The objective of this course is to provide a basic introduction to the theory of digital signal processing (DSP)

01 Introduction to digital signal processing

 01.01 Characterization and classification of signals

 01.02 Digital signal processing: pros and cons

02 Discrete-time signals

 02.01 Time domain representation

 02.02 Operations on sequences

 02.03 Classification of sequences 

 02.04 Energy and average power

 02.05 Basic sequences

 02.06 Complex sequences and sinusoids

03 Discrete-time systems

 03.01 Properties of discrete-time systems

 03.02 Example of basic systems

 03.03 LTI systems

 03.04 Impulse response and discrete convolution

 03.05 Difference equations

04 Continuous-time signals in the frequency domain

 04.01 The Fourier series

 04.02 The Continuous-Time Fourier transform (CTFT)

 04.03 Ideal sampling

 04.04 The sampling theorem

 04.05 Aliasing

 04.06 Signal reconstruction

05 Discrete-time signals in the frequency domain

 05.01 The Discrete-Time Fourier Transform (DTFT)

 05.02 Properties of DTFT

 05.03 DTFT examples

 05.04 The convolution theorem for DTFT

 05.05 Parseval's relation

 05.06 Frequency response

 05.07 Phase and group delay

06 The Discrete Fourier Transform

 06.01 The Discrete Fourier Transform (DFT)

 06.02 The relation between DFT and DTFT

 06.03 DTFT sampling with the DFT

 06.04 Properties of the DFT

 06.05 The Fast Fourier Transform (FFT)

 06.06 Linear convolution and circular convolution

 06.07 Frequency analysis with DFT

07 The Z-Transform

 07.01 The Z-Transform

 07.02 The relation between DTFT and Z-transform

 07.03 Regions of convergence

 07.04 Pole-zero diagrams

 07.05 Properties of the Z-Transform

 07.06 The inverse Z-Transform

 07.07 The Transfer Function, stability and causality

08 Discrete-time LTI systems in the frequency domain

 08.01 Ideal filters

 08.02 Practical filter specifications

 08.03 FIR filters

 08.04 Example of simple FIR filters

 08.05 Linear phase FIR filters

 08.06 Zero-phase filters

 08.07 IIR filters

 08.08 IIR filter design with pole-zero placement method

 08.09 Comb filters

 08.10 All-pass filters

 08.11 Minimum/maximum phase systems

 08.12 Inverse system

 08.13 The window method for FIR filter design

09 Laboratory

 09.01 Introduction to Python language

 09.02 DSP algorithms using Python

Although there are no mandatory prerequisites for this exam, students are strongly encouraged to take it after the exams of: Calculus, Discrete Structures and Linear Algebra.

Knowledge and understanding:

At the end of the course the student will learn the fundaments of signal analysis, will know how signals can be processed using digital filters, will know the main digital filter structures and how these can be designed and she/he will acquire the ability to understand the principles and methods at the basis of any DSP system.

Applying knowledge and understanding:

The student will learn the methodologies of digital signal processing (DSP) and will be able to apply them for processing audio signals. In particular, she/he will be able to analyze signals, to process them using digital filters and to design different kinds of DSP filters.The ability to apply these techniques will be developed and sharpened in the laboratory exercitations, where audio signals will be analyzed.

Making judgements:

The student will be able to apply the methodologies of DSP for understanding and solving novel problems involving signal processing.

Communication skills:

The student will acquire the ability to communicate the fundamental concepts of DSP with an appropriate and rigorous terminology. She/he will learn to describe the problems related to signal processing and the methodologies adopted for their solution.

Learning skills:

The student will acquire the ability to study and learn novel techniques for signal analysis and signal processing, and she/he will be able to develop autonomously solutions for novel problems related to DSP.

The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it

Teaching

Frontal lessons and laboratory activities

Attendance

Although recommended, attendance of this course is not mandatory.

Course books

S. Mitra, "Digital signal processing", McGraw-Hill, 2001, 2011.
A. V. Hoppenheim e R. W. Schafer, "Discrete-time signal processing", Prentice Hall, 2010.

Assessment

Written exam and oral exam.

The written exam is evaluated in thirtieths and it is passed if the mark, which holds for the whole academic year, is at least 15/30. The oral exam, which is composed by open answer questions, can be performed only after passing the written exam and predominantly contributes to the final mark. The evalution of the oral exam considers the acquired knowledge, the comprehension of the subject and the ability to properly present the topic.

Disabilità e DSA

Le studentesse e gli studenti che hanno registrato la certificazione di disabilità o la certificazione di DSA presso l'Ufficio Inclusione e diritto allo studio, possono chiedere di utilizzare le mappe concettuali (per parole chiave) durante la prova di esame.

A tal fine, è necessario inviare le mappe, due settimane prima dell’appello di esame, alla o al docente del corso, che ne verificherà la coerenza con le indicazioni delle linee guida di ateneo e potrà chiederne la modifica.