Course detail

Data acquisition,analysis and processing.

FEKT-LZPDAcad. year: 2010/2011

Data sequence, analysis and transformation.Discrette Convolution, Deconvolution, Correlation. Ortogonal transformation, analysys and calculating DFT. Interpolation, derivatin and integration. Trend removal methods. Numeric parameters and histograms. Spectral, Correlation and Ceptral analysis. Compression. Filtration. Indentification of linear systems.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

Ing. Marie Havlíková, Ph.D.

Department

Department of Control and Instrumentation (UAMT)

Learning outcomes of the course unit

Knowledge about realization of algoritm time series analysis

Prerequisites

The subject knowledge on the Bachelor´s degree level is requested, knowledge of 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.

Assesment methods and criteria linked to learning outcomes

30 points - computer works
70 points - final test

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

Knowledge about time series acqusistion and analysis algoritm

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

Not applicable.

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme EEKR-ML Master's

    branch ML-KAM , 2 year of study, summer semester, elective specialised

  • Programme EEKR-CZV lifelong learning

    branch EE-FLE , 1 year of study, summer semester, elective specialised

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Marie Havlíková, 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. Marie Havlíková, 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.