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Circuiti e Algoritmi per l'Elaborazione dei Segnali 2
Digital Adaptive Circuits and Learning Systems
Stefano Squartini

Seat Ingegneria
A.A. 2015/2016
Credits 9
Hours 72
Period I
Language ENG

Prerequisites
Linear Algebra, Circuit Theory, Circuits and Algorithms ofr Digital Signal Processing

Learning outcomes
Know and understand the advanced techniques of Digital Signal Processing (DSP): analysis, synthesis and implementation of circuits and discrete-time adaptive algorithms, linear and nonlinear, including artificial neural networks. Apply the techniques studied in the field of Audio Processing (both on PC and embedded platforms).

Program
Review of basic DSP concepts. Review of Esimation Theory concepts Optimal FIR filters. Linear prediction and modern spectral analysis. Adaptive FIR filter in both time and frequency domains. IIR adaptive filters. Adaptive filter relevant applications. Static and Dynamic Neural Networks. Neural Networks applications Advanced Algorithms for Audio Processing. Implementation of adaptive algorithms in Matlab/Scilab Real-time implementation of adaptive algorithms on Digital Signal Processors

Development of the examination
LEARNING EVALUATION METHODS
The learning evaluation methodology consists in the presentation and discussion of a technical report related to a project focused on the Advanced Digital Signal Processing and Computational Intelligence presented during the lectures. The project is proposed by the teacher in agreement with the student's preferences and it can be fulfilled also in groups of maximum three people. The student can also propose something on the basis of his/her interests: the teacher will carefully evaluate the suitability of student's suggestion in relationship with the course contents, and will also calibrate the implementation aspects before finalizing the project proposal. The student has 6 months to fulfill the work and present it, starting from the date in which the project proposal is given to thim.

LEARNING EVALUATION CRITERIA
The student is required to show an adequate comprehension of the concepts discussed during the lectures and to be able to apply them in an autonomous way to the fulfillment of the assigned project. The student is also asked to explain in a rigorous and synthetic way the technical report related to the project.

LEARNING MEASUREMENT CRITERIA
The test is evaluated with a score on the scale 18-30.

FINAL MARK ALLOCATION CRITERIA
To pass the exam, the student is required to show his/her ability in analyzing the problems related to the proposed project, by using the technical background acquired during the lectures, and develop suitable algorithms for their solutions. The maximum score is reached when the student show to be able to work in an autonomous and costructive way to face the technical issues experienced during the project fulfillment, and to motivate, by means of adequate experimental tests, the functional properties of the algorithms. Honours are given to students who show to have a relevant scientific rigour in addressing the faced issues and a certain brightness in exposing their answers to specific theoretical questions and in discussing the final project.

Recommended reading
1 -P. M. Clarkson, Optimal and Adaptive Signal Processing, CRC Press, 2000. 2 -S.Haykin, Neural Networks, IEEE Press, 1994 (o edizioni successive). 3- D. O'Shaughnessy, Speech Communications: Human and Machine, IEEE Press, 2001. 4- R. Chassaing, D. Reay,

Courses

• Ingegneria Elettronica (Corso di Laurea Magistrale (DM 270/04))