84457 - Signal Acquisition and Processing M

Course Unit Page

Academic Year 2019/2020

Learning outcomes

The course aims to give students the appropriate techniques for the acquisition and processing of real world data and the implementation of efficient and robust signal processing structures. Knowledge about the modern theory and practice of sampling from an engineering perspective, and classical and modern signal processing tools will be acquired.

Course contents

Random varibles and vectors

Expectation

Moments and generating function

Covariance

Stochastic processes

Joint probability

Correlations and covariances

Projections

Static linear processing of random vectors

Quantization of random variables

Linear filtering of stochastic processes

z-transform

Structure and model for discrete-time linear filters

Stability

Design methods for discrete-time linear filters

Gaussian random vectors

Gaussian stochastic processes, white noise

Power spectrum

Wiener-Khinchin theorem

Elements of estimation theory

Periodogram spectral estimation

Modified-periodogram spectral estimation

Estimation of correlation

Minimum-variance spectral estimation

Linear prediction

Orthogonality principle

Yule-Walker equations

Finite-memory processes

Finite Markov chains

Office hours

See the website of Riccardo Rovatti

See the website of Mauro Mangia