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ICRA
1999
IEEE
122views Robotics» more  ICRA 1999»
14 years 1 days ago
Learning Visual Landmarks for Pose Estimation
Abstract-- We present an approach to vision-based mobile robot localization, even without an a-priori pose estimate. This is accomplished by learning a set of visual features calle...
Robert Sim, Gregory Dudek
ICCAD
2005
IEEE
132views Hardware» more  ICCAD 2005»
14 years 1 months ago
Battery optimization vs energy optimization: which to choose and when?
— Batteries are non-ideal energy sources – minimizing the energy consumption of a battery-powered system is not equivalent to maximizing its battery life. We propose an alterna...
Ravishankar Rao, Sarma B. K. Vrudhula
ICSE
2010
IEEE-ACM
14 years 16 days ago
Characterizing and predicting which bugs get fixed: an empirical study of Microsoft Windows
We performed an empirical study to characterize factors that affect which bugs get fixed in Windows Vista and Windows 7, focusing on factors related to bug report edits and relat...
Philip J. Guo, Thomas Zimmermann, Nachiappan Nagap...
BMCBI
2007
166views more  BMCBI 2007»
13 years 7 months ago
How to decide which are the most pertinent overly-represented features during gene set enrichment analysis
Background: The search for enriched features has become widely used to characterize a set of genes or proteins. A key aspect of this technique is its ability to identify correlati...
Roland Barriot, David J. Sherman, Isabelle Dutour
SPEECH
2010
89views more  SPEECH 2010»
13 years 2 months ago
Which words are hard to recognize? Prosodic, lexical, and disfluency factors that increase speech recognition error rates
Despite years of speech recognition research, little is known about which words tend to be misrecognized and why. Previous work has shown that errors increase for infrequent words...
Sharon Goldwater, Daniel Jurafsky, Christopher D. ...