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 Название: Big Data Analytics for Cyber-Physical Systems: Machine Learning for the Internet of Things
Автор: Guido Dartmann (Editor), Houbing Song (Editor), Anke Schmeink (Editor)
Издательство: Elsevier
Год: 2019
Формат: True PDF
Страниц: 396
Размер: 37.4 Mb
Язык: English
Big Data Analytics in Cyber-Physical Systems: Machine Learning for the Internet of Things examines sensor signal processing, IoT gateways, optimization and decision-making, intelligent mobility, and implementation of machine learning algorithms in embedded systems. This book focuses on the interaction between IoT technology and the mathematical tools used to evaluate the extracted data of those systems. Each chapter provides the reader with a broad list of data analytics and machine learning methods for multiple IoT applications. Additionally, this volume addresses the educational transfer needed to incorporate these technologies into our society by examining new platforms for IoT in schools, new courses and concepts for universities and adult education on IoT and data science.
Bridges the gap between IoT, CPS, and mathematical modelling.
Features numerous use cases that discuss how concepts are applied in different domains and applications.
Provides "best practices", "winning stories" and "real-world examples" to complement innovation.
Includes highlights of mathematical foundations of signal processing and machine learning in CPS and IoT.
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Автор: buratino 31-03-2020, 12:17 | Напечатать
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Big Data Analytics for Cyber-Physical Systems: Machine Learning for the Internet of Things 
