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WSC
1997
13 years 11 months ago
Modeling Dependencies in Stochastic Simulation Inputs
We discuss some basic techniques for modeling dependence between the random variables that are inputs to a simulation model, with the main emphasis being continuous bivariate dist...
James R. Wilson
BC
2002
97views more  BC 2002»
13 years 9 months ago
A spatial stochastic neuronal model with Ornstein-Uhlenbeck input current
We consider a spatial neuron model in which the membrane potential satisfies a linear cable equation with an input current which is a dynamical random process of the Ornstein
Henry C. Tuckwell, Frederic Y. M. Wan, Jean-Pierre...
GLVLSI
2005
IEEE
103views VLSI» more  GLVLSI 2005»
14 years 3 months ago
Causal probabilistic input dependency learning for switching model in VLSI circuits
Switching model captures the data-driven uncertainty in logic circuits in a comprehensive probabilistic framework. Switching is a critical factor that influences dynamic, active ...
Nirmal Ramalingam, Sanjukta Bhanja
CCCG
2009
13 years 11 months ago
New Algorithms for Computing Maximum Perimeter and Maximum Area of the Convex Hull of Imprecise Inputs Based On the Parallel Lin
In this paper, we present new algorithms for computing maximum perimeter and maximum area of the convex hull of imprecise inputs based on the parallel line segment model. The runn...
Wenqi Ju, Jun Luo
CAD
2006
Springer
13 years 9 months ago
A curvature estimation for pen input segmentation in sketch-based modeling
A proper segmentation of pen marking enhances shape recognition and enables a natural interface for sketch-based modeling from simple line drawing tools to 3D solid modeling appli...
Dae Hyun Kim, Myoung-Jun Kim