
Course unit
DIGITAL SIGNAL PROCESSING
INP7079338, A.A. 2018/19
Information concerning the students who enrolled in A.Y. 2018/19
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Core courses 
INGINF/03 
Telecommunications 
9.0 
Course unit organization
Period 
First semester 
Year 
1st Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Lecture 
9.0 
72 
153.0 
No turn 
Start of activities 
01/10/2018 
End of activities 
18/01/2019 
Examination board
Board 
From 
To 
Members of the board 
2 A.A. 2018/2019 
01/10/2018 
15/03/2020 
CALVAGNO
GIANCARLO
(Presidente)
ZANUTTIGH
PIETRO
(Membro Effettivo)
BADIA
LEONARDO
(Supplente)
CORVAJA
ROBERTO
(Supplente)
ERSEGHE
TOMASO
(Supplente)
LAURENTI
NICOLA
(Supplente)
MILANI
SIMONE
(Supplente)
ROSSI
MICHELE
(Supplente)
TOMASIN
STEFANO
(Supplente)
VANGELISTA
LORENZO
(Supplente)
ZANELLA
ANDREA
(Supplente)
ZORZI
MICHELE
(Supplente)

Prerequisites:

Previous knowledge of the following topics is expected: Calculus, Linear Algebra, Probability, Random Variables and Stochastic Processes, Signals and Systems, basic elements of the Matlab programming language. 
Target skills and knowledge:

The educational goal of the course is to provide the following knowledge and skills:
1. To rehears and consolidate the basic concepts of digital signal processing that the student should already know from previous studies (e.g., Signals and Systems).
2. To learn the possible practical applications of the previous concepts in communications and multimedia systems.
3. To learn advanced notions of digital signal processing, among which are:
3.a. To know how to design both FIR and IIR digital filters;
3.b. To be able to design interpolation/decimation systems for digital signals;
3.c. To know how to perform the spectral analysis od digital signals.
4. To be aware of the many practical examples of application of digital signal processing systems which are essential or useful in several areas of the Information and Communications Technology.
5. To be able to develop computer simulation algorithms for the implementation of digital signal processing, and to asses if the given design specifications are met. 
Examination methods:

The grading of the expected knowledge and skills is based on two contributions:
1. A closed book written exam, where the student must solve 4 problems, needed to verify that a good knowledge of the theoretical aspects and of the fundamental characteristics of the various digital signal processing systems analyzed during the course has been acquired.
2. The development of a simple home assignment consisting in a computer simulation project using Matlab, to check the ability of the student to apply the theoretical concepts to a practical implementation. Each student must write a short report describing the methodologies used to solve the assigned project and the obtained results.
The written exam contributes 85% to the final score, while the project contributes 15% to the final score. 
Assessment criteria:

The evaluation criteria with which the verification of the knowledge and expected skills is carried out considers the following aspects:
1. The completeness and the degree of detail of the acquired knowledge of digital signal processing basics.
2. The ability to analyze advanced digital signal processing systems.
3. The ability to design digital signal processing systems.
4. The skill to apply the acquired theory to identify the appropriate tools for the design and the computer simulation of digital signal processing systems to be used in the various areas of the Information and Communications Technology. 
Course unit contents:

Shiftinvariant discrete time linear systems: convolution; stability; causality; systems defined by linear constant coefficient difference equations; FIR and IIR linear timeinvariant filters.
Ztransform and its properties; transfer function and frequency response. Allpass transfer functions. Simple examples of lowpass/highpass, bandpass/bandstop, and allpass transfer functions.
Linear phase FIR filters.
Discrete Fourier Transform (DFT): definition, properties and usage in practical contexts; FFT algorithms; fast convolution algorithms.
Design of linear phase FIR filters: windowed Fourier series technique; frequency sampling method; minimization of the Chebyschev norm (Remez algorithm).
IIR filter design using the bilinear transformation method; Butterworth, Chebyschev and Cauer filters; frequency transformations.
Direct form, cascade, and parallel realizations.
Multirate linear systems: interpolation and decimation; efficient realizations.
Examples of application. 
Planned learning activities and teaching methods:

Teaching is provided by means of lectures at the chalkboard, since we believe that this way of teaching allows to keep the right rate (speed) in the presentation of the different topics and to maintain the student attention high, with the possibility of interaction and participation.
The simulation results of some simple digital signal processing systems and the results relative to the design of several digital filters by means of Computer Aided Design tools are shown using a computer and visualized on large screen. 
Additional notes about suggested reading:

All the topics of the course will be taught in classroom. Class notes can be integrated with the reference textbook and with additional material made available on the moodle platform.
Sets of suggested problems (homework) are made available on the moodle platform and their solutions are presented later. 
Textbooks (and optional supplementary readings) 

S. Mitra, Digital Signal Processing: a Computerbased Approach. New York: McGrawHill, 2011.

A.V. Oppenheim, R.W. Schafer, DiscreteTime Signal Processing. Harlow, Hessex: Pearson, 2014.

Innovative teaching methods: Teaching and learning strategies
 Lecturing
 Loading of files and pages (web pages, Moodle, ...)
 Suggested problems with postponed solutions.
Innovative teaching methods: Software or applications used
 Moodle (files, quizzes, workshops, ...)
 Matlab
Sustainable Development Goals (SDGs)

