標(biāo)題: Titlebook: Machine Learning for Indoor Localization and Navigation; Saideep Tiku,Sudeep Pasricha Book 2023 The Editor(s) (if applicable) and The Auth [打印本頁] 作者: 富裕 時(shí)間: 2025-3-21 18:07
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書目名稱Machine Learning for Indoor Localization and Navigation讀者反饋學(xué)科排名
作者: 反話 時(shí)間: 2025-3-21 23:54
Smart Device-Based PDR Methods for Indoor Localizationon capability. Sensors embedded in these devices are relatively low-cost and convenient to carry. Consequently, leveraging the sensors embedded in smart devices has provided new opportunities for indoor PDR developments. In this chapter, we first introduce various types of smart devices and device-b作者: Conspiracy 時(shí)間: 2025-3-22 04:28
Geometric Indoor Radiolocation: History, Trends and Open Issuesrties of the received signal, the so-called geometric radiolocation techniques. A brief reference to the localization history, actors, and architectures, along of a taxonomy of the different concepts of localization, introduces the chapter, before presenting a detailed discussion of the most common 作者: Ptosis 時(shí)間: 2025-3-22 07:57
Indoor Localization Using Trilateration and Location Fingerprinting Methodsl techniques to identify the location of the intersection point of three circles. A location “fingerprinting” algorithm is normally comprised of two stages. In the first stage, a positioning fingerprint database is established and the second stage is matching the fingerprint with the database. Kalma作者: overrule 時(shí)間: 2025-3-22 10:51 作者: Allergic 時(shí)間: 2025-3-22 16:19 作者: 閑逛 時(shí)間: 2025-3-22 19:48
A Scalable Framework for Indoor Localization Using Convolutional Neural Networkstals, and underground mines. Most prior works in the domain of indoor localization deliver inadequate localization accuracies without expensive infrastructure. Alternatively, methodologies employing inexpensive off-the-shelf devices that are ubiquitous in nature lack consistency in localization qual作者: laceration 時(shí)間: 2025-3-22 21:13 作者: 多山 時(shí)間: 2025-3-23 03:47
Exploiting Fingerprint Correlation for Fingerprint-Based Indoor Localization: A Deep Learning-Based lying various fingerprints in improving localization accuracy still remains unknown. Moreover, how to design efficient indoor localization methods through fully exploiting the features of different fingerprints is to be explored as well. In this chapter, we investigate the location error of a finger作者: genesis 時(shí)間: 2025-3-23 06:25 作者: scrutiny 時(shí)間: 2025-3-23 10:38
Overview of Approaches for Device Heterogeneity Management During Indoor Localizationd positioning technology, has attracted extensive attention. In the process of localization, the difference in RSS caused by heterogeneity between different devices cannot be ignored. It leads to the degradation of positioning accuracy. A comprehensive overview of device heterogeneity management met作者: Gorilla 時(shí)間: 2025-3-23 15:39
Deep Learning for Resilience to Device Heterogeneity in Cellular-Based Localizationre suitable for providing such ubiquitous services due to their widespread availability. One of the main barriers to accuracy is a large number of models of cell phones, which have variations of the measured received signal strength (RSSI), even at the same location and time. This chapter discusses 作者: 排出 時(shí)間: 2025-3-23 19:53 作者: 政府 時(shí)間: 2025-3-23 23:43
Smartphone Invariant Indoor Localization Using Multi-head Attention Neural Network However, a few critical challenges have prevented the widespread proliferation of this technology in the public domain. One such critical challenge is device heterogeneity, i.e., the variation in the RSSI signal characteristics captured across different smartphone devices. In the real world, the sm作者: WAIL 時(shí)間: 2025-3-24 04:18
Heterogeneous Device Resilient Indoor Localization Using Vision Transformer Neural Networksgs to localize users with smartphones. Unfortunately, it has been demonstrated that the heterogeneity of wireless transceivers among various cellphones used by consumers reduces the accuracy and dependability of localization algorithms. In this chapter, we propose a novel framework based on vision t作者: 迅速成長 時(shí)間: 2025-3-24 08:26 作者: Temporal-Lobe 時(shí)間: 2025-3-24 11:40 作者: 暗指 時(shí)間: 2025-3-24 16:58 作者: craving 時(shí)間: 2025-3-24 21:57
Heterogeneous Device Resilient Indoor Localization Using Vision Transformer Neural Networks smartphone heterogeneity while improving localization accuracy from 41% to 68% over the best-known prior works. We also demonstrate the generalizability of our approach and propose a data augmentation technique that can be integrated into most deep learning-based localization frameworks to improve accuracy.作者: Pedagogy 時(shí)間: 2025-3-24 23:34 作者: curriculum 時(shí)間: 2025-3-25 03:29
Exploiting Fingerprint Correlation for Fingerprint-Based Indoor Localization: A Deep Learning-Based ough fully exploiting the features of different fingerprints is to be explored as well. In this chapter, we investigate the location error of a fingerprint-based indoor localization system with the application of hybrid fingerprints. On this basis, we propose a hybrid fingerprints localization algorithm based on machine learning.作者: NATTY 時(shí)間: 2025-3-25 07:57
A Scalable Framework for Indoor Localization Using Convolutional Neural Networksnatures into images, to create a scalable fingerprinting framework based on convolutional neural networks (CNNs). Our proposed CNN-based indoor localization framework (.) is validated across several indoor locales and shows improvements over the best-known prior works, with an average localization error of <2 meters.作者: 不知疲倦 時(shí)間: 2025-3-25 14:15 作者: Digitalis 時(shí)間: 2025-3-25 16:13 作者: 外露 時(shí)間: 2025-3-25 21:25
Smartphone Invariant Indoor Localization Using Multi-head Attention Neural Network neural network-based indoor localization framework that is resilient to device heterogeneity. An in-depth analysis of our proposed framework across a variety of indoor environments demonstrates up to 35% accuracy improvement compared to state-of-the-art indoor localization techniques.作者: STELL 時(shí)間: 2025-3-26 03:06
Book 2023dense 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 embedde作者: 刺激 時(shí)間: 2025-3-26 06:49 作者: 貞潔 時(shí)間: 2025-3-26 09:56
http://image.papertrans.cn/m/image/620623.jpg作者: Negligible 時(shí)間: 2025-3-26 12:45
https://doi.org/10.1007/978-3-031-26712-3Machine learning-based indoor localization; deep learning indoor localization; indoor positioning; indo作者: Gullible 時(shí)間: 2025-3-26 18:54 作者: Infantry 時(shí)間: 2025-3-27 00:56
prove theaccuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies severalnovel energy-efficient, real-time, and robust indoor localization techniqu978-3-031-26714-7978-3-031-26712-3作者: 開玩笑 時(shí)間: 2025-3-27 03:57 作者: 盲信者 時(shí)間: 2025-3-27 06:03
Facundo Lezama,Federico Larroca,Germán Capdehourat作者: 沒收 時(shí)間: 2025-3-27 11:34 作者: overrule 時(shí)間: 2025-3-27 15:09
Indoor Localization Using Trilateration and Location Fingerprinting Methodsthat in both the line-of-sight and non-line-of-sight experiments, the error is less than 0.5 meter within 3 meters in distance prediction by path loss models. The experimental results show that the trilateration localization algorithm is prone to error. The location fingerprinting-based method shows作者: CAMP 時(shí)間: 2025-3-27 19:10
Fusion of WiFi and IMU Using Swarm Optimization for Indoor Localizationter, we propose a new indoor localization system that integrates the inertial sensing and RSS fingerprinting via a modified Particle Swarm Optimization (PSO)-based algorithm. Different from traditional methods, our proposed method improves the accuracy by a new optimization process, in which the Ine作者: 死亡 時(shí)間: 2025-3-28 00:25
Learning Indoor Area Localization: The Trade-Off Between Expressiveness and Reliabilitycoarser location estimate (e.g., area/zone) with a higher accuracy. The size and shape of the predicted areas determine the model’s expressiveness (user gain) while influencing the degree to which the model provides a correct prediction (reliability). In this chapter we will introduce the area local作者: NATTY 時(shí)間: 2025-3-28 04:53 作者: gratify 時(shí)間: 2025-3-28 07:46
Overview of Approaches for Device Heterogeneity Management During Indoor Localizationnsformation, calibration-free function mapping method, and non-absolute fingerprint method, respectively. The principles of the implementation for these methods are presented in this chapter. Different evaluation metrics are utilized to participate in the comparison of these methods. The advantages 作者: gastritis 時(shí)間: 2025-3-28 13:59
Book 2023reless 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 techniqu作者: compose 時(shí)間: 2025-3-28 15:10
Machine Learning for Indoor Localization and Navigation作者: GRAIN 時(shí)間: 2025-3-28 19:16
Machine Learning for Indoor Localization and Navigation978-3-031-26712-3作者: 確定無疑 時(shí)間: 2025-3-29 02:24 作者: BURSA 時(shí)間: 2025-3-29 06:03 作者: 胖人手藝好 時(shí)間: 2025-3-29 10:09 作者: bioavailability 時(shí)間: 2025-3-29 11:31
Distributed Context Space (DCS) - Foundation of Semantic P2P Systemsroduces the concept of Distributed Context Space which is a concept for loosely coupled (mobile) P2P semantic systems. Shark is a reference implementation of DCS and iSphere is a social network application based on Shark. This paper shows how Semantic Web approaches combined with P2P can substitute 作者: Foolproof 時(shí)間: 2025-3-29 19:24 作者: 表示向下 時(shí)間: 2025-3-29 20:26 作者: 反話 時(shí)間: 2025-3-30 00:24 作者: 注射器 時(shí)間: 2025-3-30 05:18 作者: 任意 時(shí)間: 2025-3-30 10:31
Evaluating the?Impact of?MPI Network Sharing on?HPC Applicationsrograms. To do this, we conducted a series of experiments using synthetic noise on the Lomonosov-2 supercomputer to determine to what extent such noise can slow down the execution of widely used benchmarks and computing cores.作者: 徹底明白 時(shí)間: 2025-3-30 14:29 作者: BORE 時(shí)間: 2025-3-30 20:03 作者: 知識分子 時(shí)間: 2025-3-30 21:52 作者: Hla461 時(shí)間: 2025-3-31 03:31
ewerk, das Biologen aus unterschiedlichsten Fachrichtungen einen fundierten überblick über die Erscheinungsformen der Wirbeltiere gibt. Es vervollst?ndigt das von Wilfried Westheide und Reinhard Rieger herausge978-3-642-55436-0作者: SHOCK 時(shí)間: 2025-3-31 05:51
Triadic Insights in Astronomy, Art and Musicdie sich mit der Personalsuche besch?ftigen müssen, werden mit dieser Tatsache konfrontiert. Es wird nicht nur schwieriger, neue Mitarbeiter zu finden, es wird auch problematischer, sie zu halten. Die Fluktuationsneigung w?chst.作者: Salivary-Gland 時(shí)間: 2025-3-31 09:29 作者: OCTO 時(shí)間: 2025-3-31 16:21
