Mission-Oriented Sensor Networks and Systems: Art and Science
Volume 2: Advances
Editors: Ammari, Habib M (Ed.)
Free Preview- Provides a concise and structured presentation of deep learning applications
- Introduces a large range of applications related to vision, speech, and natural language processing
- Includes active research trends, challenges, and future directions of deep learning
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- About this book
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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.
- Table of contents (21 chapters)
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Introduction
Pages 1-8
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Autonomous Cooperative Routing for Mission-Critical Applications
Pages 11-54
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Using Models for Communication in Cyber-Physical Systems
Pages 55-81
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Urban Microclimate Monitoring Using IoT-Based Architecture
Pages 85-134
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Models for Plug-and-Play IoT Architectures
Pages 135-170
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Table of contents (21 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Mission-Oriented Sensor Networks and Systems: Art and Science
- Book Subtitle
- Volume 2: Advances
- Editors
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- Habib M Ammari
- Series Title
- Studies in Systems, Decision and Control
- Series Volume
- 164
- Copyright
- 2019
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer Nature Switzerland AG
- eBook ISBN
- 978-3-319-92384-0
- DOI
- 10.1007/978-3-319-92384-0
- Hardcover ISBN
- 978-3-319-92383-3
- Series ISSN
- 2198-4182
- Edition Number
- 1
- Number of Pages
- XVIII, 794
- Number of Illustrations
- 115 b/w illustrations, 188 illustrations in colour
- Topics
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