Course aims:

Representation of information acquisition, processing, transmission or storage
Quantitative measure of information transmission systems raw, with or without loss
Control error correction or detection
- Main types of binary codes or cyclic type non-binary
- Use of information theory and coding the current standards for storage or transmission

Course Entry Requirements:

• Fundamental knowledge in coding theory.

Course contents:

COURSE CONTENTS: The course covers the following main topics: 1. Elements of probability theory and mathematical statistics with applications in information transmission theory. Transmitting information systems (ITS) 2. Elements of the mathematical theory of information. Sources of information without memory. Information entropy. Flow of information. Sources of information to memory. 3. Discrete transmission channels. Channel capacity given by the matrix of noise. Channel capacity and signal-band given by Shannon's formula 4. Coding signals. Source coding, modulation impulses in code, lossless compression. Shannon's theorem I (lossless compression theorem). Compression algorithms. 5. Channel coding. Theorem II's Shannon (coding of channels with interference). Block codes: algebraic theory, determination and representation, control matrix and generators 6. Perfect and near-perfect course. Error syndrome. Hamming codes group. 7. Cyclical codes: definition and representation, algebraic coding, coding and decoding circuits to achieve. 8. Distance and ration code. 9 Elements of Galois field theory for cyclic codes. BCH codes 10. Convolution: definition and representation compared with block codes, algebraic coding, and implementation of feedback shift registers. 11. Decoding convolution codes algorithms 12. Differential Pulse code modulation, delta modulation linear, adaptive and others 13. Signal Processing course. Modern compression algorithms; static and dynamic algorithms, coding with a fixed or variable pitch. Conclusions regarding the source coding.

Teaching methods:

Lecture, conversation, exemplification, exercises.

Learning outcomes:

Application of basic methods for acquisition and signal processing.

Learning outcomes verification and assessment criteria:

Projects/Assignments –60%; continuous assessment – 40%.

Recommended reading:

Monica Borda, Information Theory and Coding, Editura UT PRE, 2007,
G. Wade, Signal coding and processing, Palgrave-McMillan, 200,
B. Sklar, Digital communications, Prentice Hal, 2001,
D. Salomon, A guide to data compression methods, Springer-Verlag, 2002,
James V Stone, Information Theory, http://jim-stone.staff.shef.ac.uk/BookInfoTheory/InfoTheoryBookChapter01.pdf, 2015,