International Journal of Soft Computing and Engineering
ISSN : 2231 - 2307 Website: www.ijsce.org Volume-2 Issue-2, May 2012 Published by: Blue Eyes Intelligence Engineering and Sciences Publication Pvt. Ltd.
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G INN www.ijsce.org Exploring Innovation Editor In Chief Dr. Shiv K Sahu Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT) Director, Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., Bhopal (M.P.), India
Dr. Shachi Sahu Ph.D. (Chemistry), M.Sc. (Organic Chemistry) Additional Director, Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., Bhopal (M.P.), India
Vice Editor In Chief Dr. Vahid Nourani Professor, Faculty of Civil Engineering, University of Tabriz, Iran
Prof.(Dr.) Anuranjan Misra Professor & Head, Computer Science & Engineering and Information Technology & Engineering, Noida International University, Noida (U.P.), India
Chief Advisory Board Prof. (Dr.) Hamid Saremi Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran
Dr. Uma Shanker Professor & Head, Department of Mathematics, CEC, Bilaspur(C.G.), India
Dr. Rama Shanker Professor & Head, Department of Statistics, Eritrea Institute of Technology, Asmara, Eritrea
Dr. Vinita Kumari Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., India
Dr. Kapil Kumar Bansal Head (Research and Publication), SRM University, Gaziabad (U.P.), India
Dr. Deepak Garg Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India, Senior Member of IEEE, Secretary of IEEE Computer Society (Delhi Section), Life Member of Computer Society of India (CSI), Indian Society of Technical Education (ISTE), Indian Science Congress Association Kolkata.
Dr. Vijay Anant Athavale Director of SVS Group of Institutions, Mawana, Meerut (U.P.) India/ U.P. Technical University, India
Dr. T.C. Manjunath Principal & Professor, HKBK College of Engg, Nagawara, Arabic College Road, Bengaluru-560045, Karnataka, India
Dr. Kosta Yogeshwar Prasad Director, Technical Campus, Marwadi Education Foundation’s Group of Institutions, Rajkot-Morbi Highway, Gauridad, Rajkot, Gujarat, India
Dr. Dinesh Varshney Director of College Development Counceling, Devi Ahilya University, Indore (M.P.), Professor, School of Physics, Devi Ahilya University, Indore (M.P.), and Regional Director, Madhya Pradesh Bhoj (Open) University, Indore (M.P.), India
Dr. P. Dananjayan Professor, Department of Department of ECE, Pondicherry Engineering College, Pondicherry,India
Dr. Sadhana Vishwakarma Associate Professor, Department of Engineering Chemistry, Technocrat Institute of Technology, Bhopal(M.P.), India
Dr. Kamal Mehta Associate Professor, Deptment of Computer Engineering, Institute of Technology, NIRMA University, Ahmedabad (Gujarat), India
Dr. CheeFai Tan Faculty of Mechanical Engineering, University Technical, Malaysia Melaka, Malaysia
Dr. Suresh Babu Perli Professor& Head, Department of Electrical and Electronic Engineering, Narasaraopeta Engineering College, Guntur, A.P., India
Dr. Binod Kumar Associate Professor, Schhool of Engineering and Computer Technology, Faculty of Integrative Sciences and Technology, Quest International University, Ipoh, Perak, Malaysia
Dr. Chiladze George Professor, Faculty of Law, Akhaltsikhe State University, Tbilisi University, Georgia
Dr. Kavita Khare Professor, Department of Electronics & Communication Engineering., MANIT, Bhopal (M.P.), INDIA
Dr. C. Saravanan Associate Professor (System Manager) & Head, Computer Center, NIT, Durgapur, W.B. India
Dr. S. Saravanan Professor, Department of Electrical and Electronics Engineering, Muthayamal Engineering College, Resipuram, Tamilnadu, India
Dr. Amit Kumar Garg Professor & Head, Department of Electronics and Communication Engineering, Maharishi Markandeshwar University, Mulllana, Ambala (Haryana), India
Dr. T.C.Manjunath Principal & Professor, HKBK College of Engg, Nagawara, Arabic College Road, Bengaluru-560045, Karnataka, India
Dr. P. Dananjayan Professor, Department of Department of ECE, Pondicherry Engineering College, Pondicherry, India
Dr. Kamal K Mehta Associate Professor, Department of Computer Engineering, Institute of Technology, NIRMA University, Ahmedabad (Gujarat), India
Dr. Rajiv Srivastava Director, Department of Computer Science & Engineering, Sagar Institute of Research & Technology, Bhopal (M.P.), India
Dr. Chakunta Venkata Guru Rao Professor, Department of Computer Science & Engineering, SR Engineering College, Ananthasagar, Warangal, Andhra Pradesh, India
Dr. Anuranjan Misra Professor, Department of Computer Science & Engineering, Bhagwant Institute of Technology, NH-24, Jindal Nagar, Ghaziabad, India
Dr. Robert Brian Smith International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie Centre, North Ryde, New South Wales, Australia
Dr. Saber Mohamed Abd-Allah Associate Professor, Department of Biochemistry, Shanghai Institute of Biochemistry and Cell Biology, Yue Yang Road, Shanghai, China
Dr. Himani Sharma Professor & Dean, Department of Electronics & Communication Engineering, MLR Institute of Technology, Laxman Reddy Avenue, Dundigal, Hyderabad, India
Dr. Sahab Singh Associate Professor, Department of Management Studies, Dronacharya Group of Institutions, Knowledge Park-III, Greater Noida, India
Dr. Umesh Kumar Principal: Govt Women Poly, Ranchi, India
Dr. Syed Zaheer Hasan Scientist-G Petroleum Research Wing, Gujarat Energy Research and Management Institute, Energy Building, Pandit Deendayal Petroleum University Campus, Raisan, Gandhinagar-382007, Gujarat, India.
Dr. Jaswant Singh Bhomrah Director, Department of Profit Oriented Technique, 1 – B Crystal Gold, Vijalpore Road, Navsari 396445, Gujarat. India
Technical Advisory Board Dr. Mohd. Husain Director, MG Institute of Management & Technology, Banthara, Lucknow (U.P.), India Dr. T. Jayanthy Principal, Panimalar Institute of Technology, Chennai (TN), India
Dr. Umesh A.S. Director, Technocrats Institute of Technology & Science, Bhopal(M.P.), India
Dr. B. Kanagasabapathi Infosys Labs, Infosys Limited, Center for Advance Modeling and Simulation, Infosys Labs, Infosys Limited, Electronics City, Bangalore, India
Dr. C.B. Gupta Professor, Department of Mathematics, Birla Institute of Technology & Sciences, Pilani (Rajasthan), India
Dr. Sunandan Bhunia Associate Professor & Head,, Dept. of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia, West Bengal, India
Dr. Jaydeb Bhaumik Associate Professor, Dept. of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia, West Bengal, India
Dr. Rajesh Das Associate Professor, School of Applied Sciences, Haldia Institute of Technology, Haldia, West Bengal, India
Dr. Mrutyunjaya Panda Professor & Head, Department of EEE, Gandhi Institute for Technological Development, Bhubaneswar, Odisha, India
Dr. Mohd. Nazri Ismail Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia
Dr. Haw Su Cheng Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia, 63100 Cyberjaya
Dr. Hossein Rajabalipour Cheshmehgaz Industrial Modeling and Computing Department, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia (UTM) 81310, Skudai, Malaysia
Dr. Sudhinder Singh Chowhan Associate Professor, Institute of Management and Computer Science, NIMS University, Jaipur (Rajasthan), India
Dr. Neeta Sharma Professor & Head, Department of Communication Skils, Technocrat Institute of Technology, Bhopal(M.P.), India
Dr. Ashish Rastogi Associate Professor, Department of CSIT, Guru Ghansi Das University, Bilaspur (C.G.), India
Dr. Santosh Kumar Nanda Professor, Department of Computer Science and Engineering, Eastern Academy of Science and Technology (EAST), Khurda (Orisa), India
Dr. Hai Shanker Hota Associate Professor, Department of CSIT, Guru Ghansi Das University, Bilaspur (C.G.), India
Dr. Sunil Kumar Singla Professor, Department of Electrical and Instrumentation Engineering, Thapar University, Patiala (Punjab), India
Dr. A. K. Verma Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India
Dr. Durgesh Mishra Chairman, IEEE Computer Society Chapter Bombay Section, Chairman IEEE MP Subsection, Professor & Dean (R&D), Acropolis Institute of Technology, Indore (M.P.), India
Dr. Xiaoguang Yue Associate Professor, College of Computer and Information, Southwest Forestry University, Kunming (Yunnan), China
Dr. Veronica Mc Gowan Associate Professor, Department of Computer and Business Information Systems,Delaware Valley College, Doylestown, PA, Allman China Dr. Mohd. Ali Hussain Professor, Department of Computer Science and Engineering, Sri Sai Madhavi Institute of Science & Technology, Rajahmundry (A.P.), India
Dr. Mohd. Nazri Ismail Professor, System and Networking Department, Jalan Sultan Ismail, Kaula Lumpur, MALAYSIA
Dr. Sunil Mishra Associate Professor, Department of Communication Skills (English), Dronacharya College of Engineering, Farrukhnagar, Gurgaon (Haryana), India
Dr. Labib Francis Gergis Rofaiel Associate Professor, Department of Digital Communications and Electronics, Misr Academy for Engineering and Technology, Mansoura City, Egypt
Dr. Pavol Tanuska Associate Professor, Department of Applied Informetics, Automation, and Mathematics, Trnava, Slovakia
Dr. VS Giridhar Akula Professor, Avanthi's Research & Technological Academy, Gunthapally, Hyderabad, Andhra Pradesh, India
Dr. S. Satyanarayana Associate Professor, Department of Computer Science and Engineering, KL University, Guntur, Andhra Pradesh, India
Dr. Bhupendra Kumar Sharma Associate Professor, Department of Mathematics, KL University, BITS, Pilani, India
Dr. Praveen Agarwal Associate Professor& Head, Department of Mathematics, Anand International College of Engineering, Jaipur (Rajasthan), India
Dr. Manoj Kumar Professor, Department of Mathematics, Rashtriya Kishan Post Graduate Degree, College, Shamli, Prabudh Nagar, (U.P.), India
Dr. Shaikh Abdul Hannan Associate Professor, Department of Computer Science, Vivekanand Arts Sardar Dalipsing Arts and Science College, Aurangabad (Maharashtra), India
Dr. K.M. Pandey Professor, Department of Mechanical Engineering,National Institute of Technology, Silchar, India
Prof. Pranav Parashar Technical Advisor, International Journal of Soft Computing and Engineering (IJSCE), Bhopal (M.P.), India
Dr. Biswajit Chakraborty MECON Limited, Research and Development Division (A Govt. of India Enterprise), Ranchi-834002, Jharkhand, India
Dr. D.V. Ashoka Professor & Head, Department of Information Science & Engineering, SJB Institute of Technology, Kengeri, Bangalore, India
Dr. Sasidhar Babu Suvanam Professor & Academic Cordinator, Department of Computer Science & Engineering, Sree Narayana Gurukulam College of Engineering, Kadayiuruppu, Kolenchery, Kerala, India
Dr. C. Venkatesh Professor & Dean, Faculty of Engineering, EBET Group of Institutions, Kangayam, Erode, Caimbatore (Tamil Nadu), India
Dr. Nilay Khare Assoc. Professor & Head, Department of Computer Science, MANIT, Bhopal (M.P.), India
Dr. Sandra De Iaco Professor, Dip.to Di Scienze Dell’Economia-Sez. Matematico-Statistica, Italy
Dr. Yaduvir Singh Associate Professor, Department of Computer Science & Engineering, Ideal Institute of Technology, Govindpuram Ghaziabad, Lucknow (U.P.), India
Dr. Angela Amphawan Head of Optical Technology, School of Computing, School Of Computing, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia Dr. Ashwini Kumar Arya Associate Professor, Department of Electronics & Communication Engineering, Faculty of Engineering and Technology,Graphic Era University, Dehradun (U.K.), India
Dr. Yash Pal Singh Professor, Department of Electronics & Communication Engg, Director, KLS Institute Of Engg.& Technology, Director, KLSIET, Chandok, Bijnor, (U.P.), India
Dr. Ashish Jain Associate Professor, Department of Computer Science & Engineering, Accurate Institute of Management & Technology, Gr. Noida (U.P.), India
Dr. Abhay Saxena Associate Professor&Head, Department. of Computer Science, Dev Sanskriti University, Haridwar, Uttrakhand, India
Dr. Judy. M.V Associate Professor, Head of the Department CS &IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Brahmasthanam, Edapally, Cochin, Kerala, India
Dr. Sangkyun Kim Professor, Department of Industrial Engineering, Kangwon National University, Hyoja 2 dong, Chunche0nsi, Gangwondo, Korea
Dr. Sanjay M. Gulhane Professor, Department of Electronics & Telecommunication Engineering, Jawaharlal Darda Institute of Engineering & Technology, Yavatmal, Maharastra, India
Dr. K.K. Thyagharajan Principal & Professor, Department of Informational Technology, RMK College of Engineering & Technology, RSM Nagar, Thiruyallur, Tamil Nadu, India
Dr. P. Subashini Asso. Professor, Department of Computer Science, Coimbatore, India
Dr. G. Srinivasrao Professor, Department of Mechanical Engineering, RVR & JC, College of Engineering, Chowdavaram, Guntur, India
Dr. Rajesh Verma Professor, Department of Computer Science & Engg. and Deptt. of Information Technology, Kurukshetra Institute of Technology & Management, Bhor Sadian, Pehowa, Kurukshetra (Haryana), India
Dr. Pawan Kumar Shukla Associate Professor, Satya College of Engineering & Technology, Haryana, India
Dr. U C Srivastava Associate Professor, Department of Applied Physics, Amity Institute of Applied Sciences, Amity University, Noida, India
Dr. Reena Dadhich Prof.& Head, Department of Computer Science and Informatics, MBS MArg, Near Kabir Circle, University of Kota, Rajasthan, India
Dr. Aashis.S.Roy Department of Materials Engineering, Indian Institute of Science, Bangalore Karnataka, India
Dr. Sudhir Nigam Professor Department of Civil Engineering, Principal, Lakshmi Narain College of Technology and Science, Raisen, Road, Bhopal, (M.P.), India
Dr. S.Senthilkumar Doctorate, Department of Center for Advanced Image and Information Technology, Division of Computer Science and Engineering, Graduate School of Electronics and Information Engineering, Chon Buk National University Deok Jin-Dong, Jeonju, Chon Buk, 561- 756, South Korea Tamilnadu, India
Dr. Gufran Ahmad Ansari Associate Professor, Department of Information Technology, College of Computer, Qassim University, Al-Qassim, Kingdom of Saudi Arabia (KSA)
Dr. R.Navaneethakrishnan Associate Professor, Department of MCA, Bharathiyar College of Engg & Tech, Karaikal Puducherry, India
Dr. Hossein Rajabalipour Cheshmejgaz Industrial Modeling and Computing Department, Faculty of Computer Science and Information Systems, Universiti Teknologi Skudai, Malaysia
Dr. Veronica McGowan Associate Professor, Department of Computer and Business Information Systems, Delaware Valley College, Doylestown, PA, Allman China
Dr. Sanjay Sharma Associate Professor, Department of Mathematics, Bhilai Institute of Technology, Durg, Chhattisgarh, India
Dr. Taghreed Hashim Al-Noor Professor, Department of Chemistry, Ibn-Al-Haitham Education for pure Science College, University of Baghdad, Iraq
Dr. Madhumita Dash Professor, Department of Electronics & Telecommunication, Orissa Engineering College , Bhubaneswar,Odisha, India
Dr. Anita Sagadevan Ethiraj Associate Professor, Department of Centre for Nanotechnology Research (CNR), School of Electronics Engineering (Sense), Vellore Institute of Technology (VIT) University, Tamilnadu, India
Dr. Sibasis Acharya Project Consultant, Department of Metallurgy & Mineral Processing, Midas Tech International, 30 Mukin Street, Jindalee-4074, Queensland, Australia
Dr. Neelam Ruhil Professor, Department of Electronics & Computer Engineering, Dronacharya College of Engineering, Gurgaon, Haryana, India
Dr. Faizullah Mahar Professor, Department of Electrical Engineering, Balochistan University of Engineering and Technology, Pakistan
Dr. K. Selvaraju Head, PG & Research, Department of Physics, Kandaswami Kandars College (Govt. Aided), Velur (PO), Namakkal DT. Tamil Nadu, India
Dr. M. K. Bhanarkar Associate Professor, Department of Electronics, Shivaji University, Kolhapur, Maharashtra, India
Dr. Sanjay Hari Sawant Professor, Department of Mechanical Engineering, Dr. J. J. Magdum College of Engineering, Jaysingpur, India
Dr. Arindam Ghosal Professor, Department of Mechanical Engineering, Dronacharya Group of Institutions, B-27, Part-III, Knowledge Park,Greater Noida, India
Dr. M. Chithirai Pon Selvan Associate Professor, Department of Mechanical Engineering, School of Engineering & Information Technology, Amity University, Dubai, UAE
Dr. S. Sambhu Prasad Professor & Principal, Department of Mechanical Engineering, Pragati College of Engineering, Andhra Pradesh, India.
Dr. Muhammad Attique Khan Shahid Professor of Physics & Chairman, Department of Physics, Advisor (SAAP) at Government Post Graduate College of Science, Faisalabad.
Dr. Kuldeep Pareta Professor & Head, Department of Remote Sensing/GIS & NRM, B-30 Kailash Colony, New Delhi 110 048, India
Dr. Th. Kiranbala Devi Associate Professor, Department of Civil Engineering, Manipur Institute of Technology, Takyelpat, Imphal, Manipur, India
Dr. Nirmala Mungamuru Associate Professor, Department of Computing, School of Engineering, Adama Science and Technology University, Ethiopia
Dr. Srilalitha Girija Kumari Sagi Associate Professor, Department of Management, Gandhi Institute of Technology and Management, India
Dr. Vishnu Narayan Mishra Associate Professor, Department of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Dumas Road, Surat (Gujarat), India
Dr. Yash Pal Singh Director/Principal, Somany (P.G.) Institute of Technology & Management, Garhi Bolni Road , Rewari Haryana, India.
Dr. Sripada Rama Sree Vice Principal, Associate Professor, Department of Computer Science and Engineering, Aditya Engineering College, Surampalem, Andhra Pradesh. India.
Dr. Rustom Mamlook Associate Professor, Department of Electrical and Computer Engineering, Dhofar University, Salalah, Oman. Middle East.
Dr. Ramzi Raphael Ibraheem Al Barwari Assistant Professor, Department of Mechanical Engineering, College of Engineering, Salahaddin University – Hawler (SUH) Erbil – Kurdistan, Erbil Iraq.
Dr. Kapil Chandra Agarwal H.O.D. & Professor, Department of Applied Sciences & Humanities, Radha Govind Engineering College, U. P. Technical University, Jai Bheem Nagar, Meerut, (U.P). India.
Dr. Anil Kumar Tripathy Associate Professor, Department of Environmental Science & Engineering, Ghanashyama Hemalata Institute of Technology and Management, Puri Odisha, India.
Managing Editor Mr. Jitendra Kumar Sen International Journal of Soft Computing and Engineering (IJSCE)
Editorial Board Dr. Soni Changlani Professor, Department of Electronics & Communication, Lakshmi Narain College of Technology & Science, Bhopal (.M.P.), India
Dr. M .M. Manyuchi Professor, Department Chemical and Process Systems Engineering, Lecturer-Harare Institute of Technology, Zimbabwe
Dr. John Kaiser S. Calautit Professor, Department Civil Engineering, School of Civil Engineering, University of Leeds, LS2 9JT, Leeds, United Kingdom
Dr. Audai Hussein Al-Abbas Deputy Head, Department AL-Musaib Technical College/ Foundation of Technical Education/Babylon, Iraq
Dr. Şeref Doğuşcan Akbaş Professor, Department Civil Engineering, Şehit Muhtar Mah. Öğüt Sok. No:2/37 Beyoğlu Istanbul, Turkey
Dr. H S Behera Associate Professor, Department Computer Science & Engineering, Veer Surendra Sai University of Technology (VSSUT) A Unitary Technical University Established by the Government of Odisha, India
Dr. Rajeev Tiwari Associate Professor, Department Computer Science & Engineering, University of Petroleum & Energy Studies (UPES), Bidholi, Uttrakhand, India
Dr. Piyush Kumar Shukla Assoc. Professor, Department of Computer Science and Engineering, University Institute of Technology, RGPV, Bhopal (M.P.), India
Dr. Piyush Lotia Assoc.Professor, Department of Electronics and Instrumentation, Shankaracharya College of Engineering and Technology, Bhilai (C.G.), India
Dr. Asha Rai Assoc. Professor, Department of Communication Skils, Technocrat Institute of Technology, Bhopal (M.P.), India
Dr. Vahid Nourani Assoc. Professor, Department of Civil Engineering, University of Minnesota, USA
Dr. Hung-Wei Wu Assoc. Professor, Department of Computer and Communication, Kun Shan University, Taiwan
Dr. Vuda Sreenivasarao Associate Professor, Department of Computr And Information Technology, Defence University College, Debrezeit Ethiopia, India
Dr. Sanjay Bhargava Assoc. Professor, Department of Computer Science, Banasthali University, Jaipur, India
Dr. Sanjoy Deb Assoc. Professor, Department of ECE, BIT Sathy, Sathyamangalam, Tamilnadu, India
Dr. Papita Das (Saha) Assoc. Professor, Department of Biotechnology, National Institute of Technology, Duragpur, India
Dr. Waail Mahmod Lafta Al-waely Assoc. Professor, Department of Mechatronics Engineering, Al-Mustafa University College – Plastain Street near AL-SAAKKRA square- Baghdad - Iraq
Dr. P. P. Satya Paul Kumar Assoc. Professor, Department of Physical Education & Sports Sciences, University College of Physical Education & Sports Sciences, Guntur
Dr. Sohrab Mirsaeidi Associate Professor, Department of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Skudai, Johor, Malaysia
Dr. Ehsan Noroozinejad Farsangi Associate Professor, Department of Civil Engineering, International Institute of Earthquake Engineering and Seismology (IIEES) Farmanieh, Tehran - Iran
Dr. Omed Ghareb Abdullah Associate Professor, Department of Physics, School of Science, University of Sulaimani, Iraq Dr. Khaled Eskaf Associate Professor, Department of Computer Engineering, College of Computing and Information Technology, Alexandria, Egypt
Dr. Nitin W. Ingole Associate Professor & Head, Department of Civil Engineering, Prof Ram Meghe Institute of Technology and Research, Badnera Amravati
Dr. P. K. Gupta Associate Professor, Department of Computer Science and Engineering, Jaypee University of Information Technology, P.O. Dumehar Bani, Solan, India
Dr. P.Ganesh Kumar Associate Professor, Department of Electronics & Communication, Sri Krishna College of Engineering and Technology, Linyi Top Network Co Ltd Linyi , Shandong Provience, China
Dr. Santhosh K V Associate Professor, Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal, Karnataka, India
Dr. Subhendu Kumar Pani Assoc. Professor, Department of Computer Science and Engineering, Orissa Engineering College, India
Dr. Syed Asif Ali Professor/ Chairman, Department of Computer Science, SMI University, Karachi, Pakistan
Dr. Vilas Warudkar Assoc. Professor, Department of Mechanical Engineering, Maulana Azad National Institute of Technology, Bhopal, India
Dr. S. Chandra Mohan Reddy Associate Professor & Head, Department of Electronics & Communication Engineering, JNTUA College of Engineering (Autonomous), Cuddapah, Andhra Pradesh, India
Dr. V. Chittaranjan Das Associate Professor, Department of Mechanical Engineering, R.V.R. & J.C. College of Engineering, Guntur, Andhra Pradesh, India
Dr. Jamal Fathi Abu Hasna Associate Professor, Department of Electrical & Electronics and Computer Engineering, Near East University, TRNC, Turkey
Dr. S. Deivanayaki Associate Professor, Department of Physics, Sri Ramakrishna Engineering College, Tamil Nadu, India
Dr. Nirvesh S. Mehta Professor, Department of Mechanical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, South Gujarat, India
Dr. A.Vijaya Bhasakar Reddy Associate Professor, Research Scientist, Department of Chemistry, Sri Venkateswara University, Andhra Pradesh, India
Dr. C. Jaya Subba Reddy Associate Professor, Department of Mathematics, Sri Venkateswara University Tirupathi Andhra Pradesh, India
Dr. TOFAN Cezarina Adina Associate Professor, Department of Sciences Engineering, Spiru Haret University, Arges, Romania
Dr. Balbir Singh Associate Professor, Department of Health Studies, Human Development Area, Administrative Staff College of India, Bella Vista, Andhra Pradesh, India
Dr. D. RAJU Associate Professor, Department of Mathematics, Vidya Jyothi Institute of Technology (VJIT), Aziz Nagar Gate, Hyderabad, India
Dr. Salim Y. Amdani Associate Professor & Head, Department of Computer Science Engineering, B. N. College of Engineering, PUSAD, (M.S.), India
Dr. K. Kiran Kumar Associate Professor, Department of Information Technology, Bapatla Engineering College, Andhra Pradesh, India
Dr. Md. Abdullah Al Humayun Associate Professor, Department of Electrical Systems Engineering, University Malaysia Perlis, Malaysia Dr. Vellore Vasu Teaching Assistant, Department of Mathematics, S.V.University Tirupati, Andhra Pradesh, India
Dr. Naveen K. Mehta Associate Professor & Head, Department of Communication Skills, Mahakal Institute of Technology, Ujjain, India
Dr. Gujar Anant kumar Jotiram Associate Professor, Department of Mechanical Engineering, Ashokrao Mane Group of Institutions, Vathar, Maharashtra, India
Dr. Pratibhamoy Das Scientist, Department of Mathematics, IMU Berlin Einstein Foundation Fellow Technical University of Berlin, Germany
Dr. Messaouda AZZOUZI Associate Professor, Department of Sciences & Technology, University of Djelfa, Algeria
Dr. Vandana Swarnkar Associate Professor, Department of Chemistry, Jiwaji University Gwalior, India
Dr. Arvind K. Sharma Associate Professor, Department of Computer Science Engineering, University of Kota, Kabir Circle, Rajasthan, India
Dr. R. Balu Associate Professor, Department of Computr Applications, Bharathiar University, Tamilnadu, India
Dr. S. Suriyanarayanan Associate Professor, Department of Water and Health, Jagadguru Sri Shivarathreeswara University, Karnataka, India
Dr. Dinesh Kumar Associate Professor, Department of Mathematics, Pratap University, Jaipur, Rajasthan, India
Dr. Sandeep N Associate Professor, Department of Mathematics, Vellore Institute of Technology, Tamil Nadu, India
Dr. Dharmpal Singh Associate Professor, Department of Computer Science Engineering, JIS College of Engineering, West Bengal, India Dr. Farshad Zahedi Associate Professor, Department of Mechanical Engineering, University of Texas at Arlington, Tehran, Iran
Dr. Atishey Mittal Associate Professor, Department of Mechanical Engineering, SRM University NCR Campus Meerut Delhi Road Modinagar, Aligarh, India
Dr. Hussein Togun Associate Professor, Department of Mechanical Engineering, University of Thiqar, Iraq
Dr. Shrikaant Kulkarni Associate Professor, Department of Senior faculty V.I.T., Pune (M.S.), India
Dr. Mukesh Negi Project Manager, Department of Computer Science & IT, Mukesh Negi, Project Manager, Noida, India
Dr. Sachin Madhavrao Kanawade Associate Professor, Department Chemical Engineering, Pravara Rural Education Society’s,Sir Visvesvaraya Institute of Technology, Nashik, India
Dr. Ganesh S Sable Professor, Department of Electronics and Telecommunication, Maharashtra Institute of Technology Satara Parisar, Aurangabad, Maharashtra, India
Dr. T.V. Rajini Kanth Professor, Department of Computer Science Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, India
Dr. Anuj Kumar Gupta Associate Professor, Department of Computer Science & Engineering, RIMT Institute of Engineering & Technology, NH-1, Mandi Godindgarh, Punjab, India
Dr. Hasan Ashrafi- Rizi Associate Professor, Medical Library and Information Science Department of Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
Dr. Golam Kibria Associate Professor, Department of Mechanical Engineering, Aliah University, Kolkata, India
Dr. Mohammad Jannati Professor, Department of Energy Conversion, UTM-PROTON Future Drive Laboratory, Faculty of Electrical Enginering, Universit Teknologi Malaysia,
Dr. Mohammed Saber Mohammed Gad Professor, Department of Mechanical Engineering, National Research Centre- El Behoos Street, El Dokki, Giza, Cairo, Egypt,
Dr. V. Balaji Professor, Department of EEE, Sapthagiri College of Engineering Periyanahalli,(P.O) Palacode (Taluk) Dharmapuri,
Dr. Naveen Beri Associate Professor, Department of Mechanical Engineering, Beant College of Engg. & Tech., Gurdaspur - 143 521, Punjab, India
Dr. Abdel-Baset H. Mekky Associate Professor, Department of Physics, Buraydah Colleges Al Qassim / Saudi Arabia
Dr. T. Abdul Razak Associate Professor, Department of Computer Science Jamal Mohamed College (Autonomous), Tiruchirappalli – 620 020 India
Dr. Preeti Singh Bahadur Associate Professor, Department of Applied Physics Amity University, Greater Noida (U.P.) India
Dr. Ramadan Elaiess Associate Professor, Department of Information Studies, Faculty of Arts University of Benghazi, Libya
Dr. R . Emmaniel Professor & Head, Department of Business Administration ST, ANN, College of Engineering & Technology Vetapaliem. Po, Chirala, Prakasam. DT, AP. India
Dr. C. Phani Ramesh Director cum Associate Professor, Department of Computer Science Engineering, PRIST University, Manamai, Chennai Campus, India
Dr. Rachna Goswami Associate Professor, Department of Faculty in Bio-Science, Rajiv Gandhi University of Knowledge Technologies (RGUKT) District- Krishna, Andhra Pradesh, India
Dr. Sudhakar Singh Assoc. Prof. & Head, Department of Physics and Computer Science, Sardar Patel College of Technology, Balaghat (M.P.), India
Dr. Xiaolin Qin Associate Professor & Assistant Director of Laboratory for Automated Reasoning and Programming, Chengdu Institute of Computer Applications, Chinese Academy of Sciences, China
Dr. Maddila Lakshmi Chaitanya Assoc. Prof. Department of Mechanical, Pragati Engineering College 1-378, ADB Road, Surampalem, Near Peddapuram, East Godavari District, A.P., India
Dr. Jyoti Anand Assistant Professor, Department of Mathematics, Dronacharya College of Engineering, Gurgaon, Haryana, India
Dr. Nasser Fegh-hi Farahmand Assoc. Professor, Department of Industrial Management, College of Management, Economy and Accounting, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Dr. Ravindra Jilte Assist. Prof. & Head, Department of Mechanical Engineering, VCET Vasai, University of Mumbai , Thane, Maharshtra 401202, India
Dr. Sarita Gajbhiye Meshram Research Scholar, Department of Water Resources Development & Management Indian Institute of Technology, Roorkee, India
Dr. G. Komarasamy Associate Professor, Senior Grade, Department of Computer Science & Engineering, Bannari Amman Institute of Technology, Sathyamangalam,Tamil Nadu, India
Dr. P. Raman Professor, Department of Management Studies, Panimalar Engineering College Chennai, India
Dr. M. Anto Bennet Professor, Department of Electronics & Communication Engineering, Veltech Engineering College, Chennai, India
Dr. P. Keerthika Associate Professor, Department of Computer Science & Engineering, Kongu Engineering College Perundurai, Tamilnadu, India
Dr. Santosh Kumar Behera Associate Professor, Department of Education, Sidho-Kanho-Birsha University, Ranchi Road, P.O. Sainik School, Dist-Purulia, West Bengal, India
Dr. P. Suresh Associate Professor, Department of Information Technology, Kongu Engineering College Perundurai, Tamilnadu, India
Dr. Santosh Shivajirao Lomte Associate Professor, Department of Computer Science and Information Technology, Radhai Mahavidyalaya, N-2 J sector, opp. Aurangabad Gymkhana, Jalna Road Aurangabad, India
Dr. Altaf Ali Siyal Professor, Department of Land and Water Management, Sindh Agriculture University Tandojam, Pakistan
Dr. Mohammad Valipour Associate Professor, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
Dr. Prakash H. Patil Professor and Head, Department of Electronics and Tele Communication, Indira College of Engineering and Management Pune, India
Dr. Smolarek Małgorzata Associate Professor, Department of Institute of Management and Economics, High School of Humanitas in Sosnowiec, Wyższa Szkoła Humanitas Instytut Zarządzania i Ekonomii ul. Kilińskiego Sosnowiec Poland, India S. Volume-2 Issue-2, May 2012, ISSN: 2231-2307 (Online) Page No Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. No.
Authors: Rajesh. P, Priya. S, Priyanka. R Modified Energy Efficient Backup Hierarchical Clustering Algorithm Using Residual Energy for Paper Title: Wireless Sensor Network Abstract: Clustering is a fundamental performance improvement technique in wireless sensor networks, which can increase network scalability, lifetime and power level. In this paper, we integrate the multi-hop technique with a backup-based clustering algorithm using the residual energy to organize sensors. By using an adaptive backup strategy as well as the residual energy, the algorithm not only realizes load balance among sensor node, but also achieves dynamic cluster head distribution across the network in a timeout manner. Simulation results also demonstrate our algorithm is more energy-efficient compared to other algorithms. Our algorithm is also easily extended to avoid the formation of forced cluster heads, thereby it achieves better network management, energy- efficiency and scalability.
Keywords: dynamic cluster, forced cluster head, load balance, residual energy.
References: 1. Wang J, Cao YT, Xie JY et al. Energy efficient backoff hierarchical clustering algorithms for multi-hop wireless sensor networks. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 26(2): 283{291 Mar. 2011. DOI 10.1007/s11390- 011-1131-x 1. 2. Yu M, Leung K K, Malvankar A. A dynamic clustering and energy efficient routing technique for sensor networks. IEEE Transactions on Wireless Communications, Aug. 2007, 6(8): 3069-3079. 3. Chen Y P, Liestman A L, Liu J. A hierarchical energy efficient framework for data aggregation in wireless sensor networks. IEEE Trans. 1-5 VT, May, 2006, 55(3): 789-796. 4. Cao Y, He C. A distributed clustering algorithm with an adaptive backoff strategy for wireless sensor networks. IEICE Transactions on Communications., 2006, 89-B(2): 609-613. 5. Sundararaman B, Buy U, Kshemkalyani A D. Clock synchronization for wireless sensor networks: A survey. Ad Hoc Networks, 2005, 3(3): 281-323. 6. Younis O, Fahmy S. Distributed clustering in ad-hoc sensor networks: A hybrid, energy efficient approach. In Proc. IEEE INFOCOM, Hong Kong, China, Mar. 7-11, 2004. 7. Zhao F, Guibas L. Wireless Sensor Networks: An Information Processing Approach. Morgan Kaufmann, 2004. 8. Bandyopadhyay S, Coyle E J. An energy efficient hierarchical clustering algorithm for wireless sensor networks. In Proc. IEEE INFOCOM2003, San Francisco, USA, Mar. 30-Apr. 3, April 2003, pp.1713-1723. 9. Heinzelman W B, Chandrakasan A P, Balakrishnan H. An application specific protocol architecture for wireless microsensor networks. IEEE Tran. Wireless Communications, Oct. 2002, 1(4): 660-670. 10. Akyildiz I F, Su W, Sankarasubramaniam Y et al. A survey on sensor networks. IEEE Communications Magazine, 2002, 40(8): 102-114. 11. Intanagonwiwat C, Govindan R, Estrin D. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proc. ACM/IEEE Int. Conf. Mobile Computing and Networking (MOBICOM), Boston, USA, Aug. 6-11,2000, pp.56-67. 12. Pottie G J, Kaiser W J. Wireless integrated network sensors.Communications of the ACM, 2000, 43(5): 51-58. 13. Amis A D, Prakash R, Vuong T H P, Huynh D T. Max-Min D-cluster formation in wireless ad hoc networks. In Proc. IEEEINFOCOM2000, Tel Aviv, Israel, Mar. 26, 2000, pp.32-41. 14. “ Wireless Sensor Networks for Early Detection of Forest Fires “ by Mohamed Hefeeda and Majid Bagheri. Authors: Mopsy Dhiman, Pawan Kapur, Abhijit Ganguli, Madan Lal Singla Paper Title: Impedance Study of Drinking Water and Tastants Using Conducting Polymer and Metal Electrodes Abstract: In this study the sensing capabilities of a combination of metals and conducting polymer electrodes for drinking water and dissolved tastants using an AC-impedance mode in frequency range 102 to 105 Hz at 0.1 V potential has been carried out. Classification of seven different bottled and municipal drinking water samples along with various tastants dissolved in DI water (DI water) for KCl (5mM) (salty), HCl (5 mM) (sour) quinine (0.1 mM) (bitter), sucrose (5 mM) (sweet), black tea liquor, black tea liquor with sucrose (2% sugar solution), and a bottle of “packed” orange juice has been made using six different working electrodes in a multi electrode setup using PCA. Working electrodes of Platinum (Pt), Gold (Au), Silver (Ag), Glassy Carbon (GC) and conducting polymer electrodes of Polyaniline (PANI) and Polypyrrole (PPY) grown on an ITO surface potentiostatically have been deployed in a three electrode set up. The impedance response of these water samples using number of working electrodes shows a decrease in the real and imaginary impedance values presented on nyquist plots depending upon the nature of the electrode and amount of dissolved salts present in water/tastants. The different sensing surfaces allowed a high cross-selectivity in response to the same analyte. From PCA plots it was possible to classify drinking 2. water in 3-4 classes using conducting polymer electrodes; however tastants were well separated from the PCA plots employing the impedance data of both conducting polymer and metal electrodes. 6-11
Keywords: Sensing electrodes, AC-impedance, Principal component analysis, Drinking water, tastants, conducting polymers.
References: 1. B Jakosky, “The Search for Life on Other Planets”, Cambridge University Press, Cambridge, 1998. 2. D Rajkumar, and K Palanivelu , “Electrochemical treatment of industrial wastewater”. Journal of Hazardous Materials B, vol.113, no.4 pp.123–129, 2004, 3. Kiyoshi, Toko. “Taste sensors”. Sensors and Actuators B: Chemical, vol. 64, no.1, pp. 205-215. 2000 4. C Di Natale, A Macagnano, F Davide, A Damico, A Legin, Y Vlasov, A Rudnitskaya, and B Selenzenev, “Multi component analysis on polluted water by means of impedance study”. Sensors and Acurators B: Chemical, vol. 44, no.1-2, pp. 423-28, 1997 5. A Legin, A Smirnova, A Rudnitskaya, L Lvova, E Suglobova and Y Vlasov, “Chemical sensor array for multi component analysis of biological liquids”. Analytica Chimica Acta,,vol. 385,no.1, pp.131-135, 1999 6. Y Vlasov, A V Legin, A M Rudnitskaya, A Di D'Amico, and C Natale, “Impedance study new analytical tool for liquid analysis on the basis of non-specific sensors and methods of pattern recognition”. Sensors and Actuators B: Chemical, vol.65, pp.235-236, 2000 7. C Di Natale, R Paolesse, A Macagnano, A Mantini , A D'Amico, M Ubigli, A Legin, L Lvova, A Rudnitskaya and Y Vlasov, “Application of a combined artificial olfaction and taste system to the quantification of relevant compounds in red wine”. Sensors and Actuators B: Chemical, vol.69, no.3, pp.342–347, 2000 8. H Sakai, S Iiyama, and K Toko, “Evaluation of water quality and pollution using multichannel sensors”, Sensors and Actuators B: Chemical, vol. 66, pp.251-255, 2000 9. V Martina, K Ionescu, L Pigani, F Terzi, A Ulrici, C Zanardi, and R Seeber, “ Development of an electronic toungue based on a PEDOT modified voltammetric sensor”, Anal. Bioanal. Chem.,vol. 387, no.6,pp.2101-2110. 2007 10. F Winquist, S Holmim, C Krantz-Rulcker, P Wide, and I Lundstorm, “A hybrid impedance study”. Analytica Chemical Acta, vol.406, no.2 , pp.147-157, 2000 11. C Krantz-Rulcker, M Stenberg, F Winquist, and I Lundstorm, “Electronic toungues for environmental monitoring based on arrays and pattern recognition: a review”. Analytica Chimica Acta, vol.426, pp.217-226, 2001 12. A Riul Jr., D. S dos Santos, Jr., K Wohnrath, R Di Tommazo, A C P L F Carvalho, F J Fonseca, O N Olivera, Jr., D M Taylor, and L H. C Mattoso, “Artificial taste sensor: - efficient combination of sensor from Langmuir-Blodgett films at conducting polymer and ruthenium complex and self assembled films of an azobenzene containing polymer”, Langmuir,vol. 18,no.1, pp.239-245. 2002 13. RT Kurulugama, O Wlpf David, A Takacs Sara, S Pongmayteegul, P A Garris and J E Baur, “Scanning Electrochemical Micrscopy of Model Neurons: Constant Distance Imaging”. Anal. Chem., vol.77, no.4, pp.1111 -1117, 2005 14. P Mirtaheri, S Grimnes and Ǿ G Martinsen, “ Electrode Polarization Impedance in Weak NaCl Aqueous Solutions”, IEEE Transactions on Biomedical Engineering, vol.52,no.12, pp.2093-93,2005 15. L Scribner and R Taylor, “The Measurement and Correction of Electrolyte Resistance in Electrochemical Test”, Philadelphia. ASTM Pub., 1990. 16. H Fricke, The theory of electrolyte polarization. Phil. Mag., vol. 7, pp. 310–318, 1932 17. H P Schwan, “Electrode polarization impedance and measurements in biological materials”, Ann. New York Acad. Sci., vol.148, no.1 pp. 191–209, 1968 18. E Stussi, R Stella and D De Rossi, “Chemo resistive conducting polymer based odour sensors: Influence of thickness changes on their sensing properties”, Sensors and Actuators B: Chemical, vol.43, no. 1, pp.180-185. 1997 19. Z Mark, “The effect of the PANI-free volume on impedance response”. Journal of Electroanalytical Chemistry, vol. 610, no. 1, pp. 57–66. 2007 20. K Rossberg, G Paasch, L Dunsch, and S Ludwig, “The influence of porosity and the nature of charge storage capacitance on the impedance behavior of electropolymersized PPY film”, Journal of Electroanalytical Chemistry, vol. 443,no.1 , pp.49-62. 1998 21. T Ragheb and L. A Geddes, “The polarization impedance of common electrode metals operated at low current density”, Ann. Biomed. Eng, vol. 19, no. 2 pp.151–163,1991 22. V Freger and S Bason, “Characterization of ion transport in thin film using electrochemical impedance spectroscopy: I .Principles and theory, Journal of Membrane Science, vol. 302, no.1-2, pp.1-9. 2007 23. M A Vorotyntsev, L I Daikhin and M D Levi, “Modelling the impedance properties of electrodes coated with electroactive polymer films”, Journal of Electroanalytical Chemistry , vol. 364, no. 1-2, pp. 37-49, 1994 24. SC Lenore, EG Arnold and RT Rhodes, “Standard methods for the examination of water and wastewater-17th edition”, APHA publication, Washington DC., ISBN 087553 161-X. 1989 25. D M Taylor and A G MacDonald, “AC admittance of the metal/insulator/electrolyte interface”. Journal of Physics D: Applied Physics, vol.20, no.10, pp.1277-1283, 1987 26. P Ferloni, M Mastragostino, and L Maneghello, “Impedance analysis of electrochemically conducting polymer”, Electrochimica Acta, , vol.41,no.1 ,pp. 27-33. 1996 27. Genz, M M Lohrengel and J W Schultze, “Potentiostatic pulse and impedance investigations of the redox process in PPY films”, Electrochimica Acta, vol. 39,no.2, pp.179-185. 1994 28. G Inzelt, G Láng, V Kertész and J Bácskai, “Effect of the temperature on the conductivity and capacitance of poly(aniline) film electrodes”, Electrochimica Acta, vol. 38,no.17, pp. 2503-2510, 1993 29. A P Bhondekar, M Dhiman, A Sharma, A Bhakta, A Ganguli, S S Bari, R Vig, P Kapur and M L Singla, “A novel iTongue for Indian black tea discrimination”, Sensors and Actuators B: Chemical, vol.148,no.2, pp.601–609, 2010. Authors: Mopsy Dhiman, Pawan Kapur, Madan Lal Singla, Abhijit Ganguli Paper Title: Classification of Epigallocatechin and Catechin in Impedometric Mode Using PCA Abstract: Due to the presence of innumerable compounds and their diverse contribution to tea quality an assessment of tea quality is a difficult task. As a result tea samples are assessed by experienced tea tasters and an instrumental evaluation of tea quality is not practiced in the industry. There had been a very few reports where instruments like electronic tongue/electronic nose has been used for the discrimination of taste of tea samples. In this paper, an Impedance study has been carried out at Epigallocatechin and Catechin levels present in black tea using Glassy Carbon electrode and its fingerprint mapping was done using Principal Component Analysis. Similar data has been generated from the known individual antioxidant compounds and the respective mixture. The antioxidant level has been also extracted from the complex structure of the other antioxidants present in black tea. It has been found that impedance data and their PCA have been able to clearly discriminate the presence of these two compounds. The reproducibility has been studied continuously for about month’s time which lies within the + 2% of the output.
3. Keywords: Fingerprint Mapping, Principal Component Analysis, Antioxidants, Impedance. 12-16 References: 1. M Naczk, and F Shahidi, “Phenolics in cereals, fruits and vegetables: occurrence, extraction and analysis”, J. Pharm. Biomed. Anal., vol. 41, no., pp. 1523–1542, 2006. 2. Hara, Y, “Green tea: Health benefits and applications”, New York: Marcel Dekker, 2001. 3. L D Mello, A A Alves, K V Macedo, L T Kubota, “Peroxidase-based biosensor as a tool for a fast evaluation of antioxidant capacity of tea”, Food Chemistry, vol. 92, no. pp. 515–519. 2005 4. N Khan, H Mukhtar, “Tea polyphenols for health promotion”, Life Sci., vol. 81, no., pp. 519–533, 2007 5. L Yao, X Liu, Y Jiang, N Caffin, B D’Arcy, R Singanusong, N Datta and Y Xu, “Phenolic compounds in tea from Australian supermarkets”, Food Chemistry, vol. 96, no., pp.614–620, 2006 6. R G Santana, G A Orner, A Amantana, C Provost, S-Y Wu and R H Dashwood, “Potent antimutagenic activity of white tea in comparison with green tea in the Salmonella assay”, Mutation Research, vol. 495, no., pp. 61–74, 2001 7. Y S Zhen, Z M Chen, S J Cheng and M L Chen, “Tea Bioactivity and Therapeutic Potential” , London: Taylor & Francis, 2002 8. K Hosoda, M F Wang, M L Liao, C K Chuang, M Iha, B.Clevidence and S Yamamoto, “Anti-hyperglycemic effect of Oolong tea in type 2 diabetes”, Diabetes Care, vol. 26 no.,, pp. 1715–1718, 2003 9. B Halder, S Pramanick, S Mukhopadhyay and A K Giri, “Anticlastogenic effects of black tea polyphenols theaflavins and thearubigins in human lymphocytes in vitro”, Toxicology in Vitro, vol. 20, no., pp. 608–613, 2006 10. C H Lu, L S Hwang, “Polyphenol contents of pu-erh teas and their abilities to inhibit cholesterol biosynthesis in Hep G2 cell line”, Food Chemistry, vol. 111, no., pp. 67–71, 2008 11. D Wang, K Kubota, A Kobayashi, and I-M. Juan, “Analysis of glycosidically bound aroma precursors in tea leaves.3. Change in the glycoside content of tea leaves during the oolong tea manufacturing process”, Journal of Agricultural and Food Chemistry, vol. 49, no., pp. 5391–5396, 2001 12. M M Camouse, D S Domingo, F R Swain, E P Conrad, M S Matsui, D Maes, L Declercq, K D Cooper, S R Stevens, and E D Baron, “Topical application of green and white tea extracts provides protection from solar-simulated ultraviolet light in human skin”, Experimental Dermatology, vol. 18, no., pp. 522–526, 2009 13. N Togari, A Kobayashi and T Aishima “Relating sensory properties of tea aroma to gas chromatographic data by chemometric calibration methods”, Food Res. Int., vol. 28, no. , pp. 485–493, 1995 14. Y Zuo, HChen and Y Deng, “Simultaneous determination of catechins, caffeine and gallic acids in green, Oolong, black and pu-erh teas using HPLC with a photodiode array detector”, Talanta, vol. 57, no. , pp. 307–316, 2002 15. H Horie, T Mukai and K Kohata, “Simultaneous determination of qualitatively important components in green tea infusions using capillary electrophoresis”, J. Chromatogr. A., vol.758, no. , pp. 332–335,1997 16. B Banerjee, Tea Production and Processing. New Delhi, India, Oxford & IBH Publishing Co, 1993 17. P Ivarsson, S Holmin, N E Hojer, C Krantz-Rulcker and F Winquist, “Discrimination of tea by means of a voltammetric electronic tongue and different applied waveforms”, Sens. Actuators B. Chem., vol. 76, no. , pp. 449–454, 2001 18. V Parra, A A Arrieta, J A Fernandez-Escudero, H Garcia, C Apetrei, M L Rodriguez-Mendez, J A De Saja, “E-tongue based on a hybrid array of voltammetric sensors based on phthalocyanines, perylene derivatives and conducting polymers: Discrimination capability towards red wines elaborated with different varieties of grapes”, Sens. Actuators B. Chem., vol. 115, no. , pp. 54–61, 2006 19. B A Lawton and R Pethig, “Determining the fat content of milk and cream using AC conductivity measurements”, Meas. Sci. Technol., vol. 4, no. , pp. 38–41, 1993 20. A Legin, A Rudnitskaya, Y Vlasov, C Di Natale, F Davide and A.D’Amico, “Tasting of beverages using an electronic tongue”, Sens. Actuators B. Chem., vol. 44, no. , pp. 291–296, 1997 21. Q Chen, J Zhao and S Vittayapadung, “Identification of the green tea grade level using electronic tongue and pattern recognition”, Food Res. Int., vol. 41, no. , pp. 500-504, 2008 22. P Ciosek, Z Brzózka, and W Wróblewski, “Electronic tongue for flowthrough analysis of beverages”, Sens. Actuators B. Chem., vol. 118, no. , pp. 454–460, 2006 23. A Scozzari, N Acito, and G Corsini, “A novel method based on voltammetry for the qualitative analysis of water”, IEEE Trans. Instrum. Meas.,vol. 56, no. , pp. 2688–2697, 2007 24. K Beullens, D Kirsanov, J Irudayaraj, A Rudnitskaya, A Legin, , B M Nicolai, and J Lammertyn, “The electronic tongue and ATR-FTIR for rapid detection of sugars and acids in tomatoes”, Sens. Actuators B. Chem., vol. 116, no. , pp.107–115, 2006 25. M Scampicchio, S Benedetti, S Brunetti, and S Mannino, “Amperometric electronic tongue for the evaluation of the tea astringency”, Electroanalysis, vol. 18, no. , pp.1643–1648, 2006 26. J Olsson, F Winquist, and I Lundstrom, “A self polishing electronic tongue”, Sens. Actuators B. Chem., vol. 118, no. , pp. 461–465, 2006. VI. L LVOVA, A LEGIN, Y VLASOV, AND G S CHA, H NAM, “MULTICOMPONENT ANALYSIS OF KOREAN GREEN TEA BY MEANS OF DISPOSABLE ALL-SOLID-STATE POTENTIOMETRIC ELECTRONIC TONGUE MICROSYSTEM”, SENS. ACTUATORS B. CHEM., VOL. 95, NO. ,PP. 391–399, 2003 27. M J C Pontes, S R Santos, M C U Araujo, L F Almeida, R A C Lima, E Gaiao, and U Souto, “Classification of distilled alcoholic beverages and verification of adulteration by near infrared spectrometry”, Food Research International, vol. 39, no. , pp.182–189, 2006 28. A P Bhondekar, M Dhiman, A Sharma, A Bhakta, A Ganguli, S S Bari, R Vig, P Kapur, and M L Singla, “A novel iTongue for Indian black tea discrimination”, Sen. Act. B. Chem., vol.148, no. , pp. 601–609, 2010 29. T L Alexandre, M S Bueno, K Goraieb, and M Izabel, ”Quality control of beverages using XRS allied to chemometrics: determination of fixed acidity, alcohol and sucrose contents in Brazilian cachaca and cashew juice”, X-Ray Spectrometry, vol. 39, no. , pp. 285-290, 2010. Authors: Rakesh Kumar, Tapesh Parashar, Gopal Verma Genetic Algorithm and DWT Based Multilevel Automatic Thresholding Approach for Vehicle Paper Title: Extraction Abstract: Vehicle Extraction from aerial images is an important research topic in surveillance, traffic monitoring and military applications. In this paper, an approach based on Automatic Multilevel Thresholding has been proposed for extracting vehicles from aerial imagery. The approach combines Genetic Algorithm with DWT to make segmentation faster and geometric feature of vehicles for vehicle extraction. This algorithm analyses the color and connected properties of pixels to extract the outline of vehicles. In this research, UAV colour imagery is examined experimentally. After analysis, it is examined that proposed method provides the vehicle position accurately.
Keywords: Histogram, Thresholding, Genetic Algorithm, Discrete Wavelet Transform, Morphological Processes, Edge Detection, Aerial Imagery
4. References: 1. Grejner-Brzezinska, D., C. Toth and E. Paska, “Airborne remote sensing supporting traffic flow estimates”, C. V. Tao and J. Li (eds) Advances in Mobile Mapping Technology, Taylor & Francis, London, 2007. 17-21 2. Tao Zhao and Ram Nevatia, “Car Detection in Low Resolution Aerial Images”. In Image and Vision Computing, 2001, pages 710–717. 3. Stefan Hinz, “Detection of Vehicles and Vehicle Queues in High Resolution Aerial Images”. In Photogrammetrie - Fernerkunding - Geoinformation (PFG) 3, 2004, pages 203–215. 4. ZuWhan Kim and Jitendra Malik, “Fast Vehicle Detection with Probabilistic Feature Grouping and its Application to Vehicle Tracking”, Computer Vision, IEEE International Conference on Computer Vision, 2003, pages 524-531. 5. J.C. Yen, F.J. Chang, S. Chang, “A new criterion for automatic multilevel thresholding”, IEEE Trans. Image Process. IP-4, 1995, pages 370–378. 6. B.-G. Kim, J.-I. Shim, D.-J. Park, “Fast image segmentation based on multi-resolution analysis and wavelets”, Pattern Recognit. Lett. 24, 2003, pages 2995–3006. 7. P.-Y. Yin, L.-H. Chen, “A fast iterative scheme for multilevel thresholding methods”, Signal Process. 60, 1997, pages 305–313. 8. D.E. Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison-Wesley, 1989. 9. Rafael C. Gonzalez, Richard E. Woods, “Digital Image Processing”, Pearson Education, Second Edition, 2002. 10. Li Yu, “Vehicle Extraction Using Histogram and Genetic Algorithm based Fuzzy Image Segmentation from High Resolution UAV Aerial Imagery”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing, 2008. Authors: Ashish Patel, Vasundhara Misal, Pankaja Alappanavar, Ronak Agrawal Paper Title: Unified Operating Systems 5. Abstract: Every Operating System has its different way of operation. When a novice user wants to perform some 22-25 operations with the Operating System he is not acquainted with, the problem arises. The Novice user has to learn about the basic operations about the system to perform the intended task. However, this is time consuming job and often leads to frustration when needs to be done frequently. Hence, leads to reduced productivity. One answer to the above mentioned problem can be a generic interface which would allow user to perform his task irrespective of the underlying Operating System. Under these circumstances this paper proposes a system which implements the above mentioned interface as the core concern. The unique feature that the above implemented system will provide is the same input and output syntax for performing the intended tasks under the scope of the system. Studied statistics show that this system is capable of achieving an Operating System independent interface on all JAVA supported systems.
Keywords: Operating System, Working Platform, Java Swing, GUI.
References: 1. Moving from Solaris to Red Hat Enterprise Linux | Michael Solberg 2. Forouzan, B.A. (2000). TCP/IP: Protocol Suite. 1st ed. New Delhi, India: Tata McGraw-Hill Publishing Company Limited. 3. APPNOTE.TXT - .ZIP File Format Specification, Version: 4.5, 2001-11-01, retrieved 2012-01-05 4. Unifying Operating System published in NTSACT-2012 (ISBN 978-1-4675-1444-6) Authors: K. Porkumaran, S. Manimurugan, Pradeep P Mathew Paper Title: An Assessment on Irrevocable Compression of Encrypted Grayscale Image Abstract: This paper may deals with the miscellaneous troubles that may be occurs during the irrevocable compression applied on an encrypted grayscale image. This work is a comparative learn with diverse methods of irrevocable compression such as Compressive sensing technique and Iterative reconstruction technique on encrypted grayscale image. But they practiced a multiplicity of limitations. The major obscurity is to achieve higher compression ratio as well as the better quality of the reconstructed image. The higher compression ratio and the smoother the original image may furnish the better quality of the reconstructed image.
Keywords: Image compression, image encryption, image decryption, image decompression, image reconstruction.
References: 1. Xinpeng Zhang, “Lossy Compression and Iterative Reconstruction for Encrypted Images,” IEEE Trans. Information Forensics and Security., Vol. 6, No. 1, pp. 53-58, Mar. 2011. 2. A. Kumar and A. Makur, “Lossy compression of encrypted image by compressing sensing technique,” in Proc. IEEE Region 10 Conf. (TENCON 2009), 2009, pp. 1–6. 3. Z. Xiong, A. D. Liveris and S. Cheng, ”Distributed source coding for sensor networks”, IEEE Signal Processing Magazine, Vol. 21, pp. 80-94, Sep. 2004. 4. M. Johnson, P. Ishwar, V. Prabhakaran, D. Schonberg and K. Ramachandran, ”On compressing encrypted data”, IEEE Trans. Signal Processing Vol. 52, pp. 2992-3006, Oct. 2004. 5. D. Schonberg, S. Draper and K. Ramachandran, ”On compression of encrypted images”, Proc. International conference on Image processing, Atlanta, GA, pp. 269-272, Oct. 2006. 6. A. Kumar and A. Makur, ”Distributed source coding based encryption and lossless compression of gray scale and color images”, Proc. IEEE 10th workshop on multimedia and signal processing, Cairns, Australia, pp. 760-764, Oct. 2008. 7. Y. Rachlin and D. Baron, ”The secrecy of compressive sensing measurements”, Proc. 46th Allerton conference on commun., control, and computing, Monticello, IL, Sep. 2008. 8. E. J. Candes and M. B. Wakin, ”An introduction to compressive sampling”, IEEE signal processing magazine, Vol. 25, pp. 21-30, Mar. 6. 2008. 9. S. S. Chen, D. L. Donoho, and M. A. Saunders, ”Atomic decomposition by basis pursuit”, SIAM J. Sci. Comput., Vol. 20, No. 1, pp. 3361, 26-29 1988. 10. R. Baranuik, ”Compressive Sensing”, IEEE signal processing magazine, Vol. 24, pp. 118-120, July 2007. 11. A. M. Bruckstein, D. L. Donoho and M. Elad, ”From sparse solutions of systems of equations to sparse modeling of signals and images”, to appear in SIAM review. 12. A. Mitra, Y. V. S. Rao and S. R. M. Prasanna, ” A new image encryption approach using combinational permutation techniques”, International J. of comp. Sc., Vol. 1, pp. 127-131, May 2006. 13. S. Sridharan, E. Dawson and B. Goldburg, ”Fast Fourier transform based speech encryption system”, IEE Proceedings - I, Vol. 138, pp. 215-223, June 1991. 14. Y. Wu and B. P. Ng, ”Speech scrambling with hadamard transform in transform domain”, Proc. 6th International conference on signal processing, pp. 1560-1563, Aug. 2002. 15. M. Johnson, P. Ishwar, V. M. Prabhakaran, D. Schonberg, and K. Ramchandran, “On compressing encrypted data,” IEEE Trans. Signal Process., Vol. 52, No. 10, Pt. 2, pp. 2992–3006, Oct. 2004. 16. R. G. Gallager, “Low Density Parity Check Codes,” Ph.D. dissertation, Mass. Inst. Technol., Cambridge, MA, 1963. 17. D. Schonberg, S. C. Draper, and K. Ramchandran, “On blind compression of encrypted correlated data approaching the source entropy rate,” in Proc. 43rd Annu. Allerton Conf., Allerton, IL, 2005. 18. R. Lazzeretti and M. Barni, “Lossless compression of encrypted grey-level and color images,” in Proc.16th Eur. Signal Processing Conf. (EUSIPCO 2008), Lausanne, Switzerland, Aug. 2008 [Online] Available: http://www.eurasip.org/Proceedings/Eusipco/Eusipco2008/pap- ers/1569105134.pdf 19. W. Liu, W. Zeng, L. Dong, and Q. Yao, “Efficient compression of encrypted grayscale images,” IEEE Trans. Image Process., Vol. 19, No. 4, pp. 1097–1102, Apr. 2010. 20. D. Schonberg, S. C. Draper, C. Yeo, and K. Ramchandran, “Toward compression of encrypted images and video sequences,” IEEE Trans. Inf. Forensics Security, Vol. 3, No. 4, pp. 749–762, Dec. 2008. 21. T. Bianchi, A. Piva, and M. Barni, “Composite signal representation for fast and storage-efficient processing of encrypted signals,” IEEE Trans. Inf. Forensics Security, Vol. 5, No. 1, pp. 180–187, Mar. 2010. 22. T. Bianchi, A. Piva, and M. Barni, “On the implementation of the discrete fourier transform in the encrypted domain,” IEEE Trans. Inf. Forensics Security, Vol. 4, No. 1, pp. 86–97, Mar. 2009. 23. J.-C. Yen and J.-I. Guo, “Efficient hierarchical chaotic image encryption algorithm and its VLSI realization,” Proc. Inst. Elect. Eng., Vis. Image Signal Process., Vol. 147, No. 2, pp. 167–175, 2000. 24. N. Bourbakis and C. Alexopoulos, “Picture data encryption using SCAN patterns,” Pattern Recognit., Vol. 25, No. 6, pp. 567–581, 1992. Authors: Afsane Fathi, Amir Hassan Monadjemi, Fariborz Mahmoudi 7. Paper Title: Defect Detection of Tiles with Combined Undecimated Wavelet Transform and GLCM Features Abstract: Development of an automatic defect detection system has a major impact on the overall performance of ceramic tile production industry. With this in mind, in this paper, a new algorithm has been offered for segmentation of defects in random texture tiles. firstly, by using undecimated discrete wavelet Transform (UDWT), frequency features of textures which are robust towards transition could be extracted. Then a co-occurrence matrices of sub- bands, in order to extract texture information, is obtained. Finally, after obtaining special characteristics from the combination of the two new methods, a back propagation neural network is applied for segmentation which is the final product of this. The results, both visually and computationally, show a higher accuracy while using this method than the conventional wavelet method and co-occurrence matrices that was utilized previously. The reason could be its independent from scale and rotation nature compared to the typical transform. Different locations of defects make different wavelet coefficients and ultimately increase the defect segmentation performance of a wide variety of defects.
Keywords: Defect detection, Wavelet Transform, Undecimated Wavelet Transform, Co-occurrence Matrices, Back-Propagation Neural Network.
References: 1. A. Monadjemi, B. Mirmehdi,and T. Thomas, “Reconstructured Eginfilter Matching for Novelty Detection in Random Texture,” In Proceedings of the 15th british Machine Vision Conference, 2004, pp. 637-646. 2. X. Xie and M. Mirmehdi, “TEXEMS: Texture Examplers for Defect Detection on Random Textured Surfaces,” IEEE Transaction on PAMI, Vol.29, No.8, Aug.2007, pp.1454-1464. 3. I. Novak, Z. Hocenski, “Texture Feature Extraction for a Visual Inspection of Ceramic Tiles,” IEEE ISIE, June. 2005, Dubrovnik, Croatia, 30-34 pp.1279-1283. 4. G. Loum, C. T. Haba, J. Lemoine, and P. Provent, “Texture charecterisation and classification using full wavelet decomposition,” J. Applied Sci, Vol.7, pp. 1563-1573 . 5. S.C. Kim and T. J. Kang, “Texture Classification and Segmentation using Wavelet Packet Frame and Gaussian Mixture Model,” Vol. 28, 2007, pp. 1566-1573. 6. A. L. Amet, A. Ertuzun, and A.Ercil, ”An efficient method for texture defect detection: Sub-band domain co-occurrence matrices,” Vol. 28, 2000, pp. 543-553. 7. S.Rimac, A. Keller, and Hocenski, “Neural Network Based Detection of Defects in Texture Surfaces,” IEEE ISIE,2005, Dubrovnik, Croatia, pp.1255-1260 . 8. M. Ghazvini, S. A. Monadjemi, N. Movahedinia, and K. Jamshidi, “Defect Detection of Tiles Using 2D-Wavelet Transform and Statistical Features,” World Academy of Science, Engineering and Technology, 2009. 9. N. G. Kingsburg, “Complex Wavelets and Shift Invariance,” Proceedings IEE Colloquiom on Time-Scale and Time-Frequency Analysis and Applications, London, 2000. 10. M. Mirmehdi, X. Xie, and S. Jasjit, ed. Texture Analysis. London: Imperial college, 2008. 11. S. Abdelmounaime, F. B. Mohamed, I. Tahar, “La Transform en ondelettes pour l’extraction de la texture-couleur. Application a la classification combinee des images (HRV) de SPOT,” International Journal of Remote Sensing, Vol.28, No. 18, 2006, pp. 3977-3990. 12. J. Bonnel, A. Khademi, S. Krishnan, and C. Loana, “Small bowel image image classification using Cross-co-occurrence matrices on wavelet domain,” Journal of Biomedical Signal Processing and Control, Vol. 4, 2009, pp. 7-15. 13. H. Zhang, Image Processing Via Undecimated Wavelet Systems, Doctor of Philosophy Thesis, March, 2000, Rice University. 14. S. Mallat, “Multiresolution approximation and wavelets orthonormal bases of L2(r),” Trans Amer. Math. Soc, Vol. 315, September 1989, pp. 69-87. 15. M. J. Shensa, “The discrete wavelet transform: wedding the a trous and mallat algorithm,” IEEE Transactions on Signal Processing, Vol. 40, No. 10, December 1992, pp. 2464-2482. 16. R. Haralick, “Statistical and Structural Approaches to Texture,” Proceedings of the IEEE, Vol. 67, No 5, 1979, pp. 786-803. Authors: A. Ganguly, Manoj K Kowar, H. Chandra Paper Title: Preventive Maintenance of Rotating Machines Using Signal Processing Techniques Abstract: This paper presents a method for analyzing the vibration signals of rotating machines and diagnoses preventive maintenance requirements using Vibration Signature Analysis Technique. The concept of Vibration Signature Analysis of Rotating Machines lies on the fact that all rotating machines in good condition have a fairly stable vibration pattern, which can be considered its 'Signature'. Under any anomalous condition of working of such machines, the vibration pattern gets changed. The amount of variation can be detected and the nature of anomalies can be analyzed to get an idea about the malfunctioning of the rotating machine. In order to develop the technique to be applied, it is proposed to simulate the vibration signals of a rotating machines using MATLAB to store the signature of rotating machines under healthy conditions. Deformation can now be introduced in the signature or can be acquired from other sources. Such deformed signals are to be processed in order to know the type of defect the rotating parts of the machine is suffering from. Based on the type of defect, preventive maintenance schedule can be proposed. This paper also aims at overcoming the limitations of traditional Vibration Signature Analysis techniques. 8. Keywords: Vibration Signals, Signature Analysis, Signal Processing, Rotating machines, Preventive Maintenance. 35-40
References: 1. Jack,L.B. and Nandi,A.K., Genetic Algorithm for feature selection in machine condition monitoring with vibration signals, IEE Proceedings on Vision, Image and Signal Processing, 147; 2000; 205-212. 2. Li,B.,Chow, M. Y., Tipuswan and Y. Hung, J.C., Neural Network based Motor Bearing Fault Diagnosis, IEEE Transactions on Industrial Electronics, 47(5); 2000; 1060-1069. 3. Mellor P.H., Roberts D., Turner D. R., Lumped parameter thermal model for electrical machines of TEFC design, IEE Proceedings B, 138; 1995; 208-219 4. O. I. Okoro, Steady and transient states thermal analysis of induction machine at blocked rotor operation, IEE Proceedings B, 20(4); 2005, 730-736. 5. Randall R.B. Vibration Signature Analysis : Techniques & Instrument Systems, Proceedings of Noise, Shock and Vibration Analysis at Monash University, Melbourne, May 1974. 6. J. Ellison , C. J. Moore and S. J. Yang, Methods of measurement of acoustic noise radiated by an electric machine, Proceeding of IEE, 118(1); 1971; 174-184. 7. Neelam Mehala and Ratna Ddahiya, An approach of condition monitoring of induction motor using MCSA, International Journal of Systems Applications, Engineering and Development, 1(1); 2007; 13 – 17. Authors: Ravi M. Potdar, Manoj K. Kowar, Amit Biswas, Mayur Amtey Paper Title: Multi-Scale Domain Classification Based Heart Sound Compression Abstract: In recent days, fractal compression has gained a wide popularity due to its inherent features and efficiency in compressing data. In the present communication, fractal compression technique has been applied on heart sound signals for effective compression. Fractal heart sound coding based on the representation of a heart sound signal (1D or vector) by a contractive transform, on the sound data, for which the fixed point (reconstructed heart sound) is close to the original heart sound. The work is intended to provide an approach on this process by introducing the idea of multi-scale Domain pool classification using Variance Fractal Dimension (VFD) based on complexity of the heart sound data. A pre-processing analysis of the heart sound data by VFD to identify the complexity of each sound data samples block for classification has been undertaken. The performance result of the present work has focused in terms of good fidelity signal reconstruction versus encoding time and amount of compression.
Keywords: Phonocardiogram, Fractal Compression, Variance Fractal Dimension, Domain Classification.
9. References: 1. S.P. Collins, P. Arand, CJ Lindsell, WF Peacock, AB Storrow, “Prevalence of the third and fourth heart sound in asymptomatic adults,” In 41-44 Congestive Heart Failure, 2005,Vol. 11(5), pp. 242-247. 2. M. Kompis and E. Russi, "Adaptive Heart-Noise Reduction of Lung Sounds Recorded by a Single Microphone," In Proceedings of International Conference on Engineering in Medicine and Biology Society , 1992,Vol. 2, pp. 691-692. 3. B. N. Robert. Noninvasive Instrumentation and Measurement in Medical Diagnosis, CRC Press, 2002. 4. Chissanuthat Bunluechokchai, Weerasak Ussawawongaraya, “A Wavelet-Based Factor for Classification of Heart Sounds with Mitral Regurgitation”, International Journal of Applied Biomedical Engineering, 2009,Vol. 2, No. 1. 5. M. F. Barnsley, L. Hurd, “Fractal Image Compression”, Wellesley 1993. 6. J. C. Hart, “Fractal Image Compression and Recurrent Iterated Function Systems”, IEEE Computer Graphics and Applications, 1996, Vol. 16, No. 4, 25-40. 7. Bi-Qiang Du, Gui-Ji Tang, “Fractal Data Compression Algorithm for Vibration Signal in Fault Diagnosis”, IEEE proceedings international conference on Wavelet Analysis and Pattern Recognition, Beijing, China, 2-4 Nov. 2007. 8. El-Bahlul Fgee, W. J. Phillips and W. Robertson, “Comparison Audio Compression using Wavelets with other Audio Compression Schemes”, proceedings of the 1999 IEEE Canadian Conference on Electrical and Computer Engineering, Edmonton, Alberta, Canada, May 9-12, 2009. 9. J. Gnitecki, Z. Moussavi, H. Pasterkamp, “Classification of Lung Sounds during Bronchial Provocation Using Waveform Fractal Dimensions”, San Francisco, CA, USA, Proceedings of the 26th Annual International Conference of the IEEE EMBS, Sep. 2004. 10. Dietmar Saupe, Matthias Ruhl, “Evolutionary Fractal Image Compression”, IEEE International Conference on Image Processing (ICIP'96), Lausanne, Sept. 1996. Authors: Bidishna Bhattacharya, Kamal K.Mandal, Niladri Chakravorty Cultural Algorithm Based Constrained Optimization for Economic Load Dispatch of Units Paper Title: Considering Different Effects Abstract: This paper introduces an efficient evolutionary programming based approach of cultural algorithm which is a probabilistic optimum search method using genetics and evolution theory to solve different economic load dispatch problems. The proposed algorithm is a powerful population-based algorithm in the field of evolutionary computation which can efficiently search and actively explore solutions. Also it may be employed to handle the equality and inequality constraints of the ELD problems. The salient features of its knowledge space make the proposed cultural algorithm attractive in large-scale highly constrained nonlinear and complex systems. In this paper cultural algorithm combines with evolutionary programming technique to take care of economic dispatch problem involving constraints like power balance constraints, generator limit constraints, valve point loading effect, ramp rate limits, prohibited operating zone, and transmission losses etc because of cultural algorithm's flexibility. The effectiveness and feasibility of the proposed method is tested with one example of thirteen generator system considering valve point effect and one example of three generator system considering ramp rate limits, prohibited operating zone and transmission losses. Additionally the proposed algorithm was compared with other evolutionary methods like particle swarm optimization technique, genetic algorithm, evolutionary programming etc. It is seen that the proposed method can produce comparable results. 10. Keywords: Cultural algorithm, cultural based evolutionary algorithm, evolutionary programming, ELD, prohibited 45-50 operating zone, ramp rate limits, valve-point loading.
References: 1. D.P. Kothari and J.S. Dhillon, Power System Optimization, Prentice-Hall of India, 2006. 2. A.J.Wood, and B.F. Wollenberg, Power Generation, Operation and Control, John Wiley & Sons, New York, 1984. 3. C.L.Chen, and S.C. Wang, "Branch-and bound scheduling for thermal generating units," IEEE Trans. on Energy Conversion, Vol. 8, No. 2, pp. 184-189, June 1993. 4. K.Y.Lee, e t al., "Fuel cost minimization on for both real- and reactive power dispatches," IEE b.C, Gener. Trsns. & Distr., 131, (3). pp. 85- 93,1984. 5. G.B. Sheble, and K. Brittig, "Refined genetic algorithm-economic dispatch example," IEEE Paper 94 WM 199-0 PWRS, presented at the IEEE/PES 1994 Winter Meeting. 6. D.C.Walters, and G.B. Sheble, "Genetic algorithm solution of economic dispatch with valve point loading," IEEE Trans. on Power Systems, Vol. 8, NO. 3, p ~1.32 5-1332, August 1993 7. A. G. Bakirtzis, P. N. Biskas, C. E. Zoumas, and V. Petridis, “Optimal power flow by enhance genetic algorithm,” IEEE Trans. Power Systems, vol. 17, pp. 229–236, May 2002. 8. D. B. Fogel, “An introduction to simulated evolutionary optimization,” IEEE Trans. Neural Networks, vol. 5, pp. 3–14, 1994. 9. D. B. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. Piscataway, NJ: IEEE Press, 1995. 10. K. Chellapilla and D. B. Fogel, “Two new mutation operators for enhanced search and optimization in evolutionary programming,” in SPIE Int. Symp. Optical Science and Engineering Instrum. Conf., 3165:Applic. Soft Comput.. Bellingham, WA: SPIE Press, pp. 260–269 11. K. P. Wong and Y. W. Wong, “Thermal generator scheduling using hybrid genetic/simulated annealing approach,” in IEE Proc. C, vol. 142, July1995, pp. 372–380. 12. K. P. Wong and C. C. Fung, “Simulated annealing based economic dispatch algorithm,” in Proc. Inst. Elect. Eng. C, vol. 140, 1992, pp. 544– 550. 13. R.G.Reynolds, “An Introduction to Cultural Algorithms, ” in of the 3rd Annual Conference on Evolutionary Programming, World Scientific Publishing, pp 131–139, 1994 14. J. Liu, H. Gao, J. Zhang, and B. Dai, “Urban power network substation optimal planning based on Geographic Cultural Algorithm,” The 8th Int. Conf. Power Engineering. pp. 500-504, 2007. 15. X. Yuan, and Y. Yuan, “Application of Cultural Algorithm to generation scheduling of hydrothermal systems,” Energy Conversion and Management, vol. 47, issue 15-18, pp. 2192-2201, 2006. 16. A R. Seifi, “A new Hybrid Optimization method for optimum distribution capacitor planning,” Modern Applied Science, CCSE, vol. 3, no. 4, pp. 196-202, April 2009. 17. Coelho L.D.S.,Souza R.C.T.,Mariani V.C., Improved differential evolution approach based on cultural algorithm and diversity measure applied to solve economic load dispatch problems, Mathematics and Computers in Simulation 79, 3136-3147.(2009) 18. Chan-Jin Chung,.R.G.Reynolds , “CAEP:AnEvolution-based Tool for real valued Function Optimization using Cultural algorithms”, International Journal on Artificial Intelligence Tools,7(3).(1998) 19. Y.H.Song,, G.S.Wang,, P.Y.Wang,, and Johns, A. T., “Environmental/Economic Dispatch using Fuzzy Logic Controlled Genetic Algorithm,” Proc.I.E.E Generation, Transmission and Distribution, Vol. 144, No. 4,pp. 377–382.(1997) 20. IEEE Committee Report, "Present practices in the economic operation of power systems," IEEE Trans. on Power Apparatus and Systems, Vol. PAS-90, pp. 1768-1775, July/Aug. 1971 21. Wang C. and S.M. Shahidehpour, "Effects of ramp-rate limits on unit commitment and economic dispatch," IEEE Trans. on Power Systems, Vol. 8, NO. 3, pp. 1341-1350, August 1993. 22. Po-Hung Chen and Hong-Chan Chang, Large-Scale Economic Dispatch by Genetic Algorithm, IEEE Transactions on Power System. Vol. 10. No. 4. November 1995 23. T.A.A..Victoire ,A.E.Jeyakumar , Hybrid PSO-SQP for economic dispatch with valve-point effect, Electric Power system research 71 (1)51-59. (2004). 24. C. L. Chiang, “Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels”, IEEE Transactions on Power Systems, Vol. 20, no. 4, pp. 1690-1699, 2005. 25. N. Sinha, R. Chakrabarti and P. K.Chattopadhyay, “Evolutionary programming techniques for economic load dispatch”, IEEE Transactions on EvolutionaryComputation, Vol. 7, no. 1, pp. 83-94, 2003. 26. Noman N,Iba H,Differential Evolution for economic load dispatch problem, Electric Power Systems Research 78(3)1322-1331(2008) 27. B. K. Panigrahi, V. R. Pandi, and S. Das, “Adaptive particle swarm optimization approach for static and dynamic economic load dispatch,”Energy Convers. Manage., vol. 49, no. 6, pp. 1407–1415, 2008. 28. R. Naresh, J. Dubey, and J. Sharma, “Two phase neural network based modeling framework of constrained economic load dispatch,” Proc.Inst. Elect. Eng., Gen., Transm., Distrib., vol. 151, no. 3, pp. 373–378,May 2004. Authors: Ram Kumar Singh, Akanksha Balyan Paper Title: Approach to Software Maintainability Prediction Versus Performance Abstract: The software maintainability is one of the most significant aspects in software evolution for the software product. Due to the complexity of chase maintenance demeanor, it is difficult to accurately anticipate the price and risk of maintenance afterward delivery of the software products. The value of a software system results from the interaction between its functionality and quality attribute (performance, reliability and security) and the market-place. The software maintainability is viewed considered as an inevitable evolution procedure driven through maintenance demeanor. Traditional product cost model have focused on the short term development cost of the software product. A HMM (Hidden Markov Model) is applied to simulate the maintenance demeanor demonstrated as their potential occurrence probabilities. The software metric function is the measurement of the software quality products and its measurements results of a software product existence delivered combined to from health index of the software product. When the occurrence probabilities of maintenance demeanor reach certain number which is calculate as the denotation of worsening position of software product, the software product can be considered as obsolete. The longer time, more beneficial the maintainability would be. We believe on the architectural approach to price-modeling will be able to capture these concerns so that the software can reason about the risk I the system and 11. price of mitigating them. 51-54 Keywords: Software maintainability, HMM (Hidden Markov Model), Performance modes between availability and Software metrics.
References: 1. S. Balsamo, P. Inverardi and B. Selic, Proc. Third ACM Workshop on software and Performance, Performance, Italy Rome, July 24-27, 2002. 2. Boehm B. , “Software Engineering Economics”, Prentice Hall (1981). 3. M. Hilton, “Information Technology Workers in the New Economy”, Monthly Labor Review, June 2001, pp. 41-45. 4. ISO/IEC 14764:2006 http://www.iso.org/iso/catalogue_detail.htm?csnumber=39064 5. R.L Glass, “Frequently Forgotten Fundamental Facts about Software Engineering”, IEEE Software, May/June 2001. 6. Cem Kaner, and Walter P. Bond “Software Engineering Metrics: What Do They Measure and how Dow We Know?”, 10TH INTERNATIONAL SOFTWARE METRICS SYMPOSIUM METRIS, 2004. 7. Lawrence R. Rabiner (February 1989). “A tutorial on Hidden Markov Models and selected applications in speech recognition”, Proceedings of the IEEE 77 (2); 1989. 8. Steve Cornwell, “Code Complete: A Practical Handbook of Software Construction”, Microsoft Press; 2nd edition (June 9, 2004). 9. Thomas McCabe, “A complicity Measure”, IEEE Transaction on Software Engineering, VOL, SE-2, No. 4, 1986. Authors: R. Vijay Paper Title: Intelligent Bacterial Foraging Optimization Technique to Economic Load Dispatch Problem Abstract: Bacterial Foraging optimization (BFO) is a swarm intelligence technique used to solve problem in 12. power systems. The algorithm is based on the group foraging behaviour of Escherichia coli (E-Coli) bacteria present in human intestine. This social foraging behaviour of E.coli bacteria has been used to solve optimization problems. 55-59 In this paper, an overview of the biology of bacterial foraging and the pseudo-code that models this process also explained. This paper presents a novel BFO to solve Economic Load Dispatch (ELD) problems. The results are obtained for a test system with three and thirteen generating units. In this paper the performance of the BFO is compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results clearly show that the proposed method gives better optimal solution as compared to the other methods.
Keywords: Bacteria Foraging Optimization, Escherichia coli Economic load Dispatch, Genetic Algorithm, Particle Swarm Optimization.
References: 1. Kevin M.Passino, “Bacterial Foraging optimization,” International Journal of Swarm Intelligence Research, pp. 1-16, Jan-Mar 2010. 2. Selvakumar, and K. Thanushkodi, "A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems," IEEE Trans. Power Systems, vol. 22, no. 1, pp. 42-51, Feb. 2007. 3. K.P Wong and J.Yuryevich, "Evolutionary Programming Based Algorithm for Environmentally Constrained Economic Dispatch",IEEE transaction on Power Systems”, Vol. 13,No.2,pp 301,May 1998 4. H. Chowdhury and S. Rahman, "A review of recent advances in economic dispatch," IEEE Trans. Power Syst., vol. 5, no. 4, pp. 1248- 1259, Nov. 1990. 5. BijayaKetanPanigrahi,Yuhui Shi, and Meng-Hiot Lim “Handbook of Swarm Intelligence Concepts, Principles and Applications,” Springer pp.487-502, 2011. 6. Jason Brownlee “Clever Algorithms: Nature-Inspired Programming Recipes,” Jason Brownlee pp.257-264, 2011. 7. VeyselGazi and Kevin M. Passino “Swarm Stability and Optimization,” Springer pp.233-249, 2010. 8. J. Wood, and B. F. Wollenberg, “Power generation operation and control,” John Willey and Sons, 2nd Ed, pp. 39-43, 1996. 9. T. Yang, P. C. Yang, and C. L. Huang, “Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions,” IEEE Trans. Power Syst., vol. 11, no. 1, pp. 112–118, Feb. 1996 10. Y. Liu and K. M. Passino, “Biomimicry of social foraging bacteria for distributed optimization: Models, principles, and emergent behaviors,” Journal Optimal. Theory Appl., Vol. 115, no. 3, pp. 603–628, Dec. 2002 11. SibylleD.Muller, JarnoMarchetto, StefanoAiraghi, and PetrosKoumoutsakos,”Optimization Based on Bacterial Chemotaxis,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, February 2002. 12. Kevin M.Passino, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Syst. Mag., Vol. 22, no. 3, pp. 52–67, Jun. 2002. 13. N. Sinha, R. Chakrabarti, and P. K. Chattopadhyay, “Evolutionary programming techniques for economic load dispatch,” IEEE Trans. Evol. Comput., vol. 7, no. 1, pp. 83–94, Feb. 2003. 14. Kevin M. Passino “Biomimicry for Optimization, Control, and Automation,” Springer Verlag London, pp. 768-816, 2005. 15. J.-B. Park, K.-S. Lee, J.-R. Shin, and K. Y. Lee, “A particle swarm optimization for economic dispatch with nonsmooth cost functions,” IEEE Trans. Power Syst., vol. 20, no. 1, pp. 34–42, Feb. 2005. 16. S. Das, A.Biswas, S. Dasgupta, and A. Abraham, “Foundations of Computational Intelligence,”Vol 3: Global Optimization, chapter Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications, pages 23–55. Springer, 2009. 17. J. Hazra, A.K.Sinha, “Environmental Constrained Economic Dispatch using Bacteria Foraging Optimization,” IEEE Transactions on Evolutionary Computation,2009. 18. Swagatam Das, S.Dasgupta, A. Biswas, Ajith Abraham, “On Stability of the Chemotactic Dynamics in Bacterial-Foraging Optimization Algorithm,” IEEE Transactions on systems, man, and cybernetics, Vol. 39, No. 3, May 2009. 19. Sambarta Dasgupta,ArijitBiswas,Swagatam Das, BijayaKetanPanigrahi and Ajith Abraham, “A Micro-Bacterial Foraging Algorithm for High-Dimensional Optimization,” IEEE Congress on Evolutionary Computation, pp.785-792, 2009. 20. M. E. El-Hawary and G. S. Christensen, “Optimal Economic Operation of Electric Power System”, New York: Academic, 1979. Authors: Srikanth.S, M.Jagadeeswari Paper Title: High Speed VLSI Architecture for Multilevel Lifting 2-D DWT Using MIMO Abstract: The Discrete Wavelet Transform (DWT) Lifting architecture is a powerful signal analysis technique for non-stationary data. High speed implementation of this architecture is a challenging task. This paper proposes an efficient multi-input/multi-output VLSI architecture (MIMO) for two-dimensional lifting-based discrete wavelet transform (DWT). Computing time for this high speed architecture is as low as N2/M for an N X N image with controlled increase of hardware cost. M is the throughput rate. The experimental results show that proposed architecture provides high throughput and power consumption compared to the conventional architecture.
Keywords: Discrete Wavelet Transform Lifting Scheme, MIMO, Memory Buffer, SISO.
References: 1. S. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 11, No. 7, pp. 674–693, July 1989. 2. W. Sweldens, “The Lifting Scheme: A Custom-Design Construction of Biorthogonal Wavelets,” Applied and Computational Harmonic Analysis, Vol. 3, pp.186-200, 1996. 13. 3. B.F.Wu and C.F. Lin, “High-performance memory-efficient pipeline architecture for the 5/3 and 9/7 discrete wavelet transform of JPEG 2000 codec,” IEEE Trans. on Circuit and System for Video Technology, vol.15, no.12, pp.1615-1628, Dec. 2005. 4. Andra, K. Chakrabarti, C. and Acharya, T. “A VLSI Architecture for Lifting-based Forward and Inverse Wavelet Transform,” IEEE 60-64 Trans.Signal Process., Vol. 50, No. 4, pp. 966–977, 2001. 5. Wu, P.C. and Chen, L.G. “An Efficient Architecture for Two-Dimensional Discrete Wavelet transform,” IEEE Trans. Circuits Syst. Video Technol., Vol. 11, No. 4, pp. 536–545. 6. Huang, C.T. Tseng, P.C. and Chen, L.G. “Generic RAM-based Architectures for two-Dimensional Discrete Wavelet Transform with Line based method,” IEEE Trans. Circuits Syst. Video Technol., Vol. 15, No. 7, pp. 910–920, 2005. 7. Huang, C.T. Tseng, P.C. and Chen, L.G. “Analysis and VLSI architecture for 1 D and 2 D DWT,” IEEE Trans. Signal Process., Vol.53, No.4, pp.1575-1586, Apr. 2005. 8. Xin Tian , Lin Wu , Yi-Hua Tan “Efficient Multi-Input/Multi-Output VLSI Architecture for Two-Dimensional Lifting-Based Discrete Wavelet Transform,” IEEE Transactions on Computers, vol. 60, no. 8, August 2011. 9. Chrysafis C. and Ortega A., “Line-Based, Reduced Memory, wavelet Image Compression,” IEEE Trans. Image Processing., vol. 9, no. 3, pp. 378-389, Mar.2000. 10. Dillen G. et al., “Combined Line-Based Architecture for the 5-3 and 9-7 Wavelet Transform of JPEG2000,” IEEE Trans. Circuits and Systems for Video Technology, vol. 13, no. 9, pp. 944-950, Sept. 2003. 11. Barua, S. Carletta J.E., Kotteri K.A., and Bell A.E., “An Efficient Architecture for Lifting-Based Two-Dimensional Discrete Wavelet Transform, “Integration, the VLSI J., vol. 38, no. 3, pp. 341-352, 2005. 12. Cheng C. and Parhi K.K., “High-Speed VLSI Implementation of 2D Discrete Wavelet Transform,” IEEE Trans. Signal Processing, vol. 56, no. 1, pp. 393-403, Jan. 2008. 13. Meher, P. K. Mohanty, B. K. and Patra, J. C.( 2008) ‘Memory Efficient Modular VLSI Architecture for High throughput and Low-Latency Implementation of Multilevel Lifting 2-D DWT’. IEEE Transactions on Signal processing, vol. 59, no. 5, may 2011. Authors: Mamatha. T Paper Title: Network Security for MANETS Abstract: A mobile ad hoc network (MANET) is a network consisting of a collection of nodes capable of communicating with each other without the help from a network infrastructure. Although security issues in mobile ad hoc networks have been a major focus in the recent years, the development of fully secure schemes for these networks has not been entirely achieved till now. MANETs have a unique characteristics and constraints that make traditional approaches to security inadequate. The lack of an infrastructure exacerbates the situation of using shared secret keys or authentication among members. Therefore, the issues of authentication, key distribution and intrusion detection require different methods, which are discussed here. In this paper, we propose to combine efficient techniques from elliptic curve cryptography (ECC) and a distributed intrusion detection system (IDS) based on threshold cryptography. And also propose to use a distributed certifying authority (CA) along with per-packet per- hop authentication for addressing the issues mentioned above. The model assumes that no single node can be trusted and relies instead on a distributed trust model.
Keywords: mobile ad hoc network (MANET), elliptic curve cryptography (ECC), distributed certifying authority, certifying authority (CA), threshold cryptography, intrusion detection (ID)
References: 1. B. Mukherjee, T. L. Heberlein, and K. N. Levitt, “Network Intrusion Detection,” IEEE Network, 8(3): 26–41, 1994. 2. J. S. Balasubramaniyan et al., “An Architecture for Intrusion Detection using Autonomous Agents,” Proceedings of the Fourteenth Annual 14. Computer Security Applications Conference, 1998 . 3. M. Asaka et al., “A Method of Tracing Intruders by Use of Mobile Agents,” in proceedings of the Internet Society, 1999 4. S. Kumar and E. Spafford, “An Application of Pattern Match in Intrusion Detection,” Technical Report 94-013, Dept. of Computer Science, 65-68 Purdue University, 1994. 5. M. Crosbie and G. Spafford, “Active Defense of a Computer System Using Autonomous Agents,” Technical Report 95-008, COAST Group, Dept. of Computer Science, Purdue University, 6. Y. Desmedt, “Some Recent Research Aspects of Threshold Cryptography,” in proceedings of the First International Workshop on Information Security: 158–173, 1997. 7. H. Luo, P. Zerfos, J. Kong, S. Lu, and L. Zhang, “Self-Securing Ad Hoc Wireless Networks,” in proceedings of the 2002 IEEE Symposium on Computers and Communications, Italy, July 2002. 8. A. Khalili, J. Katz, and W. Arbaugh, “Toward Secure Key Distribution in Truly Ad-Hoc Networks,” in proceedings of the 2003 Symposium on Applications and the Internet Workshops 9. G.V.S. Raju, G. Hernandez, and Q. Zou, “Quality of service routing in adhoc networks,” IEEE WCNC 2000, Vol. 1, 2000 10. G.V.S. Raju and G. Hernandez, “Routing in Ad hoc networks”, in proceedings of the IEEE–SMC International Conference, 11. G.V.S. Raju and J. Charoensakwiroj, “Wireless Communications,” Annual Review of Communications, Vol. 57 (Chicago: IEC, 2004). 12. L. Zhou and Z. Haas, “Securing Ad Hoc Networks”, IEEE Network Magazine, 13(6), November/December 1999 . 13. P Papadimitratos and Z. Haas, “Secure Routing for Mobile Ad hoc Networks,” in proceedings of the Communication Networks and Distributed Systems Modeling and Simulation Conference, January 2002. 14. A. Perrig, R. Szewcyzk, V. Wen, D. Culler, and J.D. Tygar, “Spins: Security Protocols for Sensor Networks,” in proceedings of Mobile Computing and Networking 2001. 15. G.V.S. Raju and R. Akbani, “Elliptic Curve Cryptosystems and its Applications,” in the proceedings of the IEEE-SMC Conference, October 2003. 16. V.S. Miller, “Use of Elliptic Curves in Cryptography,” in Advances in Cryptology (Proceedings of CRYPTO 1985), Springer Verlag Lecture Notes in Computer Science 218, 1986, pp. 417–426. 17. G.V.S. Raju and Rehan Akbani, “Some Security Issues in Mobile Ad-hoc Networks,” in proceedings of the Cutting Edge Wireless and IT Technologies Conference, November 2004. Authors: Ponrajan. P, Jebarani Evangeline. S, Jayakumar. J Paper Title: ANFIS Based Torque Control of Switched Reluctance Motor Abstract: This paper develops an ANFIS based torque control of SRM to reduce the torque ripple. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. This controller realizes a good dynamic behavior of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI). The above controller was realized using MATLAB/Simulink.
Keywords: ANFIS, Torque Control, Switched Reluctance Motor.
References: 15. 1. I. Husain, and M. Ehsani,“Torque Ripple minimization in Switched Reluctance Motor Drives by PWM Current Control”, IEEE Transaction on Power Electronics., vol.11, no. 1, pp. 83-88, 1996. 69-72 2. N. C. Sahoo,“A Study on Application of Modern Control techniques for Torque Control of Switched Reluctance Motors,” Ph.D. Thesis, National University of Singapore, 2001. 3. R.S. Wallace, D.G. Taylor, “Low-torque-ripple switched reluctance motors for direct-drive robotics,” IEEE Trans. on Robotics and Automation, vol. 7, no. 6 , pp. 733-742, Dec 1991. 4. I. Husain,“Minimization of torque ripple in SRM drives”, IEEE Transaction on Industrial Electronics”, vol. 49, no. 1, pp. 28-39, Feb. 2002. 5. K.J. Tseng, Shuyu Cao, “A SRM variable speed drive with torque ripple minimization control”, IEEE APEC vol. 2, pp. 1083-1089, 2001. 6. C. Shang, D. Reay, and B. Williams, “Adapting CMAC neural networks with constrained LMS algorithm for efficient torque ripple reduction in switched reluctance motors,” IEEE Transactions on Control Systems Technology, vol. 7, No. 4, pp. 401-413, July 1999. 7. Z. Lin, D. S. Reay, B. W. Williams and X. He, “Torque ripple reduction in switched reluctance motor drives using B-spline neural networks,” IEEE Transactions on Industry Applications, vol. 42, no. 6, pp. 1445-1453, Nov./Dec. 2006. 8. J. G. O' Donovan, P. J. Roche, R. C. Kavanagh, M. G. Egan, and J. M. D. Murphy, “Neural network based torque ripple minimisation in a switched reluctance motor,” in 20th International Conference on Industrial Electronics, Control and Instrumentation, vol.2, pp. 1226- 123, 1994. 9. K. M. Rahman, A. V. Rajarathnam and M. Ehsani, “Optimized instantaneous torque control of switched reluctance motor by neural network,” IEEE Industry Application Society Annual Meeting, pp. 556-563, 1997. 10. Y. Cai and C. Gao, “Torque ripple minimization in switched reluctance motor based on BP neural network,” in 2nd IEEE Conference on Industrial Electrics and Applications, pp.1198-1202, 2007. 11. M. Brown, K. M. Bossley, D. J. Mills, and C. J. Hams, “High Dimensional Neurofuzzy Systems: Overcoming the curse of Dimensionality,” IEEE International Conference. on Fuzzy Systems, vol.4, pp.2139- 2146, 1995. 12. C. T. Lin and C. S. George, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, 1st ed., New Jersey: Prentice Hall PTR, 1996, p.242. Authors: L. Sreenivasulu Reddy Paper Title: A New Modal of Hill Cipher Using Non – Quadratic Residues Abstract: This paper is improved the security on Hill cipher by using Non-Quadratic residues of a prime number p≥53. In Hill Cipher, a plain text is encrypted using a fixed value ‘26’ during the computation. The paper explains how using Non-Quadratic residues during encryption improves security.
Keywords: Modular arithmetic inverse, inner key, outer key, linear congruence’s, Quadratic residues, Non- Quadratic residues, GL (n, Z).
16. References: 1. Introduction to Analytic Number Theory, fifth edition. T. Apostol .Undergraduate Texts in Mathematics, Springer-Verlag, New York, 1995 73-74 2. An introduction to the theory of numbers, 5th ed.,I. Niven, H. S. Zuckerman and H. L. Montgomery, Wiley, New York, 1991. 3. Cryptography and Network security, William stallings, 3rd Edition, pearson Education 4. On the Modular Arithmetic Inverse in the cryptology of Hill cipher, 2005. V.U.K. sastry, V.Janaki, proceedings of North American Technology and Business conference, canada 5. Hill’s System of Data Encryption prepared by” Ben Kohler and Michael Ziegler” 6. A.Vanstone. Handbook of Applied cryptography Menezes, Alfred, paul C.Van Oorschot, and scott .New York: CRC press,1997 7. Saroj KumarPanigrahy,Bindudendra Acharya and debasish Jena,Image Encryption Using Self-Invertible Key Matrix of Hill Cipher Algorithm, 1st International Conference on Advances in Computing, Chikhli, India, 21-22 February 2008. 8. G.Sivagurunathan, V.Rajendran and Dr.T.Purusothaman. Classification of Substitution Ciphers using Neural Networks . IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.3, March 2010. Authors: L. Sreenivasulu Reddy
Paper Title: Efficient on-Board J 2 -RSA Key Generation with Smart Cards Abstract: Public key cryptography gained increasing attention from both companies and the end users who wish to use this emerging technology to secularize a wide variety of applications. A major consequence of this trend has been the growing significance of the public-key smart cards. A smart card is a tiny secure crypto processor embedded within a credit card-size or smaller(like the GSM SIM) card which provide encryption, decryption as well
as key generation within it’s security perimeter. J 2 -RSA is a simple and easy to implement public key cryptographic algorithm. Today -RSA key keys range from 512 bits to 2048 bits and some bodies envision 4096- bit -RSA keys in near future, like RSA key. In this paper, I will present a study of efficient algorithms involved in on-board -RSA key generation[1]. 17. Keywords: -RSA , Jordan arithmetic function, Prime Number and Co-primes 75-79
References: 1. J. J. Quisquater and B. Schneier, Smart Card Crypto- Coprocessors for Public-Key Cryptography, vol. 1820 of Lecture Notes in Computer Science, Springer Verlag, 2000. 2. C,. K. Koc,, "High-Speed RSA Implementation," Tech. Rep. TR 201, RSA Laboratories, 73 pages, November 1994. 3. M. Joye, P. Paillier, and S. Vaudenay, "Efficient Generation of Prime Numbers," Cryptographic Hardware and Embedded Systems, pp. 340- 354, Aug. 2000. 4. J. F. Dhem, Design of an Efficient Public-Key Cryptographic Library in RISC-based Smart Cards, Ph.D. thesis, Unbiversit Catholique de Louvain, May 1998. 5. Chenghuai Lu, A. L. M. Santos, and F. R. Pimentel, "Implementation of Fast RSA Key Generation in Smart Cards," in Proceedings of the 2002-ACM Symposium on Applied computing. 2002, pp. 214-220, ACM Press. 6. N. Feyt and M. Joye, "A better use of smart cards in pkis," Gemplus Developer Conference, Nov. 2002. 7. N. Feyt, M. Joye, and P. Paillier, "Off-line/on-line generation of RSA keys with smart cards." 2nd International Workshop for Asian Public Key Infrastructures, pp. 153-158, Oct. 2002. Authors: L. Sreenivasulu Reddy, V. Vasu, M. Usha Rani Scheduling Algorithm Applications to Solve Simple Problems in Diagnostic Related Health Care Paper Title: Centers Abstract: Scheduling algorithms focuses on the applications of analytical methods to facilitate better decision making. This paper aims to raise the awareness of diagnostic specialists with regard to practical scheduling algorithm applications. Scheduling algorithm applications used as part of mainstream decision making by diagnostic centre specialists. Common people in the real world facing so many solvable problems each and every day in 18. diagnostic centers for malaria parasite checkup. If diagnostic specialist takes proper care then it is solvable simple problems. Also it is a good encouragement to everyone for checking their blood whether it is infected with parasite 80-82 or not. It’s also helpful to supporting staff. We explained basic applications along with problems with suitable simple solutions through scheduling algorithm techniques and graph theory approach too.
Keywords: Microscopy, scheduling algorithms, waiting time, image processing, Malaria parasite.
References: 1. Andrew G Dempster and F Boray Tek: Computer vision for microscopy diagnosis of malaria. Malar J. 2009; 8: 153. 2. Brailsford S: Overcoming the barriers to implementation of operations research simulation models in healthcare. Clinical and Investigative Medicine, 28, (6), 312-315, year 2005. 3. Cayirli T,Veral E,Rosen H:Designing appointment scheduling systems for ambulatory care services. Health Care Manage Sci 2006;9:47- 58. 4. Crane AB. Management engineers: a scientific approach to pinpoint a hospital’s problems and find common-sense solutions. Hosp Health Netw 2007; 81:50-4 . 5. Fone D,Hollinghurst S, Temple M,Round A,Lester N,Weightman A: Systematic review of the use and value of computer simulation modeling in population health and health care delivery. Journal of public health medicine, 25 (4). pp. 325-335. ISSN 1741-3842 6. Krithi Ramamritham and John A. stankovic: Scheduling Algorithms and Operating SystemsSupport for Real-Time Systems. Proceedings of the IEEE :1994. 7. P.M.Rubesh Anand, Vidhyacharan Bhaskar, G.Bajpai and Sam M.Job: Detection of the malarial parasite infected blood images by 3D- Analysis of the cell curved surface. In the Proceedings of the 4th Kuala Lumpur International Conference on Biomedical Engineering, Kuala Lumpur, June 2008. 8. Proudlove N,Boaden R,Jorgensen J: Developing bed managers:the why and the how. Journal of Nursing Management Volume 15, Issue 1, pages 34–42, January 2007. 9. Osamuyimen Igbinosa, Owen Igbinosa, Chenyi Jeffery: A Sequential review on accuracy of detecting malaria parasitemia in developing countries with large restriction on resources. Journal of Medicine and Medical Sciences Vol. 1(9) pp. 385-390 October 2010 . Authors: Mukhwinder Kaur, Bhawna, G.C.Lall Paper Title: An Architecture of Integration Of 802.11 WLAN Network & UMTS Abstract: In Wireless network different technologies used for different purposes like Wireless LAN used for data services and UMTS are used for cellular networks such as provide various voice and data services, WLAN provides data services at high speed. Integration of UMTS and WLAN allows Operator to deploy used services at low cost and high speed. WLAN also allow covering hotspot areas Furthermore the architecture of WLAN and UMTS integration permits a mobile node to continue data connection (packet switch) through WLAN and voice connection (circuit switch) in parallel. In this paper the main features we are explaining WLAN and UMTS architecture along with its advantages and challenges facing during integration and handover scheme from WLAN to UMTS is being proposed.
Keywords: CIRCUIT SWITCH, PACKET SWITCH, HOTSPOT, UMTS, WLAN
References: 1. W. Song , H. Jiang, W. Zhuang, and Xuemin Shen , "Resource management for Qos support in cellular/WLAN interworking," Network, IEEE , vol.19, no.5, pp. 12- 18, Sept.-Oct. 2005. 2. W.Song,W.Zhuang,A.Saleh,” Interworking of 3G cellular networks and Wireless LAN” ,International Journal of Wireless and Mobile Computing, vol.2, no. 4, pp. 237-247, 2007. 3. Matthew Gast, 802.11 Wireless Networks – The Definitive Guide, O’Reilly, 2002. 4. IEEE Std. 802.11b, Supplement to ANSI/IEEE Std. 802.11,1999 Edition, IEEE Standard for Wireless LAN MAC and PHYSpecifications, PDF: ISBN 0-7381-1812-5, January 2000. 5. Aziz, A.; Saad, N.M.; Samir, B.B.; Dept. of Elect. & Electro Eng, Univ. Teknol. Petronas, Tronoh, Malaysia “A comparative analysis of integration schemes for UMTS and WLAN networks “,Circuits and Systems (APCCAS), 2010 IEEE Asia Pacific Conference on 6-9 Dec. 2010. 6. Christine E. Jones, Krishna M. Siva lingam, Prathima Agrawal, Jyh Cheng Chen, A survey of energy efficient network protocols for wireless networks, Wireless Networks 7 (4) (2001) 343–358. 7. A. Helmy, and M. Jaseemuddin, Efficient Micro-Mobility using Intra-domain Multicast-based Mechanisms (M&M), USCCS-TR-01-747, August 2001. 8. A Comparative Analysis of Integration Schemes for UMTS and WLAN Networks Safdar Rizvi, Asif Aziz, N.M. Saad, Brahim Belhaouari 19. Samir, Department of Electrical and Electronic Engineering, University Technology Petrona 31750 Tronoh, Perak, Malaysia, 978-1-4244- 7456-1/10, 2010 IEEE. 9. M.A. Amara,”Performance of WLAN and UMTS integration at the hot spot location using opnet“, 2003-2006 83-87 10. An Architecture for Integrating UMTS and 802.11 WLAN Networks”, Muhammad Jaseemuddin Dept. of Electrical & Computer Engineering, Ryerson University, 2009 11. J. Alba-Laurila, J. Mikkonen, and J. Rinnemaa, Wireless LANAccess Network Architecture for Mobile Operators, IEEE Communications, pp. 82-89, Vol. 39, No. 11, November 2001 12. M. Bauer et al, Comparison of Different Strategies for UMTS and WLAN integration, IPCN 2002. 13. A.K. Salkintzis, "Interworking techniques and architectures forWLAN/3G integration toward 4G mobile data networks," Wireless Communications, IEEE, vol.11, no.3, pp. 50- 61, June 2004 14. Rastin Pries, Andreas M¨ader, Dirk Staehle, and Matthias Wiesen “On the Performance of Mobile IP in Wireless LAN Environments, In Wireless Systems and Mobility in Next Generation Internet”, LNCS vol. 4369, Sitges, Spain, June 2006. 15. G. Dommety, “Fast Handovers for Mobile IPv6”, Internet Draft, July 2001. 16. A. Campbell, J. Gomez, S. Kim, A. Valko, C. Wan, Z.Turanyi, Design, Implementation, and Evaluation of Cellular IP,IEEE Personal Communications, Vol. 7, No. 4, pp. 42-49,August 2000. 17. Vahid Solouk, Borhanuddin Mohd Ali, Daniel Wong “Vertical Fast Handoff in Integrated WLAN and UMTS Networks “, ICWMC 2011, the Seventh International Conference on Wireless and Mobile Communications, 2011 18. F. Zarai, N. Boudriga, M.S. Obaidat. “WLAN-UMTS Integration: Architecture, Seamless Handoff, and Simulation Analysis”. SIMULATION, 82(6): 413-424, 2006 19. A. H. Zahran, B. Liang, A. Saleh, “Signal Threshold Adaptation for Vertical Handoff in Heterogeneous Wireless Networks”. Mobile Networks and Applications, 11: 625-640, 2006 20. Hacker, H. Labiod, G. Pujolle, H. Afifi, A. Serhrouchni, P. Urien. “A New Access Control Solution for a Multi-Provider Wireless Environment”, Telecommunication Systems, 29(2): 131-152, 2005 21. Zhi Ren, Guangyu Wang, Qianbin Chen, Hongbin Li” Modeling and simulation of Rayleigh fading, path loss, and shadowing fading for wireless mobile networks”, Simulation Modeling Practice and Theory 19 (2011) 22. V. Dasarathan, M. Muthukuma, K.N. Elankumaran, Outdoor channel measurement, path loss modeling and system simulation of 2.4 GHz WLAN IEEE802.11g in Indian rural environments, in: Asia-Pacific Microwave Conference, 2007. 23. N. Alsindi, B. Alavi, K. Pahlavan, “Empirical path loss model for indoor relocation using UWB measurements”, IET Electronics Letters 43 (7) (2007) 24. Celal Ceken, Serhan Yarkan ,Huseyin Arslan,” Interference aware vertical handoff decision algorithm for quality of service support in wireless heterogeneous networks”, Computer Networks 54 (2010) 25. S. Yarkan, A. Maaref, K.H. Teo, H. Arslan, “Impact of Mobility on the Behavior of Interference in the field of Cellular Wireless Networks”, Global Telecommunications Conference, 2008. 26. Upendra Rathnayake, Maximilian Ott, Aruna Seneviratne, “Network availability prediction with hidden context”, Performance Evaluation 68 (2011) 27. Zhu, H. Yu, Xining Wang, and H. Chen, Improvement of Capacity and Energy Saving of VoIP over IEEE 802.11 WLANs by A Dynamic Sleep Strategy, IEEE GLOBECOM09 (2009) 28. Qixiang Pang, S.C. Liew, V.C.M. Leung, Performance improvement of 802.11Wireless network with TCP ACK agent and auto-zoom backoff algorithm, in: IEEE Vehicular Technology Conference, 2005 29. M. van Der Schaar, N. Sai Shankar, Cross-layer wireless multimedia transmission: challenges, principles, and new paradigms, IEEE Wireless Communications 12 (4) (2005) 50–58. Authors: S. B. Rashmi, Siva S. Yellampalli Paper Title: Design of Phase Frequency Detector and Charge Pump for High Frequency PLL Abstract: A simple new phase frequency detector and charge pump design are presented in this paper. The proposed PFD uses only 4 transistors and preserves the main characteristics of the conventional PFD. Both PFD and charge pump are implemented using cadence 0.18 μm CMOS Process. The maximum frequency of operation is 5 GHz when operating at 1.8V voltage supply. It has free dead zone. It can be used in high speed and low power consumption applications. This makes the proposed PFD more suitable to low jitter applications.
Keywords: PFD, PLL, High speed.
References: 1. A Simple CMOS PFD for High Speed Applications Nesreen Ismail Institute of Micro-Engineering and Nano-Electronics University Kebangsaan Malaysia, MalaysiaMasuri Othman Institute of Micro-Engineering and Nano-Electronics University Kebangsaan Malaysia, Malaysia 2. Leenaerts, D., J. V. D. Tang, and C. S. Vaucher, 2001. “Circuit Design for RF Transceivers”,Kluwer Academic Publishers, USA, pp. 243- 20. 258.[2] Best, R. E., 1993. “Phase-Locked Loop Design, Simulation, and Application”, 2nd edition,McGraw Hill, New York. 3. Johansson, H., 1998. “Simple Precharged CMOS Phase Frequency Detector”, IEEE Journal of solid state circuits, Vol. 33, No. 2, pp. 295- 88-92 299. 4. Arshak, K., O. Abubaker, and E. Jafer, 2004. “Design and Simulation Difference Types CMOS Phase Frequency Detector for High Speed and Low Jitter PLL”, proceedings of 5th IEEE International Caracas Conference on Devices, Circuits, and Systems, Dominican Republic, Vol. 1, Nov.3-5, pp.188-191. 5. Johnson, T., A. Fard, and D. Aberg, 2004. “An Improved Low Voltage Phase-Frequency Detector with Extended Frequency Capability”, The 47th IEEE International Midwest Symposium on Circuits and Systems, pp. 181-184. 6. Lee, G. B., P. K. Chan, and L. Siek, 1999.“A CMOS Phase Frequency Detector for Charge Pump Phase-Locked loop”, IEEE 42nd Midwest Symposium on Circuits and Systems, Vol.2, pp. 601 – 604. 7. Kondoh, H., H. Notani, T. Yoshimura, H. Shebata, and Y. Matsuda, 1995. “A 1.5-V 250-MHz to 3-V 622-MHz operation CMOS Phase- Locked Loop with Precharge type Phase Detector”, IEICE Trans. Electron, Vol. E78-C, no.4, pp 382-388. 8. Razavi, 2003.“Design of Integrated Circuits for Optical Communications”, McGraw-Hill, USA. 9. El-Hage, M., and F. Yuan, 2003. “ Architectures and Design Consideration of CMOS Charge Pump for Phase-Locked Loops”, Electrical and Computer engineering, IEEE CCECE Canadian Conference, ON, Vol. 1, pp. 223 – 226. 10. Barrett, Curtis. Fractional/Integer-N PLL Basics. Texas Instruments, Wireless Communication Business Unit, August 1999. 11. Chou, Chien-Ping, Lin, Zhi-Ming,and Chen, Jun-Da. “ A 3-PS Dead-Zone Double- Edge-checking Phase-Frequency-Detector With 4.78 GHz Operation Frequency.” The 2004 IEEE Asia-Pacific Conference on Circuits and Systems conference. (2004) : Volume 2, Page(s): 937 – 940. Authors: Sergey Panasenko, Sergey Smagin Paper Title: On Use of Lightweight Cryptography in Routing Protocols Abstract: Cryptographic algorithms become more complex and “heavyweight” every year. This is completely correct from the viewpoint of security. But at the same time such growth increases resource requirements of the algorithms and the complexity of their implementation. This also essentially increases expenses of energy required to perform cryptographic procedures. In this paper we review applications of cryptographic algorithms in routing protocols. Also we analyze the possibilities of use of a lightweight block cipher as a cryptographic kernel to mount various types of cryptographic algorithms which do not require significant resources together over it. We propose to enlarge the set of cryptographic algorithms required to be implemented within IPsec protocol and to include lightweight encryption and authentication algorithms into the set. Implementation of lightweight algorithms to apply in IPsec and related network protocols allows to provide adequate moderate security level in various applications where it is not required to use heavy and strong cryptography; it also allows to save energy and reduce the cost of implementation.
Keywords: Lightweight cryptography, KATAN, block cipher, hash function, routing protocol, RIPv2, IPsec. 21.
References: 93-97 1. A. Poschmann. Lightweight Cryptography from an Engineers Perspective. Workshop on Elliptic Curve Cryptography (ECC 2007). 2. J. Troutman and V. Rijmen. Green Cryptography: Cleaner Engineering Through Recycling. IEEE Security & Privacy, vol. 7, no. 4, pp. 71- 73, July/August, 2009. 3. S. Kent, K. Seo. RFC 4301. Security Architecture for the Internet Protocol. December 2005. 4. G. Malkin. RFC 1388. RIP Version 2. Carrying Additional Information. January 1993. 5. C. Hedrick. RFC 1058. Routing Information Protocol. June 1988. 6. F. Baker, R. Atkinson. RFC 2082. RIP-2 MD5 Authentication. January 1997. 7. R. Rivest. RFC 1321. The MD5 Message-Digest Algorithm. April 1992. 8. R. Atkinson, M. Fanto. RFC 4822. RIPv2 Cryptographic Authentication. February 2007. 9. FIPS PUB 180-2. Secure Hash Standard. National Institute of Standards and Technology, U. S. Department of Commerce – August 2002. 10. H. Krawczyk, M. Bellare, R. Canetti. RFC 2104. HMAC: Keyed-Hashing for Message Authentication. February 1997. 11. G. Malkin, R. Minnear. RFC 2080. RIPng for IPv6. January 1997. 12. S. Kent. RFC 4302. IP Authentication Header. December 2005. 13. S. Kent. RFC 4303. IP Encapsulating Security Payload (ESP). December 2005. 14. V. Manral. RFC 4835. Cryptographic Algorithm Implementation Requirements for Encapsulating Security Payload (ESP) and Authentication Header (AH). April 2007. 15. G. Malkin. RFC 2453. RIP Version 2. November 1998. 16. C. De Cannière, O. Dunkelman, M. Knežević. KATAN & KTANTAN – A Family of Small and Efficient Hardware-Oriented Block Ciphers. CHES’09, LNCS, vol. 5747, pp. 272-288. Springer, 2009. 17. S. Panasenko, S. Smagin. Energy-efficient cryptography: application of KATAN. SoftCOM 2011. 19. International Conference on Software, Telecommunications & Computer Networks. Split – Hvar – Dubrovnik, September 15-17, 2011. Proceedings (SS2 – Special Session on Green Networking). 18. J. Patarin, L. Goubin, M. Ivascot, W. Jalby, O. Ly, V. Nachef, J. Treger, E. Volte. CRUNCH. Specification. // Available at http://csrc.nist.gov – October 28, 2008. 19. E. Volte. CRUNCH. A SHA-3 Candidate. // Available at http://www.voltee.com – 27 February 2009. 20. S. Bradner. RFC 2119. Key words for use in RFCs to Indicate Requirement Levels. March 1997. 21. S. Frankel, R. Glenn, S. Kelly. RFC 3602. The AES-CBC Cipher Algorithm and Its Use with IPsec. September 2003. 22. R. Pereira, R. Adams. RFC 2451. The ESP CBC-Mode Cipher Algorithms. November 1998. 23. R. Housley. RFC 3686. Using Advanced Encryption Standard (AES) Counter Mode With IPsec Encapsulating Security Payload (ESP). January 2004. 24. C. Madson, R. Glenn. RFC 2404. The Use of HMAC-SHA-1-96 within ESP and AH. November 1998. 25. S. Frankel, H. Herbert. RFC 3566. The AES-XCBC-MAC-96 Algorithm and Its Use With IPsec. September 2003. 26. C. Madson, R. Glenn. RFC 2403. The Use of HMAC-MD5-96 within ESP and AH. November 1998. 27. NIST Special Publication 800-38A. Recommendation for Block Cipher Modes of Operation. Methods and Techniques. National Institute of Standards and Technology, U. S. Department of Commerce – December 2001. Authors: Biswapati jana, Pabitra Pal, Jaydeb Bhaumik Paper Title: New Image Noise Reduction Schemes Based on Cellular Automata Abstract: This paper presents noise filtering technique of noisy image using cellular automata (CA). Two new approaches to reduce noise form a noisy image have been proposed. In the first approach, difference values of Moore neighbors form center pixel are calculated, then sorted in ascending order and the center pixel value is updated depending on the present pixel values using CA rule. In second approach, all pixels value of Moore neighbor including center pixel are sorted in ascending order. Then the minimum and maximum values are eliminated form sorted pixel values and the center pixel value is updated using CA rule. Results are compared with other existing filtering technique in terms of Peak Signal to Noise Ratio ( PSNR). This comparisons shows that a filter based on CA provides significant improvements over the standard filtering methods.
Keywords: Cellular Automata (CA), Image processing, Noise reduction, Peak signal-to-noise ratio (PSNR).
References: 1. Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, 2001, pp.220 – 243. 2. A. Marion, “An Introduction to Image Processing”, Chapman and Hall, 1991, pp. 274. 3. Pratt. William. K, “Digital image processing”, 2nd Edition. John Willey & Sons Inc., 2001, pp. 150-157. 4. K. R. Castleman, “Digital Image Processing”. Prentice Hall, Upper Saddle River, NJ (1995). 5. J. C. Russ. “The Image Processing Handbook”. CRC Press, Boca Raton, FL, thirded. (1998). 6. A. K. Jain. “Fundamentals of Digital Image Processing”. Prentice Hall, Engle wood Cliffs, NJ (1989). 7. NING Chun-yu, LIUShu-fen, QUMing “Research on Removing Noise in Medical Image Based on Median Filter Method”, China, pp 384- 388, 2009 8. F. Bergholm. “Edge focusing,” in Proc. 8th Int. Conf. Pattern Recognition, Paris, France, pp. 597- 600, 1986. 9. Marr. D and Hilderth. “Theory of Edge Detection”, Proc.R.Soc. London, vol. B 207, pp 187-217, 1980. 10. J. F. Canny. “A computational approach to edge detection”, IEEETrans. On Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986). 11. Renyan Zhang, Guoliang Zhao and Li Su, “A New Edge Detection Method in Image Processing”, In: IEEE Proceedings of the ISCIT’05 Oct 12-14, 1: pp. 445-448, 2005. 22. 12. Davis, L. S., “Edge detection techniques: Computer Graphics Image Process”, (4), pp. 248-270, 1995. 13. D. Marr and E. Hildreth, “Theory of edge detection”. Proceedings of the Royal Society of London, Series B 207, 187–217 1980. 14. L. S. Davis, “A Survey of Edge Detection Techniques ", Computer Graphics and Image Processing, 12, 1975, 248-270. 98-103 15. C. G. Harris and M. Stephens. “A combined corner and edge detector”. In C. J. Taylor, editor, “4th Alvey Vision Conference”, pp.147– 151, Manchester 1988. 16. Ziou, D. and Tabbone, “Edge detection techniques an overview, Pattern Recognition and Image Analysis 8 (4)”, pp. 537–559, 1998. 17. A. Popovici and D. Popovici, “Cellular automata in image processing,” in Proceedings of the 15th International Symposium on the Mathematical Theory of Networks and Systems, D. S. Gilliam and J. Rosenthal, Eds., 2002, electronic proceedings. 18. Christopher D Thomas, Riccardo Poli, “Evolution of Cellular Automata for Image Processing”, Thesis, School of Computer Science, University of Birmingham (UK), April 2000. 19. P. L. Rosin, “Training cellular automata for image processing,” in Proceedings of the 14th Scandinavian Conference on Image Analysis, H. Kalviainen, J. Parkkinen, and A. Kaarna, Eds., 2005, pp. 195–204. 20. Stephen Wolfram, “statistical mechanics of Cellular Automata”, Rev Mod Phys.55, 601-644 (July 1983). 21. W. Pries, “A Thanailakis and H.C. Card, Group properties of cellular automata and VLSI Application,” IEEE Trans on computers C-35, 1013-1024, Dec 1986. 22. N.H. Packard and S. Wolfram, “Two dimensional cellular automata,” Journal of Statistical Physics, 38 (5/6) 901-946, 1985. 23. N. Ganguly, P. Maji, S. Dhar, B. K. Sikdar, P. P. Chaudhuri, “Evolving Cellular Automata as Pattern Classifier”, ACRI 2002, LN CS2493, Springer-Verlag Berlin Heidelberg (2002) pp. 56-68. 24. P. P. Chaudhuri, D. R. Chaudhuri, S. Nandi, S. Chattrrjee, “Additive Cellular Automata, Theory and Applications”, Vol. 1, IEEE Computer Society Press, Los Alamitos, California, ISBN-0-8186-7717-1. (1997). 25. P. Jebaraj Selvapeter and Wim Hordijk, “cellular automata for image noise filtering”. 26. Von Neumann, J.: “Theory of Self-Reproducing Automata”, chapter in Essays on Cellular Automata. University of Illinois Press, Urbana, Illinois, 1970. 27. M. Delorme, “An introduction to cellular automata”: Some basic definition and concepts by LIP ENS Lyon 46, Allee d’Italie, 69364 Lyon Cedex 07, France. 28. Nie, Harald. “Introduction to Cellular Automata: Organic Computing”, USA. 29. J. E. Hanson, J. P. Crutchfield, “Computational mechanics of cellular automata: an example, Physics D103: 1-4 (1997)”, pp.169-89. 30. W.R. Ashby, “Principles of the self-organizing system, Principles of Self-organization”, pp.255-278, 1962. 31. T. Gramss, S. Bornholdt, M. Gross, M. Mitchell, and T. Pellizzari, “Computation in Cellular Automata: A selected Review of Non standard Computation”, pp.95–140. Weinheim: VCH Verlagsge sells chaft, 1998. 32. Wolfram, S.: “Cellular Automata as Models of Complexity”, Nature, 311, pp. 419-424, 1984. 33. David J. Eck. “Introduction to one dimensional cellular automaton”. 34. A. S. Mariano, G.M.B.de Oliveira, “Evolving one-dimensional radius-2 cellular automata rules for the synchronization task”, AUTOMATA-2008 Theory and Applications of Cellular Automata, Luniver Press (2008), pp.514-526. 35. P. P. Choudhury, B. K. Nayak, S. Sahoo, S.P. Rath, 2008. “Theory and Applications of Two-dimensional, Null-boundary, Nine- Neighborhood, Cellular Automata Linear Rules”, in: arXiv: 0804.2346, cs.DM; cs.CC; cs.CV. (2008). 36. D.R. Chowdhury, I.S. Gupta and P.P. Chaudhury, “A class of two-dimensional cellular automata and applications in random pattern testing”, Journal of Electronic Testing: Theory and Applications 5, 65-80, (1994). 37. Abdul Raouf Khan, “On Two Dimensional Cellular Automata and its VLSI applications”, Department of Computer Sciences, KING FAISAL UNIVERSITY, SAUDI ARABIA. 38. T. Sunand Y. Neuvo, “Detail-preserving median based filters in image processing,” Pattern Recognition Letters, vol.15, no. 4,pp. 341– 347, 1994. 39. H. Hwangand, R. A. Haddad, “Adaptive median filters: new algorithms and results,” IEEE Transactions on Image Processing, vol.4, no.4, pp.499–502, 1995. Authors: Prashant, Sarika Gupta Paper Title: Simplifying Use Case Models Using CRUD Patterns Abstract: In this paper, we have presented CRUD, a use-case patterns that is proven useful for developing maintainable and reusable use-case models. These patterns focus on designs and techniques used in high-quality models, and not on how to model specific usages. In CRUD we merge short, simple use cases such as Creating, Reading, Updating, and Deleting pieces of information into a single use case forming a conceptual unit.
Keywords: Create data, delete data, information handling, merge use cases, read data, short flow, short use case, simple operation, update data.
23. References: 1. Adolph, S., and P. Bramble . 2002. Patterns for effective use cases.Addison-Wesley. 104-106 2. Alexander, C., S. Ishikawa, and M. Silverstein . 1977. A pattern language: towns, buildings, construction. Oxford University Press. 3. Bass, L., P. Clements, and R. Kazman . 2003. Software architecture in practice. Addison-Wesley. 4. Bittner, K., and I. Spence . 2002. Use case modeling. Addison-Wesley. 5. Buschmann, F., R. Meunier, H. Rohnert, P. Sommerlad, and M. Stal . 1996. Pattern-oriented software architecture, volume 1: a system of patterns. John Wiley and Sons. 6. Jacobson, I. Concepts for modeling large real time systems. Ph.D. thesis, Royal Institute of Technology, Stockholm, Sweden. 7. Jacobson, I."Object-oriented development in an industrial environment." Proceedings of OOPSLA'87. Sigplan Notices 22(12) :183191. 8. Jacobson, I. 2003 (March). "Use cases yesterday, today, and tomorrow." The Rational Edge. 9. Jacobson, I., G. Booch, and J. Rumbaugh . 1999. The unified software development process. Addison-Wesley. 10. Jacobson, I., M. Christerson, P. Jonsson, and G. Övergaard . 1993. Object-oriented software engineering: a use- case driven approach. Addison-Wesley. Authors: Manish Ranjan Pandey, Manoj Kapil, Sohan Garg Beginning of an Effective E-Governance in India by using Informative and Communicative Paper Title: Mechanism Abstract: Good governance is characterized by skill, collaboration, transparency and openness which are the results of effective communication. Three key areas Communication Planning Process (GCPP), Government Communication Assessment Process (GCAP) and Government Communication Improvement Process (GCIP) have been identified and the catalytic impact that ICT has in these key area has been discussed. Government communication is the exchange of government-citizen specific information to citizens (G2C, C2G) and government (G2G) that serves some useful purpose of either government or citizen or both. As the interaction between the citizen and the government is crucial in democracy analyzing the role of governmental officials as service and information providers and the need for improvement in the government – citizen relationship becomes essential [1]. An effective communication mechanism will solve the variety of issues and challenges faced by governments in their efforts to apply 21st century capabilities to e-Government initiatives [2]. According to Moon [3] e-Government was initially envisioned as a means of enhancing intra-governmental communications via an intranet system. The available research on the role of communications in governance is fragmented across multiple disciplines with often conflicting priorities [4, 5].
24. Keywords: GCPP, GCAP, G2C, C2G, 107-109 References: 1. Luht K., 2002, Reforming government – citizen relationship in the information age, Tallinn 2002. 2. Sinha S., 2002, “Competition Policy in Telecommunications: The Case of the India”, International Telecommunication Union. 3. Moon, M. J., 2002, “The Evolution of E-Government Among Municipalities: Rhetoric or Reality?” Public Administration Review, 62: 4. pp. 424-433. 4. Ojo A., Janowski T., Estevez E., Khan I. K., Human Capacity Development for e-Government, April 2007, UNU-IIST Report No. 362. 5. Owen A., Johnson, Stephen F., King, Best Practice in Local E-Government: A Process Modelling Approach, E government Workshop ’05 (Egov05), September 13 2005, Brunel University, West London, Uk 6. Norris P., 2001, “Digital divide: Civic engagement, information, poverty and the Internet worldwide”, Cambridge University Press, Cambridge, pp. 232 7. O.Looney, J. A., 2002, “Wiring governments: Challenges and possibilities for public managers”, Westport: Quorum Books. 8. Subramanian M., 2007, Theory and practice of e-governance in India: a gender perspective, ACM International Conference Proceeding Series; Vol. 232. 9. Thomas J.C., Streib G., “The New Face of Government: Citizen- Initiated Contacts in the Era of E-Government,” Journal of public administration: research and theory, vol.13, No.1, pp.83-102, 2003. 10. Wilson. M., Warnock K., Schoemaker M., 2007, At the Heart of Change: The Role of Communication in Sustainable Development, Panos Institute, London. 11. Kumar T., 2010, “E-SANCHAR (e-Speech Application through Network for Communication, Help and Response”, 13th National Conference on e-Governance, http://indiagovernance.gov.in/files/E-sanchar.pdf. Authors: Bhawna, Mukhwinder Kaur, G.C.Lall 25. Paper Title: Automatic Modulation Recognition for Digital Communication Signals Abstract: Different modulation techniques are used for different signal transmission. These techniques give versatility to the transmission medium as well as make user easy to work in such computational field. With prior no knowledge of data transmitted and various unspecified parameters at receiver side like the carrier frequency, phase offsets and signal power etc., blind detection of the modulation is challenging. This becomes more difficult at the time of fading. That’s why recognizing these modulation schemes is useful for various technical purposes and especially quite significant for the military, wireless and COMINT applications. Digital modulation recognition is based on some parameters especially statistical parameters. Till now various recognition algorithms have been developed and still developing. The recognition algorithms can be divided into two major groups ‘maximum likelihood approach (MLA) and pattern recognition approach (PRA). In this paper we are emphasizing on the theoretical information of these techniques of modulation recognition along with ANN modulation recognizer for m- ary modulation techniques. A general application of modulation recognition in field of SDR is also proposed.
Keywords: Maximum likelihood, Pattern Recognition, Modulation Detection Scheme, Software Defined Radio, Artificial neural network
References: 1. E.E Azzouz and A.K. Nandi, “Automatic Modulation Recognition of Communication Signals”, Kluwar Academic Publishers, 1996 2. D. Linda Essentials of cognitive radio, Cambridge Wireless Essentials Series, Cambridge University Press, 2009 3. O.A. Dobre and Y. Bar-Ness Blind Modulation Classification: A Concept Who’s Time has Come IEEE/Sarnoff Symposium, pp. 223U˝ 228 April 18U˝ 19, 2005 4. D. L. Guen, A. Man sour, “Automatic Recognition Algorithm for Digitally Modulated Signals”, International Conference on Signal Processing, Pattern Recognition, and Applications Crete, Greece, 25-28 June,2002 5. K .N. Haq, A. Mansur, Sven Nordholm, “Comparison of digital modulation classification based on statistical approach”, 10thPostgraduate Electrical and Computer Symposium Perth Australia, September 2009 6. S.S. Soliman and Z.S. Hsue, “Signal classification using statistical moments,”IEEE Transactions on Communications, vol. 40(5), pp. 908– 916, May 1992 7. Z.S. Hsue and S.S. Soliman, “Automatic modulation classification using zero-crossing IEEE Proc. Part F, Radar and signal processing, vol. 137 (6), pp. 459–464, December 1990 8. J.E. Hipp, “Modulation Classification based on Statistical moments”, IEEE Proc. Military Communication Conference, vol2, pp. 20.2.1- 20.2.6, October 1986 9. G.Acosta, “OFDM simulation using Mat Lab”, Report, Smart Antenna Research Laboratory Georgia Institute of Technology, Georgia, USA, August 2000 10. H. Zhang, “Orthogonal Frequency Division Multiplexing for Wireless Communication Thesis, Georgia Institute of Technology, Georgia, 110-114 November 2004 11. Li Tieying, Cui yan,”A design of neural classifier based on rough sets” [J]. Computer Engineering and Applications, 2005, 32 12. Martin P. DeSimio, Glenn E. Prescott “Adaptive Generation of Decision Functions For Classıfıcatıon of Digitally Modulated Signals” NAECON, 1988 13. Adel Metref, Daniel Le Guennec, Jacques Palicot “A new digital modulation recognition technique using the phase detector reliability “2010 14. HU You-qiang, LIU Juan, TAN Xiao-hang “Digital modulation recognition based on instantaneous information “June 2010 15. Hua-Kui Wang, Bin Zhang, Juan-Ping Wu, Ying-Zhuang Han, Xiao-Wei Wu, Roué-Si Jia “A Research on Automatic Modulation Recognition with the Combination of the Rough Sets and Neural Network” 2010 DOI 10.1109/PCSPA.2010.24880\ 16. Fatima K. Faek,” Digital Modulation Classification Using Wavelet Transform and Artificial Neural network” (JZS) Journal of Zankoy Suleiman 2010 17. Asoke K. Nandi, E. E. Azzouz “Algorithms for Automatic Modulation Recognition of Communication Signals” IEEE Transactions On Communications, Vol. 46, No. 4, April 1988 18. Khandker Nada Haq, Ali Mansur, Sven Nordholm” Recognition of Digital Modulated Signals based on Statistical Parameters “, 4th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2010) 19. Octavia A. Dobre, Ali Abdi, Yeheskel Bar-Ness and Wei S “A Survey of Automatic Modulation Classification Techniques: Classical Approaches and New Trends “, Vol. 46, No. 4, April 2010 20. Liang Hong K.C. Ho” Identification of Digital Modulation Types Using the Wavelet Transform”, vol2, pp. 20.2.1-20.2.6, October 2010 21. Azzedine Zerguine,” Automatic Modulation Recognition Using Wavelet Transform and Neural Networks in Wireless Systems “, December 2009 22. Z Chaozhu, Yang Lianbai, Wang Xin,” Discrete wavelet neural network group system for digital modulation recognition”, IEEE 3rd international conference, May 2011 23. Cheng Yuanzeng, zang Hailong, Wang Yu,” Research on modulation recognition of the communication signal based on statistical model” ICMTMA, IEEE 3rd international conference May 2011 24. K Hassan, Nzeza CN,” Blind Modulation identification for MIMO system “, IEEE Global telecom conference, Dec 2010 25. N Ahmadi, “, Modulation classification based on constellation using TTSAS approach”, Journal of recognition research, May 2010 26. Mobien shoaib, Alharbi Harza, Alturki Fahd “Robustness of digital modulated signals against variation in Hf noise model”, EURASIP journal on wireless communication network, 2011 27. Wu Min. The research of Rough Set attribute reduction algorithm in numeral character recognition [D].Hefei University of Technology Master Dissertation, 2009 28. Zhao F., Hu Y. and SH., Hao, ’Classification using wavelet packet decomposition and support vector machine for digital modulation’ Journal of system Engineering and Electronics, August 2009, 19,914-918. 29. Khandker Nada Haq, Ali Man sour, Sven Nordholm” Recognition of Digital Modulated Signals based on Statistical Parameters “, 4th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2010) 30. Z.S. Hsue and S.S. Soliman, “Automatic modulation classification using zero-crossing IEEE Proc. Part F, Radar and signal processing, vol. 137 (6), pp. 459–464, December 1990. Authors: R M Potdar, Anup Mishra, Soma Kala Sammidi, Akula Nagesh Paper Title: Controlling Induced Draft Fan of Power Plant Using Labview Abstract: In this proposed work, design and development of controlling induced Draft fan in a power plant which 26. is presently working on DCS technique has been accomplished by using high computing software Lab VIEW and results has been shown with suitable examples. The goal of this work is to control the Induced Draft Fan in a 115-120 different way. A set of six interlock conditions were provided for this purpose. The objective was to design and implement the controlling of ID Fan in Lab VIEW that will control the ID Fan similar to the DCS technique. Since DCS is applicable only for big system not less than 5000 input and output but this is costly. It consists of separate server, processor and computers where as Lab VIEW does not require a separate processor, no workstation, no operator station here directly connect interfacing card with computer itself. Proposed system can cost less than two hundred times than a DCS. Keywords: Induced Draft Fan; LAB VIEW,Power Plant (WHRB), Software Control.
References: 1. Gregory K. McMillan, Douglas M. Considine (Ed), Process/Industrial Instruments and Controls Handbook Fifth Edition, McGraw-Hill, 1999 ISBN 0-07-012582-1 Section 3 Controllers 2. Li, Nan; Teng, Fei System Design Electro-motor Rotational Speed Control Based on of Lab VIEW . Computer Measurement & Control, p794-799. 2006. 14(6). 3. Prime, J.B. Valdes, J.G., “use of ladder diagram in discrete system of PLC”, IEEE Transaction, Vol. PAS-100, pp-143-153, January 1989. 4. IEEE Guide for AC Motor Protection IEEE, Std C37.96-2000 (Revision of IEEE Std C37.96-1988) 5. National Instruments Corporation. Getting started with LabVIEW [Z]. Part No.323427A-01. April 2003 Edition. 6. Peter A. Blume: The LabVIEW Style Book, February 27, 2007, Prentice Hall. Part of the National Instruments Virtual Instrumentation Series series. ISBN 0-13-145835-3 7. Jeffrey Travis, Jim Kring: LabVIEW for Everyone: Graphical Programming Made Easy and Fun, 3rd Edition, July 27, 2006, Prentice Hall. Part of the National Instruments Virtual Instrumentation Series. ISBN 0-13-185672-3. Authors: H.S. Behera, Abhishek Ghosh, Sipak Ku.Mishra A New Improved Hybridized K-MEANS Clustering Algorithm with Improved PCA Optimized with Paper Title: PSO for High Dimensional Data Set Abstract: The day to day computation has made the data sets and data objects to grow large so it has become important to cluster the data in order to reduce complexity to some extent. K-means clustering algorithm is an efficient clustering algorithm to cluster the data, but the problem with the k-means is that when the dimension of the data set becomes larger the effectiveness of k-means is lost. PCA algorithm is used with k-means to counter the dimensionality problem. However K-means with PCA does not give much optimisation. It can be experimentally seen that the optimisation of k-means gives more accurate results. So in this paper we have proposed a PSO optimised k-means algorithm with improved PCA for clustering high dimensional data set.
Keywords: Data mining, Clustering, Particle Component Analysis, Centred vector, Squared Sum Error, Lower bound, Bound Error, Particle Swarm Optimisation.
References: 27. 1. Dash et.al , “A Hybridized k-Means Clustering Algorithm for High Dimensional Dataset”, International Journal of Engineering, Science and Technology, vol. 2, No. 2, pp.59-66, 2010. 2. H.S. Beheraet.al. “An Improved Hybridized K-Means Clustering Algorithm (IHKMCA) For High dimensional Dataset &its Performance 121-126 Analysis International” Journal on Computer Science and Engineering (IJCSE) vol 3 no 3 march 2011 3. P.Prabhuet et al. “Improvising the performance of K-means clustering for high dimensional data set” International journal on computer science and engineering vol 3, Jun 2011 4. ”Dimensionality reduction: A comparative review”, by Maaten L.J.P., Postma E.O. and Herik H.J. van den, Tech. rep.University of Maastricht ,2007. 5. Davy Michael and Luz Saturnine, 2007. “Dimensionality reduction for active learning with nearest neighbour classifier” in text categorization problems, Sixth International Conference on Machine Learning and Applications, pp. 292-297 6. ”Performance analysis of K-means with different initialization for high dimensional data” by Tanjunisha and Saravan International journal of Artificial Intelligence and application vol1 no.4, October 2010. 7. ”New method of dimensionality reduction using K-means clustering algorithm for high dimensional data set” by D Napoleon and S.Paralakodi international journal of computer science application vol13 no.7, January 2011. 8. ”An efficient method to improve clustering performance for high dimensional data by principal component analysis and modified K-means” by Tanjunisha and SaravanInternationaljournalofdatabase management system vol3 no.1, February 2011. 9. ”Auto-Clustering Using Particle Swarm Optimization and Bacterial Foraging”.byJakob R. Olesen, Jorge Cordero H., and YifengZeng.Cao et al. (Eds.): ADMI 2009, LNCS 5680, pp. 69–83, 2009 Springer-Verlag Berlin Heidelberg 2009. 10. ”Particle Swarm Optimization Methods, Taxonomy and Applications” by DavoudSedighizadeh and EllipsMasehian, International Journal of Computer Theory and Engineering, Vol. 1, No. 5, December 20091793-8201. Authors: Nagamani .K , A G Ananth Paper Title: Evaluation of SPIHT Compression Scheme for Satellite Imageries Based on Statistical Parameters Abstract: Non reversible and lossy image compression techniques is known to be computationally more complex as they grow more efficient, confirming the constraints of source coding theorems in information theory that a code for a (stationary) source approaches optimality the limit of infinite computation (source length). It has been observed that when a variety of images of different types are compressed using a fixed wavelet filter, the peak signal to noise ratios (PSNR) vary widely from image to image. This variation in PSNR can be attributed to the nature and inherent statistical characteristics of image. To explore the effect of various image features on the coding performance, a set of gray level image statistics have been analyzed by using SPIHT (Set Partitioning In Hierarchical Trees) algorithm. The Mean Square Error (MSE) and Peak Signal to Noise Ratios (PSNR) determined for an image depends on the 28. statistical properties of the image and the compression scheme applied. The efficiency of the compression scheme can be evaluated by examining the statistical parameters of the image. In this paper various statistical parameters 127-130 associated with the SPIHT compression scheme are derived for three different types of images namely standard Lena, satellite urban and rural imageries based on higher order statistics. The statistical parameters include higher order image statistics like Rate Distortion and Skewness and Kurtosis which describe the shape and symmetry of the image. The statistical parameters derived for a fixed rate and fixed level of decomposition for three types of images have been are used for the explanation of the Compression Ratio and Peak Signal to Noise Ratio (PSNR) achieved for the satellite imageries. The results show that urban images are better suited for SPIHT compression scheme compared to that of satellite rural image. The results of the analysis are presented in the paper.
Keywords: Compression ratio, EZW, MSE, SPIHT, PSNR.
References: 1. S. Lewis and G. Knowles, “Image Compression Using the 2-D Wavelet Transform”, IEEE Trans. on Image Processing, Vol. 1, No. 2, pp. 244-250, April (1992). 2. J.M.Shapiro,“Embedded Image Coding Using Zerotrees of Wavelet Coefficients”, IEEE Trans. on Signal Processing, Vol. 41, pp 3445- 3462, (1993) 3. A Said and W.A. Pearlman, “A New, Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees”, IEEE Trans. on Circ and Syst for Video Tech, Vol 6, no. 3, pp 243-250, June 1996. 4. A Said and A. Pearlman, “An Image Multiresolution Representation for Losssless and Lossy Compression.” IEEE Trans. Image Processing, Vol. 5, No. 9, pp 243-250, Sept. 1996. 5. Richa Jindal ,Sonika Jindal Navdeep Kaur , Analyses of Higher Order Metrics for SPIHT Based Image Compression , International Journal of Computer Applications , Volume 1 – No. 20, 2010. 6. Sunhasis Saha and Rao Vemuri, “How do Image Statistics Impact Lossy Coding Performance?” Proceedings. International Conference of Information Technology: Coding and Computing Pages 42 - 47, 2000. 7. Sunhasis Saha and Rao Vemuri, An Analysis on the Effect of Image Features on Lossy Coding Performance, IEEE Signal Processing Letters, Volume. 7, No. 5, Pages 104-108, May 2000. Authors: B. Amarendra Reddy, Praveen Adimulam, M. Sujatha Paper Title: Signal Flow Graph Analysis of Linearized Takagi-Sugeno Fuzzy PI Controller Abstract: A systematic procedure for developing the signal flow graph model of linearized Takagi-Sugeno (TS) fuzzy PI controller is presented in this paper. This proposed method provides ease of model formulation and avoids the mathematical complexity involved in obtaining the linearized model from a non-linear model. As a first step in constructing the signal flow graph, the analytical structures of TS-fuzzy PI controller is needed. Triangular/trapezoidal membership functions are considered for input variables, Zadeh fuzzy logic AND operation and centroid defuzzifier, structural analysis of TS-fuzzy pi controller are considered. A TS-fuzzy PI controller is represented as a non-linear TS-fuzzy PI controller which is linearized around an operating point using perturbation method. For the linearized fuzzy TS-fuzzy PI controller signal flow graphs are developed.
Keywords: TS-fuzzy, PI controller.
References: 29. 1. Yongsheng Ding; Hao Ying; Shihuang Shao; “Theoretical analysis of a Takagi-Sugeno fuzzy PI controller with application to tissue hyperthermia therapy” Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on Volume: 1 On page(s): 252 - 257 vol.1 131-136 2. The Simplest Fuzzy controllers using different inference methods are different nonlinear Proportional-Integral controllers with variable gains. Automatica, Vol 29, No.6, pp.1579-1589, 1993. 3. Alwadie,A., H. Ying, and H.Shah “A Practical Two-Input Two-Output Takagi-Sugeno Fuzzy Controller” International Journal of Fuzzy Systems, Vol 5, No.2, June 2003. 4. Hao Ying, Senior Member, IEEE “Deriving Analytical Input-Output Relationshipfor Fuzzy Controllers Using Arbitrary InputFuzzy Sets and Zadeh Fuzzy AND Operator”. IEEE TRANSACTION ON FUZZY SYSTEMS, VOL 14, NO.5, OCTOBER 2006. 5. Hao Ying, William Siler and James J. Buckleys “Fuzzy Control Theory: A Nonlinear Case”. Automatica, Vol 26, No.3, pp.513-520,1990. 6. Hao Ying “Theory and application of a novel fuzzy PID controller using a simplified Takagi-Sugeno rule scheme”. Information Sciences 123 (2000) 281-293. 7. Hao Ying, Senior Member, IEEE “Constructing Nonlinear Variable Gain Controllers via the Takagi-Sugeno Fuzzy Control” IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 6, NO. 2, MAY 1998. 8. Y.Ding, H.Ying, S.Shao “Structure and stability of a Takagi-Sugeno fuzzy PI controller with application to tissue hyperthermia therapy” Soft Computing 2(1999) 183-190© Springer-Verlag 1999. 9. Hao Ying “The Takagi-Sugeno Fuzzy Controllers Using Simplified Linear Control Rules are Nonlinear Variable Gain Controllers” Automatica, Vol 34, No.2, pp.157-167, 1998. 10. A.V. Patel, B.M. Mohan “Some numerical aspects of center of area defuzzification method” Fuzzy Sets and Systems 132 (2002) 401 – 409 Authors: Binitha S, S Siva Sathya Paper Title: A Survey of Bio inspired Optimization Algorithms Abstract: Nature is of course a great and immense source of inspiration for solving hard and complex problems in computer science since it exhibits extremely diverse, dynamic, robust, complex and fascinating phenomenon. It always finds the optimal solution to solve its problem maintaining perfect balance among its components. This is the thrust behind bio inspired computing. Nature inspired algorithms are meta heuristics that mimics the nature for solving optimization problems opening a new era in computation .For the past decades ,numerous research efforts has been concentrated in this particular area. Still being young and the results being very amazing, broadens the scope and viability of Bio Inspired Algorithms (BIAs) exploring new areas of application and more opportunities in computing. This paper presents a broad overview of biologically inspired optimization algorithms, grouped by the biological field that inspired each and the areas where these algorithms have been most successfully applied. 30. Keywords: Bio Inspired Algorithm, Optimization algorithms. 137-151
References: 1. Back, T. 1996: Evolutionary algorithms in theory and practice. Oxford University Press. 2. J.H. Holland, Genetic algorithms and the optimal allocation of trials, SIAM J. Comput. 2 (2) (1973) 88–105 3. Koza, John R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: The MIT Press. 4. Beyer, H.G. and Schwefel, H.P. 2002: Evolution strategies. Natural Computing 1,3–52. 5. R. Storn, K. Price, Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization 11 (1997) 341–359. 6. Upeka Premaratne , Jagath Samarabandu, and Tarlochan Sidhu, “A New Biologically Inspired Optimization Algorithm”,Fourth International Conference on Industrial and Information Systems, ICIIS 2009,28-31 December 2009, Sri Lanka. 7. Bonabeau, E., Dorigo, M. and Theraulaz, G.1999: Swarm intelligence. Oxford University Press 8. Kennedy, J.; Eberhart, R. (1995). "Particle Swarm Optimization". Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942–1948. 9. Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics – Part B, 26, 29–41. 10. D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization 39 (2007) 459–471 11. X. Li, Z. Shao, J. Qian,Anoptimizing method base on autonomous animates: fish- swarm algorithm, Systems Engineering Theory and Practice 22 (2002) 32–38. 12. Shah_Hosseini, H. Shahid Beheshti Univ., Tehran Problem solving by intelligent water drops IEEE Congress on Evolutionary Computation, 2007. CEC 2007. 13. K. M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Syst. Mag., vol. 22, no. 3, pp.52– 67, Jun. 2002 14. D. Dasgupta, Artificial Immune Systems and Their Applications, Springer, Berlin, 1999. 15. X.S Yang, “Fire fly algorithm for multimodal optimization”,in proceedings of the stochastic Algorithms. Foundations and Applications(SAGA 109) vol.5792of Lecture notes in Computer Sciences Springer,Oct.2009 16. S.He,Q.H.Wu, Senior Member IEEE and J.R Saunders.A novel group search optimizer inspired by Animal Behavioral Ecology;2006,IEEE Congress on Evolutionary Computation,1272-1278 17. Eusuff MM and K.E Lansey; Optimization of water distribution network design using SFLA(2003) 18. Hanning Chen , Yunlong Zhu, Optimization based on symbiotic multi-species coevolution;journal on Applied Mathematics and Computation 205 (2008) 19. A.R. Mehrabian, C. Lucas, A novel numerical optimization algorithm inspired from weed colonization, Ecological Informatics 1 (2006) 355–366. 20. Simon, D., 2008. Biogeography-based optimization. IEEE Transactions on Evolu- tionary Computation. 12 (6), 702–713. Authors: Debabrata Samanta, Goutam Sanyal Paper Title: Statistical Approach for Classification of SAR Images Abstract: The statistical parameters contain high order image statistics which portray the outline and symmetry of the different image region. The good feat of recognition algorithms based on the quality of classified image. The main problem in SAR image function is accurate classification. In this paper a novel methodology has been carried out to classify SAR images using the statistical approach based on skewness. A comparison has been carried out with histogram based classification on same images for measuring the accuracy.
Keywords: SAR image, Skewness, symmetrical, normal distribution.
References: 1. Lee J S, Grunes M R, Kwok R. “Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution”, Int. J. Remote Sensing, 1994, 15(11): 2299-2311. 2. Bischof H, Schneider W, Pinz A J.”Multispectral classification of landsat-images using neural networks ”, IEEE Trans. Geosci. Remote Sensing, 1992, 30(3): 482-490. 31. 3. Chen C T, Chen K S, Lee J S. “The use of fully polarimetric information for the fuzzy neural classification of SAR images”, IEEE Trans. Geosci. Remote Sensing, 2003, 41(9 Part I): 2089-2100. 4. Du L, Lee J S. “Fuzzy classification of earth terrain covers using complex polarimetric SAR data”, International Journal of Remote 152-155 Sensing, 1996, 17(4): 809-826. 5. Fukuda S, Hirosawa H. “A wavelet-based texture feature set applied to classification of multi frequency polarimetric SAR images”. IEEE Trans. Geosci. Remote Sensing, 1999, 37(5): 2282-2286. 6. Debabrata Samanta and Goutam Sanyal, “Development of Edge Detection Technique for Images using Adaptive Thresholding”, Fifth International Conference on Information Processing (ICIP-2011), CCIS 157, pp. 671-676, 5-7 Aug.2011. @ Springer-Verlag Berlin Heidelberg. 7. Chih-Chang Lai,Ching-Chih Tasi,A Modified Stripe-RGBW TFT-LCD with Image-Processing Engine for Mobile Phone Displays, IEEE Transaction on Computer Electronics ,Vol.- 53,No. 4,Nov-2007. 8. Debabrata Samanta, Mousumi Paul and Goutam Sanyal , ”Segmentation Technique of SAR Imagery using Entropy”, International Journal of Computer Technology and Applications, Vol. 2 (5), pp.1548-1551, 2011. 9. J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, R. Zabih, Image Indexing Using Color Correlograms, Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 762-768, 1997. 10. M. J. Swain, S. H. Ballard,” Color Indexing”, Int. Journal of Computer Vision , Vol.7, No. 1, pp. 11-32, 1991. 11. W. Y. Ma, H. J. Zhang,, “Content-based Image Indexing and Retrieval, In Handbook of Multimedia Computing”, Borko Furht. Ed, CRC Press, 1998. 12. Theodor Richardson, Improving the Entropy Algorithm with Image Segmentation, [Online document] 2003, Available at HTTP: http://www.cse.sc.edu/songwang/CourseProj/proj2003/Richardson/richardson.pdf. Authors: Virender Kumar, G.C. Lall, Rishipal Paper Title: Optimum Efficient Fast Handover Support for IPv6 Abstract: International Engineering Task Force (IETF) proposed MIPv6 and HMIPv6 both mobility management solutions to support the IP mobility. Although HMIPv6 is an extension of MIPv6 still there is handover latency and packet loss in HMIPv6.In this paper a scheme is presented that supports a fast handover efficiently in hierarchical mobile IPv6 networks (HMIPv6). In HMIPv6 when a mobile node (MN) moves from a one MAP region to another, then there is a interruption of connection as well as packet loss due to long handover latency. To overcome these problems, an efficient fast handover scheme is adopted from FMIPv6 to optimize the performance of the inter-MAP 32. handover. In this paper the handover latency for MIPv6 & HMIPv6 is compared to the proposed scheme with analytical model. By analysis and by simulations, we show that the proposed scheme has better performance compared to MIPv6 & HMIPv6 in terms of handover latency and packet loss. 156-160
Keywords: Access Route, Fast Mobile IPv6, Hierarchical Mobile IPv6, Mobile IPv6, Mobility Anchor Point.
References: 1. D. Johnson, C. Perkins, and J. Arkko, “Mobility Support in IPv6”, RFC 3775, 2004. 2. H.S. Flarion, C. Castelluica, K. El-Malki, and L. Bellver, “Hierarhical Mobile IPv6 mobility mangement (HMIPv6)”, RFC 4140, 2005. 3. R. Koodli, ”Fast Handovers for Mobile IPv6”, RFC 4068, 2005 4. H.Y. Jung, S. Koh, “Fast Handover Support in Hierarchical Mobile IPv6”, Proc. Int. Conf. on Advanced Communication Technology, Feburary 2004, Phoenix Park, Vol. 2, pp. 551–554. 5. J. Vatn, “An experimental study of IEEE 802.11b handover performance and its effect on voice traffic”, Technical Report TRITA-IMIT- TSLAB R 03:01, Royal Institute of Technology, Stockholm, Sweden, 2003. 6. T. Kwon, M. Gerla, and S. Das, “Mobility Management for VoIP Service: Mobile IP vs. SIP”, IEEE Wireless Communications, 2002, 9, (5), pp. 66–75. 7. H. Fathi, R. Prasad, and S. Chakraborty, “Mobility Management for VoIP in 3G System: Evaluation of Low- Latency Handoff Schemes”, IEEE Wireless Communications, 2005, 12, (2), pp. 96–104. 8. H. Fathi, S. Chakraborty, and R. Prasad, “Optimization of Mobile IPv6-based Handovers to Support VoIP Services in Wireless Heterogeneous Networks”, IEEE Trans. Vehicular Tech., 2007, 56, (1), pp. 260–270. 9. Shengling Wang, Yong Cui and Sajal K Das, “Intelligent Mobility Support for IPv6” 978-1-4244-2413-9/08/$25.00 ©2008 IEEE 10. Zheng Wang, Xiaodong Li, Baoping Yan “Fast inter-MAP handover in HMIPv6” 978-0-7695-3557-9/09 $25.00 © 2009 IEEE Authors: Meenu Gupta, Ajay Rana Hybrid Evolutionary Techniques to Restricted Feed Forward Neural Network with Distributed Error Paper Title: for Recognition of Handwritten Hindi ‘MATRAS’ Abstract: This paper evaluates the performance of restricted feed forward neural network trained by hybrid evolutionary algorithm with generalized delta learning rule for distributed error to obtain the pattern classification for the given training set of Handwritten Hindi ‘MATRAS’. Generally, the feed forward neural network considers the performance index as back-propagated instantaneous unknown error for output of hidden layers. Within this proposed endeavor, we are considering the performance index of distributed instantaneous unknown errors i.e. different errors for different layers. In this case, the convergence is obtained only when the minimum of every error on different layer is determined. The simulation for the performance evaluation is conducted for hand-written ‘MATRAS’ of Hindi language scripted by five different people. These samples are stored as scanned images. The MATLAB is used to determine the densities of these scanned images after partitioning each image into 16 portions. These 16 densities for each character are used as an input pattern of training set. We consider five trials for each learning method and results are presented with their mean value.
Keywords: Genetic Algorithm, Handwritten Hindi MATRAS, Multilayer Feed Forward Neural Network, Pattern 33. ecognition.
References: 161-169 1. Du K.L. and Swamy, M.N.S. (2006),” Neural Networks in a Soft computing Framework”, Springer-Verlag London Limited 2. Hagan, M.T., Demuth, H.B. and Beale, M.H. (1996), “Neural Network Design,” PWS Publishing Co., Boston, MA 3. Kim, H.B., Jung, S.H., Kim, T.G., and Park, K.H. (1996), “Fast Learning method for back propagation neural network by evolutionary adaption of learning rates”, Neurocomputing, vol. 11(1), pp. 101-106 4. Mangal M. and Singh,M.P. (2007), ‘Analysis of multidimensional XOR classification problem with evolutionary feed-forward neural networks.”, International Journal on Artificial Intelligence Tools , volume 16(1),pp. 111-120. 5. Mangal,M. and Singh,M.P. (2007), “Analysis of pattern classification for the multidimensional parity-bit-checking problem with hybrid evolutionary feed-forward neural network.”, Neurocomputing, volume 70,pp. 1511-1524. 6. Mangal,M. and Singh,M.P. (2006), “Handwritten English vowels Recognition using Hybrid Evolutionary Feed-Forward Neural Networks.”, Malaysian Journal of computer science, vol. 19(2), pp. 169-187. 7. Pradeep J, Srinviasan E & Himavathi S (2010 ) “Diagonal Feature Extraction Based Handwritten Character System Using Neural Network”, International Journal of Computer Applications Volume 8– No.9 8. Rumelhart, D.E., Hinton G.E., and Williams R.J. (1986), “Learning internal representations by error propagation.”, MIT Press, Cambridge, vol. 1,pp. 318–362. 9. Shrivastava, S. and Singh, M.P. (2011), “Performance evaluation of feed-forward neural network with soft computing techniques for hand written English alphabets”, Journal of Applied Soft Computing, vol. 11, pp. 1156-1182 10. Shrivastava, S., and Singh, M. P. (2007), “ Analysis of soft computing techniques for minimizing the problem of local minima in back- propagation for handwritten English alphabets”, Proc Int. Con. of soft computing and intelligent systems, vol. 2, pp. 307-313 Authors: Muhammad Ahmad, Sungyoung Lee, Ihsan Ul Haq, Qaisar Mushtaq Paper Title: Hyperspectral Remote Sensing: Dimensional Reduction and End member Extraction Abstract: In this work, we present an algorithm to overcome the computational complexity of hyperspectral (HS) image data to detect multiple targets/endmembers accurately and efficiently by reducing time and complexity. In order to overcome the computational complexity standard deviation and chi square distance metric methods are considered. The number of endmembers is estimated by unbiased iterative correlation method. Hyperspectral remote sensing is widely used in real time applications such as; Surveillance, Mineralogy, Physics and Agriculture.
Keywords: Hyperspectral data, chi square, correlation, unbiased, Mat lab
References: 1. F. H. Goetz, G. Vane, J. Solomon, and B. Rock, “Imaging spectrometry for earth remote sensing”, in Airborne Imaging Spectrometer Data 34. Analysis workshop, JPL Publication, vol. 228, pp. 22-29, 1985. 2. Green. R. O, Pavri. B, Faust. J, and Williams. O, “AVIRIS Radiometric Laboratory Calibration, In flight Validation, and a Focused 170-175 Sensitivity Analysis”, NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 1998. 3. Haq. U. I, and Xu. X, 2008, A New Approach to Band Clustering and Selection for Hyperspectral Imagery, IEEE Proceedings on Signal Processing, , pp.1198-1202, ICSP, 2008. 4. Muhammad Ahmad et al, “A new statistical approach for band clustering and band selection using K-Means clustering”, International journal of engineering and technology (IJET), vol. 3 (6), p.p. 606-614, 2001. 5. S. Serpico and L. Bruzzone, “A new search algorithm for feature selection in hyperspectral remote sensing images”, IEEE Transactions on Geosciences and Remote Sensing, vol. 39, pp. 1360-1367, 2001. 6. Nascimento, J., Dias, J, “Does independent component analysis play a role in unmixing Hyperspectral data”, IEEE Transaction, vol 43, issue 1, p.p. 175-187, 2005. 7. Nascimento. J and Dias. J, “Vertex component analysis: A fast algorithm to unmix Hyperspectral data”, IEEE Transaction, vol 43, issue 4, p.p. 898-910, 2005. 8. Boardman J, “Automating spectral unmixing of AVIRIS data using convex geometry concepts”, In Jet Propulsion Laboratory (JPL) Publications, AVIRIS Workshop, vol 1, ppno 93-26, 1993. 9. Winter, M, “an algorithm for fast autonomous spectral end-member determination in HyperSpectral data”, In Process of SPIE. vol 3753, 1999. 10. N. Keshava and J. Mustard, \Spectral unmixing," IEEE Signal Processing Magazine, vol. 19, no. 1, pp. 44-57, 2002. 11. N. Keshava, \Best bands selection for detection in hyperspectral processing," in IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5, pp. 3149-3152, IEEE, IEEE, 2001. 12. J. M. P. Nascimento and J. M. B. Diasb, \Independent component analysis applied to unmixing hyperspectral data," in Image and Signal Processing for Remote Sensing (L. Bruzzone, ed.), vol. 5238, pp. 306-315, SPIE, SPIE, Bellingham, WA, 2004. 13. J. M. P. Nascimento and J. M. B. Dias, \Vertex component analysis: a fast algorithm to unmix hyperspectral data," IEEE Transactions on Geoscience and Remote Sensing, vol. 43, pp. 898-910, April 2005. 14. A. R. Gillespie, M. O. Smith, J. B. Adams, S. C. Willis, A. F. Fisher, and D. E. Sabol, \Interpretation of residual images: Spectral mixture analysis of aviris images, owens valley, california," vol. 2 of AVIRIS Workshop, JPL, 1990. 15. M. O. Smith, J. B. Adams, and D. E. Sabol, Spectral mixture analysis-New strategies for the analysis of multispectral data. Brussels and Luxemburg, Belgium: Kluwer Academic Publishers, 1994. 16. C. Chang, X. Zhao, M. L. G. Althouse, and J. J. Pan, \Least squares subspace projection approach to mixed pixel classi_ation for hyperspectral images," IEEE Transection on Geoscience Remote Sensing, vol. 36, pp. 898-912, May 1998. 17. C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. 2003. 18. C. Chein. I., \Estimation of number of spectrally distnct signal sources in hyperspectral imagery," IEEE Transection on Geoscience and Remote Sensing, vol. 42, March 2004. 19. A. Huck and M. Guillaume, \Independent component analysis-based estimation of anomaly abundances in hyperspectral images," pp. 168- 177, 2007. 20. D. Manolakis, \Taxonomy of detection algorithms for hyperspectral imaging applications," IEEE Signal Processig Magazine, pp. 29{43, January 2002. 21. D. Manolakis, \Taxonomy of detection algorithms for hyperspectral imaging applications," Optical Engineering, vol. 44, no. 6, p. 066403, 2005. 22. A. Ifarraguerri and C.-I. Chang, \Hyperspectral image segmentation with convex cones," Proceedings of ISSSR, 1997. 23. Winter and M. E., \Fast autonomous spectral end-member determination in hyperspectral data," in Proceedings of the Thirteenth International Conference on Applied Geologic Remote Sensing, vol. 11, (Vancouver, BC, Canada), pp. 337-344, 1999. 24. H. H. Harmon, Modern Factor Analysis. Chicago: University of Chicago Press, 1967. 25. C. S. DimitrisManolakis and G. Shaw, \Hyperspectral subpixel target detection using the linear mixing model," IEEE Transaction on Geoscience and Remote Sensing, vol. 39, July 2001. 26. M. Lennon, G. Mercier, M. C. Mouchot, and L. Hubert-Moy, \Independent component analysis as a tool for the dimensionality reduction and the representation of hyperspectral images," SPIE Remote Sensing, vol. 4541, pp. 2893-2895, Sept 19-21 2001. 27. R. B. Singer and T. B. McCord, \Mars: large scale mixing of bright and dark surface materials and implications for analysis of spectral reectance," in 10th conference on Lunar Planet Science, pp. 1835-1848, 1979. 28. W. Wei and T. Adali, \Detection using correlation bound in a linear mixure model," Signal Processing, vol. 87, pp. 1118-1127, 2007. 29. M. P. Jos_e and M. B.-D. Jos_e, \Blind hyperspectral unmixing," in Image and Signal Processing for Remote Sensing XIII (Lorenzo, ed.), vol. 6748, SIE, 2007. 30. B. Hapke, \Bidirectional reactance spectroscopy. i-theory," Geophysical Research, vol. 86, pp. 4571-4586, June 1986. 31. S. Kay, Fundamentals of Statistical Signal Processing. Englewood Cli_s, NJ: Prentice Hall, 1998. 32. J. B. Adams and M. O. Smith, \Spectral mixture modeling : a new analysis of rock and soil types at the vicking lander site [j]," Geophysical Research, vol. 91, no. B8, pp. 8098{8112, 1986. 33. J. C. Harsanyi and C. I. Chang, \Hyperspectral image classi_cation and dimensionality reduction: An orthogonal subspace projection approach," IEEE Transaction Geoscience and Remote Sensing, vol. 32, pp. 779-785, July. 34. A. Schaum and A. Stocker, \Spectrally-selective target detection," in SPIE Proceeding on ISSSR, vol. 3071, 1997. 35. A. Stocker and A. Schaum, \Application of stochastic mixing models to hyperspectral detection problems," vol. 3071, SPIE, April 1997. 36. J. Ellis, “Searching for oil seeps and oil-impacted soil with hyperspectral imagery”, Earth Observation Magazine, January 2001. 37. R. Smith, “Introduction to hyperspectral imaging with tmips”, MicroImages Tutorial Web site: www.microimages.com/documentation/Tutorials/hyprspec.pdf, July 2006. 38. G. Vane, R. Green, T. Chrien, H. Enmark, E. Hansen and W. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS)”, Remote Sensing of the Environment, vol. 44, pp. 127–143, 1993. 39. G. Swayze, S. S. R.N. Clark and A. Gallagher, “Ground-truthing AVIRIS mineral mapping at Cuprite Nevada”, in Third Annual JPL Airborne Geosciences Workshop, pp. 47–49, 1992. Authors: B. Babypriya, N. Devarajan Paper Title: Simulation and Analysis of a DFIG Wind Energy Conversion System with Genetic Fuzzy Controller Abstract: The behavior of a grid connected, wind energy conversion system (WECS) is simulated using MATLAB in this paper. This analysis is presented for different fault conditions like line to ground faults, line to line faults, double line to ground faults and three phase symmetric faults. A genetic algorithm based fuzzy controller is incorporated into the Doubly fed Induction Generator (DFIG) Wind Energy Conversion System. The dynamic behavior of a DFIG Wind Energy Conversion system with genetic fuzzy controller is simulated for different fault conditions and the results are compared to that of the system with PI Controllers. The comparison shows that the incorporation of the Genetic fuzzy controller results in an improvement in the dynamic behavior of the system under transient conditions.
Keywords: Doubly fed Induction Generator, Wind Energy Conversion System, Genetic Fuzzy Controller.
35. References: 1. Anderson P.M. and Anjan Bose (1983), ‘Stability Simulation of Wind Turbine Systems’, IEEE Transactions on power apparatus and 176-182 systems, Vol. PAS-102, No. 12, pp. 3791-3795. 2. Hinrichsen E.N. (1983), ‘Integration of Wind Turbine and Power System Controls’, Sixth Biennial Wind Energy Conference and Workshop, pp. 887- 898. 3. Nayar C.V. and Bundell J.H. (1987), ‘Output Power Controller for a Wind Driven Induction Generator’, IEEE Transactions on Aerospace and Electronic Systems, Vol. Aes-23, No. 3, pp. 388-400. 4. Filho R., Sanchez E., Sanchez B. and Armando V. (1997), ‘Control of wound rotor induction machine’, International Conference on Power Electronics and Drives, Systems Proceedings IEEE, Vol. 1, pp. 97-102. 5. Nakra H.L. and Benoit Dube (1988), ‘Slip Power Recovery Induction Generators for Large Vertical Axis Wind Turbines’, Winter Meeting of the IEEE Power Engineering Society, pp. 733-737. 6. Pena R., Clare J.C. and Asher G.M. (1996), ‘A Doubly Fed Induction Generator Using back-to-back PWM Converters Supplying an Isolated Load from a Variable Speed wind Turbine’, IEEE Proc. B, Electric Power Applications, Vol. 143, pp. 380-387. 7. Datta R. and Ranganathan V.T. (2002), ‘Variable Speed Wind Power using Doubly Fed Wound Rotor Induction Machine - A Comparison with Alternative Scheme’, IEEE Trans Energy Conversion, Vol. 17, pp. 414-421. 8. Torbjorn Thiringer (2002), ‘Grid Friendly Connecting of constant speed wind turbines using external resistance’, IEEE Transactions on Energy Conversion, Vol. 17 , No. 4 , pp. 537-542 9. Renewable Energy Sources and Technologies (TERI Energy Data Directory and Yearbook, 2005), pp. 175-183. 10. Riaz M. (1959), ‘Energy-Conversion Properties of Induction Machines in Variable-Speed Constant Frequency Generating Systems’, Applications an Industry. Authors: Isha Garg Paper Title: Multi-Area Load Frequency Control Implementation in Deregulated Power System Abstract: In power system, the main goal of load frequency control (LFC) or automatic generation control (AGC) is to maintain the frequency of each area and tie- line power flow within specified tolerance by adjusting the MW outputs of LFC generators so as to accommodate fluctuating load demands. In this paper, attempt is made to make a scheme for automatic generation control within a restructured environment considering effects of contracts between DISCOs and GENCOs to make power system network in normal state. This scheme is tested on two area system with considering deregulation using MATLAB simulink tool. The results are shown in frequency and power response for two area AGC system in restructured environment.
Keywords: Automatic generation control, load frequency control, two area control in deregulated power system.
References: 1. Javad Sadeh and Elyas Rakshani, “ Multi area load frequency control on deregulated power system using optimal output feedback method,” IEEE Transaction, 978-1-4244-1744-5/08. 2. O. I. Elgerd, C. Fosha, “Optimum Megawatt-Frequency control of multiarea Electric Energy Systems”, IEEE Transaction on Power Apparatus & Systems, Vol. PAS-89, No. 4, April 1970. 36. 3. R. Christie, A. Bose, “Load-Frequency control Issues in Power Systems sOperations AfterDeregulation”, IEEE Transactions on Power sSystems, Vol 11, Aug 1996, Pages. 1191-1200. 183-187 4. V. Donde, M. A. Pai, I. A. Hiskens ,“ Simulation of Bilateral Contracts in an AGC System After Restructuring” IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 16, NO. 3, AUGUST. 5. C. Fosha, O. I. Elgerd, “The Megawatt-Frequency control Problem: A New approach via Optimal control Theory”, IEEE Transactions on Power Apparatus & Systems, Vol. PAS-89, No. 4, April 1970. 6. E. Rakhshani, and J. Sadeh. “Load Frequency Control of Multi area Restructured Power system” IEEE 978-1-4244-1762-9/08. 7. B. Venkata Prasanth, S. V. Jayaram Kumar,“Load Frequency Control For A Two Area aInterconnected Power System ssUsing Robust Genetic Algorithm Controller” Journal of Theoretical and Applied Information Technology, pp 1204-1212. 8. Richard D. Christie, Anjan Bose, “Load Frequency Control Issues In Power System Operations After Deregulation” IEEE 0-7803-2663- 619. 9. E. Rakhshani, and J. Sadeh, “A Reduced-Order Estimator with Prescribed Degree of Stability for Two-Area LFC System in a Deregulated Environment” IEEE 978-1-4244-3811-2/09. 10. IEEE Recommended Definitions of Terms for Automatic Generation Control on Electric Power Systems, Approved September 26,1991 Standard Board. 11. S.N. Singh, S.C. Srivastava, “Electric Power Industry Restructuring in India:Present Scenario and Future Prospect” IEEE Intemational Conference on Electric Utility Deregulation, Restructuring and Power Technologies (DRPT2004) April 2004, pp 20-23 12. F. Liu, Y.H. Song, J. Ma, S. Mei and Q. Lu, “Optimal load-frequency control in restructured power systems” IEE Proc.-Gmer. Transm. Distrib &. Vol. 150, No.1 January 2003, pp s87-95. 13. A. P. Sakis Meliopoulos, George J. Cokkinides, A. G. Bakirtzis, “Load-Frequency Control Service in a Deregulated Environment” IEEE 1060-3425/98. Authors: A K Malik, Yashveer Singh, S K Gupta Paper Title: A Fuzzy Based Two Warehouses Inventory Model for Deteriorating Items Abstract: In real life situations, especially for new products, the probability is not known due to lack of historical data and adequate information. Then these parameters and variables are treated as fuzzy parameters. Fuzzy set theory is now applied to problems in engineering, business, medical and related health sciences and natural sciences. Over the years there have been successful applications and implementations of fuzzy set theory in production management. In this study, a fuzzy based two warehouses inventory model has been developed with exponential demand. Deterioration rates of two warehouses are considered to be different due to change in environment. The holding cost in RW is assumed to be higher than those in OW. To reduce the inventory costs, it will be economical for firms to store goods in OW before RW, but clear the stocks in RW before OW. The parameters such as holding costs, ordering cost and deteriorating cost for two warehouses are considered as fuzzy number. We considered the triangular fuzzy number to represents the fuzzy parameters. The total inventory cost is obtained in crisp environment as well as fuzzy sense with the help of Signed distance method.
Keywords: Exponential demand, linear deterioration, Fuzzy model, Crisp model, Signed distance. 37.
References: 188-192 1. Ghare, P.M. and Schrader, G.P. (1963), “A model for exponentially decaying inventory”, Journal of Industrial Engineering (J.I.E.), 14, 228- 243. 2. Covert, R.P. and Philip, G.P. (1973), “An EOQ model for items with Weibull distribution deterioration”, AIIE Trans., 5(4), 323-329. 3. Hertely V. Ronald. (1976), “On the EOQ model two levels of storage”, Opsearch, 13,190-196. 4. Bhunia, A.K. and Maiti, M. (1998), “A two-warehouse inventory model for deteriorating items with a linear trend in demand and shortages”, J.O.R.S., 49 (3), 287–292. 5. Lee, C.C. and Hsu, S. L. (2009), “A two-warehouse production model for deteriorating inventory items with time-dependent demands, European Journal of Operational Research, 194, 700-710. 6. Singh, S.R., Malik, A.K., (2009). Effect of inflation on two warehouse production inventory systems with exponential demand and variable deterioration, International Journal of Mathematical and Applications, 2, (1-2), 141-149. 7. Lee, Y. Y., Kramer, B. A. and Hwang, C. L. (1990) Part-period balancing with uncertainty: a fuzzy sets theory approach, International Journal of Production Research, 28(10), 1771-1778. 8. Park, K. S. (1987) Fuzzy-set theoretic interpretation of economic order quantity, IEEE Transactions on Systems, Man and Cybernetics, 17(6), 1082-1084. 9. E. A. Silver, R. Peterson, (1985) Decision Systems for Inventory Management and Production Planning, John Wiley & Sons, New York. 10. H. J. Zimmermann. (1985) Fuzzy Set Theory and Its Applications. Kluwer-Nijho, Hinghum, Netherlands. 11. M. Papadrakakis, N. D. Lagaros. (2003) Soft Computing Methodologies for Structural Optimization. Applied Soft Computing, vol. 3, no. 4, pp. 283-300. 12. R. P. Sundarraj, S. Talluri. (2003) A Multi-period Optimization Model for the Procurement of Component-based Enterprise Information Technologies. European Journal of Operational Research, vol. 146, no. 2, pp. 339-351. 13. Singh S.R. and Singh C. (2008). Fuzzy Inventory Model for finite rate of Replenishment using Signed Distance Method. International Transactions in Mathematical Sciences and Computer, 1(1), 27-34. 14. Yao J.S. and Lee H.M., (1999). Fuzzy inventory with or without backorder for fuzzy order quantity with trapezoidal fuzzy number. Fuzzy Sets and Systems, 105, 311-337. 15. Yung, K. L., W. Ip and D. Wang (2007). Soft Computing Based Procurement Planning of Time-variable Demand in Manufacturing System. International Journal of Automation and Computing, 04 (1), 80-87. 16. Y.W. Zhou, (2003). A Multi-warehouse Inventory Model for Items with Time-varying Demand and Shortages. Computers & Operations Research, vol. 30, no. 14, pp. 2115-2134. 17. Chang, H., C., Yao, J., S., and Quyang, L.Y., (2004) “Fuzzy mixture inventory model with variable lead-time based on probabilistic fuzzy set and triangular fuzzy number”, Computer and Mathematical Modeling, 39, 287-304. 18. Chang, H., C., Yao, J., S., and Quyang, L.,Y., (2006) “Fuzzy mixture inventory model involving fuzzy random variable, lead-time and fuzzy total demand”, European Journal of Operational Research, 69 65-80. 19. Dutta, P., Chakraborty, D., and Roy, A.R.,(2007) “Continuous review inventory model in mixed fuzzy and stochastic environment”, Applied Mathematics and Computation, 188, 970-980. 20. Maiti, M.K., and Maiti, M., (2007) “Two-storage inventory model with lot-size dependent fuzzy lead-time under possibility constraints via genetic algorithm”, European Journal of Operational Research, 179, 352-371. 21. Bellman, R. E. & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17, B141-B164. 22. Goni, A. & Maheswari, S. (2010). Supply chain model for the retailer’s ordering policy under two levels of delay payments in fuzzy environment. Applied Mathematical Sciences, 4, 1155-1164. 23. Halim, K.A., Giri, B.C. & Chaudhuri, K.S. (2008). Fuzzy economic order quantity model for perishable items with stochastic demand, partial backlogging and fuzzy deteriorating rate. International Journal of Operational Research, 3, 77-96. 24. Halim, K.A., Giri, B.C. & Chaudhuri, K.S. (2010). Lot sizing in an unreliable manufacturing system with fuzzy demand and repair time. International Journal of Industrial and Systems Engineering, 5, 485-500. 25. Hsieh, C.H. (2002). Optimization of fuzzy production inventory models. Information Sciences, 146, 29-40. 26. Mahapatra, N. K. & Maiti, M. (2006). A fuzzy stochastic approach to multi-objective inventory model of deteriorating items with various types of demand and time dependent holding cost, Journal of the Operational Research Society of India, 43 (2), 117-131. 27. Mahata, G. C. & Goswami, A. (2006). Production lot size model with fuzzy production rate and fuzzy demand rate for deteriorating item under permissible delay in payments. Journal of the Operational Research Society of India, 43 (4), 358-375. 28. Zadeh, L. A. (1965). Fuzzy Sets, Information and Control, 8, 338-353. Authors: R. Valarmathi, S. Palaniswami, N. Devarajan Paper Title: Simulation and Analysis of Wind Energy and Photo Voltaic Hybrid System Abstract: This paper models a hybrid system consisting of a wind turbine and a photovoltaic array as main energy sources and this is simulated using MATLAB. To connect the PV system to the grid the only adaptation required is to adjust the DC bus voltage to the conventional/isolated grids characteristics. Both energy sources are parallely linked to a common PWM voltage source inverter through individual AC/DC and DC/DC converters. A AC/DC converter transforms the 3 phase variable frequency wind turbine AC power, into variable DC power. A DC/DC converter controls variable power from the solar array DC. Though all sources have their individual controllers they have a common configuration. A VLSI based fuzzy logic controller ensures constant voltage needed for the load through the convertor’s PWM signals. The wind turbine and photovoltaic array voltage are controlled through error signal which is fed to the controller to generate pulses for the dc-dc converter. Simulation results reveal that the hybrid system provides a constant power to the load.
Keywords: Photovoltaic array, Wind turbine, VLSI, Fuzzy logic controller.
References: 38. 1. J. M. Carrasco, L. G. Franquelo, J. T. Bialasiewicz, E. Galvan, R. C. PortilloGuisado, M. A. M. Prats, J. I. Leon, and N.Moreno-Alfonso, “Power-electronic systems for the grid integration of renewable energy sources: A survey,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1002– 1016, Jun. 2006. 193-200 2. F. Valencaga, P. F. Puleston, and P. E. Battaiotto, “Power control of a solar/wind generation system without wind measurement: A passivity/ sliding mode approach,” IEEE Trans. Energy Convers., vol. 18, no. 4, pp. 501–507, Dec. 2003. 3. R. Chedid and S. Rahman, “Unit sizing and control of hybrid wind-solar power systems,” IEEE Trans. Energy Convers., vol. 12, no. 1, pp. 79–85, Mar. 1997. 4. W. D. Kellogg, M. H. Nehrir, G. Venkataramanan, and V. Greez, “Generation unit sizing and cost analysis for stand-alone wind, photovoltaic, and hybrid wind/PV systems,” IEEE Trans. Energy Convers., vol. 13, no. 1, pp. 70–75, Mar. 1998. 5. Y. Atat and N.-E. Zergainoh, “Simulink-Based MPSoC Design: New Approach to Bridge the Gap between Algorithm and Architecture Design,” Proc. IEEE CS Ann. Symp. VLSI, pp. 9-14, 2007. 6. D. Soderman and Y. Panchul, “Implementing C Designs in Hardware: A Full-Featured ANSI C to RTL Verilog Compiler in Action,” Proc. Int’l Verilog HDL Conf. and VHDL Int’l Users Forum, pp. 22-29, 1998. 7. P. Banerjee et al., “Overview of a Compiler for Synthesizing MATLAB Programs onto Fpgas,” IEEE Trans. VLSI Systems, vol. 12, no. 3, pp. 312-323, Mar. 2004. 8. MATLAB, “The MATLAB Website,” http://www.mathworks. com, 2007. 9. J.S. Kim et al., “TANOR: A Tool for Accelerating N-Body Simulations on Reconfigurable Platform,” Proc. Int’l Conf. Field Programmable Logic and Applications (FPL ’07), pp. 68-73, Aug. 2007. 10. S. Jayasoma, S. J. Dodds, and R. Perryman, “An FPGA implemented PMSM servo drive: Practical issues,” in Proc. Int. Universities Power Eng. Conf., 2004, vol. 1, pp. 499–503. 11. [11]. R.C.Bansal (2005), “Three phase Self Excited Induction Generators: An Overview”, IEEE Transactions On Energy Conversion, Vol. 20, No. 2, pp.292-299 Authors: Ravindra Kumar Sharma, Kirti Vyas, Ajay Kumar Bairwa
Flattened Dispersion of Hexagonal Chalcogenide As2Se3 Glass Photonic Crystal Fiber with a Large Paper Title: Core 39. Abstract: In this paper, we have proposed a novel structure of the fabrication of a chalcogenide As2Se3 glass photonic crystal fiber (PCF) with increased core diameter. As comparision with the normal PCFs in which silica 201-203 glass is used as core material, the proposed PCF has following feature; firstly we have used the chalcogenide As2Se3 glass as core material in which the first ring area contains no air holes. Then the proposed PCF has a large core area chalcogenide As2Se3 glass photonic crystal fiber. There are low chromatic dispersion in the proposed PCF comparied to normal As2Se3 glass PCF. The chromatic dispersion is almost flat in the range of 2.4 micrometer to 4.0 micrometer range when the air hole diameter ‘d’ is 1.0 micrometer and air hole space ‘˄’ is 2.0 micrometer. Keywords: chalcogenide As2Se3 glass, chromatic dispersion, photonic crystal fiber.
References: 1. Knight, T.A. Birks, P. St. J. Russel, and D.M. Atkin, “ All silica single- mode optical fiber with photonic crystal cladding”, Opt. Lett, 21, pp. 1547 – 1549 (1996). 2. J. Broeng, D. Mogilevstev , S.E. Barkou, and A. Bjarklev, “Photonic crystal fibers: a new class of optical waveguides”, Optical fiber Technology, 5, pp. 305-330(1999). 3. M.J. Gander, et.al. “Experimental measurement of group velocity dispersion in photonic crystal fiber”, Electron, let. 35, pp. 63-64 (1999). 4. A.V. Husakou, J. Hermann, Appl. Phys. B77 (2003)227. 5. D.I. Yeom, E.C. Magi, M.R.E. Lamont, M.A.F. Roelens, L. Fu, B.J. Eggleton, Opt. Lett. 33(2008) 660. 6. G.P. Agarwal, Nonlinear Fiber Optics, third ed., Academic Press, New York, 1995. 7. G. Boudebs, S. Cherukulappurath, M. Guignard, J. Troles, F. Smektala, and F. Sanchez,” Linear optical characterization of chalcogenide glasses”,Opt. common. 230, 331- 336 (2004). 8. L.P. Shen, W.P. Huang and S.S. Jain, “Design of photonic crystal fibers for dispersion – related applications”, J. Lightwave Technol, 21pp. 1644- 1651 (2003). 9. K. Thyagarajan, R.K. Varshney, P. Palai, A.K. Ghatak, and I.C. Goyal, “ A noval design of a dispersion compensating fiber”, IEEE Photon Technol, Lett. 8, pp. 1510- 1512 (1996). 10. Bhawana Dabas , R.K. Sinha, “ Dispersion characteristic of hexagonal and square lattic chalcogenide As2Se3 glass photonic crystal fiber”, opt. Comm. 283, 1331- 1337 (2010). Authors: A. Thirueelakandan, T. Thirumurugan An Approach towards Improved Cyber Security by Hardware Acceleration of Open SSL Paper Title: Cryptographic Functions. Abstract: Providing improved Information Security to the rapidly developing Cybernet System has become a vital factor in the present technically networked world. The information security concept becomes a more complicated subject when the more sophisticated system requirements and real time computation speed are considered. In order to solve these issues, lots of research and development activities are carried out and cryptography has been a very important part of any communication system in the recent years. Cryptographic algorithms fulfill specific information security requirements such as data integrity, confidentiality and authenticity. This work proposes an FPGA-based VLSI Crypto-System, integrating hardware that accelerates the cryptographic algorithms used in the SSL/TLS protocol. SSL v3 and TLS v1 protocol is deployed in the proposed system powered with a Nios-2 soft-core processor. The cipher functions used in SSL-driven connection are the Scalable Encryption Algorithm (SEA), Message Digest Algorithm (MD5), Secured Hash Algorithm (SHA2). These algorithms are accelerated in the VLSI Crypto-System that is on an Altera Cyclone III FPGA DE2 development board. The experimental results shows that, by hardware acceleration of SEA, MD5 and SHA2 cryptographic algorithms, the VLSI Crypto-System performance has increased in terms of speed, optimized area and enhanced level security for the target Cybernetic application.
Keywords: Cryptographic algorithm, Hardware accelerator, SSL/TLS protocol, CtoH Compiler, VLSI Crypto - System.
References: 1. Mohamed Khalil-Hani, Vishnu P., Nambiar M., Marsono N., (2010) “Hardware Acceleration of OpenSSL cryptographic functionsfor high- 40. performance Internet Security”International Conference on Intelligent Systems, Modelling and Simulation. 2. Nambiar V. P., Khalil-Hani M., and Zabidi M. M, (IJCTS 2009), “Accelerating the AES encryption function in OpenSSL for embedded systems,” International Journal of Information and Communication Technology, vol. 2, no. 1/2, pp. 83–93. 204-207 3. Khalil-Hani M., Nazrin M., and Hau Y. W., (ICED 2008) “Implementation of SHA-2 hash function for a digital signature System-on-Chip in FPGA,” in International Conference on Electronic Design. 4. Praveen Kumar B., Ezhumalai P., Ramesh P., Dr SankaraGomathi S., Dr.Sakthivel P., (Febraury 2010), “Improving the Performance of a Scalable Encryption Algorithm (SEA) using FPGA”, IJCSNS International Journal of Computer Science and Network Security, VOL. 10 No.2. 5. Maharak C. and Sowanwanichakul B., (in TENCON 2004), “Security methods for Web- based applications on embedded system,” 2004 IEEE Region 10 Conference, vol. C, 2004, pp.56–59 Vol. 3. 6. Colleen E. Garcia, Naval Postgraduate School, Monterey, California, (June 2010) “Regulating nation-state cyber attacks in Counter terrorism operations” – Master Thesis. 7. EkawatHomsirikamol, MarcinRogawski, Kris Gaj, in George Mason University, (2010) “Comparing Hardware Performance of Fourteen Round Two SHA-3 Candidates Using FPGAs” – Master Thesis. 8. Jury: Prof.Y.Willems ,voorzitter in atholiekeuniversiteitleuven, Kasteelpark, Arenberg 10, B–3001 Heverlee, (May 2007), “Analysis and design of symmetric encryption algorithms” - Master Thesis . 9. Pravir Chandra, Matt Messier, John Viega, (June 2002) Publisher: O'ReillyPub Date: ISBN : 0-596-00270. Network Security with OpenSSL.. 10. Pascal junod, in EcolePolytechnique, Federale De Lausanne, (2005)“Statistical Cryptanalysis of Block Ciphers” – Master Thesis. 11. Stephen A. Weis in Massachusett Institute of Technology, (May 2006), “New Foundations for Efficient Authentication, Commutative Cryptography, and Private Disjointness TestinG”. 12. Saar Drimer in University of Cambridge United Kingdom, (November 2009) “Security for volatile FPGAs” – Master Thesis 13. Wollinger .T, J. Guajardo, C. Paar, (2003) “Cryptography in Embedded Systems: An Overview,” in Proc. of the Embedded World 2003 Exhibition and Conference. 14. William Stallings 3’rd Edition, Publisher: Pearson Education.“Cryptography and Network Security– Principles and Practices”. 15. “Hacking Techniques – High Tech Crime Brief” An Article by Australian Institute of Criminology, 2005. 16. “2010 Data Breach Investigations Report” A study conducted by the Verizon Business RISK team in cooperation with the United States Secret Service. 17. www.openssl.org and www.cryptography.org Authors: Sandeep Kumar, Puneet Verma 41. Paper Title: Comparison of Different Enhanced Image Denoising with Multiple Histogram Techniques Abstract: There are different techniques for enhance an image by using gray scale manipulation, histogram equalization and filtering. Out of different enhancement techniques HE became a popular technique because, it is simple and effective. For preserving the input brightness of the image, there is a segment to avoid the generation of non-existing artifacts in the output image. So, these methods are used for preserving the input brightness with the significant contrast enhancement. They may produce an image which is not look like input image. HE method is used for re-mapping of the gray level and tends to introduce some annoying artifacts and unnatural enhancement. To preserve from these drawbacks brightness preserving techniques are used such as CLAHE, DSIHE and DHE. But after the enhancement some noise is also there which is further reduce for better result. Enhanced Image Denoising comparative analysis with the different techniques is carried out. In this comparison some subjective and objective parameters are used. For subjective parameter visual quality and computation time and for objective parameter PSNR and MSE are used.
Keywords: Contrast enhancement, HE, PSNR, MSE, visual quality.
References: 1. S. Lau, “Global image enhancement using local information,” Electronics Letters, vol. 30, pp. 122–123, Jan. 1994. 2. J. Zimmerman, S. Pizer, E. Staab, E. Perry, W. McCartney, and B. Brenton, “Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement,” IEEE Transactions on Medical Imaging, pp. 304-312, Dec. 1988. 3. Yu Wan, Qian Chen and Bao-Min Zhang, “Image enhancement based on equal area dualistic sub-image histogram equalization method,” IEEE Transactions Consumer Electron., vol. 45, no. 1, pp. 68-75, Feb. 1999. 208-213 4. Yeong-Taeg Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,” IEEE Trans. Consumer Electronics, vol. 43, no. 1, pp. 1-8, Feb. 1997. 5. M. Abdullah-Al-Wadud, Md. Hasanul Kabir, M. Ali Akber Dewan, and Oksam Chae, “A dynamic histogram equalization for image contrast enhancement”, IEEE Transactions. Consumer Electron., vol. 53, no. 2, pp. 593- 600, May 2007. 6. Y. Wang, Q. Chen, and B. Zhang, Soong-Der Chen, and Abd. Rahman Ramli, “Minimum mean brightness error bi-histogram equalization in contrast enhancement”, IEEE Transactions Consumer Electron. vol. 49, no. 4, pp. 1310-1319, Nov. 2003. 7. WANG Zhiming, TAO Jianhua, “A Fast Implementation of Adaptive Histogram Equalization”, IEEE 2006, ICSP 2006 Proceedings. 8. Md. Foisal Hossain, Mohammad Reza Alsharif, “Image Enhancement Based on Logarithmic Transform Coefficient and Adaptive Histogram Equalization”, 2007 International Conference on Convergence Information Technology, IEEE 2007. 9. Alex Stark “Adaptive Image Contrast Enhancement Using Generalizations of Histogram Equalization”, IEEE Transactions on Image Processing, Vol. 9, No. 5, May 2000. 10. Wang Yuanji. Li Jianhua, Lu E, Fu Yao and Jiang Qinzhong, “Image Quality Evaluation Based On Image Weighted Separating Block Peak Signal To Noise Ratio”, IEEE Int. Conf. Neural Networks & Signal Processing, Nanjing, China, December 14-17, 2003. 11. Rafael C. Gonzalez, and Richard E. Woods, “Digital Image Processing”, 2nd edition, Prentice Hall, 2002. 12. Stephen M. Pizer, R. Eugene Johnston, James P. Ericksen, Bonnie C. Yankaskas, Keith E. Muller, “Contrast-Limited Adaptive Histogram Equalization Speed and Effectiveness”, ”, IEEE Int. Conf. Neural Networks & Signal Processing, Nanjing, China, December 14-17, 2003. 13. Rafael C. Gonzalez, and Richard E. Woods, “Digital Image Processing”, 2nd edition, Prentice Hall, 2002. 14. Ashok Saini, International Journal of Electronics Engineering, 3 (2), 2011, pp. 275– 277,” Reduction of Noise from Enhanced Image Using Wavelets”. 15. Rafael. E. Herrera, Robert J. Sclabassi, “Single trial visual event related potential EEG analysis using wavelet transform” proceedings of the first joint BMES/EMB conference serving humanity advance technology Oct. 13-16, 99, ATLANTA USA. 16. Sudha, G.R.Suresh, and R. Sukanesh , “Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance”, International Journal of Computer Theory and Engineering, Vol.1, No.1, April 2009. Authors: Santosh Kumar Gupta, S. Baishya Modeling and Simulation of Triple Metal Cylindrical Surround Gate MOSFETs for Reduced Short Paper Title: Channel Effects Abstract: Due to the continuous scaling of the MOS transistors it has become absolute necessary to investigate for the new transistor architectures for better control of SCEs and HCEs. In literature triple metal and double metal gate structure has been proposed to reduce the SCEs and HCEs due to scaling of the MOS transistors. The double metal and triple metal structures screen the effect of drain voltage change on the source/channel barrier reducing the SCE. The triple metal gate structure however induces an electrical junction on source and drain side which works as ultra shallow source/drain junctions. Since the surround gate structures have been found to have best control over the channel a cylindrical surround gate structure with triple metal was recently proposed by Cong Li et al. In this paper we present the physically based analytical model for the surface potential of triple metal cylindrical surround gate MOSFET. The model takes into account for the drift-diffusion currents and continuity equations. In the latter part of the paper some 2D simulation results of triple metal gate MOS transistor has been shown. The device has also been explored for the suitable channel doping in terms of subthreshold slope, DIBL, transconductance etc.
42. Keywords: Cylindrical Surround Gate MOSFETs, Surface Potential, TCAD, Short Channel Effects, Analog. 214-221 References: 1. Chaudhry and M. J. Kumar, “Controlling Short-Channel Effect in Deep-Submicron SOI MOSFETs for Improved Reliability: A Review” IEEE Trans. Device and Materials Reliability, vol. 4, pp. 99-109, Mar. 2004. 2. G.V.Reddy and M. J. Kumar,"A New Dual-Material Double-Gate (DMDG) Nanoscale SOI MOSFET – Two-dimensional Analytical Modeling and Simulation," IEEE Trans. on Nanotechnology, Vol.4, pp.260 - 268, March 2005. 3. J.-P. Colinge, “Silicon-On-Insulator: Material to VLSI,” Amsterdam, Kluwer Academic Publishers, 2004. 4. K. K. Ng and G. W. Taylor, “Effects of hot-carrier trapping in n- and p-channel MOSFETs,” IEEE Trans. Electron Devices, vol. ED-30, pp. 871-876, 1983. 5. M. J. Kumar and A. A. Orouji, "Two-Dimensional Analytical Threshold Voltage Model of Nanoscale Fully Depleted SOI MOSFET with Electrically Induced Source/Drain Extensions," IEEE Trans. on Electron Devices, vol. 52, no. 7, pp. 1568-1575, July 2005. 6. Ali A. Orouji and M. Jagadesh Kumar, "Nanoscale SOI-MOSFETs with Electrically Induced Source/Drain Extension: Novel attributes and Design considerations for Suppressed Short-channel Effects," Superlattices and Microstructures, Vol.39, pp. 395-405, May 2006. 7. Y. Taur and T. H. Ning, Fundamentals of Modern VLSI Devices. Cambridge, U. K. Cambridge Univ. Press, 1998. 8. S. R. Banna, P. C. H. Chan, P. K. Ko, C. T. Nguyen, and M. Chan, “Threshold voltage model for deep-submicrometer fully depleted SOI MOSFETs,” IEEE Trans. Electron Devices, vol.42, no.11, pp.1949–1955, Nov.1995. 9. Biswajit Ray , Santanu Mahapatra “A New Threshold Voltage Model for Omega Gate Cylindrical Nanowire Transistor”, 21st International Conference on VLSI Design, 1063-9667/08, DOI 10.1109/VLSI.2008.52, pp. 447-452. 10. Cong Li, Yiqi Zhuang, Ru Han “Cylindrical surrounding-gate MOSFETs with electrically induced source/drain extension”, Microelectronics Journal, vol. 42, issue 2, February 2011, pp. 341-346. 11. Hamdy AbdEl Hamid, Benjamin Iñíguez, Jaume Roig Guitart “Analytical Model of the Threshold Voltage and Subthreshold Swing of Undoped Cylindrical Gate-All-Around-Based MOSFETs”, IEEE Transactions on Electron Devices, Vol.54, No.3, March 2007, pp. 572- 579 12. Hyun-Jin Cho, James D. Plummer “Modeling of Surrounding Gate MOSFETs With Bulk Trap States”, IEEE Transactions On Electron Devices, Vol. 54, No. 1, January 2007, pp. 166-169. 13. Sentaurus TCAD User’s Manual, 2009. 14. Cong Li, Yiqi Zhuang, Ru Han, Gang Jin, Junlin Bao, “Analytical threshlod voltage model for cylindrical surrounding gte MOSFET with electrically induced source/drain extensions”, Microelectronics Reliability, vol. 51, issue 12, December 2011, pp.2053-2058. 15. Santosh Kumar Gupta and S. Baishya, “Design Considerations of Electrically Induced Source/Drain Junction SOI MOSFETs for the Reduced Short Channel and Hot Carrier Effects”, International Journal of Computer and Electrical Engineering, vol. 3, No. 6, December 2011, pp. 869-872. 16. Santosh Kumar Gupta, Achinta Baidya and S. Baishya, “Simulation and Analysis of Gate Engineered Triple Metal Double Gate (TM-DG) MOSFET for Diminished Short Channel Effects”, International Journal of Advanced Science and Technology, vol. 38, January 2012, pp. 15-24. 17. Santosh Kumar Gupta, Srimanta Baishya, “3D-TCAD Simulation Study 18. of an Electrically Induced Source/Drain Cylindrically Surrounding Gate 19. MOSFETs for reduced SCEs and HCEs”, IEEE 3rd International 20. Conference on Electronics Computer Technology, 8-10 April, 2011, 21. Kanyakumari, India, vol. 2, pp. 429-432. Authors: Gurpreet Kaur, Devesh Mahor, Anil Kamboj Paper Title: CDMA vs. OFDM- Comparison and Hybrid OFDM- the Solution for the Next Generation Abstract: This paper investigates the effectiveness of OFDM and proven in other conventional (narrowband) commercial radio technologies (e.g. DS-CDMA in cell phones) (e.g. OFDM in IEEE 802.11a/g). The main aim was to assess the suitability of OFDM as a modulation technique for a fixed wireless phone system for rural areas. However, its suitability for more general wireless applications is also assessed. Most third generation mobile phone systems are proposing to use Code Division Multiple Access (CDMA) as their modulation technique. For this reason, CDMA is also investigated so that the performance of CDMA could be compared with OFDM on the basis of various wireless parameters. At the end it is concluded that the good features of both the modulation schemes can be combined in an intelligent way to get the best modulation scheme as a solution for wireless communication high speed requirement, channel problems and increased number of users.
Keywords: CDMA, OFDM, PN Sequence, Peak Power Clipping.
43. References: 1. L. Hanzo, M. Mu¨nster, B. J. Choi, and T. Keller,“OFDM and MC-CDMA for Broadband Multi-User Communications, WLANs and Broadcasting”. Piscataway, NJ: IEEE Press/Wiley, (2003). 222-225 2. R. V. Nee and R. Prasad “OFDM for Wireless Multimedia Communications”, London, U.K.: Artech House, 2000. 3. J. A. C. Bingham “Multicarrier modulation for data transmission: An idea whose time has come”, IEEE Commun. Mag., vol. 28, no. 5, pp. 5–14, May 1990. 4. Datacomm Research Company, Using MIMO-OFDM Technology to Boost WirelessLAN Performance Today, White Paper, St. Louis, MO, Jun. 2005. 5. R. S. Blum, Y. Li, J. H. Winters, Q. Yan and “Improved space-time coding for MIMO-OFDM wireless communications”, IEEE Trans. Commun., vol. 49, no. 11,pp. 1873– 1878, Nov. 2001. 6. K. S. Gilhousen, I. M. Jacobs, R. Padovani, A. J. Viterbi, L. A. Weaver, Jr., and C. E. Wheatley, III, “On the capacity of a cellular CDMA system,” IEEE Trans. Veh. Technol., vol. 40, no. 2, pp. 303-312, May 1991. 7. L. Liu, J. Tong, and Li Ping, “Analysis and optimization of CDMA systems with chip-level interleavers,” IEEE J. Select. Areas Commun., vol. 24, no. 1, pp. 141-150, Jan. 2006. 8. S. Verdu and S. Shamai, “Spectral efficiency of CDMA with random spreading,” IEEE Trans. Inform. Theory, vol. 45, no. 2, pp. 622-640, Mar. 1999. 9. I. Cosovic, S. Kaiser, M. Schnell, and A. Springer, “Performance of coded uplink MC-CDMA with combined-equalization in fading channels,” in Proc. IST Mobile & Wireless Commun. Summit (IST’04), Lyon, France, pp. 692-696, June, 2004. 10. M. Moher, “An iterative multiuser decoder for near-capacit communications,” IEEE Trans. Commun., vol. 46, pp. 870- 880, July,1998. Authors: Nirosha Joshitha J, R. Medona Selin Paper Title: Image Fusion using PCA in Multifeature Based Palmprint Recognition Abstract: Biometric technology offers an effective approach to identify personal identity by using individual’s unique, reliable and stable physical or behavioral characteristics. Palmprint is a unique and reliable biometric characteristic with high usability. The composite algorithm used estimates the orientation field of the palmprint from which multiple features is extracted. Fusion increases the system accuracy and robustness in person recognition. The first kind of fusion is multiple features from one palmprint image. The existing system uses this technique through multiple features like minutiae, density map orientation, and principal line map from each palmprint image. The 44. proposed paper uses multi-image fusion. The PCA-based image fusion technique adopted here improve resolution of the images in which images to be fused are firstly decomposed into sub images with different frequency and then the 226-230 information fusion is performed and finally these sub images are reconstructed into a result image with plentiful information. The PCA algorithm builds a fused image of several input images as a weighted superposition of all input images. The resulting image contains enhanced information as compared to individual images. This image is used for palmprint recognition. A database containing multiple images of the same palmprint is used. The task of palmprint matching is to calculate the degree of similarity between an input test image and a training image from database. A normalized Hamming distance method is adopted to determine the similarity measurement for palmprint matching.
Keywords: Density map, Hamming distance, Multi-image fusion, Minutiae, PCA, Principal line map.
References: 1. Jifeng Dai and Jie Zhou, “Multifeature- Based High Resolution Palmprint Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 945-957, May 2011. 2. A. Jain, P. Flynn, and A. Ross, “Handbook of Biometrics,” Springer, 2007. 3. PolyU Palmprint Database. 4. A. Jain and J. Feng, “Latent Palmprint Matching,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 7, pp. 1032- 1047, July 2009. 5. W. Kong, D. Zhang, and M. Kamel, “Palmprint Identification Using Feature Level Fusion,” Pattern Recognition, vol. 39, no. 3, pp. 478- 487, 2006. 6. D. Zhang, W. Kong, J. You, and M. Wong, “Online Palmprint Identification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1041-1050, Sept. 2003. 7. W. Kong, D. Zhang, and W. Li, “Palmprint Feature Extraction Using 2-D Gabor Filters,” Pattern Recognition, vol. 36, no. 10, pp. 2339- 2347, 2003. 8. N. Duta, A. Jain, and K. Mardia, “Matching of Palmprints,” Pattern Recognition Letters, vol. 23, no. 4, pp. 477-486, 2002. 9. A.Jain, P.Flynn, and A.Ross, Handbook of Biometrics. Springer,2007 & Wikipedia, free Encyclopedia. 10. Nagesh kumar.M, Mahesh.PK and M.N. Shanmukha Swamy,”An Efficient Secure Multimodal Biometric Fusion Using Palmprint and Face Image”, IJCSI International Journal of Computer Science Issues, vol. 2, pp 49-53, 2009. 11. Naidu & Raol,”Pixel-Level Image Fusion Using Wavelets And Principal Component Analysis”, Defence Science Journal, Vol. 58, No. 3, May 2008, Pp. 338-352. 12. K.Y. Rajput, Melissa Amanna, Mankhush Jagawat and Mayank Sharma,“Palmprint Recognition Using Image Processing”, International Journal of Computing Science and Communication Technologies, Vol. 3, No. 2, Jan. 2011. (ISSN 0974-3375), Pp 618-621. 13. J. B. O. Souza Filho, L. P. Caloba, J. M. Seixas,”An Accurate and Fast Neural Method for PCA Extraction“, Proc. IJCNN 2003, Portland, USA. Authors: R. Vinothkanna, Amitabh Wahi 45. Paper Title: A Novel Approach for Extracting Fingerprint Features from Blurred Images Abstract: Biometrics is the science and technology of authentication by identifying the living individual’s physiological or behavioral attributes. Fingerprint identification is one of the most well known and published biometrics. Normally in blurred fingerprints the extraction of ridges becomes very difficult. But the extraction of valleys instead of ridges from the same blurred fingerprint images will produce better results. In this paper, we have tried the extraction of features with different types of filters like Median filter, Gaussian filter, Wiener filter, Kalman filter and Gabor filter. We noticed that the extraction of valleys instead of ridges from blurred fingerprints will produce more features for forth coming processes like post-processing and matching process.
Keywords: Biometrics, Fingerprints, Valley Extraction, Ridge Extraction, and Gabor filter.
References: 1. A.K. Jain, R. Bolle, S. Pankanti (Eds.), “Biometrics: Personal Identification in Networked Society”, Kluwer Academic Publishers, Boston, 1999. 2. L.C. Jain, U. Halici, I. Hayashi, S.B. Lee, S. Tsutsui (Eds.), “Intelligent Biometric Techniques in Fingerprint and Face Recognition”, CRC Press, Boca Raton, 1999. 3. Nishiuchi. N, Soya. H, “Cancelable Biometric Identification by Combining Biological Data with Artifacts”, Biometrics and Kansei Engineering (ICBAKE), 2011 International Conference on 19-22 Sept. 2011, 61-64. 4. Nalini Ratha, Rudd Bolle, “Automatic Finger print Recognition system”, Springer New York 2004. 5. J.R. Parker,” Gray level thresholding in badly illuminated images”, IEEE Trans. Pattern Anal. Mach. Intell. 13 (8) (1991) 813–819. 6. Chowdhury. A, “An Effectual Thinning Algorithm”, Electronics Computer Technology (ICECT), 2011 3rd International Conference on 8- 10 April 2011,1, 183 – 187. 7. Luping Ji, Zhang Yi, Lifeng Shang, Xiaorong Pu, “Binary Fingerprint Image Thinning Using Template Based PCNNs”, Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on Oct. 2007, Volume : 37 , Issue:5 , 1407 – 1413. 8. Zhang Jinhai, “Fingerprint Image Enhancement based on Gabor Function” Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC), 2011 , 2, 1414 – 1417. 9. Heeyeol Yu, Mahapatra R, Bhuyan L, “A Hash-Based Scalable IP lookup using Bloom and Fingerprint filters”, Network Protocols, 2009. 231-235 ICNP 2009. 17th IEEE International Conference on 13-16 Oct. 2009, 264 – 273. 10. Takenga, C. Tao Peng, Kyamakya K, “Post-Processing of Fingerprint Localization using Kalman filter and Map-Matching Techniques” , Advanced Communication Technology, The 9th International Conference on 12-14 Feb. 2007,3, 2029 – 2034. 11. Chitresh Saraswat and Amit Kumar, “An Efficient Automatic Attendance System using Fingerprint Verification Technique”, International Journal on Computer Science and Engineering Vol. 02, No. 02, 2010, 264-269. 12. Medeiros. L.X, Flores. E.L, Arantes Carrijo. G, Paschoarelli Veiga. A.C, “Optimization of Calculation of Field Orientation Time and Binarization of Fingerprint Images”, Latin America Transactions, IEEE (Revista IEEE America Latina) Sept. 2011, Volume : 9 , Issue:5 , 868 – 874. 13. Chaur-Chin Chen and Yaw-Yi Wang, “An AFIS Using Fingerprint Classification,” Image and Vision Computing, 2003. 14. Jie-Cherng Liu, Yang-Lung Tai, “ Design of 2-D Wideband Circularly Symmetric FIR Filters by Multiplierless High- OrderTransformation”, Circuits and Systems, IEEE Transactions on April 2011,vol.58,issue 4, 746 – 754. 15. Rajapaksha, Nilanka T, Madanayake, Arjuna, “Asynchronous – QDI 2D IIR Digital Filter Circuits”, Circuits and Systems (ISCAS), 2011 IEEE International Symposium on 15-18 May 2011, 665 – 668. 16. John C. Ross, “Image Processing Hand Book”, CRC Press. 1994. 17. S.Jayaraman, S.Esakkirajan, T.Veerakumar, “Digital Image Processing”, Tata McGraw Hill Education privateLtd,NewDelhi,2009. 18. Gang Cao; Yao Zhao; Rongrong Ni; Lifang Yu; Huawei Tian, “ Forensic Detection of Median Filtering in Digital Images”, Multimedia and Expo (ICME), 2010 IEEE International Conference on 19-23 July 2010, 89 – 94. 19. Kanagalakshmi K, Chandra E, “Performance Evaluation of Filters in Noise Removal of Fingerprint Image”, Electronics Computer Technology (ICECT), 2011 3rd International Conference on 8-10 April 2011,1, 117 – 121. 20. Wan.S, Raju. B.I, Srinivasan. M.A, “Robust deconvolution of high-frequency ultrasound Ultrasonics, Ferroelectrics and Frequency Control, IEEE Transactions on Oct.wavelets” images using higher-order spectral analysis and, 2003, Volume : 50 , Issue:10 , 1286 – 1295. 21. Sun Chun-Jung, Kuo Hong-Yi, Lin Chin E, “A Sensor Based Indoor Mobile Localization and Navigation Using Unscented Kalman Filter”, Position Location and Navigation Symposium (PLANS), 2010 IEEE/ION 4-6 May 2010, 327 – 331. 22. Feng Zhao, Xiaoou Tang, “Preprocessing and Postprocessing for skeleton-based fingerprint minutiae extraction,” Pattern Recognition 40(4): 1270-1281 (2007). 23. Pham. T.Q, Perry. S.W, Fletcher. P.A, Ashman. R.A, “Paper Fingerprinting using alpha-masked image matching”, Computer Vision, IET, July 2011, Volume: 5 , Issue:4 , 232 – 243. Authors: Sarnali Basak, Md. Imdadul Islam, M. R. Amin Paper Title: Detection of Virtual Core Point of A Fingerprint: A New Approach Abstract: In a fingerprint the profile of ridges are flowed by ridge orientation curves. The slope of each point of a ridge orientation curve varies with the radius of curvature of the line. The change in gradient will attain its maximum value when the curve changes its slope from positive to negative or vice versa which occurs on immediate left and right of maxima or minima point. Every ridge on a fingerprint will provide such point of maximum gradient and the mean value of those points is considered as the virtual core point. This paper presents a new model to determine the virtual core point based on changed in gradient of maxima and minima points, so that this core point is considered to be the reference point to select the region of interest (ROI) of a fingerprint for further processing. The results of the paper show that, the proposed method can provide the virtual core point from different types of fingerprint very 46. efficiently and consequently simplifies the fingerprint recognition system. 236-239 Keywords: Change in gradient, maxima and minima points, non-minutia and minutia based detection, ridge orientation, ROI.
References: 1. B. Tan, S. Schuckers, “New approach for liveness detection in fingerprint scanners based on valley noise analysis,” Journal of Electronic Imaging, vol. 17(1), pp. 011009-1- 011009-9, Jan.-March 2008. 2. D. Batra, G. Singhal and S. Chaudhury, “Gabor filter based fingerprint classification using support vector machines,” IEEE India Annual Conference, pp. 256-261, Dec. 2004. 3. E. R.Henry, Classification and uses of finger prints, London: George Rutledge & Sons, Ltd., pp. 17-18, 1900. 4. S. W. Lee and B. H. Nam, “Fingerprint recognition using wavelet transform and probabilistic neural network,” International Joint Conference on Neural Network, vol. 5, pp. 3276 – 3279, 1999. 5. A. K. Jain, S. Prabhakar and L. Hong, “A multichannel approach to fingerprint classification,” IEEE Trans. PAMI, vol. 21, no. 4, pp. 348- 359, Apr. 1999. 6. W. Zhang and Y. Wang, "Singular point detection in fingerprint image," Asian Conference on Computer Vision, vol. 2, pp. 793-796, Jan. 2002. 7. A. Mishra and M. Shandilya, “Fingerprint core point detection using gradient field mask,” International Journal of Computer Applications, vol. 2, no. 8, June 2010. 8. Julasayvake and S. Choomchuay, “An Algorithm For Fingerprint Core Point Detection,” International Symposium on Signal Processing and Its Applications, pp. 1-4, Feb. 2007. 9. M.S. Khalil, D. Muhammad, M. K. Khan and K. Alghathbar., “Singular points detection using fingerprint orientation field reliability,” International Journal of Physical Sciences, vol. 5(4), pp. 352-357, April 2010. 10. J. C. Yang and D. S. Park, “A fingerprint verification algorithm using tessellated invariant moment features”, Neurocomputing, vol. 71, pp. 1939-1946, 2008. 11. Q. Zhao, D. Zhang, L. Zhang and N. Luo, “Adaptive fingerprint pore modeling and extraction”, Pattern Recognition, vol. 43, pp. 2833- 2844, 2010. 12. M. U. Munir and M. Y. Javed, “Fingerprint Matching using Gabor Filters,” National Conference on Emerging Technologies, Pakistan, pp. 147-152, 2004. 13. T. Hsieh1, S.R. Shyu1 and K.M. Hung, ‘An Effective Method for Fingerprint Classification,” Tamkang Journal of Science and Engineering, vol. 12, No. 2, pp. 169-182,2009. 14. A. Jain, L. Hong, and R. Bolle, “On-line fingerprint verification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, pp. 302–314, Apr. 1997. 15. A.K. Jain, S. Prabhakar, L. Hong and S. Pankanti, “Filterbank-based fingerprint matching,” IEEE Transactions on Image Processing, vol. 9, no. 5, pp. 846-859, 2000. Authors: Soumen Biswas, Sarosij Adak Paper Title: Back-Gate Biasing of the DG Transistors Abstract: DG-MOSFET programmable logic circuits have noteworthy features such as the ease of re- programming techniques and fewer transistors used in an IC package. Dynamic and reconfigurable threshold logic gates based on DG-MOSFETs are explored. Multiple functions are obtained on a single Boolean static logic circuit built with DG-MOSFETs. Our proposed work is to reconfigurable static and dynamic Boolean logic gates, as well as threshold logic gates designed with DG-MOSFETs. For reconfiguration in these circuits, a systematic back-gate biasing approach is utilized.
Keywords: CMOS integrated circuits, double-gate (DG) transistors, logic circuits,
References: 1. S. Hauck, “The roles of FPGAs in reprogrammable systems,” Proc. IEEE, vol. 86, no. 4, pp. 615–638, Apr. 1998. 2. B. Yu, H. Wang, A. Joshi, Q. Xiang, E. Ibok, and M.-R. Lin, “15-nm gate length planar CMOS transistor,” in Tech. Dig. IEDM , 2001, p. 937. 3. H.-S. P.Wong, “Beyond the conventional MOSFET,” in Proc. 31st Eur. Solid-State Device Research Conf., 2001, p. 69. 47. 4. P. Beckett, “Low-power spatial computing using dynamic threshold devices,” in Proc. IEEE Int. Symp. Circuits Syst., 2005, p. 2345. 5. S. Kaya and W. Ma, “Optimization of RF linearity in DG-MOSFETs,” IEEE Electron Devices Lett., p. 308, 2004. 240-245 6. G. Pei and E. C.-C. Kan, “Independently driven DG MOSFETs for mixed-signal circuits—I: Quasi-static and nonquasi-static channel coupling,”IEEE Trans. Electron Dev., vol. 51, no. 12, pp. 2086–2093, Dec.2004. 7. M. V. R. Reddy, D. K. Sharma, M. B. Patil, and V. R.Rao,“Power-area evaluation of various double-gate RF mixer topologies,” IEEE Electron Devices Lett., vol. 26, p. 664, 2005. 8. L. Mathewet al., “CMOS vertical multiple independent gate field effect transistor (MIGFET),” in Proc. IEEE SOI Conf., 2004, p. 187. 9. S.Varadharajan and S. Kaya, “Study of dual-gate SOI MOSFETs as RF mixers,” in Proc. Int. Semiconductor Dev. Res. Symp. (ISDRS), Dec. 7–9, 2005, p. 7. 10. J. M. Rabaey, A. Chandrakasan and B Nikolic, “Digital integrated circuits”, Second Edition, Prentice Hall, 2003. 11. K. Bernstein and N. J. Rohrer, “SOI circuit design concepts”, Kluwer Academic Publishers, 2000.104 12. A. Marshall and S. Natarajan, “SOI design: analog, memory and digital techniques”, Kluwer Academic Publishers, 2002. 13. “ISE TCAD Suite,” Synopsis, Mountain View, CA, 2006 [Online]. Available: http://www.synopsis.com 14. S. Mukhopadhyay, H. Mahmoodi and K. Roy, “A Novel High-Performance and Robust Sense Amplifier Using Independent Gate Control in Sub-50-nm Double-gate MOSFET,” IEEE Trans. Very Large Scale Integration Systems (VLSI), vol. 14, no.2, pp. 183-192, Feb. 2006. 15. S. L. Hurst, “Threshold logic”, Mills & Boon Ltd. London, 1971. 16. S. Kaya, H. F.A. Hamed, D.T. Ting and G. Creech, "Reconfigurable Threshold Logic Gates with Nanoscale DG-MOSFETs, "Solid-State Electronics, vol. 51, no.7, pp. 1301-1307, Oct. 2007. 17. Y. Taur, “Analytic solutions of charge and capacitance in symmetric and asymmetric double-gate MOSFETs,” IEEE Trans. Electron Devices, vol. 48, no.12, Dec. 2001. Authors: K.Gupta, P. T. Das, T. K. Nath, P.C.Jana, A.K.Meikap Paper Title: Polymer Coated Manganites and Its Magnetic Properties Abstract: Synthesis and analysis of magnetic properties of polypyrrole coated La0.9-xSmxSr0.1MnO3 (x= 0.2) nanoparticles is the main aim of this investigation. About 60% magneto resistance (MR) is obtained for La0.9- xSmxSr0.1MnO3 nanoparticles and it decreases with increasing temperature. Enhanced spin-polarized tunneling between two adjacent grains at the grain boundary may increase the MR. Oscillating type of MR is obtained for polypyrrole coated La0.9-xSmxSr0.1MnO3. A core shell type model is attributed to an intermediate exchange coupling between the shell (surrounding) and antiferromagnetic core mainly on the basis of uncompensated surface 48. spins. Samples may be used as multifunctional spintronic devices and magnetic recording medium. 246-250 Keywords: A. Manganites, B. Polypyrrole, C. Oscillating magneto resistance.
References: 1. J.S. Zhou, J.B. Goodenough, A. Asamitsu, Y. Tokura, Phys. Rev. Lett. 79(1997) 3234 2. R.A. Rao, D. Lavric, T.K. Nath, C.B. Eom, L. Wu, F. Tsui, Appl. Phys. Lett. 73(1998) 3294 3. Y. Tokura, Colossal Magnetoresistive Oxides (Gordon and Breach, New York, 2000) 4. D. C. Worledge, G. Jeffrey Snyder, M. R. Beasley, T. H. Geballe, J. Appl. Phys. 80(1996) 5158 5. A. Gupta, G. Q. Gong, Gang Xiao, P. R. Duncombe, P. Lecoeur, P. Trouilloud, Y. Y. Wang, V. P. Dravid, J. Z. Sun, Phys. Rev. B 54(1996) R15629 6. C. N. R. Rao, B. Raveau, Colosal magnetoresistance, charge ordering and related properties of manganese oxides, (World Scientific 1998). 7. S. Satpathy et al J.Appl.Phys 79(1996) 4555 8. Solovyev et al Phys Rev Lett 76 (1996) 4825) 9. C.Zener, Phys.Rev. 82(1951) 403 10. P. Dey, T. K. Nath, Phys. Rev. B 73(2006) 214425 11. P. Dey, T. K. Nath, U. Kumar, P.K.Mukhopadhyay, J. Appl. Phys 98(2005) 014306 12. H. Naarman, Science and Application of Conducting Polymers (Adam Hilger, Bristol, 1991) 13. Joo, A. Epstein, Appl. Phys. Lett. 65(1994) 2278 14. N. Asim, S. Radiman, M.A. Yarmo, Mater. Lett. 62(2008)1044 15. T. Skothemin, R. Elsenbaumer, Handbook of Conducting Polymers (Marcel Dekker, New York, 1998). 16. H. S. Nalwa, Handbook of organic Conductive Molecules and polymers (Wiley, 1997, Vol-2, Chapter 12) 17. Gupta, P. C. Jana, A. K. Meikap, T. K. Nath 107(2010)073704 18. Chang, J. B. Xia , F. M. Peeters Phys. Rev. B 65(2002) 115209 19. C. Mamani, G. M. Gusev, O. E. Raichev, T. E. Lamas, and A. K. Bakarov Phys. Rev. B 80(2009)075308 20. Q.Xie, B.Lv,P. Wang,P. Song,X. Wu, Materials Chem and Physics 114(2009)636 21. P. Raychaudhuri, K. Sheshadri, P. Taneja, S. Bandyopadhyay, P. Ayyub, A.K.Nigam, R. Pinto, Phys. Rev. B 59(1999) 13919 22. P.Raychaudhuri, T.K. Nath, A.K. Nigam, R. Pinto, J. Appl. Phys. 84(1998)2048 23. J.S. Helman, B. Abeles, Phys. Rev. Lett. 37(1976)1429 Authors: Shobha Sharma 22nm High K Metal Gate Inverter Comparative Analysis of Substrate Biasing Effect on Low Power Paper Title: And High Performance Ptm Models Abstract: This paper analysis the low power and high performance models of PTM with Hi-K metal gate cmos technology by using them in an cmos inverter. Also the effect of substrate body biasing is analysed on the output characteristics. The comparison tables are drawn on Voltage Transfer Characteristic in normal biasing as well as in nsubstrate and psubstrate biasing with input voltage sweeping from minimum to maximum voltage, at 22nm technology node. This analysis gives an insight into unusual leakages in the gate and supply terminal at 22nm node. All the simulations are being done with Hspice simulator using PTM models of 22nm cmos HiK-metal gate of Arizona state University, USA.
Keywords: 22nm, body biasing,BSIM473, ptm, scaling issue.
References: 1. Bohr et al, “nanotechnology goals & challenges for electronic, “IEEE T_nano, vol1_1, pp.56-62, March2002 2. Ronen et al, “Coming challenges in Architecture”, Proceedings of IEEE, Vol89, March 2011 3. S Borkar et al, ‘Design Challenges in scaling”. IEEE Micro, Vol19 Aug1999 4. K roy et al, “Low power VLSI Design”, John wiley, inc 2000 5. Borkar et al, “Obeying Moore’s law beyond. 18micron’, Proceedings of IEEE intl ASIC sept 2000 6. Brooks et al, “Power aware architecture, “IEEE micro, vol20 Dec2000 7. Flynn, “deep submicron microprocessor design issues, ”IEEE Micro, Vol19 Aug1999 8. Chanderakasan et al, “Low power CMOS digital Design, “Kluwer Academic Publications 9. Slawsby et al, “Trends in Mobile device power consumption”, Intel tech, June 2002 10. Usami et al, “automated low power techniques, “IEEE Journal of Solid State circuits, Vol 33, March 1998. 11. Kursun et al, “CMOS voltage interface circuit ..,” proceedings of IEEE int’l symp um, Vol3, May 2002 12. Chen et al,” performance and Vdd scaling” IEEE journal of solid state, Vol33, 1998 13. Kuroda et al” .9V 150MHz Core Processor with Variable threshold”, IEEE J SSC, Vol31 Nov1996. 14. Huang et al, “scalability and biasing” Proceeding of the IEEE int’l symposium, June2001 15. Keshaveri et al, “Technology scaling behavior “Proceeding of the IEEE int’l symposium, June 2006 49. 16. Keshaveri et al, “Effectiveness of Reverse body bias for leakage control”, Proceeding of the IEEE int’l symposium, August 2001 Miyazaki et all, “a 1.2 GIPS microprocessor using adaptive Threshold Voltage with Forward bias,” IEEE SSC, Vol37, Feb2002 17. Kuroda et al, Variability supply scheme for Low power CMOS design, “IEEE SSC Vol33, March 1998 251-256 18. Kao et al, “175mv Multiply Accumulate unit using Adaptive supply voltage”, IEEE J of SSC, Vol37, Nov 2002 19. G. E. Moore, “Progress in Digital integrated Electronics,” IEEE International Electron Devices Meeting, pp. 11-13, December 1975,. 20. S. Borkar, “Obeying Moore’s Law Beyond 0.18 Micron, “Proceedings of 13th Annual IEEE International ASIC/SOC Conference, pp. 26- 31, September 2000. 21. World Technology Working Groups, International Technology Roadmap for semiconductors. Semiconductor Industry Association. 1999. 22. K. Wang, S. Thomas, and M. Tanner, “SiGe Band Engineering for MOS, CMOS and Quantum Effect Devices, “Journal of Materials Science: Materials in Electronics, Vol. 6, No.5, pp.311-324, October 1995. 23. R. Bate and M. Reed, “Prospects for Quantum Integrated Circuits,” Proceedings of SPIE – Symposium on Quantum Well and Superlattice Physics, Vol. 792, pp.26-35, March 1987. 24. B. Davari and G. Shahidi, “CMOS scaling for High Performance and Low Power-the Next Ten Years,” Proceedings of the IEE, Vol. 83, No.4, pp.596-606, April 1995. 25. Y. Taur, D. Buchanan, and II. Wong, “CMOS scaling into the Nanometer Regime, “Proceedings of the IEEE. Vol. 85, No. 4, pp. 486-504, April 1997. 26. K. Wang and W. Lynch, “Scenarios of CMOS scaling, “Proceedings of the IEEE interactional Conference on Solid-state and integrated Circuit Technology. pp. 12-16, October 1998. 27. II Wong. D Frank, and J Welset, “Nanoscals CMOS, “Proceedings of the IEEE, Vol. 87, No.4, pp. 537 570, April 1999 R.C. PR Smith, and Y. L. Lowe, “inductive Crosstalk Between Integrated Passive Components M RF Wireless Modules,” Proceedings of the IEEE International. 28. Conference on Multichip Modules and High Density Packaging, pp. 496-500, April 1998. 29. A. Deutsch, H. Smith, G.A. Katopis, W.D. Becker, P.W. Coteus, and P.J. Reslte, “ The Importance of Inductance and Inductive Coupling for on-chip Wiring,” Proceedings of the IEEE Electrical performance of Electronic Packaging Conference, pp. 53-56, October 1997. 30. T. Sakurai, “Closed-Form Expressions for interconnection Delay, Coupling, and Crosstalk in VLSi’s,” IEEE Transactions on Electron Devices, Vol. 40, No.1, pp.118-124, January 1994. 31. D. H. Cho, Y. S. Eo, and H. S. Park, “Interconnect Capacitance, Crosstalk, and Signal Delay for 0.35 pm CMOS Technology, “Proceedings of the IEEE International Electron Devices Meeting, pp. 619-622, December 1996. 32. K. M. Fukuda, S. Macda, T. Tsukuda, and T. Matsuura, “Substrate Noise Reduction Using Active Guard Band Filters in Mixed-Signal Integrated Circuits,” IEICE Transactions on Fundamentals of Electronics, Communications, and computer Scvences, Vol. E80-A, No.2, pp. 313-320, February 1997. 33. D. K. Su and B. A. Wooley, “experimental Results and Modeling Techniques for substrate Noise in Mixed- Signal Integrated Circuits,” IEEE Journal of Solid-State Circuits, Vol. 28, No. 4, pp. 420-430, April 1993. 34. X. Aragones and A. Rubio, “Analysis and Modeling of Parasitic Substrate Coupling in CMOS Circuits,” IEE Proceedings-Circuits, Devices and Systems, Vol. 142, No.5, pp. 307-312, October 1995. Authors: Shobha Sharma Comparative Analysis of Low Power and High Performance PTM Models of CMOS with HiK-Metal Paper Title: Gate Technology at 22nm Abstract: This paper analysis the low power and high performance models of PTM with Hi-K metal gate cmos technology by using them in an cmos inverter at 22nm technology node.The characteristics are compared with cmos bulk technology as well. This analysis gives an insight into leakages when the input voltage is sweeping from minimum to maximum voltage.The aim of HiK metal gate technology is to reduce the leakage at sub 32nm node and is a good alternative to cmos bulk technology having high leakage and power dissipation as seen in this paper’s comparative analysis. All the simulation is done with hspice simulator at 22nm technology node with PTM models of Arizona state university.
Keywords: 22nm cmos, body biasing, Scaling issues, ptm models.
50. References: 1. Dennard A. R. LeBlanc et al, SSC 256. 257-261 2. Hiwai,ohmi microelectronics reliab 1251 42 (2002) 3. www.itrs.net 4. H lwai IEDM 2008 5. Ono et al,ieee transaction on elec devices,42 ,1510 6. www.freescale.com 7. Pille et al,Issscc Dig tech 2007, p322-323 8. Kukushima et al, iwdtf,2008 9. Kuhn et al, IEDM tech dig 2007 p471 10. Bohr et al Proceedings ICSICT 2008 p13 11. www.intel.com 12. Morita et al, Symp on VLSI circuit 2007,p256 13. Hlwai IWJT 2008 14. Zimmermann and W. Fichtner, \Low-power logic styles," IEEEJSSC, vol. 32, no. 7, pp. 1079 1090, R1997. 15. G. E. Moore et al., Cramming more components onto integrated circuits," Proceedings of the IEEE, vol. 86, no. 1, pp. 82 85, 1998. Authors: Krishnendu Chattopadhyay, Santanu Das, Sekhar Ranjan Bhadra Chaudhuri Bandwidth Enhancement of A Micro strip Line Fed Hexagonal Wide-Slot Antenna Using Fork-like Paper Title: Tuning Stub Abstract: In this paper, a printed hexagonal wide slot antenna, fed by a microstrip line with fork like tuning stub for bandwidth enhancement is proposed and experimentally investigated. The impedance, radiation and gain characteristics of this antenna are studied. Simulation and experimental results indicate that a 1.5:1 VSWR bandwidth, of about 1 GHz and 2:1 VSWR bandwidth of 1.34 GHz is achieved at operating frequency around 2.5 GHz, which is about three times larger than a microstrip line fed hexagonal wide slot antenna, with normal tuning stub, considered as reference antenna.
Keywords: Fork-like tuning stub, Hexagonal wide-slot, Microstrip line fed, Method of moment, wide band.
References: 1. M. Kahrizi, T.K. Sarkar and Z.A. Maricevic , “Analysis of a wide radiating slot in the ground plane of a microstrip line” IEEE Trans. Microwave Theory and Techniques, vol.41,No. 1, pp. 29-37.Jan.1993. 51. 2. J.Y. Jan, J.C. Kao, “Novel printed Wide-band Rhombus-Like slot antenna with an offset microstrip-fed line”, IEEE Antennas and Wireless Propagation Lett. Vol.6, pp.249-251, 2007. 3. J.Y. Jan, J.W. Su, “Bandwidth Enhancement of a printed wide slot Antenna with a rotated slot” IEEE Trans. Antennas and 262-267 Propagation.vol.53, No.6, pp.2111-2114,2005. 4. Y.W. Jang, “Experimental study of large bandwidth Three-offset microstripline-fed slot antenna” IEEE Microwave and wireless components Lett. Vol.11,No.10,pp.425-427,oct.2001. 5. J.Y. Jan, L.C. Wang, “Printed wideband Rhombus slot antenna with a pair of parasitic strips for Multiband applications” IEEE Trans. Antennas and Propagation, vol.57, No.4, pp.1267-1270, 2009. 6. Y. Sung, “A printed wide slot antenna with a modified L-shaped microstrip line for wideband applications” IEEE Trans. Antennas and Propagation,vol.59,No.10,pp. 3918-3922,2011. 7. J.Y. Sze, K.L. Wong, “Bandwidth enhancement of a microstrip-line-fed printed wide-slot antenna” IEEE Trans. Antennas and Propagation, vol.49, No.7, pp.1020-1024, 2001. 8. L. Zhu, R. Fu, K.L. Wu, “A novel broadband microstrip-fed wide slot antenna with double rejection zeros” IEEE Antennas and Wireless Propagation Lett., vol.2,pp.194-196,2003. 9. Y.F .Liu, K.L. Lau, Q. Xue, C.H. Chan, “Experimental studies of printed wide slot antennas for wideband applications” IEEE Antennas and wireless Propagation Lett., vol.3,pp.273-275,2004. 10. H. Kim, Y. J. Yoon, “Microstrip-fed slot antennas with suppressed harmonics” IEEE Trans. Antennas and Propagation,vol.53,No.9, pp. 2809-2817,2005. 11. P.H. Rao, V.F. Fusco,R. Cahill, “Linearly polarized radial stub fed high performance wideband slot antenna” Electronics Lett., vol.37,No.6,pp.335-337,March 2001. Authors: R.Parvathi , C.Malathi Paper Title: Arithmetic Operations on Symmetric Trapezoidal Intuitionistic Fuzzy Numbers Abstract: In this paper, Symmetric Trapezoidal Intuitionistic Fuzzy Numbers (STIFNs) have been introduced and 52. their desirable properties are also studied. A new type of intuitionistic fuzzy arithmetic operations for STIFN have been proposed based on (,) -cuts. A numerical example is considered to elaborate the proposed arithmetic 268-273 operations. These operations find applications in solving linear programming problems in intuitionistic fuzzy environment and also to find regression coefficient in intuitionistic fuzzy environment.
Keywords: Intuitionistic Fuzzy Index, Intuitionistic Fuzzy Number, Intuitionistic Fuzzy Set, Symmetric Trapezoidal Intuitionistic Fuzzy Number, (,) -cuts.
References: 1. K. T. Atanassov, “Intuitionistic Fuzzy Sets: Theory and Applications”, Physica-Verlag, Heidelberg, New York, 1999. 2. K. T. Atanassov, “Intuitionistic Fuzzy Sets”, Fuzzy Sets and Systems, vol. 20, 1986, pp. 87-96. 3. K. T. Atanassov, “More on intuitionistic fuzzy sets”, Fuzzy Sets and Systems, vol. 33, 1989, pp. 37-46. 4. R. E. Bellman and L. A. Zadeh, “Decision making in a fuzzy environment”, Management Science, vol. 17, 1970, pp. 141-164. 5. H. Bustine and P. Burillo, “Vague sets are intuitionistic fuzzy sets”, Fuzzy Sets and Systems, vol. 79, 1996, pp. 403- 405. 6. K. Ganesan and P. Veeramani, “Fuzzy Linear Programs with Trapezoidal Fuzzy Numbers”, Ann.Oper.Res., vol. 143, 2006, pp. 305-315. 7. W. L. Gau and D. J. Buehrer, “Vague sets”, IEEE Transactions on Systems, Man and Cybernetics, vol. 23, 2, 1993, pp. 610-614. 8. B. S. Mahapatra and G. S. Mahapatra, “Intuitionistic Fuzzy Fault Tree Analysis using Intuitionistic Fuzzy Numbers”, International Mathematical Forum, vol. 5, 21, 2010, pp. 1015 – 1024. 9. H. M. Nehi, “A New Ranking Method for Intuitionstic Fuzzy Numbers”, International Journal of Fuzzy Systems, vol. 12, 1, 2010, pp. 80- 86. 10. M. H. Shu, C. H. Cheng and J. R. Chang, “Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly”, Microelectronics Reliability, vol. 46, 2006, pp. 2139-2148. 11. J. Q. Wang, “Overview on fuzzy multi-criteria decision-making approach”, Control and decision, vol. 23, 6, 2008, pp. 601-606. 12. H. J. Zimmermann, “Fuzzy Set Theory and its Applications”, Kluwer Academic Publishers, London, 1991. 13. H. J. Zimmermann, “Fuzzy programming and linear programming with several objective functions”, Fuzzy Sets and Systems, vol. 1, 1978, pp. 45-55. Authors: R. Sudha, Vishwas Vats, Gaurav Pathak, Jayabarathi T Paper Title: Optimal Placement of Phasor Measurement Units Using Modified Invasive Weed Optimization Abstract: A modified Invasive Weed based methodology for optimal measurement of Phasor measurements units (PMUs) for complete observability of Power system is presented in this paper. The prime objective of this Optimization problem is to reduce the number of PMUs and to maximize the redundancy at power system bushes. In this paper MIWO (Modified Invasive Weed Algorithm is implemented for three bush systems namely 7, 9, IEEE 14 standard bus systems. The proposed algorithm is very easy to understand and it’s result is as satisfactory as results of other algorithm methods. 53. Keywords: Invasive Weed Algorithm, Phasor Measurement Units, Observability, Optimal Placements. 274-277
References: 1. K Mazlumi and H. Vahedi “ Optimal Placement of PMUs in power system based on Bacterial Foraging Algorithm” Proceedings of ICEE 2010, May 11-13, 2010 .. 2. State Estimation and voltage Security Monitoring using Synchronized Phasor Measurement by Reynaldo Francisco Nuqui. 3. Abhinav Sadu, Rajesh G. Kavasser, Rajesh Kumar, “Optimal Placement of Phasor Measurement Units using Particle Swarm Optimization”, IEEE, 2009, 978-1-4244-5612, World Congress on Nature & Biologically Inspired Computing. 4. B.Dadalipour, A.R. Mallahzadeh and Z. Davoodi-Rad “APPLICATION OF THE INVASIVE WEED OPTIMIZATION TECHNIQUE FOR ANTENNA CONFIGURATIONS” 2008 Loughborough Antennas & Propagation Conference. Authors: Shah Murtaza Rashid Al Masud Paper Title: An Extended and Granular Classification of Cloud’s Taxonomy and Services Abstract: In the recent time cloud computing has come forwarded as one of the most admired computing model in knowledge domain that concerns about the distributed information systems to support the whole world as a cloud community. Distributed, virtualization and service-oriented nature have given ascendancy to cloud computing to distinguish from its core descendants like grid computing, geographical information systems, and distributed system. Although cloud computing dominants the e-society, but it is still in under research, progress. The architecture of cloud’s taxonomy and its services are very significant issues for cloudifications because every day some new advancements and developments are adjoined under its umbrella. In this paper we proposed an extended and granular classification of taxonomy for cloud computing and specified services that is a detailed ontology of cloud, which will be helpful for researchers and stakeholders in better understanding, developing, and implementing cloud technology and services to their lives.
Keywords: Cloud computing, Distributed system, Granular classification, Taxonomy. 54. References: 278-286 1. M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. H. Katz, A. Konwinski, G. Lee, D. A. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, “Above the Clouds: A Berkeley View of Cloud Computing,” EECS Dept., Uni. of California, Berkeley, Tech. Rep. UCB/EECS-2009-28, Feb ’09. 2. L. Qian, Z. Luo, Y. Du, and L. Guo, “Cloud computing: An overview,” in CloudCom ’09: Proceedings of the 1st International Conference on Cloud Computing. Springer-Verlag, 2009, pp. 626–631. 3. G. Lin, D. Fu, J. Zhu, and G. Dasmalchi, “Cloud computing: It as a service,” IT Professional, vol. 11, no. 2, pp. 10–13, 2009. 4. P. Murray, “Enterprise grade cloud computing,” in WDDM ’09: Proceedings of the Third Workshop on Dependable Distributed Data Management. New York, NY, USA: ACM, 2009, pp. 1–1. 5. A. Weiss, “Computing in the clouds,” NetWorker, vol. 11, no. 4, pp. 16–25, 2007. 6. D. Hilley, “Cloud computing: A taxonomy of platform and infrastructure-level offerings,” CERCS, Georgia Institute of Technology, Tech. Rep. GIT-CERCS-09-13, April 2009. 7. cloud-outlook-for-2012.html: http://cloudonomic.blogspot.com/2011/12/cloud-outlook-for-2012.html#!/2011/12/cloud-outlook-for- 2012.html 8. T. Jowitt, “Four out of five enterprises giving cloud a try,” August 27, 2009. Computerworld UK, (visited: 2010, May 7). [Online]. Available: http://www.computerworlduk.com/management/ it-business/services-sourcing/news/index.cfm?newsId=16355 9. R. Grossman, “The case for cloud computing,” IT Professional, vol. 11, no. 2, pp. 23 –27, march-april 2009. 10. P. Mell and T. Grance, “The NIST definition of cloud computing (v15),” National Institute of Standards and Technology, Tech. Rep., 2009. 11. A. Plesser, “CC is hyped and overblown, Forrester’s Frank Gillett.....Big tech companies have ’cloud envy’,” Sep ’08, interview video (visited: 2010, Apr 23). [Online]. Available: http://www.beet.tv/2008/ 09/cloud-computing.html 12. Bhaskar Prasad Rimal, Eunmi Choi, “A Conceptual Approach for Taxonomical Spectrum of Cloud Computing”, Proceedings of the 4th International Conference on Ubiquitous Information Technologies & Applications, Dec 2009. ICUT '09. 13. D. Gottfrid, “Self-service, Prorated Super Computing Fun” Available from: http://open.blogs.nytimes.com/2007/11/01/self-service- proratedsuper- computing-fun/ 14. M. Crandell, “Defogging Cloud Computing: A Taxonomy”, Available: http://gigaom.com/2008/06/16/defogging-cloud-computing- ataxonomy/ 15. P. Laird, “Different Strokes for Different Folks: A Taxonomy of Cloud Offerings”, Enterprise Cloud Submit, INTEROP, 2009. 16. Cloud Computing Use Case Discussion Group, “Cloud Computing use Case”, White Paper version 1.0, 5 August, 2009 17. S. Ried, “Yet Another Cloud – How Many Clouds Do We Need?” Available from: Forrester Research. http://www.forrester.com/ 18. M. Crandell, “Defogging Cloud Computing: A Taxonomy”, Available: http://gigaom.com/2008/06/16/defogging-cloud-computing- ataxonomy/ 19. P. Laird, “Different Strokes for Different Folks: A Taxonomy of Cloud Offerings”, Enterprise Cloud Submit, INTEROP, 2009. 20. Cloud Computing Use Case Discussion Group, “Cloud Computing use Case”, White Paper version 1.0, 5 August, 2009 21. S. Ried, “Yet Another Cloud – How Many Clouds Do We Need?” Available from: Forrester Research. http://www.forrester.com/ 22. Cloud computing conference in China, May 2012, “http://www.ciecloud.org/2012/schedule03_en.html” 23. Cloud outlook for 2012, http://cloudonomic.blogspot.com/2011/12/cloud-outlook-for-2012.html#!/2011/12/cloud-outlook-for-2012.html. 24. Gurdev Singh, and others, “cloud computing future solution for educational systems”: http://www.ijecbs.com/January2012/29.pdf, Vol. 2 Issue 1 January 2012. 25. Medical cloud: http://www.backup-technology.com/5988/the-medical-cloud/ 26. Kevin L. Jackson, “Government Cloud Computing”, SOA-R Cloud Computing, Dataline, LLC. 27. “Salesforce Customer Relationships Management (CRM) system,” http://www.salesforce.com/. 28. “GOOGLE Apps,” http://www.google.com/apps/business/index.html. 29. “Apex: Salesforce on-demand programming language and framework,” http://developer.force.com/. 30. “Hadoop,” http://hadoop.apache.org/. 31. C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins, “Pig latin: a not-so-foreign language for data processing,” in SIGMOD ’08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data. New York, NY, USA: ACM, 2008, pp. 1099– 1110. [Online]. Available: http://dx.doi.org/10.1145 32. S. Ghemawat, H. Gobioff, and S.-T. Leung, “The google file system,” SIGOPS Oper. Syst. Rev., vol. 37, no. 5, pp. 29–43, 2003. 33. K. Petersen, M. Spreitzer, D. Terry, and M. Theimer, “Bayou: replicated database services for world-wide applications,” in EW 7: Proceedings of the 7th workshop on ACM SIGOPS European workshop. New York, NY, USA: ACM, 1996, pp. 275–280. 34. G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels, “Dynamo: amazon’s highly available key-value store,” in SOSP ’07. New York, NY, USA: ACM, 2007, pp. 205–220. 35. “Amazon simple storage service,” http://aws.amazon.com/s3/. 36. “EMC Managed Storage Service,” http://www.emc.com/. /1376616.1376726 37. W. J. et al., “Network Communication as a Service-Oriented Capability,” High Performance Computing and Grids in Action, Advances in Parallel Computing, vol. 16, March 2008. 38. A. H. et al., “Perfsonar: A service oriented architecture for multi-domain network monitoring,” in ICSOC, ser. Lecture Notes in Computer Science, B. B. et al., Ed., vol. 3826. Springer, 2005, pp. 241–254. 39. J. Hofstader, “Communications as a Service,” http://msdn. microsoft.com/en-us/library/bb896003.aspx. 40. “Microsoft Connected Service Framework,” http: //www.microsoft.com/serviceproviders/solutions/ connectedservicesframework.mspx. 41. Symantec:http://www.symantec.com/theme.jsp?themeid=symantec-cloud 42. Security: https://wiki.cloudsecurityalliance.org/guidance/index.php/Cloud_Computing_Architectural_Framework 43. Foster and C. Kesselman, “Globus: A metacomputing infrastructure toolkit,” International Journal of Supercomputer Applications, 1997. 44. T. Tannenbaum and M. Litzkow, “The condor distributed processing system,” Dr. Dobbs Journal, February 1995. 45. R. W. et al., “Grid resource allocation and control using computational economies,” in Grid Computing: Making the Global Infrastructure a Reality. John Wiley & Sons, 2003, pp. 747–772. 46. T. Severiens, “Physics portals basing on distributed databases,” in IuK, 2001. 47. P. Smr and V. Novek, “Ontology acquisition for automatic building of scientific portals,” in SOFSEM 2006: Theory and Practice of Computer Science: 32nd Conference on Current Trends in Theory and Practice of Computer Science. Springer Verlag, 2006, pp. 493–500. [Online]. http://www.fit.vutbr.cz/research/view pub.php?id=8004 48. M. Chau, Z. Huang, J. Qin, Y. Zhou, and H. Chen, “Building a scientific knowledge web portal: the nanoport experience,” Decis. Support Syst., vol. 42, no. 2, pp. 1216–1238, 2006. 49. M. Christie and S. Marru, “The lead portal: a teragrid gateway and application service architecture: Research articles,” Concurr. Comput. : Pract. Exper., vol. 19, no. 6, pp. 767–781, 2007. 50. D. G. et al., “Building Grid Portals for e-Science: A Service Oriented Architecture,” High Performance Computing and Grids in Action, IOS Press, Amsterdam, 2007. 51. D. Gannon, J. Alameda, O. Chipara, M. Christie, V. Dukle, L. Fang, M. Farellee, G. Fox, S. Hampton, G. Kandaswamy, D. Kodeboyina, C. Moad, M. Pierce, B. Plale, A. Rossi, Y. Simmhan, A. Sarangi, A. Slominski, S. Shirasauna, and T. Thomas, “Building grid portal applications from a webservice component architecture,” Proceedings of the IEEE (Special issue on Grid Computing), vol. 93, no. 3, pp. 551–563, March 2005. 52. Rough Type. http://www.roughtype.com 53. “Morgan Stanley, IBM ink utility computing deal,” http://news. cnet.com/2100-7339-5200970.html. 54. “Preboot Execution Environment (PXE) Specifications, Intel Technical Report, September 1999.” 55. “Das U-Boot: The Universal Boot Loader,” http://www.denx. de/wiki/U-Boot/WebHome. 56. J. Appavoo, V. Uhlig, and A. Waterland, “Project Kittyhawk: building a global-scale computer: Blue Gene/P as a generic computing platform,” SIGOPS Oper. Syst. Rev., vol. 42, no. 1, pp. 77–84, 2008. 57. Oracle cloud strategy, http://www.oracle.com/us/products/consulting/resource-library/cloud-servi es-ds-507966.pdf 58. Truffle Capital (2007), ‘Truffle Capital: European Commission Recognises the Need for a “European Strategy for Software” – Commenting on the 2007 Truffle 100 Europe, Viviane Reding Calls on Europe to Develop a Leadership Position in Software’ - available at http://www.businesswire.com/news/google/20071122005070/en 59. DG Information Society and Media - Directorate for Converged Networks and Service (2009), ‘Towards A European Software Strategy - Report Of An Industry Expert Group’ - available at http://www.nessi-europe.com/Nessi/LinkClick.aspx?fileticket=7teEO5hz wY%3D&tabid=304 &mid=1571 60. Strategy: http://cordis.europa.eu/fp7/ict/ssai/docs/cloud-report-final.pdf Authors: Purno Mohon Ghosh, Md. Anwar Hossain, A.F.M. Zainul Abadin, Kallol Krishna Karmakar Comparison Among Different Large Scale Path Loss Models for High Sites in Urban, Suburban and Paper Title: Rural Areas 55. Abstract: Radio propagation is essential for emerging technologies with appropriate design, deployment and management strategies for any wireless network. It is heavily site specific and can vary significantly depending on 287-290 terrain, frequency of operation, velocity of mobile terminal, interface sources and other dynamic factor. Accurate characterization of radio channel through key parameters and a mathematical model is important for predicting signal coverage. Path loss models for macro cells such as Hata Okumura, Walfisch-Ikegami and Lee models are analyzed and compared their parameters. The received signal strength was calculated with respect to distance and model that can be adopted to minimize the number of handoffs. This paper proposes path loss models for high sites in urban, suburban and rural areas.
Keywords: Cellular mobile, Propagation model, Path loss, Received signal strength.
References: 1. Armoogum.V, Soyjaudah.K.M.S, Fogarty.T and Mohamudally.N, “Comparative Study of Path Loss using Existing Models for Digital Television Broadcasting for Summer Season in the North of Mauritius”, Proceedings of Third Advanced IEEE International Conference on Telecommunication, Mauritius Vol. 4, pp 34-38, May 2007. 2. Abhayawardhana V. S, Wassell.I.J, Crosby D, Sellars. M.P. and Brown. M.G, “Comparison of empirical propagation path loss models for fixed wireless access systems”, Proceedings of IEEE Conference on Vehicular Technology , Stockholm, Sweden, Vol. 1, pp 73-77, June 2005. 3. K.Ayyappan, P. Dananjayan, “Propagation Model for Highway in Mobile Communication System”, 4. Ahmed H.Zahram, Ben Liang and Aladdin Dalch, “Signal threshold adaptation for vertical handoff on heterogeneous wireless networks”, Mobile Networks and application, Vol.11, No.4, pp 625- 640, August 2006. 5. A. Hecker, M. Neuland, and T. Kuerner, “Propagation models for high sites in urban areas”, Adv. Radio Sci., 4, pp. 345-349, 2006. 6. Vijay K. Garg, “Wireless Communications and Networking”, Morgan Kaufmann Publishers, pp 66-68, 2007. 7. Gordon L. Stüber, “Principles of Mobile Communication”, Second Edition, Kluwer Academic Publishers, pp 105-109, 2002. 8. T.S. Rappaport, “Wireless Communications”, Pearson Education, 2003. 9. William C.Y. Lee, “Mobile Cellular Telecommunications”, McGraw Hill International Editions, 1995. Authors: Seyyed Ashkan Ebrahimi, Peiman Keshavarzian, Saeid Sorouri, Mahyar Shahsavari Paper Title: Low Power CNTFET- Based Ternary Full Adder Cell for Nanoelectronics Abstract: In a VLSI circuit, about 70 percent of area occupies by Interconnection. Such a large number of area occupation leads to many limitations of fabricating and applying in binary circuit implementation. Multiple-valued logic is one of the most proper way to improve the ability of value and data transferring in binary systems. Nowadays as small portable devices consuming are largely increased, applying low power approaches are considerably taking into account. In this paper we suggest and evaluate a novel low power ternary full adder cell which is built with CNTFETs (Carbon Nano-Tube Field Effect Transistors). Using beneficial characteristics of CNTFET in our design and implementation notably increased the efficiency of this adder cell. Simulation results using HSPICE are reported to show that the proposed TFA (ternary full adder) consume significantly lower power and impress improvement in term of the power delay product compare to previous work.
Keywords: CNTFET, Low Power,Nanoelectronic, Ternary Full Adder, Ternary Logic.
References: 1. X. Wu, F. P. Prosser: 'CMOS ternary logic circuits', Circuits, Devices and Systems, IEE Proceedings G., 1990, 137, (1), pp. 21–27 2. X. Wu, X. Deng, and s. Ying: 'Design of ternary current-mode CMOS circuits based on switch-signal theory', Journal of Electronics., 1993, 10, (3), pp. 193–202 3. A. Raychowdhury and K. Roy: 'Carbon-nanotube-based voltage-mode multiple-valued logic design', Nanotechnology, IEEE Transactions on., 2005, 4, (2), pp. 168–179 4. P. Keshavarzian and K. Navi: 'Efficient carbon nanotube galois field circuit design', IEICE Electronics Express., 2009, 6, (9), pp. 546–552 5. K.Nepal: 'Dynamic circuits for Ternary computation in Carbon Nanotube based Field Effect Transistors', NEWCAS Conference (NEWCAS), 2010 8th IEEE International, June 2010: pp. 53-56 6. R. Mariani, F. Pessolano, , and R. Saletti: 'A new CMOS ternary logic design for low-power low-voltage circuits', in PATMOS, Louvain- la-Neuve, Belgium, September 1997 56. 7. S. Lin, Y.-B. Kim, and F. Lombardi: 'A novel CNTFET-based ternary logic gate design', Circuits and Systems, 2009. MWSCAS '09. 52nd IEEE International Midwest Symposium on, Aug. 2009: pp. 435–438 8. K. Navi, M. Rashtian, A. Khatir, P. Keshavarzian, and O. hashemipour: 'High Speed Capacitor-Inverter Based Carbon Nanotube Full 291-295 Adder', Nanoscale Res. Lett. Springer., 2010, 5, (5), pp. 859-862 9. S. Lin, Y. Kim, and F. Lombardi: 'CNTFET-Based Design of ternary logic Gates and Arithmetic Circuits', Nanotechnology, IEEE Transactions on., 2011, 10, (2), pp. 217-225 10. M. H. Moaiyeri, R. FaghihMirzaee, K. Nani and O. Hashemipour, "Efficient CNTFET-based ternary full adder cell for nanoelectronics", Nano-Micro Lett. 2011, 3, (1), pp. 43-50 11. Stanford University CNFET model Website: Stanford University, Stanford, CA.Available: http://nano.stanford.edu/model.php?id=23, accessed April 2012 12. S. Iijima: 'Helical microtubules of graphitic carbon', Nature., 1991, 354, pp. 56–58 13. Leonardo C. Castro, D. L. John, D. L. Pulfrey , Mahdi Purfath , Andreas Gehring ,Hans Kosina , Method of Predicting FT for Carbon Nanotube FETs,IEEE TRANSACTION on NANOTECHNOLOGY , VOL , 4 NO ,62005. 14. S. J. Tans, A. R. M. Verschueren, and C. Dekker: 'Room-Temperature Transistor based on a Single Carbon Nanotube', Nature., 1998, 393, pp. 49-52 15. V. Derycke, R. Martle, J. Appenzeller, and P. Avouris: 'Carbon Nanotubes Inter- and Intermolecular Logic Gates', Nano Letters., 2001, 1, (9), pp. 453-456 16. S. Frank, P. Poncharal, Z. L.Wang, andW. A. de Heer: 'Carbon nanotube quantum resistors', Science., 1998, 280, pp. 1744–1746 17. T. Rueckes, K. Kim, E. Joselevich, G. Y. Tseng, C. Cheung, and C. M. Lieber: 'Carbon nanotube based nonvolatile random access memory for molecular computer', Science., 2000, 289, pp. 94–97 18. P. Avouris, J. Appenzeller, R. Martel, and S. J. Wind: 'Carbon Nanotube Electronics', Proceedings of the IEEE, 2003, 91, (11), pp. 1772- 1784 19. P. L. McEuen, M. S. Fuhrer, and H. Park : 'Single Walled Carbon Nanotube Electronics', Nanotechnology, IEEE Transactions on., 2002, 1, (1), pp. 78-85 20. M. Kameyama, S. Kawahito, and T. Higuchi: 'A multiplier chip with multiple-valued bidirectional current-mode logic circuits', Computer., 1988, 21, (4), pp. 43–56. 21. P. Keshavarzian, and K. Navi: 'Universal ternary logic circuit design through carbon nanotube technology', International Journal of Nanotechnology., 2009, 6, (10), pp. 942-953. 22. J. Deng and H.-S. P.Wong: 'A compact SPICE model for carbon-nanotube field-effect transistors including nonidealities and its application—Part I: Model of the intrinsic channel region', Electron Devices, IEEE Transactions on., 2007, 54, (12), pp. 3186–3194 23. J. Deng and H.-S. P.Wong: 'A compact SPICE model for carbon-nanotube field-effect transistors including nonidealities and its application—Part II: Full device model and circuit performance benchmarking', Electron Devices, IEEE Transactions on., 2007, 54 (12), pp. 3195–3205. Authors: Peiman Keshavarzian, Mahla Mohammad Mirzaee Paper Title: A Novel Efficient CNTFET Gödel Circuit Design Abstract: Carbon nanotube field effect transistors (CNFETs) are being extensively studied as possible successors to Silicon MOSFETs. Implementable CNTFET circuits have operational characteristics to approach the advantage of using MVL in voltage mode. In this paper we used CNTFETs to implement the improved Gödel basic operators. This paper presents arithmetic operations, implication and multiplication in the ternary Godel field through carbon nanotube field effect transistors (CNFETs). Consequently, in the novel Gödel circuit design, the simulation results demonstrate an improvement in the circuit parameters such as delay, power and power delay product.
Keywords: CNTFET, MVL, TVL, Gödel.
References: 1. Z. Yao, h. W. Postma, L. Balents, and C. Dekker.Carbon nanotube intramolecular junctions. Nature.1999; 402:273-276. 2. W. Z. Li, S. S. Xie, L. X. Qian, B. H. Change, B. S. Zou, W. Y. Zhou, R.A. Zhao, and G. Wang.Large scale synthesis of aligned carbon nanotubes.Science.1996; 274(5293): 1701-1703. 3. M. Mukaidono .Regular ternary logic functions - ternary logic functions suitable for treating ambiguity. IEEE Trans. Computers .1986 ;C- 35(2):179 - 183. 4. X.Wu,F .p .Prosser, Cmos ternary logic circuits. IEEE Pro. G. Electronic Circuits and Systems.1990; 137:21-22. 5. P. Keshavarzian,K. Navi. Efficient carbon nanotube galois field circuit design .IEICE Electronic Express.2009;6 (9): 546-552. 57. 6. P . Keshavarzian and K. Navi. An improved CNTFET galois circuit design as a basic MVL field. IEICE Electronic Express.2009; 6(9): 546- 552. 7. P.Keshavarzian and M.M.Mirzaee.A novel efficient CNTFET galois design as a basic ternary valued logic field.Nanotechnology ,sience and 296-300 applications.2012,vol 5,p 1-11. 8. S. LIN, Y.-B. Kim,F. Lombardi.novel CNTFET-based ternary logic gate design.IEEEInt.MidwestSymp. Circuits Syst.2009:435-438. 9. P. Keshavarzian, K. Navi, “Universal Ternary Circuit Design Through Carbon Nanotube Technology” Int. J. Nanotechnol., Vol. 6, Nos. 10/11, p. 942, 2009. 10. P. Keshavarzian, K. Navi, “efficient carbon nanotube lukasiewicz circuit design,in proceeding of 3’rd international conference on nanosrsucture, kish Island,p.1022, 2010. 11. S. Iijima.Helical microtubules of graphic carbon.Nature.1991;345: 56-58. 12. S. LIN, Y.-B. Kim,F. Lombardi.novel CNTFET-based ternary logic gate design.IEEEInt.MidwestSymp. Circuits Syst.2009:435-438. 13. S. J. Tans, A. R. M. Verschueren,C.Dekker.Room-temperature transistor based on a single carbon nanotube,Nture.1998;393:49-52. 14. P. L. McEuen, M. S. Fuhrer,H. Park.Single-walled carbon nanotube electronics.IEEE Tran. On Nanotechnology,2002;1(1):78-85. 15. P. Avouris, J. Appenzeller, R. Martel,andS. J. Wind.Carbonnanotube electronics.Proceeding of IEEE.2004;91(11):1772-1784. 16. J. S. Hwang etal.Electronic transport properties of a single-wall carbon nanotube field effect transistor with deoxyribonucleic acid conjugation. Physica E: Low-dimensional Systems and Nanostructures. 2008;40(5): 1115-1117. 17. M. Pourfath, et al. Numerical Analysis of Coaxial Double Gate Schottky Barrier Carbon Nanotube Field Effect Transistors.Journal of Computational Electronics;2005,4: 75–78. 18. J. Guo, A. Javey, H. Dai, S. Datta,M. Lundstrom.Predicted Performance advantages of carbon nanotube transistors with doped nanotubes source/drain. Phys. Rev. B, Condens.Matter;2003,cond-mat/0 309 039. 19. J. Deng, H.S. P. Wong. A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Nonidealities and Its Application - Part I: Model of the Intrinsic Channel Region. IEEE Trans. Electron Devices; 2007.54:3186-3194. 20. J. Deng, H.S. P. Wong. A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Nonidealities and Its Application - Part II: Full DeviceModel and Circuit Performance Benchmarking.IEEE Trans. Electron Devices;2007. 54: 3195-3205. Authors: Harshal J. Jain, M. S. Bewoor, S. H Paper Title: Context Sensitive Text Summarization Using K Means Clustering Algorithm Abstract: The field of Information retrieval plays an important role in searching on the Internet. Most of the information retrieval systems are limited to the query processing based on keywords. In the information retrieval system matching of words with huge data is core task. Retrieval of the relevant natural language text document is of more challenging. In this paper we introduce the concept of OpenNLP tool for natural language processing of text for word matching. And in order to extract meaningful and query dependent information from large set of offline documents, data mining document clustering algorithm are adopted. Furthermore performance of the summary using OpenNLP tool and clustering techniques will be analysed and the optimal approach will be suggested.
Keywords: K means algorithm, Document graph, Context sensitive text summarization. 58.
References: 301-304 1. Ramakrishna Varadarajan, Vangelis Hristidis,”A System for Query-Specific Document Summarization” 2. Ravindranath Chowdary P Sreenivasa Kumar “An Incremental Summary Generation System” 3. Regina Barzilay and Michael Elhadad,”Using Lexical Chains for Text Summarization” 4. Mohamed Abdel Fattah, and Fuji Ren,”utomatic Text Summarization” 5. Jackie CK Cheung,”Comparing Abstractive and Extractive Summarization of Evaluative Text: Controversialist and Content Selection” 6. Jie Tang, Limin Yao, and Dewei Chen,”Multi-topic based Query-oriented Summarization” 7. R.M.Aliguliyev,”Automatic Document Summarization by Sentence Extraction” 8. Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Proceedings of the ACL-04 Workshop: Text Summarization Branches Out, pages 74–81, Barcelona, Spain. 9. Luhn H. P. 1958, the automatic creation of literature abstracts, IBM Journal, pages 159-165 10. http://opennlp.apache.org/ 11. L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, Authors: Hossein Etemadi, Morvarid F. Dabiri, Peiman Keshavarzian, Tahere Panahi Paper Title: Design of CNTFET-Based Invertor Inspired BiCMOS Technology 59. Abstract: In this paper we present a new combination of Carbon NanoTube Field Effect Transistors (CNTFETs) 305-308 and bipolar transistors which named Bi CNTFET and used to design a fast and low power inverter. New inverter proposes and compare to existing Bipolar-CMOS (BiCMOS) design. Propose Bi CNTFET inverter has advantages such as large load drive capabilities, low static power dissipation, fast switching and high input impedance. Extensive simulation using HSPICE to investigate the power consumption and delay of propose inverter. Simulation result shows that the propose inverter using carbon nanotube has better performance in terms of delay and power consumption, in compared to BiCMOS counterpart. Furthermore the new design reduces the chip area because of using carbon nanotubes.
Keywords: CNTFET, Nanoelectronic, Bi-CNTFET.
References: 1. Dong-Shong Liang; Kwang-Jow Gan; Jenq-Jong Lu; Cheng-Chi Tai; Cher-Shiung Tsai; Geng-Huang Lan; Yaw-Hwang Chen, “Multiple- Valued Memory Design by Standard BiCMOS Technique,” Computer Science and Information Engineering, Volume 7 , Issue 3, pp. 596 – 599, April 2009. 2. Xiaohui Hu; Jizhong Shen; City Coll., Sch. of Inf. & Electr. Eng., Zhejiang Univ., Hangzhou, “The structure of dynamic BiCMOS circuit and its switch-level design,” IEEE International conference, Issue 11, pp 319 – 322,Dec 2008. 3. J. Appenzeller, “Carbon Nanotubes for High-Performance Electronics—Progress and Prospect,” Proc. IEEE, Volume 96, Issue 2, pp. 201 - 211, Feb. 2008. 4. A. Raychowdhury,; K. Roy, “Carbon-nanotube-based voltage-mode multiple-valued logic design,” IEEE Trans. Nanotechnol., Volume 4, Issue 2, pp. 168 – 179, March 2005. 5. Y. Li, W. Kim, Y, Zhang, M, Rolandi, D. Wang, “Growth of Single-Walled Carbon Nanotubes from Discrete Catalytic Nanoparticles of Various Sizes,” J. Phys. Chem., Vol. 105, pp. 11 424, 2001. 6. J. Appenzeller, et al. Appl. Phys. Lett. 78, 3313 (2001); C.T. White and T. N. Todorov, Nature (London) 393, 240 (1998). 7. M. S. Fuhrer, M. Forero, A. Zettl, and P. L. McEuen , AIP Conference Proceedings, Electronic Properties of Novel Materials – Molecular Nanostructures, (Editors: H. Kuzmany, J. Fink, M. Mehring and S. Roth) 2001, p. 401. 8. J. Appenzeller et al. submitted. 9. Y. Bok Kim, Y. B. Kim and F. Lombardi, Proc.” Design and analysis of a high-performance CNTFET-based Full Adder” IEEE International Midwest Symposium on Circuits and Systems 1130 (2009). 10. J. Deng and H. SP Wong,” A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Nonidealities and Its Application—Part I: Model of the Intrinsic Channel Region” IEEE T.Electron. Volume : 54, Issue:12, pp 3186 – 3194, Nov. 2007. 11. J. Deng and H. SP Wong,” A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Nonidealities and Its Application—Part II: Full Device Model and Circuit Performance Benchmarking,” IEEE T. Electron.volome:54, Issue: 12, pp 3195 – 3205, Nov. 2007. 12. (2008). Stanford University CNTFET model Website. Stanford University, Stanford, CA [Online].Available: http://nano.stanford.edu/model.php? id=23 13. Keivan Navi, Rabe'e Sharifi Rad, Mohammad Hossein Moaiyeri and Amir Momeni, “A low-voltage and energy-efficient full adder cell based on carbon nanotube technology” Vol. 2, No. 2 114-120 (2010). Authors: S.Sujitha, C.Venkatesh Paper Title: Design and Analysis of Standalone Solar Assisted Switched Reluctance Motor Drives Abstract: Switched Reluctance Motor (SRM) is a simple, low cost, robust structure, reliability, controllability and high efficiency, So that it is used in variable speed and high speed applications. Renewable energy sources are a great improvement in many applications. In this paper, a switched reluctance motor with PV modeling is introduced. The implemented design is based on the optimization of solar PV modules arranged in array, integrated with rechargeable battery with existing converter models to drive the switched reluctance motor. The results of the investigations compare with SRM driven by DC source offers superior performance in terms of simulation analysis.
Keywords: Battery, Charger, Converter, PV Panel, Switched Reluctance Motor.
References: 60. 1. Tsai HL. Insolation-oriented model of photovoltaic module using MATLAB/Simulink. Solar Energy 2010; 84:1318 - 26. 2. Tsai HL, Tu CS, Su YJ. Development of generalized photovoltaic model using MATLAB/Simulink. In: Proceedings of the world congress on engineering and computer science, 2008, San Francisco, 2008. p. 1 - 6. 309-312 3. C.S. Chin , A. Babu, W. McBride, Design, modeling and testing of a standalone single axis active solar tracker using MATLAB/Simulink. In: Renewable Energy 2011; 36: 3075 – 90 4. Villalva MG, Gazoli JR, Filho ER. Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Transactions on Power Electronics 2009; 5:1198 - 208. 5. C.S.Solanki, Solar Photovoltaics: Fundamentals, Technologies and Applications. New Delhi, PHI learning Pvt. Ltd., 2011. 6. Lopes LAC, Lienhardt AM. A simplified nonlinear power source for simulating PV panels. In: IEEE 34th annual conference on power electronics specialist; 2003. p. 1729 - 34. 7. Kroposki B, DeBlasio R. Technologies for the new millennium: photovoltaics as a distributed resource. In: IEEE power engineering society summer meeting; 2000. p. 1798 - 801. 8. Ji Keyan, Zhang Zhuo. Study on direct torque control system of Switched Reluctance motor, In: ICCSE 2011; 0904 – 08. 9. Z.Zhang, N.C.Cheung. Analysis and design of cost effective converter for SRM drives using Component sharing. In: 4th International conference on power electronics system and Applications; 2011. p. 099 - 104. 10. Mehrdad Ehsani, Ramani, James. H. Galloway. Dual Decay Converter for SRM Drives in Low voltage Applications. In: IEEE Transaction on Power Electronics, April 1993. P. 224 – 230. Authors: Neelapala Anil Kumar, Mehar Niranjan Pakki Paper Title: Analyzing The Severity of The Diabetic Retinopathy and Its Corresponding Treatment Abstract: Diabetic-related eye disease is a major cause of blindness in the world. It is a complication of diabetes which can also affect various parts of the body. When the small blood vessels have a high level of glucose in the 61. retina, the vision will be blurred and can cause blindness eventually, which is known as diabetic retinopathy. Regular screening is essential to detect the early stages of diabetic retinopathy for timely treatment and to avoid further 313-315 deterioration of vision. This project aims to detect the presence of abnormalities in the retina such as the structure of blood vessels, micro aneurysms and exudates using image processing techniques by automating the detection of Diabetic retinopathy (DR). This Process is achieved by the fundus images using morphological processing techniques to extract features such as blood vessels, micro aneurysms and exudates and then we calculate the area of each extracted feature. Depending on the area of each feature we classify the severity of the disease. Then finally by knowing the severity of the disease corresponding treatment measures can be analyzed. It will surely help to reduce the risk and increase efficiency for ophthalmologists.
Keywords: Blood Vessels, De-noising, Diabetic Retinopathy, Disease Severity, Enhancement, Exudates, Fundus Camera, Micro-aneurysms, Morphological Operations, Segmentation, Treatment.
References: 1. U R Acharya, C M Lim, E Y K Ng, C Chee and T Tamura. Computer-based detection of diabetes retinopathy stages using digital fundus images. 2. Singapore Association of the Visually Handicapped. http://www.savh.org.sg/info_cec_diseases.php. 3. What is Diabetic Retinopathy? http://www.news-medical.net/health/What-is-Diabetic-Retinopathy.aspx. 4. Diabetic Retinopathy. http://www.hoptechno.com/book45.htm. 5. James L. Kinyoun, Donald C. Martin, Wilfred Y. Fujimoto, Donna L. Leonetti. Opthalmoscopy Versus Fundus Photographs for Detecting and Grading Diabetic Retinopathy. 6. Salvatelli A., Bizai G., Barbosa G.Drozdowicz and Delrieux (2007), ‘A comparative analysis of pre-processing techniques in colour retinal images’, Journal of Physics: Conference series 90. 7. Andrea Anzalone, Federico Bizzari, Mauro Parodi, Marco Storace (2008), ‘A modular supervised algorithm for vessel segmentation in red- free retinal images’, Computers in Biology and Medicine, Vol. 38, pp. 913-922. 8. Daniel Welfer, Jacob Schacanski, Cleyson M.K., Melissa M.D.P., Laura W.B.L., Diane Ruschel Marinho (2010), ‘Segmentation of the optic disc in color eye fundus images using an adaptive morphological approach’, Journal on Computers in Biology and Medicine”, Vol. 40, pp. 124-137. 9. Cemal Kose, Ugur Sevik, Okyay Gencalioglu (2008), ‘Automatic segmentation of age-related macular degeneration in retinal fundus images’, Computers in Biology and Medicine,Vol.38, pp. 611-619. 10. Dietrich Paulus and Serge Chastel and Tobias Feldmann (2005), ‘Vessel segmentation in retinal images’, Proceedings of SPIE, Vol. 5746, No.696. 11. Ana Maria Mendonca and Aurelio Campilho (2006), ‘Segmentation of Retinal Blood Vessels by Combining the Detection of centerlines and Morphological Reconstruction’, IEEE Transaction on Medical Imaging, Vol. 25, No. 9, pp. 1200-1213. 12. Jagadish Nayak, Subbanna Bhat (2008), ‘Automated identification of diabetic retinopathy stages using digital fundus images’, Journal of medical systems, Vol.32, pp. 107-115. 13. Akara Sopharak, Bunyarit Uyyanonvara, Sarah Barman, Thomas H.Williamson (2008), ‘Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods’, Computerized Medical Imaging and Graphics, Vol. 32, pp.720-727. 14. ANOVA test for severity of disease. Available: http://afni.nimh.nih.gov/pub/dist/HOWTO/howto/ht05_group/html/background_ANOVA.shtml Authors: K. Srinivas, A. A. Chari Paper Title: ECDC: Energy Efficient Cross Layered Congestion Detection and Control Routing Protocol Abstract: Here in this paper A MAC layer level congestion detection mechanism has been proposed. The proposed model aims to deliver an energy efficient mechanism to quantify the degree of congestion at victim node with maximal accuracy. This congestion detection mechanism is integrated with a Two-Step Cross Layer Congestion Control Routing Protocol. The proposed model involves controlling of congestion in two steps with effective energy efficient congestion detection and optimal utilization of resources. Packet loss in network routing is primarily due to link failure and congestion. Most of the existing congestion control solutions do not possess the ability to distinguish between packet loss due to link failure and packet loss due to congestion. As a result these solutions aim towards action against packet drop due to link failure which is an unnecessary effort and may result in loss of resources. The other limit in most of the existing solutions is the utilization of energy and resources to detect congestion state, degree of congestion and alert the source node about congestion in routing path. Here in this paper we propose cross layered model of congestion detection an control mechanism that includes energy efficient congestion detection, Zone level Congestion Evaluation Algorithm [ZCEA] and Zone level Egress Regularization Algorithm [ZERA], which is a hierarchical cross layer based congestion detection and control model in short we refer this protocol as ECDC(Energy Efficient Congestion Detection and Control). This paper is supported by the experimental and simulation results show that better resource utilization, energy efficiency in congestion detection and congestion control is possible by the proposed protocol. 62.
Keywords: Ad-hoc networks, cross-layer design, optimization, random access, wireless networks. 316-322
References: 1. Michael Gerharz, Christian de Waal, and Matthias Frank, “A Practical View on Quality-of-Service Support in Wireless Ad Hoc Networks”, BMBF 2. Xiaoqin Chen, Haley M. Jones, A .D .S. Jayalath, “Congestion-Aware Routing Protocol for Mobile Ad Hoc Networks”, IEEE, 2007 3. Hongqiang Zhai, Xiang Chen, and Yuguang Fang, “Improving Transport Layer Performance in Multihop Ad Hoc Networks by Exploiting MAC Layer Information”, IEEE, 2007 4. Yung Yi, and Sanjay Shakkottai, “Hop-by-Hop Congestion Control Over a Wireless Multi-Hop Network”, IEEE, 2007 5. Tom Goff, Nael B. Abu-Ghazaleh, Dhananjay S. Phatak and Ridvan Kahvecioglu, “Preemptive Routing in Ad Hoc Networks”, ACM, 2001 6. Xuyang Wang and Dmitri Perkins, “Cross-layer Hop-byhop Congestion Control in Mobile Ad Hoc Networks”, IEEE, 2008. 7. Dzmitry Kliazovich, Fabrizio Granelli, “Cross-layer Congestion Control in Ad hoc Wireless Networks,” Elsevier, 2005 8. Duc A. Tran and Harish Raghavendra, “Congestion Adaptive Routing in Mobile Ad Hoc Networks”, 2006 9. Nishant Gupta, Samir R. Das. Energy-Aware On-Demand Routing for Mobile Ad Hoc Networks, OPNET Technologies, Inc. 7255 Woodmont Avenue Bethesda, MD 20814 U.S.A., Computer Science Department SUNY at Stony Brook Stony Brook, NY 11794-4400 U.S.A. 10. Laura, Energy Consumption Model for performance analysis of routing protocols in MANET,Journal of mobile networks and application 2000. 11. LIXin MIAO Jian –song, A new traffic allocation algorithm in AD hoc networks, “The Journal of ChinaUniversity of Post and Telecommunication”, Volume 13. Issue3. September 2006. 12. Chun-Yuan Chiu; Wu, E.H.-K.; Gen-Huey Chen; "A Reliable and Efficient MAC Layer Broadcast Protocol for Mobile Ad Hoc Networks," Vehicular Technology, IEEE Transactions on , vol.56, no.4, pp.2296-2305, July 2007 13. Giovanidis, A. Stanczak, S., Fraunhofer Inst. for Telecommun., Heinrich Hertz Inst., Berlin, Germany This paper appears in: 7th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, 2009. WiOPT 2009 14. Outay, F.; Vèque, V.; Bouallègue, R.; Inst. of Fundamental Electron., Univ. Paris-Sud 11, Orsay, France This paper appears in: 2010 IEEE 29th International Performance Computing and Communications Conference (IPCCC) 15. Yingqun Yu; Giannakis, G.B.; , "Cross-layer congestion and contention control for wireless ad hoc networks," Wireless Communications, IEEE Transactions on , vol.7, no.1, pp.37-42, Jan. 2008 16. http://www-lih.univ-lehavre.fr/~hogie/madhoc/ 17. Prof.K.Srinivas and Prof.A.A.Chari. Article: Cross Layer Congestion Control in MANETs and Current State of Art. International Journal of Computer Applications 29(6):28-35, September 2011. Published by Foundation of Computer Science, New York, USA 18. Prof. K. Srinivas, Dr. A. A. Chari;"ZCEA&ZERA: Two-Step Cross Layer Congestion Control Routing Protocol (pp. 36-44)", Vol. 9 No. 12 December 2011 International Journal of Computer Science and Information Security. Authors: Arshdeep Kaur, Amrit Kaur Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning Paper Title: System Abstract: Fuzzy inference systems are developed for air conditioning system using Mamdani-type and Sugeno- type fuzzy models. The results of the two fuzzy inference systems (FIS) are compared. This paper outlines the basic difference between the Mamdani-type FIS and Sugeno-type FIS. It also shows which one is a better choice of the two FIS for air conditioning system.
Keywords: Air Conditioning, Fuzzy Inference System (FIS), Fuzzy Logic, Mamdani.
63. References: 1. J. Yen and R. Langari, Fuzzy Logic. Pearson Education, 2004. 2. K.P. Mohandas and S. Karimulla, “Fuzzy and Neuro-fuzzy modeling and control of non linear systems”, Second International Conference 323-325 on Electrical and Electronics, 2001. 3. G. S. Sandhu and K. S. Rattan, “Design of a neuro-fuzzy controller”, IEEE International Conference on Systems, Man, Cybern., 1997. 4. T. J. Ross, Fuzzy Logic with Engineering Applications. John Wiley and sons, 2010. 5. M. Du, T. Fan, W. Su, H. Li, “Design of a new practical expert fuzzy controller in central air conditioning control system”, IEEE Pacific- Asia Workshop on Computational Intelligence and Industrial Application, 2008 6. S. Li, J. Liu, J. Liu, “Design on the central air-conditioning controller based on LabVIEW”, ICCASM IEEE proc., 2010. 7. A. Haman, N. D. Geogranas, “Comparison of Mamdani and Sugeno Fuzzy Inference Systems for Evaluating the Quality of Experienceof Hapto-Audio-Visual Applications”, HAVE 2008 – IEEE International Workshop on Haptic Audio Visual Environments and their Applications, 2008 8. M. S. I. Md., S. Z. Sarker, K. A. A. Rafi, M. Othman, “Development of a fuzzy logic controller algorithm for air conditioning system”, ICSE Proceedings,2006. Authors: Anand Bora, Abrar Chapalgaonkar,Vanita More Paper Title: Ultrasonic 3 Dimensional Mouse Abstract: With the advent of 3D technology in our daily lives, need of the hour is to develop 3D interactive devices. In this paper, review an air mouse that interacts with PC in 3 Dimensions. The device will not only need any contact surface, but also provide the user with three degrees of freedom. Setup of the project consists of a non-echo ultrasonic system with three receivers at different corners of the display screen and one hand held transmitter, which acts as the mouse. Upon measuring the three distances, position of transmitter in three dimensions can be determined. Above calculated distances will be sent to the PC serially. A 3D image is used to demonstrate the 64. functionality in three dimensions, and changes in transmitter coordinates will result in corresponding changes in 3D image. 326-328
Keywords: 3D, mouse, ultrasonic, spatial, interrupts.
References: 1. Karl Gluck and David DeTomaso, “UltraMouse 3D”. Available: http://people.ece.cornell.edu/land/courses/ece4760/FinalProjects/s2009/kwg8_dmd54/kwg8_dmd54/index.html 2. Ronald E.Milner, United States Patent – “Sonic Positioning Device ”, Patent Number: 4862 152 3. Dave Johnson, “40KHz Ultrasound Receiver”. Available: http://www.discovercircuits.com/DJ-Circuits/40kultrasoundrvr2.htm. Authors: Ashita S. Bhagade, Parag. V. Puranik Paper Title: Artificial Bee Colony (ABC) Algorithm for Vehicle Routing Optimization Problem Abstract: This paper involves Bee Colony Optimization for travelling salesman problem. The ABC optimization is a population-based search algorithm which applies the concept of social interaction to problem solving. This biological phenomenon when applied to the process of path planning problems for the vehicles, it is found to be excelling in solution quality as well as in computation time. Simulations have been used to evaluate the fitness of paths found by ABC Optimization. The effectiveness of the paths has been evaluated with the parameters such as tour length, bee travel time by Artificial Bee Colony Algorithm. In this article, the travelling salesman problem for 65. VRP is optimized by using nearest neighbor method; evaluation results are presented which are then compared by the artificial bee colony algorithm. The pursued approach gives the best results for finding the shortest path in a 329-333 shortest time for moving towards the goal. Thus the optimal distance with the tour length is obtained in a more effective way.
Keywords: Artificial Bee Colony algorithm, Bee travel time, Nearest neighbor method, Tour length, Travelling Salesman Problem
References: 1. Karaboga, D. Artificial Bee Colony Algorithm. Scholarpedia 2010, 5,6915.Availableonline:http://www.scholarpedia.org/article/Artificial_bee_colony_algorithm/ (accessed on 27 May 2011). 2. Artificial bee colony algorithm with multiple onlookers for constrained optimization problems. Milos Subotic Faculty of Computer Science University Megatrend Belgrade Bulevar umetnosti. 3. J. F. Cordeau, M. Gendreau, G. Laporte, J. Y. Rotvin, F. Semet. A guide to vehicle routing heuristics. Journal of the Operational Research Society, 2002, 53(5): 512-522. 4. P.-W. TSai, J.-S. Pan, B.-Y. Liao, and S.-C. Chu, “Enhanced artificial bee colony optimization,” International Journal of Innovative Computing, Information and Control, vol. 5, no. 12, 2009. 5. Chaotic Bee Swarm Optimization Algorithm for Path Planning of Mobile Robots Jiann-Horng Lin and Li-Ren Huang Department of Information Management I-Shou University, Taiwan 2009 6. Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem.Adil Baykasolu1, Lale Özbakır2 and Pınar Tapkan2 1University of Gaziantep, Department of Industrial Engineering 2Erciyes University, Department of Industrial Engineering Turkey,2007. 7. Bee colony optimization: the applications survey Duˇsan teodorovi´c University of Belgrade, faculty of transport and traffic engineering Tatjana davidovi´c Mathematical institute, Serbian academy of sciences and arts And Milica ˇselmi´c University of Belgrade, faculty of transport and traffic engineering. 8. Nearest neighbor method by Sofiya Cherni, ¤Department of Mathematics and Computer Science, South Dakota School of Mines and Technology, Rapid City, SD 57701-3995). 9. An Effective Refinement Artificial Bee Colony Optimization Algorithm Based On Chaotic Search and Application for PID Control Tuning Gaowei YAN †, Chuangqin LI College of Information Engineering, Taiyuan University of Technology, Taiyuan, 030024, China 10. Artificial bee colony (abc), harmony search and bees algorithms on Numerical optimization D. Karaboga, b. Akay Erciyes University, the dept. Of computer engineering, 38039, melikgazi, kayseri, turkiye 11. Elitist artificial bee colony For constrained real-parameter optimization Efr´en mezura-montes member, ieee and ramiro ernesto velez- koeppel 12. The bee colony-inspired algorithm (bcia) – a two-stage Approach for solving the vehicle routing problem with Time windows Sascha hackle Faculty of economics and business Administration Chemnitz university of technology Chemnitz, Germany [email protected] chemnitz.de Patrick dippold Faculty of economics and business Administration Chemnitz university of technology Chemnitz, Germany [email protected] 13. Optimization of multiple vehicle routing problems using approximation algorithms.R. Nallusami1, K.Duraiswamy2, R. Dhanalaxmi3and P. Parthiban4. 1,2Department of computer science and engineering, K S Rangasamy college of technology, Tiruchengode-637215, India.Email:[email protected] 3D-Link India Ltd, Bangalore, India. 4Department of production engineering, National institute of technology, Tiruchirapalli, India 14. Bee colony optimization – a cooperative learning Approach to complex transportation problems Dušan teodorović1,2, mauro dell’ orco3 15. An improved artificial bee colony algorithm for the capacitated vehicle routing problem With time-dependent travel times Ping ji1 yongzhong wu1,2 1 department of industrial and systems engineering, the hongkong polytechnic University, hongkong 2 school of business administration, south china university of technology, Guangzhou, p.r., china. 16. An Efficient Bee Colony Optimization Algorithm for Traveling Salesman Problem using Frequency-based Pruning Li-Pei Wong† Malcolm Yoke Hean Low‡ School of Computer Engineering, Nanyang Technological University Nanyang Avenue, Singapore 639798. Email: †[email protected], ‡[email protected] Chin Soon Chong Singapore Institute of Manufacturing Technology 71 Nanyang Drive, Singapore 638075. Email: [email protected] 17. P. Curkovic, B. Jerbic, Honey-bees optimization algorithm applied to path planning problem, International Journal of Simulation Modelling, pp. 137-188, 2007. 18. D. Karaboga and B. Akay. A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, In Press, 2009. 19. Bee Colony Optimization with Local Search for Traveling Salesman Problem i Li-Pei Wong, ii Malcolm Yoke Hean Low, iii Chin Soon Chong i,ii School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, SINGAPORE 639798. iii Singapore Institute of Manufacturing Technology, 71 NanyangDrive,[email protected], ii [email protected], iii [email protected] 20. A bee colony optimization algorithm with the fragmentation statetransition rule for traveling salesman problem L.P. Wonga, M.Y.H. Lowa, C.S. Chongb a School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798.b Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075. Email: [email protected] Authors: Ashish kumar Dewangan, Majid Ahmad Siddhiqui Paper Title: Human Identification and Verification Using Iris Recognition by Calculating Hamming Distance Abstract: A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Most commercial iris recognition systems use patented algorithms developed by Daugman, and these algorithms are able to produce perfect recognition rates. However, published results have usually been produced under favorable conditions, and there have been no independent trials of the technology. The work presented in this paper involved developing an ‘open-source’ iris recognition system in order to verify both the uniqueness of the human iris and also its performance as a biometric. For determining the recognition performance of the system one databases of digitized grayscale eye images were used. The iris recognition system consists of an automatic segmentation system that is based on the Hough transform, and is able to localize the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region was then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data from 1D 66. Log-Gabor filters was extracted and quantized to four levels to encode the unique pattern of the iris into a bit-wise biometric template. The Hamming distance was employed for classification of iris templates, and two templates 334-338 were found to match if a test of statistical independence was failed. Therefore, iris recognition is shown to be a reliable and accurate biometric technology.
Keywords: Automatic segmentation, Biometric identification, Iris recognition, Pattern recognition.
References: 1. S. Sanderson, J. Erbetta. Authentication for secure environments based on iris scanning technology. IEEE Colloquium on Visual Biometrics, 2000. 2. J. Daugman. How iris recognition works. Proceedings of 2002 International Conference on Image Processing, Vol. 1, 2002. 3. R. Wildes, J. Asmuth, G. Green, S. Hsu, R. Kolczynski, J. Matey, S. McBride. A system for automated iris recognition. Proceedings IEEE Workshop on Applications of Computer Vision, Sarasota, FL, pp. 121-128, 1994. 4. W. Boles, B. Boashash. A human identification technique using images of the iris and wavelet transform. IEEE Transactions on Signal Processing, Vol. 46, No. 4, 1998. 5. C. Tisse, L. Martin, L. Torres, M. Robert. Person identification technique using human iris recognition. International Conference on Vision Interface, Canada, 2002. 6. Chinese Academy of Sciences – Institute of Automation. Database of 756 Greyscale Eye Images. http://www.sinobiometrics.com Version 1.0, 2003. 7. W. Kong, D. Zhang. Accurate iris segmentation based on novel reflection and eyelash detection model. Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong, 2001. 8. L. Ma, Y. Wang, T. Tan. Iris recognition using circular symmetric filters. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 2002. 9. D. Field. Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America, 1987. 10. P. Kovesi. MATLAB Functions for Computer Vision and Image Analys is. Available at: http://www.cs.uwa.edu.au/~pk/Research/MatlabFns/index.html. Authors: Ruchika, Mooninder Singh, Anant Raj Singh Paper Title: Compression of Medical Images Using Wavelet Transforms Abstract: Medical image compression is necessary for huge database storage in Medical Centres and medical data transfer for the purpose of diagnosis. Wavelet transforms present one such approach for the purpose of compression. The same has been explored in paper with respect to wide variety of medical images. In this approach, the redundancy of the medical image and DWT coefficients are reduced through thresholding and further through Huffman encoding. This paper presents a lossy image compression technique which works well over most of the medical images.
Keywords: Biorthogonal, DWT, Haar, Medical image compression, symlets.
References: 67. 1. M. Antonini, et al.: “Image Coding Using Wavelet Transforms” IEEE Trans. Image Processing, vol. 1, no. 2, pp. 205-220, April 1992. 2. Amir Averbuch, et al.: “Image Compression Using Wavelet Transform and Multiresolution Decomposition”, IEEE Trans. Image 339-343 Processing; vol. 5, no. 1, pp 4-15, January 1996. 3. N. Sahba, et al.: “An Optimized Two-Stage Method for Ultrasound Breast Image Compression,” 4th Kuala Lumpur International Conf. Biomedical Engg., vol. 21, June 2008, pp. 515-518. 4. M.D.Adams and R.Ward; “Wavelet Transforms in JPEG 2000 Standard”, IEEE Pacific Rem Conf. on Comm. Comp. & Signal Pross., 2001, vol. 1 pp. 160-163. 5. S. Udomhunsakul and K. Hamamoto, “Wavelet filters comparison for ultrasonic image compression,” Conf. IEEE TENCON, vol. 1, Nov. 2004, pp. 171-174. 6. T. Acharya and P. Tsai, JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures, John Wiley & Sons, 2005, pp. 24-30. 7. I. Daubechies; “Ten Lectures on Wavelet”. 8. S. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Patr. Anal. Machine Intell., vol. 11, no. 7, July 1989, pp. 674-693. 9. D.A. Huffman, "A Method for the Construction of Minimum-Redundancy Codes", Proceedings of the I.R.E., September 1952, pp. 1098– 1102. Authors: S. Roga, K.M.Pandey, A.P.Singh Computational Analysis of Supersonic Combustion Using Wedge-Shaped Strut Injector with Paper Title: Turbulent Non-Premixed Combustion Model Abstract: This paper presents the supersonic combustion of hydrogen using wedge-shaped strut injector with two-dimensional turbulent non-premixed combustion model. The present model is based on the standard k-epsilon (two equations) with standard wall functions which is P1 radiation model. In this process, a PDF (Probability Density Function) approach is created and this method needs solution to a high dimensional PDF transport equation. As the combustion of hydrogen fuel is injected from the wedge-shaped strut injector, it is successfully used to model the turbulent reacting flow field. It is observed from the present work that, the maximum temperature occurred in the recirculation areas which is produced due to shock wave-expansion and the fuel jet losses concentration and after passing successively through such areas, temperature decreased slightly along the axis. From the maximum mass fraction of OH, it is observed that there is very little amount of OH around 0.0027 were found out after combustion. By providing wedge-shaped strut injector, expansion wave is created which cause the proper mixing between the fuels and air which results in complete combustion.
Keywords: CFD, combustion, hydrogen fuel, non-premixed combustion, scramjet, standard k-epsilon turbulence 68. model, standard wall functions, steady state, supersonic combustion, two-dimensional, wedge-shaped strut injector. 343-353 References: 1. Heiser, W.H., Pratt, D.T., Hypersonic Airbreathing Propulsion. 1994: AIAA Educational Series. 2. Andreadis, D. (2004) Scramjet Engines Enabling the Seamless Integration of Air and Space Operations. 3. Gruber, M.R. & Nejad, A.S. New supersonic combustion research facility. J. Prop. Power, 1995, 11(5), 1080-83. 4. Riggins, D.W. & McClinton, C.R. “A computational investigation of flow losses in a supersonic combustor”, AIAA Paper No. 90-2093, 1990. 5. Riggins, D.W.; McClinton, C.R. & Vitt, P.H. Thrust losses in hypersonic engines–Part 1: Methodology. J. Prop. Power, 1997, 13(2). 6. Riggins, D.W.; McClinton, C.R. & Vitt, P.H., Thrust losses in hypersonic engines–Part 2: Applications. J. Prop. Power, 1997, 13(2). 7. Tomioka, S.; Kanda, T.; Tani, K.; Mitani, T.; Shimura, T. & Chinzei, N. Testing of a scramjet engine with a strut in M8 flight conditions. AIAA Paper No. 98-3134, 1998. 8. Tomioka, S. Combustion tests of a staged supersonic combustor with a strut. AIAA Paper No. 98-3273, 1998. 9. Gerlinger, P. & Bruggemann, D. Numerical investigation of hydrogen strut injections into supersonic air flows. J. Prop. Power, 2000, 16(1), 22-28. 10. P.K. Tretyakov “The Problems of Combustion at Supersonic Flow”, West-East High Speed Flow Field Conference 19-22, November 2007. 11. Shigeru Aso, Arifnur Hakim, Shingo Miyamoto, Kei Inoue And Yasuhiro Tani “Fundamental Study Of Supersonic Combustion In Pure Air Flow With Use Of Shock Tunnel”, Acta Astronautica 57 (2005) 384 – 389. 12. Andreadis, D. (2004) Scramjets Integrate Air and Space. 13. Bonanos, A.M., “Scramjet Operability Range Studies of an Integrated Aerodynamic-Ramp-Injector/Plasma-Torch Igniter with Hydrogen and Hydrocarbon Fuels”, 2005: Blacksburg, VA. 14. T. Cain And C. Walton “Review Of Experiments On Ignition And Flame Holding In Supersonic Flow”, Published By The America Institute Of Aeronautics And Astronautics, Rto-Tr-Avt-007-V2. 15. Peter Gerlinger, Peter Stoll 1, Markus Kindler, Fernando Schneider C, Manfred Aigner “Numerical Investigation Of Mixing And Combustion Enhancement In Supersonic Combustors By Strut Induced Streamwise Vorticity”, Aerospace Science And Technology 12 (2008) 159–168. 16. K. Kumaran, V. Babu “Investigation of the effect of chemistry models on the numerical predictions of the supersonic combustion of hydrogen”, Combustion And Flame 156 (2009) 826–841. 17. C. Gruenig* And F. Mayinger “Supersonic Combustion Of Kerosene/H2-Mixtures In A Model Scramjet Combustor”, Institute A For Thermodynamics, Technical University Munich, D-85747 18. P Manna, D Chakraborty “Numerical Simulation Of Transverse H2 Combustion In Supersonic Airstream In A Constant Area Duct”, Vol. 86, November 2005, ARTFC. 19. Jiyun tu, guan Heng yeoh and chaoqun liu. “Computational Fluid Dynamics”, Elsevier Inc. 2008. 20. Fluent, Software Training Guide TRN-00-002. 21. Evans, J. S., Shexnayder Jr., C. J., and Beach Jr., H. L. (1978). Application of a Two-Dimensional Parabolic Computer Program to Prediction of Turbulent Reacting Flows. NASA Technical Paper 1169. 22. K.M. Pandey and A.P Singh, “Recent Advances in Experimental and Numerical Analysis of Combustor Flow Fields in Supersonic Flow Regime”, International Journal of chemical Engineering and Application, Vol.-1, No.2, August 2010, ISSN-2010-0221, pp 132-137. 23. K.M. Pandey and A.P Singh, “Numerical Analysis of Supersonic Combustion by Strut Flat Duct Length with S-A Turbulence Model”, IACSIT International Journal of Engineering and Technology, Vol.-3, No. 2, April 2010, pp 193-198. 24. K. M. Pandey and A. P. Singh, “Numerical Analysis of Combustor Flow Fields in Supersonic Flow Regime with Spalart-Allmaras and k-ε Turbulence Models” IACSIT International Journal of Engineering and Technology, Vol.3, No.3, June 2011,pp 208-214. 25. K.M. Pandey and A.P Singh, “CFD Analysis of Conical Nozzle For Mach 3 at Various Angles of Divergence With Fluent Software” International Journal of chemical Engineering and Application ,Vol.-1, No. 2, August 2010,ISSN- 2010-0221, pp 179-185. 26. K.M.Pandey and T.Sivasakthivel,”CFD Analysis of Mixing and Combustion of a Scramjet Combustor with a Planer Strut Injector ”, International Journal of Enviromental Science and Development, Vol. 2, No. 2, April 2011. 27. K.M. Pandey, A.P.Singh, ”NUMERICAL SIMULATION OF COMBUSTION CHAMBER WITHOUT CAVITY AT MACH 3.12”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-1, March 2012. 28. K.M. Pandey, S.K. Reddy K.K., “Numerical Simulation of Wall Injection with Cavity in Supersonic Flows of Scramjet Combustion”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-1, March 2012. Authors: Arpan Ghorai, Dibyendu Chowdhury, Satyajit Das Paper Title: Design and Implementation of Public Key Steganography Abstract: Security of the digital information is becoming primary concern prior to transmitting the information itself via some media. Information security means defending information and information systems from unlicensed access, use, disclosure, disruption, modification or demolition. In this paper, a public key method of Steganography is proposed under standard cryptographic assumptions. The byte location in LSB of which the secret bit is to be embedded is found out by public key of the receiver and receiver apply private key of itself to reconstruct the secret message following RSA assumptions.
Keywords: Communication, Security, Steganography. Public Key.
69. References: 1. Dobsicek, ‘‘M.,Extended steganographic system,’’ in 8th Intl. Student Conf. On Electrical Engineering, FEE CTU 2004, Poster 04. 354-358 2. Yusuk Lim, Changsheng Xu and David Dagan Feng, “Web based Image Authentication Using Invisible Fragile Watermark,” in Pan-Sydney Area Workshop on Visual Information Processing (VIP2001), Sydney, Australia, Page(s): 31 – 34, 2001 3. Min Wu, “Data Hiding in Binary Image for Authentication and Annotation,”in IEEE Trans. Image Processing, volume 6, Issue 4, Member, IEEE, and Bede Liu, Fellow, IEEE, Aug. 2004 Page(s): 528 - 538 4. Rehab H. Alwan, Fadhil J. Kadhim, and Ahmad T. Al- Taani, “Data Embedding Based on Better Use of Bits in Image Pixels,” in International Journal of Signal Processing Vol 2, No. 2, 2005, Rehab H. Alwan, Fadhil J. Kadhim, and Ahmad T. Al- Taani, Page(s): 104 – 107 5. Nameer N. EL-Emam, "Hiding a large amount of data with high security using steganography algorithm," in Journal of Computer Science, April 2007, Page(s): 223 – 232 6. S.K.Bandyopadhyay, Debnath Bhattacharyya, Swarnendu Mukherjee, Debashis Ganguly, Poulumi Das, "A Secure Scheme for Image Transformation," in August 2008, IEEE SNPD, Page(s): 490 – 493 7. G. Sahoo, R. K. Tiwari, "Designing an Embedded Algorithm for Data Hiding using Steganographic Technique by File Hybridization," in January 2008, IJCSNS Vol. 8, No. 1,Page(s): 228 – 233. Authors: Shiv Kumar Gupta, Rajiv Kumar, Krishna Kumar Verma Application of the Fault Tolerance of Reduced Bond Graph Approach of Parallel Computing of A Paper Title: Matching Network Abstract: In this paper we present a new method for modeling high frequency systems. This method combines the scattering formalism with the bond graph model in a new technique called scattering bond graph model. This method allows describing explicitly the distribution of electromagnetic waves of any high frequency system. We applied this method to deduce the reflection and transmission coefficient as function as frequency of a parallel computing matching network of a Planar Inverted F Antenna.
70. Keywords: Matching network, scattering matrix, scattering formalism, bond graph modeling, scattering bond Graph model, PIFA. 359-365 References: 1. RAUL G.LONGORIA, “Wave- scattering formalisms for multiport energetic systems”, J.Franklin Inst.Vol.333(B), No.4,pp.539-564,1996 copyright 1996 the Franklin Institute published by Elsevier Science Ltd Printed in Great Britain 0016-0032/96 $15.00+0.00. 2. Mieczysław RONKOWSKI, Zbigniew KNEBA,” Bond-graphs based modeling of hybrid energy systems with permanent magnet brushless machines”. 3. R.G.LONGORIA,J.A.Kypuros, H.M.Payenter, “Bond Graph and Wave-Scattering Models of Switched Power Conversion”, 0-7803453- 1/97/$10.00 @ 1997 IEEE. 4. Raul G.LONGORIA,”Wave-scattering Formalisms for Multiport Energetic Systems”, J. FrankIin Inst. Vol. 333(B). No. 4. pp. 539-564, 1996 Copyright +1996 The Franklin Institute Published by Elsevier Science Ltd Printed in Great Britain 0016-0032/96 $15.00 + 0.00. 5. Hichem Taghouti,Mami Abdelkader” Extraction, Modelling and Simulation of the Scattering Matrix of a Chebychev Low-Pass Filter with cut-off frequency 100 MHz from its Causal and Decomposed Bond Graph Model”, ICGST-ACSE Journal, Volume 10, Issue 1, November 2010. 6. Hichem Taghouti,Mami Abdelkader”How to find wave- scattering paramerters from the causal bond graph model of a high frequency filter”,Americain Journal of applied sciences 7(5): 702-710, 2010 ISSN 1546-9239, 2010 Science Publications. 7. Hichem Taghouti,Mami Abdelkader,” Modeling Method of a Low-Pass Filter Based on Microstrip T-Lines with Cut-Off Frequency 10 GHz by the Extraction of its Wave-Scattering Parameters from its Causal Bond Graph Model”, American J. of Engineering and Applied Sciences 3 (4): 631-642, 2010 ISSN 1941-7020. Authors: R.Sudha, Deepak Jain, Umang Lahoty, Swati Khushalani, Nivedita G, Jayabarathi T Paper Title: State Estimation and Voltage Stability Monitoring Using ILP PMU Placement Abstract: This paper shows various cases under which optimal PMU placement is done. Zero injection busses are also considered for the placement problem which reduces the number of PMU required. For this topology method is used. In case of failure of single PMU, the reliability of the system should be improved. For this the problem formulation is modified according to which each bus is observed by at least two PMUs. The PMU placement is then used to get data for state estimation. The results-voltages and phase angles of bus system are compared with and without PMU using two algorithms- WLS and LAV. It is found that LAV is better algorithm than WLS and errors are reduced if the PMU measurements are included. The PMU data is also used for the voltage stability analysis using two indices-FVSI and LQP. Contingency analysis is done using these under different operating conditions to get an idea of stressful situation of lines randomly chosen.
Keywords: PMU, WLS and LAV, using two indices-FVSI and LQP.
71. References: 1. Abur and F. H. Magnago, “Optimal meter placement for maintaining observability during singlebranch outages,” IEEE Transactions on 366-373 Power Systems, vol. 14, no. 4, pp. 1273–1278, Nov. 1999. 2. Xu and A. Abur, “Observability analysis andmeasurement placement for systems with PMUs,”in IEEE Power Systems Conference and Exposition,vol. 2, Oct. 2004, pp. 943–946. 3. Sanjay Dambhare, DeveshDua, Rajeev Kumar Gajbhiye, S. A. Soman, “Optimal Zero Injection Considerations in PMU Placement: An ILP Approach,” 16th PSCC, Glasgow, Scotland, July 14-18, 2008. 4. Thesis by RoozbehEmami, “Enhancement of network monitoring & security analysis using phasor measurement units” 5. S.Kamireddy, “Comparison of State Estimation Algorithms Considering Phasor Measurement Units And Major And Minor Data Loss”,M.S. Thesis, Mississippi State University, December 2008 6. K.D.Jones, “Three-Phase Linear State Estimation With Phasor Measurements”, M.S. Thesis, Virginia Polytechnic Institute and State University, May 2011 7. Matlab user Manual obtained from http://www.mathworks.com/access/helpdesk/help/helpdesk.html 8. Renuga Verayiah, Izham Zainal Abidin,” A Study on Static Voltage Collapse Proximity Indicators”, 2nd IEEE International Conference on Power and Energy (PECon 08), December 1-3, 2008, Johor Baharu, Malaysia. 9. I. Musirin, T.K.A Rahman, “Novel Fast Voltage Stability Index (FVSI) for Voltage Stability Analysis in Power Transmission System,” 2002 Student Conference on Research and Development Proceedings, Shah Alam, Malaysia, July 2002. 10. A. Mohamed, G.B. Jasmon, S. Yusoff, “A Static Voltage Collapse Indicator using Line Stability Factors,” Journal of Industrial Technology, Vol. 7, N1, pp. 73-85, 1989 Authors: Aditya Kumar Singh, Bishnu Prasad De, Santanu Maity Paper Title: Design and Comparison of Multipliers Using Different Logic Styles Abstract: Low power VLSI circuits have become important criteria for designing the energy efficient electronic designs for high performance and portable devices .The multipliers are the main key structure for designing an energy efficient processor where a multiplier design decides the digital signal processors efficiency. In this paper, 4*4 unsigned Array and Tree multiplier architecture is being designed by using 1-bit full adders and AND2 function following various logic styles. The full adders and AND2 function have been designed using various logic styles following a unique pattern of structure to improve their performance in various means like less transistors, low power, minimal delay, and increased power delay product. The various types of adders used in our paper are complementary MOS (CMOS) logic style, complementary pass-transistor (CPL) logic style and double-pass transistor (DPL) logic style. The main objective of our work is to calculate the average power, delay and power delay product of 4*4 bit multipliers following various logic styles at 5v supply voltage at 25c temperature with 0.15um technology and simulating them with T-spice of Tanner EDA tool. An multiplier architecture is designed using full adder, half adder structure and AND2 function and then the above said various logic style adders and AND2 function are replaced in the multiplier architecture and then their outputs are generated, such that their 72. average power, delay, and power delay product are calculated. 374-379 Keywords: Array Multipliers, Tree multiplier, Full adder, CMOS, CPL, DPL, power delay product.
References: 1. Chandrakasan, A., and Brodersen, Low Power Digital Design, Kluwer Academic Publishers, 1995. 2. Weste, N., and Eshragian, K., Principles of CMOS VLSI Design: A Systems Perspective, Pearson Addison-Wesley Publishers, 2005. 3. Bellaouar, A., and Elmasry, M., Low-Power Digital VLSI Design: Circuits and Systems, Boston, Massachusetts: Kluwer Academic Publishers, 1995. 4. Sun, S., and Tsui, P., Limitation of CMOS supply-voltage scaling by MOSFET threshold voltage, IEEE Journal of Solid-State Circuits, vol. 30, 1995, pp. 947-949. 5. Bisdounis, L., Gouvetas, D., and Koufopavlou, O., A comparative study of CMOS circuit design styles for low-power high-speed VLSI circuits, Int. J. of Electronics, vol. 84, no. 6, 1998, pp. 599-613. 6. Gupta, A., Design Explorations of VLSI Arithmetic Circuits, Ph.D. Thesis, BITS, Pilani, India, 2003. 7. Yano, K., Yamanaka, T., Nishida, T., Saito, M., Shimohigashi, K., and Shimizu, A., “A 3.8-ns CMOS 16-b multiplier using complementary pass-transistor logic,” IEEE Journal of Solid-State Circuits, vol. 25, 1990, pp. 388-395. 8. K. Yano, T. Yamanaka, T. Nishida, M Saito, K. Shimohigashi, A. Shimizu, “A 3.8-ns CMOS 16*16-b Multiplier Using CPL Logic”, IEEE Journal of Solid-State Circuits, vol.25, 1990, pp. 388-395. . 9. Psilogeorgopoulos, M., Chuang, T.S., Ivey, P.A., and Seed, L., “Contemporary Techniques for Lower Power Circuit Design,” PREST Deliverable D2.1, Tech Report, The Department of Electronic and Electrical Engineering, University of Sheffield, 1998. 10. Zimmermann, R., and Fichtner, W., “Low Power Logic styles: CMOS versus Pass - Transistor Logic,” IEEE Journal of Solid State Circuits, vol. 32, no. 7, July 1997. 11. R. Zimmerman and W. Fichtner, “Low-power logic styles: CMOS versus pass-transistor logic,”IEEE J. Solid-State Circuits, vol. 32, no.7, Jul. 1997, pp. 1079–1090. 12. Suzuki, M., Ohkubo, N., Yamanaka, T., Shimizu, A., and Sasaki, K., “A 1.5-ns 32-b CMOS ALU in double pass-transistor logic,” IEEE Journal of Solid-State Circuits, vol. 28, 1993, pp. 1145-1151. 13. Bellaouar, A., and Elmasry, M. I., Low-Power Digital VLSI Design, Kluwer, Norwell, MA, 1995. 14. Parhami, B., Computer Arithmetic Algorithms and Hardware Designs, Oxford University Press, 2000. 15. Rabaey, J.M., Chandrakasan, A., and Nikolic, B., Digital Integrated Circuits, Second Edition, PHI Publishers, 2003 16. Ware, F.A., McAllister, W.H., Carlson, J.R., Sun, D.K., and Vlach, R.J., “64 Bit Monolithic Floating Point Processors,” IEEE Journal of Solid-State Circuits, vol. 17, no. 5, October 1982, pp. 898-90, 17. Wallace, C.S., “A Suggestion for a Fast Multiplier,” IEEE Transactions on Electronic Computers, EC-13, 1964, pp. 14-17. 18. C.S. Wallace, “A suggestion for a fast multiplier,” in IEEE Trans. On Electronic Computers, vol. EC-13, 1964, pp. 14-17. 19. P. M. Kogge and H. S. Stone, “A Parallel Algorithm for the Efficient Solution of a General Class of Recurrence Equations,” IEEE Transactions on Computers, vol. 22, no. 8, August 1973, pp. 786–793. 20. Tanner EDA Inc. 1988, User‘s Manual, 2005. 21. Najm, F., “A survey of power estimation techniques in VLSI circuits,” IEEE Transactions on VLSI Systems, vol. 2, 1995, pp. 446-455.. 22. Kang, S., “Accurate simulation of power dissipation in VLSI circuits,” IEEE Journal of Solid-State Circuits, vol. 21, 1986, pp. 889-891. Authors: Pushpalata Pujari, Jyoti Bala Gupta Paper Title: Improving Classification Accuracy by Using Feature Selection and Ensemble Model Abstract: Classification is an important technique of data mining. In this paper feature selection technique and an ensemble model is proposed to improve classification accuracy. Feature selection technique is used for selecting subset of relevant features from the data set to build robust learning models. Classification accuracy is improved by removing most irrelevant and redundant features from the dataset. Ensemble model is proposed for improving classification accuracy by combining the prediction of multiple classifiers. Three decision tree data mining classifiers CART, CHAID and QUEST are considered in this paper for classification. The ionosphere dataset investigated in this study is taken from UCI machine learning repository which is classified under two category “Bad” and “Good”. The proposed ensemble model combines the classifiers CART, CHAID and QUEST by using confidential-weighted voting scheme. A comparative study is carried on the performances of the classifiers before and after carrying out feature selection. The performance of each classifier and ensemble model is evaluated by using statistical measures like accuracy, specificity and sensitivity. Gain chart and R.O.C (Receiver operating characteristics chart) are also used for measuring performances. It is found that with feature selection the ensemble model provides a greater accuracy of 93.84% than any of the individual model. Experimental results show that the proposed ensemble model with feature selection is quite effective for the task of classification of ionosphere dataset
Keywords: Classification, Ensemble Model, Ionosphere Dataset, Feature Selection.
References: 1. Jiwaei Han, Kamber Micheline, Jian Pei “Data mining: Concepts and Techniques”, Morgam Kaufmann Publishers (Mar 2006). 2. Cabena, Hadjinian, Atadler, Verhees, Zansi “Discovering data mining from concept to implementation” International Technical Support Organization, Copyright IBM corporation 1998. 3. S.Mitra, T. Acharya “Data Mining Multimedia, Soft computing and Bioinformatics, A john Willy & Sons, INC , Publication, 2004. 4. Alaa M. Elsayad “Predicting the severity of breast masses with ensemble of Bayesian classifiers” journal of computer science 6 (5): 576- 584, 2010, ISSN 1549-3636 73. 5. Alaa M. Elsayad “ Diagnosis of Erythemato-Squamous diseases using ensemble of data mining methods” ICGST-BIME Journal Volume 10, Issue 1, December 2010 380-386 6. SPSS Clementine 12.0, 2007. Data mining workbench software. Product Of SPSS, Inc. http://www.cad100.net/247_dataminingworkbench-SPSS-Clementine-12.html 7. UCI Machine Learning Repository of machine learning databases.University of California, school of Information and Computer Science, Irvine. C.A. http://www.ics.uci.edu/~mlram,?ML.Repositary.html 8. Michael J.A .Berry Gordon Linoff “Data Mining Techniques for Marketing, Sales and Customer Support ”, John Wiley & Sons publishers, 1997 9. P.Nancy and R.Geetha Ramani, “A Comparison on Performance of Data Mining Algorithms in Classification of Social Network Data”, International Journal of Computer Applications (0975 – 8887), Volume 32– No.8, October 2011. 10. Milan Kumari and Sunila Godara “Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction”, IJCSt Vol. 2, ISSue 2, June 2011, IJCSt Vol. 2, ISSUE 2, June 2011 I S S N: 2 2 2 9 - 4 3 3 3 (P r i n t) | I S S N: 0 9 7 6 - 8 4 9 1 (On l i n e) 11. Matthew N Anyanwu &Sajjan G Shiva “Comparative Analysis of serial Decision Trees Classification Algorithms”, (IJCSS), Volume (3): Issue (3) 12. Mahesh Pal “Ensemble Learning With Decision Tree for Remote Sensing Classification”, World Academy of Science, Engineering and Technology 36 2007. 13. Kelly H. Zou, PhD; A. James O’Malley, PhD; Laura Mauri, MD, MSc “ROC Analysis for Evaluating Diagnostic Test and Predictive Models” 14. Shu-Ting Luo & Bor-Wen Cheng, “Diagnosing Breast Masses in Digital Mammography Using Feature Select ion and Ensemble Methods” J Med Syst, DOI 10.1007/s10916-010-9518-8, Springer Science+Business Media, LLC 2010. 15. R.Nithya, B.Santh “Mammogram Classification using Maximum Difference Feature Selection Method”, Journal of Theoretical and Applied Information Technology, 30 Th November 2011. Vol. 33 No.2, ISSN: 1992-8645, E-ISSN: 1817-3195. 16. Alexey Tsymbal, Pádraig Cunningham, Mykola Pechenizkiy, Seppo Puuronen “Search Strategies for Ensemble Feature Selection in Medical Diagnostics” Proceedings of the 16th IEEE Symposium on Computer-Based Medical Systems (CBMS’03) 1063-7125/03 © 2003 IEEE 17. Thomas Abeel, Thibault Helleputte, Yves Van D Peer etc, “Robust biomark identification for cancer diagnosis with ensemble feature selection methods” Oxford Journals, Bioinformatics , Volume 26 , Issue 3, PP :392-398. 18. Gidudu. A, “Random ensemble feature selection for land cover mapping”, Geo Science and remote Sensing Symposium, 2009 IEEE , International IGRSS 2009, Volume: 2, On Page(s): II-840 - II-842 19. Zhang, Zili and Yang, Pengyi 2008, “An ensemble of classifiers with genetic algorithm Based Feature Selection”, The IEEE intelligent informatics bulletin, vol. 9, no. 1, pp. 18-24. Authors: Deepak Malik, Sonam Dung, Robin Walia Paper Title: Quality of Service in Two-Stages EPON for Fiber-to- the-Home Abstract: Passive optical network (PON) is thought to be the best candidate for fiber to the home (FTTH) to solve the access network bandwidth problem. We set up Ethernet PON (EPON) system model and analyze voice and video performance through the EPON simulation model. A service-classified and QoS-guaranteed triple play mode is tested in proposed EPON system model. We present results of the detailed experiments and propose the differentiated service with different QoS level.
Keywords: Bit Error Rate, Quality of Service.
References: 1. G. Kramer et.al, “Ethernet PON (EPON): Design and Analysis of an Optical Access Network,” Photonic Network Communications, vol.3, no.3, July 2001, pp. 307- 319. 2. Paul W. Shumate, “Fiber-to-the-Home: 1977–2007,” Journal of light wave technology, vol. 26, no. 9, May, 2008, pp.1093-1103. 74. 3. Cedric F. Lam, “Passive optical networks: principles and practice,” 2007, pp. 19-20. 4. Abdallah Shami, “QoS Control Schemes for Two-Stage Ethernet Passive Optical Access Networks,” IEEE Journal on selected areas in communication, Vol. 23, No. 8, August 2005, pp.1467-1478. 387-390 5. Monika Gupta et.al, “Performance Analysis of FTTH at 10 Gbit/s by GEPON Architecture,” IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5, September 2010, pp.265-271. 6. Yoshinori Ishii et.al, “Optical Access Transport System GE-PON Platform,”FUJITSU Sci.Tech.J. Vol.45, No.4, October 2009, pp.346-354. 7. Ahmad R. Dhaini et al, “Dynamic Wavelength and Bandwidth Allocation in Hybrid TDM/WDM EPON Networks,” IEEE Journal of Lightwave Technology, Vol. 25, No. 1, January 2007, pp.277-289. 8. Kun Yang et al, “Convergence of Ethernet PON and IEEE 802.16 Broadband Access Networks and its QoS-Aware Dynamic Bandwidth Allocation Scheme,” IEEE Journal on Selected Area in Communications, Vol. 27, No. 2, February 2009, pp.101-116. 9. Ahmad R. Dhaini, “Per-Stream QoS and Admission Control in Ethernet Passive Optical Networks (EPONs),” IEEE Journal of Lightwave Technology, Vol. 25, No. 7, July 2007, pp.1659-1669. 10. Michael P. et al, “Just-in-Time Scheduling for Multichannel EPONs,” IEEE Journal of Lightwave Technology, Vol. 26, No. 10, May 2008, pp.1204-1216. 11. Mirjana R. Radivojevic et al, “Implementation of Intra - ONU Scheduling for Quality of Service Support in Ethernet Passive Optical Networks,” IEEE Journal of Lightwave Technology, Vol. 27, No. 18, September 2009, pp.4055-4062. 12. Hiroki Nishiyama et al, “Inter-Layer Fairness Problem in TCP Bandwidth Sharing in 10G-EPON,” IEEE System Journal, Vol. 4, No. 4, December 2010, pp.432-439. 13. S. P. Singh et.al, “Nonlinear Scattering Effects in Optical Fibers,” Progress In Electromagnetic Research, PIER 74, 2007, pp.379–405. Authors: Renuka R. Londhe, Dr. Vrushshen P. Pawar Paper Title: Analysis of Facial Expression and Recognition Based On Statistical Approach Abstract: Facial Expression Recognition is rapidly becoming area of interest in computer science and human computer interaction. The most expressive way of displaying the emotions by human is through the facial expressions. In this paper, Recognition of facial expression is studied with the help of several properties associated with the face itself. As facial expression changes, the curvatures on the face and properties of the objects such as, eyebrows, nose, lips and mouth area changes. Similarly, intensity of corresponding pixels of images also changes. We have used statistical parameters to compute these changes and computed results (changes) are recorded as feature vectors. Artificial neural network is used to classify these features in to six universal expressions such as anger, disgust, fear, happy, sad and surprise. Two-layered feed forward neural network is trained and tested using Scaled Conjugate Gradient back-propagation algorithm and we obtain 92.2 % recognition rate.
Keywords: Facial expression Recognition, Human Computer Interaction, Scaled Conjugate Gradient, Statistical parameters. 75. References: 391-394 1. Ioanna-Ourania and George A. Tsihrintzis, “An improved Neural Network based Face Detection and Facial Expression classification system,” IEEE international conference on Systems Man and Cybernetics 2004. 2. Ying-li Tian, Takeo Kanade, and Jaffery F. Cohn, “Recognizing Action Units for Facial Expression Analysis,” IEEE transaction on PAMI, Vol. 23 No. 2 Feb2001. 3. Y. Zhu, L. C. DE. Silva, C. C. Co, “Using Moment Invariant and HMM for Facial Expression Recognition,” Pattern Recognition Letters Elsevier. 4. Paul Ekman, “Basic Emotions,” University of California, Francisco, USA. 5. James J. Lien and Takeo Kanade, “Automated Facial Expression Recognition Based on FACS Action Units,” IEEE published in Proceeding of FG 98 in Nara Japan. 6. Maja Pantic and L.L.M. Rothkrantz, “Automatic analysis of facial expressions: The state of the art,” IEEE Trans. PAMI Vol. 22 no. 12 2000. 7. L. Ma and K. Khorasani, “Facial Expression Recognition Using Constructive Feed forward Neural Networks,” IEEE TRANSACTION ON SYSTEM, MAN AND CYBERNETICS, VOL. 34 NO. 3 JUNE 2004. 8. Praseeda Lekshmi V Dr. M. Sasikumar, “A Neural Network Based Facial Expression Analysis using Gabor Wavelets,” Word Academy of Science, Engineering and Technology. 9. Japanese Female Facial Expression Database, www.kasrl.org/jaffe_download.html 10. Martin F. Moller, “A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning,” Neural Networks, 1993. Authors: Lakshmi Balasubramanian, M. Sugumaran Paper Title: An Analysis on Update Strategies for Spatio-Temporal Indexing Abstract: Many applications such as location based systems, traffic monitoring, radio frequency identification, 76. sensor networks etc., benefit from spatio-temporal indexing. R-Tree based index structures are widely used for indexing the spatial information. The main issue to be considered is frequent updates. These applications pose 395-398 frequent updates which have to be reflected in the index structure. Frequent changes to the index structure causes more overhead. Recent research is to handle these frequent updates efficiently. This paper presents the state of art in the update strategies adopted in spatio-temporal indexing. This work provides an idea of the present development in updating techniques for spatio-temporal indexing.
Keywords: R-Trees, Spatio-Temporal Indexing, Update Strategies.
References: 1. Yannis Manolopoulos, Alexandros Nanopoulos, Apostolos N. Papadopoulos and Yannis Theodoridis, R-Trees: Theory and Applications, London: Springer, 2006, 1st Ed. 2. A. Guttman, “R-Trees: A Dynamic Index Structure for Spatial Searching”, Proceedings of the 1984 ACM SIGMOD International Conference on Management of Data, 1984, pp.47-57. 3. Raphael A. Finkel and Jon Louis Bentley, “Quad Trees: A Data Structure for Retrieval on Composite Keys", Journal of Acts Informtica, vol.4, no.1, 1974, pp.1-9. 4. Barrios. J, Makki. S.K and Karimi. M, “An Indexing Structure for Mobile Objects Utilizing Late Update”, Proceedings of the 7th International Confernce on Information Technolofy: New Generations, 2010, pp.162-167. 5. Dongseop Kwon, Sangjun Lee and Sukho Lee, “Indexing the Current Positions of Moving Objects Using the Lazy Update R-Tree”, Proceedings of the 3rdInternational Conference on Mobile Data Management, 2002, pp.113–120. 6. Xiaoyuan Wang, Weiwei Sun and Wei Wang, “Bulkloading Updates for Moving Objects”, Proceedings of the 7thInternational Conference on Web-Age Information Management, 2006. 7. Xiaopeng Xiong and Walid G. Aref, “R-Trees with Update Memos”, Proceedings of the 22nd International Conference on Data Engineering, 2006. 8. MoonBae Song and Hiroyuki Kitagawa, “Managing Frequent Updates in R-Trees for Update-Intensive Applications”, IEEE Transactions on Knowledge and Data Engineering, vol.21, no.11, 2009, pp.1573-1589. 9. M.-L. Lee, W. Hsu, C. S. Jensen, and K. L. Teo, “Supporting Frequent Updates in R-Trees: A Bottom-Up Approach”, Proceedings of of the International Conference on Very Large Databases, 2006. 10. S. Saltenis, C.S. Jensen, S. Leutenegger and M. Lopez, “Indexing the Positions of Continuously Moving Objects”, Proceedings of ACM SIGMOD Conference on Management of Data, 2000, pp.331-342. 11. Douglas Comer , “Ubiquitous B-Tree”, Journal of CAN Computing Surveys, vol. 11, no.2, 1979, pp.121-137. 12. N. Beckmann, H.-P. Kriegel, R. Schneider and B. Seeger, “The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles”, Proceedings of the ACM SIGMOD International Conference on Management of Data, 1990, pp.322-331. 13. Pan Jin and Quanyou Song, “A Novel Index Structure R*Q-Tree based on Lazy Splitting and Clustering”, Proceedings of the International Conference on Computer Science and Automation Engineering, 2011, pp.405-407. 14. Su Chen, Beng Chin Ooi and Zhenjie Zhang, “An Adaptive Updating Protocol for Reducing Moving Object Database Workload”, Journal Proceedings of VLDB Environment, vol.3, no.1, 2010, pp.735-746. Authors: Chirag Sharma, Deepak Prashar Paper Title: DWT Based Robust Technique of Watermarking Applied on Digital Images Abstract: Digital Watermarking is a process of embedding unnoticeable signal in an image in the form of text and image in such a way that intruder is no able to trace the signal to enhance Copyright Protection. This Paper presents an efficient Watermarking Technique for Digital Media Content Protection and Copyright Protection. Watermarking is a technique to embed hidden and unnoticeable signal into digital media in such a way that if an intruder wants to copy it, he can be caught on the basis of Copyright protection and Ownership Identification. There are many Techniques that are available to watermark the data, proposal we are discussing DWT Technique which is most robust to attacks rather than LSB for the protection of Digital Images. We are going to find the Quality loss after the addition of watermark after applying various attacks on Watermarked Image, the more the quality loss will be there lesser will be the efficiency of Watermarking. There will be many factors that can effect the quality of the Images after the addition of Watermarking that are discussed in Later Section. The Creating on GUI and Implementation of our purposed Algorithm is realized using MATLAB.
Keywords: Discrete Wavelet Transform (DWT), Image Watermarking, Information Hiding,, Invisible 77. Watermarking, PSNR, Visible Watermarking. 399-402 References: 1. Kamble Sushila, Maheshkar Vikas, Agarwal Suneeta, K Srivastava Vinay, “DWT-SVD Based Secured Image Watermarking For Copyright Protection using Visual Cryptography”, CS & IT-CSCP 2012, ITCS, SIP, JSE-2012, CS & IT 04, pp. 143–150, 2012. 2. Chandra Munesh, Pandey Shikha,” A DWT Domain Visible Watermarking Techniques for Digital Images” International Conference on Electronics and Information Engineering (ICEIE 2010),V2,Pp-421-427. 3. Saraswathi.M,”Lossless Visible Watermarking for Video”,IJCSIT,V2(3),Pp-1109-1103,2011. 4. Subbarayan Sadasivam, Ramanathan S.Karthick,” EffectiveWatermarking of Digital Audio and Image using Matlab Technique” Second International Conference on Machine Vision, Pp-317-319,2009. 5. A. Sangeetha , Gomathy.B , K.Anusudha,”A Watermarking Approach to Compact Geometric Attacks”International Conference on Image Processing,Pp-381-385,2009. 6. Li Xin, Shoshan Yonatan, Fish Aleander, Jullien Graham, Yadid-Pecht Orly (2008)”Hardware Implementation of Video Watermarking” Pp-9-15. 7. Jayamalar T, Radha V (2010) “Survey on Digital Video Watermarking Techniques and attacks on Watermarks”, International Journal of Engineering and Technology, Vol. 2(12), Pp 6963-6967,2010. 8. Sathik M. Mohammed and Sujatha S.S,”An Improved Invisible Watermarking Technique for Image Authentication”,International Journal of Advanceed Science and Technology,Vol-24,Pp-61-74,2010. 9. Phadilkar Amit, Verma Bhupendra,Jain Sanjeev”A New Color Image Watermarking Scheme, INFOCOMP Journal of Computer Science”,International Conference,2006. Authors: V. Anitha, S. Sri Jaya Lakshmi, M. L. S. N. S. Lakshmi, Y. Dhatri Sai, M. Aditya, Ch. Ravi Teja Paper Title: Tri-band Circularly Polarized 3-Layer Stacked Patch Antenna Abstract: A probe-fed circularly polarized 3-layer stacked patch antenna is presented and simulated by using 78. Commercial Ansoft High Frequency Structure Simulator. The design consists of three patches and three substrates. The lower and middle patches are of square-shaped where as the upper patch is of triangular-shaped. One slot is 403-406 inserted in the lower layer, two slots are inserted in the middle layer and three slots are inserted on the upper layer so that bandwidth can be improved. Return loss, VSWR, gain, axial ratio and radiation patterns are simulated and analyzed. This antenna is best suitable for C-band applications.
Keywords: Circular polarization, Stacked patch antennas.
References: 1. Koray Siirmeli and Bahattin Tiiretken, “U-Slot Stacked Patch Antenna using High and Low Dielectric Constant Material Combinations in S-band”, IEEE, 2011. 2. H. F. AbuTarboush, H. S. Al-Raweshidy and R. Nilavalan, “Bandwidth Enhancement for Microstirp Patch Antenna Using Stacked Patch and Slot”, IEEE, 2009. 3. O. Pigaglio, N. Raveu and O. Pascal, “Design of Multi-frequency band Circularly Polarized Stacked Microstrip patch Antenna”, IEEE, 2008. 4. Kwok L. Chung, “Effect of perturbation on the parasitic patch of singly-fed circularly-polarized stacked patch antennas”, Proceedings of ISAP, 2005. 5. H. Tiwari and M. V. Kartikeyan, “A Stacked Microstrip Patch Antenna with Fractal Shaped Defects”, Progress in Electromagnetics Research C, Vol. 14, 185-195, 2010. 6. Sandeep Sainkar and Amutha Jeyakumar, “Design Analysis of Broadband Circularly Polarized Compact Microstrip Antenna for Wireless Applications”, IJECT, Vol. 2, June 2011. 7. Haun-Cheng Lien, Huei-Chiou Tsai, Yung-Cheng Lee and Wen-Fei Lee, “A Circular Polarization Microstrip Stacked Structure Broadband Antenna”, PIERS ONLINE, Vol. 4, No. 2, 2008. 8. Vibha Rani Gupta and Nisha Gupta, “Gain and Bandwidth Enhancement in Compact Microstrip Antenna”. Authors: Souvik Singha, G.K.Mahanti Design and Implementation of Memory-based Cross – Talk Reducing Algorithm to Eliminate Worst Paper Title: Case Crosstalk in On- Chip VLSI Interconnect Abstract: Cross- talk induced Delay and power consumption are two of the most important constraints in an on- chip bus design. In same metal the ratio of cross-coupling capacitance between adjacent on-chip wires is quite larger. As a consequence, cross- talk interference becomes a serious problem for VLSI design. On chip bus delay maximized by cross-talk effect when adjacent wires simultaneously switch for opposite signal transition directions. In this paper we propose a memory- based cross-talk reduction technique to minimized the cross-talk for on- chip buses based on graph representation. In this approach that represents all the illegal code words canonically generates code words efficiently. As a result, a memory-based cross-talk avoidance CODEC would need to partition large buses into small groups. Our approach is applicable for reducing the cross talk, using a unified implicit formulation. It can actually speed up the bus by exploring cross talk among neighboring wire. By using this approach, we have developed a CODEC based algorithm to minimize the cross- talk or interference in on- chip buses.
Keywords: crosstalk, Bus Encoding, On-chip bus, Crosstalk Free Algorithm, Delay. 79.
References: 407-413 1. C. Duan, K. Gulati and S.P. Khatri, “Memory-based Cross-talk Canceling CODECs for On-chip busses”, ISCAS 2006, pp 4-9. 2. C. Duan, A.Tirumala and S.P.Khatri, ”Analysis and Avoidance of Cross-talk in On-chip Bus”, HotInterconnects, 2001,pp 133-138. 3. C. Duan and S. P. Khatri, ”Exploiting Crosstalk to Speed up On-chip busses”, DATE 2004, pp 778-783. 4. C.Duan, C.Zhu, S.P.Khatri, “Forbidden Transition Free Crosstalk Avoidance CODEC Design” DAC 2008, June 8-13, 2008, Anaheim, California, USA, pp-986-991. 5. C. Duan, V. Cordero and S. P. Khatri, ”Efficient On-Chip Crosstalk Avoidance CODEC Design”, IEEETransactions on VLSI Systems, to appear. 6. Madhu Mutyam, ”Preventing Crosstalk Delay using Fibonacci Representation”, Intl Conf. on VLSI Design, 2004, pp 685-688. 7. Bret Victor and K. Keutzer,”Bus Encoding to Prevent Crosstalk Delay”, ICCAD, 2001, pp 57-63. 8. M. Mutyam, “ACM Transactions on Design Automation of Electronic Systems”, Vol. 14, No. 3, pp. Article 43, pp. 1-20, 2009 9. S.R. Sridhara, A. Ahmed, and N. R. Shanbhag, ”Area and Energy-Efficient Crosstalk Avoidance Codes for On-Chip busses”, Proc. of ICCD, 2004, pp 12-17. 10. J. -S Yim and C. –M. Kyeng. “ Reducing cross- coupling among interconnect wires in deep- submicron Datapath design”.36th design Automation Conference (DAC) ,june 1999 ,pp. 485-490. 11. K. Hiroes and H. Yasuura, “ A bus delay reduction technique considering crosstalk”. Design, Automation and Test in Europe (DATE), mar 2000, pp 441-445. Authors: Amit S. Ghade, Sushil R. Lanjewar Paper Title: Design of Cost Effective Seal to Protect Bearings Used in Conveyor Roller Housing in Mines Abstract: The primary sources of bearing failure are lack of lubrication and contaminant ingress. Industrial sealing devices are the primary protection against bearing failure. When the sealing device fails, bearing failure is imminent. Therefore, extending the life of sealing devices extends bearing life and in turn improves equipment uptime. Whether the equipment in question is a pulverizer, a turbine, conveyance equipment or something else altogether, there is usually a bearing system either driving or being driven by the equipment. In any application where power is transmitted from one point to the next, a bearing system is used to support rotating elements (usually a shaft) and to support the related loads, while at the same time reducing power losses due to friction. The most 80. common types of bearings are ball and roller bearings. This paper investigates about various areas and factors that are important for designing a cost effective and a versatile bearing seal for roller conveyor typically used in dusty 414-419 and environment found in mines and excavation sites.
Keywords: Cost effective seal, Multiple Labyrinth seal, Radial Lip Seal.
References: 1. David C. Roberts; Improved Sealing Technology Extends Equipment Life , Presented at Power-Gen International 2007 New Orleans. 2. Flitney R.; Seals and sealing Handbook, Fifth Edition 2007 Butterworth-Heinemann; pp. 394. 3. Peter Jones; The Mould Design Guide, Smithers Rapra Technology Limited 2008; pp. 447- 449. 4. Heinz P. Bloch; Better bearing housing seals prevent costly machinery failures, Fifteenth National Industrial Energy Technology Conference, Houston, Tx, March 24-25, 1993. 5. K.Yamamoto, D.Ozaki, T.Nakagawa; Influence of Surface Roughness on Sliding Characteristics of Rubber Seals; Koyo Engineering Journal English Edition No.166E (2005). 6. Charles A. Harper; Modern Plastics handbook; McGraw-Hill Publication. 7. http://www.ckit.co.za/secure/conveyor/troughed/idlers/idlers_calc_bearing_life.html. Authors: Yajnaseni Dash, Sanjay Kumar Dubey, Ajay Rana Maintainability Prediction of Object Oriented Software System by Using Artificial Neural Network Paper Title: Approach Abstract: Maintainability is an imperative attribute of software quality. However the prediction of this attribute is a cumbersome process. Therefore various methodologies are proposed so far to estimate the maintainability of software. Among them Artificial Neural Network is one of the sophisticated techniques which have immense prediction capability and this paper explores its application to evaluate maintainability of the object-oriented software. In this study maintenance effort was chosen as the dependent variable and principal components of object- oriented metrics as the independent variables. Prediction of maintainability is performed by Multi Layer Perceptron (MLP) neural network model. The results obtained from the current study are also compared with other models and it is revealed that the presented model is more useful than the previous one.
Keywords: Artificial neural network, Maintainability, Object oriented metrics, Principal component analysis.
References: 1. Y. Dash, S.K. Dubey and A. Rana, “Maintainability Measurement in Object Oriented Paradigm”, International Journal of Advanced Research in Computer Science (IJARCS), Vol.3, no.2, , April 2012, pp. 207-213. 2. H. D. Rombach, “A controlled experiment on the impact of software structure on maintainability”, Software Engineering, IEEE Transactions on, SE-13(3):344–354, March 1987. 3. R. E. Johnson and B. Foote Designing Reusable Classes. Journal of Object-Oriented Programming. 1988, vol. 1, no. 2, pp. 22-35. 4. D. R Moreau and W. D. Dominick, “Object-Oriented Graphical Information Systems: Research Plan and Evaluation Metrics,” Journal of Systems and Software, vol. 10, 1989, pp. 23-28. 5. W. Li and S. Henry, “Object-Oriented Metrics that Predict Maintainability”, Journal of Systems and Software, vol 23, no.2, 1993, pp.111- 122. 6. S. R. Chidamber and C. F. Kemerer, “A metrics suite for object oriented design.” IEEE Trans. Software Eng., vol. 20, no. 6, 1994, pp. 476–493. 7. V. Basili, L. Briand and W. Melo, “A Validation of Object-Oriented Design Metrics as Quality Indicators”, IEEE Transactions on Software Engineering, vol. 22, no.10, 1996, pp. 751-761. 8. Binkley and S. Schach, “Validation of the Coupling Dependency Metric as a risk Predictor”, Proceedings in ICSE 98, 1998, pp. 452-455. 9. M.H. Tang, M.H. Kao, and M.H. Chen, “An Empirical Study on Object Oriented Metrics,” Proc. Sixth Int’l Software Metrics Symp., 1999, pp. 242-249. 10. S. Muthanna, K. Kontogiannis, K. Ponnambalaml and B. Stacey, “A Maintainability Model for Industrial Software Systems Using Design Level Metrics”, In Working Conference on Reverse Engineering (WCRE’00), 2000. 81. 11. M. Genero, M. Piattini, E. Manso, G. Cantone, “Building UML class diagram maintainability prediction models based on early metrics”, Proceedings 5th International Workshop on Enterprise Networking and Computing in Healthcare Industry, , IEEE, 2003, pp. 263-275. 12. J.H. Hayes, S.C. Patel and L. Zhao, “A Metrics-Based Software Maintenance Effort Model,” Proc. 8th European Conference on Software 420-423 Maintenance and Reengineering (CSMR'04), 24 – 26 Mar. 2004, IEEE Computer Society, 2004, pp. 254 – 258. 13. K. M. Breesam, “Metrics for Object Oriented design focusing on class Inheritance metrics”, 2nd International conference on dependability of computer system IEEE, 2007. 14. S.K. Dubey and A. Rana, “A comprehensive assessment of object oriented software system using metrics approach”, International journal of computer science and engineering (IJCSE), 2010, pp. 2726-2730. 15. T.M. Khoshgaftaar, E.D. Allen, J.P. Hudepohl and S.J. Aud "Application of neural networks to software quality modelling of a very large telecommunications system," IEEE Transactions on Neural Networks, Vol. 8, No. 4, 1997, pp. 902--909. 16. N. E. Fenton, and M. Neil, (1999), “A Critique of Software Defect Prediction Models”, Bellini, I. Bruno, P. Nesi, D. Rogai, University of Florence, IEEE Trans. Softw. Engineering, vol. 25, Issue no. 5, pp. 675-689. 17. T. M. Khoshgoftaar, E. B. Allen, Z. Xu, “Predicting testability of program modules using a neural network”, In Proc. of 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology, pp.57-62, 2000. 18. F. Fioravanti and P. Nesi “A study on fault-proneness detection of object-oriented systems”, Fifth European Conference on Software Maintenance and Reengineering, pp. 121 –130, 2001. 19. M. Genero, J. Olivas, M. Piattini and F. Romero “"Using metrics to predict OO information systems maintainability", Proceedings. of the 13th International Conference Advanced Information Systems Engineering, Interlaken, Switzerland, 2001. 20. J. T. S. Quah, M. M. T. Thwin, “Prediction of Software Readiness Using Neural Network”, In Proceedings of 1st International Conference on Information Technology & Applications, Bathurst, Australia, 2002, pp. 2312-2316. 21. L. Tian and A. Noore, “Evolutionary neural network modelling for software cumulative fault prediction”, Reliability Engineering & system safety, vol. 87, pp. 45-51, 2005. 22. M. M. T. Thwin,T. S. Quah, “Application of neural networks for software quality prediction using Object-oriented metrics”, Journal of systems and software, Vol.76, No.2, pp.147-156, 2005. 23. Q. Hu and C. Zhong, “Model of predicting software module risk based on neural network”(in Chinese), Computer Engineering and Applications, Vol.43, No.18, pp.106-110, 2007. 24. A. Kaur, K. Kaur and R. Malhotra, “Soft Computing Approaches for Prediction of Software Maintenance Effort,” International Journal of Computer Applications, Vol. 1, no.16, 2010. 25. R. Ratra, N.S. Randhawa, P. Kaur, G. Singh,” Early Prediction of Fault Prone Modules using Clustering Based vs. Neural Network Approach in Software Systems,” IJECT Vol. 2, Issue 4, Oct . –Dec. 2011 26. K. K. Aggarwal, Y. Singh,A. Kaur and R. Malhotra, “Application of Artificial Neural Network for Predicting Maintainability using Object-Oriented Metrics, World Academy of Science, pp. 140-144, 2006. 27. Y. Dash and S.K. Dubey, “Quality Prediction in Object Oriented System by Using ANN: A Brief Survey”, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), Vol. 2, no. 2, February 2012, ISSN: 2277 128X. 28. M. M. T. Thwin, T. S. Quah, "Application of Neural Networks for Estimating Software Maintainability Using Object-Oriented Metrics", Proceedings of the 15th International Conference on Software Engineering and Knowledge Engineering, San Francisco, U.S.A, 2003, pp. 69-73. Authors: Shachi Bhatnagar, Sanjay K. Dubey, Ajay Rana Paper Title: Quantifying Website Usability using Fuzzy Approach 82. Abstract: Usability is one of the most important factors for evaluating the quality of software/website. There are 424-428 different dimensions through which usability of software can be evaluated. But the concept of usability is complicated. As the evaluation of usability is dependent on user experience, the data becomes difficult to work on as it is fuzzy in nature. There are different types of fuzzy theories are available through which usability can be evaluated even in the presence of uncertain and imprecise data. ISO 9241 states that effectiveness, efficiency and satisfaction are the criteria for usability evaluation. In this paper we are trying to show that if we incorporate the learnability of software with effectiveness, efficiency and satisfaction the usability of software increases with a considerable amount.
Keywords: Analytical hierarchy process (AHP), Fuzzy comprehensive evaluation, learnability, usability, usability evaluation, user experience.
References: 1. Zhou Ronggang How to Quantify User Experience: Fuzzy Comprehensive Evaluation Model Based on Summative Usability Testing,Usability and Internationalization, Part II, HCII 2007, LNCS 4560, pp. 564–573, (2007). 2. ISO 9241-11 (1998): Ergonomic requirements for office work with visual display terminals (VDTs) Part 11: Guidance on usability, 1998. 3. IEEE Std. 1061(1992): IEEE standard for a software quality metrics methodology, New York, IEEE Computer Society Press, 1992. 4. Nielsen J. and V. Philips, “Estimating the relative usability of two interfaces: Heuristic, formal, and empirical methods compared,” in Proc. ACM/IFIP Human Factors Computing Systems (INTERCHI), Amsterdam, The Netherlands, 1993, pp. 214–221. 5. Shackel, B.: Usability - context, framework, design and evaluation. In: Shackel, B., Richardson, S. (eds.) Human factors for informatics usability. Cambridge, pp. 21–38 (1991) 6. Booth, P. (1989): An introduction to human-computer interaction, Hillsdale USA, Lawrence Erlbaum Associates Publishers, 1989. 7. Hix, D. and H. R. Hartson (1993): Developing user interfaces: Ensuring usability through product & process, New York, John Wiley, 1993. 8. Wixon, D., & Wilson, C. (1997). The usability engineering framework for product design and evaluation. In Helander, M. G., Landauer, T. K., & Prabhu, P. V. (Eds.), Handbook of human-computer interaction. 2nd ed. Amsterdam, The Netherlands: North-Holland. 9. Lecerof, A.; Paterno, F. (1998): Automatic Support for Usability Evaluation. IEEE Transaction on Software Engineering, 24(10), pp. 863- 888. 10. Kirakowski, J.: The Software Usability Measurement Inventory: Background and usage. In: Jordan, P., Thomas, B., Weerdmeester, B. (eds.) Usability Evaluation in Industry, pp. 169–178. Taylor and Francis, London (1996) 11. Chin, J.P., Diehl, V.A., Norman, K.L.: Development of an instrument measuring user satisfaction of the human-computer interface. In: Proceedings of SIGCHI ’88. ACM/SIGCHI, New York pp. 213–218 (1988) 12. Harper, B.D., Norman, K.L.: Improving User Satisfaction: The Questionnaire for User Interaction Satisfaction Version 5.5. In: Proceedings of the 1st Annual Mid-Atlantic Human Factors Conference. Virginia Beach, VA, pp. 224–228 (1993) 13. Liang, Z., Yang, K., Sun, Y., Yuan, J., Zhang, H., Zhang, Z.: Decision support for choice optimal power generation projects: Fuzzy comprehensive evaluation model based on the electricity market. Energy Policy 34, 3359–3364 (2006) 14. Saaty, T.L.: The analytic hierarchy process. McGraw Hill, New York (1980) 15. Hsiao, S-W., Chou, J-R.: A Gestalt-like perceptual measure for home page design using a fuzzy entropy approach. International Journal of Human-Computer Studies 64, 137–156 (2006) 16. Vredenburg, K., Isensee, S., Righi, C.: User-Centered Design: An Integrated Approach. Prentice Hall, New Jersey (2001) 17. Kuo, Y.-F., Chen, L.-S.: Using the fuzzy synthetic decision approach to assess the performance of university teachers in Taiwan. International journal of management 19, 593–604 (2002). Authors: Sachin Upadhyay, Yashpal Singh, Amit Kumar Jain Paper Title: An Analysis of the Attack on RSA Cryptosystem Through Formal Methods Abstract: Communication is the basic process of exchanging information. The effectiveness of computer communication is mainly based on the security aspects whether it is through internet or any communication channel. The aim of this paper is based on analyzing the results given by Wiener's, who says that if the private exponent d used in RSA cryptosystem is less than n^.292 than the system is insecure. We will focus on the result given by Weiner’s and try to increase the range of private exponent d up to n^0.5. As n is the product of p & q (which are the relative prime numbers). This paper also aims at considering the different factors that affects the performance of encryption algorithms so as to make our information more secure over the network. 83.
Keywords: Conjunctive Normal Form (CNF), Cryptanalysis, RSA Algorithm, , SAT Solver tool. 429-432
References: 1. D. Boneh. Twenty Years of Attacks on the RSA. Notices of the American Mathematical Society, vol 46(2):203–213, 1999. 2. R. L. Rivest, A. Shamir, and L. Adleman. A method for obtaining digital signatures and public key cryptosystems. Commun. of the ACM, 21:120-126, 1978. 3. Brown, Lawrie. "Classic Cryptography". 22 Feb 1996. 4. SANS Institute. "SANS GIAC Training and Certification".URL:http://www.sans.org/giactc/GIAC_certs.htm (24 Nov 2001) 5. Cryptography & Network Security by William Stallings fourth edition. 6. Cryptography & Network Security by Kumar Manoj, Krishna’s Prakashan Media (P) Ltd. Authors: D.Ujwala, D.S.Ram Kiran, B.Jyothi, Shaik Saira Fathima, P.Harish, Y.M.S.R.Koushik Paper Title: A Parametric Study on Impedance Matching of A CPW Fed T-shaped UWB Antenna Abstract: A CPW fed novel compact Ultra wide band antenna is proposed in this paper. The size of the antenna is 20mm x 20mm x 0.6mm and it is prototyped on FR4-Epoxy substrate material which has a dielectric constant of 4.4. The proposed antenna provides a bandwidth of 5.45 GHz from 4.76 GHz to 10.21 GHz which can be used for 84. wireless applications. A parametric study is carried out by varying the horizontal and vertical gaps ‘g’ and ‘d’ between the conducting patch and ground. The output parameters and the dimensional variation effects on the 433-436 proposed antenna are presented in this paper. Simulations are carried out using Finite Element based Ansoft High Frequency Structure Simulator.
Keywords: CPW fed, Ultra Wide band, Wireless applications.
References: 1. J.Y. Jan and J.-W. Su, “Bandwidth enhancement of a printed wide-slot antenna with a rotated slot,” IEEE Trans. Antennas Propag., vol. 53, no. 6, pp. 2111–2114, Jun. 2005. 2. S.I.Latif, L.Shafai, and S. K. Sharma, “Bandwidth enhancement and size reduction of microstrip slot antennas,” IEEE Trans. Antennas Propag., vol. 53, no. 3, pp. 994–1003, Mar. 2005. 3. K. H Kim, Y. J .Cho, S.H Hwang. S. O. Park, “Band notched UWB planar monopole antenna with two parasitics,” Electron. Lett., vol. 41, No 14, pp. 783-785, Jul. 2005. 4. Y. J . Cho, K. H. Kim, D. H choi, S. S. Lee and S. O. Park, “A miniature UWB planar monopole antenna with 5-GHz band rejectionfilter and time domain characteristics,” IEEE trans. Antennas Propag., vol.54, pp. 1453-1460, Mar. 2006. 5. W. S. Lee, D. Z. Kim, K. J. Kim and Y. W. Yu, “Wideband planar monopole antenna with dual band–notched characteristics”, IEEE trans. Antennas Propag., vol.54, no. 6, pp. 2800-2806, Jun. 2006. 6. Jen-Yea Jan, Liang-Chih Tseng, "Small Planar Monopole antenna With a Shorted Parasitic Inverted-L Wire for Wireless Communications in the 2.4, 5.2 and 5.8 GHz Bands," IEEE Trans. Antennas and Propagation, vol. AP-52, no. 7, pp. 19031905, July 2004. 7. Jon II Kimker and Yong Jee, “Design of Ultra wideband Coplanar waveguide-fed LI-shape planar monopole antennas”, IEEE Antennas Wireless Propag. Lett., vol. 6, pp. 383-387, 2007. 8. W.C. Liu, P.C Kao., “CPW-fed triangular monopole antenna for ultrawideband operation”, Microw. Opt. Technol. Lett., Vol. 47, No. 6, pp. 580–582, 2005. T.Krishna Kathik , T.Praveen Blessington, Fazal.Noor Basha, ALGN. Aditya, S R Sastry Authors: Kalavakolanu Paper Title: Design and Verification of UART IP Core Using VMM Abstract: In the earlier era of electronics the UART (Universal asynchronous receiver/transmitter) played a major role in data transmission. This UART IP CORE provides serial communication capabilities,which allow communication with modems or other external devices. Thiscore is designed to be maximally compatible with industry standard designs[4]. Thekey features of this design are WISHBONE INTERFACE WITH 8-BIT OR 32- BIT selectable data bus modes. Debug interface in 32-bit data bus mode. Registerlevel and functionalcompatibility. FIFO operation. The design is verified using VMM based on system verilog. The test bench is written with regression test cases in order to acquire maximum functional coverage. 85. Keywords: UART, VMM, FIFO, WISHBONE INTERFACE. 437-441 References: 1. I.E.Sutherland'Micropiplines'CommunicationACM, June 1989, Vol. 32(6), pp. 720 -738. 2. V.N. Yarmolik, Fault Diugnosis of Digital Circuits, JohnWiley & Sons, 1990. 3. “PCI6550DUniversalAsynchronousReceiver/Transmitter withFIFOs”, National Semiconductor Application Note, June 1995. 4. M.S.Harvey,“GenericUARTManual”SiliconValley. December 1999. 5. “PCI6550DUniversalAsynchronousReceiver/Transmitter withFIFOs”,NationalSemiconductor Application Note, June 1995 6. Martin S. Michael, “A Comparison of the INS8250,NS16450 and NS16550AF Series ofUARTs”National Seiniconductor Application Note 493, April1989 7. W.Elmenreicb, M.Delvai, Time Triggered Communication with UARTs. InProceedings ofthe 4'h IEEE International Workshop on FactoryCommunication Systems, Aug. 2002. Authors: Benoy Kumar Thakur, Bhusan Chettri, Krishna Bikram Shah Paper Title: Current Trends, Frameworks and Techniques Used in Speech Synthesis – A Survey Abstract: Vocalized form of human communication is Speech. Here, we have reviewed some of the most popular and effective techniques used to generate synthetics speech. In this quest we are able to find the scenario where one method is advantageous over another. We have discusses Text To Speech Architecture putting more emphasize on the two components, namely, Natural Language Processing (NLP) and Digital Signal Processing (DSP). We have also reviewed some of the most popular generic frameworks like MBROLA, FESTIVAL, and FLITE that available in public domain for the development of a TTS synthesizer.
Keywords: Speech Synthesis, Synthesized Speech, Text-to-Speech, TTS, Artificial Speech, speech synthesizer.
References: 1. Jonathan Allen, M. Sharon Hunnicutt, Dennis Klatt, “From Text to Speech: The MITalk system”, Cambridge University Press, 1987. 2. Rubin, P.; Baer, T.; Mermelstein, P., "An articulatory synthesizer for perceptual research". Journal of the Acoustical Society of America 70: 321–328, 1981 3. Dutoit T, “High-quality text-to-speech synthesis: an overview. Journal of Electrical & Electronics Engineering,” Australia: Special Issue 86. on Speech Recognition and Synthesis, vol. 17, pp 25-37 4. Allen J, “Synthesis of speech from unrestricted text. IEEE Journal”, Vol.64, Issue 4, pp 432-42, 1976 442-446 5. Allen J, Hunnicutt S, Klatt D , “From text-to-speech: the MITalk system”, Cambridge University Press, Inc., 1987 6. Klatt D, “Review of text-to-speech conversion for English”, Journal of the Acoustical Society of America, vol. 82, pp 737-93 7. Sami Lemmetty, “Review of Speech Synthesis Technology,” Master’s Thesis, Dept. of Electrical and Communication Engineering, Helsinki University of Technology, March 30, 1999. 8. O’Saughnessy D, Speech Communications – Human and Machine, University Press. 2001 9. David Öhlin, Rolf Carlson "Data-driven formant synthesis" Proceedings, FONETIK 2004, Dept. of Linguistics, Stockholm University. 10. P A TAYLOR, "Concept-to-Speech Synthesis by Phonological Structure Matching". 11. T.Yoshimura, K.Tokuda, T. Masuko, T. Kobayashi, and T. Kitamura, “Simultaneous modeling of spectrum,pitch and duration in HMM- based speech synthesis”, Proc. Eurospeech, pp.2347-2350,1999. 12. Y. Stylianou, “Harmonic plus Noise Models for Speech, combined with Statistical Methods, for Speech and Speaker Modification,” Ecole Nationale Supérieure des Telecommunications, Paris, January 1996. 13. R.J. McAulay and T.F. Quatieri, “Speech analysis-synthesis based on a sinusoidal representation,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-34, no. 4, pp. 744-754 August 1986. 14. MBROLA, “Project homepage”, 1998. Online: http://tcts.fpms.ac.be/synthesis/mbrola.html/ 15. Black,”User Manual for the Festival Speech Synthesis System”, version1.4.3, 2001. Online: http://fife.speech.cs.cmu.edu/festival/cstr/festival/1.4.3/ 16. Black A, Taylor P, Caley R (2001) The Festival speech synthesis system: system documentation. University of Edinburgh. Online: http://www.cstr.ed.ac.uk/projects/festival/. Authors: Kapil Kr Bansal Paper Title: Production Inventory Model with Price Dependent Demand and Deterioration Abstract: In this paper many inventory models demand rate are either constant or time dependent but independent of the stock level. However for certain types of commodities particularly consumer goods, the demand rate of may be depend on the on hand inventory. For this type of commodity the sale would increase as the amount of inventory increase Most of the researchers have assumed that as soon as the items arrive in stock, they begin to deteriorate at once, but for many items this is not true. In practice when most of the items arrive in stock they are fresh and new and they begin to decay after a fixed time interval called life-period of items.
Keywords: Particularly Consumer Goods. 87. References: 447-451 1. B.N. Mandal and S. Phaujdar (1989): An inventory model for deteriorating items and stock dependent consumption rate. J. Opl. Res. Soc. 40, 483-488. 2. S.P. Aggarwal and C.K. Jaggi (1989): Ordering policy for decaying inventory. Int. J. System Sci. 20, 151-155. 3. Su., C.T., Tong, L.I. and H.C., Liao (1996): An inventory model under inflation for stock dependent consumption rate and exponential decay. Opsearch, 33, 71-82. 4. Su., C.T., Lin, C.W. and C.H., Tsai (1999): A deterministic production inventory model for deteriorating items and exponential declining demand. Opsearch, 36, 95-106. 5. Naresh Kumar and Anil Kumar Sharma (2000): On deterministic production inventory model for deteriorating items with an exponential declining demand. Acta Ciencia Indica, Vol. XXVI M, 4, 305-310. 6. Kun-Shan Wu, Liang-Yuh Ouyang and Chih-Te Yang (2005) An optimal replenishment policy for non-instantaneous deteriorating items with stock-dependent demand and partial backlogging.International Journal of Production Economics. Authors: Vishwas Massey, K.J.Satao Employing CoCoMo 81 For Comparing New Proposed SDLC “VISHWAS” With Existing SDLC Paper Title: Models Abstract: Various SDLC models are available which are employed by different organizations depending upon their need and requirement of software being developed [1],[2]. Each company either follows a fixed SDLC or randomly chooses SDLC model. There were various SDLC models available but none of them were capable in addressing the issue of release management. We have developed a SDLC model – “SDLC VISHWAS” which enables the developer in handling the concept of release management along with the core SDLC phases employed for software development. We have developed software capable of generating schedules, effort, development time and staffing needed for any specified software which employs the concept of CoCoMo – 81[3],[4]. The software generates results both in text and in graphic charts which makes clear understanding for specified software being developed.
Keywords: SDLC, CoCoMo-81, LOC, SDLC-VISHWAS, Software.
88. References: 1. Software Development Life Cycle (SDLC) – the five common principles.htm 452-460 2. Roger Pressman, titled Software Engineering - a practitioner's approach 3. Software Engineering (3rd ed.), By K.K Aggarwal & Yogesh Singh, Copyright © New Age International Publishers, 2007 42 Software Project Planning(by narender sharma (istk))) The Constructive Cost Model (CoCoMo) Constructive Cost model (CoCoMo) Basic Intermediate Detailed Model proposed by B. W. Boehm’s through his book Software Engineering Economics in 1981. 4. Barry Boehm. Software Engineering Economics. Englewood Cliffs, NJ:Prentice-Hall, 1981. ISBN 0-13-822122-7 5. Roger S. Pressman, Software Engineering: A Practitioner's Approach http://www.selectbs.com/analysis-and-design/what-is-the-waterfall- model 6. Roger S. Pressman, Software Engineering: A Practitioner's Approach http://en.wikipedia.org/wiki/Incremental_build_model#Incremental_Model 7. Barry Boehm, Chris Abts, A. Winsor Brown, Sunita Chulani, Bradford K. Clark, Ellis Horowitz, Ray Madachy, Donald J. Reifer, and Bert Steece. Software Cost Estimation with CoCoMo II (with CD-ROM). Englewood Cliffs, NJ:Prentice-Hall, 2000. ISBN 0-13-026692-2 8. Software Release Management, 6th European Software Engineering Conference, LNCS 1301, Springer, Berlin, 1997 9. Hoek, A. van der, Wolf, A. L. (2003) Software release management for component-based software. Software—Practice & Experience. Vol. 33, Issue 1, pp. 77–98. John Wiley & Sons, Inc. New York, NY, USA. 10. Software Release Management: Proceedings of the 6th European Software Engineering Conference, LNCS 1301, Springer, Berlin, 1997(Andre van der Hoek, Richard S. Hall, Dennis Heimbigner, and Alexander L. Wolf Software Engineering Research Laboratory, Department of Computer Science, University of Colorado, Boulder, CO 80309 USA). Authors: Niladree De, Jaydeb Bhaumik Paper Title: A Modified XTEA Abstract: This paper presentsa modified Extended Tiny Encryption Algorithm (XTEA). A nonlinear Boolean function called Nmix is used to replace addition modulo 232.Proposed design has been implemented on a FPGA platform. Simulation result shows that it requires a reasonable hardware and provides an acceptable throughput. It is shown that proposed design requires less hardware compared to XTEA. 89. Keywords: Extended Tiny Encryption Algorithm (XTEA), Nonlinear Mixing Function, VLSI Implementation. 461-464
References: 1. D. Wheeler, R. Needham, “TEA, a Tiny Encryption Algorithm,” FSE 1994, LNCS, Springer-Verlag, vol. 1008, 1995, pp. 97-110. 2. J.P. Kaps, “Chai-tea, cryptographic hardware implementations of XTEA,”INDOCRYPT2008, LNCS, vol. 5365, HeidelbergSpringer, 2008, pp. 363-375. 3. L. Jiqiang ,"Related-key rectangle attack on 36 rounds of the XTEA block cipher," International Journal of Information Security, vol. 8 , no.1, 2009, pp. 1-11. 4. D. Moon, K. Hwang, W. Lee, S. Lee, and J. Lim, “Impossible Differential Cryptanalysis of Reduced Round XTEA and TEA”, Fast Software Encryption ’02, LNCS, Springer-Verlag,vol. 2365, 2002, pp. 49-60. 5. S.Hong,D. Hong,Y.Ko, D.Chang, W. Lee, S. Lee,“Differential cryptanalysis of TEA and XTEA,”ICISC 2003, vol. 2971, Springer Heidelberg,2004, pp. 402–417. 6. Y.Ko, S. Hong, W.Lee, S.Lee, Kang, and J. Lim, "Related key differential attacks on 27 rounds of XTEA and full rounds of GOST," FSE '04, LNCS, vol. 3017,2004,pp. 299-316. 7. E. Biham and A. Shamir, “Differential Cryptanalysis of the DES-like Cryptosytems,” CRYPTO 1990,LNCS, Springer-Verlag,vol. 537, 1990, pp.187-195 8. R. Needham and D. Wheeler, “eXtended Tiny Encryption Algorithm, ”Technical Report, Cambridge University,England, Oct. 1997. 9. J. Cesar, H. Castro and P. I. Vinuela, “New results on the genetic cryptanalysis of TEAand reduced-round versions of XTEA,” Journal of New Generation Computing, vol. 23, no. 3, 2005, pp. 233-243. 10. C. H. Lim and T. Korkishko,“mCrypton - A Lightweight Block Cipher for Security of Low-Cost RFIDTags and Sensors,” WISA, LNCS, Springer, vol. 3786,2005, pp. 243–258. 11. D.Wagner, “The boomerang attack,” Fast SoftwareEncryption Workshop, LNCS, Springer Heidelberg, vol. 1636, 1999, pp. 156–170. 12. E. Lee, D. Hong, D. Chang, S.Hong, J. Lim “ A weakkey class of XTEA for a related-key rectangle attack,” VIETCRYPT, LNCS, vol. 4341, 2006, pp. 286-297. 13. J. Bhaumik, and D. Roy Chowdhury, “Nmix: An Ideal Candidate For Key Mixing,” Proc. of Int. Conf. on Security and Cryptography (Secrypt), Milan, Italy, July 2009, pp. 285-288. 14. J.Daemen and V.Rijmen. “The Design of Rijndael - AES The Advanced Encryption Standard, “ Springer-Verlag, 2002. Authors: S. Singaravelu, S. Sasikumar Paper Title: Genetic Algorithm based Steady-State Analysis of Three-Phase Self-Excited Induction Generators Abstract: This paper presents a genetic algorithm based steady-state analysis of a three-phase self-excited induction generator (SEIG) for wind energy conversion. A generalized mathematical model based on inspection is developed for a three-phase induction generator for steady-state analysis. The proposed mathematical model is quite general in nature and can be implemented for any type of load such as resistive or reactive load. The proposed model completely avoids the tedious work of segregating real and imaginary components of the complex impedance of the equivalent circuit. Also, any equivalent circuit component can be easily included or eliminated from the model, if required. To carry out the steady-state analysis of SEIG, a genetic algorithm approach is used to find the unknown variables using the proposed model. The parameter sensitivity analysis of the generator is also carried out. The computed performance characteristics of the machine are compared with the experimentally obtained values on a laboratory machine, and a good correlation is observed.
Keywords: Genetic algorithm, Induction generator, Self-excitation, Steady-state analysis. 90. References: 465-470 1. M. Abdulla, V.C. Yung, M. Anyi, A. Kothman, K.B. Abdul Hamid, and J. Tarawe, “Review and comparison study of hybrid diesel/solar/hydro/fuel cell energy schemes for rural ICT Telecenter,” Energy, Vol. 35, pp. 639-646, 2010. 2. R.C. Bansal, “Three-phase self-excited induction generators: An overview,” IEEE Trans. Energy Conversion, Vol. 20, pp. 292-299, 2005. 3. M.I. Mosaad, “Control of self excited induction generator using ANN based SVC,” International Journal of Computer Applications, Vol. 23, pp. 22-25, 2011. 4. S.P. Singh, K. Sanjay, Jain, and Sharma, “Voltage regulation optimization of compensated self-excited induction generator with dynamic load,” IEEE Trans. on Energy Conversion, Vol. 19, pp. 724-732, 2004. 5. Tarek Ahmed, and Mutsuo Nakaoka, “Static VAr compensator based terminal voltage control for stand-alone ac and dc outputted self excited induction generator,” IEE Proc. pp. 40-45, 2004. 6. T.F. Chan, and L.L Lai, “A novel excitation scheme for a stand-alone three-phase induction generator supplying single phase loads,” IEEE Trans. on Energy Conversion, Vol. 19, pp.136-142, 2004. 7. S. Singaravelu, and S. Velusami, “Capacitive VAr requirements for wind driven self-excited induction generators,” Energy Conversion and Management, Vol. 48, pp.1367-1382, 2007. 8. S. Velusami, and S. Singaravelu, “Steady state modeling and fuzzy logic based analysis of wind driven single-phase induction generators,” Renewable Energy, Vol. 32, pp. 2386-2406, 2007. 9. M.E. Van Valkenburg, Network Analysis, Third Edition, Prentice Hall of India Pvt. Ltd., New Delhi, 1994. 10. D.E. Goldberg, Genetic algorithm in search, optimisation, and machine learning, Pearson Education, New Delhi, 2001. Authors: Yogita Gigras, Kusum Gupta Paper Title: Artificial Intelligence in Robot Path Planning
Abstract: Mobile robot path planning problem is an important combinational content of artificial intelligence and robotics. Its mission is to be independently movement from the starting point to the target point make robots in their work environment while satisfying certain constraints. Constraint conditions are as follows: not a collision with known and unknown obstacles, as far as possible away from the obstacle, sports the shortest path, the shortest time, robot-consuming energy minimization and so on. In essence, the mobile robot path planning problem can be seen as a conditional constraint optimization problem. To overcome this problem, ant colony optimization algorithm is used. 91. Keywords: Particle Swarm Optimization (PSO), Genetic Algorithm(GA), Tabu Search, Simulated Annealing (SA), Reactive Search Optimization (RSO), proportional–integral–derivative(PID). 471-474
References: 1. Yao-hong Qu, Quan Pan, Jian-guo Yan, “Flight Path Planning of UAV Based on Heuristically Search and Genetic Algorithms”, Annual Conference of IEEE on Industrial Electronics Society, (IECON),pp:5,2005. 2. Chih-Lyang Hwang, Member, IEEE, and Li-Jui Chang, “Internet-Based Smart-Space Navigation of a Car-Like Wheeled Robot Using Fuzzy-Neural Adaptive Control”, IEEE Transactions on Fuzzy Systems, pp: 1271 – 1284,2008 3. Abdullah Zawawi MOHAMED, Sang Heon LEE, Mahfuz AZIZ, Hung Yao HSU, Wahid Md FERDOUS, “A Proposal on Development of Intelligent PSO Based Path Planning and Image Based Obstacle Avoidance for Real Multi Agents Robotics System Application”, International Conference on Electronic Computer Technology (ICECT), pp: 128 – 132, 2010. Authors: Pankaj H. Chandankhede Paper Title: Soft Computing Based Texture Classification with MATLAB Tool Abstract: This paper deals with Implementation of my previous work [1]. Here MATLAB simulation software is use as a platform tool for designing the concept of Texture Classification using Soft Computing Tool as a function of MATLAB. This paper classifies Textures on the basis of two novel approaches of artificial neural network & adaptive neuro-fuzzy inference system. This paper proves that neuro-fuzzy model performed better than the neural network in classification of texture image of three different types.
Keywords: Database, Neural Network Toolbox, Training, DCT Features, ANN, ANFIS, FIS Editor.
References: 1. Pankaj H. Chandankhede, Parag V. Puranik, and P. R. Bajaj, “Design Approach of Texture Classification using Discrete Cosine Transform with Soft Computing Tool”, IJRTET, Vol. 05, No. 01, Mar 2011. 2. Hee-Jung Bae and Sung-Hwan Jung, “Image Retrieval Using Texture Based on DCT,” Proceedings of ICICS’ 97, Singapore, Vol. 2. 1997, pp. 1065-1068. 3. Salgado, M.F.P., Caminhas, W.M.;Menezes, B.R., “Soft computing approaches in reliability modeling and analysis of repairable systems”, In Proceeding of the Reliability and Maintainability Symposium (RAMS), 2010. 4. Al Nadi, D.A., Mansour, A.M. “Independent Component Analysis (ICA) for texture classification” In Proceedings of 5th International 92. Multi-Conference on the Systems, Signals and Devices, 2008. M. N. Shirazi, H. Noda and N. Takao, “Texture modeling and classification in wavelet feature space”. In Proceedings of the International Conference on Image Processing. Vol. 1, pp. 272-5, 2000. 5. J. R. Smith and S. Chang, “Transform features for texture classification and discrimination in large image databases”. In Proceedings of 475-480 ICIP-94, vol. 3, pp. 407-11, 1994. 6. G. K. Wallace, “Overview of the JPEG still image compression standard,” SPIE, Vol. 1244, 1990, pp. 220- 233. 7. D. J. Le Gall, “The MPEG video compression algorithm: a review,” SPIE Vol. 1452, 1991, pp. 444- 457. 8. F. Borko, “video and image processing in multimedia systems”, Kluwer Academic publishers, 1995, pp 225-249. 9. T. Hashiyama, T. Furuhashi, and Y. Uchikawa. “A Decision making Model Using a Fuzzy Neural Network”. In Proceedings of the 2nd International Conference on Fuzzy Logic & Neural Networks, Iizuka, Japan, 1992. 10. Sulzberger SM, Tschicholg-Gurman NN, Vestli SJ, “FUN: Optimization of Fuzzy Rule Based System Using Neural Networks”, In Processing of IEEE Conference on Neural Networks, San Francisco, pp 312-316, March 1993. 11. R. Jang. “ANFIS: Adaptive Network-Based Fuzzy Inference System”. IEEE Trans. on Syst., Man, Cybernetics, 23(3):665-685, 1993. 12. Abraham A & Nath B, “Designing Optimal Neuro-Fuzzy Systems for Intelligent Control”, In processing of the Sixth International Conference on Control Automation Robotics Computer Vision, (ICARCV 2000), Singapore, December 2000. 13. R. E. Hamdi, Njah M. and Chtourou M. “Multilayer perceptron training using an evolutionary algorithm”, Int. J. Modelling, Identification and Control, Vol. 5, No. 4, 2008. 14. J. A. Freeman and D. M. Skapura, “Neural networks algorithms, applications, and programming techniques”, Addison-Wesley, Massachusetts, 1992. 15. T. Raden and H. Husoy, “Filtering for texture classification: A comparative study”, IEEE Trans. on PAMI, vol. 21, no. 4, 1999. 16. Juang C. F. and Lin C. T. “An self-constructing neural fuzzy inference network and its applications”, IEEE trans. Fuzzy Syst. vol. 6, no.1, pp. 1231, 1998. 17. Raghu, P.P, Yegnanarayana, B., “Texture classification using a probabilistic neural network and constraint satisfaction model”, In Proceedings of the IEEE International Conference on neural network, 1996. Authors: Ram Kumar Singh, Amit Asthana, Akanksha Balyan, Shyam Ji Gupta, Pradeep Kumar Paper Title: Vertical Handoffs in Fourth Generation Wireless Networks Abstract: This book chapter presents a tutorial on vertical handoff methods in the evolving 4G wireless communication networks. Integration architectures for various wireless access networks are described. Then handoff classification, desirable handoff features, the handoff process, and multimode mobile terminals are discussed. A section is devoted to some recently proposed vertical handoff techniques. We propose a vertical handoff decision algorithm that determines whether a vertical handoff should be initiated and dynamically selects the optimum network connection from the available access network technologies to continue with an existing service or begin another service.
Keywords: Heterogeneous Wireless Access Networks, Vertical Handoffs in 4G Wireless Networks, Recently Proposed Vertical Handoff Techniques and Performance Evaluation of Network Selection.
References: 1. M. Stemm, and R. Katz, “Vertical Handoffs in Wireless Overlay Networks”, ACM Mobile Networking, Special Issue on Mobile 93. Networking in the Internet 3 (4), 1998, pp. 335-350. 2. A. K. Salkintzis, C. Fors, and R. Pazhyannur, “WLAN-GPRS Integration for Next-Generation Mobile Data Networks”, IEEE Wireless Communications, vol. 9, no. 5, October 2002, pp. 112-124. 481-490 3. 3GPP, “Feasibility Study on 3GPP System to WLAN Interworking (Release 6)”, 3GPP TR 22.934 v6.2.0, 2003. 4. 3GPP, “3GPP System to WLAN Interworking; System Description (Release 6)”, 3GPP TS 23.234 v6.1.0, 2004. 5. K. Pahlavan et al., “Handoff in Hybrid Mobile Data Networks”, IEEE Personal Communications, April 2000, pp. 34-47. 6. N. D. Tripathi et al., “Adaptive Handoff Algorithm for Cellular Overlay Systems Using Fuzzy Logic”, IEEE 49th VTC., May 1999, pp. 1413-1418. 7. N. Nasser, A. Hasswa, and H. Hassanein, “Handoffs in Fourth Generation Heterogeneous Networks”, IEEE Communications Magazine, October 2006, pp. 96-103. 8. F. Siddiqui and S. Zeadally, “Mobility Management across Hybrid Wireless Networks: Trends and Challenges”, Computer Communications, May 2006, pp. 1363-1385. 9. F. Zhu and J. McNair, “Vertical Handoffs in Fourth-Generation Multinetwork Environments”, IEEE Wireless Communications, June 2004, pp. 8-15. 10. S. McCann, et al., “Next Generation Multimode Terminals”, http://www.roke.co.uk/download/papers/next_generation_multimode_terminals.pdf 11. M. Ylianttila et al., “Optimization scheme for Mobile Users Performing Vertical Handoffs between IEEE 802.11 and GPRS/EDGE Networks”, Proc. of IEEE GLOBECOM’01, San Antonio, Texas, USA, Nov 2001, pp. 3439-3443. 12. H. Wang et al., “Policy-enabled Handoffs across Heterogeneous Wireless Networks”, Proc. of Mobile Comp. Sys. and Apps., New Orleans, LA, Feb 1999. 13. A. A. Koutsorodi et al., “Terminal Management and Intelligent Access Selection in Heterogeneous Environments”, Mobile Networks and Applications, (2006) 11, pp. 861-871 14. Q. Song and A. Jamalipour, “Network Selection in an Integrated Wireless LAN and UMTS Environment using Mathematical Modeling and Computing Techniques”, IEEE Wireless Communications, June 2005, pp. 42-48. 15. P. M. L. Chan et al., “Mobility Management Incorporating Fuzzy Logic for a Heterogeneous IP Environment”, IEEE Communications Magazine, December 2001, pp. 42-51. 16. W. M. Eddy, “At What Layer Does Mobility Belong?”, IEEE Communications Magazine, October 2004, pp. 155-159. 17. J. W. Floroiu, R. Ruppelt, and D. Sisalem, "Seamless Handover in Terrestrial Radio Access Networks: A Case Study", IEEE Communications Magazine, November 2003, pp. 110-116. 18. R. Stewart et al., “Stream Control Transmission Protocol”, IETF RFC 2960, Oct. 2000. 19. L. Ma, F. Yu, and V. C. M. Leung, “A New Method to Support UMTS/WLAN Vertical Handover using SCTP”, IEEE Wireless Communications, August 2004, pp. 44-51. 20. H. Schulzrinne, and E. Wedlund, “Application-Layer Mobility using SIP”, ACM Mobile Comp. and Commun. Rev., vol. 4, no. 3, July 2000, pp. 47-57. 21. W. Wu et al., “SIP-Based Vertical Handoff between WWANs and WLANs”, IEEE Wireless Communications, June 2005, pp. 66-72. 22. Q. Zhang et al., “Efficient Mobility Management for Vertical Handoff between WWAN and WLAN”, IEEE Communications Magazine, November 2003, pp. 102-108. 23. J-S. R. Jang and C-T. Sun, “Neuro-Fuzzy Modeling and Control”, Proceedings of the IEEE, March 1995. 24. R. R. Yager, “Multiple Objective Decision Making using Fuzzy Sets”, International Journal of Man-Machine Studies, Vol. 9, 1977, pp. 375-382. Authors: Yashpal Singh, Er .Anurag Sharma Paper Title: Study of Broadcasting and Its Performance Parameter in VANET Abstract: A Vehicular Ad-Hoc Network is a kind of ad-hoc network, and is a self-configuring network of vehicular routers connected by wireless links. Vanet presents new and promising field of research, development and standardization. Vehicular Ad-Hoc Network is a wireless network without infrastructure. Reliable broadcasting in vehicular ad-hoc networks is one of the keys to success for services and applications on intelligent transportation system. Broadcasting in VANET is very different from routing in mobile ad hoc network (MANET) due to several reasons such as network topology, mobility patterns, demographics, traffic patterns at different time of the day, etc. In this paper we report the broadcasting in VANET, three very different regimes that a vehicular broadcasting protocol needs to work and the performance parameter of broadcasting in VANET 94. Keywords: Vehicular Ad-Hoc Network is a wireless network without infrastructure. 491-493
References: 1. S. Ni, Y. Tseng, Y. Chen, and J. Sheu, “The broadcast storm problem in a mobile ad hoc network,” in Proc. ACM Intern. Conf. on Mobile Comput. and Networking (MOBICOM), Seattle, USA, 1999, pp. 151–162. 2. A. Vahdat and D. Becker, “Epidemic routing for partially connected ad hoc networks,” Duke University, Tech. Rep., April 2000, cS- 200006. 3. T. Spyropoulos, K. Psounis, and C. S. Raghavendra, “Single-copy routing in intermittently connected mobile networks,” in The 1st IEEE Comm. Society Conf. SECON’04, October 2004. 4. “Spray and wait: An efficient routing scheme for intermittently connected mobile networks,” in WDTN, 2005. 5. Dr. Yashpal Singh Head Dept. of ComputerScience/IT,BIET,Jhansi.Published 15 research papers and attented number of seminars/workshops all around the [email protected],09415030602 Authors: J. Sreedhar, S. Viswanadha Raju, A. Vinaya Babu, Amjan Shaik, P. Pavan Kumar Paper Title: Word Sense Disambiguation: An Empirical Survey Abstract: Word Sense Disambiguation(WSD) is a vital area which is very useful in today’s world. Many WSD algorithms are available in literature, we have chosen to opt for an optimal and portable WSD algorithms. We are discussed the supervised, unsupervised, and knowledge-based approaches for WSD. This paper will also furnish an idea of few of the WSD algorithms and their performances, Which compares and asses the need of the word sense disambiguity
Keywords: Supervised, Unsupervised, Knowledge-based , WSD.
References: 1. Collins,M., and Singer, Y. 1999. Unsupervised models for namedentity classification. In EMNLP/VLC-99. 2. RESNIK, P. 1997. Selectional preference and sense disambiguation. In Proceedings of the ACL SIGLEXWorkshop on Tagging Text with Lexical Semantics: Why, What, and How? (Washington, D.C.). 52–57. 95. 3. LI, H. AND ABE, N. 1998. Generalizing case frames using a thesaurus and the MDL principle. Computat. Ling. 24, 2, 217–244. 4. ABNEY, S. AND LIGHT, M. 1999. Hiding a semantic class hierarchy in a Markov model. In Proceedings of the ACL Workshop on Unsupervised Learning in Natural Language Processing (College Park, MD). 1–8. 494-503 5. CLARK, S. AND WEIR, D. 2002. Class-based probability estimation using a semantic hierarchy. Computat. Ling. 28, 2, 187–206. 6. Duda, R. O. and Hart, P. E.: Pattern Classification and Scene Analysis. Wiley, New York, 1973. 7. Blum, A., and Chawla, S. 2001. Learning from labeled and unla-beled data using graph mincuts. In ICML-2001. 8. Blum, A., and Mitchell, T. 1998. Combining labeled and unla-beled data with co-training. In COLT-98. 9. CARDIE, C. AND MOONEY, R. J. 1999. Guest editors’ introduction: Machine learning and natural language. Mach. Learn. 34, 1–3, 5–9. 10. SCHUTZE, H. 1998. Automatic word sense discrimination. Computat. Ling. 24, 1, 97–124. 11. MARTINEZ, D. 2004. Supervised word sense disambiguation: Facing current challenges, Ph.D. dissertation. University of the Basque Country, Spain. 12. MARQUEZ, L., ESCUDERO, G., MART`INEZ, D., AND RIGAU, G. 2006. Supervised corpus-based methods for WSD. In Word Sense Disambiguation: Algorithms and Applications, E. Agirre and P. Edmonds, Eds. Springer, New York, NY, 167–216. 13. RIVEST, R. L. 1987. Learning decision lists. Mach. Learn. 2, 3, 229–246. 14. YAROWSKY, D. 1994. Decision lists for lexical ambiguity resolution: Application to accent restoration in Spanish and French. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics (Las Cruces, NM). 88–95. 15. AGIRRE, E. AND MARTINEZ, D. 2000. Exploring automatic word sense disambiguation with decision lists and the web. In Proceedings of the 18th International Conference on Computational Linguistics (COLING, Saarbr ¨ ucken, Germany). 11–19. 16. Saa KELLY, E. AND STONE, P. 1975. Computer Recognition of English Word Senses. Vol. 3 of North Holland Linguistics Series. Elsevier, Amsterdam, The Netherlands. 17. BLACK, E. 1988. An experiment in computational discrimination of English word senses. IBM J. Res. Devel. 32, 2, 185–194 18. J QUINLAN, J. R. 1986. Induction of decision trees. Mach. Learn. 1, 1, 81–106. 19. QUINLAN, J. R. 1993. Programs for Machine Learning. Morgan Kaufmann, San Francisco, CA. 20. MOONEY, R. J. 1996. Comparative experiments on disambiguating word senses: An illustration of the role of bias in machine learning. In Proceedings of the 1996 Conference on Empirical Methods in NaturalLanguage Processing (EMNLP). 82–91. 21. NG, T. H. 1997. Getting serious about word sense disambiguation. In Proceedings of the ACL SIGLEX 22. Workshop on Tagging Text with Lexical Semantics: Why, What, and How? (Washington D.C.). 1–7. 23. MCCULLOCH, W. AND PITTS, W. 1943. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133. 24. COTTRELL, G. W. 1989. A Connectionist Approach to Word Sense Disambiguation. Pitman, London, U.K. 25. TSATSARONIS, G.VAZIRGIANNIS, M., AND ANDROUTSOPOULOS, I. 2007. Word sense disambiguation with spreading activation networks generated from thesauri. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI, Hyderabad, India). 1725–1730. 26. TOWELL, G. AND VOORHEES, E. 1998. Disambiguating highly ambiguous words. Computat. Ling. 24, 1, 125–145. 27. LEACOCK, C., TOWELL, G., AND VOORHEES, E. 1993. Corpus-based statistical sense resolution. In Proceedings of the ARPA Workshop on Human Language Technology (Princeton, NJ). 260–265. 28. GALE, W. A., CHURCH, K., AND YAROWSKY, D. 1992b. A method for disambiguating word senses in a corpus. Comput. Human. 26, 415–439. 29. SCHUTZE, H. 1998. Automatic word sense discrimination. Computat. Ling. 24, 1, 97–124. 30. SCHUTZE, H. 1992. Dimensions of meaning. In Supercomputing ’92: Proceedings of the 1992 ACM/IEEE Conference on Supercomputing. IEEE Computer Society Press, Los Alamitos, CA. 787–796. 31. GOLUB, G. H. AND VAN LOAN, C. F. 1989. Matrix Computations. The John Hopkins University Press, Baltimore, MD. 32. DEMPSTER, A., LAIRD, N., AND RUBIN, D. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. 39, 1–38. 33. PEDERSEN, T. AND BRUCE, R. 1997. Distinguishing word senses in untagged text. In Proceedings of the 1997 Conference on Empirical Methods in Natural Language Processing (EMNLP, Providence, RI). 197–207. 34. SAVOVA, G., PEDERSEN, T., PURANDARE, A., AND KULKARNI, A. 2005. Resolving ambiguities in biomedical text with unsupervised clustering approaches. Res. rep. UMSI 2005/80. University of Minnesota Supercomputing Institute, Minneapolis, MN. 35. PURANDARE, A. AND PEDERSEN, T. 2004. Improving word sense discrimination with gloss augmented feature vectors. In Proceedings of theWorkshop on Lexical Resources for theWeb andWord Sense Disambiguation (Puebla, Mexico). 123–130. 36. NIU, C., LI, W., SRIHARI, R., AND LI, H. 2005. Word independent context pair classification model for word sense disambiguation. In Proceedings of the 9th Conference on Computational Natural Language Learning(CoNLL, Ann Arbor, MI). 37. IDE, N., ERJAVEC, T., AND TUFIS, D. 2001. Automatic sense tagging using parallel corpora. In Proceedings of the 6th Natural Language Processing Pacific Rim Symposium (Tokyo, Japan). 83–89. 38. LIN,D. 1998a. Automatic retrieval and clustering of similar words. In Proceedings of the 17th International Conference on Computational linguistics (COLING, Montreal, P.Q., Canada). 768–774. 39. LIN, D. AND PANTEL, P. 2002. Discovering word senses from text. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Edmonton, Alta., Canada). 613–619. 40. WIDDOWS, D. AND DOROW, B. 2002. A graph model for unsupervised lexical acquisition. In Proceedings of the 19th International Conference on Computational Linguistics (COLING, Taipei, Taiwan). 1–7. 41. DOROW, B. AND WIDDOWS, D. 2003. Discovering corpus-specific word senses. In Proceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics (Budapest, Hungary). 79–82. 42. VAN DONGEN, S. 2000. Graph Clustering by Flow Simulation, Ph.D. dissertation. University of Utrecht,Utrecht, The Netherlands. 43. V´ERONIS, J. 2004. Hyperlex: Lexical cartography for information retrieval. Comput. Speech Lang. 18, 3,223–252. 44. BRIN, S. AND PAGE, M. 1998. Anatomy of a large-scale hypertextual Web search engine. In Proceedings of the 7th Conference on World Wide Web (Brisbane, Australia). 107–117. 45. AGIRRE, E. AND EDMONDS, P., Eds. 2006. Word Sense Disambiguation: Algorithms and Applications.Springer, New York, NY. 46. LESK, M. 1986. Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone. In Proceedings of the 5th SIGDOC (New York, NY). 24–26. 47. HINDLE, D. AND ROOTH,M. 1993. Structural ambiguity and lexical relations. Computat. Ling. 19, 1, 103–120. 48. RESNIK, P. S., Ed. 1993. Selection and information: A class-based approach to lexical relationships, Ph.D. dissertation. University of Pennsylvania, Pennsylvania, Philadelphia, PA. 49. LI, H. AND ABE, N. 1998. Generalizing case frames using a thesaurus and the MDL principle. Computat. Ling. 24, 2, 217–244. 50. ABNEY, S. AND LIGHT, M. 1999. Hiding a semantic class hierarchy in a Markov model. In Proceedings of the ACL Workshop on Unsupervised Learning in Natural Language Processing (College Park, MD). 1–8. 51. CLARK, S. AND WEIR, D. 2002. Class-based probability estimation using a semantic hierarchy. Computat. Ling. 28, 2, 187–206. 52. AGIRRE, E. ANDMARTINEZ, D. 2001. Learning class-to-class selectional preferences. In Proceedings of the 5th Conference on Computational Natural Language Learning (CoNLL, Toulouse, France). 15–22. 53. FU, K. 1982. Syntactic Pattern Recognition and Applications. Prentice-Hall, Engelwood Cliffs, NJ. 54. JOACHIMS, T. 1998. Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning (ECML, Heidelberg,Germany). 137–142. 55. KUDO, T. AND MATSUMOTO, Y. 2001. Chunking with support vector machines. In Proceedings of NAACL(Pittsburgh, PA). 137–142. 56. COLLINS,M. 2004. Parameter estimation for statistical parsing models: Theory and practice of distributionfree methods. In New Developments in Parsing Technology,H. Bunt, J. Carroll, andG. Satta, Eds. Kluwer, Dordrecht, The Netherlands, 19–55. 57. ESCUDERO, G., M`ARQUEZ, L., AND RIGAU, G. 2000c. On the portability and tuning of supervised word sense disambiguation. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC, Hong Kong, China). 172–180. 58. KEOK, L. Y. AND NG, H. T. 2002. An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP, Philadelphia, PA). 41–48. Authors: Om Prakash Sharma, M. K. Ghose, Krishna Bikram Shah An Improved Zone Based Hybrid Feature Extraction Model for Handwritten Alphabets Recognition Paper Title: Using Euler Number Abstract: This paper presents an Improved Zone based Hybrid Feature Extraction Model using Euler Number, which not only improves the feature extraction process which was implemented in Diagonal Based Feature 96. Extraction [1] but also helps in efficient classification of the handwritten alphabets. The use of Euler Number in addition to zoning increases the speed and the accuracy of the classifier as we are able to reduce the search space by 504-508 dividing the character set into three groups.
Keywords: Handwritten Character Recognition, Feature Extraction, Binary Image, Euler Number, Feed Forward Neural Networks. References: 1. J Pradeep, E Shrinivasan and S.Himavathi, “Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System Using Neural Network”, International Journal of Computer Science & Information Technology (IJCSIT), vol . 3, No 1, Feb 2011. 2. M. Alata — M. Al-Shabi, “ Text Detection And Character Recognition Using Fuzzy Image Processing”, Journal of Electrical Engineering, vol. 57, no. 5, 2006, 258–267 3. R. Plamondon and S. N. Srihari, “On-line and off- line handwritten character recognition: A comprehensive survey,”IEEE. Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 63-84, 2000. 4. N. Arica and F. Yarman-Vural, “An Overview of Character Recognition Focused on Off-line Handwriting”, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2001, 31(2), pp. 216 - 233. 5. U. Bhattacharya, and B. B. Chaudhuri, “Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals,” IEEE Transaction on Pattern analysis and machine intelligence, vol.31, No.3, pp.444-457, 2009. 6. U. Pal, T. Wakabayashi and F. Kimura, “Handwritten numeral recognition of six popular scripts,” Ninth International conference on Document Analysis and Recognition ICDAR 07, Vol.2, pp.749-753, 2007. 7. Devinder Singh and Baljit Singh Khehra, “Digit Recognition System Using Back Propagation Neural Network”, International Journal of Computer Science and Communication Vol. 2, No. 1, January-June 2011, pp. 197-205 8. VENTZAS, DIMITRIOS 1, NTOGAS, NIKOLAOS , “A BINARIZATION ALGORITHM FOR HISTORICAL MANUSCRIPTS”, 12th WSEAS International conference on Communications, Heraklion, Greece, July 23-25, 2008. 9. Bindu Philip, R. D. Sudhaker Samuel and C. R. Venugopal, Member, IACSIT, “A Novel Segmentation Technique for Printed Malayalam Characters”, International Journal of Computer and Electrical Engineering, Vol. 2, No. 4, August, 2010 1793-8163 Printed Malayalam Characters. 10. Anil Kumar Jain and Torfinn Taxt, “Feature extraction Methods for Character Recognition- A Survey”, Pattern Recognition, Vol.29, No.4, pp. 641-662, 1996. 11. S.V. Rajashekararadhya, and P.VanajaRanjan, “Efficient zone based feature extraction algorithm for handwritten numeral recognition of four popular south-Indian scripts,” Journal of Theoretical and Applied Information Technology, JATIT vol.4, no.12, pp.1171-1181, 2008. 12. Anita Pal & Dayashankar Singh, “ Handwritten English Character Recognition Using Neural Network”, International Journal of Computer Science & Communication”, Vol. 1, No.2, July-December 2010, pp. 141-144 13. G. Vamvakas, B. Gatos, I. Pratikakis, N. Stamatopoulos, A. Roniotis, S.J. Perantonis, "Hybrid Off-Line OCR for Isolated Handwritten Greek Characters", The Fourth IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA’07), pp. 197-202, Innsbruck, Austria, February 2007. 14. G. Vamvakas, B. Gatos, S. Petridis and N. Stamatopoulos, ''An Efficient Feature Extraction and Dimensionality Reduction Scheme for Isolated Greek Handwritten Character Recognition'', Proceedings of the 9th International Conference on Document Analysis and Recognition, Curitiba, Brazil, 2007, pp. 1073-1077. 15. Dinesh Acharya U, N V Subba Reddy and Krishnamurthy, “Isolated handwritten Kannada numeral recognition using structural feature and K-means cluster,” IISN-2007, pp-125 -129. 16. N. Sharma, U. Pal, F. Kimura, "Recognition of Handwritten Kannada Numerals", 9th International Conference on Information Technology (ICIT'06), ICIT, pp. 133-136. 17. Ciar´ an ´ O Conaire, “Efficient Euler-Number Thresholding”, Centre for Digital Video Processing, Dublin City University, Ireland. 18. M Arijit Bishnu, Bhargab B. Bhattacharya, Malay K. Kundu b C.A. Murthy, Tinku Acharya, “A pipeline architecture for computing the Euler number of a binary image”, Journal of Systems Architecture 51 (2005) 470–487. Authors: Shalini Batra, Charu Tyagi Paper Title: Comparative Analysis of Relational And Graph Databases Abstract: Relational model has been dominating the computer industry since the 1980s mainly for storing and retrieving data. Lately, however, relational database is losing its importance due to its dependence on a rigid schema which makes it difficult to add new relationships between the objects. Another important reason of its failure is that as the available data is growing manifolds, it is becoming difficult to work with relational model as joining large number of tables is not working efficiently. One of the proposed solutions is to shift to the Graph databases as they aspire to overcome such type of problems. This paper provides a comparative analysis of a graph database Neo4j with the most prevalent relational database MySQL.
Keywords: MySQL
References: 97. 1. Chad Vicknair, Michael Macias, Zhendong Zhao, Xiaofei Nan, Yixin Chen, Dawn Wilkins “A Comparison of a Graph Database and a Relational Database”, ACM Southeast Regional Conference,2010. 2. Database Trends And Applications. Available http://www.dbta.com/Articles/Columns/Notes-on-NoSQL/Graph-Databases-and-the-Value- 509-512 They-Provide-74544.aspx,2012 3. M. Kleppmann. Should you go beyond relational databases? Available http://carsonified.com/blog/dev/should-you-go-beyond-relational- databadat. 4. Neo4j Blog, Available http://blog.neo4j.org/2009/04/current-database-debate-and-graph.html. 5. M. I. Jordan (ed). (1998) ."Learning in Graphical Models". MIT Press. 6. HypergraphDB website, Available http://www.kobrix.com/hgdb.jsp 7. Jena documentation, Available http://jena.sourceforge.net/documentation.html 8. Infogrid. Blog, Available http://infogrid.org/blog/2010/03/operations-on-a-graph-databae-papa-4. 9. Neo4j. Home. http://neo4j.org, 2012. 10. D. Dominguez-Sal, P. Urb´on-Bayes, A. Gim´enez-Va˜n´o, S. G´omez-Villamor, N. Mart´ınez-Baz´an, and J.L. Larriba-Pey, “Survey of Graph Database Performance on the HPC Scalable Graph Analysis Benchmark”, Proceeding WAIM’10 Proceedings of the 2010 international conference on Web-age information management. 11. T. Ivarsson. [neo] security, Available http://lists.neo4j.org/pipermail/user/2009-November/001955.html, 2011. 12. R. Angles and C. Gutierrez,” Survey of graph database moDels”,. ACM Comput. Surv., 40(1):1–39, 2008. 13. Neo4j. The neo database (2006), Available http://dist.neo4j.org/neo-technology-introduction.pdf Authors: Sachin Bhutani, Deepti Kakkar, Arun Khosla Paper Title: Throughput Analysis of Multi-channel TD-CSMA System and Reinforcement Learning Abstract: This study generates a cognitive radio scenario based on non-persistent carrier sense multiple access 98. (CSMA) and time division multiple access (TDMA) systems sharing a multi-channel wireless network. TDMA users are considered as primary users who can access the channel at any time, and non-persistent CSMA users are 513-516 considered as secondary users who can share the channel when it is free. Then system performance is evaluated for a variety of proportions of non-persistent CSMA and TDMA traffic levels. Simulation results are presented and effect on throughput for different traffic ratio is shown. Further effect of reinforcement learning on system model is shown how throughput increases.
Keywords: Cognitive Radio, Monte Carlo Method, Reinforcement Learning, TD-CSMA System.
References: 1. Parliamentary Office of Science and Technology: Radio Spectrum Management, 2007, POSTNOTE 2. Parliamentary Office of Science and Technology: Radio Spectrum Management, 2007, POSTNOTE 292 3. Office of Communication: Spectrum Framework Review: a consultation on Ofcom’s views as to how radio spectrum should be managed, 2004 4. COSOVIC I., YAMADA T., MAEDA K.: ‘Improved policy for resource allocation in decentralized dynamic spectrum sharing systems’, IEEE Commun. Lett., 2008, 12, (9), pp. 639–641 5. MITOLA J., MAGUIRE G.: ‘Cognitive radio: making software radios more personal’, IEEE Pers. Commun., 1999, 6, (4), pp. 13–18 6. AKYILDIZ I.F., LEE W.-Y., VURAN M.C., MOHANTY S.: ‘NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey’, Comput. Netw., 2006, 50, (13), pp. 2127–2159 7. SHUKLA A., ALPTEKIN A., BRADFORD J., ET AL.: ‘Cognitive radio technology: a study for Ofcom’ (QinetiQ Ltd 2007), vol. 1 8. PEHA J.M.: ‘Approaches to spectrum sharing’, IEEE Commun. Mag., 2005, 43, (2), pp. 10–12 9. FETTE B. (ED.): ‘Cognitive radio technology’, ‘Communication Engineering Series’ (Newnes, 2006), p. 622 10. ERKIP E., AAZHANG B.: ‘A comparative study of multiple accessing schemes’, IEEE, 1998, 1, pp. 614–619 11. PAHLAVAN K., LEVESQUE A.H.: ‘Wireless information networks’, ‘Wiley Series in Telecommunications and Signal Processing’ (Wiley, 1995), p. 572 12. ABRAMSON N.: ‘The throughput of packet broadcasting channels’, IEEE Trans. Commun., 1977, COM-25, (1), pp. 117–127 13. MORAN P.A.P.: ‘An introduction to probability theory’ (Oxford University Press, 1984), p. 524 14. H. LI, D GRACE, P.D. MITCHELL:‘Throughput analysis of non-persistent carrier sense multiple access combined with time division multiple access and its implication for cognitive radio’ Journal in IET Communication, 2010 15. KALOS M.H., WHITLOCK P.A.: ‘Monte Carlo methods’ (Wiley, 1986), p. 208 16. HASSAN M., JAIN R.: ‘High performance TCP/IP networking’ (Pearson/Prentice-Hall, 2004), p. 383 17. TAO JIANG, DAVID GRACE, YIMING Liu : ‘Performance of Cognitive Radio Reinforcement Spectrum Sharing Using Different Weighting Factors’, CROWNCOM 2008 18. R. S. SUTTON And A. G. BARTO, Reinforcement learning: an introduction: The MIT Press, 1998. 19. L. P. KAELBLING, M. L. LITTMAN, And A. W. MOORE, "Reinforcement Learning: A Survey," Journal of artificial intelligence Research, vol. 4, pp. 237-285, May. 1996 D.Venkata.Ratnam, B.Venkata Dinesh, B.Tejaswi, D.Praveen Kumar, T.V.Ritesh, Authors: P.S.Brahmanadam, G.Vindhya Paper Title: TEC Prediction Model using Neural Networks over a Low Latitude GPS Station Abstract: Ionospheric nowcasting and forecasting tools are necessary for high precision applications in equatorial regions such as India and Brazil, etc. An algorithm capable of predicting the ionospheric behavior in advance can be used to setup early warnings for GPS applications. In this paper, Neural Network (NN) model using back propagation algorithm is implemented over a low latitude GPS station (Hyderabad). The preliminary results indicate that, NN model values are closely following with actual data. It is found that, the prediction error is varied maximum up to 1TECU. Advanced NN models would be useful for forecasting ionospheric characteristics in a robust manner.
Keywords: 1TECU, GPS, (NN)
References: 1. Hofmann-Wellenhof, B.,Lichtenegger.H and Collins.J, “ Global Positioning System: theory and practice”,Springer-verlag, wein, New York, 99. fifth revised edition, 2003. 2. Haykin S., 1999. Neural Networks - A Comprehensive Foundation. Prentice Hall, Upper Saddle River, New Jersey, USA 3. Venkata Ratnam, D., A. D. Sarma, V. Satya Srinivas, and P. Sreelatha (2011), Performance evaluation of selected ionospheric delay models 517-521 during geomagnetic storm conditions in low-latitude region, Radio Sci., 46, RS0D08, doi:10.1029/2010RS004592. 4. Komjathy A., 1997. “Global Ionospheric Total Electron Content Mapping Using the Global Positioning System”. Ph.D. Thesis, Department of Geodesy and Geomatics Engineering Technical Report No. 188, University of New Brunswick, Fredericton, New Brunswick, Canada. 5. R.F.Leandro and M.C. Santos “A Neural Network approach for regional Vertical Total Electron content modelling”.in Stud. Geophys. Geod., 51 (2007), 279-292 6. Sarawut Nontasud, Nipha Leelaruji. “ Mitigate the GPS position error by neural network technique” SICE Annual Conference 2008August 20-22, 2008, The University Electro-Communications, Japan 7. McKinnell L., 2002. “A Neural Network Based Ionospheric Model for the Bottomside Electron Density Profile over Grahamstown, South Africa”. Ph.D. Thesis, Rhodes University,Grahamstown, South Africa. 8. Laurene Fausett “Fundamentals of Neural Networks” . Bancroft S.,“An algebraic solution of the GPS equation.” IEEE Transactions on Aerospace and Electronic Systems, vol. 21, pp. 56 – 59, 1985 9. Venkata Ratnam D and Sarma A.D (2006), “Modeling of Indian Ionosphere using MMSE model for GAGAN Applications”, J. Indian Geophysical Union, October, 10, 4, 303-312. 10. Tulunay E., E.T. Senalp, S.M. Radicella, Y. Tulunay, Forecasting total electron content maps by neural network technique, Radio Sci., 41(4), RS0416, 2006. Authors: J.Meenakshi, G. Rakesh Chowdary, A.L.G.N.Aditya Implementations of DPDE for Delay Locked Loop for High Frequency Clock of 2.5GHz High Speed Paper Title: Applications Abstract: Variable delay elements are often used to manipulate the rising or falling edges of the clock or any other signal in integrated circuits (ICs). Delay elements are also used in delay locked loops (DLLs). Variable delay elements have many applications in VLSI circuits. They are extensively used in digital delay locked loops phase 100. locked loops (PLLs), digitally controlled oscillators (DCOs), and microprocessor and memory circuits. In all these circuits, the variable delay element is one of the key building blocks. Its precision directly affects the overall 522-527 performance of the circuit. In this a new proposed digitally controlled delay element is implemented in 130nm technology for DLL Delay locked loop for higher clock rates greater than 2.5GHz. This is implemented in Micro wind tool.
Keywords: DLL, PLL, Delay element, Microprocessor, Clock frequency.
References: 1. M. Saint-Laurent and M. Swaminathan, "A digitally adjustable resistor for path delay characterization in high frequency microprocessors," in Proc. Southwest Symp. Mixed-Signal Design, 2001, pp. 61-64. 2. M. G. Johnson, E. L. Hudson, and H. Kopka, “A variable delay line PLL for CPU-Coprocessor synchronization,” IEEE J. Solid-State Circuits,vol. 23, pp. 1218–1223, Oct. 1988. 3. Current Starved Delay Element with symmetric load by G.S.Jovanovic, M.K.Stojcev. Published in International Journal of Electronics, Volume 93, Issue 3 March 2006, pages 167-175 4. Rothermel and F. Dell’ova, “Analog phase measuring circuit for digital CMOS ICs,” IEEE J. Solid-State Circuits, vol. 28, no. 7, pp. 853– 856, Jul. 1993. 5. M. Saint-Laurent and G. P. Muyshondt, “A digitally controlled oscillator constructed using adjustable resistors,” in Proc. Southwest Symp. Mixed-Signal Design, 2001, pp. 80–82. 6. “Digital Integrated Circuits –A Design Perspective” by JAN M. RABAEY, ANANTHA CHANDRAKASAN and BORIVOJE NIKOLIC. Authors: B.Santosh Kumar, L. Ravi Chandra, A. L. G. N. Aditya, Fazal Noor Basha, T. Praveen Blessington Paper Title: Design and Functional Verification of I2C Master Core using OVM Abstract: This paper contrasts physical implementation aspects of the protocol through a number of recent Xilinx’s FPGA families, showing up the protocol features are responsible of substantial area overhead and power overhead. These help designers to make careful and tightly tailored architecture decisions. These RTL coding is carried out for the I2C protocol using the HDL code. The verification methodology carries a important role in design of the VLSI, As the functional verification of the I2C is covered using Open Verification Methodology (OVM) which does not interfere with DUT. This verification method provides the I2C with fault free and useable for modern day applications. The OVM is carried using Questasim10.0b. 101. Keywords: I2C, FPGA, OVM, Functional verification, HDL. 528-533
References: 1. AN_108_Command_Processor_for_MPSSE_anMCU_Host_Bus_Emulation_Modes. Philips I2C datasheet. 2. D2XX Programmer‟s Guide 3. Datasheet for FT2232H V202 I2C protocol 4. Datasheet for Microchip 24LC256 – 2K I2C Serial EEPROM. 5. “Introduction to I2C Bus”: Available at http://www.semiconductors.philips.com/i2c. 6. P. Venkateswaran, “FPGA Based Efficient Interface Model for Scalefree Computer Network using I2C Bus Protocol”; Spl. Issue – Advances in Computer Sci. & Engg., ISSN 1870-4069, Pub. By 7. National Polytechnic Institute, Mexico, Vol.23, pp. 191- 198, Nov. 21-24, 2006. Authors: L.Veera Raju, B.Kali Vara Prasad,A.L.G.N.Aditya, A.Jhansi Rani, D.Naga Dilip Kumar Paper Title: Functional Verification of GPIO Core Using OVM Abstract: The OPB GPIO design provides a general purpose input/output interface to a 32-bit On-Chip Peripheral Bus (OPB). The GPIO IP core is user-programmable general-purpose I/O controller. That is use is to implement functions that are not implemented with the dedicated controllers in a system and require simple input and/or output software controlled signals. It is one of the important peripheral that is listed on any FPGA board. In this project we are atomizing the operation of the GPIO by writing the code in SYSTEM-VERILOG and simulating it in QUESTA MODELSIM. The main aim of this project is to verify the output by using GPIO pins depending up on the preference the code. We verify the GPIO modules by using OVM [Open verification Methodology]. The functional verification of the RTL design of the GPIO is carried out for the better optimum design. 102. Keywords: GPIO, OPB, QUESTA MODELSIM, System Verilog, FPGA 534-537
References: 1. D.Gajski et al, “Essential Issues for IP Reuse”, Proceedings of ASP-DAC, pp.37-42, Jan. 2000 2. C.K.Lennard et al, “Industrially proving the SPIRIT Consortium Specifications for Design Chain Integration”, Proceedings of DATE 2006, pp. 1-6, March 2006 3. K.Cho et al, “Reusable Platform Design Methodology For SOC Integration And Verification”, Proceedings of ISOCC 2008, pp. I-78-I-81, Nov. 2008 4. W.Kruijtzer et al, “Industrial IP integration flows based on IP-XACT standards” proceedings of DATE 2008, pp. 32-37, March 2008 5. M.Strik et al, “subsystem Exchange in a Concurrent Design Process Environment” Proceedings of DATE 2008, pp. 953-958, March 2008 6. GensysIO, http://www.atrenta.com/solutions/gensys-family/gensys-io.htm 7. SocratesSpinner, http://www.duolog.com/technical-documents Authors: S.Prasanna, Srinivasa Rao Paper Title: An Overview of Wireless Sensor Networks Applications and Security Abstract: Wireless communication technologies continue to undergo rapid advancement. In recent years, there has been a steep growth in research in the area of wireless sensor networks (WSNs). In WSNs, communication takes place with the help of spatially distributed, autonomous sensor nodes equipped to sense specific information. WSNs 103. can be found in a variety of both military and civilian applications worldwide. Examples include detecting enemy intrusion on the battlefield, object tracking, habitat monitoring, patient monitoring and fire detection. Sensor 538-540 networks are emerging as an attractive technology with great promise for the future. However, challenges remain to be addressed in issues relating to coverage and deployment, scalability, quality-of-service, size, computational power, energy efficiency and security. This paper presents an overview of the different applications of the wireless sensor networks and various security related issues in WSNs.
Keywords: Network, Security, Sensor, Wireless.
References: 1. J. Hill, R. Szewczyk, A, Woo, S. Hollar, D. Culler, and K. Pister, System Architecture Directions for Networked Sensors, ASPLOS, November 2000. 2. Culler, D. E and Hong, W., “Wireless Sensor Networks”, Communication of the ACM, Vol. 47, No. 6, June 2004, pp. 30-33. 3. Undercoffer, J., Avancha, S., Joshi, A., and Pinkston, J., “Security for Sensor Networks”, CADIP Research Symposium, 2002, available at, http://www.cs.sfu.ca/~angiez/personal/paper/sensor-ids.pdf 4. A.D. Wood and J.A. Stankovic, (2002) “Denial of Service in Sensor Networks,” Computer, vol. 35, no. 10, 2002, pp. 54– 62. 5. J. R. Douceur,(2002) “The Sybil Attack,” in 1st International Workshop on Peer-to-Peer Systems (IPTPS ‟02). 6. Zaw Tun and Aung Htein Maw,(2008),” Worm hole Attack Detection in Wireless Sensor networks”, proceedings of world Academy of Science, Engineering and Technology Volume 36, December 2008, ISSN 2070-3740. Authors: R.Harikumar , M.Balasubramani, T.Vijayakumar Performance Analysis of Patient Specific Epilepsy Risk Level Classifications from EEG Signals Using Paper Title: Two Tier Hybrid (Fuzzy, Soft Decision Trees Models and MLP Neural Networks) Classifiers Abstract: This paper compares the performance analysis of a two tier hybrid Fuzzy, Soft Decision Tree (SDT) models and Multi layer Perceptron (MLP) neural networks in optimization of patient specific epilepsy risk levels classifications from EEG (Electroencephalogram) signals. The fuzzy classifier (level one) is used to classify the risk levels of epilepsy based on extracted parameters like energy, variance, peaks, sharp and spike waves, duration, events and covariance from the EEG signals of the patient. Soft Decision Tree (post classifier with max-min and min-max criteria) of three models and MLP neural networks are applied on the classified data to identify the optimized risk level (singleton) which characterizes the patient’s state. The efficacies of these methods are compared with the bench mark parameters such as Performance Index (PI), Sensitivity, Specificity and Quality Value (QV). A group of twenty patients with known epilepsy findings are analyzed. High PI such as 95.88 % was obtained at QV’s of 22.43 in the SDT model of (16-4-2-1) with Method-II (min-max criteria) and for MLP (4-4-1) 99.9%and 24.43 when compared to the value of 40% and 6.25 through fuzzy classifier respectively. It was identified that the SDT models and MLP (4-4-1) are good post classifier in the optimization of epilepsy risk levels. SDT models were well accounted for low training cost over heads. A part from the training cost MLP neural networks outperformed SDT classifiers in classifying the epilepsy risk levels.
Keywords: EEG Signals, Epilepsy, Fuzzy Logic, Soft Decision Trees, Multi Layer Perceptron (MLP) neural networks, Risk Levels.
References: 1. Li Gang etal, “An Artificial –Intelligence Approach to ECG Analysis,” IEEE EMB Magazine, vol 20, April 2000, pp 95-100. 2. K. P.Adlassnig, “Fuzzy Set Theory in Medical diagnosis”, IEEE Transactions on Systems Man Cybernetics, vol,16 , March 1986, pp 260- 265. 3. Donna L Hudson, “Fuzzy logic in Medical Expert Systems”, IEEE EMB Magazine, vol, 13,no.6, November/December 1994, pp 693-698. 4. R.Harikumar, Dr.(Mrs). R.Sukanesh, P.A. Bharathi, “Genetic Algorithm Optimization of Fuzzy outputs for Classification of Epilepsy Risk Levels from EEG signals,” Journal of Interdisciplinary panels I.E. (India),vol.86, no.1, May 2005,pp1-10. 5. S.Watanabe, “Pattern Recognition as a quest for Minimum Entropy”, Pattern Recognition, vol 13, 1981, pp 381-387. 6. R.P.Lippmann, “An Introduction to Computing With Neural Nets,” IEEE ASP Magazine, April 1989, pp 4-22. 104. 7. Guoqiang Peter Zhang,“Neural Networks for Classification: A Survey,” IEEE Transactions on Systems, Man, And Cybernetics-Part C, vol,30,no.4, November 2000, pp 451-462. 8. S.Gelfand and H.Guo, “Tree Classifier With Multilayer Perceptron Feature Extraction,” M.S. Thesis Purdue University, 1990. 541-549 9. Nurettin Acir etal., “ Automatic Detection of Epileptiform Events In EEG by A Three-Stage Procedure Based on Artificial Neural Networks,” IEEE Transactions on Bio Medical Engineering, vol,52,no.1,January 2005, pp30-40. 10. Moreno.L.etal.,“Brain Maturation Estimation Using Neural Classifier,” IEEE Transactions on Bio Medical Engineering , vol,42,no.2, April 1995, pp-428-432. 11. Dr.(Mrs.).R.Sukanesh, R.Harikumar,“A Patient Specific Neural Networks (MLP) for Optimization of Fuzzy Outputs in Classification of Epilepsy Risk Levels from EEG Signals”, Journal of Engineering Letters, vol 13, No.2, September 2006, pp 50-56. 12. I.K.Sethi, “Entropy Nets: from Decision Trees to Neural networks,” Proceedings of the IEEE vol 78, no 10 (Special Issue on Neural Networks), October 1990, pp 1605-1613. 13. I.K.Sethi, “Layered Neural net Design through Decision Trees,” In Proceedings of International Symposium on Circuits and Systems, New Orleans, LA, May 1-3, 1990. 14. I.K.Sethi, and M.Otten, “Comparison Between Entropy net and Decision Tree Classifiers,” In Proceedings of International Joint Conference on Neural Networks, (IJCNN) Sandiego, CA 1989 15. Manuca, R.,Casdagil, M.C.Savit, “Non Stationarity in Epileptic EEG and Implications for Neural Dynamics,” Mathematical Bio Sciences,147, 1998, pp 1-22. 16. D.Zumsteg and H.G.Wieser,“Presurgical Evaluation: Current Role of Invasive EEG,” Epilepsia,vol.41, no. suppl 3, 2000,pp, S55-60. 17. Joel.J etal, “Detection of Seizure Precursors from Depth EEG Using a Sign Periodogram Transform,” IEEE Transactions on Bio Medical Engineering, vol 51, no,4,April 2004, pp449-458, 18. W.R.S.Webber, R.P.Lesser, R.T.Richardson and K.Wilson, “An Approach to Seizure Detection Using an Artificial Neural Network (ANN)”,Electroenceph. Clin. Neurophysio., vol 98, 1996,pp 250-274. 19. Arthur C Guyton, “Text Book of Medical Physiology”, Prism Books Pvt. Ltd., Bangalore, 9th Edition, 1996. 20. S. Balanco, H.Garcia, R.Quian Quiroja, L.Romanelli, and O.A.Rossa, “Stationarity of EEG Series,” IEEE EMBS Magazine, July/August 1995, pp 395-399. 21. Haoqu and Jean Gotman, “A Patient Specific Algorithm for Detection Onset in Long-Term EEG Monitoring-Possible Use as Warning Device”, IEEE Transactions on Biomedical Engineering, vol, 44, no.2, February 1997, pp 115-122. 22. J. Seunghan Park et al, “TDAT Domain Analysis Tool for EEG Analysis”, IEEE Transactions on Biomedical Engineering ,vol, 37,no.8,August 1990, pp 803-811. 23. Alison A Dingle et al, “A Multistage System to Detect Epileptic Form Activity in the EEG”, IEEE Transactions on Biomedical Engineering ,vol, 40, no.12, December 1993, pp 1260-1268. 24. Pamela McCauley-Bell and Adedeji B.Badiru, “ Fuzzy Modeling and Analytic Hierarchy Processing to Quantify Risk levels Associated with Occupational Injuries- Part I: The Development of Fuzzy- Linguistic Risk Levels,” IEEE Transactions on Fuzzy Systems, vol,4,no.2, 1996, pp 124-31. 25. Dr .(Mrs.).R.Sukanesh, R.Harikumar, “ A Comparison of Elman and MLP Feed Forward Neural Networks for Classification of Epilepsy Risk Level Using EEG Signals,”AMSE Journal ,Modeling C, vol.67 no.1, September 2006, pp 43-60. 26. Dr .(Mrs.).R.Sukanesh, R.Harikumar, “Diagnosis and Classification of Epilepsy Risk Levels From EEG Signals Using Fuzzy Aggregation Techniques,” Journal of Engineering Letters, vol 14, no.1, March 2007, pp 90-95. 27. C B Gupta and Vijay Gupta, “An Introduction to Statistical Methods”, 22nd Ed., Vikas Publishing House Lt., 2001 28. L.Hyafil and Rivest, “Constructing Optimal Binary Decision Trees is NP- Complete”, Information Processing Letters, vol 5, no, 1 1976, pp15-17. 29. Ronald.R.Yager, “Hierarchical Aggregation Functions Generated From Belief Structures,” IEEE Transactions on Fuzzy Systems,vol.8, no.5, May 2005,pp 481-490. 30. K.Srirama murty and B.Yegnannarayana, “Combining Evidence from Residual Phase and MFCC Features for Speaker Recognition”, IEEE Signal Processing Letters, vol,13,no.1, 2006, pp 52-55. 31. Drazen. S.etal., “Estimation of Difficult –to- Measure Process Variables Using Neural Networks”, Proceedings of IEEE MELECON 2004,May 12-15,2004,Dubrovnik, Croatia, pp 387-390. 32. Tarassenko.L, Y.U.Khan, M.R.G.Holt, “Identification of Inter-Ictal Spikes in The EEG Using Neural Network Analysis,” IEE Proceedings –Science Measurement Technology, vol, 145, no.6,November 1998, pp270-278. 33. H.Demuth and M..Beale,“Neural Network Tool Box: User’s guide, Version 3.0,” the Math works, Inc., Natick, MA, 1998. 34. Dr.(Mrs.).R.Sukanesh, R.Harikumar,“Fuzzy Techniques and Statistical Tests for Epilepsy Risk Level Classification Using EEG Signals,” Journal of Interdisciplinary panels I.E(.India)vol.86 ,no.1, May 2005, pp 29-36. Authors: Siavash Sheikhizadeh Paper Title: Optimization of OSPF Weights Using a Metaheuristic Based on Random Samplings Abstract: OSPF is the best-known intra-domain routing protocol which is employed all over the internet. In this research, an optimization problem has been introduced for optimizing OSPF weights and after mentioning the formulation of the problem [1][2], a heuristic search method based on random samplings[3] has been implemented to solve it. Finally results on some artificial networks has been compared to computations of a linear programming algorithm.
Keywords: Linear Programming, OSPF weights, Random Sampling, Routing Protocol.
105. References: 1. B. Fortz, M. Thorup, “Increasing internet capacity using local search”, Technical Report, AT&T Labs Research, 2000. 550-554 2. M. Ericsson, M.G.C Resende, P.M. Pardalos, " A genetic algorithm for the weight setting problem in OSPF routing ", Journal of Combinatorial Optimization, 2001. 3. T. Ye, S. KalyanaramanPoor, " A recursive random search algorithm for optimization network protocol parameters ", Technical Report, ECSE Department, Rensselaer Polytechnic Institue, 2001. 4. J. T. Moy, OSPF Anatomy of an internet routing protocol, Addison-Wesley, 1998. 5. B. Fortz, M. Thorup, "Internet traffic engineering by optimizing OSPF weights", IEEE INFOCOM – The Conference on Computer Communications, 2000. 6. L. G. Khachiyan, "A polynomial time algorithm for linear programming", Dokl. Akad. Nauk SSSR 244, 1979. 7. E. W. Zegura, GT-ITM Georgia tech internetwork topology models, 1996. 8. B. M. Waxman, "Routing of multipoint connections", IEEE Journal, Selected area in communications(Special issue on broadband packet communications),6(9), 1617-1622, 1998. Authors: B.Sada Siva Rao, T.Raghavendra Vishnu, Habibulllah Khan, D.Venkata Ratnam Paper Title: Spiral Antenna Array Using RT-Duroid Substrate for Indian Regional Navigational Satellite System Abstract: India is planned to develop a satellite based navigation systems known as Indian Regional Navigational Satellite System (IRNSS) for positioning applications. Design of IRNSS antenna at user segment is necessary. In order to design antenna, a new planar, wideband feed for a slot spiral antenna is designed using HFSS software simulations. This paper describes a spiral antenna on RT DUROID Substrate for the operating frequency range of 1.2 -1.6 GHz These specifications should be satisfied at the frequency L5 (1175 MHz).Array of spiral antennas can be used to increase the gain. Spiral antennas are reduced size antennas with its windings making it an extremely small structure. The antenna uses four spiral elements to provide broadband satellite coverage and can also be used in conjunction with a space-time adaptive processor (STAP) for interference suppression. This paper presents the input impedance, radiation pattern and gain. 106. Keywords: Spiral antenna, RT Duroid Substrate. 555-557
References: 1. S. Anandan (2010-04-10).Launch of first satellite for Indian Regional Navigaion Satellite system next year” 2. N. Padros, J.I. Ortigosa, J. Baker, M.F. Iskander, B. Thornberg, “Comparative study of high-performance GPS receiving antenna designs,” IEEE Trans. Antennas Propag., vol. 45, no. 4, pp. 698-706, April 1997. 3. J.M. Baracco, L. Salghetti-Drioli, P. de Maagt, “AMC low profile wideband reference antenna for GPS and GALILEO systems,” IEEE Trans. Antennas Propag., vol. 56, no. 8, August 2008. 4. M.W. Nurnberger, T. Ozdemir, J.L. Volakis, “A planar slot spiral for multi-function communications apertures,” Proc. IEEE Antenna Propag. Int. Symp., Jun. 21-26, 1998. pp. 774-777. 5. C. A. Balanis, Antenna Theory: Analysis and Design, 3rd Ed., John Wiley and Sons, Inc., Hoboken, NJ. 2005. pp 813. 6. Y. Zhio, C.-C. Chen, and J.L. Volakis, “Tri-band miniature GPS Array with a pp. 3049-3052. 7. L. Boccia, G. Amendola, G. Di Massa, “A dual frequency microstrip patch antenna for high-precision GPS applications,” IEEE Antennas and Wireless Propagation Letters, vol.3, no. 1. pp. 157-160, 2004. Authors: K.Aditya,M.Sivakumar, Fazal Noorbasha,T.Praveen Blessington Paper Title: Design and Functional Verification of A SPI Master Slave Core Using System Verilog Abstract: Synchronous serial interfaces are widely used to provide economical board level interfaces between 107. different devices such as microcontrollers, DACs ADCs and other. Many IC manufacturers produce components that are compatible with SPI and Microwire/plus. The SPI Master core is compatible with both protocols as master with 558-563 some additional functionality. At the hosts side,the core acts like a Wishbone compliant slave device. The SPI master core consists of three parts, Serial interface, clock generator and Wishbone interface. The SPI core has five 32-bit registers through the Wishbone compatible interface. The serial interface consists of slave select lines, serial clock lines, as well as input and output data lines. All transfers are full duplex transfers of a programmable number of bits per transfer(upto 64 bits).It has 8 slave select lines but only one is selected at a time. We design the SPI Master-Slave core design using system verilog and do functional verification for our design in modelsim.
Keywords: SPI, Wishbone, coverage.
References: 1. www.opencore.org.Simon Srot. “SPI Master Core Specification”,Rev.0.6. May 16,2007. 2. “Design and Implementation of a Reused Interface” 978-0-7695-3887-7/09/$26.00 ©2009 IEEE. 3. Wikipedia, the free encyclopedia, “Serial Peripheral Interface Bus”, Available http://en.wikipedia.org/wiki/Serial_Peripheral_Interface_Bus. 4. Tianxiang Liu ”IP Design of Universal Multiple Devices SPI Interface” 978-1-61284-632-3/11/$26.00 ©2011 IEEE. 5. Specification for the:“WISHBONE System-on-Chip (SoC) Interconnection Architecture for Portable IP Cores”Revision: B.3, Released: September 7, 2002. 6. Chris spear “System verilog for verification” second edition. 7. F. Leens, “An Introduction to I2C and SPI Protocols,” IEEE Instrumentation & Measurement Magazine, pp. 8-13, February 2009. Authors: Anusha. J Paper Title: Entropy Based Detection of DDOS Attacks Abstract: Distributed Denial of service (DDOS) attacks is a critical threat to the internet. Due to the memory less features of the internet routing mechanism makes difficult to trackback the source of the attacks. In this paper, I find out the source of the attack with the help of entropy variation in dynamic by calculating the packet size, which shows the variation between normal and DDOS attack traffic, which is fundamentally different from commonly used packet marking techniques. In comparison to the existing DDOS trackback methods, the proposed one posses dynamic entropy variations as per the clients behavior.
Keywords: DDOS, Method, Router
References: 1. “IP Flow-Based Technology,” ArborNetworks, http://www.arbornetworks.com, 2010. 2. C. Patrikakis, M. Masikos, and O. Zouraraki, “Distributed Denialof Service Attacks,” The Internet Protocol J., vol. 7, no. 4, pp. 13-35, 2004. 3. T. Peng, C. Leckie, and K. Ramamohanarao, “Survey of Network-Based Defense Mechanisms Countering the DoS and DDoS Problems,” ACM Computing Surveys, vol. 39, no. 1, p. 3, 2007. 108. 4. Y. Kim et al., “PacketScore: A Statistics-Based Packet Filtering Scheme against Distributed Denial-of-Service Attacks,” IEEE Trans. Dependable and Secure Computing, vol. 3, no. 2, pp. 141-155, Apr.-June 2006. 5. H. Wang, C. Jin, and K.G. Shin, “Defense against Spoofed IP Traffic Using Hop-Count Filtering,” IEEE/ACM Trans. Networking, vol. 15, 564-567 no. 1, pp. 40-53, Feb. 2007. 6. Y. Chen and K. Hwang, “Collaborative Detection and Filtering of Shrew DDoS Attacks Using Spectral Analysis,” J. Parallel and Distributed Computing, vol. 66, pp. 1137-1151, 2006. 7. K. Lu et al., “Robust and Efficient Detection of DDoS Attacks for Large-Scale Internet,” Computer Networks, vol. 51, no. 9, pp. 5036- 5056, 2007. 8. R.R. Kompella, S. Singh, and G. Varghese, “On Scalable Attack Detection in the Network,” IEEE/ACM Trans. Networking, vol. 15, no. 1, pp. 14-25, Feb. 2007. 9. P.E. Ayres et al., “ALPi: A DDoS Defense System for High-Speed Networks,” IEEE J. Selected Areas Comm., vol. 24, no. 10, pp. 1864 - 1876, Oct. 2006. 10. R. Chen, J. Park, and R. Marchany, “A Divide-and-Conquer Strategy for Thwarting Distributed Denial-of-Service Attacks,” IEEE Trans. Parallel and Distributed Systems, vol. 18, no. 5, pp. 577- 588, May 2007. 11. A. Yaar, A. Perrig, and D. Song, “StackPi: New Packet Marking and Filtering Mechanisms for DDoS and IP Spoofing Defense,” IEEE J. Selected Areas Comm., vol. 24, no. 10, pp. 1853-1863, Oct. 2006. 12. Bremler-Bar and H. Levy, “Spoofing Prevention Method,” Proc.IEEE INFOCOM, pp. 536- 547, 2005. 13. J. Xu and W. Lee, “Sustaining Availability of Web Services under Distributed Denial of Services Attacks,” IEEE Trans. Computers, vol. 52, no. 2, pp. 195-208, Feb. 2003. 14. W. Feng, E. Kaiser, and A. Luu, “Design and Implementation of Network Puzzles,” Proc. IEEE INFOCOM, pp. 2372-2382, 2005. 15. X. Yang, D. Wetherall, and T. Anderson, “A DoS-Limiting Network Architecture,” Proc. ACM SIGCOMM, pp. 241-252, 2005. Authors: Atluri.Jhansi rani, K.Harikishore, Fazal Noor Basha, L.Veera Raju, K.Purnima Paper Title: An Advanced Online Memory Testing For Fault Diagnostic Systems Abstract: An increasing part of microelectronic systems is implemented on the basis of pre designed and pre verified modules, so-called cores, which are reused in many instances. Core-providers offer RISC-kernels, embedded memories, DSPs, and many other functions, and built-in self-test is the appropriate method for testing complex systems composed of different cores. The main objective of this project is to designing a system, in such a way that; it is having the capability to detect the faults in the Read only memories. Whether the faults may be Software or Hardware, all faults can be recognized by our designed system. BIST (Built in self test) concept is introduced to detect the faults in the memories. The proposed approach offers a simple test flow and does not require intensive interactions between a BIST controller and a tester. The scheme rests on partitioning of rows and columns 109. of the memory array by employing low cost test logic. It is designed to meet requirements of at-speed test thus enabling detection of timing defects. 568-571
Keywords: DSPs, RISC-Kernels, BIST
References: 1. J. T. Chen, J. Rajski, J. Khare, O. Kebichi, and W. Maly, “Enabling embedded memory diagnosis via test response compression,” in Proc. VTS, 2001, pp. 292–298. 2. J. T. Chen, J. Khare, K. Walker, S. Shaikh, J. Rajski, and W. Maly, “Test response compression and bitmap encoding for embedded memories in manufacturing process monitoring,” in Proc. ITC, 2001, pp. 258–267. 3. D. W. Clark and L.-J. Weng, “Maximal and near-maximal shift register sequences: Efficient event counters and easy discrete logarithms,” IEEETrans. Comput., vol. 43, no. 5, pp. 560–568, May 1994. 4. X. Du, N. Mukherjee, W.-T. Cheng, and S. M. Reddy, “Full-speed fieldprogrammable memory BIST architecture,” in Proc. ITC, 2005, paper 45.3. 5. D. Gizopoulos, Ed., Advances in Electronic Testing—Challenges and Methodologies. Dordrecht, The Netherlands, Springer, 2006. 6. International Technology Roadmap for Semiconductors. (2009) [Online]. Available: www.itrs.net 7. Y.-H. Lee, Y.-G. Jan, J.-J. Shen, S.-W. Tzeng, M.-H. Chuang, and J.-Y. Lin, “A DFT architecture for a dynamic fault model of the embedded mask ROM of SoC,” in Proc. Int. Workshop Memory Technol. Design Testing, 2005, pp. 78–82. 8. J.-F. Li and C.-W. Wu, “Memory fault diagnosis by syndrome compression,” in Proc. DATE, 2001, pp. 97–101. 9. G. Mrugalski, J. Rajski, and J. Tyszer, “Ring generators: New devices for embedded deterministic test,” IEEE Trans. Comput.-Aided Design, vol. 23, no. 9, pp. 1306–1320, Sep. 2004. 10. N. Mukherjee, A. Pogiel, J. Rajski, and J. Tyszer, “High throughput diagnosis via compression of failure data in embedded memory BIST,” in Proc. ITC, 2008, paper 3.1. 11. D. Niggemeyer and E. M. Rudnick, “Automatic generation of diagnostic memory tests based on fault decomposition and output tracing,” IEEE Trans. Comput., vol. 53, no. 9, pp. 1134–1146, Sep. 2004. 12. J. Rajski and J. Tyszer, “Diagnosis of scan cells in BIST environment,” IEEE Trans. Comput., vol. 48, no. 7, pp. 724–731, Jul. 1999. speed memory testing with a slow memory tester,” U.S. Patent 6 404 250, Jun. 11, 2002. 13. J. Rajski, N. Tamarapalli, and J. Tyszer, “Automated synthesis of phase shifters for built-in self-test applications,” IEEE Trans. Comput.- Aided Design, vol. 19, no. 10, pp. 1175–1188, Oct. 2000. 14. J. Rajski, N. Mukherjee, J. Tyszer, and A. Pogiel, “Fault diagnosis in memory BIST environment,” U.S. Patent Applicat. 20110055646, Sep.18, 2008.