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The students become thoroughly familiar with the continuous-time signals and systems, discrete-time signals and systems, and their representation in the frequency domain, continuous-time and discrete-time random signals, sampling and signal recovery, analog and, in particular, digital communication systems. Students will learn how to use Matlab for signal processing applications in different practical areas. Students will have an idea of how to implement signals and systems in microprocessors and digital signal processors.

The subject knowledge on the secondary school level especially of maths and physics is required. Furthermore, a general knowledge of programming and computer skills are important. Emphasis is placed on the knowledge of complex numbers and their application.

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Lab exercises are mandatory for successfully passing this course and the students have to obtain the required credits. For lab work and numerical exercise tests they can get 40 points of 100 points. The remaining 60 points can be obtained by successfully passing the final written examination.

1. Signals and systems and their mathematical models
Real signals and their mathematical continuous-time models. Basic signal operations (time scaling, flipping, time shifting translation, time shifting translation and flipping, convolution, correlation). Signal classification, unit impulse, unit step, harmonic signal. Real systems with continuous- and discrete-time. Dynamic system, its input and output, status. Linear time-invariant system. Impulse response. Response of LTI system using convolution, superposition.
2. Periodic signals and their spectrum
Function substitution by functional series. Periodic continuous-time signal, harmonic signal and its representation by phasors. Periodic and harmonic discrete-time signals. Fourier series, spectrum of periodic rectangular pulses, spectrum theorems.
3. Fourier representation of aperiodic continuous-time signals
Definition of the Fourier transform of aperiodic continuous-time signals. Spectra of selected signals. Spectrum theorems. Definition of the inverse Fourier transform. The inverse Fourier transform of rectangular spectral impulse. Relationship between the Fourier series and the Fourier transform.
4. Continuous-time systems
The characteristics of a linear time-invariant (non-parametric) system (frequency response, hodograf). System transfer function, zero-pole plot. Ideal transfer circuit. Frequency filters. Non-linear systems. Superheterodyne.
5. Sampling of continuous-time signals
Ideal sampling of continuous-time signal and its reconstruction. Sampling theorem. Amplitude quantization. A/D and D/A conversions. Aliasing. Sampling of bandpass signals.
6. Discrete-time signals
Discrete time axis. Basic discrete signals. Signal theorems. Discrete linear, periodic and circular convolutions. Using FFT for convolution calculation.
7. Fourier transform of discrete-time signals.
The discrete Fourier series and the discrete Fourier transform. The fast Fourier transform (FFT). Decimation-in-Time (DIT) and Decimation-in-Frequency (DIF) algorithms, FFT algorithm properties.
8. Z transform and its properties
Definition of the Z transform and its properties. The inverse Z transform and its calculation. The relationship between the Z transform and the discrete Fourier transform.
9. Modulation signals in base-band and transition-band
Communication system and its properties, modulation and transmission rates, spectrum of communication channel. Amplitude, frequency, and phase analog modulations and their spectra. Digital modulations.
10. Stochastic variables and processes and their properties
Continuous and discrete time variables. Definition of stochastic processes with continuous- and discrete-time and their representations. Cumulative distribution function, probability density function. Moments (mean, variance, standard deviation, etc.). Stationarity and ergodicity.
11. Power spectral density and its calculation
Power spectral density of continuous- and discrete-time stochastic processes. Periodogram, using FFT for its calculation. White noise. Processing of stochastic signal by linear system. Non-parametric and parametric models.
12. Discrete-time systems
Linear time-invariant discrete system, impulse response. System transfer function, frequency response, zero-pole plot. Systems of the type of IIR and FIR. Connection of LTI systems. Series, parallel and feedback connections of partial sections.
13. Realization of LTI discrete system
Design of LTI discrete system based on analog prototype. Structures of realization. Mason’s gain rule. Implementation of LTI system on microprocessor. Calculation of frequency response based on time responses.

The aim of the course is to make students familiar with the continuous-time signals and systems, discrete-time signals and systems, continuous-time and discrete-time random signals, sampling and signal recovery, analog and digital communication systems.

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.

MITRA, S. K. Digital signal procesing. A computer-base approach. 1. vyd. New York: The McGraw-Hill Companies, 1998
OPPENHEIM, A. V., WILLSKY, A. S. Signals and Systems. Englewood Cliffs: Prentice Hall, 1997