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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.

Electronic downloads

• http://www.eecs.berkeley.edu/~yang/paper/AllenYang-Proceedings2009.pdf

• 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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