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A totally distributed and scalable support vector machine (DSVM) for classification in ad hoc wireless sensor networks (WSNs) is proposed. A sequential gradient ascent based algorithm is first introduced and adapted for distributed and parallel SVM training using only the local dataset for each classification agent. Then the global nonlinear classifier is(More)
We have investigated how LTB4, an endogenous chemoattractant encountered early in the inflammatory process, and fMLP, a bacteria-derived chemotactic peptide emanating from the site of infection, mediate inside-out regulation of the beta2-integrin. The role of the two chemoattractants on beta2-integrin avidity was investigated by measuring their effect on(More)
To avoid both the inconsistency of the Kalman filter and the performance conservation of the covariance intersection (CI) in the case of unknown correlations, an internal ellipsoidal approximation (IEA) method is proposed to fuse the local estimations. A numerical example of three-state radar tracking system is presented to illustrate the implementation and(More)
—A scalable Sigma-Point Kalman filter (DSPKF) is proposed for distributed target tracking in a sensor network in this paper. The main idea is to use dynamic consensus strategy to the information form sigma-point Kalman filter (ISPKF) that derived from weighted statistical linearization perspective. Each node estimates the global average information(More)
Based on M-estimate, the problem of robust estimation fusion in decentralized architecture when the sensor noises are contaminated by outliers is considered. A simple robust Kalman filtering (RKF) scheme with weighted matrices of innovation sequences is introduced for local state estimation. Then, to avoid both the inconsistency of the Kalman filter and the(More)