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» Approximation schemes for wireless networks
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IWANN
1999
Springer
15 years 10 months ago
Using Temporal Neighborhoods to Adapt Function Approximators in Reinforcement Learning
To avoid the curse of dimensionality, function approximators are used in reinforcement learning to learn value functions for individual states. In order to make better use of comp...
R. Matthew Kretchmar, Charles W. Anderson
DCOSS
2008
Springer
15 years 8 months ago
Improving the Data Delivery Latency in Sensor Networks with Controlled Mobility
Unlike traditional multihop forwarding among homogeneous static sensor nodes, use of mobile devices for data collection in wireless sensor networks has recently been gathering more...
Ryo Sugihara, Rajesh K. Gupta
ALGOSENSORS
2007
Springer
15 years 10 months ago
Efficient Sensor Network Design for Continuous Monitoring of Moving Objects
We study the problem of localizing and tracking multiple moving targets in wireless sensor networks, from a network design perspective i.e. towards estimating the least possible n...
Sotiris E. Nikoletseas, Paul G. Spirakis
PC
2008
119views Management» more  PC 2008»
15 years 6 months ago
Atomic wavefunction initialization in ab initio
We present a distributed scheme for initialization from atomic wavefunctions in ab initio molecular dynamics simulations. Good initial guesses for approximate wavefunctions are ve...
Constantine Bekas, Alessandro Curioni, Wanda Andre...
ICA
2010
Springer
15 years 6 months ago
An Alternating Minimization Method for Sparse Channel Estimation
The problem of estimating a sparse channel, i.e. a channel with a few non-zero taps, appears in many fields of communication including acoustic underwater or wireless transmissions...
Rad Niazadeh, Massoud Babaie-Zadeh, Christian Jutt...