An energy-aware framework for cascaded detection algorithms

David M. Jun, Douglas L. Jones

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Low-power, scalable detection systems require aggressive techniques to achieve energy efficiency. Algorithmic methods that can reduce energy consumption by compromising performance are known as being energy-aware. We propose a framework that imposes energy-awareness on cascaded detection algorithms. This is done by setting the detectors' thresholds to make a systematic trade-off between energy consumption and detection performance. The thresholds are determined by solving our proposed energy-constrained version of the Neyman-Pearson detection criterion. Our proposed optimization method systematically determines the energy-optimal thresholds and dynamically adjusts to time-varying system requirements. This framework is applied to a two-stage cascade, and simulations show that our energy-aware cascaded detectors outperform an energy-aware detection algorithm based on incremental refinement. Finally, combining our framework with incremental refinement reveals a promising approach to the design of energy-efficient detection systems.

Original languageEnglish (US)
Title of host publication2010 IEEE Workshop on Signal Processing Systems, SiPS 2010 - Proceedings
Number of pages6
StatePublished - 2010
Event2010 IEEE Workshop on Signal Processing Systems, SiPS 2010 - San Francisco, CA, United States
Duration: Oct 6 2010Oct 8 2010

Publication series

NameIEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
ISSN (Print)1520-6130


Other2010 IEEE Workshop on Signal Processing Systems, SiPS 2010
Country/TerritoryUnited States
CitySan Francisco, CA


  • Energy-aware
  • Incremental refinement
  • Passive vigilance
  • Scalable systems
  • Signal detection

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Applied Mathematics
  • Hardware and Architecture


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