Articles liés à Machine Learning for Indoor Localization and Navigation
- ÉditeurSpringer International Publishing AG
- Date d'édition2023
- ISBN 10 3031267117
- ISBN 13 9783031267116
- ReliureRelié
- Langueanglais
- Nombre de pages567
- ÉditeurTiku Saideep, Pasricha Sudeep
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MACHINE LEARNING FOR INDOOR LOCALIZATION AND NAVIGATION
Vendeur : Romtrade Corp., STERLING HEIGHTS, MI, Etats-Unis
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Etat : New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. N° de réf. du vendeur ABNR-277417
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Machine Learning for Indoor Localization and Navigation
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Etat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. N° de réf. du vendeur ABEJUNE24-261374
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Machine Learning for Indoor Localization and Navigation
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Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10 : 3031267117
ISBN 13 : 9783031267116
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Couverture rigide
Vendeur : moluna, Greven, Allemagne
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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. N° de réf. du vendeur 799921344
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Machine Learning for Indoor Localization and Navigation
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Etat : New. 1st ed. 2023 edition NO-PA16APR2015-KAP. N° de réf. du vendeur 26396294583
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Machine Learning for Indoor Localization and Navigation
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Machine Learning for Indoor Localization and Navigation
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Machine Learning for Indoor Localization and Navigation
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Machine Learning for Indoor Localization and Navigation
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Machine Learning for Indoor Localization and Navigation
ISBN 10 : 3031267117
ISBN 13 : 9783031267116
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : 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. N° de réf. du vendeur 9783031267116
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Machine Learning for Indoor Localization and Navigation
ISBN 10 : 3031267117
ISBN 13 : 9783031267116
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - 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.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. 584 pp. Englisch. N° de réf. du vendeur 9783031267116
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