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A sensor is a device that detects or measures a physical property and records, indicates, or otherwise responds to it. In other words, a sensor allows us to interact with the surrounding environment, by measuring qualitatively or quantitatively a given phe ...
In this paper, we extend the generalized approximate message passing (G-AMP) approach, originally proposed for high-dimensional generalized-linear regression in the context of compressive sensing, to the generalized-bilinear case, which enables its applica ...
Institute of Electrical and Electronics Engineers2014
Ultra-low power optimization is a challenging research topic in the de- sign of the sensor front-ends, especially in the area of Wireless Body Sensor Nodes (WBSN), where a limited amount of power and hardware resources are available. In this talk, I analyz ...
In this paper, we extend the generalized approximate message passing (G-AMP) approach, originally proposed for high-dimensional generalized-linear regression in the context of compressive sensing, to the generalized-bilinear case. In Part I of this two-par ...
This paper deals with the problem of sparse H-infinity dynamic output-feedback control of LTI interconnected systems with polytopic uncertainty. The main objective is to find a controller structure with minimum information exchange between subsystems and l ...
Is it possible to predict the intrusiveness of background noise in speech signals as perceived by humans? Such a question is important to the automatic evaluation of speech enhancement systems, including those designed for new wideband speech telephony, an ...
Posterior features have been shown to yield very good performance in multiple contexts including speech recognition, spoken term detection, and template matching. These days, posterior features are usually estimated at the output of a neural network. More ...
Cycle spinning is a widely used approach for improving the performance of wavelet-based methods that solve linear inverse problems. Extensive numerical experiments have shown that it significantly improves the quality of the recovered signal without increa ...
We present a primal-dual algorithmic framework to obtain approximate solutions to a prototypical constrained convex optimization problem, and rigorously characterize how common structural assumptions affect the numerical efficiency. Our main analysis techn ...
We introduce a model-based excessive gap technique to analyze first-order primal- dual methods for constrained convex minimization. As a result, we construct new primal-dual methods with optimal convergence rates on the objective residual and the primal fe ...
