In this paper, we present a novel probabilistic technique, based on the Bayes filter, able to estimate the user location, even with unreliable sensor data coming only from fixed sensors in the monitored environment. Our approach has been extensively tested in a home-like environment, as well as in a real home, and achieves very good results. We present results on two datasets, representative of real life conditions, collected during the testing phase. We detect the patient location with subroom accuracy, an improvement over the state of the art for localization using only environmental sensors. The main drawback is that it is only suitable for applications where a single person is present in the environment, like as with other approaches that do not use any mobile device. For this reason, we introduced the "telehomecare" term, therefore differentiating from generic telemedicine applications, where many people can be in the same environment at the same time.

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High Level Synthesis (HLS) frameworks allow to describe hardware designs in a high-level language (C/C++), without burdening developers with the error-prone task of specifying their implementation in detail. The HLS process is usually controlled by user-specified directives, which influence the implementation area and latency. Nonetheless, the correlation between directives and performance is often difficult to foresee and to quantify. Addressing this challenge, we herein propose a heuristic that, by only exploring a subset of possible configurations for an HLS design, is able to retrieve a close approximation of its Pareto Frontier of non-dominated implementations. Our framework identifies regions of interest in the design space, and iteratively searches for new solutions within such regions, or in their combinations. Experimental evidence across multiple benchmarks showcases that our approach to HLS design space exploration reaches better Pareto approximations, and with less required synthesis runs, with respect to State of the Art alternatives.