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Roles:PV, PS Extent of teaching:2P+2C
Department:13131 Language of teaching:EN
Guarantors:Bortel R. Completion:Z,ZK
Lecturers:Sovka P. Credits:6
Tutors:Illner V., Sovka P. Semester:Z

Anotation:

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.

Study targets:

This course aims to provides the basic knowledge in the area of algorithms for filtering, decorrelation, separation and beamforming.

Course outlines:

 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

Exercises outline:

 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

Literature:

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.

Requirements:

The knowledge of basic digital signal processing techniques - primarily the spectral analysis and non-adaptive linear filtering. Ability to use Matlab.

Keywords:

adaptive filtering, LMS and RLS algorithms, blind separation, beamforming

Subject is included into these academic programs:

 Program Branch Role Recommended semester MEBIO2_2018 Medical Instrumentation PV 3 MEBIO4_2018 Signal Processing PS 3 MEBIO3_2018 Image Processing PV 3 MEBIO1_2018 Bioinformatics PV 3

 Page updated 23.4.2024 17:51:08, semester: Z/2024-5, Z,L/2023-4, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)