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Distributed Sensor Perception via Sparse representation
Allen Yang, Michael Gastpar, Ruzena Bajcsy, Shankar Sastry

Citation
Allen Yang, Michael Gastpar, Ruzena Bajcsy, Shankar Sastry. " Distributed Sensor Perception via Sparse representation". Proceedings of IEEE, 2010.

Abstract
Abstract—Sensor network scenarios are considered where the underlying signals of interest exhibit a degree of sparsity, which means that in an appropriate basis, they can be expressed in terms of a small number of nonzero coefficients. Following the emerging theory of compressive sensing, an overall architecture is considered where the sensors acquire potentially noisy projections of the data, and the underlying sparsity is exploited to recover useful information about the signals of interest, which will be referred to as distributed sensor perception. First, we discuss the question of which projections of the data should be acquired, and how many of them. Then, we discuss how to take advantage of possible joint sparsity of the signals acquired by multiple sensors, and show how this can further improve the inference of the events from the sensor network. Two practical sensor applications are demonstrated, namely, distributed wearable action recognition using low-power motion sensors and distributed object recognition using high-power camera sensors. Experimental data support the utility of the compressive sensing framework in distributed sensor perception.

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  • HTML
    Allen Yang, Michael Gastpar, Ruzena Bajcsy, Shankar Sastry.
    <a
    href="http://www.truststc.org/pubs/738.html">  
             Distributed Sensor Perception via Sparse
    representation</a>, <i>Proceedings of
    IEEE</i>,  2010.
  • Plain text
    Allen Yang, Michael Gastpar, Ruzena Bajcsy, Shankar Sastry.
    "            Distributed Sensor Perception via Sparse
    representation". <i>Proceedings of
    IEEE</i>,  2010.
  • BibTeX
    @article{YangGastparBajcsySastry10_DistributedSensorPerceptionViaSparseRepresentation,
        author = {Allen Yang and Michael Gastpar and Ruzena Bajcsy
                  and Shankar Sastry},
        title = {            Distributed Sensor Perception via
                  Sparse representation},
        journal = {Proceedings of IEEE},
        year = {2010},
        abstract = {Abstract—Sensor network scenarios are considered
                  where the underlying signals of interest exhibit a
                  degree of sparsity, which means that in an
                  appropriate basis, they can be expressed in terms
                  of a small number of nonzero coefficients.
                  Following the emerging theory of compressive
                  sensing, an overall architecture is considered
                  where the sensors acquire potentially noisy
                  projections of the data, and the underlying
                  sparsity is exploited to recover useful
                  information about the signals of interest, which
                  will be referred to as distributed sensor
                  perception. First, we discuss the question of
                  which projections of the data should be acquired,
                  and how many of them. Then, we discuss how to take
                  advantage of possible joint sparsity of the
                  signals acquired by multiple sensors, and show how
                  this can further improve the inference of the
                  events from the sensor network. Two practical
                  sensor applications are demonstrated, namely,
                  distributed wearable action recognition using
                  low-power motion sensors and distributed object
                  recognition using high-power camera sensors.
                  Experimental data support the utility of the
                  compressive sensing framework in distributed
                  sensor perception.},
        URL = {http://www.truststc.org/pubs/738.html}
    }
    

Posted by Jessica Gamble on 4 May 2010.
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