Course detail

Data Acquisition, Analysis and Processing

FEKT-MZPDAcad. year: 2019/2020

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

Language of instruction

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

Guarantor

Ing. Stanislav Pikula, 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 12 points (for credit minimum 6) for computer exercises
up to 18 points (for credit minimum 9) for project
up to 70 points (minimum 35) 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

Not applicable.

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme EEKR-M Master's

    branch M-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.

Laboratory exercise

39 hod., compulsory

Teacher / Lecturer

Ing. Jakub Krejčí
Ing. Stanislav Pikula, Ph.D.

Syllabus

Organization + Introduction to LabVIEW programming I.
Introduction to LabVIEW programming II.
Practical tasks
Correlation and convolution of signals
DFT, inverse DFT
FFT properties + windows
Regression, derivation, integration, interpolation
Filter design

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.