Course detail

Advanced Methods of Signal Processing

FEKT-MMZSAcad. year: 2019/2020

Formalised optimum filtering and signal restoration in unified view: Wiener filter in clasical formulation and generalised discrete Wiener-Levinson filter, Kalman filtering; source modelling and signal restoration, further approaches. Adaptive filtering and identification, algorithms of adaptation, classification of typical applications of adaptive filtering. Neural networks - error-backpropagation networks, feed-back networks, self-organising networks, and their application in signal processing and classification. Non-linear filtering - polynomial and ranking filters, homomorphic filtering and deconvolution, non-linear matched filters. Typical applications of the above methods.

Learning outcomes of the course unit

The graduate of the course is capable of:
- understanding principles of advanced signal processing methods and their relations,
- choosing a suitable method for a specific practical purpose,
- implementing the chosen method in a computing environment as a commercial or individually developed software,
- properly interpreting the results of the analyses.


The knowledge on the Bachelor´s degree level is requested, namely on digital signal processing


Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Jan, J.: Číslicová filtrace, analýza a restaurace signálů. Vutium Brno, 2002
J.Jan: Digital Signal Filtering, Analysis and Restoration. IEE Publishing, London, UK, 2000
P.M.Clarkson: Optimal and Adaptive Signal Processing. CRC Press, 1993
S. Haykin: Modern Filters. MacMillan Publ., 1990
B.Mulgrew, P.M.Grant J.S.Thompson: Digital Signal Processing, Concepts and Applications, Mac-Millan Pres Ltd.1999
V.K.Madisetti, D.B.Williams (eds.): The Digital Signal Processing Handbook. CRC Press & IEEE Press, 1998
Vích,R., Smékal,Z.: Číslicové filtry. Academia Praha 2000, ISBN 80-200-0761-X
B. Kosko (ed.): Neural Networks for signal processing. Prentice Hall 1992
J.G.Proakis, et al.: Advanced digital signal processing. McMillan Publ. 1992

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. Identification of stochastic signals. Introduction to signal restoration, formalised optimum LMS signal restoration in unified presentation
2. Wiener filter in classical and generalised discrete representation
3. Scalar and vector Kalman filtering, modelling of signal sources
4. Principles of adaptive filtering, algorithms of adaptation
5. Applications of adaptive filtering, classifying applications
6. Introduction to non-linear filtering – polynomial and ranking filters, homomorphic filtering and deconvolution, nonlinear matched filters
7. Introduction to neural networks, individual neuron and its learning
8. Feedforward layered networks learning by error back propagation, radial base networks
9. Feedback networks: Hopfield and Boltzmann nets, competing and Jordan networks
10. Self-organising networks, Kohonen maps
11. Applications of neural networks in signal processing and analysis
12. Principal component analysis in signal processing
13. Independent component analysis in signal processing


The goal of the course is to provide insight into principles of advanced signal processing methods and their relations, and demonstrating some practical applications.

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

Delimitation of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year (see Rozvrhové jednotky).
- obligatory computer-lab tutorial
- voluntary lecture

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MIN , any year of study, summer semester, 6 credits, elective

  • Programme EEKR-M1 Master's

    branch M1-BEI , 1. year of study, summer semester, 6 credits, optional specialized
    branch M1-EST , 1. year of study, summer semester, 6 credits, optional interdisciplinary

  • Programme EEKR-CZV lifelong learning

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

Type of course unit



39 hours, optionally

Teacher / Lecturer

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer