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» Using Goal-Models to Analyze Variability
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AUTOMATICA
2010
73views more  AUTOMATICA 2010»
13 years 9 months ago
Realization of Boolean control networks
Based on the linear expression of the dynamics of Boolean networks, the coordinate transformation of Boolean variables is defined. It follows that the state space coordinate trans...
Daizhan Cheng, Zhi Qiang Li, Hongsheng Qi
TSMC
2008
113views more  TSMC 2008»
13 years 8 months ago
Computational Methods for Verification of Stochastic Hybrid Systems
Stochastic hybrid system (SHS) models can be used to analyze and design complex embedded systems that operate in the presence of uncertainty and variability. Verification of reacha...
Xenofon D. Koutsoukos, Derek Riley
BMCBI
2004
106views more  BMCBI 2004»
13 years 8 months ago
Spotting effect in microarray experiments
Background: Microarray data must be normalized because they suffer from multiple biases. We have identified a source of spatial experimental variability that significantly affects...
Tristan Mary-Huard, Jean-Jacques Daudin, Sté...
ICPR
2008
IEEE
14 years 10 months ago
Classification of perceived running fatigue in digital sports
This paper presents methods for collecting and analyzing physiological and biomechanical data during recreational runs in order to classify an athlete's perceived fatigue sta...
Bjoern Eskofier, Florian Hoenig, Pascal Kuehner
CVPR
2006
IEEE
14 years 11 months ago
Edge Suppression by Gradient Field Transformation Using Cross-Projection Tensors
We propose a new technique for edge-suppressing operations on images. We introduce cross projection tensors to achieve affine transformations of gradient fields. We use these tens...
Amit K. Agrawal, Ramesh Raskar, Rama Chellappa