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Case studies 
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: Ztransform, difference equations, system description

Case Study 1: DSP in environmental engineering: Spatial modelling of air pollution data

Matlab


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

Project MME

C 
Timescale Analysis: Discrete Wavelet transform, basic definitions, signal decomposition, denoising, reconstruction 
Applications: DSP in signal and image processing 
Case Study 3: DSP in biomedical signal and image processing


Project DSP

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

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


TOPICS
