Energy-Efficient Detection System in Time-Varying Signal and Noise Power
Long Le

Citation
Long Le. "Energy-Efficient Detection System in Time-Varying Signal and Noise Power". Master's thesis, University of Illinois, Urbana-Champaign, June, 2013.

Abstract
In many detection applications with battery-powered or energy-harvesting sensors, energy constraints preclude the use of the optimal detector all the time. Optimal energy-performance trade-off is therefore needed in such situations. In many signal processing applications, the signal and noise power may vary greatly over time, which can be exploited to constrain energy consumption while maintaining the best possible performance. A detector scheduling algorithm based on the signal and noise power information is developed in this thesis. The resulting algorithm is simple due to its threshold-test structure and can be easily implemented with almost no overhead. A detection system with two detectors using the proposed scheduling scheme is estimated to greatly reduce the energy consumption for a wildlife monitoring application. Hardware implementation also consolidates the empirical evidence for the effectiveness of the proposed method.

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Citation formats

• HTML

  Long Le. <a
  href="http://www.terraswarm.org/pubs/76.html"
  ><i>Energy-Efficient Detection System in
  Time-Varying Signal and Noise Power</i></a>,
  Master's thesis,  University of Illinois, Urbana-Champaign,
  June, 2013.

• Plain text

  Long Le. "Energy-Efficient Detection System in
  Time-Varying Signal and Noise Power". Master's thesis, 
  University of Illinois, Urbana-Champaign, June, 2013.

• BibTeX

  @mastersthesis{Le13_EnergyEfficientDetectionSystemInTimeVaryingSignalNoise,
      author = {Long Le},
      title = {Energy-Efficient Detection System in Time-Varying
                Signal and Noise Power},
      school = {University of Illinois, Urbana-Champaign},
      month = {June},
      year = {2013},
      abstract = {In many detection applications with
                battery-powered or energy-harvesting sensors,
                energy constraints preclude the use of the optimal
                detector all the time. Optimal energy-performance
                trade-off is therefore needed in such situations.
                In many signal processing applications, the signal
                and noise power may vary greatly over time, which
                can be exploited to constrain energy consumption
                while maintaining the best possible performance. A
                detector scheduling algorithm based on the signal
                and noise power information is developed in this
                thesis. The resulting algorithm is simple due to
                its threshold-test structure and can be easily
                implemented with almost no overhead. A detection
                system with two detectors using the proposed
                scheduling scheme is estimated to greatly reduce
                the energy consumption for a wildlife monitoring
                application. Hardware implementation also
                consolidates the empirical evidence for the
                effectiveness of the proposed method.},
      URL = {http://terraswarm.org/pubs/76.html}
  }
  

Posted by Mila MacBain on 2 Jul 2013.