Biomedical and industrial signal processing

A.Y. 2018/2019
Lesson for
6
Max ECTS
48
Overall hours
SSD
ING-INF/06
Language
English
Learning objectives
The course is a one semester advanced class in signal processing with specific application to biomedical signals, taught in English. The course will start with a review of digital signal processing fundamentals, to then move to the study of biomedical signals and techniques meant for their analysis. At the end of the course, the students will be able to acquire and process biomedical digital signals while extracting information ready to be use in applications.

Biomedical and Industrial Signal Processing è un corso avanzato di elaborazione numerica dei segnali, con particolare enfasi ai segnali di origine biologica. Il corso è tenuto in inglese e inizia con una review dei concetti fondamentali di digital signal processing (per permettere anche a chi non ha mai frequentato un corso base di DSP di seguire le lezioni), per poi continuare con lo studio dei principali segnali di origine biologica e delle tecniche specifiche per la loro analisi. Alla fine del corso, gli studenti saranno in grado di acquisire ed elaborare segnali di origine biologica, al fine di estrarre caratteristiche da utilizzare in applicazioni pratiche.

Course structure and Syllabus

Linea Milano - disponibile in streaming da Crema
Active edition
Yes
Responsible
ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING - University credits: 6
Lessons: 48 hours
Professor: Sassi Roberto
Syllabus
Introduction & review
· Properties of biological signals
· Statistical characterization of signals
· Stochastic processes
· Linear Time-invariant(LTI) systems, frequency response and transfer function.
· Finite and infinite impulse response filters(FIR & IIR). Linear-phase FIR filter design by windows method. IIR filter design by poles and zeros placement. Classical IIR filters.
· Autoregressive (stochastic) processes as models of signals. AR models order selection.
· Estimation theory basics: accuracy, trueness, precision.

Spectral analysis
· Non parametric and Parametric spectral estimators
· Spectral analysis of non-evenly sampled series

Source separation
· Enhancement of repetitive patterns through averaging
· Mean and exponential average
· Cross-correlation& matched filters

Long time correlations and fractals signals
· Long memory processes
· Estimation of scaling in time series

Entropies and regularity
· Entropy as a measure of information rate
· Entropy as a measure of regularity
· Entropy practical estimators
Lesson period
Second semester
Lesson period
Second semester
Assessment methods
Esame
Assessment result
voto verbalizzato in trentesimi
Professor(s)
Reception:
By appointment (email or phone)
Dipartimento di Informatica, via Celoria 18, stanza 6004 (6 piano, ala Ovest), Milano or Polo didattico di Crema (CR)