Biomedical and Industrial Signal Processing
A.Y. 2018/2019
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.
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.
Expected learning outcomes
Undefined
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Linea Milano - disponibile in streaming da Crema
Responsible
Lesson period
Second semester
Course 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
· 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
ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING - University credits: 6
Lessons: 48 hours
Professor:
Sassi Roberto
Professor(s)
Reception:
By appointment (email or phone)
Dipartimento di Informatica, via Celoria 18, stanza 6004 (6 piano, ala Ovest), Milano or remotely via Microsoft Teams