Course detail

Data Acquisition, Analysis and Processing

FEKT-MPC-ZPDAcad. year: 2021/2022

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.

Language of instruction

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

Guarantor

doc. Ing. Petr Beneš, Ph.D.

Department

Department of Control and Instrumentation (UAMT)

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.

Prerequisites

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.

Co-requisites

Not applicable.

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.

Course curriculum

1. Signal and its properties
2. Time series and its model
3. Linear time-invariant systems, discrete convolultion
4. Discrete correlation, evaluation of dependency phenomena
5. Orthogonal function, discrete Fourier transform
6. Properties of DFT
7. Principles of fast DFT algorithms (FFT)
8. Introduction to digital filters (FIR and IIR)
9. Digital filter design
10. Numerical derivation and integration, data interpolation
11. Spectral analysis, Cepstrum
12. Other orthogonal transformations (Hilbert, Wavelets)
13. Time-frequency analysis (STFT and other)

Work placements

Not applicable.

Aims

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.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

UHLÍŘ, Jan, Pavel SOVKA a Roman ČMEJLA. Úvod do číslicového zpracování signálů. Praha: Vydavatelství ČVUT, 2003. ISBN 80-01-02799-6. (CS)
LYONS, Richard G. Understanding digital signal processing. 3rd ed. Upper Saddle River, NJ: Prentice Hall, c2011. ISBN 9780137027415. (CS)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme MPC-KAM Master's, 1. year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

doc. Ing. Petr Beneš, Ph.D.
Ing. Stanislav Pikula, Ph.D.

Syllabus

1. Signal and its properties
2. Time series and its model
3. Linear time-invariant systems, discrete convolultion
4. Discrete correlation, evaluation of dependency phenomena
5. Orthogonal function, discrete Fourier transform
6. Properties of DFT
7. Principles of fast DFT algorithms (FFT)
8. Introduction to digital filters (FIR and IIR)
9. Digital filter design
10. Numerical derivation and integration, data interpolation
11. Spectral analysis, Cepstrum
12. Other orthogonal transformations (Hilbert, Wavelets)
13. Time-frequency analysis (STFT and other)

Exercise in computer lab

39 hours, compulsory

Teacher / Lecturer

Ing. Jan Kunz, Ph.D.
Ing. Stanislav Pikula, Ph.D.

Syllabus

1. Organization + Introduction to LabVIEW programming I.
2. Introduction to LabVIEW programming II.
3. Practical tasks
4. Correlation and convolution of signals
5. DFT, inverse DFT
6. FFT properties + windows
7. Regression, derivation, integration, interpolation
8. Filter design
9. Work on project
10. Work on project
11. Work on project
12. Project defense
13. Project defense + awarding the credit