English

5

Not applicable.

The graduate is able: (1) to display and describe digital signals, (2) to define and generate required digital signal, (3) to estimate spectrum and properties of digital signals, (4) to analyze digital systems, (5) to discuss advantages and disadvantages of signal processing methods.

Knowledge of bachelor mathematics is requested (derivations, integrals, solution of equations, fundamentals of probability analysis, statistical distributions).

Not applicable.

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

Students can obtain maximally 30 points for their activities during semester and 70 points for the final exam. The honored activities are as follows: one midterm test oriented to calculations of signal problems (10 points) and computer exercises (20 points). The written final exam is based on the theory of signals and systems as well as calculations (70 points).

Lectures:
1. Introduction to digital signals and systems, classification of signals, applications.
2. Signal processing in time domain, periodic and aperiodic signals, typical examples.
3. Signal processing in frequency domain, discrete Fourier transform, applications of DFT.
4. Spectral analysis of signals, time-frequency analysis, sliding DFT.
5. Correlation and convolution, properties, application examples, interrelationship.
6. Discrete transforms, cosine transform, wavelet transform.
7. Random signals, statistical properties, stacionarity, stochastic processes.
8. Digital filters, basic filter structures, block diagram representation, filter design.
9. Signals in noise, properties of noise, white noise, filtering, signal restoration.
10. Analysis of finite word length effects, sampling, quantization, signal-to-noise ratio.
11. Discrete‐time systems, system blocks, LTI systems.
12. Identification and analysis of discrete‐time systems, impulse and step responses.
13. Examples of signal processing in multimedia, medicine, and security.

Computer exercises:
1. Waveform generation using MATLAB, time vectors, periodic and aperiodic waveforms.
2. Visualization of signals, 2D and 3D representations, multichannel signals.
3. Spectra of typical periodic and aperiodic signals.
4. Short-time spectral analysis of speech signal.
5. Generation of echo in acoustic signals.
6. Calculation of mel-frequency coefficients using cosine transform.
7. Generation of typical random signals.
8. Design of simple digital filters.
9. Denoising of acoustic signals.
10. Statistical analysis of measured aperiodic signals.
11. Effect of nonlinear amplifier on signal spectrum.
12. Signal modulation and demodulation for data transmission in communication.
13. Fundamental frequency of voice as biometric feature.

Not applicable.

The course is aimed to present common types of digital signals and their processing, and to show analysis of systems as well as principles of interactions between signals and systems.

Computer exercises are compulsory. Missed lessons can be made up usually by the end of semester.

Not applicable.

Not applicable.

MITRA, S.K. Digital signal processing. A computer-base approach. New York: The McGraw-Hill Companies, 2011. (EN)
KAMEN, E.W., HECK, B.S. Fundamentals of Signals and Systems. Englewood Cliffs: Prentice Hall, 2007. (EN)
IFEACHOR, E.C., JERVIS, B.W. Digital signal processing. A practical approach. Englewood Cliffs: Prentice Hall, 2002. (EN)

Not applicable.

eLearning: currently opened course

26 hours, compulsory

1. Waveform generation using MATLAB, time vectors, periodic and aperiodic waveforms.
2. Visualization of signals, 2D and 3D representations, multichannel signals.
3. Spectra of typical periodic and aperiodic signals.
4. Short-time spectral analysis of speech signal.
5. Generation of echo in acoustic signals.
6. Calculation of mel-frequency coefficients using cosine transform.
7. Generation of typical random signals.
8. Design of simple digital filters.
9. Denoising of acoustic signals.
10. Statistical analysis of measured aperiodic signals.
11. Effect of nonlinear amplifier on signal spectrum.
12. Signal modulation and demodulation for data transmission in communication.
13. Fundamental frequency of voice as biometric feature.

eLearning: currently opened course