Course detail

Data Acquisition, Analysis and Processing

FEKT-LZPDAcad. year: 2016/2017

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

Ing. Marie Havlíková, 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 KMA1, KMA2, knowledge about programming Matlab.

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 laboratories.
Course is taking advantage of e-learning (Moodle) system.

Assesment methods and criteria linked to learning outcomes

up to 30 points for the individual works
up to 70 points for the final written examination.

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

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, J. Sovka, P. Číslicové zpracování signálů, ČVUT Praha, 1995 (CS)

Recommended reading

Kadlec,F. Zpracování akustických signálů, ČVUT Praha, 1996 (CS)

Classification of course in study plans

  • Programme EEKR-ML Master's

    branch ML-KAM , 1 year of study, winter semester, compulsory

  • Programme EEKR-ML Master's

    branch ML-KAM , 1 year of study, winter semester, compulsory

  • Programme EEKR-CZV lifelong learning

    branch EE-FLE , 1 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Stanislav Pikula, Ph.D.

Syllabus

Time series data. Data formats. Data operaton speed. Generatin time series data.
Display time series data. Basic work on time series. Discrette convolutin.
Discrette corelation. Discrette deconvolution.
Discrette ortogonal transform. DFT, characteristics.
Principle of FFT, other discrette ortogonal transform.
Preprocessing time series data. Derivation and integration.
Trend removal.Numeric parameters and histograms.
Spectral, Correlation and Cepstral analysis.
Interpolation problem.
Compression.
Filtration.
Designing digital filter methods.
Indentification of linear systems.

Exercise in computer lab

39 hod., compulsory

Teacher / Lecturer

Ing. Stanislav Pikula, Ph.D.

Syllabus

Introduction.
Simple data display system.
Generation time series. Sorting, Data operation speed.
Individual work.
Discrette convolution and corelation.
DFT comparation.
Discrette Haar transform. Time window.
Individual work
Amplitude, phase and power spectrum.
Regress analze.
Interpolation in time series data.
Histograms. Digital filters.
Finish.