Windowed State-Space Filters for Signal Detection and Separation - 2018


This Project introduces a toolbox for model-primarily based detection, separation, and reconstruction of signals that is especially suited to biomedical signals, like electrocardiograms (ECGs) or electromyograms (EMGs). The modeling is predicated on autonomous linear state house models (LSSMs), which are localized with versatile windows. The models are work to observations by minimizing the squared error whereas the employment of LSSMs ends up in economical recursive error computations and minimizations. Multisection windows enable complicated models, and per-sample weights enable multistage processing or adaptive smoothing. This Project is motivated by, and intended for, practical applications, for that many examples and tabulated value computations are given.

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PROJECT TITLE :A New Regularized Adaptive Windowed Lomb Periodogram for Time–Frequency Analysis of Nonstationary Signals With Impulsive ComponentsABSTRACT :This paper proposes a new class of windowed Lomb periodogram
PROJECT TITLE :Windowed Decoding of Protograph-Based LDPC Convolutional Codes Over Erasure ChannelsABSTRACT:We consider a windowed decoding scheme for LDPC convolutional codes that is based on the belief-propagation (BP) algorithm.

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