Course detail
Fundamentals of Digital Signal Processing
FEKT-GFSPAcad. year: 2019/2020
The course deals with fundamentals of digital signal processing and digital system analysis − a topic that forms an integral part of engineering systems in many diverse areas. The course presents basic principles of discrete-time signals and systems. Signal representations are developed for both time and frequency domains. Basic types of signals and their properties, useful signal operations, as well as classification and analysis of systems, are discussed and illustrated. In addition, students become familiar with visualization and processing of signals using computer with MATLAB. Students will use gained knowledge in subsequent courses oriented to specific applications of signal processing.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
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.
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
KAMEN, E.W., HECK, B.S. Fundamentals of Signals and Systems. Englewood Cliffs: Prentice Hall, 2007. (EN)
MITRA, S.K. Digital signal processing. A computer-base approach. New York: The McGraw-Hill Companies, 2011. (EN)
Recommended reading
Elearning
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
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.
Exercise in computer lab
Teacher / Lecturer
Syllabus
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