Signals and Systems
FIT-ISSAcad. year: 2019/2020
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.
Learning outcomes of the course unit
Students will learn and understand the basis of the description and analysis of discrete and continuous-time signals and systems. They will also obtain practical skills in analysis and filtering in MATLAB/Octave.
Students will deepen their knowledge in mathematics and statistics and apply it to real problems of signal processing.
Recommended optional programme components
Recommended or required reading
Jan, J., Kozumplík, J.: Systémy, procesy a signály. Skriptum VUT v Brně, VUTIUM, 2000. (CS)
Šebesta V.: Systémy, procesy a signály I., Skriptum VUT v Brně, VUTIUM, 1997. (CS)
Jan J., Číslicová filtrace, analýza a restaurace signálů, VUT v Brně, VUTIUM, 2002, ISBN 80-214-1558-4. (CS)
Oppenheim A.V., Wilski A.S.: Signals and systems, Prentice Hall, 1997
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
- 6 tests in numerical exercises, each 2 pts, total 12 pts.
- half-semester exam, written materials, computers and calculators prohibited, 25 pts.
- submission of project report - 12 pts.
- 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.
Language of instruction
To learn and understand the 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 - two basic building blocks of modern communication and machine learning systems.
Specification of controlled education, way of implementation and compensation for absences
- participation in numerical exercises is not checked, but tests are conducted in them, each worth 2 points.
- Groups in numerical exercises are organized according to
inscription into schedule frames.
- Replacing missed exercises (and obtaining the points) is possible by (1) attending the exercise and the test with another group, (2) solving all tasks in given assignment and presenting them to the tutor, (3) examination by the tutor or course responsible after an appointment. Options (2) and (3) are valid max. 14 days after the missed exercises, not retroactively at the end of the course.
Type of course unit
39 hours, optionally
Teacher / Lecturer
- Digital filters - fundamentals and practical usage
- Frequency analysis using DFT - fundamentals and practical usage
- Image processing (2D signals) - fundamentals and practical usage
- Random signals - fundamentals and practical usage
- Applications of signal processing and introduction to the theory
- Frequency analysis of continuous time signals
- Continuous time systems
- From continuous to discrete - sampling, quantization
- The discrete signal in more detail
- Spectral analysis of discrete signals in more detail.
- Digital filtering in more detail
- Random signals in more detail
- Applications and advanced topics of signal processing
12 hours, compulsory
Teacher / Lecturer
- Complex numbers, cosines and complex exponentials and operations therewith
- Basics, filtering, frequency analysis
- Continuous time signals: energy, power, Fourier series, Fourier transform
- Continuous time systems and sampling
- Operations with discrete signals, convolutions, DTFT, DFT
- Digital filtering and random signals
14 hours, compulsory
Teacher / Lecturer
The project aims at the practical experience with signals and systems in Matlab/Octave. Its study etap contains solved exercises on the following topics:
- Introduction to MATLAB
- Projection onto basis, Fourier series
- Processing of sounds
- Image processing
- Random signals
- Sampling, quantization and aliasing
eLearning: currently opened course