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DIGITAL SIGNAL AND IMAGE PROCESSING WITH APPLICATIONS |
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*** Czech Technical University *** Czech Institute of Informatics, Robotics and Cybernetics Supported by EU Socrates Programme *** University of Chemistry and Technology *** Department of Computing and Control Engineering Computational Intelligence and Digital Signal and Image Processing Research Group Prof. Ing. Aleš Procházka, CSc |
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| BLOCK | SECTION 1 |
SECTION 2 |
SECTION 3 |
Text | Case studies |
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| A | Numerical Methods: Introduction to signal processing, MATLAB programming, visualization, programming tools, structured arrays, selected methods of data processing, linear algebra, the least square method, approximation and interpolation | Signal and System Modelling: Z-transform, difference equations, system description |
Case Study 1: DSP in environmental engineering: Spatial modelling of air pollution data |
Matlab 
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| B | Spectral Analysis: Discrete Fourier transform, properties, frequency components detection, short time Fourier transform, window functions, applications |
Digital Filters: Digital filtering using difference equations, FIR and IIR filters, frequency domain filtering |
Case Study 2: DSP in prediction of energy consumption |
DSP
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Project MME
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| C |
Time-scale Analysis: Discrete Wavelet transform, basic definitions, signal decomposition, de-noising, reconstruction |
Applications: DSP in signal and image processing |
Case Study 3: DSP in biomedical signal and image processing |
Project DSP
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| D | Neural Networks: Computational intelligence, artificial neural networks, mathematical description, error surface, optimization, adaptive linear element, signal denoising |
Classification: Feature extraction, classification, nearest neighbour method, Kohonen learning, deep learning |
Case Study 4: Classification of EEG segments |
Project NN
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| F | Signal Prediction: Neural networks in signal prediction. recurrent systems, applications of data processing in physiological signal processing, robotic systems and computer vision |
Conclusion: History and interdisciplinary applications of digital signal and image processing |
COLLOQUIUM
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TOPICS
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| DATA FILES | Real Data Files: Biomedical Signals (EEG, MRI), Environmental Signals (Air Pollution), Energy Consumption (Gas) |
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| INFORMATIONS |
COURSE OVERVIEW:
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REFERENCES:
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