Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

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System Theory

A tantárgy neve magyarul / Name of the subject in Hungarian: Rendszerelmélet

Last updated: 2019. február 13.

 Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics
 Course ID Semester Assessment Credit Tantárgyfélév VIHVAB00 3 2/2/0/f 4
3. Course coordinator and department Dr. Nagy Lajos,
4. Instructors Lajos Nagy, Department of Broadband Infocommunication and Electromagnetic Theory
6. Pre-requisites
Kötelező:
(TárgyEredmény( "BMETE90AX22" , "jegy" , _ ) >= 2
VAGY
TárgyEredmény( "BMETE90AX05" , "jegy" , _ ) >= 2)

ÉS NEM ( TárgyEredmény( "BMEVIHVA214", "jegy" , _ ) >= 2
VAGY TárgyEredmény("BMEVIHVA214", "FELVETEL", AktualisFelev()) > 0)

ÉS (Training.Code=("5N-A8") VAGY Training.Code=("5NAA8"))

A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.

A kötelező előtanulmányi rendek grafikus formában itt láthatók.

7. Objectives, learning outcomes and obtained knowledge The objective of the course is to present the most important notions and principles of the signal and system theory and to establish their mathematical relationships. The presentation is focused on the analysis of the discrete and continuous time, linear and time-invariant systems. The system analysis is discussed in time, frequency (Fourier transform) and complex frequency domain (Laplace and Z transform).

The theory is illustrated by practical examples taken from real engineering problems - signal and image processing and telecommunication channel description - modulation.

Obtained skills and expertise:
Ability to analyze linear, time-invariant systems both in the time and in the frequency domains.

8. Synopsis Lectures

Lecture 1. Definition of signals, systems and networks. Signal characterization - discrete and continuous time signals. Deterministic and stochastic signals. Special signals - step, impulse signals. Finite time, finite energy signals. Even and odd signals. Dirac delta (impulse) signal.

Lecture 2. System Classification. SISO, SIMO, MISO, MIMO systems. Causality, linearity, time invariance. Systems with memory. Deterministic and stochastic systems.

Lecture 3. Network characterization. Input and output of the netork. Basic operations on discrete time (DT) and continuous time (CT) signals. Time domain description of DT and CT systems. Impulse response, convolution.

Lecture 4. Input-output (BIBO) stability. Stability criteria of LTI systems using impulse response.

Lecture 5. State space representation, system response calculation using matrix functions. System eigenvalues - system response.

Lecture 6. Asymptotic stability. Asymptotic and BIBO stability.

Lecture 7. Engineering problems. Signal flow networks (SFN), signals low graphs. Feedback in networks - negative feedback.

Lecture 8. Sinusoidal signal description - complex phasor representation. System in steady-state. Transfer factor of system.

Lecture 9. Periodic signals - Fourier series. Periodic response of linear systems.

Lecture 10. Signal spectrum - Fourier transform. Band and time limited signals. Windowing of signals. Fourier transform, system transfer characteristics, distortionless signal transmission, Bode plot.

Lecture 11. Signal description in complex frequency domain. Laplace transform. Inverse Laplace transorm. Transfer function.

Lecture 12. Discrete time signal and system description - Z transform, inverse Z transform.

Lecture 13. Sampling of signals. Shannon sampling theorem.

Lecture 14. Control theory introduction - feedback, loop gain, phase margin.

Practicum

Practicum 1. Signal characterization - discrete and continuous time signals. Special signals - step, impulse signals. Even and odd signals. Dirac delta (impulse) signal.

Practicum 2. Impulse response. Convolution. Step response. Multipath channel impulse response.

Practicum 3. Impulse response and step response calculation for LTI systems. System response calculation using convolution.

Practicum 4. State space representation, system impulse response calculation using matrix functions. System eigenvalues - impulse response.

Practicum 5. State space representation, system response calculation.

Practicum 6. Signal flow networks (SFN), signals low graphs. Analogy of electrical, mechanical systems.

Practicum 7. Sinusoidal signal description - complex phasor representation. System in steady-state. Transfer factor of system.

Practicum 8. Periodic signals - Fourier series. Periodic response of linear systems. Realization of transfer characteristics - direct and cascade realization. FIR and IIR systems.

Practicum 9. Signal spectrum - Fourier transform. Band and time limited signals. Fourier transform, system transfer characteristics, distortionless signal transmission, Bode plot.

Practicum 10. Signal description in complex frequency domain. Laplace transform. Inverse Laplace transorm. Transfer function.

Practicum 11. Discrete time signal and system description - Z transform, inverse Z transform.

Practicum 12. Analysis in complex frequency domain - signal flow graph, state space model.

Practicum 13. Sampling of signals. Shannon sampling theorem.

Practicum 14. Simple control system model and analysis. Feedback, loop gain, phase margin

9. Method of instruction 2 lecture and 2 practicum pro week
13. References, textbooks and resources
14. Required learning hours and assignment
 Kontakt óra Félévközi készülés órákra Felkészülés zárthelyire Házi feladat elkészítése Kijelölt írásos tananyag elsajátítása Vizsgafelkészülés Összesen