Course detail

Data acquisition,analysis and processing

FEKT-MZPDAcad. year: 2015/2016

The course is dedicated to the analysis of digital signals in time and frequency domain. Emphasis is placed on the orthogonal transformation in particular DFT, fast algorithms FFT, and wavelet transformations. Part of the course is devoted to mathematical perations with time series and digital filtering.

Learning outcomes of the course unit

Student is able to:
- describe the types of physical signals,
- interpret the basic principles of data analysis methods,
- explain the importance of orthogonal transformations and give examples,
- explain the principles of FFT algorithms and methods for time - frequency analysis,
- describe the principles of wavelet transformations and discuss the results,
- explain the results of spectral and cepstral analysis,
- explain the principles of digital signal filtering,
- design a filter with the required properities.


The student who writes the subject should be discuss the basic terms of signal theory. Generally, the required knowledge of the subjects BMA1, BMA2, knowledge about programming LabVIEW.


Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Otnes,R.K.-Enochson,L. Applied Time Series Analysis,Wiley (EN)
Rabiner,R.L.-Gold,B. Theory and Application of Digital Signal Processing.,Prentice Hall (EN)
Smith, S.W. Digital Signal Processing. California Technical Publishing, San Diego, California 1999 (EN)
Uhlíř, J. Sovka, P. Číslicové zpracování signálů, ČVUT Praha, 1995 (CS)
Kadlec,F. Zpracování akustických signálů, ČVUT Praha, 1996 (CS)

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.
Techning methods include lectures and computer laboratorie.
Students have to write a single project/assignment during the course.

Assesment methods and criteria linked to learning outcomes

up to 30 points for the evaluation computer.
up to 70 points for the final written examination.

Language of instruction


Work placements

Not applicable.

Course curriculum

1. Classification and description of the physical signals
2. Operations with time series data
3. Linear time-invariant systems, discrete convolultion
4. Discrete correlation, evaluation of dependency phenomena
5. Orthogonal function, discrete Fourier transform
6. Principles of FFTalgorithms
7. Discrete orthogonal transformations (Walsch, Haar, Hadamard, Hilbert)
8. Time-frecvency analysis, STFT, wavelet transforms
9. Spectral and cepstral analysis
10. Numerical derivate and integration, interpolation of data sequence
11. Reduction and data compression
12. Methods of digital filtering, characteritics of digital filters
13. Desisgn of digital filters


The aim of the course is to provide students with an overview and information in digital signal processing. The emphasis is placed to frequency and spectral analysis and digital filtering of signals.

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

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Classification of course in study plans

  • Programme EEKR-M1 Master's

    branch M1-KAM , 1. year of study, summer semester, 5 credits, optional specialized

  • Programme EEKR-M Master's

    branch M-KAM , 2. year of study, summer semester, 5 credits, optional specialized

  • Programme EEKR-CZV lifelong learning

    branch ET-CZV , 1. year of study, summer semester, 5 credits, optional specialized

Type of course unit



26 hours, optionally

Teacher / Lecturer

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer