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» Optimization Algorithms for Large Self-Structuring Networks
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GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
14 years 3 months ago
Reinforcement learning for games: failures and successes
We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforcement learning tasks. In both cases these algorithms se...
Wolfgang Konen, Thomas Bartz-Beielstein
TWC
2008
120views more  TWC 2008»
13 years 10 months ago
Binary Power Control for Sum Rate Maximization over Multiple Interfering Links
We consider allocating the transmit powers for a wireless multi-link (N-link) system, in order to maximize the total system throughput under interference and noise impairments, and...
Anders Gjendemsjø, David Gesbert, Geir E. &...
GLOBECOM
2008
IEEE
14 years 4 months ago
Network Resource Allocation for Competing Multiple Description Transmissions
Providing real-time multimedia services over a best-effort network is challenging due to the stringent delay requirements in the presence of complex network dynamics. Multiple des...
Ying Li, Chao Tian, Suhas N. Diggavi, Mung Chiang,...
EUROSYS
2007
ACM
14 years 7 months ago
Removing the memory limitations of sensor networks with flash-based virtual memory
Virtual memory has been successfully used in different domains to extend the amount of memory available to applications. We have adapted this mechanism to sensor networks, where,...
Andreas Lachenmann, Pedro José Marró...
SSDBM
2005
IEEE
122views Database» more  SSDBM 2005»
14 years 4 months ago
Detection and Tracking of Discrete Phenomena in Sensor-Network Databases
This paper introduces a framework for Phenomena Detection and Tracking (PDT, for short) in sensor network databases. Examples of detectable phenomena include the propagation over ...
Mohamed H. Ali, Mohamed F. Mokbel, Walid G. Aref, ...