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ICUMT
2009
15 years 2 months ago
A Bayesian analysis of Compressive Sensing data recovery in Wireless Sensor Networks
Abstract--In this paper we address the task of accurately reconstructing a distributed signal through the collection of a small number of samples at a data gathering point using Co...
Riccardo Masiero, Giorgio Quer, Michele Rossi, Mic...
CORR
2010
Springer
97views Education» more  CORR 2010»
15 years 1 months ago
On the Scaling Law for Compressive Sensing and its Applications
1 minimization can be used to recover sufficiently sparse unknown signals from compressed linear measurements. In fact, exact thresholds on the sparsity, as a function of the ratio...
Weiyu Xu, Ao Tang
IJAMCIGI
2010
90views more  IJAMCIGI 2010»
15 years 1 months ago
A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling
Hyper-heuristics are identified as the methodologies that search the space generated by a finite set of low level heuristics for solving difficult problems. One of the iterative h...
Ender Özcan, Mustafa Misir, Gabriela Ochoa, E...
CORR
2010
Springer
198views Education» more  CORR 2010»
15 years 1 months ago
Convex Graph Invariants
The structural properties of graphs are usually characterized in terms of invariants, which are functions of graphs that do not depend on the labeling of the nodes. In this paper ...
Venkat Chandrasekaran, Pablo A. Parrilo, Alan S. W...
CVPR
2011
IEEE
14 years 12 months ago
Learning Temporally Consistent Rigidities
We present a novel probabilistic framework for rigid tracking and segmentation of shapes observed from multiple cameras. Most existing methods have focused on solving each of thes...
Jean-Sebastien Franco, Edmond Boyer