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We address two important issues in causal discovery from nonstationary or heterogeneous data, where parameters associated with a causal structure may change over time or across data sets. First, we investigate how to efficiently estimate the "driving force" of the nonstationarity of a causal mechanism. That is, given a causal mechanism that varies over time or across data sets and whose...
Aggressive maneuvers for micro unmanned aerial vehicle (MAV) require short settling time for attitude reference tracking and small overshoot for stabilization, which leads to the implementation of composite nonlinear feedback (CNF) control method. In this paper, an MAV platform is customized with all of the sub-systems designed to be small in size factor and simplified with their functionality. A...
Focusing on the estimation of the hopping period of the frequency-hopping (FH) signals, this paper proposes a novel method based on the binary-sum in the frequency domain. First, the time-frequency (TF) spectrograph of the signal is obtained and transformed into a binary matrix. Then the sum in frequency domain is calculated and the hopping period is accurately estimated by spectrum analyzing. The...
Anomaly detection in streaming data is of high interest in numerous application domains. In this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies in streaming data. Underlying the algorithm is a fast and accurate density estimator implemented by multiple fully randomized space trees (RS-Trees), named RS-Forest. The piecewise constant density estimate of each RS-tree...
Ultrasound elastography has been well applied in medical detection as a tool to aid diagnosis. However, in conventional ultrasound elastograms, there are patterned artifacts from non-white estimation errors. In this paper, we investigate spatial angular compounding methods to reduce the errors. The method involves averaging ultrasound angular elastograms around the same region-of-interest but from...
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