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Existing supervised learning models are generally built upon the basis of only one single objective function, through the minimizing of the square-loss (neural networks) or the minimizing of the information entropy (decision tree). Due to the inherent complexity of the real-life data, learning models merely based on only one single objective function are always inadequate. Consequently, many well-known...
In this paper a novel way to control the amount of generated video surveillance data by controlling the spatial and temporal samplings of video is proposed. The samplings are controlled adaptively using the speed, distance and dimension of the object extracted dynamically from the surveillance video. We also present a method of estimating the actual 2D dimension, location and speed of moving objects...
In wireless sensor networks, balanced energy dissipation is crucial to achieving satisfactory network lifetime. Since all data packets usually have to be forwarded to the base station via multi-hop routing, the traffic pattern is highly non-uniform, putting a high burden on the sensor nodes close to the base station. This paper proposes novel centralized and distributed algorithms to efficiently balance...
In order to analyze the performance of lunar rover, draw bar pull and motion resistance are analyzed in this paper based on terramechanics. The relationships between configuration parameters (like wheel width, wheel bases et al.) and mobility performance parameters are obtained from the analysis. Meanwhile, the sensitive factors of the motion resistance are found. Then main performance evaluation...
By Johnson and Lindenstrauss lemma, n points in d-dimensional Euclidean space can be projected down to k=O(epsiv-2logn) dimensions while incurring at most 1+- bi-Lipschitz distortion in pairwise distance. However, most current projection methods requires a k-by-d matrix; and mapping n point takes O(kdn) time. In this paper, a O(dn) complexity random projection method, directly random projection (DRP),...
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