Signal Processing - S445012
Title: Signal Processing
Guaranteed by: Department of Computing and Control Engineering (445)
Faculty: Faculty of Chemical Engineering
Actual: from 2022
Semester: winter
Points: winter s.:5
E-Credits: winter s.:5
Examination process: winter s.:
Hours per week, examination: winter s.:2/2, Ex [HT]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
Language: English
Teaching methods: full-time
Is provided by: AB445007
For type:  
Guarantor: Švihlík Jan doc. Ing. Ph.D.
Is interchangeable with: AB445007
Examination dates   Schedule   
Last update: Procházka Aleš prof. Ing. CSc. (04.07.2012)
The subject covers the analysis and processing of observed signals and images. Basic mathematical methods include discrete Fourier transform and selected numerical methods including the use of difference equations for rejection of undesirable signal components. Algoritmic tools assume the use of the MATLAB environment. Projects include the application of selected methods for biomedical data analysis and environmental signal processing.
Last update: Procházka Aleš prof. Ing. CSc. (04.07.2012)

[1] T. Bose: Digital Signal and Image Processing, Wiley, 2004


Requirements to the exam
Last update: Procházka Aleš prof. Ing. CSc. (04.07.2012)

In the frame of computational laboratories it is necessary to evaluate 3 projects including the verification of programs proposed in the MATLAB/Simulink environment. During exam the knowledge of the theoretical background of digital signal processing is verified together with their algorithmic implementation.

Last update: Procházka Aleš prof. Ing. CSc. (04.07.2012)

1. Signal and image description, sampling, applications

2. Discrete Fourier transform, basic properties, visualization

3. Short time Fourier transform, interpretation, application

4. Window functions, spectrum estimation, convolution

5. Signal and image decomposition and reconstruction, spectum modification

6. Principles of digital filters, system description by difference equations

7. Statistical methods of signal processing, histograms, corelation

8. Block oriented methods of signal and image processing

9. Time series description and processing, signal prediction

10. Real signal and image acquisition, Simulink and signal processing

11. Fundamentals of wavelet transforms

12. Image coding, analysis and visualization

13. Computational intelligence and adaptive methods, principles of neural networks

14. Applications of signal and image processing