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An energy-aware framework for cascaded detection algorithms
Jun, David M.
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https://hdl.handle.net/2142/18395
Description
- Title
- An energy-aware framework for cascaded detection algorithms
- Author(s)
- Jun, David M.
- Issue Date
- 2011-01-14T22:49:01Z
- Director of Research (if dissertation) or Advisor (if thesis)
- Jones, Douglas L.
- Department of Study
- Electrical & Computer Eng
- Discipline
- Electrical & Computer Engr
- Degree Granting Institution
- University of Illinois at Urbana-Champaign
- Degree Name
- M.S.
- Degree Level
- Thesis
- Date of Ingest
- 2011-01-14T22:49:01Z
- Keyword(s)
- energy-aware
- signal detection
- incremental refinement
- passive vigilance
- scalable systems
- Abstract
- 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. The cascade architecture is known for being energy-efficient, but without proper operation can end up being energy-inefficient in practice. In this thesis, we propose a framework that imposes energy-awareness on cascaded detection algorithms, which enforces proper operation of the cascade to maximize detection performance for a given energy budget. This is achieved by solving our proposed energy-constrained version of the Neyman-Pearson detection criterion, resulting in detector thresholds that can be updated to dynamically adjust to time-varying system resources and requirements. Sufficient conditions for a global solution for a cascade of an arbitrary number of detectors are given. Explicit solutions are derived for a two-stage cascade. Applied to a canonical detection problem, simulations show that our energy-aware cascaded detectors outperform an energy-aware detection algorithm based on incremental refinement, an existing alternate approach to developing energy-aware algorithms. Combining our framework with incremental refinement reveals a promising approach to developing energy-aware energy-efficient detection systems.
- Graduation Semester
- 2010-12
- Permalink
- http://hdl.handle.net/2142/18395
- Copyright and License Information
- Copyright 2010 David Moses Jun
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Dissertations and Theses - Electrical and Computer Engineering
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