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Books on information theory and coding have proliferated over the last few years, but few succeed in covering the fundamentals without losing students in mathematical abstraction. Even fewer build the essential theoretical framework when presenting algorithms and implementation details of modern coding systems.

Without abandoning the theoretical foundations, Fundamentals of Information Theory and Coding Design presents working algorithms and implementations that can be used to design and create real systems. The emphasis is on the underlying concepts governing information theory and the mathematical basis for modern coding systems, but the authors also provide the practical details of important codes like Reed-Solomon, BCH, and Turbo codes. Also setting this text apart are discussions on the cascading of information channels and the additivity of information, the details of arithmetic coding, and the connection between coding of extensions and Markov modelling.

Complete, balanced coverage, an outstanding format, and a wealth of examples and exercises make this an outstanding text for upper-level students in computer science, mathematics, and engineering and a valuable reference for telecommunications engineers and coding theory researchers.

ENTROPY AND INFORMATION
Structure
Structure in Randomness
First Concepts of Probability Theory
Surprise and Entropy
Units of Entropy
The Minimum and Maximum Values of Entropy
A Useful Inequality
Joint Probability Distribution Functions
Conditional Probability and Bayes' Theorem
Conditional Probability Distributions and Conditional Entropy
Information Sources
Memoryless Information Sources
Markov Sources and n-Gram Models
Stationary Distributions
The Entropy of Markov Sources
Sequences of Symbols
The Adjoint Source of a Markov Source
Extensions of Sources
Infinite Sample Spaces
INFORMATION CHANNELS
What Are Information Channels?
BSC and BEC Channels
Mutual Information
Noiseless and Deterministic Channels
Cascaded Channels
Additivity of Mutual Information
Channel Capacity: Maximum Mutual Information
Continuous Channels and Gaussian Channels
Information Capacity Theorem
Rate Distortion Theory
SOURCE CODING
Introduction
Instantaneous Codes
The Kraft Inequality and McMillan's Theorem
Average Length and Compact Codes
Shannon's Noiseless Coding Theorem
Fano Coding
Huffman Coding
Arithmetic Coding
Higher-Order Modelling
DATA COMPRESSION
Introduction
Basic Concepts of Data Compression
Run-Length Coding
The CCITT Standard for Facsimile Transmission
Block-sorting Compression
Dictionary Coding
Statistical Compression
Prediction by Partial Matching
Image Coding
FUNDAMENTALS OF CHANNEL CODING
Introduction
Code Rate
Decoding Rules
Hamming Distance
Bounds on M, Maximal Codes and Perfect Codes
Error Probabilities
Shannon's Fundamental Coding Theorem
ERROR-CORRECTING CODES
Introduction
Groups
Rings and Fields
Linear Spaces
Linear Spaces over the Binary Field
Linear Codes
Encoding and Decoding
Codes Derived from Hadamard Matrices
CYCLIC CODES
Introduction
Rings of Polynomials
Cyclic Codes
Encoding and Decoding of Cyclic Codes
Encoding and Decoding Circuits for Cyclic Codes
The Golay Code
Hamming Codes
Cyclic Redundancy Check Codes
Reed-Muller Codes
BURST-CORRECTING CODES
Introduction
Finite Fields
Irreducible Polynomials
Construction of Finite Fields
Bursts of Errors
Fire Codes
Minimum Polynomials
Bose-Chaudhuri-Hocquenghem Codes
Other Fields
Reed-Solomon Codes
CONVOLUTIONAL CODES
Introduction
ASimple Example
Binary Convolutional Codes
Decoding Convolutional Codes
The Viterbi Algorithm
Sequential Decoding
Trellis Modulation
Turbo Codes
INDEX

Each chapter also contains a section of exercises and a section of references.