ECTS - University of Pisa
Scheda programma d'esame
SIGNAL PROCESSING
MAURIZIO VARANINI
Academic year2016/17
CoursePHYSICS
Code199BB
Credits6
PeriodSemester 1
LanguageItalian

ModulesAreaTypeHoursTeacher(s)
ELABORAZIONE DEI SEGNALIFIS/01LEZIONI36
MAURIZIO VARANINI unimap
Programma non disponibile nella lingua selezionata
Learning outcomes
Knowledge
Students are expected to acquire: knowledge to extract signal from noise applying: - optimum/adaptive filtering (regressive model) - principal component / total least squares modelling - independent component model some knowledge of pattern recognition / classification: - elements of Bayesian decision theory - discriminant functions - clustering
Assessment criteria of knowledge
The student's ability to explain correctly the main topics presented during the course at the board will be assessed.

Methods:

  • Final oral exam

Further information:
Final oral exam 100%
Teaching methods

Delivery: face to face

Learning activities:

  • attending lectures

Attendance: Mandatory

Teaching methods:

  • Lectures
Syllabus
Optimum filtering: Wiener filter; adaptive filters (LMS and RLS algorithm). Autoregressive model, parametric spectrum estimation. Total least squares model, singular value decomposition, signal and noise subspaces. Independent Component Analysis: maximization of non gaussianity, maximization of likelihood, minimization of mutual information. Pattern recognition/classification: feature selection (principal component analysis, generalized variance ratio); Elements of Bayesian decision theory; maximum likelihood and Bayesian estimation; linear and quadratic discriminant functions. Classifier training and validation. Clustering: k-means; hierarchical clustering.
Bibliography
Recommended reading: Professor's lecture notes. Additional reading includes: - S. Haykin. Adaptive Filter Theory. Prentice Hall. - A. Hyvarinen, J. Karhunen, E. Oja. Independent Component Analysis. Wiley-Interscience Publication. - R. O. Duda, P. E. Hart and D. G. Stork. Pattern Classification. Wiley Interscience. Further bibliography: S.T. Alexander. Adaptive Signal Processing, Theory and Applications. Springer-Verlag. B. Widrow, S.D. Stearns. Adaptive Signal Processing. Prentice Hall. S.L.Marple. Digital spectral estimation. Prentice-Hall. S. Van Huffel, J. Vanderwalle. The Total Least Squares Problem: Computational Aspects and Analysis. SIAM, Philadelphia, PA, 1991. G.H. Golub, C. Van Loan. Matrix Computations. The Johns Hopkins University Press.
Work placement
Yes
Updated: 14/11/2016 17:27