COMPUTATIONAL INTELLIGENCE AND DIGITAL SIGNAL PROCESSING RESEARCH GROUP
Panorama
S4
COMPUTATIONAL INTELLIGENCE AND
DIGITAL SIGNAL AND IMAGE PROCESSING
WITH APPLICATIONS

*** 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
S5

S5

ATHENS

COURSE TOPICS


BLOCK GENERAL
LECTURES		 LABORATORIES
DSP		 ASSOCIATED
STUDIES		 Text PROJECTS
SELECTION
A Numerical Methods: Selected methods of data processing, the least square method, approximation and interpolation MATLAB MODELLING:
1. Matlab variables, approximation, data acquisition, AI implementation 		Private studies:
DSP in data analysis 		Matlab summary
MATLAB
B Spectral Analysis: Discrete Fourier transform, frequency components detection, STFT, window functions FFT USE:
2. FFT implementation, interpolation, STFT
Private studies:
Spectral Analysis 		DSP summary
DSP 		Project MME
PROJECTenNN
C Signal and System Modelling:
Z-transform, difference equations, system description 		DATA MODELLING:
3. System modelling, Z-transform
Private studies:
AI in biomedical signal and image processing
D Digital Filters:
Digital filtering, FIR and IIR filters, frequency domain processing
NOISE REJECTION:
4. Digital filtering
5. Project
6. Image denoising
PROJECT 1:
Biomedical EEG project solution 		Project DSP
PROJECTenNN
E Time-scale Analysis:
Discrete Wavelet transform, basic definitions, signal decomposition, de-noising, reconstruction
FEATURE EXTRACTION:
7. Project

DWT DATA ANALYSIS:
8. Functional data processing
PROJECT 2:
Walking symmetry analysis
F Neural Networks:
Computational intelligence, artificial neural networks, mathematical description, signal denoising
SENSORS:
9. Feature extraction
10. Applications
Private studies:
AI in EEG signal processing 		Project NN
PROJECTenNN
G Signal Prediction:
Neural networks in signal prediction

PREDICTION AND CLASSIFICATION:
11. Prediction
12. Simulink modelling
13. AI methods
Private studies:
AI use for feature classification
H Conclusion:
Integration of methodological tools, robotic systems and computer vision

EVALUATION:
14. Colloquium 		COLLOQUIUM
ATHENS_EXAM 		TOPICS
ATHENS_EXAM

DATA FILES Real Data Files: Biomedical Signals (EEG, MRI, motion data), Environmental Signals (Air Pollution), Energy Data (Gas consumption, photovoltaics), ... DATA

CS1 CS2 CS3

INFORMATIONS COURSE OVERVIEW:      PRESENTATION REFERENCES:      REFERENCES