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Optimal Actuation Strategies for Sensor/Actuator Networks F. Thouin, R. Thommes and M.J. Coates McGill University Department of Electrical and Computer Engineering 3480 University, Montreal, Quebec, Canada H3A 2A7
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Document Date: 2006-07-19 14:26:54


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Belmont / IEEE ICASSP / Montreal / Nashville / Hillsdale / Hilton Head Island / /

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UDA LDA / CDA LDA / Princeton University Press / I. I NTRODUCTION Wireless / J. Hybrid Intelligent Systems / /

Country

Canada / China / /

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Facility

Penn. State University / Stanford University / University of Cambridge / L. Hall / /

IndustryTerm

low-power wireless / medical and agricultural applications / fusion site / Wireless sensors / important communications savings / potential applications / reinforcement learning algorithm / neural network / sensor networks / actuator networks / on-line reinforcement learning / self-propelled irrigation systems / approximate algorithm / food industry / Wireless sensor-actuator networks / learning algorithm / /

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Department of Statistics / Princeton University / USDA / Methodology Center / Penn. State University / Summer School / University of Cambridge / Stanford University / UDA LDA / McGill University Department of Electrical and Computer Engineering / /

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Island / Lawrence Erlbaum / /

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Position

appropriate model for the agricultural system / /

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Quebec / YT / Tennessee / South Carolina / /

PublishedMedium

Machine Learning / /

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Stanford University / /

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three algorithms / approximate algorithm / proposed algorithms / LDA algorithm / Machine Learning / distributed LDA algorithm / two algorithms / learning algorithm / distributed algorithm / neural network / two distributed algorithms / second algorithm / simulation / reinforcement learning algorithm / Q-learning algorithm / /

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