Academic Year 2021/2022
- Docente: Riccardo Rovatti
- Credits: 6
- SSD: ING-INF/01
- Language: Italian
- Moduli: Riccardo Rovatti (Modulo 1) Mauro Mangia (Modulo 2)
- Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
- Campus: Bologna
-
Corso:
Second cycle degree programme (LM) in
Electronic Engineering (cod. 0934)
Also valid for Second cycle degree programme (LM) in Advanced Automotive Electronic Engineering (cod. 9238)
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
Mathematical tools:
- euclidean vector spaces of functions and random variables
- operators
- Optimization problme, Lagrange's multipliers
- pseudo-differentiation of functioncs of complex variables
Basics of probability and statistics
- random variable and their characterization (PDF, CDF, expectation, moments, characteristic function)
- covariance and linear prediction, orthogonality principle
- independence and unpredictability
- stochastic processes and their charcterization (joint probabilities, correlation/covariance functions, projections)
Processing of stochastic quantities
- linear algebraic processing (importance of pre-images, pseudo-inversion)
- linear dynamics processing (universal characterization of linear filters)
- scalar quantization of random variables (conditions ofr uniformity and incorrelation of quantization error)
Gaussian vectors and processes
- definitions and properties
- White Gaussian Noise
Power spectrum
- definition for continuous-time and discrete-time processes
- Wiener-Kinchine theorem
- estimation in general and its application to power spectrum
- periodogoram and modified periodogram
- minimum-variance estimation
- regular and predictable processes
- Wold theorem
- maximum-entropy estimator
Office hours
See the website of Riccardo Rovatti
See the website of Mauro Mangia