This note serves as a comprehensive guide to fundamental concepts in
information theory and coding. This pdf provides discrete probability theory,
information theory, and coding principles. Beginning with Shannon's measure of
information, then delves into the efficient coding of information, the
methodology of typical sequences is introduced, emphasizing the distinction
between lossy and lossless source encoding. The text also discusses coding for
noisy digital channels, block coding principles and tree and trellis coding
principles.
This note explains the following topics: uncertainty
and information, Efficient coding of information, Stationary processes and
markov chains, Coding for noisy transmission, Complements to efficient coding of
Information, Error correcting codes and cryptography.
This note serves as a comprehensive guide to fundamental concepts in
information theory and coding. This pdf provides discrete probability theory,
information theory, and coding principles. Beginning with Shannon's measure of
information, then delves into the efficient coding of information, the
methodology of typical sequences is introduced, emphasizing the distinction
between lossy and lossless source encoding. The text also discusses coding for
noisy digital channels, block coding principles and tree and trellis coding
principles.
This PDF covers the following
topics related to Information Theory : Information measures, Lossless data
compression, Binary hypothesis testing, Channel coding, Lossy data compression,
Advanced topics.
This is a graduate-level
introduction to mathematics of information theory. This note will cover both
classical and modern topics, including information entropy, lossless data
compression, binary hypothesis testing, channel coding, and lossy data
compression.