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Measurements from sensor networks consisting of thousands of nodes are often correlated, since nearby sensors observe the same phenomenon. Using Compressed Sensing, that data can be reconstructed with a high probability from a small collection of random linear combinations of those measurements. This opens a new approach to simultaneously extract, transmit and distribute information in wireless sensor...
We present a model-based environment for the probabilistic analysis of systems operating under uncertain conditions. This uncertainty may result from either the environments in which they operate or the platforms on which they execute. Available probabilistic analysis methods require to capture the system specification using languages that are semantically very close to Markov Chains. However, designers...
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