Course details
Advanced Methods of Signal Processing
MMZS FEKT MMZS Acad. year 2019/2020 Summer semester 6 credits
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.
Guarantor
Language of instruction
Completion
Time span
- 39 hrs lectures
- 26 hrs pc labs
Department
Lecturer
Instructor
Subject specific learning outcomes and competences
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.
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Learning objectives
The goal of the course is to provide insight into principles of advanced signal processing methods and their relations, and demonstrating some practical applications.
Prerequisite knowledge and skills
The knowledge on the Bachelor´s degree level is requested, namely on digital signal processing
Study literature
- 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
Fundamental literature
- Jan, J.: Číslicová filtrace, analýza a restaurace signálů. Vutium Brno, 2002
- J.Jan: Digital Signal Filtering, Analysis and Restoration. IEE Publishing, London, UK, 2000
Syllabus of lectures
More profound view of linear filtering, state models, design methods of FIR and IIR filters
Multirate systems, banks of decimation and interpolation filters
Non-linear filtering, polynomial filters, generalised and adaptive median filter, homomorphic filtering, non-linear matched filters
Classical and modern methods of statistical characteristics identification of stochastic signals
Unifying approach to methods of formalised signal restoration. Discrete Wiener filter as a golden standard
Kalman filtering, stationary and non-stationary, aaplication in signal restoration and in modelling of signal sources
Restoration via frequency domain. Constrained deconvolution, deconvolution via optimisation of impulse response
Concept of adaptive filtering, filter with recursive optimum adaptation, filter with stochastic gradient adaptation
Classification of adaptive filtering applications: system identification and modelling, channel equalisation, adaptive linear prediction, adaptive noise adn interference cancelling
Introduction to architecture and properties of neural networks: feed-forward networks, learning, knowledge generalisation; feed-back networks; self-organising maps.
Neural-network based signal processing: learned and adaptive neural filter, formalised restoration by feed-back networks
Typical applications of the above methods in communication, speech and acoustic signal processing
Typical applications of the above methods in processing of measurement and diagnostic signals, system identification and in biomedical applications
Syllabus of computer exercises
Becoming acquainted with MATLAB - Signal Processing Toolbox and DSP Blockset environment
Design and verification of an FIR or IIR filter
Application of adaptive median- or homomorphic filtering
Identification of statistical properties of given stochastic signals
Design and application of a discrete Wiener filter
Kalman filtering, aaplication in modelling of signal sources
Restoration by a modified inverse filter via frequency domain
Experiment with an adaptive filter with stochastic gradient adaptation
Adaptive cancelling of given interference
Experimenting with a feed-forward network, learning and knowledge generalisation
Signal processing by a learned neural filter
Applications of given methods in acoustic signal processing
Applications of the above methods in processing of given measurement and diagnostic signals
Progress assessment
Requirements for completion of a course are elaborated by the lecturer responsible for the course every year;
basically:
- 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)
Teaching methods and criteria
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.
Controlled instruction
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).
Basically:
- obligatory computer-lab tutorial
- voluntary lecture
Course inclusion in study plans