PV291 Introduction to Digital Signal Processing

Faculty of Informatics
Spring 2025
Extent and Intensity
2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Mgr. Lucia Hradecká (seminar tutor)
doc. RNDr. Martin Maška, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. David Svoboda, Ph.D.
Department of Visual Computing – Faculty of Informatics
Supplier department: Department of Visual Computing – Faculty of Informatics
Prerequisites (in Czech)
MB151 Linear models && MB152 Calculus
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of this course is to introduce the basic concepts related to digital signal and the common operations used in digital signal processing. It covers the simple signal modifications as well as transforms converting the original data into different representations. At the end of this course, students should be able to:
- know what the digital signal is a how to process it;
- understand the concept of convolution and correlation;
- upsample, downsample, resample the digital signal;
- understand the basic principles of frequency analysis;
- understand the principle of linear and non-linear filters;
- implement and apply the selected filters;
- analyze time series;
- manipulate with multidimensional data;
- understand commonly used compression methods.
Learning outcomes
After completing the course, the student should be able to:
- analyze the signal both in time and frequency domain;
- properly resample the digital signal;
- design, implement, and apply linear/non-linear filters;
- discuss the problems in the field of frequency analysis;
- propose her/his own efficient and optimized compression methods;
- demonstrate the general principles of compression algorithms;
- use wavelet and Fourier transform appropriately and efficiently;
- work with multidimensional data;
- find specific patterns in time series.
Syllabus
  • Signal, Digitization, Sampling & Resampling
  • Convolution, Correlation
  • Continuous and Discrete Fourier Transform
  • Fourier transform and discrete Fourier transform properties
  • Fast Fourier transform, Discrete cosine transform
  • Linear & Non-linear filters
  • Z-transform
  • Discrete Wavelet Transform
  • Fast wavelet transform, Lifting scheme
  • Recursive filters
  • Time series
  • Signal compression
Teaching methods
Working in PC labs requires knowledge of the theory presented in the lectures. During the PC labs, students will work in Python to better understand theoretical concepts and experiment with some practical signal processing problems.
Assessment methods
After successfully solving all practical exercises during semester, students will be allowed to register for a written exam. The written part of the exam will be optionally followed by oral part.
Language of instruction
English
Further Comments
The course is taught annually.
The course is taught: every week.
Teacher's information
https://cbia.fi.muni.cz/education/
The course is also listed under the following terms Spring 2024.
  • Enrolment Statistics (recent)
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