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CMSB
2009
Springer
13 years 11 months ago
Probabilistic Approximations of Signaling Pathway Dynamics
Systems of ordinary differential equations (ODEs) are often used to model the dynamics of complex biological pathways. We construct a discrete state model as a probabilistic appro...
Bing Liu, P. S. Thiagarajan, David Hsu
CDC
2010
IEEE
169views Control Systems» more  CDC 2010»
12 years 11 months ago
Consensus-based distributed linear filtering
We address the consensus-based distributed linear filtering problem, where a discrete time, linear stochastic process is observed by a network of sensors. We assume that the consen...
Ion Matei, John S. Baras
CVPR
1999
IEEE
14 years 6 months ago
Time-Series Classification Using Mixed-State Dynamic Bayesian Networks
We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...
ICML
2009
IEEE
13 years 11 months ago
Learning linear dynamical systems without sequence information
Virtually all methods of learning dynamic systems from data start from the same basic assumption: that the learning algorithm will be provided with a sequence, or trajectory, of d...
Tzu-Kuo Huang, Jeff Schneider
ICCAD
2006
IEEE
152views Hardware» more  ICCAD 2006»
14 years 1 months ago
System-wide energy minimization for real-time tasks: lower bound and approximation
We present a dynamic voltage scaling (DVS) technique that minimizes system-wide energy consumption for both periodic and sporadic tasks. It is known that a system consists of proc...
Xiliang Zhong, Cheng-Zhong Xu