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Kalman Filter in Biomedical Signal Processing Vangelis P. Oikonomou, Alexandros T. Tzallas, Spiros Konitsiotis, Dimitrios G. Tsalikakis and Dimitrios I. Fotiadis Department of Computer Science, University of Ioannina GR 45110 Ioannina, Greece 1.Introduction The
Kalman Filter (KF) is a powerful tool in the analysis of the evolution of a dynamical model in time.
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Midnattssol över Lappland Den danske salmebog I joined Brunel as Professor of Computing in 2000. Prior to that, I was a member of academic staff in computer science at Birkbeck, University of London and research staff in engineering at Durham and Heriot-Watt Universities.At Brunel, I was Director of Research (2006-14) for the School of Information Systems, Computing and Mathematics, Doctoral Programme Director (2008-13) and Chair of ...
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Kalman Filtering Originally developed for use in spacecraft navigation, the
Kalman filter turns out to be useful for many applications.
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Sensor fusion is combining of sensory data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. The term uncertainty reduction in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision (calculation …
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Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution ...
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