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The subject presents fundamental mathematical, algorithmic and programming tools for digital signal and image analysis, including z-transform, discrete Fourier and wavelet transforms and solution of difference equations. A special attention is paid to the description of discrete systems in time and frequency domains, to methods of spectral analysis, to adaptive methods of signal processing and to digital filters of multichannel and multidimensional signals. Applications and case studies include tools and methods of data acquisitions and their processing in selected engineering and biomedical problems.
Last update: Procházka Aleš (20.01.2018)
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Participation on the final colloquium with the presentation and discussion of a selected research topic. Last update: Procházka Aleš (20.01.2018)
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Z: J. Uhlíř, P. Sovka, Číslicové zpracování signálů, Vydavatelství ČVUT, 2002
Z: http://uprt.vscht.cz/prochazka/pedag/ lectures/ATHENS_DSP.pdf
D: T. Bose: Digital Signal and Image Processing, Wiley, 2004
D: J. Jan, Číslicová filtrace, analýza a restaurace signálů, BEN 2004
D: Vaseghi S.V.: Multimedia Signal Processing, Wiley, 2007
D: WIKIBOOK: Signals and Systems, https://en.wikibooks.org/wiki/Signals_and_Systems, 2018 Last update: Procházka Aleš (05.01.2018)
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Lectures and practical verification of proposed algorithms in the computer laboratory. Last update: Procházka Aleš (05.01.2018)
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During studies of this subject it is necessary either to submit the own paper using signal and image processing methods in the area of own research or to evaluate three projects devoted to specific signal and image processing problems and their solution in MATLAB. Last update: Procházka Aleš (20.01.2018)
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1. Fundamentals of computational, programming and visualization system MATLAB / SIMULINK 2. Computational intelligence in signal processing 3. Description of signals and systems, Z-transform 4. Solution of difference equations, discrete and frequency transfer functions, stability 5. Spectral analysis, discrete Fourier transform, windowing 6. The short time Fourier transform, analysis of multidimensional signals 7. Discrete Wavelet transform 8. Digital filters in time and frequency domains 9. Description of filters, construction 10. Rejection of noise signal components 11. Adaptive methods of signal processing, computational intelligence 12. CASE STUDY 1: Spectral analysis in signal analysis 13. CASE STUDY 2: Digital filters 14. CASE STUDY 3: Image analysis Last update: Procházka Aleš (05.01.2018)
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http://uprt.vscht.cz/prochazka/pedag/lectures/ATHENS_DSP.pdf
http://uprt.vscht.cz/prochazka/pedag/lectures/SP0_MATLAB_2006EN.pdf Last update: Procházka Aleš (05.01.2018)
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Students will know how
(i) to analyze time series and images by discrete Fourier and wavelet transforms,
(ii) to use z-transform for discrete systems description,
(iii) to apply digital filters in time and frequency domains,
(iv) to use digital signal processing methods for real data analysis
(v) to propose computational environment for analysis of real (biomedical) data Last update: Procházka Aleš (20.01.2018)
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Fundamentals of computer intelligence, fundamentals of numerical methods Last update: Procházka Aleš (05.01.2018)
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Mathematics 1, 2 Last update: Procházka Aleš (20.01.2018)
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