Signals and Systems
|Language of Instruction:||Czech|
|Hour/sem||Lectures||Sem. Exercises||Lab. exercises||Comp. exercises||Other|
|Guarantor:||Černocký Jan, doc. Dr. Ing., DCGM|
|Lecturer:||Burget Lukáš, doc. Ing., Ph.D., DCGM|
Černocký Jan, doc. Dr. Ing., DCGM
Hubeika Valiantsina, Ing., DCGM
|Instructor:||Fapšo Michal, Ing., DCGM|
Grézl František, Ing., Ph.D., DCGM
Hannemann Mirko, Dipl.-Ing., DCGM
Hubeika Valiantsina, Ing., DCGM
Jančík Zdeněk, Ing., DCGM
Janda Miloš, Ing., DCGM
Kombrink Stefan, Dipl.-Inf -Ling, DCGM
Mikolov Tomáš, Ing., DCGM
Plchot Oldřich, Ing., Ph.D., DCGM
|Faculty:||Faculty of Information Technology BUT|
|Department:||Department of Computer Graphics and Multimedia FIT BUT|
| || ||To learn and understand basic theory of signals and linear systems with
continuous and discrete time. To introduce to random signals. The
emphasis of the course is on spectral analysis and linear filtering - 2
basic building blocks of modern communication systems.|
| || ||Continuous and discrete time signals and systems. Spectral analysis in
continuous time - Fourier series and Fourier transform. Systems with
continuous time. Sampling and reconstruction. Discrete-time signals and
their frequency analysis: Discrete Fourier series and Discrete-time
Fourier transform. Discrete systems. Two-dimensional signals and
systems. Random signals.|
|Knowledge and skills required for the course:|
| || ||basic maths and statistics|
|Subject specific learning outcomes and competences:|
| || ||Students will learn and understand basis of description and
analysis of discrete and continuous-time signals and systems. They will
also obtain practical skills in analysis and filtering in MATLAB.|
|Generic learning outcomes and competences:|
| || ||Students will deepen their knowledge in mathematics and statistics and
apply it on real problems of signal processing. During the course, they
will get acquainted with math- and visualization-SW Matlab.|
|Syllabus of lectures:|
- Introduction, motivation, organization of the course. Examples of
signal processing systems. Basic classification of signals -
continuous/discrete time, periodic/non-periodic. Transformation of
- Continuous and discrete time periodic signals: sinusoids and
complex exponentials. Overview of basic notions in complex numbers.
Discrete and continuous time systems. Linear, time invariant systms
(LTI). Representation of signals as series of pulses, convolution.
Describing systems using differential and difference equations.
- Continuous time signals and their frequency analysis:
periodic - Fourier series, coefficients. Non-periodic - Fourier
transform, spectral function. Spectra of typical signals. Signal energy
- Parseval's theorem.
- Continuous-time systems - Laplace transform, transfer
function, frequency response, stability. Example of a simple analog
- Sampling and reconstruction - ideal sampling, aliasing,
sampling theorem. Spectrum of sampled signal, ideal reconstruction.
Normalized time and frequency. Quantization.
- Discrete-time signals and their frequency analysis - Discrete
Fourier series, Discrete-time Fourier transform. Circular convolution,
- Discrete Fourier transform (DFT) and what it really
computes. Fast Fourier transform.
- Discrete systems - z-transform, finite and infinite impulse
response systems (FIR and IIR), transfer function, frequency response,
stability. Example of a digital filter: MATLAB and C.
- Discrete systems cont'd: design of simple digital filters,
sampling of frequency response, windowing. Links between
continuous-time and discrete-time systems.
- Two-dimensional (2D) signals and systems: space frequency,
spectral analysis (2D-Fourier transform), filtering using a mask.
Example - JPEG.
- Random signals - random variable, realization, distribution
function, probability density function (PDF). Stationarity and
ergodicity. Parameters of a random signal: mean, etc. Estimation -
ensemble and temporal.
- Random signals cont'd: correlation function, power spectral density (PSD). Processing of random signals by LTI systems.
- Summary of basic notions, systematic organization of signal processing knowledge. Examples.
|Syllabus of computer exercises:|
- Introduction to MATLAB
- Projection onto basis, Fourier series
- Processing of sounds
- Image processing
- Random signals
- Sampling, quantization and aliasing
|Syllabus - others, projects and individual work of students:|
| ||The individual project aims at image processing, see http://www.fit.vutbr.cz/study/courses/ISS/public/#proj|
- Oppenheim A.V., Wilski A.S.: Signals and systems, Prentice Hall, 1997
- Jan, J., Kozumplík, J.: Systémy, procesy a signály. Skriptum VUT v Brně, VUTIUM, 2000.
- Šebesta V.: Systémy, procesy a signály I., Skriptum VUT v Brně, VUTIUM, 1997.
- Jan J., Číslicová filtrace, analýza a restaurace signálů, VUT v Brně, VUTIUM, 2002, ISBN 80-214-1558-4.
| || |
- participation in computer labs is not checked but active
participation and presentation of results to the tutor is evaluated by
- Groups in computer labs are organized according to
inscription into schedule frames.
| || |
- active participation in computer labs, presentation of results to the tutor - 2 pts. each, total 12 pts.
- half-semester exam, written materials, computers and calculators prohibited, 25 pts.
- submission of project report - 12b.
- final exam - 51 pts., written materials, computers and calculators prohibited, list of basic equations will be at your disposal. The minimal number of points which can be obtained from the final
exam is 17. Otherwise, no points will be assigned to the student.