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Local convergence is a limitation of many optimization approaches for multimodal functions. For hybrid model learning, this can mean a compromise in accuracy. We develop an approach for learning the model parameters of hybrid discrete-continuous systems that avoids getting stuck in locally optimal solutions. We present an algorithm that implements this approach that 1) iteratively learns the locations...
A Voronoi diagram of a set of sites partitions a bounded space into regions of different areas. A capacity-constrained Voronoi diagram is a partition in which the area for each Voronoi region is predefined. In this paper, we present two approaches for computing such capacity-constrained Voronoi diagrams in continuous spaces. Our first approach is based on ordinary (non-weighted) distance functions...
The dramatic increase in leakage current, coupled with the swell in process variability in nano-scaled CMOS technologies, has become a major issue for future IC design. Moreover, due to the spread of leakage power values, leakage variability cannot be neglected anymore. In this work an accurate analytic estimation and modeling methodology has been developed for logic gates leakage under statistical...
A new approach for signal expansion with respect to hybrid dictionaries, based upon probabilistic modeling is proposed and studied. The signal is modeled as a sparse linear combination of waveforms, taken from the union of two orthonormal bases, with random coefficients. The behavior of the analysis coefficients, namely inner products of the signal with all basis functions, is studied in details,...
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