Anno Accademico 17/18

Sicurezza II 2017 2018

Course objectives

The course represents an introduction to classical results of Shannon information theory.

 

Results of learning outcomes

At the end of the course the student will have the capacity to apply information theory tools and approaches to both theoretical and practical problems related to information management, coding, representation, protection and information metrics. 

 

Syllabus

The course is structured in two parts.

The first part of the course is devoted to the classical information theory. In particular, the addressed topics are: definition of information and source types, the concept of entropy, source coding, Shannon's first theorem (source coding), uniquely decodable codes, optimality of Huffman coding, models of noisy channels, definition of the channel capacity according to Shannon's theorem (channel coding).

The second part of the course is devoted to the study of source coding and channel coding algorithms used in many applications, communication systems and networks. The selected topics include arithmetic coding, the Lempel-Ziv-Welch algorithms and state of the art standards for image and video compression. As far as channel coding is regarded the course will introduce linear block codes, cyclic codes, convolutional codes and fountain codes.

 

Course delivery

The course will be based on theretical lessons followed by in class exercises and computer based experiments. Personal training on assigned exercises is important for the success in this class.

 

Learning assessment methods

The assesment comprises a written test followed by an oral examination.

 

Suggested readings and bibliography

  • R. W. Yeung, "Information Theory and Network Coding,  ISBN: 978-0-387-79233-0
  • S. Benedetto, E. Biglieri, "Principles of Digital Transmission: with wireless applications", Springer Science & Business Media, Chapter 10 and Chapter 11 (only two chapters!!!).


Other references

  • T. M. Cover, J. A. Thomas, "Elements of Information Theory", Wiley.
  • V. Guruswami, A. Rudra and M. Sudan, “Essential Coding Theory”, available on-line at http://www.cse.buffalo.edu/faculty/atri/courses/coding-theory/book/index.html

Prerequisites

The basis of  Probability Theory and Elements of Statistics are assumed to be known by the students.

The knowledge of a general purpose programming language is necessary in order to implement  the simulators required as part of the homework exercises and of the final project.

 

 

Objectives

Simulation is one of the most common techniques used for the evaluation of the performance and if the reliability of Discrete Event Dynamic Systems (DEDS) often modelled with Stochastic Processes. Discrete Event Simulation consists on the execution of a program which results in the production of a realization of a stochastic process driven by Monte-Carlo methods. Learning how to construct a simulator is the main objective of this course, together with the development of the techniques needed for the statistical analysis of the simulation output. To deeply understand the difficulty of writing an efficient simulator equipped with the output analysis components, students will be required to write a few simple simulators “from scratch” without using available tools and libraries.

Part I  (6 CFUs) Simulation and Operational Analysis – The first part of the course is devoted to the presentation and discussion of Discrete Event Simulation. Special attention is devoted to the measures that must be implemented to quantify the simulation results and to operational relations that may be derived among these measures useful to gain confidence on the correctness of the reliability of simulation outputs.

Part II (3 CFUs) Stochastic Processes and Markov Chains – The second part of the course introduces probability based analysis techniques that may be used to further improve the confidence on simulation results

 

Results of learning outcomes

At the end of the course the students will be able to perform the simulation of non-trivial Discrete Event Sysrtems. The exercises and the final project will provide the students with the capability of writing the simulators using a general purpose programming language of their choice. Having developed the simulators “fromn scratch” will allow the students to understand the potentials and the limits of the Discrete Event Simultauion technique, thus providing them with the capability of using professional simulators with competence

 

Course delivery

The course will be based on theretical lessons as well as on the solution of class exercises. Computer implementations will be required as homework assignments. Personal training on assigned exercises is important for the success in this class.

 

Learning assessment methods

The final examination will consist in the discussion of a project developed individually by te students used as the basis for asking questions on the theoretical aspects of the exercise. Students will not be required to be able to reproduce the derivations used  to obtain the results discussed during the course, but will have to know the definitions and the applications of the theory.

The final grade will be out of thirty.

 

Support activities

Exercises will be assigned as homework.  The course will include sessions devoted to the discussion of the solutions of selected homework, as well as to the solution of additional exercises.