001470149 000__ 06923cam\\22006017i\4500
001470149 001__ 1470149
001470149 003__ OCoLC
001470149 005__ 20230803003404.0
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001470149 007__ cr\un\nnnunnun
001470149 008__ 230704s2023\\\\sz\a\\\\ob\\\\001\0\eng\d
001470149 019__ $$a1389353482
001470149 020__ $$a9783031267123$$q(electronic bk.)
001470149 020__ $$a3031267125$$q(electronic bk.)
001470149 020__ $$z3031267117
001470149 020__ $$z9783031267116
001470149 0247_ $$a10.1007/978-3-031-26712-3$$2doi
001470149 035__ $$aSP(OCoLC)1388634099
001470149 040__ $$aYDX$$beng$$cYDX$$dUNBCA$$dGW5XE$$dN$T$$dEBLCP
001470149 049__ $$aISEA
001470149 050_4 $$aTK5103.48323
001470149 08204 $$a621.3841/91$$223/eng/20230713
001470149 24500 $$aMachine learning for indoor localization and navigation /$$cSaideep Tiku, Sudeep Pasricha, editors.
001470149 264_1 $$aCham, Switzerland :$$bSpringer,$$c2023.
001470149 300__ $$a1 online resource (567 pages)
001470149 336__ $$atext$$btxt$$2rdacontent
001470149 337__ $$acomputer$$bc$$2rdamedia
001470149 338__ $$aonline resource$$bcr$$2rdacarrier
001470149 504__ $$aIncludes bibliographical references and index.
001470149 5050_ $$aPart 1. Introduction to indoor localization and navigation -- An overview of indoor localization techniques / Saideep Tiku and Sudeep Pasricha -- Smart device-based PDR methods for indoor localization / Siya Bao and Nozomu Togawa -- Geometric indoor radiolocation: history, trends and open issues / Antonello Florio, Gianfranco Avitabile, and Guiseppe Coviello -- Indoor localization using trilateration and location fingerprinting methods / Lu Bai, Maurice D. Mulvenna, and Raymond R. Bond -- Localization with Wi-Fi ranging and built-in sensors: self-learning techniques / Jeongsik Choi, Yang-Seok Choi, and Shilpa Talwar -- Part II. Advanced pattern-matching techniques for indoor localization and navigation -- Fusion Wi-Fi and IMU using swarm optimization for indoor localization / He Huang, Jianfei Yang, Xu Fang, Hao Jiang, and Lihua Xie -- A scalable framework for indoor localization using convolutional neural networks / Saideep Tiku, Ayush Mittal, and Sudeep Pasricha -- Learning indoor area localization: the trade-off between expressiveness and reliability / Marius Laska and Jörg Blankenbach -- Exploiting fingerprint correlation for fingerprint-based indoor localization: a deep learning-based approach / Yang Zheng, Junyu Liu, Min Sheng, and Chengyi Zhou -- On the application of graph neural networks for indoor positioning systems / Facundo Lezama, Frederico Larroca, and Germán Capdehourat -- Part III. Machine learning approaches for resilience to device heterogeneity -- Overview of approaches for device heterogeneity management during indoor localization  / Cunyi Yin, Hao Jiang, and Jing Chen -- Deep learning for resilience to device heterogeneity in cellular-based localization / Hamada Rizk -- A portable indoor localization framework for smartphone heterogeneity resilience / Saideep Tiku and Sudeep Pasricha -- Smartphone invariant indoor localization using multi-head attention neural network / Saideep Tiku, Danish Gufran, and Sudeep Pasricha -- Heterogeneous device resilient indoor localization using vision transformer neural networks / Danish Gufran, Saideep Tiku, and Sudeep Pasricha -- Part IV. Enabling temporal variation resilience for ML-based indoor localization and navigation -- Enabling temporal variation resilience for ML-based indoor localization / Nobuhiko Nishio, Kota Tsubouchi, Masato Sugasaki, and Masamichi Shimosaka -- A few-shot contrastive learning framework for long-term indoor localization / Saideep Tiku and Sudeep Pasricha -- A manifold-based method for long-term indoor positioning using WiFi fingerprinting / Yifan Yang, Saideep Tiku, Mahmood R. Azimi-Sadjadi, and Sudeep Pasricha -- Part V. Deploying indoor localization and navigation frameworks for resource constrained devices -- Exploring model compression for deep machine learning-based indoor localization / Saideep Tiku, Liping Wang, and Sudeep Pasricha -- Resource-aware deep learning for wireless fingerprinting localization / Gregor Cerar, Blaž Bertalaniĉ, and Carolina Fortuna -- Toward real-time indoor localization with smartphones with conditional deep learning / Saideep Tiku, Prathmesh Kale, and Saideep Pasricha -- Part VI. Securing indoor localization and navigation frameworks -- Enabling security for fingerprinting-based indoor localization on mobile devices / Saideep Tiku and Sudeep Pasricha.
001470149 506__ $$aAccess limited to authorized users.
001470149 520__ $$aWhile 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 the accuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies several novel 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.
001470149 650_0 $$aIndoor positioning systems (Wireless localization)
001470149 650_0 $$aEmbedded computer systems$$xReliability.
001470149 650_0 $$aMachine learning$$xTechnique.
001470149 655_0 $$aElectronic books.
001470149 7001_ $$aSaideep, Tiku,$$eeditor.
001470149 7001_ $$aSudeep Pasricha,$$eeditor.
001470149 77608 $$iPrint version: $$z3031267117$$z9783031267116$$w(OCoLC)1365060675
001470149 852__ $$bebk
001470149 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-26712-3$$zOnline Access$$91397441.1
001470149 909CO $$ooai:library.usi.edu:1470149$$pGLOBAL_SET
001470149 980__ $$aBIB
001470149 980__ $$aEBOOK
001470149 982__ $$aEbook
001470149 983__ $$aOnline
001470149 994__ $$a92$$bISE