CTU FEE Moodle
Advanced DSP methods
B232 - Summer 23/24
Advanced DSP methods - B2M31DSP
| Credits | 6 |
| Semesters | Both |
| Completion | Assessment + Examination |
| Language of teaching | Czech |
| Extent of teaching | 2P+2C |
Annotation
The course follows the basic course in signal processing and introduces advanced methods of analysis and digital signal processing. Graduates will learn the methods of digital signals analysis and be able to practically use them. They learn to know the conditions of use of correlation, spectral and coherent analysis of random signals. They will became familiar with methods of signal decomposition and independent component analysis and the time-frequency transformations. Emphasis will be placed on an ability to interpret the results of signal analyses.
Study targets
Students will acquire theoretical and practical experiences about advanced signal processing methods. They will deepen the ability of solving problems of digital signal processing in MATLAB within exercises and within their individual projects.
Course outlines
1. Modeling and representation of linear systems in time-, correlation- and spectral-domain
2. Measurement of the delay using correlation and spectral analysis
3. Coherence, partial coherence and their use
4. Cepstral analysis and its use for signal deconvolution
5. Spectral and cepstral distances and their use
6. Methods of additive and convolution noise reduction and signal restoration
7. Methods of 1-D signal interpolation
8. Principal component analysis and its use for lossy compression of signals
9. Principles of methods of blind source separation
10. Principles of methods of blind signal deconvolution
11. Implementation of the discrete wavelet transform using filter bank, quadrature filters
12. Granger causality and Hilbert-Huang transform
13. Robust estimates of characteristics of random signals
14. Reserve
2. Measurement of the delay using correlation and spectral analysis
3. Coherence, partial coherence and their use
4. Cepstral analysis and its use for signal deconvolution
5. Spectral and cepstral distances and their use
6. Methods of additive and convolution noise reduction and signal restoration
7. Methods of 1-D signal interpolation
8. Principal component analysis and its use for lossy compression of signals
9. Principles of methods of blind source separation
10. Principles of methods of blind signal deconvolution
11. Implementation of the discrete wavelet transform using filter bank, quadrature filters
12. Granger causality and Hilbert-Huang transform
13. Robust estimates of characteristics of random signals
14. Reserve
Exercises outlines
1. Representation of systems in time-, correlation- and spectral- domain
2. Methods of delay estimation and conditions of their proper use
3. Implementation of coherence function and its use
4. The use of cepstral analysis for signal deconvolution
5. Examples of spectral and cepstral distance use
6. Implementation of methods for additive and convolution noise reduction
7. Examples of interpolation of 1-D signals
8. Principal component analysis and its use for lossy compression of signals
9. Estimation of moments and cumulants of random signals
10. Examples of methods for blind separation and blind deconvolution
11. The use of discrete wavelet transform for noise reduction and signal analysis
12. Hilbert-Huang Transform use and properties
13. Robust estimates of random signal characteristics
14. Reserve, individual projects
2. Methods of delay estimation and conditions of their proper use
3. Implementation of coherence function and its use
4. The use of cepstral analysis for signal deconvolution
5. Examples of spectral and cepstral distance use
6. Implementation of methods for additive and convolution noise reduction
7. Examples of interpolation of 1-D signals
8. Principal component analysis and its use for lossy compression of signals
9. Estimation of moments and cumulants of random signals
10. Examples of methods for blind separation and blind deconvolution
11. The use of discrete wavelet transform for noise reduction and signal analysis
12. Hilbert-Huang Transform use and properties
13. Robust estimates of random signal characteristics
14. Reserve, individual projects
Literature
Saeed V. Vaseghi: Advanced Digital Signal Processing and Noise Reduction, Wiley,2009, ISBN: 978-0-470-75406-1
Monson Hayes: Statistical digital signal processing and modeling. Wiley, 1999, ISBN: 978-0-471-59431-4.
Monson Hayes: Statistical digital signal processing and modeling. Wiley, 1999, ISBN: 978-0-471-59431-4.
Requirements
Basic knowledge of system theory and digital signal processing.