Course detail
Data Acquisition, Analysis and Processing
FEKT-MPC-ZPDAcad. year: 2023/2024
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
Entry knowledge
Rules for evaluation and completion of the course
up to 70 points for the final written examination.
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.
Aims
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.
Study aids
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
1. Organization + Review of LabVIEW I.
2. Basic operations with signals
3. Time series analysis
4. Work with HW
5. Convolution, DFT (graded task 1)
6. Spectral analysis
7. Design of filters I
8. Design of filters II (graded task 2)
9. Noise, correlation
10. Modulation
11. Demodulation (graded task 3)
12. STFT
13. Test