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A Comprehensive Guide to Information Theory and Coding by K Giridhar PDF 69



Information Theory and Coding by K Giridhar PDF 69: A Comprehensive Guide




Are you interested in learning about information theory and coding, the mathematical foundations of communication and data processing? Do you want to know more about the concepts, applications, and challenges of this fascinating field? If yes, then you are in the right place.




information theory and coding by k giridhar pdf 69


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In this article, we will introduce you to information theory and coding by K Giridhar PDF 69, a comprehensive book that covers all the essential topics in a clear and concise manner. We will also show you how to download this book for free from the internet. But before we do that, let us first understand what information theory and coding are, why they are important, and who is K Giridhar.


What is information theory and coding?




Information theory is the branch of mathematics that deals with the quantification, transmission, storage, and manipulation of information. It was founded by Claude Shannon in his landmark paper "A Mathematical Theory of Communication" in 1948. Information theory provides the theoretical limits and optimal methods for various aspects of information processing, such as compression, encryption, error correction, detection, estimation, inference, etc.


Coding is the process of transforming information from one form to another according to some rules or algorithms. Coding can be used for various purposes, such as reducing redundancy, increasing efficiency, enhancing security, correcting errors, etc. Coding can be divided into two main types: source coding and channel coding. Source coding deals with the representation of information at the source (e.g., text, image, audio, video), while channel coding deals with the transmission of information over a noisy or unreliable channel (e.g., wire, radio, optical fiber).


Why is information theory and coding important?




Information theory and coding are important because they have many applications in various fields of science, engineering, technology, and society. Some examples are:


  • Data compression: Information theory provides the fundamental limits on how much information can be compressed without losing any essential information. Coding techniques such as Huffman coding, arithmetic coding, run-length encoding, etc., are used to reduce the size of data files or streams for efficient storage or transmission.



  • Cryptography and security: Information theory provides the basis for measuring the secrecy and complexity of cryptographic systems. Coding techniques such as substitution cipher, transposition cipher, stream cipher, block cipher, public-key cryptography, etc., are used to encrypt or decrypt data for protecting confidentiality, integrity, authenticity, etc.



  • Error control coding: Information theory provides the criteria for designing optimal codes that can detect or correct errors that may occur during transmission or storage of data. Coding techniques such as parity check code, Hamming code, cyclic code, Reed-Solomon code, convolutional code, turbo code, etc., are used to improve the reliability and performance of communication systems and networks.



  • Communication systems and networks: Information theory provides the framework for analyzing the capacity and efficiency of communication channels and networks. Coding techniques such as modulation, demodulation, multiplexing, demultiplexing, coding, decoding, etc., are used to transmit or receive data over various media such as wire, radio, optical fiber, etc.



  • Machine learning and artificial intelligence: Information theory provides the tools for measuring the complexity and uncertainty of data and models. Coding techniques such as clustering, classification, regression, dimensionality reduction, feature extraction, etc., are used to learn from data and make predictions or decisions.



Who is K Giridhar and what is his book about?




K Giridhar is a professor of electrical engineering at the Indian Institute of Technology Madras. He has more than 25 years of teaching and research experience in the areas of information theory, coding, communication systems, signal processing, etc. He has published more than 100 papers in reputed journals and conferences and has received several awards and honors for his contributions.


His book "Information Theory & Coding" is a comprehensive textbook that covers all the essential topics in information theory and coding in a clear and concise manner. The book is divided into 10 chapters, each with a brief introduction, a detailed exposition, a summary, a set of solved examples, a set of review questions, and a set of exercise problems. The book also includes appendices on mathematical preliminaries, probability theory, linear algebra, etc. The book is suitable for undergraduate and postgraduate students of engineering and science as well as for practicing engineers and researchers who want to learn or refresh their knowledge on information theory and coding.


Basic Concepts of Information Theory and Coding




In this section, we will briefly review some of the basic concepts of information theory and coding that are covered in the first four chapters of K Giridhar's book. These concepts include information measures, entropy and mutual information, source coding and data compression, channel capacity and coding theorem.


Information measures




Information measures are quantitative ways of expressing how much information is contained in or conveyed by a source or a channel. Some of the common information measures are:


  • Information content: The amount of information contained in an individual message or symbol from a source. It is inversely proportional to the probability of occurrence of the message or symbol. It is measured in bits (binary digits) or nats (natural digits).



  • Entropy: The average amount of information contained in a source or produced by a source per message or symbol. It is also a measure of the uncertainty or randomness of a source. It is maximized when all the messages or symbols from a source are equally likely. It is measured in bits per symbol (bps) or nats per symbol (nps).



  • Joint entropy: The average amount of information contained in two sources or produced by two sources per pair of messages or symbols. It is also a measure of the uncertainty or randomness of two sources together. It is measured in bits per pair (bpp) or nats per pair (npp).



  • Conditional entropy: The average amount of information contained in one source or produced by one source per message or symbol given that another source or message or symbol is known. It is also a measure of the uncertainty or randomness of one source given another source. It is measured in bits per symbol (bps) or nats per symbol (nps).



  • Mutual information: The amount of information shared by two sources or conveyed by a channel between two sources. It is also a measure of the dependence or correlation between two sources or the reduction in uncertainty or randomness of one source due to another source. It is measured in bits (b) or nats (n).



Entropy and mutual information




Entropy and mutual information are two fundamental concepts in information theory that relate the information content, uncertainty, and dependence of sources and channels. Some of the important properties and formulas involving entropy and mutual information are:


  • The entropy H(X) of a discrete source X with alphabet x1,x2,...xn and probabilities p1,p2,...pn is given by: $$H(X) = -\sum_i=1^n p_i \log p_i$$ where log denotes logarithm to base 2 for bits or base e for nats.



The joint entropy H(X,Y) of two discrete sources X and Y with joint alphabet (x1,y1),(x2,y2),...(xn,yn) and joint probabilities p11,p12,...pnn is given by: $$H(X,Y) = -\sum_{i 71b2f0854b


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