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Risorse bibliografiche
Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2018/2019
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 052471 - ADVANCED DIGITAL SIGNAL PROCESSING
Docente Spagnolini Umberto
Cfu 10.00 Tipo insegnamento Monodisciplinare
Didattica innovativa L'insegnamento prevede  1.0  CFU erogati con Didattica Innovativa come segue:
  • Blended Learning & Flipped Classroom

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing Ind - Inf (Mag.)(ord. 270) - MI (474) TELECOMMUNICATION ENGINEERING - INGEGNERIA DELLE TELECOMUNICAZIONI*AZZZZ052471 - ADVANCED DIGITAL SIGNAL PROCESSING
Ing Ind - Inf (Mag.)(ord. 270) - MI (476) ELECTRONICS ENGINEERING - INGEGNERIA ELETTRONICA*AZZZZ093267 - DIGITAL SIGNAL PROCESSING
Ing Ind - Inf (Mag.)(ord. 270) - MI (487) MATHEMATICAL ENGINEERING - INGEGNERIA MATEMATICA*AZZZZ093267 - DIGITAL SIGNAL PROCESSING

Obiettivi dell'insegnamento

The course deals with fundamentals of the estimation theory, statistical signal processing and time-data analytics using an application-driven approach with several interdisciplinary engineering examples from audio/video and digital communications, vibration analysis, imaging and remote sensing, GPS and navigation systems.

Goal is to gain practice on the following key-topics: algebra for signal processing and estimation theory, fundamentals of the estimation theory (BLUE, MLE, CRB, MMSE, MAP), parameter tracking and Kalman filtering, and adaptive LMS/RLS filtering. Spectral analysis (AR/MA/ARMA) and high-resolution methods for line spectra and array processing. Detection theory, pattern and feature detection/classification, and supervised/unsupervised classification methods.

Exercises are on theoretical aspects and practical cases with the use of Matlab software and Montecarlo simulation. During the semester there are 3 interactive exercises on practical cases by students organized in working groups.

 


Risultati di apprendimento attesi

Students are expected to achieve the following outcomes:

Knowledge and understanding:  students will learn how to manipulate signals using algebraic tools; apply the estimation theory and bounds to signals related problems; manipulate multidimensional signals for estimation and decision.   

Applying knowledge and undestanding: students will be able to define a statistical model for signals' related problems and their solution with fundamental estimation theory methods; solve a broad range of statistical signals' related engineering problems.

Making judgements: students will be able to evaluate the fundamental limits on statistical signal processing applications even in presence of uncomplete/inaccurate conditions; tailor and adapt the statistical methods to complex problems. 

Communication: student will learn how to describe a method and sinthetize the limits and results; write a technical report.

 


Argomenti trattati

The course focuses on statistical signal processing and covers the following topics:

  • Review of basics: matrix and linear algebra; quadratic and constrained optimization problems.
  • Introduction to the estimation problem and models: definitions, performance, sufficient statistics, linear and non-linear models.
  • Estimators: minimum variance unbiased estimation (MVUE), best linear unbiased estimation (BLUE), maximum likelihood estimation (MLE), least squares method. Cramer Rao lower bound.
  • Bayesian estimators: a-posteriori estimation (MAP, MMSE and LMMSE); Wiener filter; linear prediction and Yule-Walker equations.
  • Adaptive filters: LMS, RLS methods, convergence analysis and step-size selection.
  • Spectral analysis: sample autocorrelation and power spectrum; non-parametric method (periodogram); parametric methods (MA, AR, ARMA models, and line spectra).
  • Bayesian tracking: dynamic model and Kalman filter; examples of positioning.
  • Array processing and direction of arrivals (DOA), beamforming methods and multichannel systems.
  • Pattern and sequence recognition: supervised and unsupervised classification, classification of signals in noise, linear discriminant and support vectors, clustering methods.
  • Montecarlo simulation and numerical analysis.

Prerequisiti

It is preferable (but not necessary) to have basic knowledge of matrix computations and stochastic processes


Modalità di valutazione

Written and (optional) oral exam. Written exam are multiple exercises that saturates to 24 points. Saturation is to 27 points if student requests the oral discussion; oral exam is possible only if written is passed >24 points.

During the semester there will be assigned up to 3 homeworks (Hws) involving some Matlab-based computer simulations to be delivered for exam without time-constraints. Each Hw is delivered in form of short report describing methods and main results, this should be discussed with the Matlab-codes; solution of at least 1 Hw is mandatory. Total Hws cumulates up to 6points, but 30 cum laude is only with oral exam.


Bibliografia
Risorsa bibliografica obbligatoriaU.Spagnolini, Statistical Signal Processing in Engineering, Editore: Wiley Ed. (ISBN: 978-1-119-29397-2), Anno edizione: 2017
Note:

Notes/slides on the book can be downloaded from the folder of the course shared with students during the semester

Risorsa bibliografica facoltativaP. Stoica and R. L. Moses, Introduction to spectral analysis, Editore: Prentice Hall, Anno edizione: 1997

Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
(hh:mm)
Ore di studio autonome
(hh:mm)
Lezione
60:00
90:00
Esercitazione
40:00
60:00
Laboratorio Informatico
0:00
0:00
Laboratorio Sperimentale
0:00
0:00
Laboratorio Di Progetto
0:00
0:00
Totale 100:00 150:00

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.6.4 / 1.6.4
Area Servizi ICT
10/07/2020