![]() ![]() It can also be used as a textbook for graduate courses in electrical engineering and computer science. The book will be an invaluable reference for scientists and engineers working in communications, control or any other field related to signal processing. A key feature of the book is that many carefully designed simulation examples are included to help guide the reader in the development of systems for new applications. ![]() The authors then discuss a number of powerful algorithms and architectures for a range of important problems, and describe practical implementation procedures. It begins by covering the basic principles and models of neural networks in signal processing. Temporal coding (that is, neural processing related to the timing of spikes). ![]() As is evident from the literature neural networks have already been used for a wide variety of tasks within medicine. Intelligent signal processing: wideband radio-frequency signal processing 17, fibre-optic communication 18,19. Requisites: course 131A, Mathematics 33A. Based on the backstepping technique, an adaptive neural boundary controller is developed that effectively suppresses the effects of input saturation. Applied Neural Networks for Signal Processing is the first book to provide a comprehensive introduction to this broad field. The current applications of neural networks to in vivo medical imaging and signal processing are reviewed. Description: Lecture, four hours discussion, one hour outside study, seven hours. The use of neural networks in signal processing is becoming increasingly widespread, with applications in many areas. FIR filter bank is an important tool for neural signal processing, and has been widely used in various biomedical applications, such as motor imagery-based BMIs 40, epilepsy detection 20, and. ![]()
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