Introduction to Signal Processing (FSI-RSZ-A)

Academic year 2020/2021

Supervisor:	doc. Ing. Jiří Krejsa, Ph.D.
Supervising institute:	ÚMTMB	all courses guaranted by this institute
Teaching language:	English
Aims of the course unit:
The goal of the subject is to introduce to the students the fundamentals of signal processing, mainly the diginal processing of one dimensional signals. The practical tasks realized in Matlab are the essential part of the course.
Learning outcomes and competences:
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.
Prerequisites:
fundamentals of Matlab (or Octave), in particular: working with matrices, vectors, simple graphs, basics of programming
Course contents:
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)
Teaching methods and criteria:
The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.
Assesment methods and criteria linked to learning outcomes:
Course unit credist will be awarded based on evaluation of the project. In the project the student has to prove the ability to solve given task: design of the filter for onedimensional signal using Matlab
Controlled participation in lessons:
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.
Type of course unit:
Lecture	13 × 1 hrs.	optionally
Computer-assisted exercise	13 × 2 hrs.	compulsory
Course curriculum:
Lecture	1. Signal: definition, classification, why we need to process, applications. 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	1. Matlab review. Signal Processing Toolbox. 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

Literature - fundamental:
1. Zaplatílek K., Doňar B.: Matlab - začínáme se signály, BEN,Praha, 2006
2. Jan. J.: Číslicová filtrace, analýza a restaurace signálů, VUT v Brně, 1997
3. SMITH, S. W. The Scientist and Engineer’s Guide to Digital Signal Processing. California Technical Pub., 1997. 626 p. ISBN: 9780966017632.