Advanced Methods of Signal Processing
FEKT-LMZSAcad. 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
Recommended optional programme components
Recommended or required reading
J.Jan: Digital Signal Filtering, Analysis and Restoration. IEE Publishing, London, UK, 2000
J.Jan: Číslicová fitlrace, analýza a restaurace aignálů. VUTIUM 2002
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
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