Course detail

Advanced Signal Processing

FIT-MZSAcad. year: 2014/2015

Formalised inverse filtering and restoration of signals. Wiener filter, constrained deconvolution and further higher restoration approaches. Kalman filtering, scalar and vector formulation, system modelling based on Kalman filtering. Adaptive filtering and identification, algorithms of adaptive filters, typical applications of adaptive filtering. Multirate systems. Non-linear filtering: polynomial filters, rank filters, homomorphic filtering and deconvolution, non-linear matched filters. Signal processing by neural networks. Time-frequency analysis, wavelet transform and its applications. Concept of multidimensional signal and spectrum, 2D and 3D Fourier transform, discrete unitary multidimensional transforms. Applications in formalised image processing: restoration approaches, tomographic reconstruction from projections, 3D reconstruction from stereo data.

Language of instruction

Czech

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

prof. Ing. Jiří Jan, CSc.

Department

Department of Biomedical Engineering (UBMI)

Learning outcomes of the course unit

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.

Prerequisites

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

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Obtaining at least 15 points from the computer-labs.

Course curriculum

Syllabus of lectures:

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

Syllabus of computer exercises:

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

Work placements

Not applicable.

Aims

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.

Specification of controlled education, way of implementation and compensation for absences

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

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

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

VUT

Faculties

University Institutes

Parts

Advanced Signal Processing