Course detail

Introduction to Signal Processing

FSI-RSZAcad. year: 2018/2019

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

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Jiří Krejsa, Ph.D.

Department

Institute of Solid Mechanics, Mechatronics and Biomechanics (ÚMTMB)

Learning outcomes of the course unit

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

Co-requisites

Not applicable.

Planned learning activities and teaching methods

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

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

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.

Specification of controlled education, way of implementation and compensation for absences

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.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Jan. J.: Číslicová filtrace, analýza a restaurace signálů, VUT v Brně, 1997
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

Not applicable.

Classification of course in study plans

  • Programme M2A-P Master's

    branch M-MET , 1 year of study, winter semester, compulsory-optional
    branch M-IMB , 1 year of study, winter semester, compulsory-optional

Type of course unit

 

Lecture

13 hod., optionally

Teacher / Lecturer

doc. Ing. Jiří Krejsa, Ph.D.

Syllabus

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

26 hod., compulsory

Teacher / Lecturer

doc. Ing. Jiří Krejsa, Ph.D.

Syllabus

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