Adaptive signal processing
Code | Completion | Credits | Range | Language |
---|---|---|---|---|
BEAM31ADA | Z,ZK | 6 | 2P+2C | English |
- Relations:
- In order to register for the course BEAM31ADA, the student must have registered for the required number of courses in the group BEZBM no later than in the same semester.
- Course guarantor:
- Lecturer:
- Tutor:
- Supervisor:
- Department of Circuit Theory
- Synopsis:
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This course provides a basic discourse on adaptive algorithms for filtering, decorrelation, separation and beamforming. The course explains adaptive algorithms for estimation and prediction, including analysis, implementation and practical applications. Next, it describes the algorithms for adaptive decorrelation and separation of multidimensional signals. Last, the course provides analysis of adaptive beamforming techniques.
- Requirements:
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The knowledge of basic digital signal processing techniques - primarily the spectral analysis and non-adaptive linear filtering. Ability to use Matlab.
- Syllabus of lectures:
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1. Block algorithms for estimation
2. Block algorithms for prediction
3. LMS and RLS algorithms and their use for estimation and prediction
4. Convergence of LMS and RLS algorithms
5. Structures for implementation of adaptive filters
6. Use of adaptive algorithms for signal compression
7. Use of adaptive algorithms for noise suppression
8. Kalman filters
9. Grid filters and particle filters
10. Adaptive algorithms for decorrelation of multidimensional signals
11. Adaptive algorithms for separation of multidimensional signals
12. Adaptive beamforming - LCMV and MVDR algorithms
13. Adaptive beamforming - MUSIC algorithm
14. Reserved
- Syllabus of tutorials:
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1. Implementation of block algorithms for estimation
2. Implementation of block algorithms for prediction
3. Implementation of LMS and RLS algorithms
4. Convergence of LMS and RLS algorithms
5. Comparisoin of structures for implementation of adaptive filters
6. Vocoder
7. Adaptive supression of narrowband interference.
8. Application of Kalman filters
9. Use of grid filters and particle filters
10. Implementation of algorithms for decorrelation of multidimensional signals
11. Implementation of algorithms for separation of multidimensional signals
12. Application of LCMV and MVDR algorithms
13. Application of MUSIC algorithm
14. Reserved
- Study Objective:
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This course aims to provides the basic knowledge in the area of algorithms for filtering, decorrelation, separation and beamforming.
- Study materials:
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Sayed, A.H., Adaptive Filters, Wiley-IEEE Press, 2008.
Bellanger, M.B., Adaptive Digital Filters, Marcel Dekker, NY 2001.
Hyvarinen, A, Karhunen, J, Oja, E. Independent Component Analysis, John Wiley & Sons, 2004.
- Note:
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans:
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- Medical Electronics and Bioinformatics - Specialization Image Processing (compulsory elective course)
- Medical Electronics and Bioinformatics - Specialization Signal Processing (PS)
- Medical Electronics and Bioinformatics - Specialization Bioinformatics (compulsory elective course)
- Medical Electronics and Bioinformatics - Specialization Medical Instrumentation (compulsory elective course)