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Mission-oriented sensor networks and...

Ammari, Habib M.

Mission-oriented sensor networks and systems.art and scienceVolume 2,Advances :

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mission-oriented sensor networks and systems.edited by Habib M. Ammari.
其他題名:	art and science
其他題名:	Advances
其他作者:	Ammari, Habib M.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	xviii, 794 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Sensor networks.
電子資源:	https://doi.org/10.1007/978-3-319-92384-0
ISBN:	9783319923840$q(electronic bk.)

Mission-oriented sensor networks and systems.art and scienceVolume 2,Advances :
Mission-oriented sensor networks and systems.art and scienceVolume 2,Advances :[electronic resource] /Advancesedited by Habib M. Ammari. - Cham :Springer International Publishing :2019. - xviii, 794 p. :ill., digital ;24 cm. - Studies in systems, decision and control,v.1642198-4182 ;. - Studies in systems, decision and control ;v.3..

Introduction -- Autonomous Cooperative Routing for Mission-Critical Applications -- Using Models for Communication in Cyber-Physical Systems -- Urban Micro-Climate Monitoring Using IoT Based Architecture -- Digital Forensics for IoT and WSNs -- An Overview of Wearable Computing -- Wearable Computing and Human Centricity -- Wireless transfer of energy alongside information in wireless sensor networks -- Efficient Protocols for Peer-to-Peer Wireless Power Transfer and Energy Aware Network Formation -- DeepCharge: Next-generation Software-defined Wireless Charging Systems -- Robotic Wireless Sensor Networks -- Robot and Drone Localization in GPS-Denied Areas -- Middleware for Multi-Robot System -- Interference Mitigation Techniques in Wireless Body Area Networks -- Radiation Control Algorithms in Wireless Networks -- Subspace based Encryption.

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain-computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.

ISBN: 9783319923840$q(electronic bk.)

Standard No.: 10.1007/978-3-319-92384-0doiSubjects--Topical Terms:

214195
Sensor networks.

LC Class. No.: TK7872.D48 / M577 2019

Dewey Class. No.: 006.25

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505 0 $a Introduction -- Autonomous Cooperative Routing for Mission-Critical Applications -- Using Models for Communication in Cyber-Physical Systems -- Urban Micro-Climate Monitoring Using IoT Based Architecture -- Digital Forensics for IoT and WSNs -- An Overview of Wearable Computing -- Wearable Computing and Human Centricity -- Wireless transfer of energy alongside information in wireless sensor networks -- Efficient Protocols for Peer-to-Peer Wireless Power Transfer and Energy Aware Network Formation -- DeepCharge: Next-generation Software-defined Wireless Charging Systems -- Robotic Wireless Sensor Networks -- Robot and Drone Localization in GPS-Denied Areas -- Middleware for Multi-Robot System -- Interference Mitigation Techniques in Wireless Body Area Networks -- Radiation Control Algorithms in Wireless Networks -- Subspace based Encryption.
520 $a This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain-computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.
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