Fusion and Signal Quality Assessment for Sensors in the Swarm
Viswam Nathan, Roozbeh Jafari

Citation
Viswam Nathan, Roozbeh Jafari. "Fusion and Signal Quality Assessment for Sensors in the Swarm". Talk or presentation, 26, October, 2016; TerraSwarm Annual Review 2016 Theme 3 poster presentation.

Abstract
The world is gearing up for smart cities and the internet of things (IoT) and it is very likely that successful signal processing techniques would take advantage of the expected multitude of sensors. Therefore we present a signal processing framework that is capable of supporting multiple simultaneous and heterogeneous sensors and fusing them effectively for more accurate estimates, with heart rate estimation results from real cardiac sensor data. Moreover, we show how features of the proposed signal processing framework can be leveraged to estimate the signal quality and reliability of each sensor, which would be a useful metric to help manage the swarm.

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Citation formats

• HTML

  Viswam Nathan, Roozbeh Jafari. <a
  href="http://www.terraswarm.org/pubs/843.html"
  ><i>Fusion and Signal Quality Assessment for
  Sensors in the Swarm</i></a>, Talk or
  presentation,  26, October, 2016; TerraSwarm Annual Review
  2016 Theme 3 poster presentation.

• Plain text

  Viswam Nathan, Roozbeh Jafari. "Fusion and Signal
  Quality Assessment for Sensors in the Swarm". Talk or
  presentation,  26, October, 2016; TerraSwarm Annual Review
  2016 Theme 3 poster presentation.

• BibTeX

  @presentation{NathanJafari16_FusionSignalQualityAssessmentForSensorsInSwarm,
      author = {Viswam Nathan and Roozbeh Jafari},
      title = {Fusion and Signal Quality Assessment for Sensors
                in the Swarm},
      day = {26},
      month = {October},
      year = {2016},
      note = {TerraSwarm Annual Review 2016 Theme 3 poster
                presentation},
      abstract = {The world is gearing up for smart cities and the
                internet of things (IoT) and it is very likely
                that successful signal processing techniques would
                take advantage of the expected multitude of
                sensors. Therefore we present a signal processing
                framework that is capable of supporting multiple
                simultaneous and heterogeneous sensors and fusing
                them effectively for more accurate estimates, with
                heart rate estimation results from real cardiac
                sensor data. Moreover, we show how features of the
                proposed signal processing framework can be
                leveraged to estimate the signal quality and
                reliability of each sensor, which would be a
                useful metric to help manage the swarm. },
      URL = {http://terraswarm.org/pubs/843.html}
  }
  

Posted by Viswam Nathan on 21 Oct 2016.
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