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Title:An energy-aware framework for cascaded detection algorithms
Author(s):Jun, David M.
Advisor(s):Jones, Douglas L.
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
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.
Issue Date:2011-01-14
Rights Information:Copyright 2010 David Moses Jun
Date Available in IDEALS:2011-01-14
Date Deposited:December 2

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