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Course Catalog 2012-2013
SGN-2206 Adaptive Signal Processing, 5 cr |
Additional information
Suitable for postgraduate studies
Person responsible
Ioan Tabus
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
Assignment and final exam.
Principles and baselines related to teaching and learning
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Learning outcomes
Student will learn basic adaptive signal processing methods, especially linear adaptive filters and learning of supervised neural networks. After completing the course, the student - Is familiar with the most important adaptive filter generic problems: optimal design, convergence, recursiveness in time, frequency domain implementations; - Is able to start from the formulation of a problem formulation and utilize a number of typical algorithmic tools to derive the solution; - Knows what are the most important structures for adaptive filters: LMS, NLMS,RLS etc. - Acquires practice on simulating adaptive algorithms with given input data and extracting useful performance indices helpful in comparing various algorithms. - Knows how to integrate an adaptive filter in a number of important applications: echo cancelation, noise cancellation, channel equalization etc.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Optimal Wiener filtering | ||
| 2. | Gradient based adaptation: Steepest descent, LMS, NMLS | Frequency domain adaptive filters | |
| 3. | Linear prediction, lattice filters, adaptive lattice filters | Levinson algorithm | |
| 4. | Least squares filtering, Recursive least squares | ||
| 5. | Neural networks as adaptive filters | Backpropagation in time |
Evaluation criteria for the course
Course is graded on the basis of answers to exam questions. Very good grade is obtained when exam questions are correctly answered and homework is accepted. Course acceptance threshold is approx. half of the maximum exam points.
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Study material
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Book | "Adaptive Filter Theory" | S. Haykin | Prentice-Hall, 2002. | English |
Prerequisites
| Course | Mandatory/Advisable | Description |
| MAT-33317 Statistics 1 | Advisable | |
| SGN-1201 Signal Processing Methods | Advisable |
Prerequisite relations (Requires logging in to POP)
Correspondence of content
| Course | Corresponds course | Description |
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More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |