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Titlebook: Compressed Sensing for Distributed Systems; Giulio Coluccia,Chiara Ravazzi,Enrico Magli Book 2015 The Author(s) 2015 Compressed Sensing.Di

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Strategisches Technologiemanagementhe distributed framework in which multiple devices acquire multiple signals. In particular, we focus on two key problems related to the distributed setting. The former is the definition of sparsity models for an ensemble of signals, as opposed to just one signal. The second is the structure of the c
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Strategisches Technologiemanagementr nodes that collect measurements from different sensors. This estimation problem can be recast into an optimization problem where a convex and separable loss function should be minimized subject to sparsity constraints. The goal of the network is to handle distributed sparse estimation. Clearly, to
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Centralized Joint Recovery, measurements of a set of signals obeying a specific joint sparsity model, while a centralized fusion center collects the measurements of the entire network and jointly processes them to reconstruct the acquired signals.
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Randomized Partial Checking Revisitedent mix-net..We identify serious issues in the original description of mix-nets with RPC and show how to exploit these to break both correctness and privacy, both for Chaumian and homomorphic mix-nets. Our attacks are practical and applicable to real world mix-net implementations, e.g., the Civitas and the Scantegrity voting systems.
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