Network Coding And Data Compression

In this book, new developments in network coding and data compression are examined. The first chapter proposes a new multipath transmission scheme that employs network coding at the source for rate-delay adaptation. The second chapter presents the theory, applications and challenges of a physical-layer network coding technique that promises significant improvement to the spectral efficiency and interference mitigation challenges in future heterogenous cellular and wireless networks. In this third chapter, network and distributed turbo coding based HARQ protocol are examined for wireless broadcasting systems. The last three chapters included in this book present three models that are based on Data Envelopment Analysis (DEA) methodology to appropriately evaluate the efficiency of multicast energy and further minimizes energy transmission in wireless networks without affecting the overall network performance; the development of methods of experimental statistical modeling of physical laws and the cascade scheme of data compression in multimode fibre systems.

Introduction -- Network coding Fundamentals -- Harnessing Network Coding in Wireless Systems -- Network Coding for Content Distribution and Multimedia Streaming in Peer-to-Peer Networks -- Network Coding in the Real World -- Network Coding and User Cooperation for Streaming and Download Services in LTE Networks -- CONCERTO: Experiences with a Real-World MANET System Based on Network Coding -- Secure Network Coding: Bounds and Algorithms for Secret and Reliable Communications -- Network Coding and Data Compression -- Scaling Laws with Network Coding -- Network Coding in Disruption Tolerant Networks.

Following an exchange of correspondence, I met Ross in Adelaide in June 1988. I was approached by the University of Adelaide about being an external examiner for this dissertation and willingly agreed. Upon receiving a copy of this work, what struck me most was the scholarship with which Ross approaches and advances this relatively new field of adaptive data compression. This scholarship, coupled with the ability to express himself clearly using figures, tables, and incisive prose, demanded that Ross's dissertation be given a wider audience. And so this thesis was brought to the attention of Kluwer. The modern data compression paradigm furthered by this work is based upon the separation of adaptive context modelling, adaptive statistics, and arithmetic coding. This work offers the most complete bibliography on this subject I am aware of. It provides an excellent and lucid review of the field, and should be equally as beneficial to newcomers as to those of us already in the field.

Digital data is heavily used when generating, storing, and transmitting information, and special codes are used to represent the data and to control its size, reliability, and security. Data coding is therefore a highly important, and indeed increasingly critical, discipline for data and computer communications. In a unique, single volume, this highly versatile text/reference introduces readers to the importance of channel coding (error-correcting codes), secure coding (cryptography), and source coding (data compression). It is organized in three main parts and uses clear, nonmathematical explanations to develop the concepts, principles, and techniques in each area of coding. Requiring only a general familiarity with computer methods, the book deals with all aspects of coding and its relevance to fast, secure, and reliable data transmission and storage. Features & Benefits: *Presents comprehensive coverage of areas of coding often found in separate books, and stresses data coding's relevance in today's world *Provides a wealth of examples and exercises (with solutions) to help readers easily grasp the material *Incorporates an extensive chapter on data hiding, a sparsely documented topic of increasing importance in the community *Includes an author-supplied website with supplementary material *Possesses a detailed bibliography and helpful glossary, index, and appendixes (including projects for self-study). Computer scientists, electrical engineers, and other professionals will find this up-to-date, accessible volume an essential resource about key areas, problems, and methods associated with data coding. Its straightforward approach also makes it very suitable for students taking courses on computer networks, coding and encryption, data communications, and information theory and coding. David Salomon is a professor emeritus of computer science at California State University, Northridge. He has authored numerous articles and books, including Data Compression: The Complete Reference (Third Edition), Guide to Data Compression Methods, and Data Privacy and Security, and Computer Graphics and Geometric Modeling. Key Topics:” Error-control codes” Image compression methods” Cipher types” Public-key cryptography” Check digits” Error detection” Statistical methods of source coding” Steganography (data hiding)” Encryption codes” Security / Cryptography” Beginning / Intermediate.

This book is an evolution from my book A First Course in Information Theory published in 2002 when network coding was still at its infancy. The last few years have witnessed the rapid development of network coding into a research ?eld of its own in information science. With its root in infor- tion theory, network coding has not only brought about a paradigm shift in network communications at large, but also had signi?cant in?uence on such speci?c research ?elds as coding theory, networking, switching, wireless c- munications,distributeddatastorage,cryptography,andoptimizationtheory. While new applications of network coding keep emerging, the fundamental - sults that lay the foundation of the subject are more or less mature. One of the main goals of this book therefore is to present these results in a unifying and coherent manner. While the previous book focused only on information theory for discrete random variables, the current book contains two new chapters on information theory for continuous random variables, namely the chapter on di?erential entropy and the chapter on continuous-valued channels. With these topics included, the book becomes more comprehensive and is more suitable to be used as a textbook for a course in an electrical engineering department.

Information Systems and Data Compression presents a uniform approach and methodology for designing intelligent information systems. A framework for information concepts is introduced for various types of information systems such as communication systems, information storage systems and systems for simplifying structured information. The book introduces several new concepts and presents a novel interpretation of a wide range of topics in communications, information storage, and information compression. Numerous illustrations for designing information systems for compression of digital data and images are used throughout the book.

James A. Storer Computer Science Dept. Brandeis University Waltham, MA 02254 Data compression is the process of encoding a body of data to reduce stor age requirements. With Lossless compression, data can be decompressed to be identical to the original, whereas with lossy compression, decompressed data may be an acceptable approximation (according to some fidelity criterion) to the original. For example, with digitized video, it may only be necessary that the decompressed video look as good as the original to the human eye. The two primary functions of data compression are: Storage: The capacity of a storage device can be effectively increased with data compression software or hardware that compresses a body of data on its way to the storage device and decompress it when it is retrieved. Communications: The bandwidth of a digital communication link can be effectively increased by compressing data at the sending end and decom pressing data at the receiving end. Here it can be crucial that compression and decompression can be performed in real time.

From archiving data to CD-ROMs, and from coding theory to image analysis, many facets of computing make use of data compression in one form or another. This is an overview of the many different types of compression, including a taxonomy, an analysis of the most common systems of compression, discussion of their relative benefits and disadvantages, and their most common uses. Readers are presupposed to have a basic understanding of computer science -- essentially the storage of data in bytes and bits and computing terminology -- but otherwise this book is self-contained. It divides neatly into four main parts based on the main branches of data compression: run length encoding, statistical methods, dictionary-based methods, and lossy image compression. All of the most well-known compression techniques are covered including Zip, BinHex, Huffman coding, and GIF.