Course detail
Data Acquisition, Analysis and Processing
FEKT-MPC-ZPDAcad. year: 2020/2021
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
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
- 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
Co-requisites
Planned learning activities and teaching methods
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 70 points for the final written examination.
Course curriculum
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
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
UHLÍŘ, Jan, Pavel SOVKA a Roman ČMEJLA. Úvod do číslicového zpracování signálů. Praha: Vydavatelství ČVUT, 2003. ISBN 80-01-02799-6. (CS)
Recommended reading
Classification of course in study plans
- Programme MPC-KAM Master's 1 year of study, winter semester, compulsory
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
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
Teacher / Lecturer
Syllabus
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