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177
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IROS
2008
IEEE
211views Robotics» more  IROS 2008»
16 years 1 months ago
GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models
Abstract— Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. The most common instantiations of Bayes filters are Kalman filt...
Jonathan Ko, Dieter Fox
DMSN
2007
ACM
15 years 10 months ago
Declarative temporal data models for sensor-driven query processing
Many sensor network applications monitor continuous phenomena by sampling, and fit time-varying models that capture the phenomena's behaviors. We introduce Pulse, a framework...
Yanif Ahmad, Ugur Çetintemel
HICSS
2003
IEEE
131views Biometrics» more  HICSS 2003»
16 years 1 days ago
Requirements Elicitation and Elicitation Technique Selection: A Model for Two Knowledge-Intensive Software Development Processes
By its very nature, software development consists of many knowledge-intensive processes. One of the most difficult to model, however, is requirements elicitation. This paper prese...
Ann M. Hickey, Alan M. Davis
DAC
2005
ACM
15 years 8 months ago
Mapping statistical process variations toward circuit performance variability: an analytical modeling approach
A physical yet compact gate delay model is developed integrating short-channel effects and the Alpha-power law based timing model. This analytical approach accurately predicts bot...
Yu Cao, Lawrence T. Clark
ICIP
2009
IEEE
16 years 7 months ago
Lidar Waveform Modeling Using A Marked Point Process
Lidar waveforms are 1D signal consisting of a train of echoes where each of them correspond to a scattering target of the Earth surface. Modeling these echoes with the appropriate...