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The subject is devoted to selected problems of computational intelligence and machine learning including architecture of artificial neural networks, their optimization for signal and image processing and their use for adaptive noise rejection. A special attention is paid to signal and image features extraction, pattern recognition and to the use of neural networks for their classification into given number of classes. Selected case studies presented in the MATLAB computational environment are devoted to biomedical and engineering data processing.
Last update: Kubová Petra (12.04.2018)
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Participation on the final colloquium with the presentation and discussion of a selected research topic. Last update: Kubová Petra (12.04.2018)
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Z: S. Haykin: Neural Networks, Prentice Hall, 1999, ISBN 0132733501
Z: S. Samarasinghe: Neural Networks for Applied Science and Engineering, CRC Press, 2016
D: Vaseghi S.V.: Multimedia Signal Processing, Wiley, 2007
D: WIKIBOOK: Artificial Neural Networks, https://en.wikibooks.org/wiki/Artificial_Neural_Networks, 2018 Last update: Kubová Petra (12.04.2018)
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Lectures and practical verification of proposed algorithms in the computer laboratory. Last update: Kubová Petra (12.04.2018)
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During studies of this subject it is necessary either to submit the own paper using computational intelligence in the area of own research or to evaluate three projects and their solution in MATLAB. Last update: Kubová Petra (12.04.2018)
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1. Computational intelligence in data processing 2. Architecture of artificial neural networks, their modelling and optimization in the MATLAB environment 3. Learning and verification 4. Adaptive lineare element and their use in signal denoising 5. Multilayesr feedforward and recurrent neural networks in signal prediction 6. Feature matrix construction and classification methods in signal and image processing 7. Neural networks with topology, alternative methods of data classification 8. Machine learning, pattern recognition 9. Neural networks in image processing 10. Neural networks based on deep learning 11. Selected applications of adaptive signal processing, neural networks in robotics 12: CASE STUDY 1: Real data denoising 13. CASE STUDY 2: Signal prediction 14. CASE STUDY 3: Feature extraction and classification in biomedicine Last update: Kubová Petra (12.04.2018)
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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: Kubová Petra (12.04.2018)
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Students will know how
(i) to model multilayer and reccurent neural networks,
(ii) to create pattern matrices forming inputs for neural networks,
(iii) to optimize neural networks for data classification,
(iv) to use neural networks for noise cancelling and signal prediction
(v) to propose computationa environment for real data segmmentation and classification Last update: Kubová Petra (12.04.2018)
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none Last update: SOUSKOVH (12.06.2018)
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none Last update: SOUSKOVH (12.06.2018)
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