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Kernel-based machine learning methods are gaining increasing interest in flow modeling and prediction in recent years. Gaussian process (GP) is one example of such kernel-based methods, which can provide very good performance for nonlinear problems. In this work, we apply GP regression to flow modeling and prediction of athletes in ski races, but the proposed framework can be generally applied to...
Driven by the promising beaconing techniques, this paper presents a general received-signal-strength (RSS) threshold optimization procedure for proximity reports to support network-based indoor positioning. The desired RSS threshold is found through optimizing a metric function (for instance the localization root-mean-square-error) in terms of both the deployment information and the RSS modeling in...
Boundary effect is one of the most concernful problems in analysis of geomagnetic anomaly field data, it introduces great error in analytical result and results in serious thread in engineering practice. This problem reflects on many aspects, such as reference map preparation, multi-resolution decomposition, etc. Improved bi-dimensional empirical mode decomposition (BEMD) is firstly proposed and used...
Device-to-Device (D2D) communications in cellular systems have a lot of potential value for both commercial and public safety (PS) applications. In D2D communications, two or more devices exchange data using a direct communication link, i.e., without having to go through the network infrastructure. This paper focuses on the study of broadcast voice communication for PS and the resource allocation...
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