A Student's Guide to Coding and Information Theory

A Student's Guide to Coding and Information Theory

by Stefan M. Moser, Po-Ning Chen
     
 

ISBN-10: 1107601967

ISBN-13: 9781107601963

Pub. Date: 01/31/2012

Publisher: Cambridge University Press

This easy-to-read guide provides a concise introduction to the engineering background of modern communication systems, from mobile phones to data compression and storage. Background mathematics and specific engineering techniques are kept to a minimum so that only a basic knowledge of high-school mathematics is needed to understand the material covered. The authors

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Overview

This easy-to-read guide provides a concise introduction to the engineering background of modern communication systems, from mobile phones to data compression and storage. Background mathematics and specific engineering techniques are kept to a minimum so that only a basic knowledge of high-school mathematics is needed to understand the material covered. The authors begin with many practical applications in coding, including the repetition code, the Hamming code and the Huffman code. They then explain the corresponding information theory, from entropy and mutual information to channel capacity and the information transmission theorem. Finally, they provide insights into the connections between coding theory and other fields. Many worked examples are given throughout the book, using practical applications to illustrate theoretical definitions. Exercises are also included, enabling readers to double-check what they have learned and gain glimpses into more advanced topics, making this perfect for anyone who needs a quick introduction to the subject.

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Product Details

ISBN-13:
9781107601963
Publisher:
Cambridge University Press
Publication date:
01/31/2012
Pages:
208
Sales rank:
951,774
Product dimensions:
6.00(w) x 8.90(h) x 0.30(d)

Table of Contents

1. Introduction Chung-Hsuan Wang; 2. Error-detecting codes Chung-Hsuan Wang; 3. Repetition and hamming codes Francis Lu; 4. Data compression: efficient coding of a random message; 5. Entropy and Shannon's source coding theorem; 6. Mutual information and channel capacity Jwo-Yuh Wu; 7. Achieving the Shannon limit by turbo coding; 8. Other aspects of coding theory Francis Lu.

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