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In this paper, we focus on the design of reduced-complexity sensor compensation modules based on learning-from-examples techniques. A multiobjective optimization design framework is proposed, where system complexity and compensation uncertainty are considered as two conflicting costs to be jointly minimized. In addition, suitable statistical techniques are applied to cope with the variability in the...
In this work we focus on the design of reduced-complexity sensor compensation modules based on learning-from-examples techniques. A multi-objective optimization design framework is proposed, where system complexity and compensation uncertainty are considered as two conflicting costs to be jointly minimized. In addition, suitable statistical techniques are applied to cope with the variability in the...
In this paper we present a novel technique based on a learning-from-examples approach for dynamic compensation of sensors. The context of application is the area of Wireless Sensor Networks, where simple but at the same time efficient signal processing methods have to be implemented on local 8-bits microcontrollers.
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