COURSE UNIT DESCRIPTION

Poznan University of Technology
European Credit Transfer System
Faculty of Electronics and Telecommunications
Title
Code
POZ04WTS2ICC15
Advanced Signal Processing Algorithms
Field
Year / Semester
Electronics and Telecommunications
1 / spring
Specialty
Course
Hours
Number of credits
core
Lectures: 2
Classes: 2
Laboratory: -
Projects / seminars: -
5
Lecturer:
prof. dr hab. inż. Ryszard Stasiński
Katedra Systemów Telekomunikacyjnych i Optoelektroniki
tel. +48 61 665 3839, fax. +48 61 665 3830
e-mail: [email protected]
Faculty:
Faculty of Electronics and Telecommunications
ul. Piotrowo 3A
60-965 Poznań
tel. (061) 665-2293, fax. (061) 665-2572
e-mail: [email protected]
Status of the course in the study program:
Compulsory course on Electronics and Telecommunications studies, specialization Information
and Communication Technologies
Objectives of the course:
Advanced topics in digital signal processing: idea of linear prediction/Wiener filtering and its
applications, and multirate systems.
Course description:
The theory of linear prediction and Wiener filtering – modeling of stationary stochastic
processes (ARMA, AR, MA), Wiener-Hopf equations and fast algorithms for solving them
(Levinson-Durbin, Schur), lattice filters, Wiener filtering – FIR and IIR case. Adaptive filters –
their connection with Wiener filters, gradient algorithms, in particular dynamic behaviour of LMS
algorithm, least squares (LS) error formulation, recursive LS (RLS) algorithms – Kalman,
square-root, fast RLS, and their lattice-ladder versions, comparison of gradient and RLS
algorithms. Parametric spectrum estimation – its connection with linear prediction, Yule-Walker,
Burg, and unconstrained LS methods, eigenanalysis approach to spectrum estimation
(Pisarenko, MUSIC, ESPRIT). Multirate systems – sampling rate conversion (decimators,
interpolators, approximate methods), multiplierless modulation and demodulation, polyphase
filter structures, filter banks (general, uniform, critically sampled, perfectly reconstructing), QMF
filters, and their use for implementation of discrete Wavelet transform. Introduction to spatiotemporal analysis – short-time Fourier transform, Gabor and wavelet transforms.
Initial knowledge:
Analog and digital signal processing theory.
Teaching methods:
Lectures supported by multimedia presentations.
Assessment methods:
Exam ending the lecture.
Bibliography:
1.
2.
Digital Signal Processing, J.G. Proakis, D.G. Manolakis, Pearson – Prentice-Hall (there are
several editions of this book).
Discrete-Time Signal Processing, A.V. Openheim, R.W. Schafer, Prentice-Hall, e.g. 1999.
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