Sparsity-Aware Online Signal Processing with Compressive Sensing


The goal of the project is to derive customized algorithms for two compressed sensing (CS) applications.

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Multiple sources (Focus area spearhead projects)

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University of Oulu

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Project description

The emerging compressed sensing (CS) theory provides fundamentally new ways for acquisition, compression, transmission, processing, storage, and sharing of data. The goal of the project is to derive customized algorithms for two CS applications:

1) compressive wireless sensor networks for automatic process monitoring; the sensors convey wirelessly their compressive measurements to a central processor that has to detect (in real time) anomalies in the monitored process; we seek computationally efficient algorithms able to perform feature extraction and real time classification directly in the compressed domain  (without first reconstructing the signal of interest); and,

2) high resolution image & video signal reconstruction from block compressive measurements; here, the main challenge comes from the fact that the measurements vector corresponding to the entire image/video signal is too large to be processed all at once.

Strategic research project of the University of Oulu
Focus institute: Infotech Oulu
Faculty: Faculty of Information Technology and Electrical Engineering (ITEE)