Advanced Signal Processing

• 39 hrs lectures
• 26 hrs pc labs

Understanding of advanced methods of signal processing and analysis, ability to utilise and modify them, and to design and verify a realisation aimed at a particular practical task.

Becoming familiar with advanced methods of digital signal processing and their application in practice. Design and testing of systems for advanced signal processing, aiming at optimum and adaptive noise reduction, identification and modelling of systems, reconstruction and restoration, analysis and classification of signals and images.

The course knowledge on the Bachelor´s degree level is requested, namely on digital signal processing.

• Jan, J.: Číslicová filtrace, analýza a restaurace signálů, Nakl. VUT Brno 1997, ISBN 80-214-0816-2, 2. vydání, 2001
• Jan, J.: Digital Signal Filtering, Analysis and Restoration, IEE London, United Kingdom 2000, 407+14 pp., ISBN 0 85296 760 8
• Gonzales, R.C. , Wintz, P.: Digital Image Processing, 2nd ed., Addison-Wesley Publ. Comp., 1987

• Madisetti, V.K., Williams, D.B. (ed.): The Digital Signal Processing Handbook, CRC & IEEE Press, USA, 1998, ISBN 0-8493-8572-5
• Vích, R., Smékal, Z.: Číslicové filtry, Academia Praha 2000, ISBN 80-200-0761-X
• Mulgrew, B., Grant, P., Thompson, J.: Digital Signal Processing - Concepts & Applications, MacMillan Press Ltd., UK, 1999, ISBN 0-333-74531-0
• Banks, S., Signal Processing, Image Processing and Pattern Recognition, Prentice Hall Int., UK, Ltd., 1990
• Jain, A.K.: Fundamentals of Digital Image Processing, Prentice Hall Int. Edit., 1989
• Jan, J.: Číslicová filtrace, analýza a restaurace signálů, Nakl. VUT Brno 1997, ISBN 80-214-0816-2
• Gonzales, R.C., Wintz, P.: Digital Image Processing, 2nd ed., Addison-Wesley Publ. Comp., 1987
• Pratt, W.K.: Digital Image Processing, 2nd ed., J. Wiley & Sons, 1991
• Rosenfeld, A., Kak, A.C.: Digital Picture Processing, 2nd. edit., Academic Press, 1982
• Schalkoff, R.J.: Digital Image Processing and Computer Vision, J. Wiley & Sons, 1989

1. Formalised inverse filtering and restoration of signals. Wiener filter: classical and discrete formulation
2. Constrained deconvolution, deconvolution with impulse response optimisation, maximum posterior-probability method
3. Kalman filtering, scalar and vector formulation, system modelling based on Kalman filtering
4. Concept of adaptive filtering and identification, algorithms of adaptive filtering
5. Typical applications of adaptive filtering: system identification and modelling, linear adaptive prediction, adaptive noise and interference suppression
6. Multirate systems of digital signal processing, multirate filter banks
7. Non-linear filtering: polynomial filters, rank filters, homomorphic filtering and deconvolution, non-linear matched filters
8. Signal processing by neural networks: learning neuronal filters and classifiers, restoration by feed-back neuronal networks
9. Time-frequency analysis, wavelet transform and its applications in processing and compression of signals
10. Concept of multidimensional signal and spectrum, 2D and 3D Fourier transform. Discrete unitary 2D transforms: cosine, Hadamard and Walsh, Haar and 2D wavelet tr.
11. Applications of signal-theory-based approaches to formalised image processing: restitution and restoration approaches, formalised image segmentation
12. Tomographic methods of image reconstruction from 1D projections
13. Motion analysis and 3D reconstruction from stereo data

1. Simulation of discrete Wiener filter and evaluation of efficiency in stationary case
2. Simulation of a 3rd order Kalman filter and comparison with the above Wiener filter in stationary environment
3. Simulation of adaptive filters of RLS and LMS type as applied to a system modelling. Comparison of both results in stationary and slowly varying environment
4. Wavelet transform: application to analysis and denoising of a signal, verification of compression ability
5. Restoration of blurred and noisy image by pseudoinversion and by 2D classical Wiener filter - comparison of results
6. 2D image reconstruction from tomographic data (1D projections) via frequency domain - evaluation of artefacts
7. Learning 2D neuronal filter: applied for texture analysis. Comparison with feature-oriented classification

Obtaining at least 15 points from the computer-labs.

Active participation in the computer-lab tutorials is checked, the minimum participation is 4 out of 7 tutorials, can not be substituted.