An energy-aware framework for cascaded detection algorithms

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Jun, David M.

Contributors dc:contributor

• Jones, Douglas L.

• energy-aware
• signal detection
• incremental refinement
• passive vigilance
• scalable systems

Statement dc:rights

• Copyright 2010 David Moses Jun

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/18395

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/18395