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ADCM
2008
136views more  ADCM 2008»
15 years 4 months ago
Learning and approximation by Gaussians on Riemannian manifolds
Learning function relations or understanding structures of data lying in manifolds embedded in huge dimensional Euclidean spaces is an important topic in learning theory. In this ...
Gui-Bo Ye, Ding-Xuan Zhou
AHSWN
2010
130views more  AHSWN 2010»
15 years 4 months ago
How Good is Opportunistic Routing? - A Reality Check under Rayleigh Fading Channels
Considerations of realistic channel dynamics motivate the design of a new breed of opportunistic schemes, such as opportunistic transmission, scheduling and routing. Compared to t...
Rong Zheng, Chengzhi Li
AUTOMATICA
2008
75views more  AUTOMATICA 2008»
15 years 4 months ago
Probabilistic performance of state estimation across a lossy network
We consider a discrete time state estimation problem over a packet-based network. In each discrete time step, a measurement packet is sent across a lossy network to an estimator u...
Michael Epstein, Ling Shi, Abhishek Tiwari, Richar...
AUTOMATICA
2008
139views more  AUTOMATICA 2008»
15 years 4 months ago
Structured low-rank approximation and its applications
Fitting data by a bounded complexity linear model is equivalent to low-rank approximation of a matrix constructed from the data. The data matrix being Hankel structured is equival...
Ivan Markovsky
CGF
2008
137views more  CGF 2008»
15 years 4 months ago
Exploiting Visibility Correlation in Direct Illumination
The visibility function in direct illumination describes the binary visibility over a light source, e.g., an environment map. Intuitively, the visibility is often strongly correla...
Petrik Clarberg, Tomas Akenine-Möller
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