
Course unit
DIGITAL SIGNAL PROCESSING
INP9086622, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2019/20
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Core courses 
INGINF/03 
Telecommunications 
6.0 
Course unit organization
Period 
First semester 
Year 
1st Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Lecture 
6.0 
48 
102.0 
No turn 
Examination board
Board 
From 
To 
Members of the board 
1 A.A. 2019/2020 
01/10/2019 
15/03/2021 
ERSEGHE
TOMASO
(Presidente)
BADIA
LEONARDO
(Membro Effettivo)
BENVENUTO
NEVIO
(Supplente)
CISOTTO
GIULIA
(Supplente)
CORVAJA
ROBERTO
(Supplente)
LAURENTI
NICOLA
(Supplente)
MILANI
SIMONE
(Supplente)
ROSSI
MICHELE
(Supplente)
TOMASIN
STEFANO
(Supplente)
VANGELISTA
LORENZO
(Supplente)
ZANELLA
ANDREA
(Supplente)
ZANUTTIGH
PIETRO
(Supplente)
ZORZI
MICHELE
(Supplente)

Common characteristics of the Integrated Course unit
Prerequisites:

This course has the following prerequisites: fundamentals of signals and systems, knowledge on Fourier analysis, and basics of Computer Programming in any language which is appropriate for signal analysis (e.g., MatLab, Python, C, Java). Moreover: 1. for the DIGITAL SIGNAL PROCESSING module: Laplace and/or Z transforms; 2. for the EHEALTH module: fundamentals of telecommunications and of network protocols; any further knowledge or previous experience on telemedicine and biological signals acquisition or processing is also useful. 
Target skills and knowledge:

The educational goal of the course is to provide the following knowledge and skills:
DIGITAL SIGNAL PROCESSING module:
1. To rehears and consolidate the basic concepts of digital signal processing that the student should already know from previous studies
2. To be aware of the many practical examples of application of digital signal processing systems which are essential or useful in several areas of ICT and multimedia
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 of digital signals
4. To be able to develop computer simulation algorithms for the implementation of digital signal processing systems, and to asses if the given design specifications are met
EHEALTH module:
1. To apply fundamentals of signal processing to biosignals and bioimages
2. To know telemedicine/wireless body area network (WBAN) components and architectures, including the most common solutions
3. To know existing standards and regulations for telemedicine/WBAN systems
4. To evaluate the performance of existing telemedicine/WBAN systems, based on their context differences and effectiveness
5. To know the main scenarios of application, possibly in crossdisciplinary contexts, of the techniques studied
6. To be aware of future perspectives, directions and challenges of this field 
Examination methods:

The course has the following methods of examination:
DIGITAL SIGNAL PROCESSING module:
The grading of the expected knowledge and skills is based on two contributions: 1. a closed book WRITTEN EXAM, where the student must solve four 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 HOMEWORK 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 homework and the obtained results.
EHEALTH module:
For ATTENDING STUDENTS the verification of the expected knowledge and skills is carried out with: 1. an ORAL EXAM (individual assessment) with few questions to test the knowledge of the whole course program; 2. a simple PROJECT (56 pages) on a selected topic to be agreed with the teacher; the project is presented in about 15 minutes (using slides) during the oral exam; 3. a twopages report at the end of each LAB EXPERIENCE. Projects and lab experiences are carried out either individually or in pairs at the discretion of the teacher.
For NONATTENDING STUDENTS the verification of the expected knowledge and skills is carried out with: 1. an ORAL EXAM (individual assessment) with few questions to test the knowledge of the whole course program; 2. a more complex PROJECT on a selected topic to be agreed with the teacher; the project requires both a theoretical part and a Matlabbased part in order to test the abilities developed by the attending students during laboratories; the project is presented in about 15 minutes (using slides) during the oral exam.
The final grade of the course is expressed as a combination of the judgments in the two modules (50%+50%). 
Assessment criteria:

The evaluation criteria with which the verification of knowledge and expected skills will be carried out will be:
DIGITAL SIGNAL PROCESSING module:
1. Completeness of the acquired knowledge about digital signal processing basics
2. Property in the technical terminology used
3. Ability in the design and analysis of advanced digital signal processing systems, to be used in the diverse areas of ICT and multimedia
4. Skill in applying the acquired theory to identify the appropriate tools for designing and testing (via computer simulations) a digital signal processing system
EHEALTH module:
1. The completeness of the acquired knowledge
2. The skill to analyze a telemedicine/WBAN system based on such knowledge
3. The correct use of the technical terminology, both in the project and in the oral assessment
4. The skill to autonomously derive conclusions from numerical results obtained from simulations or measurement campaigns
5. The skill to use ICT tools in the evaluation of telemedicine/WBAN systems and their main parameters
6. The skill to synthesize key points of different topics during the oral assessment and the quality of the oral presentation
7. The skill to effectively work with the partner during lab experiences and project development 
Specific characteristics of the Module
Course unit contents:

The module will cover the following topics:
1. Shiftinvariant discrete time linear systems; Systems defined by linear constant coefficient difference equations; Ztransform and its properties.
2. Discrete Fourier Transform (DFT): definition, properties and usage in practical contexts; FFT algorithms; fast convolution algorithms.
3. Design of linear phase FIR filters: windowed Fourier series technique; frequency sampling method; minimization of the Chebyschev norm (Remez algorithm).
4. IIR filter design using the bilinear transformation method; Butterworth, Chebyschev and Cauer filters; frequency transformations.
5. Multirate linear systems: interpolation and decimation; Efficient realizations; Examples of application. 
Planned learning activities and teaching methods:

The module includes:
 20 lectures which will give an overview of the main subjects and methodologies, a discussion on the main scenarios of application, and a deeper mathematical analysis on the subjects introduced;
 4 laboratory lectures to guide students to the use of computer programs for digital signal processing (MatLab).
The frontal teaching activities involve the use of tablet computers (transparencies + digital ink). 
Additional notes about suggested reading:

All the topics of the course will be taught in the classroom. All the teaching material presented during the lectures is made available on the platform "http://elearning.dei.unipd.it". Class notes can be integrated with the reference textbook and with additional material made available on the elearning platform. 
Textbooks (and optional supplementary readings) 

Oppenheim, Alan V.; Schafer, Ronald W., Discretetime signal processingAlan V. Oppenheim, Ronald W. Schafer. Harlow: Essex, Pearson, 2014.

Innovative teaching methods: Teaching and learning strategies
 Lecturing
 Laboratory
 Problem based learning
 Case study
 Problem solving
 Loading of files and pages (web pages, Moodle, ...)
Innovative teaching methods: Software or applications used
 Moodle (files, quizzes, workshops, ...)
 One Note (digital ink)
 Latex
 Matlab
Sustainable Development Goals (SDGs)

