The Course has the objective of introducing the basic concepts of signals and presenting the fundamental principles and methods for signal analysis and signal processing using discrete time systems

01 Signals and signal processing:
 01.01 Characterization and classification of signals.
 01.02 Digital signal processing: pros and cons.

02 Discrete-time signals in the time domain.
 02.01 Time domain representation.
 02.02 Operations on sequences. 
 02.03 Classification of sequences.
 02.04 Basic sequences.

03 Discrete-time signals in the frequency domain:
 03.01 The Fourier series.
 03.02 The Continuous-Time Fourier transform (CTFT).
 03.03 The Discrete-Time Fourier Transform (DTFT).
 03.04 Properties of DTFT.
 03.05 The sampling theorem.

04 Discrete-time systems:
 04.01 Examples of simple systems.
 04.02 Classification of discrete-time systems. 
 04.03 Impulse response and convolution sum.
 04.04 Frequency response.

05 The Z-Transform:
 05.01 Definition of the Z-Transform. 
 05.02 The inverse Z-Transform. 
 05.03 Properties of the Z-Transform. 
 05.04 The Transfer Function. 

06 The Discrete Fourier Transform:
 06.01 Definition of the Discrete Fourier Transform (DFT).
 06.02 The relation between DFT, DTFT and Z Transform.
 06.03 Properties of the DFT.
 06.04 The Fast Fourier Transform (FFT).
 06.05 Linear convolution and circular convolution. 
 06.06 Frequency analysis with DTFT and DFT.
 06.07 The Discrete Cosine Transform (DCT).

07 Discrete-time LTI systems in the frequency domain:
 07.01 Ideal filters. 
 07.02 Phase delay and Group delay. 
 07.03 Zero-phase filters.
 07.04 Linear phase FIR filters.
 07.05 Geometric interpretation of frequency response computation.
 07.06 Simple digital filters.
 07.07 Comb filters.
 07.08 All-pass filters.
 07.09 Minimum-phase and maximum-phase transfer functions.
 07.10 Inverse system.
 07.11 Deconvolution. 
 07.12 Magnitude equalizer and phase equalizer. 
 07.13 Stability test for IIR filters (stability triangle, Schur-Cohn stability test).

08 Laboratory: 
 08.01 Introduction to Python. 
 08.02 DSP algorithms using Python

Although there are no mandatory prerequisites for this exam, students are strongly recommended to take it after Calculus, Discrete Structures and Linear Algebra, Probability and Statistics.

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 he will acquire the ability to understand the principles and methods at the basis of any digital signal processing 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, he will be able to analyze signals, to process them using digital filters, to design different kinds of DSP filters, to understand and apply the main signal compression methods. 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 digital signal processing for understanding and solving novel problems involving signal analysis, signal processing, or signal compression. The critical discussions in class and the exercitations will be used to stimulate and develop the making judgements ability of the student.

Communication skills:

The student will acquire the ability to communicate the fundamental concepts of digital signal processing with an appropriate and rigorous terminology. He will learn to describe the problems related to signal analysis, signal processing, and signal compression and the methodologies adopted for their solution.

Learning skills:

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

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 determines a spread of the previous mark, thus yielding the final mark. The evalution of the oral exam consider 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.