Czech

Not applicable.

The student is able to:
- implement the sampling rate conversion
- explain the principles and advantages of multirate filtering
- implement of the various types of wavelet transforms (CWT, DTWT)
- explain the principles of the parametric methods for power spectrum estimation
- explain the principle of the Stockwell transform and its relation to STFT and DTWT
- explain the principle of the EMD and Hilbert-Huang transform
- explain the importance and possibilities of using complex signals
- explain the principles of linear and nonlinear deconvolution

Students should have knowledge of digital signal processing, be familiar with the ways of describing the linear filters (transfer function, impulse response, difference equations, frequency response). Assuming knowledge of the discrete Fourier transform (DFT) and the ability to interpret the result DFT. The laboratory work is expected knowledge of Matlab programming environment.

Not applicable.

Teaching methods include lectures and computer laboratories. Course is taking advantage of e-learning system. Students have to write a single project/assignment during the course.

Requirements for completion of a course are elaborated by the lecturer responsible for the course every year;
basically:
- obtaining at least 12 points (out of 24 as course-unit credit based on active presence in demonstration exercises),
- successful passing of final written exam (up to 76 points)

1. Sampling rate conversion
2. Design of multirate filters
3. Time-frequency analysis, wavelet transforms (CTWT, DTWT)
4. Use of DTWT in compression and for filtering and analysis of biosignals
5. Adaptive filters
6. Spectral analysis of biosignals and parametric methods for power spectrum estimation
7. Stockwell transform (S-transform), theory and use
8. Empirical mode decomposition (EMD), principle and use
9. Complex signals, Hilbert transform, Hilbert-Huang transform
10. Signal envelope and instantaneous signal frequency, their estimates
11. Multiplicative modulation, SSB modulation
12. Linear deconvolution
13. Nonlinear filtering: median filtering and homomorphic filtering
14. Mobile phone applications

Not applicable.

Gaining knowledge about multirate signal processing, the wavelet transforms for processing and analysis of biosignals. Use of wavelet transforms in compression and for filtering and analysis of biosignals. Principle and use of the Stockwell transform. Empirical mode decomposition (EMD), complex signals a Hilbert-Huang transform. Nonlinear methods of the filtering. Mobile phone applications for sensing and processing of the biosignals.

Delimitation of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year (see Rozvrhové jednotky).
Basically:
- obligatory computer-lab tutorial
- voluntary lecture

Not applicable.

Not applicable.

Kozumplík, J.: Multitaktní systémy. Elektronická skripta FEKT VUT v Brně, 2005 (CS)

Not applicable.