Course detail

Introduction to Signal Processing

FSI-RSZAcad. year: 2027/2028

The course introduces the introduction to theory of digital processing of one-dimensional signals. Students apply this knowledge to practical tasks in MATLAB. Emphasis is placed on basic terminology and processing methods: signals, sampling, discretisation, quantisation, convolution, the Fourier transform, and IIR and FIR filtering. Part of the course is focused on estimation of dynamic systems states using Kalman filter and its nonlinear variants.

Language of instruction

Czech

Number of ECTS credits

5

Assignment to study programme types

Master's

Mode of study

Not applicable.

Guarantor

doc. Ing. Jiří Krejsa, Ph.D.

Department

Institute of Solid Mechanics, Mechatronics and Biomechanics (ÚMTMB)

Entry knowledge

Basic knowledge of MATLAB or Octave, particularly working with matrices and vectors, creating simple plots and programming.

Rules for evaluation and completion of the course

The course grade is based on a functional software project that uses selected method of signal processing. Students must submit a report describing the task together with the source code and present the project to their classmates in a short presentation.

The report, in PDF format, must be at least 5 pages long and include the rationale for the assignment, detailed instructions, a description of the procedure, and the results achieved. The source code must be executable on the instructor’s computer. The presentation must be in PowerPoint or a compatible format.

Assessment is based on the fulfillment of the project’s objectives, as well as the quality of the code and text, taking into account the difficulty of the assignment.

Attendance at lectures is recommended, attendance at exercises is compulsory. The teacher specifies how students can substitute missed exercises.

Aims

The course introduces the fundamentals of signal processing, particularly the digital processing of one-dimensional signals. Practical tasks in MATLAB form an essential part of the course.

After completing the course, students will understand basic signal-processing terminology, analyse a one-dimensional signal and design a filter for a given application using MATLAB.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

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

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme N-IMB-P Master's

    specialization BIO , 1 year of study, winter semester, compulsory-optional
    specialization IME , 1 year of study, winter semester, compulsory, profile core courses

  • Programme N-MET-P Master's 1 year of study, winter semester, compulsory, fundamental theoretical courses of the profile core

Type of course unit

 

Lecture

13 hours, 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 their description, discrete systems
4. Linear systems, superposition principle, decomposition
5. Convolution
6. Fourier transform
7. Fourier transform, properties
8. Digital filters, introduction, FIR filters
9. IIR filters
10. Kalman filtering
11. Extended Kalman filter
12 Unscented Kalman filter, particle filter
13. Laplace transform, Z-transform

Computer-assisted exercise

26 hours, 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