Stefano MARANO | Information Coding and Compression
Stefano MARANO Information Coding and Compression
cod. 0622700032
INFORMATION CODING AND COMPRESSION
| 0622700032 | |
| DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA | |
| COMPUTER ENGINEERING | |
| 2015/2016 |
| YEAR OF COURSE 1 | |
| YEAR OF DIDACTIC SYSTEM 2015 | |
| SECONDO SEMESTRE |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| ING-INF/03 | 9 | 90 | LESSONS |
| Objectives | |
|---|---|
| THE COURSE IS AIMED TO PROVIDE THE METHODOLOGICAL BACKGROUND FOR UNDERSTANDING CHANNEL CODING AND DATA COMPRESSION. - KNOWLEDGE AND UNDERSTANDING: MATHEMATICAL AND STATISTICAL MODELING OF INFORMATION. PRINCIPLES OF INFORMATION THEORY. REDUNDANCY AND INFORMATION REPRESENTATION. FUNDAMENTAL LIMITS OF DATA COMPRESSION AND DATA TRANSMISSION. - APPLIED KNOWLEDGE AND UNDERSTANDING: MODELING AND ANALYSIS OF SOURCE AND CHANNEL CODING. DESIGN AND IMPLEMENTATION OF CODING AND DATA COMPRESSION ALGORITHMS. - PERSONAL JUDGEMENTS: ABILITY TO SELECT THE PROPER METHODOLOGIES FOR THE DESIGN AND THE ANALYSIS OF CODING AND DATA COMPRESSION SYSTEMS. - COMMUNICATION SKILLS: ORAL EXPOSITION OF THE COURSE TOPICS. TERMINOLOGY OF INFORMATION THEORY, CODING AND DATA COMPRESSION. - LEARNING SKILLS: APPLYING THE INTRODUCED THEORY AND METHODS TO DIFFERENT CONTEXTS. ABILITY TO DEEPEN THE KNOWLEDGE BY INDIVIDUAL STUDY OF THE SCIENTIFIC LITERATURE. |
| Prerequisites | |
|---|---|
| MATHEMATICS, PROBABILITY THEORY, FUNDAMENTALS OF DIGITAL COMMUNICATIONS |
| Contents | |
|---|---|
| - SOURCE CODING REVIEW OF INFORMATION THEORY. DIFFERENTIAL ENTROPY. DIVERGENCE AND MUTUAL INFORMATION FOR CONTINUOUS VARIABLES. THE RATE DISTORTION FUNCTION R(D). - QUANTIZATION SCALAR QUANTIZATION. DISTORTION MEASURES. UNIFORM AND NON-UNIFORM QUANTIZATION. OPTIMAL DESIGN OF QUANTIZERS. NEAREST NEIGHBOR. LLOYD ALGORITHM. PRINCIPLES OF OPTIMAL PREDICTION. ORTHOGONALITY PRINCIPLE. TRANSFORM CODING. BIT ALLOCATION. QUANTIZATION FOR INFERENCE IN NETWORKS. DISTRIBUTED LEARNING IN SENSOR NETWORKS WITH COMMUNICATION CONTRAINTS. - COMPRESSED SENSING INTRODUCTION AND MOTIVATIONS. CONNECTIONS WITH SAMPLING THEORY. SAMPLING OF SPARSE SIGNALS. SENSING MATRICES. RESTRICTED ISOMETRY PROPERTY (RIP). (PSEUDO-) NORM L0. RECONSTRUCTION WITH L1 MINIMIZATION. ROBUSTNESS AND STABILITY OF THE RECONSTRUCTION. - CHANNEL CODING SHANNON'S II THEOREM AND CHANNEL CAPACITY. REDUNDANCY AND RATE. CODING GAIN. RANDOM CODING. GAUSSIAN CHANNEL AND BINARY SYMMETRIC CHANNEL. HARD AND SOFT DECODING. LINEAR BLOCK CODING. CONVOLUTIONAL CODES. VITERBI ALGORITHM AND TRELLIS DECODING. LDPC CODES AND ITERATIVE DECODING ON BIPARTITE GRAPHS. |
| Teaching Methods | |
|---|---|
| LECTURES PROVIDING THE THEORETICAL BACKGROUND, COMPUTER-BASED DEMONSTRATIONS, CLASSROOM EXERCISES AND HOMEWORK ASSIGNMENTS. |
| Verification of learning | |
|---|---|
| THE FINAL EXAM IS AIMED TO EVALUATE: KNOWLEDGE AND UNDERSTANDING SKILLS ABOUT THE TOPICS AND CONCEPTS ADDRESSED DURING THE COURSE; THE ABILITY OF SOLVING CODING AND DATA COMPRESSION PROBLEMS IN COMMUNICATION SYSTEMS; LEARNING SKILLS, SCIENTIFIC APPROACH TO COMPLEX PROBLEMS AND INCLINATION TO CRITICAL REASONING. THE EVALUATION IS MAINLY BASED ON AN ORAL EXAMINATION, DEALING WITH ALL THE TOPICS ADDRESSED DURING THE COURSE, AND THE FINAL RATING TAKES INTO ACCOUNT, ALONG WITH THE MENTIONED CRITERIA, THE QUALITY OF THE PRESENTATION. |
| Texts | |
|---|---|
| - T. M. COVER, J. A. THOMAS, ELEMENTS OF INFORMATION THEORY, JOHN WILEY & SONS, 1991 - A. GERSHO, R. GRAY: VECTOR QUANTIZATION AND SIGNAL COMPRESSION, KLUWER, 1991 - E. BIGLIERI, CODING FOR WIRELESS CHANNELS, SPRINGER-VERLAG, 2008 - JOHN G. PROAKIS, DIGITAL COMMUNICATIONS, MCGRAW-HILL, 2008 |
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