DIGITAL SIGNAL PROCESSING
Stampa
Enrollment year
2018/2019
Academic year
2019/2020
Regulations
DM270
Academic discipline
ING-INF/03 (TELECOMMUNICATIONS)
Department
DEPARTMENT OF ELECTRICAL,COMPUTER AND BIOMEDICAL ENGINEERING
Course
ELECTRONIC ENGINEERING
Curriculum
Space Communication and Sensing
Year of study
Period
1st semester (30/09/2019 - 20/01/2020)
ECTS
6
Lesson hours
50 lesson hours
Language
English
Activity type
WRITTEN AND ORAL TEST
Teacher
SAVAZZI PIETRO (titolare) - 6 ECTS
Prerequisites
Basic concepts in analog signal processing, spectral analysis and filtering.
Learning outcomes

Developing a strong working knowledge on signal processing algorithms for modeling discrete-time signals, designing optimum digital filters, estimating the power spectrum of a random signal, and designing and implementing adaptive filters.
Ability to implement the studied algorithms in Matlab standalone and hardware-oriented applications.
Course contents

Introduction to digital signal theory.

Discrete time signals, sampling theorem, linear shift invariant digital systems.

Analysis of digital systems in the Fourier and Z transform domains.

Discrete-time random processes.

Digital filtering of deterministic and stochastic signals.

Deterministic and stochastic signal modeling.

Wiener Filter: linear prediction, white noise filtering, unwanted signal canceling.

Adaptive filtering: LMS, RLS and Kalman algorithms.
Spectrum estimation.

Application examples in Matlab and programmable hardware platforms.
Teaching methods
The course is based on lectures, practical exercises, case studies, and project examples, aimed at describing applications of statistical digital signal processing to practical utility projects.
Lectures (hours/year in lecture theatre): 44
Practicals / Workshops (hours/year in lecture theatre): 8
Reccomended or required readings

Monson H. Hayes Statistical Digital Signal Processing and Modeling. John Wiley & Sons Inc, 1996.
Assessment methods

The exam consists of an oral test during which questions will be asked on two/three different topics regarding the main course objectives, i.e., signal modeling, adaptive filtering, and spectrum estimation, in order to cover most of the course topics.

Alternatively, each student can choose to implement a laboratory project, assigned by the teacher, followed by an in-depth interview. The assigned projects will cover most of the course topics.

The final mark is in thirtieths.
Further information
Sustainable development goals - Agenda 2030