Course detail

Digital Signal Processing and Analysis

FEKT-KCZAAcad. year: 2015/2016

Properties of discrete and digital methods of signal processing, advantages and drawbacks. Linear signal filtering, digital filters of FIR and IIR types, design and realisation. Averaging methods of enhancement of signal in noise. Complex signals and their application, modulation and frequency trasnlation. Correlation and spectral analysis of deterministic and stochastic signals, identification of systems. Detection, inverse filtering and restoration of distorted signals in noise.

Language of instruction

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

Learning outcomes of the course unit

The graduate of the course
- has a good insight into the theory of digital methods of signal processing and analysis,
- is capable of assessing suitability of a particular method for a given practical task,
- has the basic application skills for implementation of these methods.

Prerequisites

Knowledge of elements of signal and system theory, mathematics on Bc level

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations. Techning methods include lectures and computer laboratories. Course is taking advantage of e-learning (Moodle) system.

Assesment methods and criteria linked to learning outcomes

final written exam 72 points (minimum 36 points)
7 homeworks 4 points each, i.e. 28 points (min. 1 point per homework, total minimum 14 points)


Course curriculum

1. Classification of discrete methods of signal processing, properties and applications
2. Linear filtering - FIR filters 1
3. Linear filtering - FIR filters 2
4. Linear filtering - IIR filters 1
5. Linear filtering - IIR filters 2
6. Averaging of signals
7. Complex signals and analytic filters
8. Spectrum translation of signals
9. Correlation analysis of signals - estimation methods of the correlation function
10. Correlation analysis of signals - applications
11. Spectral analysis of deterministic signals
12. Spectral analysis of stochastic signals
13. Inverse filtering and principles of signal restoration

Work placements

Not applicable.

Aims

To provide basic theoretical knowledge and practical experience in the area of digital signal processing, analysis and restoration

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

Five tutorials covering the described curriculum, two computer labs, seven homeworks

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

J.Jan: Digital Signal Filtering, Analysis and Restoration. IEE Publishing, London, UK, 2000 (EN)
J.Jan: Číslicová filtrace, analýza a restaurace signálů (druhé rozsírené vydání),VUTIUM Brno, 2002

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme EEKR-BK Bachelor's

    branch BK-EST , 2. year of study, summer semester, compulsory

  • Programme EEKR-CZV lifelong learning

    branch ET-CZV , 1. year of study, summer semester, compulsory

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

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 hours, compulsory

Teacher / Lecturer

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