Machine Learning Modeling for IoUT Networks : Internet of Underwater Things / by Ahmad A. Aziz El-Banna, Kaishun Wu
By: Aziz El-Banna, Ahmad A., autor
Contributor(s): Wu, Kaishun, autor
Material type:
E-bookSeries: (SpringerBriefs in Computer Science, 2191-5776); (Computer Science (SpringerNature-11645)); (Computer Science (R0) (SpringerNature-43710)).Publisher: Cham : Springer International Publising, 2021Edition: First edition 2021.Description: 1 recurso en línea (XII, 63 páginas) : 32 ilustraciones, 24 ilustraciones a color.ISBN: 9783030685676.Subject: Aprendizaje automático
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|
LIBRO-E NO PRÉSTAMO
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Madrid Digital Acceso Electrónico (UEM) | Ciencias e Ingeniería | Q325.5 2021 EB (Browse shelf(Opens below)) | Acceso electrónico | eBook.19122158 |
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| Q325.5 2021 EB Deep Learning and Practice with MindSpore | Q325.5 2021 EB Machine Learning | Q325.5 2021 EB Explainable AI with Python | Q325.5 2021 EB Machine Learning Modeling for IoUT Networks : Internet of Underwater Things | Q325.5 2021 EB Deployable Machine Learning for Security Defense : Second International Workshop, MLHat 2021, Virtual Event, August 15, 2021, Proceedings | Q325.5 2021 EB An Introduction to Machine Learning | Q325.5 2021 EB Synthetic Data for Deep Learning |
Introduction -- Seawater's Key Physical Variables -- Opportunistic Transmission -- Localization and Positioning -- ML Modeling for Underwater Communication -- Open Challenges -- Conclusion.
This book discusses how machine learning and the Internet of Things (IoT) are playing a part in smart control of underwater environments, known as Internet of Underwater Things (IoUT). The authors first present seawater's key physical variables and go on to discuss opportunistic transmission, localization and positioning, machine learning modeling for underwater communication, and ongoing challenges in the field. In addition, the authors present applications of machine learning techniques for opportunistic communication and underwater localization. They also discuss the current challenges of machine learning modeling of underwater communication from two communication engineering and data science perspectives.
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