Dettagli sull'Insegnamento per l'A.A. 2018/2019

Prerequisiti

The student must know the notions of mathematical analysis, algebra and probability contained in the programs of Mathematical Analysis I and II, Geometry and Theory of Probability

Obiettivi

The goal of this module is to provide the fundamental set of concepts and techniques that relate to the analysis of signals in the broad area of information engineering. On successful completion of this module, the student should understand i) the fundamental concepts of representation of signals in both the time and the frequency domain and their relation, ii) how signals get processed by both linear and non-linear systems, iii) the characterization of random signals in both time and frequency domain. The student is finally introduced to digital representation and processing of signals, and becomes confident with practical operations through specific computer-assisted tools. The module is tightly connected with the modules in System Theory, Electronics, Object programming, that are thaugth in the same semester, and to the module of Communication Systems that is thaught in the 3rd year. 

Sillabo

• Definition and classification of signals: continuous and analogic signals, discrete time signals, digital signals, energy and power of signals, the space of signals. 
• Representation of signals in the frequency domain: The Fourier series and the Series Fourier Transform for periodic signals; the Fourier integral and the Continuous Fourier Transform; convergence, computation and transformations from time to frequency and vice-versa; properties of Fourier transforms, Parseval's equality, and their applications to relevant examples; main parameters in time and frequency domains.
• Systems in time and frequency domains: mail properties of systems; LTI systems, impulse response and frequency response, filters and their applications; characterization of non-linear systems and examples related to amplifiers, clippers and clampers.
• Correlation and detection: auto- and cross-correlation of signals, energy and power spectra and the Wiener Theorem; applications to the matched filter and communication chains.
• Random signals: remind of probability theory and its application to information and communication theory: the binary channel and relevant models for random variates (uniform, Poisson, Gaussian, one-sided exponential, geometrical); definition and characterization of stochastic processes, auto- and cross-correlation, Wide Sense Stationary (WSS) processes and their power spectral density, filtering of WSS processes; the thermal (white) noise and narrowband noise.
• Sampling of signals and discrete-time processing: from continuous signals to discrete time signals and vice-versa, the Nyquist frequency and the Sampling Theorem; sampling and interpolation under realistic constraints, aliasing and anti-aliasing filtering, sample-and-hold devices; A/D conversion and quantization noise; discrete-time signals and sequences, Discrete Fourier Series and Transforms and related properties, Fast Fourier Trasform (FFT); linear and circular convolution, applications of FFT in circular convolution, spectral analysis, sampling frequency conversion (up-sampling and decimation); applications of digital signal processing in the audio domain.

Descrittori di Dublino

Alla fine del corso, lo studente dovrebbe

Testi di riferimento

• A. Papoulis, Probability, Random Variables and Stochastic Processes , McGraw-Hill.    
• A. W. Oppenheim, A. S. Willsky (with S. H. Nawab), Signals and Systems , Prentice Hall.    
• M. Luise, G. M. Vitetta, Teoria dei Segnali , McGraw-Hill.    

Modalità d'esame

Written and oral exam. Preparation of a small project work is required for the section on digital signal processing.

Note

• The course is taught in Italian

Risorse Internet

Aggiornamenti alla pagina del corso

• A.A. 2012/2013
• A.A. 2013/2014
• A.A. 2014/2015
• A.A. 2015/2016
• A.A. 2016/2017
• A.A. 2017/2018
• A.A. 2018/2019
• A.A. 2019/2020