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ICIP
2005
IEEE
16 years 4 months ago
Sampling in practice: is the best reconstruction space bandlimited?
Shannon's sampling theory and its variants provide effective solutions to the problem of reconstructing a signal from its samples in some "shift-invariant " space, ...
Sathish Ramani, Dimitri Van De Ville, Michael Unse...
213
Voted
CVPR
2010
IEEE
13 years 11 months ago
Abrupt motion tracking via adaptive stochastic approximation Monte Carlo sampling
Robust tracking of abrupt motion is a challenging task in computer vision due to the large motion uncertainty. In this paper, we propose a stochastic approximation Monte Carlo (...
Xiuzhuang Zhou and Yao Lu
JMLR
2010
137views more  JMLR 2010»
14 years 9 months ago
Importance Sampling for Continuous Time Bayesian Networks
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
Yu Fan, Jing Xu, Christian R. Shelton
130
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ICML
2007
IEEE
16 years 3 months ago
Beamforming using the relevance vector machine
Beamformers are spatial filters that pass source signals in particular focused locations while suppressing interference from elsewhere. The widely-used minimum variance adaptive b...
David P. Wipf, Srikantan S. Nagarajan
142
Voted
IEEECIT
2010
IEEE
15 years 27 days ago
Metadata-Based Adaptive Sampling for Energy-Efficient Collaborative Target Tracking in Wireless Sensor Networks
—The increasingly complex roles for which Wireless Sensor Networks (WSNs) are being employed have driven the desire for energy-efficient reliable target tracking. In this paper, ...
Yousef E. M. Hamouda, Chris Phillips