The Existence of Fibonacci Numbers in the Algorithmic Generator for Combinatoric Pascal Triangle

Total Page:16

File Type:pdf, Size:1020Kb

The Existence of Fibonacci Numbers in the Algorithmic Generator for Combinatoric Pascal Triangle British Journal of Science 62 September 2014, Vol. 11 (2) THE EXISTENCE OF FIBONACCI NUMBERS IN THE ALGORITHMIC GENERATOR FOR COMBINATORIC PASCAL TRIANGLE BY Amannah, Constance Izuchukwu [email protected]; +234 8037720614 Department of Computer Science, Faculty of Natural and Applied Sciences, Ignatius Ajuru University of Education, P.M.B. 5047, Port Harcourt, Rivers State, Nigeria. & Nanwin, Nuka Domaka Department of Computer Science, Faculty of Natural and Applied Sciences, Ignatius Ajuru University of Education, P.M.B. 5047, Port Harcourt, Rivers State, Nigeria © 2014 British Journals ISSN 2047-3745 British Journal of Science 63 September 2014, Vol. 11 (2) ABSTRACT The discoveries of Leonard of Pisa, better known as Fibonacci, are revolutionary contributions to the mathematical world. His best-known work is the Fibonacci sequence, in which each new number is the sum of the two numbers preceding it. When various operations and manipulations are performed on the numbers of this sequence, beautiful and incredible patterns begin to emerge. The numbers from this sequence are manifested throughout nature in the forms and designs of many plants and animals and have also been reproduced in various manners in art, architecture, and music. This work simulated the Pascal triangle generator to produce the Fibonacci numbers or sequence. The Fibonacci numbers are generated by simply taken the sums of the "shallow" diagonals (shown in red) of Pascal's triangle. The Fibonacci numbers occur in the sums of "shallow" diagonals in Pascal's triangle. This Pascal triangle generator is a combinatoric algorithm that outlines the steps necessary for generating the elements and their positions in the rows of a Pascal triangle. The Pascal triangle generator is symbolized i with E j. The is denote the element of a row while the js represent the respective positions of the elements. The generated Fibonacci sequence from i the E j model can be used in the following way; in the computational run- time analysis of Euclid's algorithm to determine the greatest common divisor of two integers- the worst case input for this algorithm is a pair of consecutive Fibonacci numbers; as pseudorandom number generators; The Fibonacci numbers are also an example of a complete sequence. This means that every positive integer can be written as a sum of Fibonacci numbers, where any one number is used once at most. This work succeeded in simulating the Pascal triangle to produce 20 Fibonacci numbers namely; 0,1,1,2,3,5,8,13,21,34,55,89,144,233,377,610,987,1597,2584,4181,6765. KEY WORDS: Algorithm, Generator, Triangle, Fibonacci Numbers, Sequence, Shallow Diagonal INTRODUCTION The discoveries of Leonard of Pisa, better known as Fibonacci, are revolutionary contributions to the mathematical world. His best-known work is the Fibonacci sequence, in which each new number is the sum of the two numbers preceding it. When various operations and manipulations are performed on the numbers of this sequence, beautiful and incredible patterns begin to emerge. The numbers from this sequence are manifested throughout © 2014 British Journals ISSN 2047-3745 British Journal of Science 64 September 2014, Vol. 11 (2) nature in the forms and designs of many plants and animals and have also been reproduced in various manners in art, architecture, and music. The mathematician Leonardo of Pisa, better known as Fibonacci, had a significant impact on mathematics. His contributions to mathematics have intrigued and inspired people through the centuries to delve more deeply into the mathematical world. He is best known for the sequence of numbers bearing his name. Leonardo Pisano Bigollo (c. 1170 – c. 1250) [Wikipedia) – known as Fibonacci, and also Leonardo of Pisa, Leonardo Pisano, Leonardo Bonacci, Leonardo Fibonacci – was an Italian mathematician, considered by some "the most talented western mathematician of the Middle Ages, Howard (1990). Fibonacci is best known to the modern world for (Encyclopædia Britannica), the spreading of the Hindu–Arabic numeral system in Europe, primarily through his composition in 1202 of Liber Abaci (Book of Calculation), and for a number sequence named the Fibonacci numbers after him, which he did not discover but used as an example in the Liber Abaci, (Parmanand, 1986). The Fibonacci sequence is named after Leonardo Fibonacci. His 1202 book Liber Abaci introduced the sequence to Western European mathematics, Goonatilake (1998), although the sequence had been described earlier in Indian mathematics. (Knuth 2006; Singh 1985; Douady and Couder 1996). By modern convention, the sequence begins either with F0 = 0 or with F1 = 1. The Liber Abaci began the sequence with F1 = 1, without an initial 0. Fibonacci numbers are closely related to Lucas numbers in that they are a complementary pair of Lucas sequences. They are intimately connected with the golden ratio; for example, the closest rational approximations to the ratio are 2/1, 3/2, 5/3, 8/5,.... Applications include computer algorithms such as the Fibonacci search technique and the Fibonacci heap data structure, and graphs called Fibonacci cubes used for interconnecting parallel and distributed systems. They also appear in biological settings, Jones and Wilson (2006) such as branching in trees, phyllotaxis (the arrangement of leaves on a stem), the fruit sprouts of a pineapple, Brousseau (1969), the flowering of artichoke, an uncurling fern and the arrangement of a pine cone. Knuth (2008). The Fibonacci sequence appears in Indian mathematics, in connection with Sanskrit prosody,(Singh 1985; Knuth 1968). In the Sanskrit oral tradition, there was much emphasis on how long (L) syllables mix with the short (S), and counting the different patterns of L and S within a given fixed length results in the Fibonacci numbers; the number of patterns that are m short syllables long is the Fibonacci number Fm + 1, (Knuth 2006). © 2014 British Journals ISSN 2047-3745 British Journal of Science 65 September 2014, Vol. 11 (2) Liber Abaci also posed, and solved, a problem involving the growth of a population of rabbits based on idealized assumptions. The solution, generation by generation, was a sequence of numbers later known as Fibonacci numbers. The number sequence was known to Indian mathematicians as early as the 6th century Donald (2006) and Rachel (2008), but it was Fibonacci's Liber Abaci that introduced it to the West. In the Fibonacci sequence of numbers, each number is the sum of the previous two numbers. Fibonacci began the sequence not with 0, 1, 1, 2, as modern mathematicians do but with 1, 1, 2, etc. He carried the calculation up to the thirteenth place (fourteenth in modern counting), that is 233, though another manuscript carries it to the next place: 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377. The problem, dealing with the regeneration of rabbits, calculated the number of rabbits after a year if there is only one pair the first month. The problem states that it takes one month for a rabbit pair to mature, and the pair will then produce one pair of rabbits each month following. Fibonacci’s solution stated that in the first month there would be only one pair; the second month there would be one adult pair and one baby pair; the third month there would be two adult pairs and one baby pair; and so forth (Posamentier and Lehmann, 2007). When the total number of rabbits for each month is listed, one after the other, it generates the sequence of numbers for which Fibonacci is most famous: 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377… This string of numbers is known as the Fibonacci sequence, and each successive term is found by adding the two preceding terms together. The Fibonacci sequence is the oldest known recursive sequence, which is a sequence where each successive term can only be found through performing operations on previous terms. Interestingly, Fibonacci does not comment on the recursive nature of this sequence. The relationship between the terms was not identified in publication until four hundred years later. At the time of the publication of Liber Abaci, no special notice was taken of these numbers. It was not until the mid- 1800s that mathematicians began to be intrigued by what would later be known as the Fibonacci numbers (Posamentier and Lehmann, 2007). A closer inspection of the numbers making up the Fibonacci sequence brings to light all sorts of fascinating patterns and mathematical properties. Fibonacci himself makes no mention of these patterns in his book, but the following patterns are a few that have been brought to light over years of examination of the numbers in the sequence. Any two consecutive Fibonacci numbers are relatively prime, having no factors in common with each other (Garland, 1987). For example: 5, 8,13,21,34 © 2014 British Journals ISSN 2047-3745 British Journal of Science 66 September 2014, Vol. 11 (2) 5 = 1 · 5; 8 = 2 · 2 · 2; 13 = 1 · 13; 21 = 3 · 7; 34 = 2 · 17 Summing together any ten consecutive Fibonacci numbers will always result in a number which is divisible by eleven (Posamentier and Lehmann, 2007). 1 + 1 + 2 + 3 + 5 + 8 + 13 + 21 + 34 + 55 = 143 143/11 = 13 89 + 144 + 233 + 377 + 610 + 987 + 1,597 + 2,584 + 4,181 + 6,675 = 17,567 17,567/11 = 1,597 Following tradition, Fn will be used to represent the n-th Fibonacci number in the sequence. n Fn 1 1 2 1 3 2 4 3 5 5 6 8 7 13 8 21 9 34 10 55 11 89 12 144 13 233 14 377 © 2014 British Journals ISSN 2047-3745 British Journal of Science 67 September 2014, Vol. 11 (2) 15 610 Every third Fibonacci number is divisible by two, or F3.
Recommended publications
  • The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on Iot Nodes in Smart Cities
    sensors Article The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities Ammar Nasif *, Zulaiha Ali Othman and Nor Samsiah Sani Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science & Technology, University Kebangsaan Malaysia, Bangi 43600, Malaysia; [email protected] (Z.A.O.); [email protected] (N.S.S.) * Correspondence: [email protected] Abstract: Networking is crucial for smart city projects nowadays, as it offers an environment where people and things are connected. This paper presents a chronology of factors on the development of smart cities, including IoT technologies as network infrastructure. Increasing IoT nodes leads to increasing data flow, which is a potential source of failure for IoT networks. The biggest challenge of IoT networks is that the IoT may have insufficient memory to handle all transaction data within the IoT network. We aim in this paper to propose a potential compression method for reducing IoT network data traffic. Therefore, we investigate various lossless compression algorithms, such as entropy or dictionary-based algorithms, and general compression methods to determine which algorithm or method adheres to the IoT specifications. Furthermore, this study conducts compression experiments using entropy (Huffman, Adaptive Huffman) and Dictionary (LZ77, LZ78) as well as five different types of datasets of the IoT data traffic. Though the above algorithms can alleviate the IoT data traffic, adaptive Huffman gave the best compression algorithm. Therefore, in this paper, Citation: Nasif, A.; Othman, Z.A.; we aim to propose a conceptual compression method for IoT data traffic by improving an adaptive Sani, N.S.
    [Show full text]
  • The Generalized Principle of the Golden Section and Its Applications in Mathematics, Science, and Engineering
    Chaos, Solitons and Fractals 26 (2005) 263–289 www.elsevier.com/locate/chaos The Generalized Principle of the Golden Section and its applications in mathematics, science, and engineering A.P. Stakhov International Club of the Golden Section, 6 McCreary Trail, Bolton, ON, Canada L7E 2C8 Accepted 14 January 2005 Abstract The ‘‘Dichotomy Principle’’ and the classical ‘‘Golden Section Principle’’ are two of the most important principles of Nature, Science and also Art. The Generalized Principle of the Golden Section that follows from studying the diagonal sums of the Pascal triangle is a sweeping generalization of these important principles. This underlies the foundation of ‘‘Harmony Mathematics’’, a new proposed mathematical direction. Harmony Mathematics includes a number of new mathematical theories: an algorithmic measurement theory, a new number theory, a new theory of hyperbolic functions based on Fibonacci and Lucas numbers, and a theory of the Fibonacci and ‘‘Golden’’ matrices. These mathematical theories are the source of many new ideas in mathematics, philosophy, botanic and biology, electrical and computer science and engineering, communication systems, mathematical education as well as theoretical physics and physics of high energy particles. Ó 2005 Elsevier Ltd. All rights reserved. Algebra and Geometry have one and the same fate. The rather slow successes followed after the fast ones at the beginning. They left science at such step where it was still far from perfect. It happened,probably,because Mathematicians paid attention to the higher parts of the Analysis. They neglected the beginnings and did not wish to work on such field,which they finished with one time and left it behind.
    [Show full text]
  • Survey on Inverted Index Compression Over Structured Data
    Volume 6, No. 3, May 2015 (Special Issue) ISSN No. 0976-5697 International Journal of Advanced Research in Computer Science RESEARCH PAPER Available Online at www.ijarcs.info Survey on Inverted Index Compression over Structured Data B.Usharani M.TanoojKumar Dept.of Computer Science and Engineering Dept.of ComputerScience and Engineering Andhra Loyola Institute of Engineering and Technology Andhra Loyola Institute of Engineering and Technology India India e-mail:[email protected] e-mail:[email protected] Abstract: A user can retrieve the information by providing a few keywords in the search engine. In the keyword search engines, the query is specified in the textual form. The keyword search allows casual users to access database information. In keyword search, the system has to provide a search over billions of documents stored on millions of computers. The index stores summary of information and guides the user to search for more detailed information. The major concept in the information retrieval(IR) is the inverted index. Inverted index is one of the design factors of the index data structures. Inverted index is used to access the documents according to the keyword search. Inverted index is normally larger in size ,many compression techniques have been proposed to minimize the storage space for inverted index. In this paper we propose the Huffman coding technique to compress the inverted index. Experiments on the performance of inverted index compression using Huffman coding proves that this technique requires minimum storage space as well as increases the key word search performance and reduces the time to evaluate the query.
    [Show full text]
  • Image Compression Using Extended Golomb Code Vector Quantization and Block Truncation Coding
    ISSN(Online): 2319-8753 ISSN (Print): 2347-6710 International Journal of Innovative Research in Science, Engineering and Technology (A High Impact Factor, Monthly, Peer Reviewed Journal) Visit: www.ijirset.com Vol. 8, Issue 11, November 2019 Image Compression Using Extended Golomb Code Vector Quantization And Block Truncation Coding Vinay Kumar1, Prof. Neha Malviya2 M. Tech. Scholar, Department of Electronics and Communication, Swami Vivekanand College of Science & Technology, Bhopal, India1 Assistant Professor, Department of Electronics and Communication, Swami Vivekanand College of Science & Technology, Bhopal, India 2 ABSTRACT: Text and image data are important elements for information processing almost in all the computer applications. Uncompressed image or text data require high transmission bandwidth and significant storage capacity. Designing an efficient compression scheme is more critical with the recent growth of computer applications. Extended Golomb code, for integer representation is proposed and used as a part of Burrows-Wheeler compression algorithm to compress text data. The other four schemes are designed for image compression. Out of four, three schemes are based on Vector Quantization (VQ) method and the fourth scheme is based on Absolute Moment Block Truncation Coding (AMBTC). The proposed scheme is block prediction-residual block coding AMBTC (BP-RBCAMBTC). In this scheme an image is divided into non-overlapping image blocks of small size (4x4 pixels) each. Each image block is encoded using neighboring image blocks which have high correlation among IX them. BP-RBCAMBTC obtains better image quality and low bit rate than the recently proposed method based on BTC. KEYWORDS: Block Truncation Code (BTC), Vector Quantization, Golomb Code Vector I.
    [Show full text]
  • Lelewer and Hirschberg, "Data Compression"
    Data Compression DEBRA A. LELEWER and DANIEL S. HIRSCHBERG Department of Information and Computer Science, University of California, Irvine, California 92717 This paper surveys a variety of data compression methods spanning almost 40 years of research, from the work of Shannon, Fano, and Huffman in the late 1940s to a technique developed in 1986. The aim of data compression is to reduce redundancy in stored or communicated data, thus increasing effective data density. Data compression has important application in the areas of file storage and distributed systems. Concepts from information theory as they relate to the goals and evaluation of data compression methods are discussed briefly. A framework for evaluation and comparison of methods is constructed and applied to the algorithms presented. Comparisons of both theoretical and empirical natures are reported, and possibilities for future research are suggested Categories and Subject Descriptors: E.4 [Data]: Coding and Information Theory-data compaction and compression General Terms: Algorithms, Theory Additional Key Words and Phrases: Adaptive coding, adaptive Huffman codes, coding, coding theory, tile compression, Huffman codes, minimum-redundancy codes, optimal codes, prefix codes, text compression INTRODUCTION mation but whose length is as small as possible. Data compression has important Data compression is often referred to as application in the areas of data transmis- coding, where coding is a general term en- sion and data storage. Many data process- compassing any special representation of ing applications require storage of large data that satisfies a given need. Informa- volumes of data, and the number of such tion theory is defined as the study of applications is constantly increasing as the efficient coding and its consequences in use of computers extends to new disci- the form of speed of transmission and plines.
    [Show full text]
  • ABSTRACT Title of Thesis: a LOGIC SYSTEM for FIBONACCI
    ABSTRACT Title of Thesis: A LOGIC SYSTEM FOR FIBONACCI NUMBERS EQUIVALENT TO 64-BIT BINARY Jiani Shen, Master of Science, 2018 Thesis Directed By: Professor Robert W. Newcomb, Electrical & Computer Engineering Compared to the most commonly used binary computers, the Fibonacci computer has its own research values. Making study of Fibonacci radix system is of considerable importance to the Fibonacci computer. Most materials only explain how to use binary coefficients in Fibonacci base to represent positive integers and introduce a little about basic arithmetic on positive integers using complicated but incomplete methods. However, rarely have materials expanded the arithmetic to negative integers with an easier way. In this thesis, we first transfer the unsigned binary Fibonacci representation with minimal form(UBFR(min)) into the even-subscripted signed ternary Fibonacci representation(STFRe), which includes the negative integers and doubles the range over UBFR(min). Then, we develop some basic operations on both positive and negative integers by applying various properties of the Fibonacci sequence into arithmetic. We can set the arithmetic range equivalent to 64-bit binary as our daily binary computers, or whatever reasonable ranges we want. A LOGIC SYSTEM FOR FIBONACCI NUMBERS EQUIVALENT TO 64-BIT BINARY by Jiani Shen Thesis submitted to the Faculty of the Graduate School of the University of Maryland, College Park, in partial fulfillment of the requirements for the degree of [Master of Science] [2018] Advisory Committee: Professor [Robert W. Newcomb], Chair Professor A. Yavuz Oruc Professor Gang Qu © Copyright by [Jiani Shen] [2018] Acknowledgements Sincere thanks to dear Professor Robert Newcomb. He guides me to do the thesis and reviews the papers for me diligently.
    [Show full text]
  • Variable-Length Coding: Unary Coding Golomb Coding Elias Gamma and Delta Coding Fibonacci Coding
    www.vsb.cz Analysis and Signal Compression Information and Probability Theory Michal Vašinek VŠB – Technická univerzita Ostrava FEI/EA404 [email protected] 2020, February 12th Content Classes of Codes Variable-length coding: Unary coding Golomb coding Elias Gamma and Delta coding Fibonacci coding Michal Vašinek (VŠB-TUO) Analysis and Signal Compression 1 / 22 Codes Obrázek: Classes of codes, Cover and Thomas, Elements of Information Theory, p. 106. Michal Vašinek (VŠB-TUO) Analysis and Signal Compression 2 / 22 Nonsigular code Let X be a range of random variable X, for instance the alphabet of input data. Let D be d-ary alphabet of output, for instance binary alphabet D = f0; 1g. Nonsingular Code A code is said to be nonsingular if every element of the range of X maps into different string in D∗; that is: x 6= x0 ! C(x) 6= C(x0) Michal Vašinek (VŠB-TUO) Analysis and Signal Compression 3 / 22 Nonsigular code Nonsingular Code A code is said to be nonsingular if every element of the range of X maps into different string in D∗; that is: x 6= x0 ! C(x) 6= C(x0) Let C(’a’) = 0, C(’b’)=00, C(’c’)=01 be codewords of code C. Encode the sequence s = abc, i.e. C(s) = 0 00 01 = 00001. We can decode in many ways: aaac, bac, abc. Can be solved by adding special separating symbol. For instance with code 11. Michal Vašinek (VŠB-TUO) Analysis and Signal Compression 4 / 22 Uniquely Decodable Codes Definition The extension C∗ of a code C is the mapping from finite length strings of X to finite-length strings of D, defined by: C(x1x2; : : : ; xn) = C(x1)C(x2) :::C(xn) For instance, if C(x1) = 00 and C(x2) = 11 then C(x1x2) = 0011.
    [Show full text]
  • New Classes of Random Sequences For
    NEW CLASSES OF RANDOM SEQUENCES FOR CODING AND CRYPTOGRAPHY APPLICATIONS By KIRTHI KRISHNAMURTHY VASUDEVA MURTHY Bachelor of Engineering in Instrumentation Technology Visvesvaraya Technological University Bangalore, Karnataka 2013 Submitted to the Faculty of the Graduate College of the Oklahoma State University in partial fulfillment of the requirements for the Degree of MASTER OF SCIENCE May, 2016 NEW CLASSES OF RANDOM SEQUENCES FOR CODING AND CRYPTOGRAPHY APPLICATIONS Thesis Approved: Dr. Subhash Kak Thesis Adviser Dr. Keith A Teague Dr. George Scheets ii ACKNOWLEDGEMENTS I would like to express my deep gratitude to my master’s thesis advisor Dr. Subhash Kak. He continually and convincingly conveyed a spirit of adventure and motivation in regard to research with profound patience handling me in all my tough situations. Without his persistent support, this thesis would not have been possible. I render my sincere thanks to my committee members, Dr. Keith Teague and Dr. George Scheets for their support and guidance in final stages of thesis presentation and document review. I thank Oklahoma State University for giving opportunity to utilize and enhance my technical knowledge. Lastly, I would like to thank my parents and friends for constant encouragement emotionally and financially to pursue masters and complete thesis research and document at OSU. iii Acknowledgements reflect the views of the author and are not endorsed by committee members or Oklahoma State University. Name: KIRTHI KRISHNAMURTHY VASUDEVA MURTHY Date of Degree: MAY, 2016 Title of Study: NEW CLASSES OF RANDOM SEQUENCES FOR CODING AND CRYPTOGRAPHY APPLICATIONS Major Field: ELECTRICAL ENGINEERING Abstract: Cryptography is required for securing data in a digital or analog medium and there exists a variety of protocols to encode the data and decrypt them without third party interference.
    [Show full text]
  • Entropy Coding and Different Coding Techniques
    Journal of Network Communications and Emerging Technologies (JNCET) www.jncet.org Volume 6, Issue 5, May (2016) Entropy Coding and Different Coding Techniques Sandeep Kaur Asst. Prof. in CSE Dept, CGC Landran, Mohali Sukhjeet Singh Asst. Prof. in CS Dept, TSGGSK College, Amritsar. Abstract – In today’s digital world information exchange is been 2. CODING TECHNIQUES held electronically. So there arises a need for secure transmission of the data. Besides security there are several other factors such 1. Huffman Coding- as transfer speed, cost, errors transmission etc. that plays a vital In computer science and information theory, a Huffman code role in the transmission process. The need for an efficient technique for compression of Images ever increasing because the is a particular type of optimal prefix code that is commonly raw images need large amounts of disk space seems to be a big used for lossless data compression. disadvantage during transmission & storage. This paper provide 1.1 Basic principles of Huffman Coding- a basic introduction about entropy encoding and different coding techniques. It also give comparison between various coding Huffman coding is a popular lossless Variable Length Coding techniques. (VLC) scheme, based on the following principles: (a) Shorter Index Terms – Huffman coding, DEFLATE, VLC, UTF-8, code words are assigned to more probable symbols and longer Golomb coding. code words are assigned to less probable symbols. 1. INTRODUCTION (b) No code word of a symbol is a prefix of another code word. This makes Huffman coding uniquely decodable. 1.1 Entropy Encoding (c) Every source symbol must have a unique code word In information theory an entropy encoding is a lossless data assigned to it.
    [Show full text]
  • STUDIES on GOPALA-HEMACHANDRA CODES and THEIR APPLICATIONS. by Logan Childers December, 2020
    STUDIES ON GOPALA-HEMACHANDRA CODES AND THEIR APPLICATIONS. by Logan Childers December, 2020 Director of Thesis: Krishnan Gopalakrishnan, PhD Major Department: Computer Science Gopala-Hemachandra codes are a variation of the Fibonacci universal code and have ap- plications in data compression and cryptography. We study a specific parameterization of Gopala-Hemachandra codes and present several results pertaining to these codes. We show that GHa(n) always exists for n ≥ 1 when −2 ≥ a ≥ −4, meaning that these are universal codes. We develop two new algorithms to determine whether a GH code exists for a given a and n, and to construct them if they exist. We also prove that when a = −(4 + k) where k ≥ 1, that there are at most k consecutive integers for which GH codes do not exist. In 2014, Nalli and Ozyilmaz proposed a stream cipher based on GH codes. We show that this cipher is insecure and provide experimental results on the performance of our program that cracks this cipher. STUDIES ON GOPALA-HEMACHANDRA CODES AND THEIR APPLICATIONS. A Thesis Presented to The Faculty of the Department of Computer Science East Carolina University In Partial Fulfillment of the Requirements for the Degree Master of Science in Computer Science by Logan Childers December, 2020 Copyright Logan Childers, 2020 STUDIES ON GOPALA-HEMACHANDRA CODES AND THEIR APPLICATIONS. by Logan Childers APPROVED BY: DIRECTOR OF THESIS: Krishnan Gopalakrishnan, PhD COMMITTEE MEMBER: Venkat Gudivada, PhD COMMITTEE MEMBER: Karl Abrahamson, PhD CHAIR OF THE DEPARTMENT OF COMPUTER SCIENCE: Venkat Gudivada, PhD DEAN OF THE GRADUATE SCHOOL: Paul J. Gemperline, PhD Table of Contents LIST OF TABLES ..................................
    [Show full text]
  • Fibonacci Codes for Crosstalk Avoidance
    IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 8, Issue 3 (Nov. - Dec. 2013), PP 09-15 www.iosrjournals.org Fibonacci Codes for Crosstalk Avoidance 1Sireesha Kondapalli, 2Dr. Giri Babu Kande 1PG Student (M.Tech VLSI), Dept. Of ECE, Vasireddy Venkatadri Ins. Tech., Nambur, Guntur, AP, India 2 Professor & Head, Dept. Of ECE, Vasireddy Venkatadri Ins. Tech., Nambur, Guntur, AP, India Abstract: In the deep sub micrometer CMOS process technology, the interconnect resistance, length, and inter- wire capacitance are increasing significantly, which contribute to large on-chip interconnect propagation delay. Data transmitted over interconnect determine the propagation delay and the delay is very significant when adjacent wires are transitioning in opposite directions (i.e., crosstalk transitions) as compared to transitioning in the same direction. Propagation delay across long on-chip buses is significant when adjacent wires are transitioning in opposite direction (i.e., crosstalk transitions) as compared to transitioning in the same direction. By exploiting Fibonacci number system, we propose a family of Fibonacci coding techniques for crosstalk avoidance, relate them to some of the existing crosstalk avoidance techniques, and show how the encoding logic of one technique can be modified to generate code words of the other technique. Keywords: On-chip bus, crosstalk, Fibonacci coding. I. Introduction The advancement of very large scale integration (VLSI) technologies has been following Moore’s law for the past several decades: the number of transistors on an integrated circuit is doubling every two years and the channel length is scaling at the rate of 0.7/3 years.
    [Show full text]
  • Source Coding and Channel Coding for Mobile Multimedia Communication
    50 Source Coding and Channel Coding for Mobile Multimedia Communication Hammad Dilpazir1, Hasan Mahmood1, Tariq Shah2 and Hafiz Malik3 1Department of Electronics, Quaid-i-Azam University, Islamabad 2Department of Mathematics, Quaid-i-Azam University, Islamabad 3Department of Electrical and Computer Engineering, University of Michigan - Dearborn, Dearborn, MI 1,2Pakistan 3USA 1. Introduction In the early era of communications engineering, the emphasis was on establishing links and providing connectivity. With the advent of new bandwidth hungry applications, the desire to fulfill the user’s need became the main focus of research in the area of data communications. It took about half a century to achieve near channel capacity limit in the year 1993, as predicted by Shannon (Shannon, 1948). However, with the advancement in multimedia technology, the increase in the volume of information by the order of magnitude further pushed the scientists to incorporate data compression techniques in order to fulfill the ever increasing bandwidth requirements. According to Cover (Cover & Thomas, 2006), the separation theorem stated by Shannon implies that it is possible for the source and channel coding to be accomplished on separate and sequential basis while still maintaining optimization. In the context of multimedia communication, the former represents the compression while the latter, the error protection. A major restriction, however, with this theorem is that it is applicable only to asymptotically lengthy blocks of data and in most situations cannot be a good approximation. So, these shortcomings have tacitly led to devising new strategies for joint-source channel coding. This chapter addresses the major issues in source and channel coding and presents the techniques used for efficient data transmission for multimedia applications over wireless channels.
    [Show full text]