PV291 Introduction to Digital Signal Processing

Fakulta informatiky
jaro 2024
Rozsah
2/1/0. 3 kr. (plus ukončení). Ukončení: zk.
Vyučující
doc. RNDr. David Svoboda, Ph.D. (přednášející)
Mgr. Lucia Hradecká (cvičící)
doc. RNDr. Martin Maška, Ph.D. (cvičící)
Garance
doc. RNDr. David Svoboda, Ph.D.
Katedra vizuální informatiky – Fakulta informatiky
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky
Rozvrh
Út 10:00–11:50 D2
  • Rozvrh seminárních/paralelních skupin:
PV291/02: St 14:00–14:50 A215, D. Svoboda
PV291/03: St 12:00–12:50 A215, M. Maška
PV291/04: St 13:00–13:50 A215, L. Hradecká
Předpoklady
MB151 Lineární modely && MB152 Dif. a integrální počet
Omezení zápisu do předmětu
Předmět je nabízen i studentům mimo mateřské obory.
Mateřské obory/plány
předmět má 33 mateřských oborů, zobrazit
Cíle předmětu
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.
Výstupy z učení
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.
Osnova
  • 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
Výukové metody
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.
Metody hodnocení
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.
Vyučovací jazyk
Angličtina
Informace učitele
https://cbia.fi.muni.cz/education/
Další komentáře
Studijní materiály
Předmět je vyučován každoročně.
  • Statistika zápisu (nejnovější)
  • Permalink: https://is.muni.cz/predmet/fi/jaro2024/PV291