Sparse Sensing for Statistical Inference: Theory, Algorithms, and Applications (2016)
Abstract / truncated to 115 words
In today's society, we are flooded with massive volumes of data in the order of a billion gigabytes on a daily basis from pervasive sensors. It is becoming increasingly challenging to locally store and transport the acquired data to a central location for signal/data processing (i.e., for inference). To alleviate these problems, it is evident that there is an urgent need to significantly reduce the sensing cost (i.e., the number of expensive sensors) as well as the related memory and bandwidth requirements by developing unconventional sensing mechanisms to extract as much information as possible yet collecting fewer data. The first aim of this thesis is to develop theory and algorithms for data reduction. We develop ... toggle 8 keywordssparse sensing – sensor networks – sampling – estimation – detection – filtering – localization – synchronization
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