Course detail

Signals 2

FEKT-BKC-SI2Acad. year: 2020/2021

The subject is focused on the analysis and digital processing of signals from the field of telecommunication. It provides a theoretical basis of signal modulation, discrete linear transformations, description of random process and its characteristics. It also deals with the problems of filtration (FIR, IIR, adaptive, inverse), correlation and spectral analysis of signals, detection of signals in noise. The subject provides both theoretical background and practical verification. To this end, the Matlab programming environment will be used.

Language of instruction

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

Guarantor

doc. RNDr. Jitka Poměnková, Ph.D.

Department

Department of Radio Electronics (UREL)

Learning outcomes of the course unit

Students should have knowledge of the basic theory of digital processing and analysis of signals in the telecommunication field after completing the course. They should also be able to independently solve practical tasks, i.e. choose and justify a suitable method and implement it.

Prerequisites

Objects are required BPC-SI1, BPC-PP1, BPC-MA1, BPC-MA2
Knowledge of elements of signal and system theory, mathematics on Bc level, Matlab.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the way of teaching and are described in Article 7 of the BUT Rules for Studies and Examinations.
Teaching methods include lectures and computer exercises. Student develops individual tasks during computer exercises.

Assesment methods and criteria linked to learning outcomes

The conditions for the successful completion of the course are specified in the annually updated guarantor's order. In general
- obtaining credit on the basis of individual works (max. 30 points, minimum 15 points),
- Written parts of the final exam (max 70 points, minimum 35 points)

Course curriculum

Lectures:
1. Introduction to signal analysis.
2. Discrete signals.
3. Discrete linear transformations.
4. Discrete linear systems
5. Linear filtering of signals. FIR type filters
6. Type IIR filters
7. Signal representation.
8. Modulation
9-10. Random signals and their characteristics
11. Correlation analysis of signals
12. Spectral analysis of deterministic signals.
13. Spectral analysis of stochastic signals.

Work placements

Not applicable.

Aims

The aim of the course is to provide students with theoretical knowledge in the field of digital processing and signal analysis and practical verification of acquired skills.

Specification of controlled education, way of implementation and compensation for absences

The definition of the controlled education and the way of its implementation are stipulated by the updated guarantor's annual regulation.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Not applicable.

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme BKC-EKT Bachelor's 2 year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. RNDr. Jitka Poměnková, Ph.D.

Syllabus

Properties of discrete and digital methods of signal processing
Linear signal filtering
FIR filters
IIR filters
Averaging methods of signal enhancement in noise
Complex signals and their applications
Frequency translation of signals
Correlation analysis of signals
Spectral analysis of deterministic signals
Spectral analysis of stochastic signals
Detection of signals in noise, plain inverse filtering
Restoration of signals, Wiener filter

Exercise in computer lab

26 hod., compulsory

Teacher / Lecturer

doc. RNDr. Jitka Poměnková, Ph.D.

Syllabus

Becoming acquinted with the MATLAB
Linear signal filtering
FIR filters
IIR filters
Averaging methods of signal enhancement in noise
Complex signals and their applications
Frequency translation of signals
Correlation analysis of signals
Spectral analysis of deterministic signals
Spectral analysis of stochastic signals
Detection of signals in noise, plain inverse filtering
Restoration of signals

Elearning

eLearning: currently opened course