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This paper looks at the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a data gathering point. The techniques that the author exploits to do so are Compressive Sensing (CS) and Principal Component Analysis (PCA). PCA is used to find transformations that sparsify the signal, which are required for CS to retrieve, with good approximation, the original signal from a small number of samples. The approach dynamically adapts to non-stationary real world signals through the online estimation of their correlation properties in space and time; these are then exploited by PCA to derive the transformations for CS.
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