Course detail
Data acquisition,analysis and processing.
FEKT-NZPDAcad. year: 2015/2016
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.
Supervisor
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, LabVIEW
Co-requisites
Not applicable.
Recommended optional programme components
Not applicable.
Recommended or required reading
Blachut,R.E.:Fast Algorithms for Digital Signal Processing,Springer (EN)
Otnes,R.K.-Enochson,L.:Applied Time Series Analysis,Wiley (EN)
Rabiner,R.L.-Gold,B.:Theory and Application of Digital Signal Processing.,Prentice Hall (EN)
Smith, S.W.:Digital Signal Processing. California Technical Publishing, San Diego, California 1999 (EN)
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 30 points for the evaluation computer.
up to 70 points for the final written examination.
Language of instruction
English
Work placements
Not applicable.
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
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.
Type of course unit
eLearning
eLearning: currently opened course