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Machine Learning for Indoor Localization and Navigation
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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£ 44.67
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Add to basketCondition: Hervorragend. Zustand: Hervorragend | Seiten: 582 | Sprache: Englisch | Produktart: Bücher.
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Machine Learning for Indoor Localization and Navigation
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Machine Learning for Indoor Localization and Navigation
Published by Springer International Publishing AG, 2024
ISBN 10: 3031267141 ISBN 13: 9783031267147
Language: English
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
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Machine Learning for Indoor Localization and Navigation
Published by Springer International Publishing AG, 2024
ISBN 10: 3031267141 ISBN 13: 9783031267147
Language: English
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Machine Learning for Indoor Localization and Navigation
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Machine Learning for Indoor Localization and Navigation
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Paperback. Condition: Brand New. 582 pages. 9.25x6.10x9.25 inches. In Stock.
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Machine Learning for Indoor Localization and Navigation
Published by Springer International Publishing, Springer International Publishing, 2024
ISBN 10: 3031267141 ISBN 13: 9783031267147
Language: English
£ 78.94
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - While GPS is the de-facto solution for outdoor positioning with a clear sky view, there is no prevailing technology for GPS-deprived areas, including dense city centers, urban canyons, buildings and other covered structures, and subterranean facilities such as underground mines, where GPS signals are severely attenuated or totally blocked. As an alternative to GPS for the outdoors, indoor localization using machine learning is an emerging embedded and Internet of Things (IoT) application domain that is poised to reinvent the way we navigate in various indoor environments. This book discusses advances in the applications of machine learning that enable the localization and navigation of humans, robots, and vehicles in GPS-deficient environments. The book explores key challenges in the domain, such as mobile device resource limitations, device heterogeneity, environmental uncertainties, wireless signal variations, and security vulnerabilities. Countering these challenges can improve theaccuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies severalnovel energy-efficient, real-time, and robust indoor localization techniques that utilize emerging deep machine learning and statistical techniques to address the challenges for indoor localization and navigation.In particular, the book:Provides comprehensive coverage of the application of machine learning to the domain of indoor localization;Presents techniques to adapt and optimize machine learning models for fast, energy-efficient indoor localization;Covers design and deployment of indoor localization frameworks on mobile, IoT, and embedded devices in real conditions.
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Add to basketCondition: New. 1st ed. 2023 edition NO-PA16APR2015-KAP.
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Machine Learning for Indoor Localization and Navigation
Published by Springer Verlag GmbH, 2024
ISBN 10: 3031267141 ISBN 13: 9783031267147
Language: English
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Machine Learning for Indoor Localization and Navigation
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Machine Learning for Indoor Localization and Navigation
Published by Springer International Publishing, Springer Nature Switzerland Jul 2024, 2024
ISBN 10: 3031267141 ISBN 13: 9783031267147
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
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Add to basketTaschenbuch. Condition: Neu. Neuware -While GPS is the de-facto solution for outdoor positioning with a clear sky view, there is no prevailing technology for GPS-deprived areas, including dense city centers, urban canyons, buildings and other covered structures, and subterranean facilities such as underground mines, where GPS signals are severely attenuated or totally blocked. As an alternative to GPS for the outdoors, indoor localization using machine learning is an emerging embedded and Internet of Things (IoT) application domain that is poised to reinvent the way we navigate in various indoor environments. This book discusses advances in the applications of machine learning that enable the localization and navigation of humans, robots, and vehicles in GPS-deficient environments. The book explores key challenges in the domain, such as mobile device resource limitations, device heterogeneity, environmental uncertainties, wireless signal variations, and security vulnerabilities. Countering these challenges can improve theaccuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies severalnovel energy-efficient, real-time, and robust indoor localization techniques that utilize emerging deep machine learning and statistical techniques to address the challenges for indoor localization and navigation.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 584 pp. Englisch.
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Machine Learning for Indoor Localization and Navigation
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Machine Learning for Indoor Localization and Navigation
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Machine Learning for Indoor Localization and Navigation
Published by Springer International Publishing, Springer International Publishing, 2023
ISBN 10: 3031267117 ISBN 13: 9783031267116
Language: English
£ 111.44
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Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - While GPS is the de-facto solution for outdoor positioning with a clear sky view, there is no prevailing technology for GPS-deprived areas, including dense city centers, urban canyons, buildings and other covered structures, and subterranean facilities such as underground mines, where GPS signals are severely attenuated or totally blocked. As an alternative to GPS for the outdoors, indoor localization using machine learning is an emerging embedded and Internet of Things (IoT) application domain that is poised to reinvent the way we navigate in various indoor environments. This book discusses advances in the applications of machine learning that enable the localization and navigation of humans, robots, and vehicles in GPS-deficient environments. The book explores key challenges in the domain, such as mobile device resource limitations, device heterogeneity, environmental uncertainties, wireless signal variations, and security vulnerabilities. Countering these challenges can improve theaccuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies severalnovel energy-efficient, real-time, and robust indoor localization techniques that utilize emerging deep machine learning and statistical techniques to address the challenges for indoor localization and navigation.In particular, the book:Provides comprehensive coverage of the application of machine learning to the domain of indoor localization;Presents techniques to adapt and optimize machine learning models for fast, energy-efficient indoor localization;Covers design and deployment of indoor localization frameworks on mobile, IoT, and embedded devices in real conditions.
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£ 118.57
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Machine Learning for Indoor Localization and Navigation
Published by Springer International Publishing, Springer Nature Switzerland Jun 2023, 2023
ISBN 10: 3031267117 ISBN 13: 9783031267116
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
£ 111.44
Convert currency£ 29.50 shipping from Germany to United KingdomQuantity: 2 available
Add to basketBuch. Condition: Neu. Neuware -While GPS is the de-facto solution for outdoor positioning with a clear sky view, there is no prevailing technology for GPS-deprived areas, including dense city centers, urban canyons, buildings and other covered structures, and subterranean facilities such as underground mines, where GPS signals are severely attenuated or totally blocked. As an alternative to GPS for the outdoors, indoor localization using machine learning is an emerging embedded and Internet of Things (IoT) application domain that is poised to reinvent the way we navigate in various indoor environments. This book discusses advances in the applications of machine learning that enable the localization and navigation of humans, robots, and vehicles in GPS-deficient environments. The book explores key challenges in the domain, such as mobile device resource limitations, device heterogeneity, environmental uncertainties, wireless signal variations, and security vulnerabilities. Countering these challenges can improve theaccuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies severalnovel energy-efficient, real-time, and robust indoor localization techniques that utilize emerging deep machine learning and statistical techniques to address the challenges for indoor localization and navigation.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 584 pp. Englisch.
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Hardcover. Condition: Brand New. 582 pages. 9.25x6.10x1.42 inches. In Stock.
