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» New models and algorithms for programmable networks
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ICRA
2010
IEEE
137views Robotics» more  ICRA 2010»
14 years 11 months ago
Robot reinforcement learning using EEG-based reward signals
Abstract— Reinforcement learning algorithms have been successfully applied in robotics to learn how to solve tasks based on reward signals obtained during task execution. These r...
Iñaki Iturrate, Luis Montesano, Javier Ming...
WAW
2007
Springer
85views Algorithms» more  WAW 2007»
15 years 7 months ago
A Spatial Web Graph Model with Local Influence Regions
We present a new stochastic model for complex networks, based on a spatial embedding of the nodes, called the Spatial Preferred Attachment (SPA) model. In the SPA model, nodes have...
William Aiello, Anthony Bonato, C. Cooper, Jeannet...
ICC
2007
IEEE
137views Communications» more  ICC 2007»
15 years 7 months ago
A Novel Algorithm and Architecture for High Speed Pattern Matching in Resource-Limited Silicon Solution
— Network Intrusion Detection Systems (NIDS) are more and more important for identifying and preventing the malicious attacks over the network. This paper proposes a novel cost-e...
Nen-Fu Huang, Yen-Ming Chu, Chi-Hung Tsai, Chen-Yi...
JCP
2008
119views more  JCP 2008»
15 years 25 days ago
Performance Comparisons, Design, and Implementation of RC5 Symmetric Encryption Core using Reconfigurable Hardware
With the wireless communications coming to homes and offices, the need to have secure data transmission is of utmost importance. Today, it is important that information is sent con...
Omar S. Elkeelany, Adegoke Olabisi
GECCO
2007
Springer
155views Optimization» more  GECCO 2007»
15 years 7 months ago
Solving the MAXSAT problem using a multivariate EDA based on Markov networks
Markov Networks (also known as Markov Random Fields) have been proposed as a new approach to probabilistic modelling in Estimation of Distribution Algorithms (EDAs). An EDA employ...
Alexander E. I. Brownlee, John A. W. McCall, Deryc...