84457 - Signal Acquisition and Processing M

Course Unit Page

  • Teacher Riccardo Rovatti

  • Credits 6

  • SSD ING-INF/01

  • Teaching Mode Traditional lectures

  • Language English

Academic Year 2018/2019

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

Refreshing basic concepts about random variables , vectors, and processes. Gaussian statistics.

Refreshing transforms: Fourier and zeta.

Brief notes on optimization and optimality conditional.

Linear predictability and predictors.

Regular and predictable processes. World's theorem.

Second-order statistical features and their link to energy and power. Power spectrum. Wiener-Khinchine theorem.

The concept of statistical estimation and its application to spectral estimation: (modified) peridodogram, minimum-variance estimation, and maximum-entropy estimation.

Characterization and synthesis of discrete-time filters: FIR and IIR cases.

Wiener filters.

Analog-to-digital conversion. Statistical characterization quantization and quantization error. Optimal quantization.

Sigma-delta modulators. Statistical linearization. Noise shaping. Basic design criteria.

Office hours

See the website of Riccardo Rovatti