International Journal of Soft Computing and Engineering
ISSN : 2231 - 2307 Website: www.ijsce.org Volume-3 Issue-4, September 2013 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-3 Issue-4, September 2013, ISSN: 2231-2307 (Online) Page No Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. No.
Authors: Vipin Kumar, Himadri Chauhan, Dheeraj Panwar Paper Title: K-Means Clustering Approach to Analyze NSL-KDD Intrusion Detection Dataset Abstract: Clustering is the most acceptable technique to analyze the raw data. Clustering can help detect intrusions when our training data is unlabeled, as well as for detecting new and unknown types of intrusions. In this paper we are trying to analyze the NSL-KDD dataset using Simple K-Means clustering algorithm. We tried to cluster the dataset into normal and four of the major attack categories i.e. DoS, Probe, R2L, U2R. Experiments are performed in WEKA environment. Results are verified and validated using test dataset. Our main objective is to provide the complete analysis of NSL-KDD intrusion detection dataset.
Keywords: Clustering, K-means, NSL-KDD Dataset, WEKA.
References: 1. Lee W., and Stolfo S. J., (1998): “Data mining approaches for intrusion detection”, 7th USENIX Security Symposium, San Antonio, 635-660. 2. Gaol F. L., Yi S., Deng F., (2012): “Research of Network Intrusion-Detection System Based on Data Mining”, Recent Progress in Data Engineering and Internet Technology, vol. 157: 141-148. Springer Berlin, Heidelberg. 3. Fayyad U. M., Piatetsky-Shapiro G., Smyth P., Uthurusamy R., (1996): editors, “Advances in Knowledge Discovery and Data Mining”, MIT Press. 4. Kaufman K. A., Michalski R. S. and Kerschberg L., (1991): “Mining for Knowledge in Databases: Goals and General Description of the INLEN System”, in G. Piatetsky-Shapiro and W. J. Frawley (eds.), Knowledge Discovery in Databases, AAAI/MIT Press, 449-462. 5. Duda, R. O., Hart P. E., and Stork D.G., “Unsupervised Learning and Clustering”, Chapter 10 in Pattern classification (2nd edition), p. 571, New York, NY: Wiley, ISBN 0-471-05669-3. 6. Fisher D. H., (1987): “Knowledge Acquisition via Incremental Conceptual Clustering”, Machine Learning 2: 139-172. 7. Zanero S., Savaresi S. M., (2004): “Unsupervised learning techniques for an intrusion detection system”, ACM Symposium on Applied Computing (SAC’04), Nicosia, Cyprus. 1. 8. Jang J. S. R., Sun C. T., and Mizutani E. (1997): "Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]," Automatic Control, IEEE Transactions on, vol. 42, no. 10, pp. 1482-1484. 1-4 9. Muda Z., Yassin W., Sulaiman M. N., and Udzir N. I., (2011): "Intrusion detection based on K-Means clustering and Naive Bayes classification," in Information Technology in Asia (CITA 11), 7th International Conference on, Kuching, Sarawak, pp. 1-6. 10. Om H., and Kundu A., (2012): "A hybrid system for reducing the false alarm rate of anomaly intrusion detection system," in Recent Advances in Information Technology (RAIT), 1st International Conference on, Dhanbad, pp. 131-136. 11. Nadiammai G. V., and Hemalatha M., (2012): "An Evaluation of Clustering Technique over Intrusion Detection System," in International Conference on Advances in Computing, Communications and Informatics (ICACCI'12) Chennai, pp. 1054-1060. 12. Ye Q., Wu X., and Huang G., (2010): "An intrusion detection approach based on data mining," in Future Computer and Communication (ICFCC), 2nd International Conference on, Wuhan, pp. V1-695-V1-698. 13. Haque M. J., Magld K. W., and Hundewale N., (2012): "An intelligent approach for Intrusion Detection based on data mining techniques," in Multimedia Computing and Systems (ICMCS), International Conference on, Tangier, pp. 12-16. 14. Poonam P., and Dutta M., (2012): "Performance Analysis of Clustering Methods for Outlier Detection," in Advanced Computing & Communication Technologies (ACCT), Second International Conference on, Rohtak, Haryana, pp. 89-95. 15. Denatious D. K., and John A., (2012): "Survey on data mining techniques to enhance intrusion detection," in Computer Communication and Informatics (ICCCI), International Conference on, Coimbatore, pp. 1-5. 16. MacQueen J. B., (1967): "Some Methods for classification and Analysis of Multivariate Observations," in 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, pp. 281-297. 17. Velmurugan T., and Santhanam T., (2010): "Performance Evaluation of K-Means and Fuzzy C-Means Clustering Algorithms for Statistical Distributions of Input Data Points," European Journal of Scientific Research, vol. 46, no. 3, pp. 320-330. 18. Borah S., and Ghose M. K., (2009): "Performance analysis of AIM-K-Means and K-Means in quality cluster generation," Journal of Computing, vol. 1, no. 1. 19. WEKA (2008): Data Mining Machine Learning Software [Available Online] http://www.cs.waikato.ac.nz/ml/weka/ 20. Chandrashekhar A. M., and Raghuveer K., (2012): "Performance evaluation of data clustering techniques using KDD Cup-99 Intrusion detection data set," International Journal of Information & Network Security (IJINS), vol. 1, no. 4, pp. 294-305. 21. “KDDCup 1999 Dataset”. [Available online]: http://kdd.ics.uci.edu/databases/kddcup1999.html/ 22. McHugh J., (2000): “Testing Intrusion detection system: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory”, ACM Transaction on Information and system security, vol. 3, no. 4, pp.262-294. 23. NSL-KDD dataset, (2006): [Available Online] http://iscx.ca/NSL-KDD/ Authors: Ruby Singh, Ramandeep Kaur Paper Title: Improved Skew Detection and Correction Approach Using Discrete Fourier Algorithm Abstract: The main objective of Image processing is to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. But when they are needed to be converted into electronic form, it has to be done through scanning. One of the major problems in this field is that if the document to be read is not placed at 900. This will result with a skewed image. Skew refers to the text which neither parallel nor at right angles to a specified or implied line. Character recognition is very sensitive to the page skew, skew detection and correction in document images are the critical 2. steps before ayout analysis. To achieve the objective of Skew detection and correction, a new improved strategy is proposed which uses the Fourier transformation and angle of elevation theory to detect the skewness 5-7 angle first and then skew correction algorithm will come in action. As Fourier transform will speed up the algorithm so speed no longer is an issue for proposed algorithm. Also angle of elevation theory has ability to detect angle in efficient manner. Proposed algorithm is designed and implemented in Matrix Laboratory by taking a sample of 120 different Skewed images. These images include documents having different languages: - English, Hindi, Punjabi and Pictures.
Keywords: Skew detection, Fast Fourier transform, Discrete Fourier transform, Radon transform, Moments.
References: 1. Deepak Kumar, Dalwinder Singh, "Modified Approach of Hough Transform for Skew Detection and Correction in Documented Images". International Journal of Research in Computer Science, 2 (3): pp. 37-40, April 2012. 2. Chandan Singh, Nitin Bhatia, Amandeep Kaur, ―Hough transform based fast skew detection and accurate skew correction methods‖, Pattern Recognition, Volume 41, Issue 12, December 2008, Pages 3528-3546. Improved Skew Detection and Correction approach using Discrete Fourier Algorithm 3. Yang Cao, Shuhua Wang, Heng Li ―Skew detection and correction in document images based on straight-line fitting‖, Pattern Recognition Letters, Volume 24, Issue 12, August 2003, Pages 1871-1879. 4. K. George ,K. Nicholas., ―A Fast High Precision Algorithm for the Estimation of Skew Angle Using Moments‖, Department of Informatics and Telecommunications University of Athens, (2002). 5. Brodić, Darko, and Z. Milivojević. "Estimation of the Handwritten Text Skew Based on Binary Moments." Radioengineering 21.1 (2012): 162-169. 6. Y. Lu, C. L. Tan., ―Improved Nearest Neighbor Based Approach to Accurate Document Skew Estimation‖, Department of Computer Science, School of Computing, National University of Singapore, Kent Ridge Singapore, (2003). 7. K. Iuliu, E. Stefan et al., ―Fast Seamless Skew and Orientation Detection in Document Images‖, Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS) Schloss Birlinghoven, 53754 Sankt Augustin, Germany, (2010). 8. S. Lowther, V. Chandhran, et al. ―An accurate method for skew determination in document images‖. In Proceedings of the Digital Image Computing Techniques and Applications. Melbourne ,Australia, (2002). Authors: Krishna Kumar Tripathi, Lata Ragha Paper Title: Hybrid Approach for Credit Card Fraud Detection Abstract: Due to a rapid growth in the e-commerce technology, the use of credit cards has increased. As credit card becomes the most popular mode of payment for both online as well as normal purchase, cases of credit card fraud also rising. Financial fraud is increasing significantly with the development of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. The fraudulent transactions are scattered with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques were proposed & implemented in literature, they have their own advantages and disadvantages. We proposed hybrid approach used in credit card fraud detection mechanism.
Keywords: Credit card fraud, Credit Card Fraud detection methods, Electronic Commerce, Rule based filter, History database, Bayesian theorem, Dempster Shaper Adder.
3. References: 1. Linda Delamaire”Credit card fraud and detection techniques: a review”. Bank and Bank Systems, Volume4, Issue2. (2009). 8-11 2. Ghosh, D.L.Reilly, “Credit card fraud and detection with a Neural-Network”. Proceeding of the International Conference on System Science ;( 1994). (621-630). 3. E.W.T.Ngai, Yong Hu, Y.H.Wong, Yijun Chen, Xin Sun “The application of data mining techniques in an academic review of literature”. Elsevier-Decision Support System (2011).50; (559-569). 4. Sherly K.K. (2012) “A comparative assessment of supervised data mining techniques for fraud prevention” TIST.Int.J.Sci.Tech.Res, Vol. 1; (1-6). 5. S.Maes, K.Tuyls, B.Vanschoenwinkel, B.Manderick” Credit card fraud detection using Bayesian and neural networks”. Processing of the First International NAISO Congress on Neuro Fuzzy Technologies ;( 1993). (261-270). 6. Suvasini Panigrahi, Amlan Kundu, Shamik Sural, A.K.Majumdar “Credit card fraud detection: A fusion approach using Dempster-Shafer Theory and Bayesian learning”.Elsevier, Information Fusion 10 :( 2009). (354-363). 7. N. Cristianini, J. Shawe-Taylor “An Introduction to Support Vector Machines and Other Kernal-based Learning Methods”. Cambridge University Press. (2000). 8. Cortes, C. & Vapnik, V “Support vector networks Machine Learning”. (1995).Vol.20 ;( 273-297). 9. http://en.wikipedia.org/wiki/Luhn_algorithm 10. http://en.wikipedia.org/wiki/One-time_password 11. http://en.wikipedia.org/wiki/Security_question 12. William M. Hayden (1906), Systems in Savings Banks, The Banking Law Journal, volume 23, page 909. Authors: Payal, Sudesh Kr. Jakhar CBR Traffic Based Performance Investigations of DSDV, DSR and AODV Routing Protocols for Paper Title: MANET Using NS2 Abstract: A Mobile Ad-Hoc Network (MANET) is self-configuring network of mobile nodes connected by wireless links to form an arbitrary topology without the use of existing infrastructure. This paper does the comparative investigations on the performance of routing protocols Dynamic Source Routing (DSR), Ad-hoc On demand distance vector (AODV) and Destination-Sequenced Distance-Vector (DSDV) for wireless ad-hoc networks in a simulated environment against varying parameters considering UDP as transport protocol and CBR as traffic generator. In this paper, we have studied the effects of varying node mobility rate, scalability and maximum speed on the performance of ad-hoc network routing protocols. Simulation results indicate that despite in most 4. simulations reactive routing protocols DSR and AODV performed significantly better than proactive routing protocol DSDV, DSR is less scalable with respect to network size because DSR introduces high overheads with the 12-16 increase in network size. Simulations presented clearly show that there is a need for routing protocol specifically tuned to the characteristics of ad-hoc networks.
Keywords: CBR, AODV, DSDV, DSR, MANET, NS-2, Performance Evaluation.
References: 1. Charles E. Perkins, “Mobile Ad-hoc Networks”, Addison Welsey, 2000. 2. S. R. Das, C. Perkins, and E. Royer, “Performance comparison of Two On-demand Routing Protocols for Ad Hoc Networks”, IEEE INFOCOM 2000, vol.1, pp. 3-12, March 2000. 3. J. Macker and S. Corson, Mobile Ad hoc Networks (MANET), http://www.ietf.org/charters/manet-charter.html, IETF Working Group Charter, 1997. 4. J. Broch, D.A. Maltz, D.B. Johnson, Y.C. Hu, and J. Jetcheva, “A Performance Comparison of Multi-Hop Wireless Ad Hoc Network Routing Protocols”, ACM/IEEE International Conference on Mobile Computing and Networking MOBICOM ’98, pp. 85-97, October 1998. 5. E. M. Royer and C.-K. Toh, “A Review of Current Routing Protocols for Ad-Hoc Mobile Wireless Networks”, IEEE Personal Communications, pp. 46-55, April 1999. 6. S.R. Das, R. Castaneda, J. Yan, and R. Sengupta, “Comparative performance evaluation of routing protocols for mobile ad hoc networks”, 7th International Conference on Computer Communications and Networks (IC3N), pp. 153–161, October 1998. 7. Charles E. Perkins and Pravin Bhagwat, “Highly dynamic Destination-Sequenced Distance-Vector routing (DSDV) for mobile computers”, SIGCOMM '94 Conference on Communications Architectures, Protocols and Applications, pp. 234-244, August 1994. 8. David B. Johnson and David A. Maltz, “Dynamic Source Routing in Ad Hoc Wireless Networks”, In Mobile Computing, Tomasz Imielinski and Hank Korth (Eds.), Chapter 5, pp. 153-181, Kluwer Academic Publishers, 1996. 9. Charles Perkins and Elizabeth Royer, “Ad hoc on demand distance vector routing”, IEEE Workshop on Mobile Computing Systems and Applications, pp. 90–100, February 1999. 10. C.E. Perkins, E.M. Royer and S.R. Das, Ad hoc on-demand distance vector (AODV) routing, IETF MANET Working Group, Internet- Draft (March 2000). 11. P. Johansson et al., Scenario-based Performance Analysis of Routing Protocols for Mobile Ad-Hoc Networks, In Proc. IEEE/ACM Mobicom ’99, pages 195–206, Seattle, WA, Aug. 1999. 12. Yi Lu, Weichao Wang, Yuhui Zhong, Bharat Bhargava, Study of Distance Vector Routing Protocols for Mobile Ad Hoc Networks, Proc. IEEE International Conference on Pervasive Computing and Communications (Per-Com’2003), 2003. 13. Azzedine Boukerche, Performance Evaluation of Routing Protocols for Ad Hoc Wireless Networks, Proc. Mobile Networks and Applications 9, Pages 333–342, 2004 14. P. Chenna Reddy, Dr. P. Chandrasekhar Reddy, Performance analysis of Adhoc network routing protocols, Academic Open Internet Journal ISSN, 1311-4360, Volume 17, 2006. 15. Geetha Jayakumar and Gopinath Ganapathy, Performance Comparison of Mobile Ad-hoc Network Routing Protocol, IJCSNS International Journal of Computer Science and Network Security, VOL.7 No.11, November 2007 16. S. Corson and J. Macker, “Mobile Ad hoc Networking (MANET): Routing Protocol Performance Issues and Evaluation Considerations” IETF RFC 2501, January 1999. 17. Network Simulator - ns-2. Available at http://www.isi.edu/nsnam/ns/ 18. Kevin Fall and Kannan Varadhan, editors, “ns notes and documentation”, The VINT Project, UC Berkeley, LBL, USC/ISI and Xerox PARC, Available from http://www.isi.edu/nsnam/ns/ns-documentation.html, Dec 25, 2007. Authors: Prashant Kumar, Ashish Vashishtha, Md. Khalid Imam Rahmani Paper Title: An Internet and Intranet Based Real Time Medical Imaging System Abstract: Today computer is an essential part of our life. The computer database is used for medical decision making. Doctors basically used to take decision on particular diesis on the help of old and current history of the patients or same diesis procured by other patients. This paper describe a tool developed in java/j2EE which enables the doctors to retrieved old record of same dieses treatment using internet and even allow them view medical image of blood slides, ECG, CT-scan, X-Ray etc. even allow them to mark and/or zoom important area of the image. It is secure and multi party medical image database consultant system.
Keywords: Server side programming, Servlet; Medical image processing, Client-server;
References: 1. W. Cai, D. D. Feng, and R. Fulton, “Web-based digital medical images”, IEEE Computer Graphics and Applications, vol. 21, no. 1, 2001, pp. 44–47. 2. J.-S. Lee, C.-T. Tsai, C.-H. Pen, and H.-C. Lu, “A real time collaboration system for teleradiology consultation”, International Journal of Medical Informatics, vol. 72, December 2003, pp. 73–79. 3. L. Ling, Y. Dezhong, L. Jianqing, L. Bin, and W. Ling, “A multimedia telemedicine system”, Proceedings of Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, 2005, pp. 3746–3748. 4. S. K. Yoo, K. M. Kim, S. M. Jung, K. J. Lee and N. H. Kim, “Design of multimedia telemedicine system for inter-hospital consultation”, Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA, USA, 2004, pp. 3109– 3111. 5. H. Huang, Ed., “PACS: Basic Principles and Applications”, New York: Wiley-Liss Publisher, 1999. 5. 6. K. Maji, A. Mukhoty, A. K. Majumdar, J. Mukhopadhyay, S. Sural, S. Paul, and B. Majumdar, “Security analysis and implementation of web-based tele-medicine services with a four-tier architecture”, International Workshop on Connectivity, Mobility and Patients’ Comfort (CMPC), Tampere, Finland: IEEE Computer Society Press, 2008, pp. 46–54. 17-21 7. A.K. Maji, A. Mukhoty, A. K. Majumdar, J. Mukhopadhyay, and S. Sural, “Secure healthcare delivery over the web: A multi-tier approach in India”, Proceedings of the Indian Conference on Medical Informatics and Telemedicine (ICMIT 2006), India, 2006, pp. 62–67. 8. D Durga Prasad, Saikat Ray, Arun K. Majumdar, Jayanta Mukherjee, Bandana Majumdar, Soubhik Paul and Amit Kumar Verma, “Real Time Medical Image Consultation System Through Internet”, Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, India. 9. Note on HIV sentiniel surveillance and HIV estimation, 2006, available at, http://www.nacoonline.org/NationalAIDSControl Program/Surveillance/, Accessed on 10.05.2013. 10. S. Bhattacharyya and A. Mukherjee, “Pediatric HIV: There is hope”, Indian Journal of Dermatology, 2006, pp. 244-249. 11. Sudar, “Improving standard of care: An electronic Health Record for Management of Pediatric HIV”, May 2008. 12. Jazayeri D, Farmer P, Nevil P, Mukherjee JS, Leandre F and Fraser HS, “An Electronic medical record system to support HIV treatment in rural Haiti”, AMIA 2003 Symposium, 2003, pp. 878. 13. Siika AM, Rotich JK, Simiyu CJ, Kigotho EM, Smith FE, Sidle JE,Wools-Kaloustian K, Kimaiyo SN, Nyandiko WM, Hannan TJ and Tierney WM, “An electronic medical record system for ambulatory care of HIV-infected patients in Kenya”, International Journal of Medical Informatics, 2005, pp. 345-355. 14. B. Atalay, W.D. Potter and D. Haburchak, “HIVPCES: A WWW-based HIV patient care expert system”, Computer-Based Medical Systems 1999, 18-20 June 1999, pp. 214 - 219. 15. “Information Economy and Healthy Citizenry: Role of Internet in Implementing India’s Health Policy”, Available at http://virtualmed.netfirms.com/internethealth/ih200431e04.html, Accessed on 15.04.2013. 16. Amiya K. Maji, Arpita Mukhoty, A.K. Majumder, J.Mukherjee and Shamik Sural, “Secure Healthcare Delivery Over the Web: A Multi Tier Approach”, ICMIT 2006, IIT Kharagpur, India, December 2006, pp. 53-58. 17. iMedik Website: http://tmportal.iitkgp.ernet.in.Guidelines for HIV care and treatment Infants and Children-IAP, NACO with support from WHO, UNICEF and Clinton Foundation. Published by Indian Academy of Paediatrics and National Aids Control Organisation, November 2006. Accessed on 10.12.2012. 18. Bates DW, Teich JM, Lee J, Seger D, Kuperman GJ, Ma’Luf N, Boyle D and Leape L, “The impact of computerized physician order entry on medication error prevention”, J Am Med Inform Assoc. 1999, 6:313-321. 19. V. Roy, P. Gupta and S. Srivastava, “MEDICATION ERRORS: CAUSES & PREVENTION”, Health Administrator Vol: XIX, pp: 60- 64. 20. Sarah Chippindale and Lesley French, “HIV counselling and the psychosocial management of patients with HIV or AIDS”, BMJ Journal, 2001, pp. 1533-1535. 21. N. Kumar, C. Shekhar, P. Kumar and A.S. Kundu, “Kuppuswamy’s Socioeconomic Status Scale-Updatingfor 2007”, Indian Jouran of Pediatrics, Vol 74, December 2007, pp. 1131-1132. 22. S. Paul, B. Majumdar, J. Mukhopadhyay, A.K. Majumdar and A. K.Maji, “PDA Based Telemedicine System in a Web-Based Environment”, ADCOM 2007, December 2007, pp. 170-174. Authors: Eliza Gail Maxwell, Tripti C A Comparison between Contrast Limited Adaptive Histogram Equalization and Gabor Filter Sclera Paper Title: Blood Vessel Enhancement Techniques Abstract: The importance of the human sclera lies in the uniqueness of the blood vessel structure which can be easily obtained non-intrusively in visible wavelengths. The unique and persistent characteristic of the sclera vessel patterns makes it a suitable candidate for human identification (ID). The proper segmentation of the sclera region and obtaining the sclera vessel patterns are the main problems that this biometric has to deal with. Images of sclera vessel patterns are often defocused or blurry and, most importantly, the vessel structure in the sclera is multilayered and has complex nonlinear deformations. This paper compares two sclera vessel enhancement methods. The first method uses a contrast limited adaptive histogram equalization with region growing technique. The other technique uses a directional Gabor filter bank to enhance the sclera veins. Experimental results showed that the Gabor filter-enhancement method yields faster and better results as compared to the histogram equalization and region growing method.
Keywords: Gabor filter, histogram, region-growing, sclera vessel pattern.
References: 1. . Zhou, E. Du, N. L. Thomas, and E. J. Delp, A new human identification method: Sclera recognition, IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 42, no. 3, pp. 571-583, May 2012. 2. Kanai and H. Kaufman, Electron microscopic studies of the elastic fiber in human sclera, Investigative Ophthalmol. Vis. Sci., vol. 11, no.10, pp. 816-821, Oct. 1972 6. 3. R. C. Gonzalez, Digital image processing, 2nd ed. Upper Saddle River, NJ: Prentice Hall, 2002. 4. J. Daugman, "Demodulation By Complex-ValuedWavelets For Stochastic Pattern Recognition," International Journal of Wavelets, Multi-resolution and Information Processing, vol. 1, pp. 1-17, 2003. 22-25 5. J. Daugman, "How iris recognition works," IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, pp. 21-30, 2004. 6. J. Daugman, "New Methods in Iris Recognition," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 37, pp. 1167-1175, 2007. 7. H. Narasimha-Iyer, J. M. Beach, B. Khoobehi, and B. Roysam, "Automatic Identi_cation of Retinal Arteries and Veins From Dual-Wavelength Images Using Structural and Functional Features," IEEE Transactions on Biomedical Engineering, vol. 54, pp. 1427-1435, 2007. 8. M.-K. Hu, "Visual pattern recognition by moment invariants," IRE Transactions on Information Theory, vol. 8, pp. 179-187, 1962. 9. C.-L. Lin and K.-C. Fan, "Biometric veri_cation using thermal images of palmdorsa vein patterns," IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, pp. 199-213, 2004. 10. J. Hashimoto, "Finger Vein Authentication Technology and Its Future," in Symposium on VLSI Circuits, 2006, pp. 5-8. 11. R. Derakhshani, A. Ross, and S. Crihalmeanu, "A New Biometric Modality Based on Conjunctival Vasculature," Proc. of Artificial Neural Networks in Engineering, 2006. 12. M. E. Martinez-Perez, A. D. Hughes, A. V. Stanton, S. A. Thom, A. A. Bharath, and K. H. Parker, "Segmentation of retinal blood vessels based on the second directional derivative and region growing," in Proceedings of International Conference on Image Processing, 1999, pp. 173-176. vol. 13. N. Otsu, A threshold selection method from gray-level histograms, Automatica, vol. 11, pp. 285-296, 1975. 14. R. Derakhshani and A. Ross, "A Texture-Based Neural Network Classifier for Biometric Identi_cation using Ocular Surface Vasculature," in Proc. of the Internationl Joint Conference on Neural Networks, Orlando, FL, 2007, pp. 2982- 2987. 15. S. Crihalmeanu, A. Ross, and R. Derakhshani, "Enhancement and Registration Schemes for Matching Conjunctival Vasculature," in Proceedings of the Third International Conference on Advances in Biometrics Alghero, Italy: Springer- Verlag, 2009. 16. N. L. Thomas ; Yingzi Du ; Zhi Zhou; A new approach for sclera vein recognition. Proc. SPIE 7708, Mobile Multimedia/Image Processing, Security, and Applications 2010. Authors: Aeesha S. Shaheen Paper Title: Partial Web Mining Website Mining Individually Abstract: The growth of numbers of pages that loaded on the web and the difference of the structures and styles of these pages involves significant challenges. So in this paper we apply web mining to the web site pages in order to benefit from the redundant data that appears in the most pages in the website to decrease the memory size needed to save these pages in each web site alone. The web mining applied by extracting the matched data between the website pages and the home page or the index page then discharges it (the matched data), but the difference data is saved in the server’s memory until the page is accessed. After the page is requested by any user, combination 7. applied between the index page file and the difference file for viewing the page on the browser. 26-29 Keywords: Pages, data
References: 1. http://novella.mhhe.com/sites/0079876543/ student_view0/power_google.html 2. http://tech.slashdot.org/submission/1888512/ average-web-page-is-now- almost -1mb?sdsrc=rel 3. http://www.andesandassociates.com/ How_many_words_on_web_pa.html. 4. Jaideep Srivastava, , Robert Cooley, Mukund Deshpande, Pang-Ning Tan, “Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data” Department of Computer Science and EngineeringUniversity of Minnesota 200 Union St SE Minneapolis, MN 55455 fsrivasta,cooley,deshpand,[email protected] 5. Jaideep Srivastava, Prasanna Desikan, Vipin Kumar,” Web Mining - Concepts, Applications & Research Directions Department of Computer Science200 Union Street SE, 4-192, EE/CSC Building University of Minnesota, Minneapolis, MN 55455, USA srivasta, desikan, kumar @cs.umn.edu 6. Johannes F¨urnkranz, “Web Structure Mining Exploiting the Graph Structure of the World-Wide Web”Austrian Research Institute for Artificial IntelligenceSchottengasse 3, A-1010 Wien, AustriaE-mail: [email protected] 7. John Wiley & Sons, Inc. “DATA MINING THE WEB,Uncovering Patterns in Web Content, Structure,and Usage”All rights reserved Copyright 2007.Published by John Wiley & Sons, Inc., Hoboken, New JerseyPublished Simultaneously in Canada 8. Katherine Andes http://battellemedia.com/archives/2005/08/ how_many_pages_does_yahoo_index.php 9. McGraw-Hill/Dushkin, “Power Google”Copyright ©2003 Guilford, CT 06437, A Division of The McGraw-Hill Companies. 10. Miguel Gomes da Costa Júnior Zhiguo Gong, “Web Structure Mining: An Introduction “Department of Computer and information Science Faculty of Science and Technology University of Macau Av. Padre Tomás, S.J., Taipa, Macao S.A.R., China {mcosta, fstzgg}@umac.mo Authors: Nadhem Sultan Ali Ebrahim, V.P Pawar Paper Title: Identifying the Software Failure Mechanisms Using Data Mining Techniques Abstract: Software is ubiquitous in our daily life. It brings us great convenience and a big headache about software reliability as well: Software is never bug-free, and software bugs keep incurring monetary loss or even catastrophes. In the pursuit of better reliability, software engineering researchers found that huge amount of data in various forms can be collected from software systems, and these data, when properly analyzed, can help improve software reliability. Unfortunately, the huge volume of complex data renders simple analysis techniques incompetent; consequently, Studies have been resorting to data mining for more effective analysis. In the past few years, we have witnessed many studies on mining for software reliability reported in data mining as well as software engineering forums. These studies either develop new or apply existing data mining techniques to tackle reliability problems from different angles. In order to keep data mining researchers abreast of the latest development in this growing research area, we propose this Paper on mining for software reliability.
Keywords: Reliability, Techniques 8. References: 30-32 1. Breuker J. and Van Der Velde W: Common KADS Library for Expertise Modelling, Amsterdam: IOS Press, 1994. 2. Carroll J. M., Scenario-Based Design: Envisioning Work and Technology in System Development, New York: Wiley, 1995. 3. Fenton N. and Pfleeger S.L. Software Metrics: A Rigorous Approach. 2nd ed. London: International Thomson Computer Press, 1997. 4. Fenton N.: Applying Bayesian belief networks to critical systems assessment. Critical Systems. Club Newsletter, Vol 8, 3 Mar. 1999,pp. 10–13. 5. Fenton N., Maiden N.: Making Decisions: Using Bayesian Nets and MCDA. Computer Science Dept, Queen Mary and Westfield College, London, 2000 6. Frawley J, Piatetsky-Shapiro G, Matheus C. J.: Knowledge Discovery in Databases: An Overview, in Knowledge Discovery in Databases. Cambridge, MA: AAAI/MIT, 1991, pp. 1-27. 7. Goebel M. and Gruenwald L.: A Survey of Data Mining and Knowledge Discovery Software Tools. ACM SIGKDD, June 1999, Volume I, Issue 1-page20. 8. Gregoriades A., Sutclife A. and Shin J. E.: Assessing the Reliability of Sociotechnical Systems. 12th Annual Symposium INCOSE (Las Vegas, July 2002). 9. Hollnagel E.: Human Reliability Analysis-Context and Control. Academic Press, Inc., New York, NY, 1993. 10. Pearl, J., Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Mateo, CA, 1988. 11. Reason J.: Human Error, Cambridge University Press. New York, NY,1990. 12. Reason J.: Managing the Risks of Organizational Accidents. Aldershot: Ashgate, 2000. Authors: Vipin Kumar, Gopal Pathak, C.B.Gupta A Deterministic Inventory Model for Deterioraing Items with Selling Price Dependent Demand and Paper Title: Parabolic Time Varying Holding Cost under Trade Credit Abstract: In the present study, we have developed a deterministic inventory model for deteriorating environment under the price dependent demand with parabolic time varying holding cost and trade credit. Supplier offers a credit limit to the customer and during that span of time no interest is charged, but after the expiry of the specified time limit, the supplier will charge some interest. The customer has the reserve capital to make the payments at the beginning, but however, he decides to take the advantage of the credit limit facility provided by the supplier. This study has main focus to establish the mathematical model for an inventory system under the above conditions. At the end of the paper, numerical examples are provided to illustrate the problem and sensitivity analyses have been carried out for showing the effect of variation in the parameters. Mathematical subject classification: 90B05
Keywords: Deterioration, Price dependent demand, Trade credit, parabolic varying holding cost 9. References: 1. M. M. Jamal, B. R. Sarkar and S. Wang, (1997), an ordering policy for deteriorating items with allowable shortage and permissible delay 33-37 in payment, Journal of Operational Research Society, 48 , 826-833. 2. G. Kingsmand, (1983), The effect of payment rules on ordering and stockholding in purchasing, Journal of Operational Research Society, 34, 1085-1098. 3. Burwell T. H., Dave D. S., Fitzpatrick K. E. and Roy M. R. (1997), Economic lot size model for price-dependent demand under quantity and freight discounts, International Journal of Production Economics, 48(2) 141-155. 4. B. Chapman, S. C. Ward, D. F. Ward and M. G. Page (1985), Credit policy and inventory control, Journal of Operational Research Society, 35, 1055-1065. 5. W. Haley and R. C. Higgin (1973), Inventory policy and trade credit financing, Management Science, 20, 464-471. 6. C. K. Jaggi and S. P. Aggarwal (1994), Credit financing in economic ordering policies of deteriorating items, International Journal of Production Economics, 34, 151-155. 7. Chung K. and Ting P. (1993), An heuristic for replenishment of deteriorating items with a linear trend in demand, Journal of Operational Research Society, 44, 1235-1241. 8. Covert R. P. and Philip G. C.(1973), An EOQ model for items with Weibull distribution deterioration, AIIE Transactions, 5, 323-326. 9. Chang CT, Wu SJ, Chen LC. (2009a), Optimal payment time with deteriorating items under inflation and permissible delay in payments. Int. J. Syst. Sci., 40,985-993. 10. Chang HC, Ho C.H, Ouyang L.Y and Su C.H(2009b),. The optimal pricing and ordering policy for an integrated inventory model when trade credit linked to order quantity. Appl. Math. Model, 33,2978-2991. 11. Chen L.H, Kang F.S(2010) , Integrated inventory models considering permissible delay in payment and variant pricing strategy. Appl. Math. Model, 34,36-46. 12. Chen M.L and Cheng M.C.(2011), Optimal order quantity under advance sales and permissible delays in payments, African Journal of Business Management 5(17), 7325-7334. 13. Deb M. and Chaudhuri K. S. (1986), An EOQ model for items with finite rate of production and variable rate of deterioration, Opsearch, 23 ,175-181. 14. Giri B. C. and Chaudhuri K. S. (1997), Heuristic models for deteriorating items with shortages and time-varying demand and costs, International Journal of Systems Science, 28, 53-159. 15. Goh M. (1994), EOQ models with general demand and holding cost functions, European Journal of Operational Research, 73, 50-54. 16. Hariga M. A. and Benkherouf L. (1994), Optimal and heuristic inventory replenishment models for deteriorating items with exponential time-varying demand, European Journal of Operational Research, 79 ,123-137. 17. J. T. Teng, C. T. Chang and S. K. Goyal (2005), Optimal pricing and ordering policy under permissible delay in payments, International Journal of Production Economics, 97 ,121-129. 18. Jaggi C. K., Goel S. K and Mittal M (2011) , Pricing and Replenishment Policies for Imperfect Quality Deteriorating Items Under Inflation and Permissible Delay in Payments, International Journal of Strategic Decision Sciences (IJSDS) 2( 2), 20-35. 19. Jalan A. K. and Chaudhuri K. S. (1999), Structural properties of an inventory system with deterioration and trended demand, International Journal of Systems Science, 30 , 627-633. 20. Kumar M., Tripathi R. P. and Singh S. R. (2008), Optimal ordering policy and pricing with variable demand rate under trade credits, Journal of National Academy of Mathematics, 22 111-123. 21. Kumar M, Singh S. R. and Pandey R. K. (2009), An inventory model with quadratic demand rate for decaying items with trade credits and inflation, Journal of Interdisciplinary Mathematics, 12(3), 331-343. 22. Kumar M, Singh S. R. and Pandey R. K. (2009), An inventory model with power demand rate, incremental holding cost and permissible delay in payments, International Transactions in Applied Sciences, 1, 55-71. 23. Mondal B., Bhunia A. K. and Maiti M. (2003), An inventory system of ameliorating items for price dependent demand rate, Computers and Industrial Engineering, 45(3), 443-456. 24. Muhlemann A. P. and Valtis-Spanopoulos N. P. (1980), A variable holding cost rate EOQ model, European Journal of Operational Research, 4, 132-135. 25. Ray, Ajanta (2008), an inventory model for deteriorating items with price dependent demand and time-varying holding cost, Journal of AMO, 10, 25-37. 26. R. L. Bregman (1993), The effect of extended payment terms on purchasing, Computers in Industry, 22 ,311-318. 27. S. P. Aggarwal and C. K. Jaggi (1995), Ordering policies of deteriorating items under permissible delay in payments, Journal of Operational Research Society 46 , 458-462. 28. S. K. Goyal (1985), Economic order quantity under conditions of permissible delay in payments, Journal of Operational Research Society, 36, 335-338. 29. Shah Y. K. and Jaiswal M. C. (1977), an order-level inventory model for a system with constant rate of deterioration, Opsearch, 14, 174- 184. 30. Weiss H. J. (1982), Economic order quantity models with non-linear holding cost, European Journal of Operational Research, 9 , 56-60. 31. You S. P. (2005), Inventory policy for products with price and time-dependent demands, Journal of Operational Research Society, 56 , 870-873. Authors: Anilkumar N Holambe, Ravindra C Thool Combining Multiple Feature Extraction Technique and Classifiers for Increasing Accuracy for Paper Title: Devanagari OCR Abstract: In this paper we combining statistical, structural, Global transformation and moments features to form hybrid feature vector .We are combining Classifiers for achieving high accuracy for Devanagari Script. To abolish the hitch of misclassification and increase the classifier accuracy, we are combining SVM and KNN together. The dataset used for experiment are created by us.
Keywords: Devanagari, SVMKNN, Features, Zernike moment.
References: 1. C.Y. Suen, M. Berthod, and S. Mori, “Automatic Recognition of Hand printed Characters – The State of The Art,” Proc. IEEE, vol.68, no. 4, pp. 469-487, Apr. 1980. 2. J. Mantas, “An Overview of Character Recognition Methodologies,” Pattern Recognition, vol. 19, no. 6, pp. 425-430, 1986. 3. V.K. Govindan and A.P. Shivaprasad, “Character Recognition – AReview,” Pattern Recognition, vol. 23, no. 7, pp. 671-683, 1990. 4. S. Mori, C.Y. Suen, and K. Yamamoto,“Historical Review of OCRResearch and Development,” Proc. IEEE, vol. 80, no. 7, pp. 1029- 1058, Jul. 1992. 5. H. Bunke and P.S.P. Wang (eds.), Handbook of Character Recognition and Document Image Analysis, World Scientific Publishing, 10. Singapore,1997 6. N. Nagy, “Twenty Years of Document Image Analysis in PAMI,”IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no.1, pp. 38-62, Jan. 2000. 38-41 7. D. Trier, A. K. Jain, T. Taxt, “Feature Extraction Method for Character Recognition - A Survey”, Pattern recognition, vol.29, no.4, pp.641-662, 1996 8. Ludmila IK, “A Theoretical Study on Six Classifier Fusion Strategies,” IEEE Trans. On Pattern Analysis and Machine Intelligence, v. 24, No. 2, pp. 281-286, Feb. 2002. 9. J Kittler, M Hatef, RPW Duin, and J Matas,“On Combining Classifiers,” IEEE Trans. On Pat. Analysis and Machine Intel., Vol. 20, No. 3, Mar. 1998. Mar. 1998. 10. R.M.K. Sinha, H. Mahabala,, “Machine recognition of Devanagri script”, IEEE Trans. System, Man Cybern. 9(1979) 435-441. 11. Plamondon, R. Srihari, S.N. ,Ecole Polytech.,Montreal, Que. , “ Online and Offline HandwritingRecognition : A comprehensive Survey,1EEE Transactions On Pattern Analysis And Machine Intelligence. VOL. 22, NO. 1. JANUARY 2000 12. U. Pal , B.B. Chaudhuri , “Printed Devanagri script OCR system”, Vivek 10 (1997) 12-24 13. S. Palit, B.B. Chaudhuri, “A feature-based scheme for the machine recognition of printed Devanagri script”, P.P. Das, B.N. Chatterjee (Eda.) Pattern Recognition, Image Processing and Computer Vision, Narosa Publishing House: New Delhi, India 1995, pp. 163-168 14. I.K. Sethi, B. Chatterjee, “Machine recognition of constrained hand-printed Devanagri numerals”, J. Inst.Electron. Telecom. Eng. 22 (1976) 532-535. 15. I.K. Sethi, B. Chatterjee,“Machine recognition of constrained hand-printed Devanagri characters”, Pattern Recognition 9 (1977) 69-76 16. R.M..K. Sinha, “A syntactic pattern analysis system and its application to Devanagri script recognition”, Ph.D. Thesis , Electrical Engineering Department, Indian Institute of Technology, India, 1973. 17. V. Bansal and R. M. K. Sinha, “Integrating Knowledge Sources in Devnagri Text Recognition,” IEEE Transactions on Systems, Man, & Cybernetics Part A: Systems . the International Society for Optical Engineering. v 3964 2000. p305-312 18. S. Madhvanath and V. Govindaraju. , “ The role of holistic paradigms in handwritten word recognition. ,”IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 23(2):149-164, 2001. 19. M. Blumenstein, B. K. Verma and H. Basli, , “A Novel Feature Extraction Technique for the Recognition of Segmented Handwritten Characters,”7th International Conference on Document Analysis and Recognition (ICDAR ’03) Eddinburgh, Scotland: pp.137-141, 2003. 20. Alireza khotanzad and Yaw hua hong, “Invariant Image Recognition by Zernike Moments,”IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 12. No. 5. Pp489-497 may 1990 21. Ovidiu Ivanciuc, “Applications of Support Vector Machines in Chemistry", In: Reviews in Computational Chemistry, Volume 23, 2007, pp. 291–400. 22. Tao Hong, Stephen W. Lam, Jonathan J. Hull and Sargur N. Srihari, “The Design of a Nearest-Neighbor Classifier and Its Use for Japanese Character Recognition”, in the Proc.of the thied Int. Conf.on Document Analysis and Recognition,Aug.1995,270-273. 23. Rong Li, Hua-Ning Wang, Han He, Yan-Mei Cui and Zhan-Le Du, “ Support Vector Machine combined with K-Nearest Neighbors for Solar Flare Forecasting,” Chin. J. Astron. Astrophys. Vol. 7, No. 3, 441–447-2007 24. http://www.mathworks.com/help/images/ref/regionprops.html Authors: B. Kishore, M.R.S.Satyanarayana, K.Sujatha Paper Title: Adaptive Particle Swarm Optimization with Neural Network for Machinery Fault Detection Abstract: Rotating machines are one of the most important elements in almost all the industries and continuous condition monitoring of these crucial parts is essential for preventing early failure, production line breakdown, improving plant safety, efficiency and reliability. Faults may also be developed over a long period of time or even suddenly. However manual fault detection techniques are error prone. This paper identifies and utilizes the distribution information of the population to estimate the evolutionary states. Based on the states, Adaptive control strategies are developed for the inertia weight and acceleration coefficients for faster convergence speed. The Particle Swarm Optimization (PSO) is thus systematically extended to Adaptive Particle Swarm Optimization (APSO), so as to bring about outstanding performance when solving global optimization problems. This paper proposes an adaptive particle swarm optimization with adaptive parameters. Adaptive control strategies are developed for the inertia weight and acceleration coefficients for faster convergence speed.
Keywords: Artificial Neural Network, Adaptive Particle swarm Optimization (APSO), Fault detection, Particle swarm Optimization (PSO).
References: 1. McCormick A.C. And Nandi A.K., “Classification of the rotating machines condition using Artificial Neural Networks”, Proceedings of Institution of Mechanical Engineers, Part C 211, 439 -450 [1997] 2. Azadeha, M. Saberib, A. Kazemc, V. Ebrahimipoura, A. Nourmohammadzadeha, Z. Saberid, “A flexible algorithm for fault diagnosis 11. in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization”, Applied Soft Computing 13 1478–1485 [2013] 42-46 3. Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948, 1995. 4. Zhang, Huihe Shao, Yu Li, Particle swarm optimization for evolving artificial neural network, in: Proc. of IEEE Int. Conf. on System, Man, and Cybernetics, vol. 4 (2000) 2487–2490. 5. Y.H. Shi, R.C. Eberhart, Parameter selection in particle swarm optimization, in: Proc. of 1998 Annual conference on Evolutionary Programming, San Diego, 1998. 6. J. Salerno, Using the particle swarm optimization technique to train a recurrent neural model, in: Proc. of Ninth IEEE Int. Conf. on Tools with Artificial Intelligence (1997) 45–49. 7. Bäck T, Fogel DB, Michalewicz Z (eds) Evolutionary computation 1: basic algorithms and operators.Institute of physics publishing, Bristol, UK[2000a]. 8. Bäck T, Fogel DB, Michalewicz Z (eds) Evolutionary computation 2: basic algorithms and operators.Institute of physics publishing, Bristol, UK [2000b]. 9. Jing-Ru Zhang,Jun Zhang,Tat-Ming Lok, Michael R. Lyu,”A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training”, Applied Mathematics and Computation, 185,1026–1037[2007] 10. Y. Shi, R.C. Eberhart, A modified particle swarm optimizer, in: Proc. of IEEE World Conf. on Computation Intelligence 69–73[1998] 11. Kuang Yu Huang “A hybrid particle swarm optimization approach for clustering and classification of datasets” Knowledge-Based Systems 24, 420–426[2011] 12. B. Irie, S. Miyake, Capability of three-layered perceptrons, in: Proc. of IEEE Int. Conf. On Neural Networks, San Diego, USA 641– 648.[1998]. 13. D.B. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, IEEE Press, Piscataway, NJ, [1995]. 14. X. Yao (Ed.), Evolutionary Computation: Theory and Applications, World Scientific, Singapore, [1999]. 15. D. Thierens. Adaptive strategies for operator allocation. In F. Lobo, C. Lima, and Z. Michalewics (Eds.), Parameter Setting in Evolutionary Algorithms, Chapter 4, 77–90, [2007]. Authors: Rachna H. B., M. S. Mallikarjuna Swamy Paper Title: Detection of Tuberculosis Bacilli using Image Processing Techniques Abstract: Tuberculosis (TB) is one of the major diseases in developing countries. TB detection is based on sputum examination microscopically by using Ziehl-Neelsen stain (ZN-stain) method, which is used worldwide. This method needs human expertise and intensive examination. The availability of expertise, time and cost are the constraints of the human intervention based examinations. Therefore, there is a need of automation of examination and detection of TB bacteria using digital image of ZN-stain sample. In this work, an algorithm based on image 12. processing is developed for identification of TB bacteria in sputum. The method is based on Otsu thresholding and k-means clustering approach. The performance of clustering and thresholding algorithms for segmenting TB bacilli 47-51 in tissue sections is compared. The developed automated technique shows good accuracy and efficiency.
Keywords: Clustering, Image segmentation, Otsu thresholding, Tuberculosis
References: 1. G Tuberculosis Control 2010, World Health Organization, 2010 2. M.K. Osman, M.Y. Mashor, and H. Jaafar, ‘Combining Thresholding and Clustering Techniques for Mycobacterium tuberculosis Segmentation in Tissue Sections’, Australian Journal of Basic and Applied Sciences, 5, (12), pp. 1270-1279. 2011. 3. K. Veropoulos, C. Campbell, and G. Learmonth, ‘Image Processing and Neural Computing Used in the Diagnosis of Tuberculosis’. Proc. IEE Colloquium on Intelligent Methods in Healthcare and Medical Applications (Digest No. 1998/514), York, UK, 20 pp. 8/1- 8/4, 1998. 4. M.G. Forero, F. Sroubek, and G. Cristobal, ‘Identification of tuberculosis bacteria based on shape and color’, Real-Time Imaging 10, (4), pp. 251–262. 2004. 5. J. Minion, H. Sohn, and M. Pai, ‘Light-emitting diode technologies for TB diagnosis: what is on the market?’ Expert Rev. Med. Devices, 6, (4), pp. 341-345. 2009. 6. M. Costa, F. Cicero Filho, J. Sena, J. Salem, and M. de Lima, ‘Automatic identification of mycobacterium tuberculosis with conventional light microscopy’. Proc. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008. EMBS 2008. , British Columbia, Canada. pp. 382-385, 20-24 August 2008 . 7. P. Sadaphal, J. Rao, G. Comstock, and M. Beg, ‘Image Processing Techniques for Identifying Mycobacterium Tuberculosis in Ziehl- Neelsen Stains’, The International Journal of Tuberculosis and Lung Disease: The Official Journal of the International Union against Tuberculosis and Lung Disease, 12, (5), pp. 579-582. 2008. 8. R. Khutlang, S. Krishnan, R. Dendere, A. Whitelaw, K. Veropoulos, G. Learmonth, and T.S. Douglas, ‘Classification of Mycobacterium tuberculosis in images of ZN-stained sputum smears’, IEEE Transactions on Information Technology in Biomedicine, 2009. 9. Vandenbroucke, and L. Macaire, “Color spaces and image segmentation”, Advances in Imaging and Electron Physics, vol. 151, pp. 65-168, 2008. 10. R. C. Gonzalez and R. E. Woods. Digital Image Processing, Prentice Hall, New Jersey 07458, second edition, 2001. 11. L. Busin, N. Vandenbroucke, and L. Macaire, “Color spaces and image segmentation”, Advances in Imaging and Electron Physics, vol. 151, pp. 65-168,2008. 12. J. V. Llahi, “Color Constancy and Image Segmentation Techniques for Applications to Mobile Robotics”, Universitat Politècnica de Catalunya, Doctoral thesis, 2005. 13. N. Otsu, ‘A Threshold Selection Method from Gray-level Histograms’, IEEE Trans.Syst .Man Cybernetics, 9, (1), pp. 62-66. 1979. Authors: Vivek Hanumante, Sahadev Roy, Santanu Maity Paper Title: Low Cost Obstacle Avoidance Robot Abstract: This paper deals with a low cost solution to obstacle avoidance for a mobile robot. This paper also presents a dynamic steering algorithm which ensures that the robot doesn't have to stop in front of an obstacle which allows robot to navigate smoothly in an unknown environment, avoiding collisions. The obstacle avoidance strategy has been described. Obstacle avoidance strategy and working of robot is greatly dependent on the detection of obstacles by sensors and corresponding response of robot. Working principle of sensors, limitation of their use and obstacle avoidance algorithm has been discussed in detail.
Keywords: AVR Studio, Binary Logic, Dynamic steering algorithm, RS232.
13. References: 1. Engelberger, J., Transitions Research Corporation, private communication, 1986. 52-55 2. Borenstein, J., "The Nursing Robot System." Ph. D. Thesis, Technion, Haifa, Israel, 1987. 3. Borenstein, J. and Koren, Y., "Obstacle Avoidance With Ultrasonic Sensors." IEEE Journal of Robotics and Automation. Vol. RA-4, No. 2, 1988, pp. 213-218. 4. Borenstein, J. and Koren, Y., "Real-time Obstacle Avoidance for Fast Mobile Robots." IEEE Transactions on Systems, Man, and Cybernetics, Vol. 19, No. 5, Sept./Oct. 1989, pp. 1179-1187. 5. Paul Kinsky and Quan Zhou, “Obstacle Avoidance Robot.” Major Qualifying Project Report, WORCESTER POLYTECHNIC INSTITUTE, Project Number: YR-11E1. 6. Ibrahim KAMAL, “Infra-Red Proximity Sensor Part 1.” Article, Sensor & Measurement, January 22, 2008. Available: http://www.ikalogic.com 7. Harivardhagini S., Pranavanand S and Ghali Bharadwaja Sharma, “Voice Guided Robot Using LabVIEW.” CVR Journal of Science and Technology, Volume 3, December 2012. 8. Jnaneshwar Das, Mrinal Kalakrishnan and Sharad Nagappa, “Obstacle Avoidance for a Mobile Exploration Robot with Onboard Embedded Ultrasonic Range Sensor”, PES Institute of Technology, Bangalore. Authors: Tamal Sarkar Paper Title: Design of D Flip-Flip Using Nano-Technology based Quantum Cellular Automata Abstract: Lot of research has been going on for implementing digital systems at nano-scale level. Quantum- dot based Quantum Cellular Automata (QCA) is a promising as well as emerging technology for implementing of digital systems at nano-scale. By taking full advantage of the unique feature of this technology, it is possible to have device which functions consuming ultra low power and very high operating speeds. In this work, we have selected few basic flip-flops and studied them well. Using the computational tools “QCADesigner” proposed for designing QCA based digital circuits; we have designed the QCA circuits of D-Flip Flop. The correctness of the proposed circuits is also verified using simulation results obtained using QCADesigner.
Keywords: Quantum Dot, QCA, Nano-electronic circuit, Binary logic, D-Flip Flop, Cell-Cell Response. 14.
References: 56-60 1. M. A. Reed, J. N. Randall, R. J. Aggarwal, R. J. Matyi, T. M. Moore, and A. E. Wetsel, “Observation of discrete electronic states in a zero-dimensional semiconductor nanostructure”, Phys. Rev. Lett. vol. 60, 1988, pp. 535-539. 2. U. Meirav, and M. A. Kastner, and S. J Wind, , “Single-electron charging and periodic conductance resonances in GaAs nanostructures”, Phys. Rev. Lett. ,Vol. 65 (6), 1990, pp. 771—774, doi. 10.1103/PhysRevLett.65.771 3. F. Hofmann, T. Heinzel, D.A. Wharam, and J.p. Kotthaus, “Single electron switching in a parallel quantum dot”, Physical Review B,, vol. 51(19), 1995, pp. 13872-13875 4. W. Porod, C. Lent, G. H. Bernstein, A. O. Orlov, I. Hamlani, G. L. Snider & J. L. Merz, “Quantum-dot cellular automata: computing with coupled quantum dots”, vol. 86(5), 1999, pp. 549-590 5. C. S. Lent, P D Tougaw, W. Porod and G. H. Bernstein, “Quantum cellular automata”, vol. 4(1), 1993, pp.49-57 6. C. S. Lent and P. D. Tougaw, “A device architecture for computing with Quantum dots”, Proc. IEEE,vol. 85(4), 1997, pp.541-557 7. T. Sarkar, S.C.Das and T.Chattopadhyay “Transition from MOSFET to Quantum Dot: An Overview”, International conference on fundamental & applications of nanoscience & technology, (ICFANT-2010), Jadavpur University, Dec 9-11, 2010, pp.183-186. 8. T.Sarkar, S.C.Das and T.Chattopadhyay, “Mathematical Modeling of Quantum Dot”, International Conference On Laser, Materials Science And Communication (ICLMSC-2011), University of Burdwan, December 07 to 09, 2011, 183-186, ISBN: 93-80813-14-7. 9. K. Das and D. De, “Characterization, test and logic synthesis of novel conservative and reversible logic gates for QCA”, International Journal of Nanoscience,vol. 9(3), 2010, pp.201-214 10. Amlani, A. O. Orlov, G. Toth, G. H. Bernstein, C. S. Lent and G. L. Snider, “Digital logic gate using quantum-dot cellular automata”, Science, vol. 284 (9), 1999, pp.289-291 11. K. Das and D. De, “Characterization, applicability and defect analysis for tiles nanostructure of quantum dot cellular automata”, Molecular Simulation, vol.37(3), 2011, pp. 210-225. 12. Lee Ai Lim, Azrul Ghazali, Sarah Chan Tji Yan, Chien Fat, “Sequential circuit using Quantum-Dot Cellular Automata (QCA)”, 978-1- 4673-3119-7, Vol. 12,IEEE, 2012, pp. 162-167 13. M. Morris Mano, Digital Logic and Computer Design, N.J., USA: Prentice –Hall .(Indian edition, published by Prentice-Hall of India Private Limited, Twenty-first Printing), 2000, pp. 204-233 14. J. Huang, M. Momenzadeh, F. Lombardi, “Design of sequential circuits by quantum-dot cellular automata, Microelectronic .J. 38, 2007, pp. 426-429 15. A.S. Shamsabadi, B.S. Ghahfarokhi, K. Zamianifar, N. Movahedinia, “Applying inherent capabilities of quantum-dot cellular automata to design D flip-flop case study”, Journal of Systems Architecture, 55, 2009, pp. 180-187 Authors: Metin Zontul Paper Title: Rule Based Aircraft Performance System Abstract: For aircrafts, there are many rules for takeoff and landing performance calculations. These rules are defined according to aircraft configuration, MEL/CDL items, Turn Procedures, Airport and Obstacle Database and Weather Conditions. In this study, a general database is created to include all kinds of aircraft, airport information and rules effecting aircraft performance. The rules include aircraft configuration, MEL/CDL items and external conditions. Rules can be simple or complex in such a way that composite conditions can be used all together and simple rules can be combined in a complex rule. This study is different from other studies in that all aircraft types can be combined in a single server based database systems and rule checking process is very dynamic and flexible. This server database can be extended to create national or international aircraft and airport information system in the future. Besides the advantages of operational safety resulting from making point calculation instead of conventional calculations, with the implementation of this system fuel conservation, emissions of CO2 decrement, aircraft engine health management are achieved.
Keywords: Aircraft Performance, EFB Database, Performance Rule, Rule Based System.
References: 1. P. Skaves, “Electronic flight bag (EFB) policy and guidance”, Digital Avionics Systems Conference (DASC), 2011, IEEE/AIAA 30th (pp. 8D1-1). 2. F. D. Florio, An Introduction to Aircraft Certification, 2011, Pages 97-136, ISBN: 978-0-08-096802-5, Elsevier Inc. http://dx.doi.org/10.1016/B978-0-08-096802-5.10005-4. 3. R. A. Lusardi, S. M. Crockard, European Patent No. EP 1610094. Munich, Germany: European Patent Office, 2005. 4. E. Theunissen, G. J. M. Koeners, F. D. Roefs, P. Ahl, , & O. F. Bleeker, Evaluation of an electronic flight bag with integrated routing and runway incursion detection functions. In Digital Avionics Systems Conference, 2005. DASC 2005. The 24th (Vol. 1, pp. 4-E). IEEE. 15. 5. D. C. Chandra, M. Yeh, V. Riley, & , S. J. Mangold , Human Factors Considerations in the Design and Evaluation of Electronic Flight Bags (EFBs): Version 2 (No. DOT-NTSC-FAA-03-07,). Office of Aviation Research. 61-66 6. D. C. Chandra, & M.Yeh, Evaluating Electronic Flight Bags in the Real World. Volpe National Transportation Systems Center,2006. 7. D. C. Chandra, M.Yeh, & V. Riley, Designing and testing a tool for evaluating electronic flight bags. In Proceedings of the International Conference on Human-Computer Interaction in Aeronautics (HCI–Aero) ,2004, Vol. 29. 8. M.N. Kamel,”A prototype rule-based front end expert system for integrity enforcement in relational data bases: An application to the naval aircraft flight records data base”, Expert Systems with Applications, 1995, Volume 8, Issue 1, Pages 47-58. 9. Y. Cheng, ”A rule-based reactive model for the simulation of aircraft on airport gates”, Knowledge-Based Systems, 1998,Volume 10, Issue 4, Pages 225-236. 10. S.-F. Wu, C.J.H. Engelen, R. Babuška, Q.-P. Chu, J.A. Mulder, “Fuzzy logic based full-envelope autonomous flight control for an atmospheric re-entry spacecraft”, Control Engineering Practice, Volume 11, Issue 1, January 2003, Pages 11-25. 11. S. Alam, K. Shafi, H. A. Abbass, M. Barlow,”An ensemble approach for conflict detection in Free Flight by data mining”, Transportation Research Part C: Emerging Technologies, Volume 17, Issue 3, June 2009, Pages 298-317. 12. F. Li, C. Tian, H. Zhang, W. Kelley, “Rule-based optimization approach for airline load planning system”, Procedia Computer Science, Volume 1, Issue 1, May 2010, Pages 1455-1463. 13. J. J. H . Liou, L.Yen, G.-H. Tzeng,, “Using decision rules to achieve mass customization of airline services”, European Journal of Operational Research, 2010, Volume 205, Issue 3, Pages 680-686. 14. Airport & Obtacle Database, http://www.aodb.iata.org/index.htm 15. Standardised Computerised Aircraft Performance (SCAP) Task Force, IATA, http://www.iata.org/whatwedo/workgroups/Pages/scaptf.aspx 16. Boeing 737-800 Airplane Flight Manual, 2010, http://fly.shanghai-air.com/flyiis/handbook/ B737/737_800_AFM.pdf 17. J.-M. Roy, Weather Technology in the Cockpit, Airbus Document. 18. Boeing 737-600/700/800/900/900 ER Flight Crew Training Manual, 2008, http://www.flightdeck737.be/wp- content/uploads/2010/03/B738W-FCTM.pdf 19. Boeing 737-900ER with Winglets SCAP (BTM/BLM) Database Release Notes, October 2, 2007. 20. Boeing Land User Manual, October 23, 2009, A guide to LAND Version 1.4 21. Boeing Standard Takeoff Analysis Software (STAS) USER MANUAL, October 27, 2009. 22. AirBus OCTOPUS:SCAP DLL Manual 23. Airbus, Flight Operations Briefing Notes, Airbus, 2004. 24. Airbus, Performance Programs Manual, Airbus, 2000. Authors: ASM Delowar Hossain, M. Kouar, M. Ummy, M. Razani Paper Title: Performance Analysis of QoS Aware Distributed Schemes over a Ring- based EPON Architecture 16. Abstract: In this work, we introduce decentralized Dynamic Bandwidth Allocation (DBA) schemes capable of supporting upstream Quality of Service (QoS) through differentiated class of service (CoS). In contrast to the 67-75 centralized approach, the proposed QoS aware distributed DBA supports differentiated services through the integration of both scheduling mechanisms (intra-ONU and inter-ONU) at the Optical Network Unit (ONU). This integration of both scheduling can only be supported by a decentralized architecture. We demonstrate, in addition to the added flexibility and reliability, that the distributed approach has characteristics that make it far better suited than its centralized counterpart for provisioning QoS necessary for properly handling voice, video, and data services over a single line.
Keywords: Distributed Control Scheme, Quality of Service, Passive Optical Access Network.
References: 1. M. Ngo, et al, "Controlling QoS in EPON-based FTTX access networks ", Telecommunication Systems, Volume 48, Issue 1-2, pp 203- 217, October 2011. 2. M. Ma, Y. Zhu, and T. H. Cheng, “A bandwidth guaranteed polling MAC protocol for Ethernet passive optical networks,” in Proc. IEEE INFOCOM’ 03, San Francisco, CA, Mar.–Apr. 2003, pp. 22–31. 3. G. Kramer, B. Mukherjee, and G. Pesavento, “IPACT: a dynamic protocol for an Ethernet PON (EPON),” IEEE Commun. Mag., pp. 74–80, Feb. 2002. 4. G. Kramer et al., “Ethernet PON: design and analysis of an optical access network,” Photon. Network Commun. J., vol. 3, no. 3, July 2001. 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. M. 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, Sept 15, 2009. 7. Assi et al., “Dynamic bandwidth allocation for quality of service over Ethernet PONs,” IEEE J. Select. Areas Commun., Dec. 03. 8. 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. 9. G. Kramer et al., “On supporting differentiated classes of service in EPON-based access network,” J. Opt. Networks, 2002. 10. ASM Delowar Hossain, et. al, "Downstream Bandwidth Allocation Scheme in Local Access over a Distributed Control Plane", in Proceedings of IEEE ICCIT 2011, Jordan, March 29-31, 2011 11. Hossain, R. Dorsinville, M. Ali, A. Shami, and C. Assi, "Ring-based local access PON architecture for supporting private networking capability," J. Opt. Network. 5, 26-39, 06. 12. J.M. Jaffe “Bottleneck Flow Control.”, IEEE Transactions on Communications, 29(7), 1981. 13. Hou, H. Tzeng and S. Panwar, “A generalized max-min rate allocation policy and its distributed implementation using the ABR flow control mechanism,” IEEE INFOCOM ’98, pp. 1366-1375, Apr. 1998. 14. Malla, M. El-Kadi, S. Olariu and P. Todorova, “A fair resource allocation protocol for multimedia wireless networks,” IEEE Trans. Parallel and Distributed Systems, vol. 14, no. 1, pp. 63-71, Jan. 2003. 15. Y. Zhou and H. Sethu, “On achieving fairness in the joint allocation of processing and bandwidth resources: principles and algorithms,” IEEE/ACM Trans. Networking, vol. 13, no. 5, pp. 1054 - 1067, Oct. 2005. 16. M. Hosaagrahara and H. Sethu, “Max-min fairness in input queued switches,” ACM SIGCOMM Student Poster Session, August 2005, Philadelphia, PA, USA. 17. V. Paxson and S. Floyd, “Wide area traffic: the failure of Poisson modeling,” IEEE/ACM Trans. Networking, vol. 3, pp. 226–244, June 1995. 18. W. Willinger, M. S. Taqqu, and A. Erramilli, “A bibliographical guide to self-similar traffic and performance modeling for modern high-speed networks,” in Stochastic Networks. Oxford, U.K.: Oxford Univ., 1996, pp. 339–366. Authors: Hariom Soni, Pradeep Kumar Mishra Paper Title: Congestion Control Using Predictive Approach in Mobile Ad Hoc Network Abstract: The Active Queue Management algorithms stabilize the instantaneous queue length through mapping the congestion measurement into packet drop probability to achieve high throughput and low average delay. Random Early Detection [RED] is widely used AQM mechanism detecting and avoiding the incipient congestion. The detection of congestion is based on priori estimation of congestion and calculation of average queue length and RED queue parameter settings. This paper describes the research work done in recent and new mechanism to estimates the network congestion and avoiding them, so that network performance will improve. Our goal in this paper is to achieve high throughput and low end to end delay in congested network.
Keywords: [RED] AQM RED,
References: 1. Patel j. k., Dubey j., “Mobile Ad Hoc Network Performance Improvement Using Strategical RED”, Ninth International Conference on 17. Wireless and Optical Communications Networks WOCN2012, 2012, IEEE. 2. R. Braden, D. clark, J. Crowcroft, B. Davie, S. Deering, D. Estrin, S. Floyd, V. J. G.Minshall, C. Patridge, L. Peterson, K. Ramakrishanan, S. Shenker, J. Wroclawski, and L. Zhang. Rfc-2309 recommendation on queue management and congestion avoidance 76-78 in the internet.Technical report, IETF, pp 246-350, 1998. 3. S. Floyd and V. Jacobson. “Random early detection gateways for congestion avoidance”, IEEE/ACM Transactions on Networking, 1(4):397–413, 1993. 4. V. Firoiu and M. Borden, “A study of active queue management for congestion control”, In Proceedings of the IEEE Infocom, pages 1435–1444, Tel Aviv, Mar 2000. 5. Chin-Hui Chien; Wanjiun Liao,“A self-configuring RED gateway for quality of service (QoS) networks, Multimedia and Expo”, 2003. ICME '03., Page(s): I - 793-6. 6. Kuzmanovic, A. Mondal, S. Floyd, K. Ramakrishnan. RFC 5562 -“Adding Explicit Congestion Notification Capability to TCP's SYN/ACK Packets”,AT&T Labs Research, June 2009. 7. K.Dinesh Kumar, I.Ramya & M.Roberts Masillamani, “Queue Management in Mobile Adhoc Networks (Manets)” 2010 IEEE. 8. Zhenyu Na and Qing Guo “An Improved AQM Scheme with Adaptive Reference Queue Threshold” 978-1-4577, 2011 IEEE. 9. Guan-Yi Su and Chian C. Ho “Random Early Detection Improved by Progressive Adjustment Method” Proceedings of IEEE 2008 6th National Conference on Telecommunication Technologies and IEEE 2008 2nd Malaysia Conference on Photonics, August 2008, Putrajaya, Malaysia. 2008 IEEE. 10. Rob Torres, John Border, George Choquette, Jun Xu, and Je-Hong Jong, “Congestion Control using RED and TCP Window Adjustment”978-1-4673,2013IEEE. Authors: Eltyeb E.Abed Elgabar, Haysam A. Ali Alamin 18. Paper Title: Comparison of LSB Steganography in GIF and BMP Images Abstract: Data encryption is not the only safe way to protect data from penetration given the tremendous development in the information age particularly in the field of speed of the processors and the cryptanalysis. In some cases, we need new technologies or methods to protect our secret data. There has emerged a set of new technologies that provides protection with or without data encryption technology such as data hiding. The word steganography literally means ‘covered writing’. It is derived from the Greek words “stegos” meaning “cover”, and “grafia” meaning “writing”. Steganography functions to hide a secret message embedded in media such as text, image, audio and video. There are a lot of algorithms used in the field of information hiding in the media; the simplest and best known technique is Least Significant Bit (LSB). In this paper we have made a comparison and of the (LSB) algorithm using the cover object as an image. From this image, we have chosen two types: Gif and BMP. The comparison and is based on a number of criteria to find out the strengths and weaknesses when using any of the two types.
Keywords: About four key words or phrases in alphabetical order, separated by commas. ,
References: 1. Watermarking Application Scenaros and Related Attacks ", IEEE international Conference on Image Processing, Vol. 3, pp. 991 – 993, Oct. 2001. 2. Moerland, T., “Steganography and Steganalysis”, Leiden Institute of Advanced Computing Science, www.liacs.nl/home/ tmoerl/privtech.pdf. 3. Henk C. A. van Tilborg (Ed.), "Encyclopedia of cryptography and security", pp.159. Springer (2005). 4. Johnson, N.F. & Jajodia, S., “Exploring Steganography: Seeing the Unseen”, Computer Journal,February 1998. 5. Krenn, R., “Steganography and Steganalysis”, http://www.krenn.nl/univ/cry/steg/article.pdf 6. Pallavi Hemant Dixit, Uttam L. Bombale, " Arm Implementation of LSB Algorithm of Steganography", International Journal of 79-83 Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-3, February 2013. 7. “Reference guide:Graphics Technical Options and Decisions”, http://www.devx.com/ /Article/1997. 8. Artz, D., “Digital Steganography: Hiding Data within Data”, IEEE Internet Computing Journal, June 2001. 9. NXP & Security Innovation Encryption for ARM MCUs ppt. 10. Owens, M., “A discussion of covert channels and steganography”, SANS Institute, 2002. 11. “MSDN:About Bitmaps”
Keywords: Segmentation Methods, Image, Pixon, Cluster, Graph-cut, Hybrid Methods.
References: 19. 1. Ajala Funmilola A , Oke O.A, Adedeji T.O, Alade O.M, Adewusi E.A. “Fuzzy k-c-means Clustering Algorithm for Medical Image Segmentation” Journal of Information Engineering and Applications ISSN 2224-5782 Vol 2, No.6, 2012 84-92 2. Alamgir Nyma, Myeongsu Kang, Yung-Keun Kwon, Cheol-Hong Kim, and Jong-Myon Kim “A Hybrid Technique forMedical Image Segmentation” Article ID 830252,Journal of Biomedicine and Biotechnology Hindawi Publishing Corporation Volume 2012. 3. Ali, Ameer; Karmakar, Gour C. and Dooley, Laurence S. “ Fuzzy Image Segmentation using Suppressed Fuzzy C-Means Clustering (SFCM)”, International Conference on Computer and Information Technology, December 2004. 4. Arnau Oliver, Xavier Mu˜noz, Joan Batlle, Llu´ıs Pacheco, and Jordi Freixenet, “Improving Clustering Algorithms for Image Segmentation using Contour andRegion Information”, IEEE transactions, 2006. 5. Yuri Boykov and Gareth Funka-Lea, “Graph Cuts and Efficient N-D Image Segmentation”, International Journal of Computer Vision 70(2), page no. 109–131, 2006. 6. Ravi S and A M Khan, “Operators Used in Edge Detection: A Case Study”, International Journal of Applied Engineering Research, ISSN 0973-4562 vol. 7 No 11, 2012. 7. Bryan S. Morse, Lecture 18: Segmentation (Region Based), Brigham Young University, 1998. 8. http://www.cs.uu.nl/docs/vakken/ibv/reader/chapter10.pdf 9. Dr.K.Duraiswamy and V. Valli Mayil “Similarity Matrix Based Session Clustering by Sequence Alignment Using Dynamic Programming” , Computer and Information Science journal, Vol 1, No.3, August 2008. 10. Dr. (Mrs.) G.Padmavathi, Dr.(Mrs.) P.Subashini and Mrs.A.Sumi “Empirical Evaluation of Suitable Segmentation Algorithms for IR Images”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 4, No 2, July 2010. 11. Jun Zhang and Jinglu Hu “Image Segmentation Based on 2D Otsu Method with Histogram Analysis”International Conference on Computer Science and Software Engineering, IEEE 2008. 12. Jos B.T.M. Roerdink and Arnold Meijster The Watershed Transform: De_nitions, Algorithms and Parallelization Strategies Fundamenta Informaticae 41, page no. 187-228 IOS Press, 2001. 13. Dr.S.V.Kasmir Raja, A.Shaik Abdul Khadir And Dr.S.S.Riaz Ahamed, “MOVING TOWARD REGION-BASED IMAGE SEGMENTATION TECHNIQUES: A STUDY”, Journal of Theoretical and Applied Information Technology. 14. S. A. Hojjatoleslami and J. Kittler, “Region Growing: A New Approach”, IEEE Transactions on Image Processing, Vol. 7, No. 7, July 1998. 15. Kostas Haris, Serafim N. Efstratiadis, Nicos Maglaveras, and Aggelos K. Katsaggelos, “Hybrid Image Segmentation Using Watersheds and Fast Region Merging”, IEEE Transactions on Image Processing, Vol. 7, No. 12, December 1998. 16. Lamia Jaafar Belaid and Walid Mourou, “IMAGE SEGMENTATION: A WATERSHED TRANSFORMATION ALGORITHM”, Image Anal Stereol 28 page no. 93-102 2009; 17. Matthew Marsh, “A Literature Review of Image Segmentation Techniques and Matting for the Purpose of Implementing “Grab-Cut”. 18. D. Muhammad Noorul Mubarak, M. Mohamed Sathik, S.Zulaikha Beevi and K.Revathy, “A HYBRID REGION GROWING ALGORITHM FOR MEDICAL IMAGE SEGMENTATION”, International Journal of Computer Science & Information Technology (IJCSIT) Vol 4, No 3, June 2012. 19. Nalina.P, Muthukannan.K “Survey on Image Segmentation Using Graph Based Methods”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-6, January 2013. 20. Nassir Salman, “Image Segmentation Based on Watershed and Edge Detection Techniques”, The International Arab Journal of Information Technology, Vol. 3, No. 2, April 2006. 21. Nobuyuki Otsu, “A Threshold Selection Method From Gray-Level Histograms”, IEEE Transactions on Systems, Man, And Cybernetics, Vol. Smc-9, No. 1, January 1979. 22. Dzung L. Pham, Chenyang Xu, and Jerry L. Prince, “CURRENTMETHODS INMEDICAL IMAGE SEGMENTATION”, Annu. Rev. Biomed. Eng. 2000. Vol. 02 page no. 315–37. 23. Pinaki Pratim Acharjya and Dibyendu Ghoshal “A Modified Watershed Segmentation Algorithm using Distances Transform for Image Segmentation”, International Journal of Computer Applications Volume 52– No.12, August 2012. 24. H.S.Prasantha ,Dr.Shashidhara.H.L, Dr.K.N.B.Murthy and Madhavi Lata.G , “MEDICAL IMAGE SEGMENTATION”, (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 04, 2010. 25. Rolf Adams and Leanne Bischof, “Seeded Region Growing”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.16, No. 6, June 1994. 26. N. Senthilkumaran and R. Rajesh, “Edge Detection Techniques for Image Segmentation – A Survey of Soft Computing Approaches”, International Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009. 27. Nikhil R Pal and Sankar K Pal, “ A Review on Image Segmentation Techniques”, Pattern Recognition, Vol 26, No. 29, 1993. 28. Shilpa Kamdi and R.K.Krishna, “ Image Segmentation and Region Growing Algorithm”, International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume 2, Issue 1. 29. X. Munoz, J. Freixenet, X. Cuf_ı, J. Mart, “Strategies for image segmentation combining region and boundary information”, Pattern Recognition Letters 24, page no 375–392, 2003. 30. Tranos Zuva, Oludayo O. Olugbara, Sunday O. Ojo and Seleman M. Ngwira “Image Segmentation, Available Techniques, Developments and Open Issues”, Canadian Journal on Image Processing and Computer Vision Vol. 2, No. 3, March 2011. 31. WANG Hongzhi and DONG Ying, “An Improved Image Segmentation Algorithm Based on Otsu Method”, International Symposium on Photoelectronic Detection and Imaging 2007: Related Technologies and Applications, Vol. 6625, 2008. 32. Jingdong Wang,”Graph Based Image Segmentation” A Thesis August 2007. 33. Yong Yang, “Image Segmentation By Fuzzy C-Means Clustering Algorithm With A Novel Penalty Term”, Computing And Informatics, Vol. 26, 17–31, 2007. 34. Yuri Y. Boykov and Marie-Pierre Jolly, “Interactive Graph Cuts For Optimal Boundary & Region Segmentation Of Objects In N-D Images”, Proceedings Of “Internation Conference On Computer Vision”, Vancouver, Canada, Vol.I, P.105, July 2001. 35. Yu-Hsiang Wang, “Tutorial: Image Segmentation”, National Taiwan University, Taipei, Taiwan, ROC. 36. Jianping Fan , Guihua Zeng, Mathurin Body and Mohand-Said Hacid, “Seeded region growing: an extensive and comparative study”, Pattern Recognition Letters 26, 1139–1156, 2005. 37. Zheng Lin, Jesse Jin and Hugues Talbot, “Unseeded region growing for 3D image segmentation” 38. S.Beucher, “The Watershed Transformation applied to Image Segmentation” 39. Hua Li, Abderrahim Elmoataz, Jaral Fadili and Su Ruan, “An improved image segmentation approach based on level set and mathematical morphology”. 40. Bo Peng, Lei Zhang, and David Zhang, “A Survey of Graph Theoretical Approaches to Image Segmentation”. 41. X. Cufi, X. Munoz, J. Freixenet and J. Marzi, “ A Review on Image Segmentation Techniques Integrating Region and Boundary Information”, 2001. 42. R.C. Gonzalez and R.E. Woods, “Digital Image Processing”, third edition, PHI publication, 2008. 43. S. Nagabhushana, “Computer Vision and Image Processing”, New Age International Publishers, 2005. Authors: Metin Zontul Paper Title: Rule Based Aircraft Performance System Abstract: For aircrafts, there are many rules for takeoff and landing performance calculations. These rules are defined according to aircraft configuration, MEL/CDL items, Turn Procedures, Airport and Obstacle Database and Weather Conditions. In this study, a general database is created to include all kinds of aircraft, airport information and rules effecting aircraft performance. The rules include aircraft configuration, MEL/CDL items and external conditions. Rules can be simple or complex in such a way that composite conditions can be used all together and simple rules can be combined in a complex rule. This study is different from other studies in that all aircraft types can be combined in a single server based database systems and rule checking process is very dynamic and flexible. 20. This server database can be extended to create national or international aircraft and airport information system in the future. Besides the advantages of operational safety resulting from making point calculation instead of 93-97 conventional calculations, with the implementation of this system fuel conservation, emissions of CO2 decrement, aircraft engine health management are achieved.
Keywords: Aircraft Performance, EFB Database, Performance Rule, Rule Based System
References: 1. P. Skaves, “Electronic flight bag (EFB) policy and guidance”, Digital Avionics Systems Conference (DASC), 2011, IEEE/AIAA 30th (pp. 8D1-1). 2. F. D. Florio, An Introduction to Aircraft Certification, 2011, Pages 97-136, ISBN: 978-0-08-096802-5, Elsevier Inc. http://dx.doi.org/10.1016/B978-0-08-096802-5.10005-4. 3. R. A. Lusardi, S. M. Crockard, European Patent No. EP 1610094. Munich, Germany: European Patent Office, 2005. 4. E. Theunissen, G. J. M. Koeners, F. D. Roefs, P. Ahl, , & O. F. Bleeker, Evaluation of an electronic flight bag with integrated routing and runway incursion detection functions. In Digital Avionics Systems Conference, 2005. DASC 2005. The 24th (Vol. 1, pp. 4-E). IEEE. 5. D. C. Chandra, M. Yeh, V. Riley, & , S. J. Mangold , Human Factors Considerations in the Design and Evaluation of Electronic Flight Bags (EFBs): Version 2 (No. DOT-NTSC-FAA-03-07,). Office of Aviation Research. 6. D. C. Chandra, & M.Yeh, Evaluating Electronic Flight Bags in the Real World. Volpe National Transportation Systems Center,2006. 7. D. C. Chandra, M.Yeh, & V. Riley, Designing and testing a tool for evaluating electronic flight bags. In Proceedings of the International Conference on Human-Computer Interaction in Aeronautics (HCI–Aero) ,2004, Vol. 29. 8. M.N. Kamel,”A prototype rule-based front end expert system for integrity enforcement in relational data bases: An application to the naval aircraft flight records data base”, Expert Systems with Applications, 1995, Volume 8, Issue 1, Pages 47-58. 9. Y. Cheng, ”A rule-based reactive model for the simulation of aircraft on airport gates”, Knowledge-Based Systems, 1998,Volume 10, Issue 4, Pages 225-236. 10. S.-F. Wu, C.J.H. Engelen, R. Babuška, Q.-P. Chu, J.A. Mulder, “Fuzzy logic based full-envelope autonomous flight control for an atmospheric re-entry spacecraft”, Control Engineering Practice, Volume 11, Issue 1, January 2003, Pages 11-25. 11. S. Alam, K. Shafi, H. A. Abbass, M. Barlow,”An ensemble approach for conflict detection in Free Flight by data mining”, Transportation Research Part C: Emerging Technologies, Volume 17, Issue 3, June 2009, Pages 298-317. 12. F. Li, C. Tian, H. Zhang, W. Kelley, “Rule-based optimization approach for airline load planning system”, Procedia Computer Science, Volume 1, Issue 1, May 2010, Pages 1455-1463. 13. J. J. H . Liou, L.Yen, G.-H. Tzeng,, “Using decision rules to achieve mass customization of airline services”, European Journal of Operational Research, 2010, Volume 205, Issue 3, Pages 680-686. 14. Airport & Obtacle Database, http://www.aodb.iata.org/index.htm 15. Standardised Computerised Aircraft Performance (SCAP) Task Force, IATA, 16. Boeing 737-800 Airplane Flight Manual, 2010, 17. J.-M. Roy, Weather Technology in the Cockpit, Airbus Document. 18. Boeing 737-600/700/800/900/900 ER Flight Crew Training Manual, 2008, http://www.flightdeck737.be/wp- content/uploads/2010/03/B738W-FCTM.pdf 19. Boeing 737-900ER with Winglets SCAP (BTM/BLM) Database Release Notes, October 2, 2007. 20. Boeing Land User Manual, October 23, 2009, A guide to LAND Version 1.4 21. Boeing Standard Takeoff Analysis Software (STAS) USER MANUAL, October 27, 2009. 22. AirBus OCTOPUS:SCAP DLL Manual 23. Airbus, Flight Operations Briefing Notes, Airbus, 2004. 24. Airbus, Performance Programs Manual, Airbus, 2000. Authors: K. Vinod Kumar, G. Kishor Paper Title: Digital Simulation of Power Converter and it’s Control in Microgrid Abstract: Microgrids (MGs) and Distributed Generation are becoming an important role because of their peculiar characteristics. MGs are composed of small power sources which can be renewable, placed near customer sites. Moreover, they have the inherent property of islanding. The attention focuses on the control technique of the converters during grid connected and islanding operation of the MGs. The converters can be represented as an ideal converter with its specified parameters and proper control techniques. Some of the power converters like voltage source converters or current source converters are analysed based on the grid requirement in the system. Similarly some of the control loops like voltage control, current control or power control can be analysed based on the performance of the grid connection. Based on these conditions we may have the attention towards the phase transformations for the control strategies. All the control operations with their performances can be simulated using MATLAB/SIMULINK. 21. Keywords: Microgrid, PWM, Voltage Source Inverter, SRF-PLL, Micro sources. 98-101
References: 1. VECHIU, A. LLARIA O. CUREA and H. CAMBLONG, Control of Power Converters for Microgrids, by ever ecologic vehicles and renewable energies, march 26-29,2009. 2. H. Ibrahim, A. Ilinca and J. Perron, Energy Storage Systems Characteristics and Comparisons, Elsevier Renewable & Sustainable Energy Reviews, Vol. 12, pp. 1221-1250, 2008. 3. Singh. M.D and Khanchandani. K.B, Power Electronics, 2nd ed., Tata McGraw-Hill Publishing Company Limited-New Delhi, India, 2005. 4. Muhammad H. Rashid, Power Electronics Circuits, Devices, and Applications, 3rd ed., Eastern Economy Ed. New Delhi, India: Prentice-Hall -India, 2006. 5. Joan Rocabert, Member, IEEE, Alvaro Luna, Member, IEEE, Frede Blaabjerg, Fellow, IEEE, and Pedro Rodr´ıguez, Senior Member, Control of Power Converters in AC Microgrids, IEEE, IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 27, NO. 11, NOVEMBER 2012 Authors: Aleck W. Leedy Paper Title: Simulink / MATLAB Dynamic Induction Motor Model for Use as A Teaching and Research Tool Abstract: A dynamic model of the induction motor developed using Simulink / MATLAB that is beneficial for use as a teaching tool in electric machines and power electronics courses, or that can be used as a research tool in the laboratory is presented. The model can be used to study the dynamic behavior of the induction motor, or can be used in various motor-drive topologies with minor modifications. The motor model presented is based on the T- 22. type d-q model of the induction motor. A block model approach is used in the construction of the motor model that will allow users of the model to resolve reference frame theory issues. The model developed is intuitive, easy to 102-107 use, and allows all motor parameters to be easily accessed for monitoring and comparison purposes. The model presented is capable of being used with various inverter topologies and PWM schemes with minor modifications.
Keywords: Dynamic Model, Inverter, Induction Motor.
References: 1. . Mariethoz, A. Domahidi, and M. Morari, "High-Bandwidth Explicit Model Predictive Control of Electrical Drives," IEEE Transactions on Industry Applications, vol. 48, no. 6, pp. 1980-1992, November/December 2012. 2. M. H. Moradi and P.G. Khorasani, “A New MATLAB Simulation of Induction Motor,” in Proceedings of the 2008 Australasian Universities Power Engineering Conference (AUPEC), 2008, pp. 1-6. 3. K. Borisov, T.E. Calvert, J.A. Kleppe, E. Martin, and A.M. Trzynadlowski, “Experimental Investigation of a Naval Propulsion Drive Model With the PWM-Based Attenuation of the Acoustic and Electromagnetic Noise,” IEEE Transactions on Industrial Electronics, vol. 53, no. 2, pp. 450-457, 2006. 4. J. Mazumdar, W. Koellner, and R. Moghe, “Interface Issues of Mining Haul Trucks Operating on Trolley Systems,” in Proceedings of the Applied PowerElectronics Conference and Exposition (APEC), 2010,pp. 1158 – 1165. 5. K.J. Karimi, “Future aircraft power systems – integration challenges,” 4th Annual Carnegie Mellon Conference on the Electricity Industry, Future Energy Systems:Efficiency, Security, Control, Carnegie Mellon University, Pittsburgh, PA (2008). 6. D. A. Kocabas, E. Salman, and A.K. Atalay, "Analysis Using D-Q Transformation of a Drive System Including Load and Two Identical Induction Motors," in Proceedings of the IEEE International Electric Machines & Drives Conference (IEMDC), 2011, pp. 1575 – 1578. 7. T.A. Lipo, “Electric Machine Analysis and Simulation,” Research Report, Wisconsin Electric Machines & Power Electronics Consortium, 2000, pp. 313-324. 8. K.S. Graeid, H.W. Ping, and H.A.F. Mohamed, “Simulink Representation of Induction Motor Reference Frames,” in Proceedings of the 2009 InternationalConference for Technical P 9. M.S. Kumar, P.R. Babu, and S. Ramprasath , "Four Quadrant Operation of Direct Torque Control-SVPWM Based Three Phase Induction Motor Drive in MATLAB/Simulink Environment," in Proceedings of the IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), 2012, pp. 397 – 402. 10. O. Dordevic, N. Bodo, and M. Jones, “Model of anInduction Machine with an Arbitrary Phase Number inMATLAB / Simulink for Educational Use,” inProceedings of the 2010 International UniversitiesPower Engineering Conference (UPEC), 2010, pp. 1-6. 11. O. D. Momoh, “Dynamic Simulation of Cage Rotor Induction Machine – A Simplified and ModularApproach,” in Proceedings of the 44th IEEE Southeastern Symposium on System Theory, 2012, pp. 200-203. 12. D.M. Bellur and M.K. Kazimierczuk, “PSPICE and MATLAB Applications in Teaching Power Electronics to Graduate Students at Wright State University,” inProceedings of the 2008 ASEE North Central Section Conference, 2008. 13. P.C. Krause, O. Wasynczuk, and S.D. Sudhoff, Analysis of Electric Machinery, IEEE Press, NY: 1995. 14. T.A. Lipo, “Electric Machine Analysis and Simulation,” Research Report, Wisconsin Electric Machines & Power Electronics Consortium, 2000, pp. 313-324. 15. A.W. Leedy and R.M. Nelms “Simplified Model of anInverter-Fed Induction Motor for the Analysis of a DC Power System”, in Proceedings of the 36th IEEE Southeastern Symposium on System Theory, 2004, pp. 275-279. 16. B. Ozpineci and L.M. Tolbert , “Simulink Implementation of Induction Machine Model-A Modular Approach,” in Proceedings of the IEEE International Electric Machines and Drives Conference (IEMDC), 2003, pp. 728-734. Authors: K. Sankar, K.Nirmala Nonsubsampled Contourlet Transformation Based Image Enhancement with Spatial and Statistical Paper Title: Feature Extraction for Classification of Digital Mammogram Abstract: In this paper, an efficient automated microcalcification classification system for breast cancer in mammograms using NonSubsampled Contourlet Transform (NSCT), Single Value Decomposition (SVD), Jacobi Moments and Support Vector Machine (SVM) is presented. The image is enhanced by using the preprocessing technique NonSubsampled Contourlet Transform (NSCT). The classification of microcalcification is achieved by extracting the features by using SVD and Jacobi moments and the outcomes are used as an input to the SVM classifier for classification.
Keywords: Mammography, breast cancer, texture features, NSCT, SVD, Jacobi moments and SVM.
References: 1. ACS, “Cancer prevention & Early Detection Facts & Figures 2008”, 2008. 2. H.D. Cheng, X.Cai, X. Chen, L. Hu and X. Lou, “Computer-aided detection and classification of microcalcification in mammograms: a survey”, Pattern Recognition, Vol.36,pp. 646-668, 2006. 3. P. Sakellaropoulos, L. Costariou and G. Panayiotakis, “ A wavelet- based spatially adaptive method for mammographic contrast enhancement”, Physics in Medicine and biology, Vol. 48, pp.787-803, 2003 4. Laine, J. Fan and W. yang, “Wavelets for contrast enhancement of digital mammography”, Engineering in Medicine and Biology Magazine, IEEE, Vol. 14, pp. 536-550,1995. 5. Laine, M.Lewis and F.Taylor, “A wavelet based mammographic system” , in IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP1994),1994. 23. 6. J.K. Romberg, M.B. Wakin and R.G. Baraniuk, “ Multiscale geometric image processing “, in Proceedings of the SPIE : Visual Communications and Image Processing 2003, pp.1265-1272, 2003. 7. M.N. Do and M. Vetterli, “The finite ridgelet transform for image representation”, IEEE Trans. Image Process, Vol.12 pp.16-18, 2003. 108-110 8. J.L. Starck, E.J. Candes and D.L. Donoho, “The curvelet transform for image denoising”, IEEE Trans. Image Process, Vol.11,pp. 670- 684, 2002. 9. E.Le pennec and S.Mallat, “ Sparse geometric image representations with bandelets”, IEEE Trans. Image Process, Vol.4, pp.423-438, 2005. 10. M.N. Do and M. Vetterli, “ The contourlet transform : an efficient directional multiresolution image representation”, IEEE Trans. Image Process, Vol. 14, pp.2091-2106, 2005. 11. H.P. Chan, K.Doi, S.Galhotra, C.J.IIVyborny, H.Macmahon & P.M. Jokich, “Image feature Analysis and Computer- Aided Diagnosis in Digital Radiography”, Medi.Phy.,Jul-Aug 1987,Vol.14 ,pp. 538-548. 12. Davis DH, Dance DR. “Automated computer detection of clustered calcifications in digital mammogram”, Physics in medicine and Biology. Vol.35, Aug -1990, pp.1111-1118. 13. Bazzani A, Bevilacqua a, Bollini D, Brancaaio R, Camanini R, Lancoonelli N et al “Automated detection of clustered microcalcifications in digital mammograms using SVM classifier”, In Proceedings of the European Symposium on Artificial Networks, April 2000, pp.195- 200. 14. Zaiane ),Maria- Luiza A & Alexandru “Apllication of data mining technique for medical image classification” , In proceedings of second International Workshop on Multimedia, USA. 2001, pp 94-101. 15. Chan HP, Lo SCB Sahiner B, Lam Helvie MA, “Computer adided detection of mammographic microcalcifications: Pattern recognition with an artificial neural network”, Medical. Physics, Vol. 22, Oct 1995, Pg 1555-67. 16. Nigel RH, Nishikawa RM, Papaioannou J, Doi K, “Analysis of methods for reducing false positives in the automated detection of clustered microcalcifications in mammograms”, Medical physics, Vol. 25 , Aug- 1998, pg.1502-1506. 17. M.-K. Hu, “Visual pattern recognition by moment invariants”, IRE Trans. Inform. Theory, pp. 179–187, Feb. 1962. 18. Teh and R. Chin, “On image analysis by the methods of moments”, IEEE Trans. Pattern Anal. Machine Intell., vol. 10, pp. 496–513, July 1988. 19. Hasan Abdel Qader,and Syed Al-Haddad,” Fingerprint Recognition using Zernike moments”, The International Arab journal of information technology, vol 4, Oct 2007. 20. Y. Abu-Mostafa and D. Psaltis, “Recognitive aspects of moment invariants”, IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, pp. 698–706, Nov. 1984. 21. T.A.Sangeetha and Dr.A.Saradha, “ An effiecient way to Enhance Mammogram Image using Non Subsampled Contourlet Transform”, International Journal of Current Research, Vol 4,Issue 12, pp. 385-390, December 2012. 22. N.V.S.Sree Rathna Lakshmi and C.Manoharan, “An Automated System for Classification of MicroCalcification in Mammogram Based on Jacobi Moments”, International Journal of Computer Theory and Engineering, Vol. 3, No. 3, June 2011. 23. Pew-Thian Yap and Raveendran paramesram,”Jacobi Moments as Image Features”. International Journal of Pattern Recognition and Artificial Intelligence, 7(6):594–597, 2004. G.K.Viju, Mohammed Merghany Abd Elsalam, Khalid Ahmed Ibrahim, Mohammed Jassim Authors: Mohammed Jassim Paper Title: The Impact of Software Process Improvements in Small and Medium Scale Enterprises Abstract: Most of the software development companies around the globe are small and medium scale enterprises. These organizations are considered as the back-bone of the world economy (In the year 2008, more than 85% of the companies in US, China, India, Finland, Ireland etc are small and medium scale enterprises). These small and medium firms have realized that improving their process and working methods are crucial for their business, but they are lacking in the knowledge and resources to implement it. Successful software process improvement implementation is a herculean task for these small and medium enterprises since they are not capable of investing the cost of these programs. There is insufficient knowledge about which innovations are effective, and which factors influence the adoption of software process improvement in small and medium enterprises. There is enough evidence that the majority of small software organizations are not adopting existing standards as they perceive them as being oriented towards large organizations and studies have shown that small firms’ negative perceptions of process model standards are primarily driven by negative views of cost, documentation and bureaucracy. In this paper, we present the current significant software process improvement methodologies for small and medium enterprises comparisons, and a proposed methodology for future studies.
Keywords: Software quality, Small and Medium Enterprises, Software Process Improvement, Software Process Improvement Methodology.
References: 1. Abbott, J.J, Software Process Improvement in a Small Commercial Software Company, Proceedings of the 1997 Software Engineering Process Group Conference, San Jose, CA, 17-20 (1997) 2. A Cater-Steel, “An evaluation of software development practice and assessment based process improvement in small 24. software developmen firms,”, PhD dissertation, Sch.Com Info Tech, Grifith University, Australia, 2004 . 3. A Hersh, “Where’s the return on process improvement?,”, IEEE Software, vol.10, no.4, 1993, p.12. 4. C. Hofer, “Software development in Austria: results of an empirical study among small and very small enterprises,” 2002, 111-115 pp. 361-366.. 5. Bae, “Panel: Software Process Improvement for Small Organizations,” COMPSAC, 2007.. 6. D. Johnson and J Brodman, “Tailoring the CMM for Small Businesses, Small Organizations, and Small Projects”, Software Process Newsletter No 8, 1997. 7. G. Maggie, et al., "Applicability of CMMI for Small to Medium Enterprises," ed: Software Engineering Institute Northrop Grumman Corporation, 2010 8. H. Saiedian and N. Carr, “Characterizing a software process maturity model for small organizations,” ACM SIGICE Bulletin, vol. 23, no. 1, 1997, pp. 2-11. 9. I.Sommerville, “Software Process”, in Software Engineering, 6th edn, Addison-Wesley, 2001, Chapter 25, pp 558. 10. Johnson, D. L., Brodman, J.G.: Applying the CMM to Small Organizations and Small Projects. Proceedings of the 1998 Software Engineering Process Group Conference, Chicago, IL. 9-12 March. (1998) 11. J. Rockart, “Chief executives define their own data needs,” Harvard Business Review, Vol 57, no.2, 1979, pp 81-93. 12. , K., Hansen, H.W., Thaysen, K.: Applying and Adjusting a Software Process Improvement Model in Practice: The use of the IDEAL Model in a Small Software Enterprise. Proceedings of ICSE 2000, Limerick, ACM Press, 626-633. (2000) 13. K.El Emam, et al, SPICE: The theory and practice of software process improvement and capability determination, IEEE Computer Society Press Los Alamitos, CA, USA, 1997. 14. M T Aileen Cater-Steela, Terry Routb, “Process improvement for small firms: an evaluation of the RAPID assessment-based method,” ed, Australia, 2006. 15. R. S. Pressman, Software Engineering: A Practitioner’s Approach, 6th international edn, McGraw- Hill Education, Singapore, 2005, p.53. 16. S.Winistorfer and H.Steudel, “ISO 9000: Issues for the structural composite lumber industry,”, Forest Products Journal, Vol 47, 1997, pp43-47. 17. T.Komiyama, et al, “Software process assessement and improvement in NEC-current status and future direction,” Software Process Improvement and Practice, vol 5, no.1, 2000. 18. T. Hall, et al, “ Implementing software process improvement:an empirical study,”, Software Process Improvement and Practice: vol 7, no.1, 2002, pp 3-15. 19. V. M. Paula and R. d. S. Alberto, "PIT-ProcessM: A Software Process Improvement Meta-model," presented at the Seventh International Conference on the Quality of Information and Communications Technology, 2010 Authors: K. Wisaeng Paper Title: A Comparison of Different Classification Techniques for Bank Direct Marketing Abstract: Classification is a data mining techniques used to predict group membership for data instance. In this paper, we present the comparison of different classification techniques in open source data mining software which consists of a decision tree methods and machine learning for a set of bank direct marketing dataset. All decision tree 25. methods tested are J48-graft and LAD tree while machine learning tested are radial basis function network and support vector machine. The experiment results show are a bout classification sensitivity, specificity, accuracy, 116-119 mean absolute error and root mean squared error. The results on bank direct marketing data also the efficiency of machine learning methods by using support vector machine is better than that of all algorithms.
Keywords: Data mining, bank direct marketing, J48-graft, LAD tree, radial basis function network, support vector machine.
References: 1. Z. Hao, X. Wang, L. Yao, and Y. Zhang, “Improved classification based on predictive associative rules”, IEEE International Conference on System, Man and Cybernetics, 2009, pp. 1165–1170. 2. Q. Niu, S.X. Xia, and L.Zhang, “Association classification based on compactness of rules”, International workshop on knowledge discovery and data mining, WKDD, 2009, pp. 245–247. 3. V. Bartik, “Association based classification for relational data and its use in web mining”, IEEE Symposium on Computational Intelligence and Data Mining, 2009, pp. 252-258. 4. R. Sumitha, and S. Paul, “Using Distributed Apriori Mining Algorithms for Grid Based Knowledge Discovery,” International Conference on Computing Communication and Networking Technologies (ICCCNT), 2010, pp.1-5. 5. Trnka, “Market Basket Analysis with Data Mining Methods,” International Conference on Networking and Information Technology (ICNIT), 2010, pp. 446-450. 6. W.X. Xie, H.N. Qi, and M.I. Huang, “Market Basket Analysis Based on Text Segmentation and Association Rule Mining,” International Conference on Neural Networking and Distributed Computing (ICNDC), 2010, pp. 309-313. 7. K.S.Y. Chiu, R.W.P. Luk, K.C.C. Chan, and K.F.L. Chung, “Market-basket Analysis with Principal Component Analysis: An Exploration,” IEEE International Conference on Systems, 2002. 8. X. Wang, Z. Ni, and H. Cao, “Research on Association Rules Mining Based-On Ontology in E-Commerce,” International Conference on Wireless Communications, Networking and Mobile Computing, 2007, pp. 3549-3552. 9. D. Jachyra, K. Pancerz, and J. Gomula, “Classification of MMPI Profiles using Decision Trees” Concurrency, Specification and Programing, Poland, 2011, pp. 397.407. 10. C.S. Trilok, J. Manoj, “WEKA Approach for Comparative Study of Classification Algorithm,” International Journal of Advanced Research in Computer and Communication Engineering, 2013, pp. 1925-1931. 11. P. Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer, and R. Wirth, “CRISP-DM 1.0 Step by Step data mining guide,” CRISP-DM Consortium, 2000. Gabriela Alexe, Sorin Alexe, Tibérius O. Bonates, Alexander Kogan, “Logical analysis of data - -- the vision of Peter L. Hammer,” Annals of Mathematics and Artificial Intelligence archive Volume 49 Issue 1-4, April 2007 Pages 265 - 312 12. D. Buhmann, “Radial Basis Functions: Theory and Imple- mentations,” Cambridge Monographs on Applied and Computational Mathematics, Cambridge University Press, Cambridge, 2003. 13. S. V. Chakravarthy, and J. Ghosh, “Scale Based Clustering using Radial Basis Function Networks,” In Press of Proceeding of IEEE International Conference on Neural Networks, Florida, 1994, pp. 14. J. Park, and I. W. Sandberg, “Universal Approximation Using Radial-Basis-Function Networks,” Neural Computation, 2013, pp.246- 257. 15. A.M., Samuel, M.D., Daniel, K., James, and M.W. Gray, “Are decision trees always greener on the open (source) side of the fence?,” In Proc. of the International Conference on Data Mining, 2009, pp. 185–188 16. M., Hall, E., Frank, G., Holmes, B., Pfahringer, P., Reutemann, and I.H Witten, “The WEKA Data Mining Software: An Update,” SIGKDD Explorations, Volume 11, 2009. Authors: V V Vijetha Inti, Karthika Mangamuri, Ch Phani Kumar Paper Title: A New Topology for Power Factor Correction using Resonant Converters Abstract: A new parallel-connected power flow technique is proposed to improve the input power factor with simultaneously output voltage regulation taking consideration of current harmonic standards. Paralleling of converter modules is used in medium-power applications to achieve the desired output power by using smaller size of high frequency transformers. A parallel-connected interleaved structure offers smaller passive components with less loss in continuous conduction inductor current mode and also reduces the volt-ampere rating of resonant (DC/DC) converter. MATLAB/SIMULINK is used for implementation and simulation results show the performance improvement.
Keywords: Power Factor Correction (PFC), Total Harmonic Distortion, Forward converter, Flyback converter, Parallel Operation 26. References: 120-123 1. R. Erickson, M. Madigan and S. Singer, “Design of a simple high power factor rectifier based on the flyback converter,” IEEE Applied Power Electronics Conference, 1990, pp. 792- 801. 2. Zaohang Yang and P.C.Sen, “Recent developments in high power factor switch –mode converters”, IEEE Canadian Conference on, Volume:2,pp. 477-488, May 1998. 3. B.A. Miwa, D.M. Otten, and M.F. Schlecht, "High efficiency power factor correction using interleaving techniques," Proc. IEEE Appl. Power Electron. Conf., 1992, pp. 557- 568. 4. F.S. Tsai and W.W. Ng, "A low-cost, low-loss active voltage-clamp circuit for interleaved single-ended forward PWM converter," Proc. IEEE Appl. Power Electron. Conf., 1993, pp. 729-733. 5. Sangsun Kim and Prasad N. Enjeti, “A Parallel-Connected Single Phase Power Factor Correction Approach With Improved Efficiency”, IEEE transactions on power electronics, vol. 19, no. 1, January 2004. 6. N. Mohan, T. M. Undeland and W. P. Robbins, Power electronics, converters, applications, and design, 2nd Edition, John Wiley and Son, Inc., New York, 1995. 7. Jindong Zhang, M. M. Jovanovic and Fred C. Lee, “Comparison between CCM single-stage and two-stage boost PFC converters”, APEC 1999, Volume: 1, pp. 335-341, March 1999. Authors: Firouz Abdullah Al-Wassai, N.V. Kalyankar Paper Title: The Classification Accuracy of Multiple-Metric Learning Algorithm on Multi-Sensor Fusion Abstract: this paper focuses on two main issues; first one is the impact of Similarity Search to learning the training sample in metric space, and searching based on supervised learning classification. In particular, four 27. metrics space searching are based on spatial information that are introduced as the following; Chebyshev Distance (CD); Bray Curtis Distance (BCD); Manhattan Distance (MD) and Euclidean Distance(ED) classifiers. The second 124-131 issue investigates the performance of combination of mul-ti-sensor images on the supervised learning classification accura-cy. QuickBird multispectral data (MS) and panchromatic data (PAN) have been used in this study to demonstrate the enhance-ment and accuracy assessment of fused image over the original images. The supervised classification results of fusion image generated better than the MS did. QuickBird and the best results with ED classifier than the other did.
Keywords: Similarity Search, Metric Spaces, Distance Classifier, Image Fusion, Classification, Accuracy Assessment.
References: 1. Lu D. & Weng Q. 2007. “A survey of image classification meth-ods and techniques for improving classification performance”. International Journal of Remote Sensing, 28, pp. 823- 870. 2. Foody GM 2002. “Status of land cover classification accuracy assessment”. Remote Sensing of Environment,Vol. 80, pp. 185-201. 3. Duda R. O. , P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. NewYork:Wiley, 2001. 4. Campbell J., 2006. “Introduction to Remote Sensing”. London: Taylor & Francis. 5. Gao J., 2009. “Digital Analysis of Remotely Sensed Imagery”. New York: McGraw Hill. 6. Varma M., Zisserman A., 2008. “A Statistical Approach to Mate-rial Classification Using Image Patch Exemplars”. IEEE Trans-actions on Pattern Analysis And Machine Intelligence, Vol. 31, No. 11, NOVEMBER 2009, pp. 2032- 2047. 7. Chi M., Bruzzone L., 2007. “Semisupervised Classification of Hyperspectral Images by SVMs Optimized in the Primal”. IEEE Transactions on Geoscience and Remote Sensing,Vol. 45, No. 6, JUNE 2007, pp. 1870- 1880. 8. Mura D. M., Benediktsson J. A., Bovolo F., Bruzzone L., 2008. “An Unsupervised Technique Based on Morphological Filters for Change Detection in Very High Resolution Images”. IEEE Geo-science and Remote Sensing Letters, Vol. 5, No. 3, JULY 2008 pp. 433- 437. 9. Bovolo F., Camps-Valls G., L. Bruzzone, 2010. “A support vector domain method for change detection in multitemporal images”. Pattern Recognition Letters Vol.31, No.10, 15 July 2010, pp. 1148-1154. 10. Yang C., Bruzzone L., Sun F., Lu L., Guan R., Liang Y., 2010. “A Fuzzy-Statistics-Based Affinity Propagation Technique for Clustering in Multispectral Images”. IEEE Transactions on Geo-science and Remote Sensing, Vol. 48, No. 6, JUNE 2010, PP. 2647- 2659. 11. Gupta S., Rajan K. S., 2011. “Extraction of training samples from time-series MODIS imagery and its utility for land cover classification”. International Journal of Remote Sensing, Vol. 32, No. 24, 20 December 2011, pp .9397 – 9413. 12. Burbidge R., Rowland J. J., and R. D. King, 2007. “Active learning for regression based on query by committee” Intelligent Data Engineering and Automated Learning, pp. 209–218. 13. Sugiyama M., Rubens N., 2008.“A batch ensemble approach to active learning with model selection” Neural Networks, vol. 21, no. 9, pp. 1278–1286. 14. Tuia D., Ratle F., Pacifici F., Kanevski M. F., Emery W. J., 2009 . “Active learning methods for remote sensing image classification,” IEEE Trans. Geosci. Remote Sens., Vol. 47, No. 7, pp. 2218–2232. 15. Ch´avez, E., Navarro, G., Baeza-Yates, R., Marroqu´ın, J.L., 2001. ‘ Searching in metric spaces”. ACM Computing Surveys 33 (2001), pp. 273–321. 16. Zezula P., Amato G., Dohnal V., Batko M., 2005. Similarity Search: The Metric Space Approach”. Advances in database sys-tems, Springer, New York. 17. Al-Wassai F. A., N.V. Kalyankar, A. A. Al-zuky, 2011. “Mul-tisensor Image Fusion Based on Feature-Level”. International Journal of Advanced Research in Computer Science, 2011, , Volume 2, No. 4, July-August 2011, pp. 354 – 362. 18. Al-Wassai F. A., N.V. Kalyankar, 1012. "A Novel Metric Ap-proach Evaluation for the Spatial Enhancement of Pan-Sharpened Images". International Conference of Advanced Computer Science & Information Technology, 2012 (CS & IT), 2(3), 479 – 493. DOI : 10.5121/ csit.2012.2347. 19. Al-Wassai F. A., N.V. Kalyankar, A. A. Al-zuky, 2011. “Feature-Level Based Image Fusion Of Multisensory Images”. International Journal of Software Engineering Research & Practices, Vol.1, No. 4, pp. 8-16. 20. Al-Wassai F. A., N.V. Kalyankar, 1012. “The Segmentation Fusion Method On10 Multi-Sensors”. International Journal of Latest Technology in Engineering, Management & Applied Sci-ence, Vol. I, No. V ,pp. 124- 138. 21. Al-Wassai F. A., N.V. Kalyankar, A. A. Al-Zaky, "Spatial and Spectral Quality Evaluation Based on Edges Regions of Satellite: Image Fusion". 2nd International Conference on Advanced Computing & Communication Technologies ACCT 2012, pre-siding in IEEE Computer Society, pp. 265-275. DOI:10.1109/ACCT.2012.107. 22. Al-Wassai F. A., N.V. Kalyankar, A. A. Al-zuky, 2011. “Studying Satellite Image Quality Based on the Fusion Techniques”. In- ternational Journal of Advanced Research in Computer Science, 2011, Volume 2, No. 5, pp. 516- 524. 23. Al-Wassai F.A., N.V. Kalyankar, A. A. Al-zuky, 2011. “Mul-tisensor Images Fusion Based on Feature”. International Journal of Advanced Research in Computer Science, Volume 2, No. 4, July-August 2011, pp. 354 – 362. 24. Al-Wassai F. A., N.V. Kalyankar, A. A. Zaky, 2012. "Spatial and Spectral Quality Evaluation Based on Edges Regions of Satellite: Image Fusion”, IEEE Computer Society, 2012 Second International Conference on ACCT 2012, pp.265-275. 25. Al-Wassai F. A., N.V. Kalyankar, 1013. “Influences Combination of Multi-Sensor Images on Classification Accuracy”. International Journal of Advanced Research in Computer Science, Vol. 4, No. 9, pp. 10- 20. Authors: Tejal Chauhan, Hemant Soni, Sameena Zafar Paper Title: A Review of Automatic Speaker Recognition System Abstract: In the recent time, person authentication in security systems using biometric technologies has grown rapidly. The voice is a signal of infinite information. Digital signal processes such as Feature Extraction and Feature Matching are introduced to authenticate person in security system. In this paper concept of speaker recognition is discussed. Several methods such as Liner Predictive Coding (LPC), Mel Frequency Cepstral Coefficients (MFCCs) etc. are utilized for feature extraction and methods like Dynamic Time Warping (DTW), Vector Quantization (VQ), Hidden Markov Model (HMM), Gaussian Mixture Models (GMM) etc are used with a view to identify voice signal.
28. Keywords: LPC, MFCC, DTW, VQ, HMM, GMM. 132-135 References: 1. Yuan Yujin, Zhao Peihua, Zhou Qun,, “Research of speaker recognition based on combination of LPCC and MFCC”, Intelligent Computing and Intelligent Systems (ICIS), IEEE International Conference , vol.3, 29-31 Oct. 2010, pp.765-767. 2. Sinith, M.S., Salim, A., Gowri Sankar, K., Sandeep Narayanan, K.V. Soman, V., “A novel method for Text-Independent speaker identification using MFCC and GMM”, Audio Language and Image Processing (ICALIP), 2010 International Conference, Nov. 2010, pp.292-296. 3. Zufeng Weng, Lin Li, Donghui Guo , “Speaker recognition using weighted dynamic MFCC based on GMM”, Anti-Counterfeiting Security and Identification in Communication (ASID), International Conference, china, July 2010 IEEE, , pp.285-288 4. R. Hasan, M. Jamil, G. Rabbani, S. Rahman, “Speaker Identification Using Mel Frequency Cepstral Coefficients”, 3rd International Conference on Electrical & Computer Engineering, Dhaka, Bangladesh, December 2004. 5. Shende, S. Mishra, Shiv Kumar,”Comparision of different parameters used in GMM based Automatic Speaker Recognition” International Journal of Soft Computing and Engineering(IJSCE), Volume 1, Issue 3 July 2011. 6. Douglas A. Reynolds and Richard C. Rose, “Robust Text-Independent Speaker Identification using Gaussian Mixture Speaker Models”, Ieee Transactions On Speech And Audio Processing, Vol. 3, January 1995 7. Jiang Hai, Er Meng Joo, “Improved linear predictive coding method for speech recognition”, Information, Communications and Signal Processing and Fourth Pacific Rim Conference on Multimedia. Proceedings of the Joint Conference of the Fourth International Conference , vol.3, Dec. 2003, pp. 1614- 1618 8. Bin Amin, T., Mahmood, I., “Speech Recognition using Dynamic Time Warping”, Advances in Space Technologies, 2nd International Conference, Nov. 2008, pp.74-79 9. Ashish Kumar Panda, Amit Kumar Sahoo, “Study Of Speaker Recognition Systems”, Rourkela 2011. Authors: Manisha Boora, Monika Gambhir Paper Title: Arnold Transform Based Steganography Abstract: Steganography exploits the use of host data to hide a piece of information in such a way that it is imperceptible to human observer. Its main objectives are robustness, high payload, and imperceptibility. Digital images, videos, sound files, and other computer files can be used as carrier to embed the information. This paper deals with digital gray scale digital images, and presents a secure steganography scheme for hiding a larger size secret-image into smaller size cover- image. Arnold Transformation is performed to obtain scrambled secret image. Discrete Wavelet Transform (DWT) is performed on both host / cover image and secret image / information, and this is followed by alpha blending operation. The approach shows satisfactory results. Results show that proposed algorithm is highly secured with good perceptual invisibility.
Keywords: Alpha Blending, Arnold Transformation, DWT, Steganography.
References: 1. Po-Yueh Chen*and Hung-Ju Lin, “A DWT Based Approach for Image Steganography”, International Journal of Applied Science and Engineering, pp. 275-290, vol.- 4,Issue- 3, 2006 2. Bhavyesh Divecha, Ajith Abraham, Crina Grosan and Sugata Sanyal, “Analysis of Dynamic Source Routing and Destination- Sequenced Distance-Vector Protocols for Different Mobility models”, First Asia International Conference on Modeling and Simulation, AMS2007, 27-30 March, 2007, Phuket, Thailand. Publisher: IEEE Press, pp. 224-229. 29. 3. S. Gowrishankar, S. Sarkar, T.G.Basavaraju, "Simulation Based Performance Comparison of Community Model, GFMM, RPGM, Manhattan Model and RWP-SS Mobility Models in MANET," First International Conference on Networks and Communications (NETCOM '09), 27-29 Dec. 2009, pp.408-413. 136-140 4. Jonghyun Kim, Vinay Sridhara, Stephan Bohacek, “Realistic mobility simulation of urban mesh networks”, Journal of Ad Hoc Networks, Vol. 7, Issue 2, March 2009, Publisher: Elsevier, pp. 411-430. 5. Liu Tie-yuan, Chang Liang, Gu Tian-long, "Analyzing the Impact of Entity Mobility Models on the Performance of Routing Protocols in the MANET”, 3rd International Conference on Genetic and Evolutionary Computing, 14-17 Oct. 2009, pp.56-59. 6. M.F. Sjaugi, M. Othman, M.F.A. Rasid, "Mobility models towards the performance of geographical-based route maintenance strategy in DSR", IEEE International Symposium on Information Technology, ITsim 2008 , Vol. 3, 26-28 Aug. 2008, pp. 1-5. 7. Po-Yueh Chen*and Hung-Ju Lin, “A DWT Based Approach for Image Steganography”, International Journal of Applied Science and Engineering, pp. 275-290, vol.- 4,Issue- 3, 2006 8. S.Arivazhagan, W.Sylvia Lilly Jebarani, M.Shanmugaraj,” An Efficient Method for the Detection of Employed Steganographic Algorithm using Discrete Wavelet Transform”, pp. 1-6, Second International conference on Computing, Communication and Networking Technologies,2010 IEEE. 9. Kalra, G.S., R. Talwar and H. Sadawarti,” Blind Digital Image Watermarking Robust Against Histogram Equalization”, Journal of Computer Science 8 (8): 1272-1280, 2012. 10. Zhenjun Tang and Xianquan Zhang,” Secure Image Encryption without Size Limitation Using Arnold Transform and Random Strategies”,Journal Of Multimedia, Vol. 6, No. 2, April 2011 11. Lingling Wu, Jianwei Zhang, Weitao Deng, Dongyan He,” Arnold Transformation Algorithm and Anti-Arnold Transformation Algorithm”, The 1st International Conference on Information Science and Engineering (ICISE2009), pp. 1164- 1167, IEEE 2009 12. Nilanjan Dey, Sourav Samanta, Anamitra Bardhan Roy,” A Novel Approach of Image Encoding and Hiding using Spiral Scanning and Wavelet Based Alpha-Blending Technique”, Int. J. Comp. Tech. Appl., Vol. 2 (6), pp. 1970-1974, 2011 13. Pratibha Sharma, Shanti Swami,” Digital Image Watermarking Using 3 level Discrete Wavelet Transform”, Conference on Advances in Communication and Control Systems 2013 (CAC2S 2013), pp. 129- 133, 2013 Authors: Amruta S. Dulange, R.B.Kulkarni, Supriya S. Ambarkar Paper Title: Opinion Mining From Blogosphere for Analysis of Social Networking Abstract: Blogs are most common medium over web where user posts their opinion. It is considered to be a web space of the users where they share their views, beliefs and other philosophy. The blogs are generally categorized of two types: Itemized blogs, where the user posts his views and opinions against a web news or news item and personal blogs where users posts random topics of their interest under the header of their choice. As more and more number of users publish their data over the web, it becomes significant that Meanings are extracted from blog and they are indexed properly for information retrieval. In this work we develop a crawler to read data from RSS feeds of blogs and save them locally. Finally we apply data mining technique to index the blogs for easy searching and information extraction. 30. Keywords: Web Mining, Blog, Blog Search, Blog Mining 141-146
References: 1. . Nanno et al., “Automatically Collecting, Monitoring, and Mining Japanese Weblogs,” Proc.13th Int’l Conf. WWW, (WWW 2004), ACM Press, 2004, 320–321W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135. 2. N. Glance et al., “Analyzing Online Discussion for Marketing Intelligence,” Proc. 14th Int’l Conf. WWW (WWW 2005), ACM Press, 2005, pp. 1172–1173. 3. Geetika T. Lakshmanan IBM T.J. Watson Research Center Martin A. Oberhofer IBM Software Group, Germany Knowledge Discovery in the Blogosphere Approaches and Challenges 4. M. Kobayashi, K. Takeda, "Information retrieval on the web". ACM Computing Surveys (ACM Press) 32 (2): 144–173, 2000 5. S. Thies, Content-Interaktionsbeziehungen im Internet. Ausgestaltungund Erfolg, 1st ed., Gabler, 2005 6. C. Marlow, “Audience, structure and authority in the weblog community,” in Proceedings of the International Communication Association Conference, 2004 7. N. Agarwal, H. Liu, L. Tang, and P. S. Yu, “Identifying the influential bloggers in a community,” in WSDM ’08: Proceedings of the international conference on Web search and web data mining. New York, NY, USA: ACM, 2008, pp. 207–218. 8. M. Chau and J. XU, “Mining communities and their relationships in blogs: A study of online hate groups,” International Journal of Human- Computer Studies, vol. 65, no. 1, pp. 57–70, January 2007. 9. R. Kumar, J. Novak, P. Raghavan, and A. Tomkins, “On the bursty evolution of blogspace,” World Wide Web, vol. 8, no. 2, pp. 159– 178, 2005. 10. M. Goetz, J. Leskovec, M. Mcglohon, and C. Faloutsos, “Modeling blog dynamics,” in International Conference on Weblogs and Social Media, May 2009. 11. S. C. Herring, I. Kouper, J. C. Paolillo, L. A. Scheidt, M. Tyworth P. Welsch, E. Wright, and N. Yu, “Conversations in the blogosphere: An analysis ”from the bottom up”,” in Proceedings of the 38th Annual Hawaii International Conference on System Sciences. IEEE Computer Society, 2005, p. 107.2. 12. P. Wortmann, “Topic-based blog article search for trend detection,” Technical University of Kaiserslautern, Project Thesis, 2009. [Online]. Available: http://www.dfki.uni-kl.de/_obradovic/downloadpa-wortmann.pdf 13. L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking: Bringing order to the web,” Stanford University, Technical Report, 1998. [Online Available: http://explorer.csse.uwa. edu.au/reference/browse paper.php?pid=233281827 14. S. Wasserman, K. Faust, and D. Iacobucci, Social Network Analysis Methods and Applications (Structural Analysis in the Social Sciences). Cambridge University Press, 1994. 15. Bruno Ohana and Brendan Tierney,” Sentiment Classification of Reviews Using SentiWordNet”, Dublin Institute of Technology, School of Computing Kevin St. Dublin 8, Ireland 16. G. Mishne, N. Glance, Predicting Movie Sales from Blogger Sentiment, American Association for Artificial Intelligence, 2005. 17. R. Kumar, J. Novak, P. Raghavan, and A. Tomkins, “On the bursty evolution of blogspace,” World Wide Web, vol. 8, no. 2, pp. 159– 178, 2005. 18. Darko Obradovi´c, Stephan Baumann and Andreas Dengel, “A Social Network Analysis and Mining Methodology for the Monitoring of Specific Domains in the Blogosphere”, International conference on Advances in Social Network Analysis and Mining 2010. 19. Barbara Poblete (2009), “Query-Based Data Mining for the Web” TESI DOCTORAL UPF, University Pompeu Fabra. 20. Bo Pang, Lillian Lee, “Opinion Mining and Sentiment Analysis” Foundations and Trends in Information Retrieval Vol. 2, Nos. 1-2 (2008). 21. Senthil Kumar & N. Palanisamy ,”CHALLENGES FOR WEB MINING” Proceedings of the 2008 International Conference on Computing, Communication and Networking 2008 IEEE. 22. Shamanth Kumar, “Towards Building a Social Computing Tool for Social Scientists”, Computer Science and Engineering Arizona State University Tempe, Arizona 85281. 23. Daniel E. O’Leary, “Blog mining-review and extensions: From each according to his opinion”, Marshall School of Business, University of Southern California, Los Angeles, CA 90089-0441, United States. Authors: Teerawong Laosuwan, Poramate Chunpang, Nutcha Laosuwan Development of Decision support System Using Management Information System and Geographic Paper Title: Information System Abstract: The objective of this study is to develop of decision support system using management information system and geographic information system, a case study in the Faculty of Science Mahasarakham University. This system was created by Open Source Software (OSS) and web GIS solution i.e. University of Minnesota Map Server (UMN Map Server), Appserv software, Quantum GIS, PHP, and Edit Plus etc. The UMN Map Server program will process spatial data from vector and raster, and then illustrate the result in webpage by used Common Gateway Interface (CGI) on the client. Although this system serves chemical management methods in Faculty of Science Mahasarakham University, the methodology can be applied elsewhere for similar processes.
Keywords: Decision support system, Open Source Software, MIS and GIS
References: 1. Pollution Control Department, People manual handling, Online available at http://webdb.dmsc.moph.go.th/ifc_toxic/ez.mm _main. asp. 2. Teerawong Laosuwn, Chatphong Bunyu, Kuakul Pimdee. (2008). A Design of Traditional Healer Data Warehouse Using Internet GIS. Journal of Science and Technology, Mahasarakham University, Special Issue Vol 4, 315-322. 3. Laosuwan, T., Uttaruk, P., Klinhom, U., Butthep, C., Samek, J.H., Skole, D. L. (2011). Development of Web-GIS Application for Carbon Sequestration in Thailand, International Journal of Geoinformatics, vol 7 (2), 41-47. 4. Teerawong Laosuwan. Geographic Information System with Earth System Science. (2011). Journal of Science and Technology, 31. Mahasarakham University, vol 30 (3), 353-359. 5. McKee L. (2012). Web Mapping Guide - Technology Trends, GEO Resources, Online available at http://www.geoplace.com/gr/ webmapping/technology.asp. 147-150 6. Raghavan, V., Santitamont, P., Masumoto, S., and Honda, K. (2002). Implementing Web GIS Applications using Open Source Software, Map Asia 2002, Online available at http://www.gisdevelopment.net/ technology/gis/ techgi0062pf.htm. 7. Ramsey, P., Open Source GIS Fights the Three-Horned Monster, GEO World, Online available at http://www.geoplace.com/gw/ 2002/0208 /0208 gis.asp. 8. F. Milano. (2005). An Open Source Power System Analysis Toolbox, IEEE Transactions on Power Systems, vol 20 (3), 1199–1206. 9. Gary E. Sherman, Tim Sutton, Radim Blazek, Lars Luthman, Quantum GIS User Guide Version 0.7 ’Seamus’, Online available at http://download.osgeo.org/qgis/doc/manual/qgis-0.7.4_user_guide_ en.pdf 10. Quantum GIS, User Guide Version 1.1.0 ’Pan’, Online available at http://download.osgeo.org/qgis/doc/manual/qgis- 1.1.0_user_guide_en.pdf 11. QGIS Project, QGIS User Guide Release 1.8.0, Online available at http://docs.qgis.org/pdf/ QGIS-1.8-UserGuide-en.pdf. 12. Gomasathit, T., Laosuwan, T., Chunpang, P., Uraichuen, Y. (2011). The Real Experience in GIS Teaching Aid by Using GIS Open- Source Software. International Journal of Geoinformatics vol 7 (4), 63- 67. 13. Teerawong Laosuwan. (2012). Online Web Based Services for Spatial Data and Sharing of Leptospirosis Epidemiology Information; Development of Pilot Project in Mahasarakham Province, Thailand, Journal of Geoinformatics and Geoscience, vol 3 (1), 121-133. 14. Teerawong Laosuwan and Poramate Chunpang.(2012) Design and Development a Novel Web-based GIS for Surveillance and Monitoring of Diarrhea by Using of Free Open Source Software, Innovation Systems Design and Engineering Journal, vol 3 (6), 42-53. 15. Teerawong Laosuwan. (2012). Design and Development GIS Web Services for Epidemiology Data Distribution: The Pilot Project for Dengue Fever in Maha Sarakham Province Thailand, International Journal of Advances in Engineering, Science and Technology, vol 2 (3), 193 -201. 16. Laosuwan, T. (2012). A Web-based GIS Development for Natural Resources and Environmental Management, Journal of Applied Technology in Environmental Sanitation, vol 2, 103-108. Authors: Jayanthi.R, M Lilly Florence 32. Paper Title: A Study on Software Metrics and Phase based Defect Removal Pattern Technique for Project Management Abstract: The Software engineering is a application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software; that is, the application of engineering to software. Metrics are used by the software industry to quantify the development, operation and maintenance of software. They give us knowledge of the status of an attribute of the software and help us to evaluate it in an objective way. In software development, a metric is the measurement of a particular characteristic of a program's performance or efficiency. Basically, as applied to the software product, a software metric measures (or quantifies) a characteristic of the software. Metrics provide information to support quantitative managerial decision-making during the software lifecycle and also software metrics is a collective term used to describe the very wide range of activities concerned with measurement in software engineering. The practice of applying software metrics to a software process and to a software product is a complex task that requires study and discipline and which brings knowledge of the status of 151-155 the process and / or product of software in regards to the goals to achieve Phase-based defect removal pattern.
Keywords: Metrics,ModelsProject, Product, Quantitative
References: 1. G. Atkinson, An Analysis of Process and Product Metrics for Monitoring Software Project Status, Masters Thesis, Software Engineering Testing Laboratory, Univ. of Idaho, May 1997. 2. V. R. Baislli & D. M. Weiss, “A Methodology for Collecting Valid Software Engineering Data”, IEEE Transactions of Software Engineering, SE-10(6), November 1984, pp. 728-738. 3. V. R. Basili, G. Caldiera, and H. D. Rombach, The Goal Question Metric Approach, http://www.cs.calpoly.edu/~ssenkere/metric.html 4. Stephen H. Kan, “Metrics and Models in Software Quality Engineering”, Pearson Education Limited 2003, Boston, United States Authors: G. Lakpathi, S. Manohar Reddy,K. Lakshmi Ganesh, G. Satyanarayana Paper Title: An Effective High Step-Up Interleaved DC-DC Converter Photovoltaic Grid Connection System Abstract: Within the photovoltaic (PV) power generation systems in the market, the ac PV module has shown obvious growth. However, a high voltage gain converter is concentrate for the module’s grid connection with dc-ac inverter. This paper proposed a converter that employs a floating active switch to isolate energy from the PV panel when the ac module is OFF; this particular design protects installers and users from electrical hazards. Without extreme duty ratios and numerous turns-ratio of a coupled inductor, this converter achieves a high step-up voltage- conversion ratio; the leakage inductor energy of the coupled inductor is efficiently recycled to the load. These features explain about module’s high-efficiency performance. The detailed operating principles and steady-state analyses of continuous mode is described. A 15 volts produces from photovoltaic which is connects to high step-up dc-dc converter to produces output voltage of 170 volts. The novel proposed system is “an effective high step up interleaved dc-dc converter photovoltaic grid connection system”. In this configuration consists of PV array, high step-up interleaved dc-dc converter and three-phase inverter with grid connected system. In this system, the THD value per phase voltage of three phase inverter output without filter is 1.74% with grid connection system. In this same configuration with 2nd order filter connected of three phase inverter output, the THD value reduced to 0.08%. The results are shown in Matlab/simulink 2009a.
Keywords: AC module, coupled inductor, high step-up voltage gain, single switch, three-phase inverter, grid connection.
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Power Electron., vol.23, no.1 pp.369-378, Jan.2008. 28. J.M. Kwon and B.H. Kwon, “High step-up active-clamp converter with input-current doubler and output-voltage doubler for fuel cell power systems,” IEEE Trans. Power Electron., vol.24, no.1 pp.108-115, Jan-2009 29. S.Dwai ad L. Parsa, “An efficient high stp-up interleaved dc-dc converter with a common active clamp,” IEEE Trans. Power Electron., vol.26, no.1, pp.66-78, Jan.2011. 30. C.Restrepo, J. Calvente ,A.Cid, A.ElAroudi, and R. Giral, “ A non-inverting buck-boost dc-dc switching converter with high efficiency and wide bandwidth,” IEEE Trans. Power Electron., vol.26, no.9,pp.2490-2503, sep.2011. 31. K.B.Prk, G.W.Moon and M.J.Youn, “Nonisolated high step-up boost converter integrated with sepic converter”, IEEE Trans. Power Electron., vol.25, no-9,pp.2266-2275,sep.2010. 32. L.S.Yang, T.J.Liang and J.F.Chen, “Transformer less dc-dc converters with high step-up voltage gain, “IEEE Trans. Ind. Electron.,vol.56, no,.8,pp.3144-3153,Aug.2009. 33. N.Pogaku, M.Prodanovic and T.C.Green, “Modeling analysis and testing of autonomous operation of an inverter-based micro grid,” IEEE Trans. Power Electron., vol.22, no.2, pp.613-625, Mar.2007. 34. M.Buresch: photovoltaic Energy Systems Design and Installation, McGraw-Hill, New York,1983. Authors: Zhibin Gui, Yongxiang Fan Paper Title: An Application of Wireless Sensor Networks in Health Care Setting, Part I Abstract: This paper and the following papers present an application of wireless sensor networks (WSNs)in health care setting. The application involves building a reliable transmission system to transmit physiological data from ICU gateway to central server wirelessly in hospitals. Due to the length of this project, this paper will include several parts, which are published separately. This paper mainly focuses on background introduction.
Keywords: Wireless sensor networks, health care, implementation.
References: 1. Z. X. Luo, “Anti-attack and channel aware target localization in wireless sensor networks deployed in hostile environments,” International Journal of Engineering and Advanced Technology, vol. 1, no. 6, Aug. 2012. 2. C. Intanagonwiwat, R. Govindan, and D. Estrin,"Directed Di 3. usion: A Scalable and Robust Communication Paradigm for Sensor Networks," in Proc. ACM MOBICOM '00, Boston, Massachusetts, August 2002. 4. K. Sohrabi, B. Manriquez and G. Pottie,"Near-Ground Wideband Channel Measurements," in Proc. IEEE VTC'99, New York, 1999. 5. Z. X. Luo, “Modeling sensor position uncertainty for robust target localization in wireless sensor networks,” in Proc. of the 2012 IEEE Radio and Wireless Symposium, Santa Clara, CA, Jan. 2012. 6. R. Rajagopalan and P. K. Varshney, "Data-aggregation techniques in sensor networks: A survey," IEEE Communications Surveys and Tutorials, vol. 8, pp. 48-63, Fourth Quarter, 2006. 7. Z. X. Luo, “Robust energy-based target localization in wireless sensor networks in the presence of byzantine attacks,” International Journal of Innovative Technology and exploring Engineering, vol. 1, no.3, Aug. 2012. 8. C. Yao, P.-N. Chen, T.-Y. Wang, Y. S. Han, and P. K. Varshney, "Performance analysis and code design for minimum Hamming 34. distance fusion in wireless sensor networks," IEEE Trans. Inform. Theory, vol. 53, pp. 1716-1734, May 2007. 9. Z. X. Luo, “A new direct search method for distributed estimation in wireless sensor networks,” International Journal of Innovative 163-166 Technology and Exploring Engineering, vol. 1, no. 4, Sept. 2012. 10. Woo, D. E. Culler, “A Transmission Control Scheme for Media Access in Sensor Networks," in Proc. ACM MOBICOM 2001, pp.221- 235, Rome, Italy 2001. 11. W. Ye, J. Heidemann and D. Estrin, "An Energy-Efficient MAC Protocol for Wireless Sensor Networks," in Proc. INFOCOM'02, New York, USA,June, 2002. 12. Akyildiz, I.F., W. Su, Y. Sankarasubramaniam, E. Cayirci, "A Survey on Sensor Networks", IEEE Communications Magazine, August,2002, pp.102-114. 13. David Culler, Deborah Estrin, Mani Srivastava, “Overview of Sensor Networks,” IEEE Computer, Special Issue in Sensor Networks, Aug 2004. 14. J. Z. Li, J. B. Li, and S. F. Shi, “Concepts, issues and advance of sensor networks and data management of sensor networks,” Journal of Software, 2003, vol. 14 no.10, pp.1717-1727. 15. Z. X. Luo, and T. C. Jannett, “Energy-based target localization in multi-hop wireless sensor networks,” in Proc. of the 2012 IEEE Radio and Wireless symposium, Santa Clara, CA, Jan. 2012. 16. K. Liu, J. Deng, P. K. Varshney, and K. Balakrishnan, "An acknowledgment-based approach for the detection of routing misbehavior in MANETs," IEEE Trans. Mobile Comput., vol. 6, pp. 536-550, May 2007. 17. Q. Cheng, P. K. Varshney, J. H. Michels, and C. M. Belcastro, "Fault detection in dynamic systems via decision fusion," IEEE Trans. Aerosp. Electron. Syst., vol. 44, pp. 227-242, Jan. 2008. 18. Z. X. Luo and T. C. Jannett, “Optimal threshold for locating targets within a surveillance region using a binary sensor network,” in Proc. of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 09), Dec. 2009. 19. R. Niu, P. K. Varshney, and Q. Cheng, "Distributed detection in a wireless sensor network with a large number of sensors," Information Fusion, vol. 7, pp. 380-394, Dec. 2006. 20. Z. X. Luo and T. C. Jannett, “A multi-objective method to balance energy consumption and performance for energy-based target localization in wireless sensor networks,” in Proc. of the 2012 IEEE SoutheastCon, Orlando, FL, Mar. 2012. 21. Engin Masazade, Ramesh Rajagopalan, Pramod K. Varshney, Chilukuri Mohan, Gullu Kiziltas Sendur, and Mehmet Keskinoz, “A Multi-objective Optimization Approach to Obtain Decision Thresholds for Distributed Detection in Wireless Sensor Networks," IEEE Transactions on Systems, Man, and Cybernetics - Part B, Vol. 40, No. 2, April 2010. 22. Z. X. Luo and T. C. Jannett, “Performance comparison between maximum likelihood and heuristic weighted average estimation methods for energy-based target localization in wireless sensor networks,” in Proc. of the 2012 IEEE SoutheastCon, Orlando, FL, Mar. 2012. 23. F. Stann and J. Heidemann, “RMST: Reliable Data Transport in Sensor Networks,” in Proc. 1st IEEE Workshop on Sensor Network Protocols and Applications (SNPA), 2003. 24. B. Hull, K. Jamieson, and H. Balakrishnan, “Mitigating Congestion in Wireless Sensor Networks,” in Proc.2nd ACM Conference on Embedded Networked Sensor Systems (SenSys’04), 2004. 25. Z. X. Luo, “A censoring and quantization scheme for energy-based target localization in wireless sensor networks,” Journal of Engineering and Technology, vol.2, no.2, Aug. 2012. 26. H. Chen and P. K. Varshney, "Theory of the stochastic resonance effect in signal detection---Part II: Variable detectors," IEEE Trans. Signal Process., vol. 56, pp. 5031-5041, Oct. 2008. 27. Z. X. Luo, “A coding and decoding scheme for energy-based target localization in wireless sensor networks,” International Journal of Soft Computing and Engineering, vol.2, no. 4, Sept. 2012. 28. C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and F. Silva, “Directed diffusion for wireless sensor networking,” ACM/IEEE Transactions on Networking, vol. 11, no. 1, 2002. 29. Z. X. Luo, “Distributed estimation in wireless sensor networks with heterogeneous sensors,” International Journal of Innovative Technology and Exploring Engineering, vol. 1, no.4, Sept. 2012. 30. M. Karpinski, A. Senart, and V. Cahill, “Sensor networks for smart roads,” in Proceedings of the Second IEEE International Workshop on Sensor Networks and Systems for Pervasive Computing (PerSeNS'06), Pisa, Italy, pp. 13–17 March 2006. 31. Z. X. Luo, “Overview of applications of wireless sensor networks,” International Journal of Innovative Technology and Exploring Engineering, vol. 1, no. 4, Sept. 2012 32. Y. Wang, G. Zhou, T. Li, “Design of a wireless sensor network for detecting occupancy of vehicle berth in car park,” in PDCAT'06: Proceedings of the Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies, IEEE Computer Society, Washington, DC, USA, 2006, pp. 115–118. 33. S. C. Ergen and P. Varaiya, “Optimal placement of relay nodes for energy efficiency in sensor networks,” in Proceedings of IEEE International Conference on Communication, Istanbul, Turkey, June 2006. 34. W. Chen, L. Chen, Z. Chen, S. Tu, “A realtime dynamic traffic control system based on wireless sensor network,” in Proceedings of the 2005 International Conference on Parallel Processing—Workshops, IEEE, Oslo, Norway 2005, pp. 258–264. 35. Z. X. Luo, P. S. Min, and S. J. Liu, “Target localization in wireless sensor networks for industrial control with selected sensors” International Journal of Distributed Sensor Networks, 2013. 36. M. Wiering, J. Vreeken, J. V. Veenen, and A. Koopman, “Simulation and optimization of traffic in a city,” in IEEE Intelligent Vehicles Symposium, Parma, Italy, June 2004. 37. Z. X. Luo, “Parameter estimation in wireless sensor networks with normally distributed sensor gains,” International Journal of Soft Computing and Engineering, vol. 2, no. 6, Jan. 2013. 38. Q. Cheng, B. Chen, and P. K. Varshney, "Detection performance limits for distributed sensor networks in the presence of nonideal channels," IEEE Trans. Wireless Commun., vol. 5, pp. 3034-3038, Nov. 2006. 39. Z. X. Luo, “Parameter estimation in wireless sensor networks based on decisions transmitted over Rayleigh fading channels,” International Journal of Soft Computing and Engineering, vol. 2, no. 6, Jan. 2013. 40. R. Niu and P. K. Varshney, "Performance analysis of distributed detection in a random sensor field," IEEE Trans. Signal Process., vol. 56, pp. 339-349, Jan. 2008. 41. Z. X. Luo, “Distributed estimation and detection in wireless sensor networks,” International Journal of Inventive Engineering and Sciences, vol. 1, no. 3, Feb. 2013. 42. D. Chen and P. K. Varshney, "On demand geographic forwarding for data delivery in wireless sensor networks," Computer Communications, vol. 30, pp. 2954-2967, Oct. 2007. 43. S. Zhang, W. Xiao, J. Gong, and Y. Yin, “A novel human motion tracking based on wireless sensor network,” International Journal of Distributed Sensor Networks, 2013. Authors: Zhibin Gui, Yongxiang Fan Paper Title: An Application of Wireless Sensor Networks in Health Care Setting, Part II Abstract: This paper and the following papers present an application of wireless sensor networks (WSNs)in health care setting. The application involves building a reliable transmission system to transmit physiological data from ICU gateway to central server wirelessly in hospitals. Due to the length of this project, this paper will include several parts, which are published separately. This part mainly focuses on implementation issues.
Keywords: Wireless sensor networks, health care, implementation.
References: 1. Z. X. Luo, “Anti-attack and channel aware target localization in wireless sensor networks deployed in hostile environments,” International Journal of Engineering and Advanced Technology, vol. 1, no. 6, Aug. 2012. 2. C. Intanagonwiwat, R. Govindan, and D. Estrin,"Directed Di 3. usion: A Scalable and Robust Communication Paradigm for Sensor Networks," in Proc. ACM MOBICOM '00, Boston, Massachusetts, August 2002. 35. 4. K. Sohrabi, B. Manriquez and G. Pottie,"Near-Ground Wideband Channel Measurements," in Proc. IEEE VTC'99, New York, 1999. 5. Z. X. Luo, “Modeling sensor position uncertainty for robust target localization in wireless sensor networks,” in Proc. of the 2012 IEEE Radio and Wireless Symposium, Santa Clara, CA, Jan. 2012. 167-171 6. R. Rajagopalan and P. K. Varshney, "Data-aggregation techniques in sensor networks: A survey," IEEE Communications Surveys and Tutorials, vol. 8, pp. 48-63, Fourth Quarter, 2006. 7. Z. X. Luo, “Robust energy-based target localization in wireless sensor networks in the presence of byzantine attacks,” International Journal of Innovative Technology and exploring Engineering, vol. 1, no.3, Aug. 2012. 8. C. Yao, P.-N. Chen, T.-Y. Wang, Y. S. Han, and P. K. Varshney, "Performance analysis and code design for minimum Hamming distance fusion in wireless sensor networks," IEEE Trans. Inform. Theory, vol. 53, pp. 1716-1734, May 2007. 9. Z. X. Luo, “A new direct search method for distributed estimation in wireless sensor networks,” International Journal of Innovative Technology and Exploring Engineering, vol. 1, no. 4, Sept. 2012. 10. Woo, D. E. Culler, “A Transmission Control Scheme for Media Access in Sensor Networks," in Proc. ACM MOBICOM 2001, pp.221- 235, Rome, Italy 2001. 11. W. Ye, J. Heidemann and D. Estrin, "An Energy-Efficient MAC Protocol for Wireless Sensor Networks," in Proc. INFOCOM'02, New York, USA,June, 2002. 12. Akyildiz, I.F., W. Su, Y. Sankarasubramaniam, E. Cayirci, "A Survey on Sensor Networks", IEEE Communications Magazine, August,2002, pp.102-114. 13. David Culler, Deborah Estrin, Mani Srivastava, “Overview of Sensor Networks,” IEEE Computer, Special Issue in Sensor Networks, Aug 2004. 14. J. Z. Li, J. B. Li, and S. F. Shi, “Concepts, issues and advance of sensor networks and data management of sensor networks,” Journal of Software, 2003, vol. 14 no.10, pp.1717-1727. 15. Z. X. Luo, and T. C. Jannett, “Energy-based target localization in multi-hop wireless sensor networks,” in Proc. of the 2012 IEEE Radio and Wireless symposium, Santa Clara, CA, Jan. 2012. 16. K. Liu, J. Deng, P. K. Varshney, and K. Balakrishnan, "An acknowledgment-based approach for the detection of routing misbehavior in MANETs," IEEE Trans. Mobile Comput., vol. 6, pp. 536-550, May 2007. 17. Q. Cheng, P. K. Varshney, J. H. Michels, and C. M. Belcastro, "Fault detection in dynamic systems via decision fusion," IEEE Trans. Aerosp. Electron. Syst., vol. 44, pp. 227-242, Jan. 2008. 18. Z. X. Luo and T. C. Jannett, “Optimal threshold for locating targets within a surveillance region using a binary sensor network,” in Proc. of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 09), Dec. 2009. 19. R. Niu, P. K. Varshney, and Q. Cheng, "Distributed detection in a wireless sensor network with a large number of sensors," Information Fusion, vol. 7, pp. 380-394, Dec. 2006. 20. Z. X. Luo and T. C. Jannett, “A multi-objective method to balance energy consumption and performance for energy-based target localization in wireless sensor networks,” in Proc. of the 2012 IEEE SoutheastCon, Orlando, FL, Mar. 2012. 21. Engin Masazade, Ramesh Rajagopalan, Pramod K. Varshney, Chilukuri Mohan, Gullu Kiziltas Sendur, and Mehmet Keskinoz, “A Multi-objective Optimization Approach to Obtain Decision Thresholds for Distributed Detection in Wireless Sensor Networks," IEEE Transactions on Systems, Man, and Cybernetics - Part B, Vol. 40, No. 2, April 2010. 22. Z. X. Luo and T. C. Jannett, “Performance comparison between maximum likelihood and heuristic weighted average estimation methods for energy-based target localization in wireless sensor networks,” in Proc. of the 2012 IEEE SoutheastCon, Orlando, FL, Mar. 2012. 23. F. Stann and J. Heidemann, “RMST: Reliable Data Transport in Sensor Networks,” in Proc. 1st IEEE Workshop on Sensor Network Protocols and Applications (SNPA), 2003. 24. B. Hull, K. Jamieson, and H. Balakrishnan, “Mitigating Congestion in Wireless Sensor Networks,” in Proc.2nd ACM Conference on Embedded Networked Sensor Systems (SenSys’04), 2004. 25. Z. X. Luo, “A censoring and quantization scheme for energy-based target localization in wireless sensor networks,” Journal of Engineering and Technology, vol.2, no.2, Aug. 2012. 26. H. Chen and P. K. Varshney, "Theory of the stochastic resonance effect in signal detection---Part II: Variable detectors," IEEE Trans. Signal Process., vol. 56, pp. 5031-5041, Oct. 2008. 27. Z. X. Luo, “A coding and decoding scheme for energy-based target localization in wireless sensor networks,” International Journal of Soft Computing and Engineering, vol.2, no. 4, Sept. 2012. 28. C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and F. Silva, “Directed diffusion for wireless sensor networking,” ACM/IEEE Transactions on Networking, vol. 11, no. 1, 2002. 29. Z. X. Luo, “Distributed estimation in wireless sensor networks with heterogeneous sensors,” International Journal of Innovative Technology and Exploring Engineering, vol. 1, no.4, Sept. 2012. 30. M. Karpinski, A. Senart, and V. Cahill, “Sensor networks for smart roads,” in Proceedings of the Second IEEE International Workshop on Sensor Networks and Systems for Pervasive Computing (PerSeNS'06), Pisa, Italy, pp. 13–17 March 2006. 31. Z. X. Luo, “Overview of applications of wireless sensor networks,” International Journal of Innovative Technology and Exploring Engineering, vol. 1, no. 4, Sept. 2012 32. Y. Wang, G. Zhou, T. Li, “Design of a wireless sensor network for detecting occupancy of vehicle berth in car park,” in PDCAT'06: Proceedings of the Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies, IEEE Computer Society, Washington, DC, USA, 2006, pp. 115–118. 33. S. C. Ergen and P. Varaiya, “Optimal placement of relay nodes for energy efficiency in sensor networks,” in Proceedings of IEEE International Conference on Communication, Istanbul, Turkey, June 2006. 34. Z. X. Luo, P. S. Min, and S. J. Liu, “Target localization in wireless sensor networks for industrial control with selected sensors,” International Journal of Distributed Sensor Networks, 2013. 35. W. Chen, L. Chen, Z. Chen, S. Tu, “A realtime dynamic traffic control system based on wireless sensor network,” in Proceedings of the 2005 International Conference on Parallel Processing—Workshops, IEEE, Oslo, Norway 2005, pp. 258–264. 36. M. Wiering, J. Vreeken, J. V. Veenen, and A. Koopman, “Simulation and optimization of traffic in a city,” in IEEE Intelligent Vehicles Symposium, Parma, Italy, June 2004. 37. Z. X. Luo, “Parameter estimation in wireless sensor networks with normally distributed sensor gains,” International Journal of Soft Computing and Engineering, vol. 2, no. 6, Jan. 2013. 38. Q. Cheng, B. Chen, and P. K. Varshney, "Detection performance limits for distributed sensor networks in the presence of nonideal channels," IEEE Trans. Wireless Commun., vol. 5, pp. 3034-3038, Nov. 2006. 39. Z. X. Luo, “Parameter estimation in wireless sensor networks based on decisions transmitted over Rayleigh fading channels,” International Journal of Soft Computing and Engineering, vol. 2, no. 6, Jan. 2013. 40. R. Niu and P. K. Varshney, "Performance analysis of distributed detection in a random sensor field," IEEE Trans. Signal Process., vol. 56, pp. 339-349, Jan. 2008. 41. Z. X. Luo, “Distributed estimation and detection in wireless sensor networks,” International Journal of Inventive Engineering and Sciences, vol. 1, no. 3, Feb. 2013. 42. D. Chen and P. K. Varshney, "On demand geographic forwarding for data delivery in wireless sensor networks," Computer Communications, vol. 30, pp. 2954-2967, Oct. 2007. 43. S. Zhang, W. Xiao, J. Gong, and Y. Yin, “A novel human motion tracking based on wireless sensor network,” International Journal of Distributed Sensor Networks, 2013. Authors: Radhakrishna Karne, Chakradhar.A Paper Title: An Enhanced Dc Motor Control Using the Speech Recognition System Abstract: In this paper we present a new enhanced methodology for increasing the accuracy among the speech recorded in order to control the DC motor. The novel method takes in to the consideration the MFCC and VQ algorithm in order to calculate the coeefiecients . The speech moves DC motor with the different speeds and controls it’s direction also with the only on command which can be issued inorder to stop the motor.
Keywords: DC, MFCC, VQ
36. References: 1. An Efficient MFCC Extraction Method in Speech Recognition. Wei HAN, Cheong-Fat CHAN, Chiu-Sing CHOY and Kong-Pang PUN, 172-173 Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, IEEE 2006. 2. Differential MFCC and Vector Quantization used for Real-Time Speaker Recognition System, 2008 IEEE Congress on Image and Signal Processing, Wang ChenˈMiao Zhenjiang, Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China 3. “Speaker Identification Using MEL Frequency Cepstral Coefficient”, Md. Rashidul Hasan, Mustafa Jamil, Md. Golam Rabbani Md. Saifur Rahman. Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology. 3rd International Conference on Electrical & Computer Engineering ICECE 2004, 28-30 December 2004, Dhaka, Bangladesh. 4. Lawrence Rabiner and Biing-Hwang Juang, Fundamental of Speech Recognition”, Prentice-Hall, Englewood Cliffs, N.J., 1993. 5. Zhong-Xuan, Yuan & Bo-Ling, Xu & Chong-Zhi, Yu. (1999). “Binary Quantization of Feature Vectors for Robust Text-Independent Speaker Identification” in IEEE Transactions on Speech and Audio Processing, Vol. 7, No. 1, January 1999. IEEE, New York, NY, U.S.A. 6. F. Soong, E. Rosenberg, B. Juang, and L. Rabiner, "A Vector Quantization Appr Authors: Ishani Bishnu, Jyoti Vimal, Neha Kumari, Pooja Singh Paper Title: Multiple Data Abstraction in a Single Plane Using Different Transformation Techniques Abstract: In this paper we suggest embedding and extraction algorithms through which we can abstract multiple data in a single color plane by using different transformation techniques such as wavelet transformation, cosine transformation and single value decomposition.
Keywords: Wavelet Transformation, Cosine Transformation, Singular Value Decomposition.
References: 1. Satyanarayan Murty P Hima BindoR , Rajesh Kumar “Novel Semi – Blind Reference using DWT- DCT- SVD” ,International Journal 37. of Computer Application (0975-8887), Volume 53- N015,September 2012 2. Nidhi M.Divecha, N.N.Jani “Image Watermarking Algorithm Using DCT, DWT, SVD” ,National Conference on Inovative Paradigm 174-176 in Engineering and Technology (NCIPET-2012), Proceeding published by International Journal of Computer Application 13CA. 3. Tanmay Bhattacharya, Nilanjan Dey , S.R Bhadra Chaudhuri “A session based Multiple image Hiding Technique using DWT and DCT”. 4. Pallavi Patil, D.S.Bormane “DWT Based Invisible Watermarking Techniques for Digital Images”. ISSN-2249-8959, Volume 2, Issue- 4,April 2013. 5. K B Shiva Kumar, KB Raja,R K chhotaray,SabyasachiPattanaik “Bit Length replacement Steganography based on DCT coefficient” Internationa journal of engineering science and technology,Vol.2(8),2010,3561-3570 6. Emir Gainc, Ahmed M Eskicioglu Robust DWT- SVD Domain image Watermarking Embedding Data in the frequencies. 7. Dan Kalman “A Singularly Valuable Decomposition: The SVD of a Matrix” february13,2002 8. Brian Potetz and Tai Singlee “Statistical correlations between two Dimensional images and three Dimensional structure in natural scenes Authors: Shyam Sundar Prasad, Chanakya Kumar Paper Title: A Methodology for an Efficient and Reliable M2M Communication Abstract: M2M is a new advent in technology where uncountable machines can share their information between other homogenous or heterogeneous machine. It is very similar o IOT.M2M is going to be a stimulation force for current Market scenario with large area of application like Smart warrior system, Real-Time Agriculture System- health ,Home Automation and many more This paper addresses the research issues related with the deployment of M2M with architecture of M2M Communication .In this paper we have tried to introduce all research issues and efforts have been made propose a Energy Efficient and Reliable (EER)M2M Communication.
Keywords: M2M, IOT
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Authors: Hairudin Abd Majid, Nur Izzati Jamahir, Azurah A.Samah Using Reliability Information and Neuro-Fuzzy to Predict Warranty Cost: A Case Study in Fleet Paper Title: Vehicle Abstract: Nowadays, the great market competition makes that the companies look for high reliability and quality of the products manufacturing. The effective ways to ensure reliability signals of the product is by offering better warranty terms and period associated with sale of the product. In fact, warranty is a legal obligation of the manufacturer or dealer in connection with the sale of the product that defines the liability of the manufacturer or dealer in the event of the premature failure or defects of the product. The purpose of this paper is to propose a method for reliability analysis of the warranty data and to validate the warranty policy. In order to applied neuro- fuzzy approach by optimizing warranty cost and period, modelling the reliability of the product must be beneficial.
Keywords: reliability, failure rate, neuro-fuzzy, warranty cost, warranty period.
References: 1. Ang J.C..Application of Artificial Neural Network in Two- Dimensional Warranty Modelling,Universiti Teknologi Malaysia, Skudai: Projek Sarjana.2011. 2. Birolini A. Reliability Engineering Theory and Practice 5th Edition. Springer, New York. 2006. 3. Blischke W.R. and Murthy D.N.P. Reliability Modelling and Optimization. Wiley, Canada. 2000. 4. Chukova, S. and Johnston, M.R.(2006). Two-dimensional warranty repair strategy based on minimal and complete repairs. Journal of Mathematical and Computer Modelling 44(11-12_: 1133-1143. 5. Hrycej T. and Grabert M. (2007), Warranty Cost Forecast Based on Car Failure Data. International Joint Conference on Neural Networks. 6. K.Rai B. and Singh N., Reliability Analysis and Prediction with Warranty Data, U.S:CRC . 2009. 39. 7. LeBlanc , B.(2008). Analysis of decisions involved in offering a product warranty. Annual of Reliability and Maintainability Symposium, RAMS. 8. Lee S. H. and Moon K. L. (2009). Fuzzy Failure Analysis of Automotive Warranty Claims using Age and mileage Rate. ICIC, 434-439. 184-188 9. Lee S. H., Cho S. E., and Moon K. L. (2010). Fast Fuzzy Control of Warranty Claims System. Journal of Information Processing Systems, 6(2). 10. Lee S. H., Lee D. S., Park C. S., Lee J. H., Park S. B., Moon K. L., and Kim B. K. (2008b). A Fuzzy Logic-Based Approach to Two- Dimensional Warranty System. ICIC, 326-331. 11. Lee S. H., Lee J. H., Park S. B., Lee M. T., Lee S. J., Kim B.K. (2008a). A Fuzzy Reasoning Model of Two-dimensional Warranty System. International Conference on Advanced Language Processing and Web Information Technology. 287-292. 12. Lee S. H., Lee S. J., and Moon K. L. (2011). Application of Fuzzy Feedback Control for Warranty Claim. SCI, 279-288. 13. Lee S. H., Seo, S. C., Yeom, S. J., Moon, K. I., Kang, M. S. and Kim, B. G. (2007). A Study on Warning/ Detection Degree of Warranty Claims Data using Neural Network Learning. Proceeding of Sixth International Conference on Advanced Language Processing and Web Information Technology (ALPIT), 492-497. 14. Lee S.H., Lee J.H., Park S.B., Lee M.T., Lee S.J., Kim B.K.(2008), A Fuzzy Reasoning Model of Two-dimensional Warranty System. International Conference on Advanced Language Processing and Web Information Technology. 287-292. 15. Lemes D.V, Felix E.P, and Martha G.F. (2008). Reliability Analysis of Electronic Equipment Based on Warranty Failure Database. ABCM Symposium Series in Mechatronics.pp. 395-404. 16. Majid H.A, Kasim N.H, Jamahir N.I, and Samah A.A. (2012). Soft Computing Method in Warranty Problems: Review and Recent Applications. IJCSI International Journal of Computer Science Issues.190-196 17. Murthy D.N.P and Djamaludin I. (2002). New product warranty: A literaturwe review. 231-260. 18. S.Yang, J.Kobza, J.Naclas, “Bivariate failure modeling”, Proc.Ann. Reliability & Maintainability Symp., 2000 Jan, pp 281-287. 19. Singpurwalla N.D. and Wilson S.(1993). The warranty problem: Its statistical and game theoretic aspects. Society for Industrial and Applied Mathematics.Vol. 35, 1 7 -4 2 . 20. Wasserman G.S, An application of dynamic linear models for predicting warranty claims, IEE Transactions 28 (1996) 967-977. 21. Yadav O. P., Singh N., Chinnam R. B., and Goel P. S. (2003). A fuzzy logic based approach to reliability improvement estimation during product development. Reliability engineering & system safety. 63-74. 22. Yang G. and Zaghati Z. (2002). Two-Dimensional Reliability Modeling From Warranty Data.272-278. Authors: Akshay A. Pawle, Vrushsen P. Pawar Paper Title: Face Recognition System (FRS) on Cloud Computing for User Authentication Abstract: Cloud computing is a new technology in the market. In cloud computing user can access their files or 40. data from anyplace using internet. There are several benefits of cloud computing like increase throughput, reduce 189-192 costs, improve accessibility and requires less training but on the other hand it has some security issues. In that, identifying authorized user is a major issue. The user wanting to access the data or services needs to be registered and before every access to data or services; his/her identity must be authenticated for authorization. There are several authentication techniques including traditional and biometric but has some drawbacks. In this paper, we proposed new face recognition system (FRS) which overcome all drawbacks of traditional and other biometric authentication techniques and enables only authorized users to access data or services from cloud server.
Keywords: Authorized User, Cloud Computing, Face recognition system, Throughput
References: 1. Rajesh Piplode and Umesh Kumar Singh, “An Overview and Study of Security Issues & Challenges in Cloud Computing,” International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277 128X, Volume-2, Issue-9, September 2012. 2. P. Senthil, N. Boopal and R.Vanathi, “Improving the Security of Cloud Computing using Trusted Computing Technology,” International Journal of Modern Engineering Research (IJMER), ISSN: 2249-6645, Volume-2, Issue-1, Jan-Feb 2012, pp-320-325. 3. Ganesh V. Gujar, Shubhangi Sapkal and Mahesh V. Korade, “STEP-2 User Authentication for Cloud Computing,” International Journal of Engineering and Innovative Technology (IJEIT), ISSN: 2277-3754, Volume-2, Issue-10, April 2013. 4. "The NIST Definition of Cloud Computing". National Institute of Science and Technology. 5. R. Kalaichelvi Chandrahasan, S Shanmuga Priya and Dr. L. Arockiam, “Research Challenges and Security Issues in Cloud Computing,” International Journal of Computational Intelligence and Information Security, Volume-3, No-3, March 2012. 6. Nils Gruschka and Meiko Jensen, “Attack Surfaces: A Taxonomy for Attacks on Cloud Services,” Proceedings of the IEEE 3rd International conference on Cloud Computing, 2010, PP-276-279. 7. Minhaz Fahim Zibran, “Biometric Authentication: The Security Issues,” University of Saskatchewan, 2012. 8. S. O. Kuyoro, F. Ibikunle and O. Awodele, “Cloud Computing Security Issues and Challenges,” International Journal of Computer Networks (IJCN), Volume-3, Issue-5, 2011. 9. Maninder Singh and Sarbjeet Singh, “Design and Implementation of Multi-tier Authentication Scheme in Cloud,” International Journal of Computer Science Issues (IJCSI), ISSN: 1694-0814 Volume-9, Issue-5, No-2, Sep. 2012. 10. Aviel D. Rubin Bellcore, “Independent One-Time Passwords,” Fifth USENIX UNIX Security Symposium, Salt Lake City, Utah, Jun. 1995. 11. Chunming Rong and Hongbing Cheng, “A Secure Data Access Mechanism for Cloud Tenants,” Cloud computing 2012: The Third International Conference on Cloud Computing, GRIDs, and Virtualization, ISBN: 978-1-61208-216-5, IARIA, 2012. 12. Jitendra Choudhary, “Survey of Different Biometrics Techniques,” International Journal of Modern Engineering Research (IJMER), ISSN: 2249-6645, Volume-2, Issue-5, Sep.-Oct. 2012, pp-3150-3155. Authors: R.Harikumar, R.Prabu, S.Raghavan Paper Title: Electrical Impedance Tomography (EIT) and Its Medical Applications: A Review Abstract: This paper reviews the principles of Electrical Impedance Tomography (EIT), different types of current patterns and reconstruction algorithms to assess its potential in medical imaging. A current injection pattern in EIT has its own current distribution profile within the subject under test. Hence, different current patterns have different sensitivity, spatial resolution and distinguishability. Image reconstruction studies with subject or practical phantoms are essential to assess the performance of EIT systems for their validation, calibration and comparison purposes. Impedance imaging of real objects or tissue phantoms with different current injection methods is also essential for better assessment of the biomedical EIT systems. More specifically, this work reviews the three different image reconstruction techniques including back-projection method, iterative method and one-step linearized method. In this review, Resistivity images are reconstructed from the boundary data using Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EIDORS).
Keywords: Electrical Impedance Tomography (EIT), Stimulation patterns, regularization, Images.
References: 1. J.G. Webster, Electrical Impedance Tomography. Adam Hilger Series of Biomedical Engineering, Adam Hilger, New York, USA, 1990. 2. S. Feliu, J.C. Galvan, and M. Morcillo, "interpretation of electrical impedance diagram for painted galvanized steel," Prog. in Organic Coatings, vol. 17, no. 2, pp.143- 153, 1989. 3. K.A.Dines and R.J.Lytle, "Analysis of ele-ctrical conductivity imaging," Geophysics, vol. 46, no. 7, pp. 1025- 1036, July 1981. 4. D.C.Barber, B.H. Brown, and J. L. Freeston, "Imaging spatial distributions of resistivity using applied potential tomography," Electron. Lett., vol. 19, pp. 933 - 935, 1983. 41. 5. B.H.Brown, “Medical impedance tomography and process impedance tomography: a brief review”, Measure. Sci. Technol. 12 (August) (2001) 991–996. 193-198 6. A.P.Bagshaw, A.D.Liston, R.H.Bayford, A. Tizzard, A.P.Gibson, A.T.Tidswell, M.K. Sparkes, H.Dehghani, C.D.Binnie, D.S. Holder, “Electrical impedance tomography of human brain function using reconstruction algorithms based on the finite element method”, NeuroImage 20 (2003) 752–764. 7. F.S. Moura, J.C.C. Aya, A.T. Fleury, M.B.P. Amato, R.G. Lima, “Dynamic imaging in electrical impedance tomography of the human chest with online transition matrix identification”, IEEE Trans. Biomed. Eng. 57 (2) (2010). 8. F. Ferraioli, A. Formisano, R. Martone, “Effective exploitation of prior information in electrical impedance tomography for thermal monitoring of hyperthermia treatments”, IEEE Trans. Mag. 45 (3) (2009). 9. A.E.Hoetink, T.J.C.Faes, J.T. Marcus, H.J.J. Kerkkamp, R.M. Heethaar, “Imaging of thoracic blood volume changes during the heart cycle with electrical impedance using a linear spot-electrode array”, IEEE Trans. Med. Imaging 21 (6) (2002) 653. 10. D. Holder, “Clinical and Physiological Applications of Electrical Impedance Tomography”. London, U.K.: UCL Press, 1993. 11. J.C. Newell, D. Isaacson, G.J. Saulnier, M. Cheney, and D.G. Gisser, “Acute pulmonary edema assessed by electrical impedance tomography,” in Proc. Annui. Int. Conf. IEEE Engineering in Medicine and Biology Soc., 1993, pp. 92-93. 12. B.H. Brown, A.D. Segar, “The Sheffield data collection system”, Clin Phys Physiol Measur 8 (Suppl. A) (1987) 91–97. 13. Avis NJ, Barber DC, “Image reconstruction using non-adjacent drive configuration”, Physiol Meas, 1994, 16, A153-A160. 14. Hua P, Webster JG, Tompkins WJ,” Effect of the measurement method on noise handling and image quality of EIT imaging”, IEEE 9th Annual Conf on Engineering in Medicine and Biological Science, 1993, 1429-30. 15. Gisser G, Isaacson D, Newell JC, “Current topics in impedance imaging”, Clin Physiol Meas, 1987, 8:38-46. 16. D.C.Barber and B.H.Brown “Recent developments in applied potential tomography”-apt Information Processing in Medical Imaging 1986 pp 106–21 17. T.J.Yorkey, J.G.Webster, and W.J. Tompkins. “Comparing reconstruction algorithms for electrical impedance tomography”. IEEE Trans. Biomed. Eng, 34:843–852, 1987. 18. M. Cheney, D. Isaacson, J.C. Newell, S. Simske, and J.C. Goble. “NOSER: an algorithm for solving the inverse conductivity problem”, Int.J.Imaging Syst.Technol., 2:66–75, 1990. 19. Adler and R. Guardo. “Electrical impedance tomography: regularized imaging and contrast detection”, IEEE Trans. Med. Imaging, 15:170–179, 1996a. 20. Nick Polydorides and William R B Lionheart “A Matlab toolkit for three-dimensional electrical impedance tomography: a contribution to the Electrical Impedance and Diffuse Optical Reconstruction Software project” Meas. Sci. Technol. 13 1871-1883, 2002 21. Yasheng Maimaitijiang, Stephan Böhm, Obaydah Jaber, Andy Adler “A phantom based system to evaluate EIT performance” Int. Conf. Electrical Bio-Impedance & Electrical Impedance Tomography Gainville, Fl, USA, 4-8 April 2010 22. GK Wolf, C Gómez-Laberge, JN Kheir, D Zurakowski, BK Walsh, A Adler, JH Arnold. “Reversal of Dependent Lung Collapse Predicts Response to Lung Recruitment in Children with Early Acute Lung Injury”, Pediatr Crit Care Med, 13:509−515, 2012 23. Eckhard Teschner, Michael Imhoff, “Electrical impedance tomography: the realization of regional ventilation monitoring”, Drager Medical GmbH, 2011 Authors: Hairudin Abdul Majid, Nur Hayati Kasim, Azurah A. Samah Paper Title: Optimization of Warranty Cost using Genetic Algorithm: A Case Study in Fleet Vehicle Abstract: Optimization is a process of making something better. Genetic Algorithm (GA) has been widely used in many applications specifically in optimization problem. This paper describes GA and its application to warranty problem in optimizing total cost of warranty. A detail procedure of GA process is provided in this paper. Initial point were created randomly and the survival are depends on fitness criterion. Improvement in every generation of GA process gives the fittest solution which is the most optimum. The results of GA give small error of parameter values while comparing with estimated values by using Maximum Likelihood Estimation (MLE).
Keywords: Fleet vehicle, Genetic Algorithm, Optimization, Warranty
42. References: 1. D. N. P. Murthy, O. Solem and T. Roren, “Product warranty logistics: Issues and challenges,” European Journal of Operational 199-202 Research, Vol. 156, 2004, pp. 110–126. 2. H. A. Majid, N. H. Kasim, N. I. Jamahir and A. A. Samah, “Soft computing methods in warranty problems: review and recent application (2003-2012),” International Journal of Computer Science Issues, Vol. 9, Issue 4, No. 3, July 2012, pp. 190-196. 3. Jin, T. and Ozalp, Y., “Minimizing Warranty Cost by Design for Reliability in Product Development Phase,” International Journal of Six Sigma and Competitive Advantage.5, 2009, pp. 42-58. 4. P. A. Scarf and H. A. Majid, “Modelling warranty extensions: a case study in the automotive industry,” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2011, pp. 225- 251. 5. R. Verma and P. A. Lakshminarayanan, “A case study on the application of genetic algorithm for optimization of engine parameters,” Proceeding of Automobile Engineering, Vol. 220, 2006, pp. 471-479. 6. S. S. Chaudhry and W. Luo, “Application of Genetic Algorithms in Production and Operations Management: A Review,” International Journal of Production Research. 43, 2005, pp. 4083-4101. 7. S. Y. Sohn, T. H. Moon, and K. J. Seok, “Optimal pricing for mobile manufacturers in competitive market using genetic algorithm,” Expert Systems with Applications. 36, 2009, pp. 3448-3453. Authors: Pranamika Kakati Paper Title: A New Similarity Measure for Fuzzy Sets with the Extended Definition of Complementation Abstract: Similarity measure for Fuzzy sets is one of the researched topics of Fuzzy set theory. Till now there have been several methods to measure similarity between two Fuzzy sets. The existing methods are based on traditional Zadehian Theory of Fuzzy sets where it is believed that there is no difference between Fuzzy membership function and Fuzzy membership value for the complement of a Fuzzy set which is already proved to be wrong. In this article, effort has been made to put forward a new similarity measure with the help of extended definition of complementation of Fuzzy sets using reference function. Our proposed method is based on the fact that Fuzzy membership function and Fuzzy membership value for the complement of a Fuzzy set are two different things. With this new approach an attempt has been made to design a new Similarity measure for Fuzzy sets so that it becomes free from any further controversy.
Keywords: Complement of a Fuzzy set, Fuzzy membership function, Fuzzy membership value, Fuzzy reference function, Fuzzy set, Similarity measure. 43. References: 203-207 1. L.A.Zadeh, “ Fuzzy Sets” , Information and Control, 8, pp. 338-353, 1965. 2. Hemanta K. Baruah, “Towards Forming A Field Of Fuzzy Sets” International Journal of Energy, Information and Communications, Vol.2, Issue 1, pp. 16-20, February 2011. 3. Hemanta K. Baruah, “The Theory of Fuzzy Sets: Beliefs and Realities”, International Journal of Energy, Information and Communications, Vol. 2, Issue 2,pp. 1-22, May,2011. 4. Tridiv Jyoti Neog , Dusmanta Kumar Sut, “Theory of Fuzzy Soft Sets from a New Perspective”, International Journal of Latest Trends in Computing, Vol. 2, No 3, September,2011. 5. Eulalia Szmidt, Janusz Kacprzyk, “Medical Diagnostic Reasoning Using a Similarity Measure for Intuitionistic Fuzzy Sets” Eighth Int. Conf. on IFSs, Varna, 20-21 June 2004, NIFS Vol. 10 (2004), 4, 61-69. 6. Hong-mei Ju , Feng-Ying Wang , “A Similarity Measure for Interval –valued Fuzzy Sets and Its Application in Supporting Medical Diagnostic Reasoning”, The Tenth International Symposium on Operation Research and Its Applications(ISORA 2011), Dunhuang, China, August 28-31, 2011. 7. Szmidt E., Kacprzyk J. (2001), “Entropy for intuitionistic Fuzzy sets”. Fuzzy Sets and Systems, vol. 118, No. 3, pp. 467-477. 8. Hongmei Ju (2008). “Entropy for Interval-valued Fuzzy Sets”,Fuzzy information and engineering, Volume 1,358-366. 9. Pranamika Kakati(2013). “A Study on Similarity Measure for Fuzzy Sets” International Journal of Advanced Research in Computer Science and Softaware Engineering, vol. 3 Issue 8 August 2013. Authors: CH.Renu Madhavi, A.G.Ananth Finding a Suitable Nonlinear Technique to Distinguish Between HRV Data of Cardiac & Non - Paper Title: Cardiac Diseased Subjects 44. Abstract: The objective of this paper is to find a nonlinear technique which has better resolution in distinguishing between healthy, cardiac and Non-cardiac diseased subjects. Heart Rate Variability (HRV) data of 208-210 healthy, cardiac disease and Non-cardiac diseased subjects are analysed using nonlinear techniques. The nonlinear techniques such as Approximate Entropy (ApEn), Sample Entropy (SampEn), Symbolic Entropy (SymbEn), Spectral Entropy (SE) and Correlation Dimension(CD) are applied to the HRV data and the corresponding nonlinear parameters are estimated and compared. Comparison of the estimated parameters revealed best resolution for SampEn to distinguish between healthy, cardiac and Non-cardiac diseased subjects. Further among the Non-cardiac diseased subjects also, SampEn showed higher resolution to distinguish between them. The lowest values of Nonlinear parameters for Non-cardiac diseased subjects when compared to cardiac diseased subjects indicated higher risk of sudden cardiac death for Non-cardiac diseased subjects when compared to cardiac diseased subjects.
Keywords: Cardiac disease, Non-cardiac disease, nonlinear techniques, Thyroid, Depression.
References: 1. Bhat.A.N,Kalsotra L,Yograj .S” Autonomic reactivity with altered thyroid status”,J.K.Sci,8,pp70-74,2007 2. Vijaylakshmi, N.Vaney and S.V.Madhu,”Effect of Thyroxine therapy on autonomic status on hypothyroid patients”,Indian J.Physiol Pharmacol,53(3):pp219-226,2009]. 3. Kernel Sayer,Huseyin Gulec,Mustafa Gokce,Ismail AK “Heart rate variability in depressed patients “,Bulletin Clinical Psychophamacology Vol 12,N:3,pp130-133,2002, 4. U.RajendraAcharya,Pauljoseph.K,kannathalN,LimCM, SuriJS,”Heartrat 5. V.C.Veera Reddy and Anuradha”Cardiac Arrhythmia classification using fuzzy classifiers”Journal of theoretical and applied information technology,pp352-359,2005 6. Ziafuzhu.WeiHe and Hao Yang “Comparitive analysisof heart rate variability between morbid group based on Correlation Dimension” IEEE,2008, 978-4244-,pp2252-2255 ,2008, 7. Joan E Deffeyes,Regina T Harbourne,stacey L,Dejong, Anastasia Kyvelidou,Wayne A Stuberg and Nicholas Sterigo” Use of information entropy measures of sitting postural sway to quantify developmental delay in infants” Journal of NeuroEngineering and Rehabilitation 2009,pp 6:34 8. CH.RenuMadhavi and A.G.Ananth ” Analysis and Characterisation of Heart Rate Variability (HRV) Data of Different Sets of Subjects Using Nonlinear Measure (Approximate Entropy)” International Journal of Computer Theory and Engineering, Vol 2(4), pp 619-623, 2010 9. CH.RenuMadhavi and A.G.Ananth ” Estimation of Multiple scale Sample Entropy (MPE) of Heart RateVariability(HRV) of normal and CongestiveHeartFailure(CHF)cases by selecting ‘m’ and ‘r’ values” published in The Journal Of Material Science and Engineering” David Publishing, USA, volume 4 (34), No.9, ,pp 66-71, 2010 10. CH.RenuMadhavi and A.G.Ananth.”Quantification of Heart Rate Variability (HRV) data using Symbolic Entropy to Distinguish between Healthy and Disease subjects” International Journal of Computer Applications ,Vol 8(12), pp10-13, 2010 11. CH.RenuMadhavi and A.G.Ananth”Noninvasive Detection of Thyroid Dysfunction using Bicoherence plot and Bicoherence metric of HRV data “ International Journal of Multidisciplinary Research and Advances in Engineering (IJMRAE),Ascent Publications, vol 3,No1, ,pp81-90, 2011 12. CH.RenuMadhavi and A.G.Ananth “Modified Multiple Scale/ Segment Entropy(MMPE) Analysis of Heart Rate Variability of NHH, CHF and AF subjects “ International Journal Of Life Sciences” , David Publishing, USA.Volume 5, Number 8, pp593-597, 2011 13. CH.RenuMadhavi and A.G.Ananth “Spectral Entropy Estimation of HRV Data of Thyroid & Healthy subjects”,International Journal of Computer Applications ,Volume 30– No.1, pp39-41, 2011 14. CH.RenuMadhavi and A.G.Ananth “A Review of Heart Rate Variability and It’s Association with Diseases “International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307,Volume-2, Issue-3, July 05,pp86-90 , 2012. 15. CH.RenuMadhavi and A,G.Ananth “Estimation Of ANS Status Of Healthy And Diseased Subjects Using Spectral Entropy“ International journal of Biomedical Engineering and Consumer Health Informatics,Serial Publications(in the Press) 16. CH.RenuMadhavi and A,G.Ananth “Areview of Heart Rate Variability and its association with diseases”, International journal of Soft computing and Engineering,(IJSCE), Volume-2,Issue-3,July Volume-2,Issue-3, pp86-90, 2012.(all the References there in) Authors: Thati. Venkata M Lakshmi, M.V.Ramesh Paper Title: Wavelet Analysis Based Overcurrent Protection for Permanent Magnet Brushless DC Motor Abstract: The objective of this paper is to progress the over current protection for Permanent Magnet Brushless DC Motors (PMBLDCM) operating under the various operating conditions ranging from no-load to full load operation. From operating point of view, an operation where the operating point of motor is continuously changing with time and the motor is never operating at a constant speed throughout its operation is termed as non-stationary. Signal processing algorithms such as the Fast Fourier Transform (FFT) cannot be used in non-stationary signals analysis is essentially complicated. This means that under assumptions of near stationary more sophisticated signal processing techniques are needed.In wavelet analysis, a signal is analyzed at different scales or resolutions. To gaze at the approximate stationary of the signal, a large window is used to and a small window is simultaneously used to look for transients. This multi-resolution or multi-scale view of the signal is the core of wavelet analysis. A single prototype function is used to perform wavelet analysis called a wavelet. As measurement of the instantaneous frequency is a significant feature of the anticipated fault-detection algorithm[1]. The Haar wavelet will be chosen for this application. 45. Keywords: PMBLDCM, over-currents, wavelets and MATLAB. 211-215
References: 1. Satish Rajagopalan, “Detection of rotor and load faults in brushless dc motors operating under stationary and non-stationary conditions”, Georgia Institute of Technology August 2006. 2. Microchip Technology, “Brushless Dc (BLDC) motor fundamentals, Application note, AN885, 2003. 3. S. Rajagopalan, w. Le roux, r. G. Harley and t. G. Habetler, “Diagnosis of potential rotor faults in brushless DC machines,” in Proc. Second International Conference on Power Electronics, Machines and Drives (PEMD 2004), 2004, pp. 668-673 4. N. Mohan, electric drives – An Integrative Approach. Minnesota: MNPERE, ISBN: 0-9663530-1-3, 2001. 5. M. Vetterli and c. Herley, “Wavelets and filter banks: theory and design, ”IEEE Trans. Signal Process., vol.40, no. 9, pp. 2207-2232, 1992. 6. The Wavelet Tutorial By Robi Polikar 7. S. Mallat, A Wavelet Tour of Signal Processing. Academic Press, CA, 1999. 8. Wavelet Toolbox, Computation Visualization Programming, User’s Guide Version 1,by Michel, Misiti, Yves Misiti, Georges Oppenheim, Jean-Michel Poggi.. 9. T. M. Jahns, “Variable frequency permanent magnet AC machine drives,” in Power Electronics and Variable Frequency Drives: Technology and Applications. B. K. Bose (ed.), Piscataway, NJ: IEEE Press, ISBN: 81-86308-74-1, 1996. 10. J. M. Aller, t. G. Habetler, r. G. Harley, r. M. Tallam, and s. B.lee, “Sensorless speed measurement of AC machines using Analytic Wavelet Transform,” IEEE Trans. Ind. Appl., vol.38, no. 5, pp. 1344-1350,200 11. N. BIANCHI, S. BOLOGNANI, and M. ZIGLIOTTO, “Analysis of PM synchronous motor drive failures during flux weakening operation,” in Proc. 27th Annual IEEE Power Electronics Specialist Conference - PESC'96, 1996, pp.1542-1548. 12. A. KUSKO and S. M. PEERAN, “Definition of the brushless DC motor,” in Proc.IEEE-IAS Annual Meeting, 1988, pp. 20-22. Authors: Janagam.SrinivasaRao, Gulivindala.Suresh Paper Title: Performance Analysis of Full Adder & It’s Impact on Multiplier Design Abstract: Full adder is an indispensable component for the design and development of all types of processors viz. digital signal processors (DSP), microprocessors etc. Adders are the core element of complex arithmetic operations like addition, multiplication, division, exponentiation etc. The various full adders available are conventional CMOS full adder, parallel prefix adders, hybrid full adders, mirror full adders, adders using mux and transmission gate logic. The main objective is to compare the existing full adder circuit’s performance and to design a Low Power Full Adder and to analyze its impact on 4x4 Wallace Tree Multiplier design. The design and implementation of proposed full adder and multiplier is done by using Mentor Graphics tool in 180 nm technology.
Keywords: Delay; Full adder; Low Power; Performance Analysis; Logic Impact; Wallace Tree multiplier.
References: 1. I.Hassoune, A.Neve, J.Legat, and D.Flandre, “Investigation of Low Power Circuit Techniques for a Hybrid Full Adder cell” in Proc. PATMOS, 2004, pp.189-197, Springer-Verlag. 2. H.Eriksson, P.L.Edefors, T.Henriksson, C.Svensson, “Full Custom Versus Standard Cell Design flow: an adder case study” in 46. Proceedings of 2003Asia and South Pacific Design Automation Conference, 2003, pp.507-510. 3. T.Vigneswaran, B.Mukundhan, P.Subbarami Reddy, “A Novel Low Power and High-Speed Performance 14 Transistor CMOS Full adder cell” Journal of Applied Science, 6(9); 1978-1981, 2006. 216-220 4. H.T.Bui, Y.Wang, and Y.Jiang, “Design and Analysis of Low Power 10 transistor Full Adders using XOR/XNOR gates” IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process, Vol.49, no.1, Jan. 2002, pp.25-30. 5. T.Sharma, K.G.Sharma, B.P.Singh, “High Performance Full adder cell: A Comparative Analysis” in Proceedings of the 2010 IEEE Students’ Technology Symposium, 3-4 April, 2010,pp.156-160. 6. M.Hossein, R.F.Mirzaee, K.Navi and K.Nikoubin, “New High Peerformance Majority function based full adder” 14th International CSI conference 2009, pp.100-104. 7. Y. Jiang, A. Al-Sheraidah, Y. Wang, E. Sha, and J. Chung, “A novel multiplexer based lowpower full adder cell,” IEEE Trans. Circuits Syst.II, Exp. Briefs, vol. 51, no. 7, pp. 345–348, Jul. 2004. 8. N. Weste and K. Eshragian, Principles of CMOS VLSI Design: A Systems Perspective, 2nd ed. Boston, MA: Addison Wesley, 1993. 9. W. R. Rafati, S. M. Fakhraie, and K. C. Smith, “Low-power data-driven dynamic logic (D3L),” in Proc. IEEE Int. Symp. Circuits Syst. (ISCAS),2000, pp. 752–755. 10. F. Frustaci, M. Lanuzza, P. Zicari, S. Perri, and P. Corsonello, “Low power split-path data driven dynamic logic,” IET Circuits, Devices, Syst., vol. 3, no. 6, pp. 303–312, 2009. 11. Pardeep Kumar “Existing full adders and their comparision on the basis of simulation result and to design a improved LPFA (Low Power Full Adder)”, International Journal of Engineering Research and Applications, ISSN:2248-9622, 2012, Vol.2, Issue-6, pp.599- 606. 12. Sohan Purohit, Martin MArgala “Investigating the impact of logic and circuit Implementation on Full Adder Performance”, IEEE Transactions on VLSI Systems, Vol.20, No.7, July 2012, pp-1327-1331. Authors: Nitesh Kumar, Ashish Kumar, Md. Imran Alam Paper Title: Enhanced " One Phase Commit Protocol " In Transaction Management Abstract: Transaction management in homogeneous distributed database system generates complexity and creates replication and distribution of data. It has been widely used in the area of distributed database system with multiple sites. One phase commit protocol was commonly used in transaction management. When a transaction runs across two sites one site may commit and another one may fail due to an inconsistent state of the transaction. The choice of commit protocol is an important design decision for distributed database system. A commit protocol in a distributed database transaction which should uniformly commit to ensure that all the participating sites agree to the final outcome and the result may be either a commit or an abort situation. In this paper, we have enhanced the one phase commit protocol based on two phase commit protocol. This phase either the " commit " or the " abort " both sides which results the query process of transaction management.
Keywords: Database system, transaction management, Homogeneous distributed database system, two phase commit protocol.
47. References: 1. This article incorporates public domain material from the General Services Administration document "Federal Standard 1037C". 221-225 2. O'Brien, J. & Marakas, G.M.(2008) Management Information Systems (pp. 185-189). New York, NY: McGraw-Hill Irwin . 3. Ozsu, Tamer M., and Valduriez, Patrick [1991], Principles of Distributed Database Systems, Prentice H. 4. Mohan, C.; Lindsay, B.; and Obermarck, R. [1986], ''Transaction Management in the R* Distributed Database Management System." ACM Transaction on Database Systems, Vol. 11, No. 4, December 1986, 379-395. 5. G. Coulouris, J. Dollimore, T. Kindberg: Distributed Systems, Concepts and Design, Addison-Wesley, 1994. 6. S. Ceri, M.A.W. Houtsma, A.M. Keller, P. Samarati: A Classification of Update Methods for Replicated Databases, via Internet, May 5, 1994. 7. D. Agrawal, A.EI. Abbadi: The Tree Quorum Protocol: An Efficient Approach for Managing Replicated Data. in Proc. of VLDB Conf. pp 243-254, 1990. 8. W. Cellary, E. Gelenbe, and T. Morzy. Concurrency Control in Distributed Database Systems. North-Holland, 1988. 9. R. Ramakrishnan. Database Management Systems. McGraw-Hill Book Company, 1998. 10. Oberg R., Mastering RMI: Developing Enterprise Applications in JAVA and EJB, John Wiley & Sons, 2001. 11. Pitt E. and MCNiff K., java.rmi: The Remote Method Invocation Guide, Addison-Wesley, 2001. 12. Oracle8 Server Distributed Database Systems, Oracle, 3-1-3-35. 13. B. Lindsay et. al., Computation and Communication in R*: A Distributed Database Manager . ACM Trans. on Computer Systems, 2(1): 24-38, February 1984. 14. R. Obermarck C. Mohan and B. Lindsay. Transaction Management in the R* Distributed Database Management System. ACM Trans. on Database Systems,11(4): 378-396, December 1986. 15. B. H. Liskov B. M. Oki and R. W. Scheifler. Reliable Object Storage to Atomic Actions. In Proc. Tenth Symp. on Operating System Principles, page 147-159, ACM, December 1985. Authors: Sweta Jain, AlkaBani Agrawal Coupled Thermal – Structural Finite Element Analysis for Exhaust Manifold of an Off-road Vehicle Paper Title: Diesel Engine Abstract: This paper present the Sequential Coupled Thermal - Structural Analysis to investigate the associated thermal stresses and deformations under simulated operational conditions close to the real situation on different materials. Analysis carried out by reference environmental testing conditions; in different ambient temperatures on different materials i.e. cast iron, structural steel.The finite element analysis software ANSYS Workbench 14.0 used to calculate the linear steady state temperature distribution under the thermal field & structural analysis. Thermal analysis calculates the temperature distributions and related thermal quantities in an exhaust manifold. Structural analysis takes inputs from thermal analysis to calculate deformation, stress and strain. FEM analysis is done by using tetrahedral element of first order and convergence test is performed for structural load. The purpose of this analysis is to ensure the appropriateness of material for the defined design from the view point of serviceability of the exhaust manifold. Selected details and results of the overall investigation are presented and discussed within the framework of this paper. 48. Keywords: Exhaust Manifold, FEM, Heat Transfer Coefficient, Thermal-Structural Analysis. 226-230 References: 1. Zhu Maoqina, LvJuncheng and ZhongXiangbo “Development for Stainless Steel Exhaust Manifold and CAE Analysis”. Advance Material Research Vol. 462. (2012).Trans Tech Publications, Switzerland. 2. YasarDeger*, BurkhardSimperl*, Luis P. Jimenez* “CFD-FE-Analysis for the Exhaust Manifold of a Diesel Engine”, *SulzerInnotec, Sulzer Markets and Technology Ltd, Winterthur, Switzerland and * *Guascor I+D S.A., Miñano, Spain.(2004). 3. HavvaKazdalZeytin “Effect of Microstructure on Exhaust Manifold Cracks Produced From SiMo Ductile Iron” Journal of Iron and Steel Research, International, 16, 3, May 2009. 4. J.DavidRathnaraj “Thermomechanical Fatigue Analysis of Stainless steel Exhaust Manifolds”, IRACST – Engineering Science and Technology: An International Journal (ESTIJ), ISSN: 2250-3498, Vol.2, No. 2, April 2012. 5. Bin Zou, Yaqian Hu, Zhien Liu,Fuwu Yan and Chao Wang in Zou,” The Impact of Temperature Effect on Exhaust Manifold Thermal Modal Analysis” Research Journal of Applied Sciences, Engineering and Technology 6(15): 2824-2829, 2013 ISSN: 2040-7459; e- ISSN: 2040-7467. 6. ANSYS Tutorials “Introduction to ANSYS Mechanical Guide”. 7. “Heat Transfer Coefficient Values” http://www.thermopedia.com. 8. “Central Farm Machinery Training and Testing Institute”, Tractor Nagar, Budni (M.P.). Authors: Nishi Sharma, Vandna Verma Paper Title: An Energy Efficient Routing for Wireless Sensor Networks: Hierarchical Approach Abstract: Wireless sensor networks (WSNs) is one of the emerging field of research in recent era of communication world. These networks collect information from the environments and deliver the same to the applications to determine characteristics of the environment or detect an event. Since the sensor nodes have limited amount of energy, it is a major issue in wireless sensor networks to develop an energy-efficient routing protocol. Many energy efficient routing protocols have been proposed to solve this problem and increase the lifetime of the network. This paper proposes an energy efficient hierarchical based routing protocol. In this, clusters are made based on their geographical location & cluster heads are chosen on the basis of highest residual energy within the cluster as well as minimum distance to the base station from the cluster heads. Simulation is done using MATLAB. The results show that the network lifetime & residual energy of the nodes increases as we increase the number of cluster in the network.
Keywords: Energy efficient routing, Hierarchical routing, Improvement in LEACH protocol, Network lifetime, Wireless Sensor Networks.
References: 49. 1. G. J. Pottie and W. J. Kaiser. Wireless integrated network sensors Commun. ACM, vol. 43, pages 51_58, May 2000. 6 2. Al-Karaki, J. N. and A. E. Kamal, "Routing Techniques in Wireless Sensor Networks: A Survey," IEEE Wireless Communications, 231-237 11(6), Dec., 2004. 3. Xu-Xun Liu “ A survey on Clustering Routing Protocols in Wireless Sensor Networks”, Sensors 2012. 4. W.Heinzelman, A.Chandrakasanand and H. Balakrishnan, "Energy-efficient communication protocol for wireless microsensor networks," in Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Maui, HI, 2000, pp. 3005-3014. 5. O. Younis, S. Fahmy, “HEED: A Hybrid, Energy-Efficient, Distributed clustering approach for Ad Hoc sensor networks”, IEEE Transactions on Mobile Computing, vol. 3 no.4, pp. 366–379, 2004. 6. Ding, P.; Holliday, J.; Celik, A. Distributed Energy Efficient Hierarchical Clustering for Wireless Sensor Networks. In Proceedings of the 8th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), Marina Del Rey, CA, USA, 8–10 June 2005; pp. 322–339. 7. Loscri, V.; Morabito, G.; Marano, S. A Two-Level Hierarchy for Low-Energy Adaptive Clustering Hierarchy. In Proceedings of the 2nd IEEE Semiannual Vehicular Technology Conference, Dallas, TX, USA, 25–28 September 2005; pp. 1809–1813 8. S. Lindesy and C. Raghavendra, "PEGASIS: Power-Efficient Gathering in Sensor Information System," in Proceedings of the Aerospace Conference,IEEE, vol. 3, Big Sky, Montana, 2002, pp. 9-16. 9. Manjeshwar and D. Agrawal, "TEEN: a Routing Protocol for Enhanced Efficient in Wireless Sensor Networks," in Proceedings of the 15th International Parallel and Distributed Processing Symposium, San Francisco, April 2001, pp. 2009-2015. 10. Manjeshwar, D.P. Agrawal, "APTEEN: a hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks," in Proceedings of the 2nd International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile computing, Ft. Lauderdale, FL, April 2002. 11. W.B. Heinzelman, A.P. Chandrakasan, H. Balakrishnan, "Application specific protocol architecture for wireless microsensor networks," Wireless Communications, IEEE, vol. 1, pp. 660-670, october 2002. 50. Authors: Shyam Sundar Prasad, Chanakya Kumar Comparison between Innovative Approaches of RFID Based Localization Using Fingerprinting Paper Title: Techniques for Outdoor and Indoor Environments Abstract: In this paper, two novel localization algorithms using a fingerprint technique are proposed. The performance evaluation of these algorithms is compared by calculating the location estimation error. The feasibility for indoor and outdoor applications is investigated. The Radio Frequency Identification (RFID) System is utilized by the reader being the localized target. The passive tags are chosen as reference tags. The basic principle of the fingerprint technique is to find the target location by comparing its signal (or information) pattern to a beforehand recorded database of known location data. Consequently, there are two main steps for estimating the target location: the detected tags, in the first place, discovered by the reader at each fingerprint location are stored in the database and called a fingerprint. The reader location, in the second place, can be estimated by using two proposed methods. The performance of each method is verified by experiment data. The best results of location estimation error among one another for outdoor environment is less than 38 cm and for indoor environment is less than 35 cm. Furthermore, the investigation found that proposed methods can be used in the real applications for both indoor and outdoor environment.
Keywords: Fingerprint, localization, outdoor environment, indoor environment, RFID,
References: 1. H. Liu, H. Darabi, P. Ganerjee, and J. Liu, ”Survey of Wireless Indoor Positioning Techniques and Systems,” IEEE Trans. on System, Man, and Cybernetics-Part C: Applications and Reviews, Vol. 37, No. 6,pp. 1067– 1080, November 2007. 2. H. Sayed, A. Tarighat, and N. Khajehnouri, ”Network-based Wire-less Location: Challenges faced in Developing Techniques for Accurate Wireless Location Information,” IEEE Signal Processing Magazine, pp. 24–40, July 2005. 3. T. S. Rappaport, J. H. Reed, and B. D. Woerner, ”Position Location using Wireless Communications on Highways of the Future,” 238-241 IEEE Communication Magazine, pp. 33–41, October 1996. 4. L. M. Ni, Y. Liu, U.C. Lau, and A. P. Patil, ”LANDMARC: Indoor Location Sensing Using Active RFID,” IEEE Internation Conference on Pervasive Computing and Communication, pp. 407–415, March 2003. 5. J. Hightower, R. Wantand, G. Borriello, ”SpotON: An Indoor 3D Location Sensing Technology based on RF Signal Strength,” Technical Report UW-CSE, University of Washington, Dep. of Comp. Science and Eng., Seattle, WA, 2000. 6. S. Choi and J. W. Lee, ”An Improved Localization System with RFID Technology for a Mobile Robot,” IEEE 34th Annual Conference on Industrial Electronics (IECON), pp. 3409–3413, November 2008. 7. T. Shiraishi, N. Komuro, H. Ueda,H. Kasai and T. Tsuboi, ”Indoor Location Estimation Technique using UHF band RFID” International Conference on Information Networking (ICOIN), pp. 1-5, January 2008. 8. L. Andrew and Z. Kaicheng, ”A Robust RFID-Based Method for Precise Indoor Positioning,” Springer Berlin / Heidelberg. ISSN 0302-9743 1611-3349 Advances in Applied Artificial Intelligence, pp. 1189-1199, January 2008. 9. R. Ahmed Wasif and G. Tan Kim, ”Investigation of Indoor Location Sensing via RFID Reader Network Utilizing Grid Covering Algorithm,” Springer Netherlands, Wireless Personal Communications, Vol. 49, No. 1, pp. 67-80, April, 2009. 10. M.C. Lee, ”Localization of Mobile Robot Based on Radio Frequency Identi®cation Devices,” SICE-ICASE, International Joint Conference, pp.5934-5939, October 2006 11. G. Sun, J. Chen, W. Guo, and K. J. R. Liu, ”Signal processing techniques in network aided positioning,” IEEE Signal Processing Magazine, pp. 12– 23, July 2005. 12. Lim and K. Zhang,”A Robust RFID-based method for precise indoor positioning,” Advances in applied artificial intelligence, Vol. 4031, pp. 1189-1199, June 2006. 13. Kaemarungsi and P. Krishnamurthy,”Modeling of indoor positioning systems based on location ®ngerprinting,” The 23 annual joint conference of the IEEE computer and communications societies, Vol. 2, pp. 1012– 1022, March 2004. 14. P. Cherntanomwong, J. Takada, and H. Tsuji,”Signal Subspace Interpo-lation from Discrete Measurement Samples in Constructing a Database for Location Fingerprint Technique,” IEICE Transactions on Communi-cations, Vol. E92-B, No. 9, pp. 2922-2930, September 2009. Authors: Ujwala T. Tayade, Seema Biday, Lata Ragha Paper Title: Forensic Sketch-Photo Matching using LFDA Abstract: The advancement of biometric technology has provided criminal investigators additional tools to determine the identity of criminals. In addition to DNA and circumstantial evidence, if a latent fingerprint is found at an investigative scene or a surveillance camera captures an image of a suspect’s face, then these cues may be used to determine the culprit’s identity using automated biometric identification. However, many crimes occur where none of this information is present, but instead an eye-witness of the crime is available. In these circumstances a forensic artist is often used to work with the witness or the victim in order to draw a sketch that depicts the facial appearance of the culprit according to the verbal description. These sketches are known as forensic sketches. This problem of matching a forensic sketch to a gallery of mugshot images is addressed here using a robust framework called local feature-based discriminant analysis (LFDA). Since, forensic sketches or digital face images can be of poor quality, a pre -processing technique is used to enhance the quality of images and improve the identification performance.In this paper experiments are carried out using 52 forensic sketches for matching against a gallery of 51. 264 photo images. The experimental results demonstrate the matching performance of the proposed algorithm with the use of preprocessing approach yields better identification accuracy compared to other methods. 242-246
Keywords: Forensic sketch, Mugshots, Feature-based approach, Local feature-based discriminant analysis, Feature descriptors.
References: 1. X. Tang and X. Wang, “Face sketch recognition,” IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 50– 57, 2004. 2. X. Wang and X. Tang, “Face photo-sketch synthesis and recognition,” IEEE Trans Pattern Analysis & MachineIntelligence, vol. 31, no. 11, pp.1955–1967, Nov. 2009. 3. Amit R. Sharma and Prakash. R. Devale “An Application to Human Face Photo- Sketch Synthesis and Recognition,” International Journal of Advances in Engineering & Technology May 2012. 4. Timo Ahonen, Abdenour Hadid and Matti Pietikainen, “FaceDescription with Local Binary Patterns: Application to Face Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 12, December 2006. 5. Mohd. Ahmed and F. Bobere, “Criminal Photograph Retrieval based on Forensic Face Sketch using Scale Invariant Feature Transform,” International Conference on Technology and Business Management, Mar 2012. 6. B. Klare and A. Jain, “Sketch to photo matching: A feature-based approach,” in Proc.SPIE Conference on Biometric Technology for Human IdentificationVII, 2010. 7. Anil K. Jain, Z. Li and Brendan Klare, “Matching ForensicSketches and Mug Shot Photos,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 639–646, March 2011. 8. Anil K. Jain, B. Klare, and Unsang Park, “Face Matching and Retrieval in Forensics Applications,” Multimedia in Forensics, Security, and Intelligence, Published by the IEEE Computer Society, 2012. 9. H. S. Bhatt, S. Bharadwaj, R. Singh, Mayank Vatsa,“ Memetic Approach for Matching Sketches with Digital Face Images”, Submitted to IEEE transactions on IFS. 10. Scott Klum, Hu Han, Anil K. Jain, “Sketch Based Face Recognition: Forensic vs. Composite Sketches”, InternationalConference on Biometrics, June 4-7, 2013. 11. Lois Gibson's website at:http://www.loisgibson.com/sketches.asp 12. H. Bhatt, S. Bharadwaj, R. Singh, and M. Vatsa, “On matching sketches with digital face images”, in Proceedings of International Conference on Biometrics: Theory Applications and Systems, 2010. Authors: Svetlana Zhelyazkova Vasileva, Aleksandar Petrov Milev Models of 2PL Algorithms with Timestamp Ordering for Distributed Transactions Concurrency Paper Title: Control Abstract: In this paper simulation models of two-phase locking in distributed database systems are presented. A mechanism of timestamp ordering (“wait – die” method) is embedded in the modeling algorithms to prevent deadlocks. The results of running model of Centralized, Distributed and Primary copy Two-phase locking algorithms are gathered and analyzed and represented. The main characteristics of transaction processing in distributed database management systems such as throughput, response time and probability service are given.
Keywords: Simulation models, GPSS transactions, Distributed transactions, Two-phase locking, Timestamp ordering. 52. References: 247-252 1. T. Connolly, C. Begg, Database systems: Addison-Wesley, 2002. 2. C. J. Date, Chris J. Introduction to Database Systems. 7th edn. Reading, MA: Addison-Wesley, 2000 3. N. Krivokapic, A. Kemper, E. Gudes, Deadlock detection in distributed database systems: A new algorithm and a comparative performance analysis [Online]. Available: http://masters.donntu.edu.ua/2005/fvti/kovalyova/library/d1.pdf. 4. TPC BenchmarkTMC. Revision 5.11 February 2010. [Online]. Available: http://www.tpc.org/tpcc/spec. 5. S. Z. Vasileva, A. P. Milev, “Simulation Models of Two-Phase Locking of Distributed transactions” International Conference on Computer Systems and Technologies: V.12-1-V.12-6, ACM, New York, 2008. 6. Bodyagin. “Deadlocks. Что такое взаимоблокировки и как с ними бороться”, RSDN Magazine #5, 2003, [Online]. Available: http://sasynok.narod.ru /index.htm?omvs.htm. 7. Simeonov, S., Ts. Tswetanov Network Flow Security Baselining IT-Incidents Management IT-Forensics - IMF, pp. 143-156, 2008 8. Simeonova, N, S. Simeonov, A. Iliev Concepts for Creating Operating Systems with Special Purpose, UNITECH’12, Gabrovo, 16-17 November 2012, pp 377-381, ISSN 1313-230X. Authors: A. Montazeri, J. Haddadnia Paper Title: MCM and CPM Combination as Compared to The Use of FDE for CPM Abstract: This paper compares Multi-Carrier Modulation (MCM) and Continuous Phase Modulation (CPM) combination, with the use of Frequency Domain Equalization (FDE) for Continuous Phase Modulation (CPM). It is shown that these two constant envelope methods exploit the frequency diversity of multi path channel. In addition, they have 0dB Peak to Average Power Ratio (PAPR) and high power efficiency. However, MCM-CPM combination outperforms applying FDE for CPM due to superior performance of multicarrier systems in frequency selective channels by using orthogonal subcarriers.
Keywords: Multi Carrier Modulation (MCM), Continuous Phase Modulation (CPM), Frequency Domain Equalization (FDE).
References: 53. 1. Z. Wang, Xiaoli Ma, and G. B. Giannakis, “OFDM or Single-Carrier Block Transmissions?” IEEE Transaction on Communications, VOL.52, NO.3, March 2004. 253-257 2. Falconer, D.; Ariyavisitakul, S.L.; Benyamin-Seeyar, A.; Eidson, B.; "Frequency domain equalization for single-carrier broadband wireless systems" IEEE Communications Magazine, Volume 40, Issue 4, Page(s):58 – 66, April 2002. 3. Thompson, S.C.; Ahmed, A.U.; Proakis, J.G.; Zeidler, J.R.; Geile, M.J.;" Constant Envelope OFDM", IEEE Transactions on Communications, Volume 56, Issue8, Page(s):1300–1312, August 2008. 4. Tsai, Y.; Zhang, G.; Pan, J., L.; "Orthogonal frequency division multiplexing with phase modulation and constant envelope design", IEEE Military Communications Conference 2005, Page(s):2658 – 2664, 17-20 October 2005. 5. Vakily, V. T., Montazeri, Am., "OFDM-CPM BER Performance in SUI Multi path channels". IEEE International Conference on Circuits & Systems for Communications 2008, Page(s):877-881, 26-28 May 2008. 6. Tan, J.; Stuber, G., L.; "Frequency-domain equalization for continuous phase modulation", IEEE Transactions on Wireless Communications, Volume 4, Issue 5, Page(s):2479 – 2490, September 2005. 7. IEEE 802.162004,” IEEE Standard for Local and Metropolitan Area Networks Part16: Air Interface for Fixed Broadband Wireless Access Systems”, 1October, 2004. 8. V. Erceg, K.V.S. Hari, M.S. Smith, D.S. Baum et al, “Channel Models for Fixed Wireless Applications”, IEEE 802.16.3 Task Group Contributions, February 2003. Authors: Ajit P. Gosavi, S. R. Khot Paper Title: Facial Expression Recognition Using Principal Component Analysis Abstract: Expression detection is useful as a non-invasive method of lie detection and behaviour prediction. 54. However, these facial expressions may be difficult to detect to the untrained eye. In this paper we implements facial 258-262 expression recognition techniques using Principal Component analysis (PCA). Experiments are performed using standard database like Japanese Female Facial Expression (JAFFE) database. The universally accepted six principal emotions to be recognized are: Angry, Happy, Sad, Disgust, Fear and Surprise along with neutral. Euclidean distance based matching Classifier is used.
Keywords: Facial Expression Detection, Feature Extraction Japanese Female Facial Expression (JAFFE) database, Principal Component Analysis (PCA),
References: 1. Bartlett, M. S., Donato, G., Ekman, P., Hager, J. C., Sejnowski, T.J., 1999,"Classifying Facial Actions", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 21, No. 10, pp. 974-989. 2. Yang, J., Zhang, D., 2004, “Two-dimensional pca: a new approach to appearance-based face representation and recognition”, IEEE Trans. Pattern Anal. Mach. Intell.Vol.26, No. 1, pp. 131–137. 3. Yi, J., R. Qiuqi et al. (2008),“Gabor-based Orthogonal Locality Sensitive Discriminant Analysis for face recognition’’, Signal Processing, 2008. ICSP 2008. 9th International Conference on. 4. Menaka Rajapakse, Jeffrey Tan, Jagath Rajapakse,“Color Channel Encoding With NMF for Face Recognition”, International Conference on Image Processing; Proceedings; ICIP, pp 2007-2010 (October 2004). 5. Cohn, J.F., Kanade, T., Lien, J.J., 1998,"Automated Facial Expression Recognition Based on FACS Action Units", Proc. Third EEE Int. Conf. Automatic Face and Gesture Recognition, pp. 390-395. 6. M. Turk, A. and Pentland, "Eigen faces for face recognition ", Journal cognitive neuroscience, Vol. 3, No.1, 1991. 7. Ching-Chih, T., C. You-Zhu et al. (2009),"Interactive emotion recognition using Support Vector Machine for human-robot interaction. Systems”, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on.M. Turk, A. and Pentland, "Eigen faces for face recognition ", Journal cognitive neuroscience, Vol. 3, No.1, 1991. 8. Pantic, M. and Rothkrantz, L., 2000, “Automatic analysis of facial expressions: The state of the art”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 12, pp.1424–1445. 9. Jain, A.K., Duin R.P.W., Mao J., 2000,"Statistical Pattern Recognition: A Review", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, pp. 4-37. 10. Pantic, M. and Rothkrantz, L., 2000, “Automatic analysis of facial expressions: The state of the art”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 12, pp.1424–1445. Authors: Joanne N. Gakungu Qualitative Assessment of Rain Water Harvested from Roof Top Catchments: Case Study of Paper Title: Embakasi, Nairobi County Abstract: Rain Water Harvesting (RWH), in its broadest sense, is a technology used for collecting and storing rainwater for human use from rooftops, land surfaces or rock catchments using simple techniques such as jars and pots as well as engineered techniques [6]. This paper aims to assess the quality of harvested roof-top rainwater from three different roofing materials in Embakasi area in Nairobi County. The roofing materials are corrugated iron sheets, clay tiles and concrete tiles. Chemical analysis included testing for iron, copper, zinc, fluoride, aluminium, lead, zinc, manganese, sodium and potassium. Physical analysis of pH and turbidity was carried out as well as bacteriological analysis for Escherichia coli (E. Coli) and total coliform. All the rainwater samples results for the samples taken after first run-off, were within the guidelines for both chemical and microbiological parameters established by the World Health Organization [10]. On the contrary, turbidity levels were higher than the maximum allowable concentration for drinking purposes hence the need to allow the first run –off during any rainfall event. As revealed from the analysis, all the samples require some level of treatment e.g. chlorination in order to ensure they meet regulatory standards for drinking water. However all water samples are quite safe for all other domestic uses including laundry, toilet flushing, bathing and other general cleaning. The integrated management must consist of regular cleaning of the catchment areas and the storage tanks and the employment of automated mechanical systems for discarding the first portion of each rainfall. The use of clay roofing tiles is preferable but due to cost, 55. corrugated iron sheets maybe adopted.
Keywords: Rain Water Harvesting, Roofing Materials, Water Quality 263-266
References: 1. Ahmed, W., Huygens, F., Goonetilleke, A., and Gardner, T. 2008. Real-time PCR detection of pathogenic microorganisms in roof harvested rainwater in southeast Queensland, Australia. Appl. Environ.Microbiol. 74:5490-5496. 2. Ariyananda, T. 1999. Rainwater harvesting for domestic supply in Sir Lanka. Integrated development for water supply and sanitation, 25th WEDC Conference in Addis Ababa, Ethiopia. 3. Birks, R., Colbourne, J., Hills, S., and Hobson, R. 2004. Microbiological water quality in a large in-building, 430 water recycling facility. Water Science Technology 50:165-172. 4. Dillaha, T.A., III, and Zolan, W.J. 1985. Rainwater catchment water quality in Micronesia. Water Research 5. Heyworth, J.S., G. Glonek, E.J. Maynard, P.A. Baghurst and J. Finlay-Jones, 2006. Consumption of untreated tank rainwater and gastroenteritis among young children in South Australia. International Journal of Epidemiology, 35: 1051-1058. 6. K. Najmi (2008): Rainwater Harvesting: A blessing in disguise, E-Journal, Earth Science. 7. Lye D.J. 1992. “Microbiology of rainwater cistern systems: A review.” Journal of Environmental Science and Health A27(28): 2123- 2166. 8. Quek U. and Förster J. 1993. “Trace metals in roof runoff.” Water, Air, and Soil Pollution 68(3-4): 373-389. 9. Uba, B.N., and Aghogho, O. 2000. Rainwater quality from different roof catchments in the Port Harcourt district, River State, Nigeria. J.Wat. Supp: Res. and Technol-AQUA. 49: 281-288. 10. World Health Organization (WHO) Guidelines, 2003. 11. Yaziz M., Gunting H., Sapari N., and Ghazali A. 1989. “Variations in rainwater quality from roof catchments.” Water Research 23(6): 761-765.