Signals and Systems

Public info:http://www.fit.vutbr.cz/study/courses/ISS/public/
Completion:examination (written)
Type of
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Guarantee: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
Discrete Mathematics (IDA), DMAT
Mathematical Analysis (IMA), DMAT
Learning objectives:
  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:
  1. Introduction, motivation, organization of the course. Examples of signal processing systems. Basic classification of signals - continuous/discrete time, periodic/non-periodic. Transformation of time.
  2. 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.
  3. 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.
  4. Continuous-time systems - Laplace transform, transfer function, frequency response, stability. Example of a simple analog circuit.
  5. Sampling and reconstruction - ideal sampling, aliasing, sampling theorem. Spectrum of sampled signal, ideal reconstruction. Normalized time and frequency. Quantization.
  6. Discrete-time signals and their frequency analysis - Discrete Fourier series, Discrete-time Fourier transform. Circular convolution, fast convolution.
  7. Discrete Fourier transform (DFT) and what it really computes. Fast Fourier transform.
  8. 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.
  9. Discrete systems cont'd: design of simple digital filters, sampling of frequency response, windowing. Links between continuous-time and discrete-time systems.
  10. Two-dimensional (2D) signals and systems: space frequency, spectral analysis (2D-Fourier transform), filtering using a mask. Example - JPEG.
  11. 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.
  12. Random signals cont'd: correlation function, power spectral density (PSD). Processing of random signals by LTI systems.
  13. Summary of basic notions, systematic organization of signal processing knowledge. Examples.
Syllabus of computer exercises:
  1. Introduction to MATLAB
  2. Projection onto basis, Fourier series
  3. Processing of sounds
  4. Image processing
  5. Random signals
  6. 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
Fundamental literature:
  • Oppenheim A.V., Wilski A.S.: Signals and systems, Prentice Hall, 1997
Study literature:
  • http://www.fit.vutbr.cz/study/courses/ISS/public/
  • 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.
Controlled instruction:
  • participation in computer labs is not checked but active participation and presentation of results to the tutor is evaluated by 2 pts.
  • Groups in computer labs are organized according to inscription into schedule frames.
Progress assessment:
  • 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.