Information Theory: Introduction, Measure of information, Information content ofmessage, Average Information content of symbols in Long Independent sequences,Average Information content of symbols in Long dependent sequences, MarkovStatistical Model of Information Sources, Entropy and Information rate of MarkoffSources (Section 4.1, 4.2 of Text 1).
Source Coding: Source coding theorem, Prefix Codes, Kraft McMillan Inequalityproperty – KMI (Section 2.2 of Text 2).
Encoding of the Source Output, Shannon’s Encoding Algorithm (Sections 4.3, 4.3.1 ofText 1).
Shannon Fano Encoding Algorithm, Huffman codes, Extended Huffman coding,Arithmetic Coding, Lempel – Ziv Algorithm (Sections 3.6,3.7,3.8,3.10 of Text 3).
Information Channels: Communication Channels ( Section 4.4 of Text 1).Channel Models, Channel Matrix, Joint probabilty Matrix, Binary Symmetric Channel,System Entropies, Mutual Information, Channel Capacity, Channel Capacity of :Binary Symmetric Channel, Binary Erasure Channel, Muroga,s Theorem, ContineuosChannels (Sections 4.2,4.3,4.4,4.6,4.7 of Text 3).
Error Control Coding:
Introduction, Examples of Error control coding, methods of Controlling Errors, Typesof Errors, types of Codes, Linear Block Codes: matrix description of Linear BlockCodes, Error Detection and Error Correction Capabilities of Linear Block Codes, SingleError Correcting hamming Codes, Table lookup Decoding using Standard Array.
Binary Cyclic Codes: Algebraic Structure of Cyclic Codes, Encoding using an (n-k) BitShift register, Syndrome Calculation, Error Detection and Correction(Sections 9.1, 9.2,9.3,9.3.1,9.3.2,9.3.3 of Text 1).
Some Important Cyclic Codes: Golay Codes, BCH Codes ( Section 8.4 – Article 5 of Text 2).
Convolution Codes: Convolution Encoder, Time domain approach, Transform domainapproach, Code Tree, Trellis and State Diagram, The Viterbi Algorithm) (Section 8.5 –Articles 1,2 and 3, 8.6- Article 1 of Text 2).