INFORMATION THEORY AND CODING BY GIRIDHAR PDF
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Information Theory and Coding by K Giridhar: A Review
Information theory and coding are two important topics in the field of communication engineering. Information theory deals with the quantification, transmission, and processing of information, while coding refers to the techniques for efficient and reliable representation and communication of information. In this article, we will review a book that covers these topics in a comprehensive and accessible way: Information Theory and Coding by K Giridhar.
The book is divided into 12 chapters, covering the following topics:
Introduction to information theory and coding
Entropy and information measures
Source coding and data compression
Channel models and capacity
Channel coding and error control
Linear block codes
Cyclic codes
BCH and Reed-Solomon codes
Convolutional codes
Trellis coded modulation
Turbo codes
LDPC codes
The book provides a clear and rigorous exposition of the fundamental concepts and principles of information theory and coding, with numerous examples, exercises, and illustrations. The book also covers some of the recent advances and applications of these topics, such as turbo codes, LDPC codes, space-time codes, etc. The book is suitable for undergraduate and postgraduate students of engineering, as well as for researchers and practitioners in the field.
The book is available as a PDF document that can be downloaded from various online sources[^1^] [^2^]. The book has received positive feedback from readers and reviewers for its clarity, depth, and relevance.
Information theory and coding have many applications in various fields of engineering, science, and technology. Some of the most prominent examples are:
Data compression: Information theory provides the theoretical basis for efficient and lossless compression of data, such as text, images, audio, and video. Data compression reduces the storage space and transmission bandwidth required for data, which is essential for many applications such as multimedia, web browsing, cloud computing, etc. Some of the popular compression algorithms based on information theory are Huffman coding, arithmetic coding, Lempel-Ziv coding, JPEG, MP3, etc.[^4^] [^2^]
Error detection and correction: Information theory also provides the theoretical foundation for designing codes that can detect and correct errors that occur during the transmission or storage of data. Error detection and correction codes add redundancy to the data to enable the receiver to recover the original data even if some bits are corrupted by noise or interference. Some of the common error detection and correction codes based on information theory are parity check codes, Hamming codes, cyclic codes, BCH codes, Reed-Solomon codes, convolutional codes, turbo codes, LDPC codes, etc.[^2^] [^3^]
Cryptography: Information theory also has applications in cryptography, which is the science of secure communication. Cryptography uses mathematical techniques to encrypt and decrypt data, so that only the intended parties can access it. Information theory helps to measure the security and efficiency of cryptographic schemes, such as symmetric-key encryption, public-key encryption, hash functions, digital signatures, etc.[^4^]
Machine learning: Information theory also has connections to machine learning, which is the field of artificial intelligence that deals with learning from data. Information theory helps to quantify the amount of information gained from data, which is useful for tasks such as feature selection, clustering, classification, regression, etc. Information theory also helps to measure the complexity and generalization ability of machine learning models, such as neural networks, decision trees, support vector machines, etc.[^4^] 061ffe29dd