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Lecturer(s)
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Filip Aleš, doc. Ing. CSc.
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Course content
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The content of the course are the following chapters: Random signals - characteristics of random signals in time and frequency domain. Estimates of random and nenáhodných parameters. Cramer-Raova limit. Formalized filtering and restoration of signals. Wiener filtering for continuous and discrete time. Kalman filtering for continuous and discrete time, its use for modeling system Adaptive filtering and identification. Adaptive filtering algorithms.
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Learning activities and teaching methods
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Dialogic (discussion, interview, brainstorming), Methods of individual activities, Laboratory work
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Learning outcomes
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The aim of the course is to acquaint students with modern methods of signal processing.
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Prerequisites
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unspecified
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Assessment methods and criteria
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Oral examination, Written examination, Home assignment evaluation
The student completes at least 3 consultations during the semester concerning the theoretical content of the course. The student will pass at least 2 consultations concerning the assigned practical work. In the framework of the practical work the student will work on the topic of advanced methods of signal processing, filtering methods, adaptive filtration, nonlinear filtration or spectral analysis. The specific topic will be determined regarding the topic of dissertation.
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Recommended literature
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Boualem, Boashash. Time-frequency Siganl Analysis and Processing a Comprehensive Review. ISBN 978-012-3984-999.
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Goodwin, Graham C., Kwai Sang Sin. Adaptive filtering prediction and control. ISBN 978-0-486-46932-4.
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Kay, Steven M.. Fundamentals of statistical signal processing : practical algorithm development.. Upper Saddle River: Prentice Hall, 2013. ISBN 978-0-13-280803-3.
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Morrison, Norman. Tracking filter engineering: the Gauss-Newton and polynomial filters. ISBN 978-1-84919-554-6.
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