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IJCNN
2006
IEEE
15 years 10 months ago
Learning to Segment Any Random Vector
— We propose a method that takes observations of a random vector as input, and learns to segment each observation into two disjoint parts. We show how to use the internal coheren...
Aapo Hyvärinen, Jukka Perkiö
VTC
2006
IEEE
121views Communications» more  VTC 2006»
15 years 10 months ago
Location-Dependent Parameterization of a Random Direction Mobility Model
— Mobility models are widely used in simulation-based performance analyses of mobile networks. However, there is a trade-off between simplicity and realistic movement patterns. S...
Bernd Gloss, Michael Scharf, Daniel Neubauer
ECCV
2008
Springer
16 years 6 months ago
Learning Optical Flow
Assumptions of brightness constancy and spatial smoothness underlie most optical flow estimation methods. In contrast to standard heuristic formulations, we learn a statistical mod...
Deqing Sun, Stefan Roth, J. P. Lewis, Michael J. B...
CDC
2008
IEEE
184views Control Systems» more  CDC 2008»
15 years 10 months ago
Almost sure convergence to consensus in Markovian random graphs
— In this paper we discuss the consensus problem for a network of dynamic agents with undirected information flow and random switching topologies. The switching is determined by...
Ion Matei, Nuno C. Martins, John S. Baras
WIMOB
2008
IEEE
15 years 10 months ago
Random Beacon for Privacy and Group Security
Abstract—Most contemporary security mechanisms and protocols include exchange of random or time-variant nonces as an essential means of protection against replay and other threat...
Aleksi Saarela, Jan-Erik Ekberg, Kaisa Nyberg