39 hours, optionally

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