19M031AOS - Adaprive Signal Processing
Course specification | ||||
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Course title | Adaprive Signal Processing | |||
Acronym | 19M031AOS | |||
Study programme | Electrical Engineering and Computing | |||
Module | Applied Mathematics, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering | |||
Type of study | master academic studies | |||
Lecturer (for classes) | ||||
Lecturer/Associate (for practice) | ||||
Lecturer/Associate (for OTC) | ||||
ESPB | 6.0 | Status | elective | |
Condition | ||||
The goal | The 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 solve 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 | ||||
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). Programming of the DSP processor (evaluation board). | |||
Literature | ||||
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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 |