Course detail

Digital Signal Processing and Analysis

FEKT-BCZAAcad. year: 2018/2019

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.

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.


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


Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

J.Jan: Digital Signal Filtering, Analysis and Restoration. IEE Publishing, London, UK, 2000
B.Mulgrew, P.M.Grant J.S.Thompson: Digital Signal Processing, Concepts and Applications, Mac-Millan Pres Ltd.1999
M. Bellanger:Digital Processing of Signals,J.Wiley 1990
J.Jan: Číslicová filtrace, analýza a restaurace signálů (druhé rozsírené vydání),VUTIUM Brno, 2002
V.K.Madisetti, D.B.Williams (eds.): The Digital Signal Processing Handbook. CRC Press & IEEE Press, 1998
JAN, J. Medical Image Processing, Reconstruction and Restoration - Concepts and Methods. Signal Processing and Comm. Signal Processing and Comm. Boca Raton, FL, USA: CRC Press, Taylor and Francis Group, 2006. 760 s. ISBN: 0-8247-5849- 8.

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

Requirements for completion of a course are elaborated by the lecturer responsible for the course every year;
- obtaining at least 12 points (out of 24 as course-unit credit based on active presence in demonstration exercises),
- successful passing of final written exam (up to 76 points)

Language of instruction


Work placements

Not applicable.

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


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

computer labs

Classification of course in study plans

  • Programme EEKR-B Bachelor's

    branch B-EST , 2. year of study, summer semester, 6 credits, compulsory

  • Programme EEKR-CZV lifelong learning

    branch ET-CZV , 1. year of study, summer semester, 6 credits, compulsory

Type of course unit



39 hours, optionally

Teacher / Lecturer

Computer exercise

26 hours, compulsory

Teacher / Lecturer


eLearning: opened course