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Many Cyber-Physical Systems (CPS) are composed of low-cost devices that are deeply integrated with physical environments. As a result, the performance of a CPS system is inevitably undermined by various physical uncertainties, which include stochastic noises, hardware biases, unpredictable environment changes and dynamics of the physical process of interest. Traditional solutions to these issues (e.g., device calibration and collaborative signal processing) work in an open-loop fashion and hence often fail to adapt to the uncertainties after system deployment. In this paper, the authors propose an adaptive system-level calibration approach for a class of CPS systems whose primary objective is to detect events or targets of interest.
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