Course detail
Signals and Systems
FSI-VSG Acad. year: 2021/2022 Winter semester
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.
Supervisor
Department
Learning outcomes of the course unit
Students will deepen their knowledge in mathematics and statistics and apply it to real problems of signal processing.
Prerequisites
Mathematical Analysis (M1, M3)
Planned learning activities and teaching methods
The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.
Assesment methods and criteria linked to learning outcomes
ASSESSMENT POINTS
51 exam, 25 half-term test, 12 labs, 12 projects
Language of instruction
Czech
Aims
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.
The study programmes with the given course
Programme N-AIŘ-P: Applied Computer Science and Control, Master's
branch ---: no specialisation, 5 credits, compulsory
Type of course unit
Lecture
39 hours, optionally
Teacher / Lecturer
Syllabus
1. Digital filters – fundamentals and practical usage
2. Frequency analysis using DFT – fundamentals and practical usage
3. Image processing (2D signals) – fundamentals and practical usage
4. Random signals – fundamentals and practical usage
5. Applications of signal processing and introduction to the theory
6. Frequency analysis of continuous time signals
7. Continuous time systems
8. From continuous to discrete – sampling, quantization
9. The discrete signal in more detail
10. Spectral analysis of discrete signals in more detail.
11. Digital filtering in more detail
12. Random signals in more detail
13. Applications and advanced topics of signal processing
Computer-assisted exercise
26 hours, compulsory
Teacher / Lecturer
Syllabus
NUMERICAL EXERCISES
1. Complex numbers, cosines and complex exponentials and operations therewith
2. Basics, filtering, frequency analysis
3. Continuous time signals: energy, power, Fourier series, Fourier transform
4. Continuous time systems and sampling
5. Operations with discrete signals, convolutions, DTFT, DFT
6. Digital filtering and random signals
The project aims at the practical experience with signals and systems in Matlab/Octave. Its study etap contains solved exercises on the following topics:
1. Introduction to MATLAB
2. Projection onto basis, Fourier series
3. Processing of sounds
4. Image processing
5. Random signals
6. Sampling, quantization and aliasing