19M031AOS - Adaprive Signal Processing

Course specification
Course title		Adaprive Signal Processing
Acronym		19M031AOS
Study programme		Electrical Engineering and Computing
Module
Type of study		master academic studies
Lecturer (for classes)		professor PhD Jelena Ćertić asst. professor PhD Miloš Bjelić
Lecturer/Associate (for practice)		asst. professor PhD Miloš Bjelić professor PhD Jelena Ćertić
Lecturer/Associate (for OTC)		professor PhD Jelena Ćertić asst. professor PhD Miloš Bjelić MA Tatjana Miljković, electrical and computer engineer
ESPB		6.0	Status	elective
Condition
The goal		Objective of the course is to teach students adaptive signal processing techniques. The focus is on usage of adaptive signal processing concepts in telecommunications.
The outcome		Students will be able to: -implement defined adaptive filter structures and related adaptive algorithms -modify and adjust adaptive systems -apply adaptive signal processing techniques to solving problems related to telecommunications -use MATLAB (MATLAB clone) for design and analysis of adaptive digital filters and algorithms and for realization and simulation of adaptive systems.
Contents
URL to the subject page		http://telit.etf.rs/kurs/adaptivna-obrada-signala/
Contents of lectures		Discrete stochastic processes, autoregressive models, general properties of adaptive algorithms, Wiener filter, linear prediction, Least-mean-square LMS adaptive filters, algorithms derived from LMS, Recursive Least Square RLS algorithm, Kalman filter, filter structures, application of adaptive filters in telecommunications, channel equalization, blind equalization.
Contents of exercises		Algorithms analysis and synthesis in MATLAB (MATLAB clone). Programing of DSP processor (evaluation board).
Literature
S. Haykin, Adaptive Filter Theory, Prentice Hall, 2002 (Original title) A. D. Poularikas, Z. M. Ramadan, Adaptive Filtering Primer with MATLAB, CRC Press, 2006 (Original title) S. Mitra, Digital Signal Processing: A Computer Based Approach, Mc Graw Hill, 2006. (Original title)
Number of hours per week during the semester/trimester/year
Lectures	Exercises	OTC	Study and Research	Other classes
3	1	1
Methods of teaching		lectures, lectures, exercises, laboratory exercises, homeworks
Knowledge score (maximum points 100)
Pre obligations		Points	Final exam	Points
Activites during lectures		0	Test paper	0
Practical lessons		0	Oral examination	30
Projects		70
Colloquia		0
Seminars