Course detail
Introduction to Signal Processing
FSI-RSZAcad. year: 2023/2024
The subject gives the introduction to theory of digital signal processing of one dimensional signals. The gained knowledge will be used in practical examples using Matlab software. The emphasis in the subject is on understanding the basic terms and processing method of the field (signal, sampling, discretization, quantizing, convolution, Fourier transform, IIR and FIR filters)
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Entry knowledge
Rules for evaluation and completion of the course
Attendance at practical training is obligatory. Attendance is checked systematically by the teachers, as well as students’ active participation in the seminars and fundamental knowledge. Unexcused absence is the cause for not awarding the course-unit credit. One absence can be compensated for by attending a seminar with another study group in the same week, or by solving supplemental tasks. Longer absence may be compensated for by solving supplemental tasks according to teacher’s requirements.
Aims
The students will be able after passing the course to understand the basic terms in the signal processing field, analyze onedimensional signal and design filter for given application using Matlab.
Study aids
Prerequisites and corequisites
Basic literature
SMITH, S. W. The Scientist and Engineer’s Guide to Digital Signal Processing. California Technical Pub., 1997. 626 p. ISBN: 9780966017632.
Zaplatílek K., Doňar B.: Matlab - začínáme se signály, BEN,Praha, 2006
Recommended reading
Elearning
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
2. Continuous / discrete signal, sampling, discretization, quantizing
3. Continuous systems and its description
4. Discrete systems
5. Linear systems, superposition principle, decomposition
6. Convolution
7. Fourier transform
8. Fourier transform, properties
9. Digital filters, introduction
10. IIR filters
11. FIR filters
12. Applications
13. Kalman filtering
Computer-assisted exercise
Teacher / Lecturer
Syllabus
2. Reading the signal, signal generation
3. Original (time) domain analysis
4. SPTool
5. Convolution
6. Correlation analysis
7. Spectral analysis - introduction
8. Discrete Fourier transform
9. Filtering - introduction
10. Filtering – using FDATool
11. Filtering, applications
12. Frequency analysis in time, spectrogram
13. Kalman filter, nonlinear versions of KF
Elearning