Course catalogue

Discrete information sources, alphabets. Entropy. Source coding: Huffman codes, Lempel-Ziv, arithmetic codes. Channel capacity. Second Shannon’s theorem. Binary symmetric channel. Source modeling with Markov chains. Modulation and channel restrictions. Sequences (d, k) and codes RLL. Linear error detection and error correction codes. Codes representation in a binary vectorial space. Hamming distance. Decoding of linear codes. Codes Hamming: design, binary code, extended Hamming codes. Performance bounds of linear codes. ARQ protocols. This course offers an introduction to the theory of information and its applications to communication systems. Emphasis is given on the design, analysis and application of error detection and correction codes.

• The student will learn the foundations of information theory.
• The student will be able to compute information that a source produces and examine the possibility to transmit it over a specific channel.
• The student will be able to choose the most adequate compression algorithms.
• The student will be able to evaluate the impact of the application of compression algorithms.
• The student will be able to choose the most adequate error correction algorithms under specific noise conditions and transmission rate.
• The student will be able to develop coding systems and applications for compression and reliable transmission of data.

Not required.

Lectures with an optional assignment.

Evaluation is performed through a final exam. 

Greek (English for Erasmus students)