Download or read book Positioning and Navigation Using Machine Learning Methods written by Kegen Yu and published by Springer. This book was released on 2024-10-21 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book completely dedicated to positioning and navigation using machine learning methods. It deals with ground, aerial, and space positioning and navigation for pedestrians, vehicles, UAVs, and LEO satellites. Most of the major machine learning methods are utilized, including supervised learning, unsupervised learning, deep learning, and reinforcement learning. The book presents both fundamentals and in-depth studies as well as practical examples in positioning and navigation. Extensive data processing and experimental results are provided in the major chapters through conducting experimental campaigns or using in-situ measurements.

Download or read book Positioning and Navigation Using Machine Learning Methods written by Kegen Yu and published by Springer Nature. This book was released on with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning for Indoor Localization and Navigation written by Saideep Tiku and published by Springer Nature. This book was released on 2023-06-29 with total page 563 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 the accuracy, 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.

Download or read book Positioning Navigation and Robot Motion Planning in GPS Denied Environments written by Chinmaya V. Kaji and published by . This book was released on 2019 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on the development of navigational and positioning algorithms for autonomous vehicles in multiple challenging environments. Fundamentally we concentrate on advancing navigation algorithms for GPS-denied underwater environments. Specifically, we apply the following machine learning algorithms: feedforward neural networks (FFNN), and cascaded feedforward neural networks (CFNN).

Download or read book Intelligent Sensors for Positioning Tracking Monitoring Navigation and Smart Sensing in Smart Cities written by Tiancheng Li and published by MDPI. This book was released on 2021-03-04 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid development of advanced, arguably, intelligent sensors and their massive deployment provide a foundation for new paradigms to combat the challenges that arise in significant tasks such as positioning, tracking, navigation, and smart sensing in various environments. Relevant advances in artificial intelligence (AI) and machine learning (ML) are also finding rapid adoption by industry and fan the fire. Consequently, research on intelligent sensing systems and technologies has attracted considerable attention during the past decade, leading to a variety of effective applications related to intelligent transportation, autonomous vehicles, wearable computing, wireless sensor networks (WSN), and the internet of things (IoT). In particular, the sensors community has a great interest in novel, intelligent information fusion, and data mining methods coupling AI and ML for substantial performance enhancement, especially for the challenging scenarios that make traditional approaches inappropriate. This reprint book has collected 14 excellent papers that represent state-of-the-art achievements in the relevant topics and provides cutting-edge coverage of recent advances in sensor signal and data mining techniques, algorithms, and approaches, particularly applied for positioning, tracking, navigation, and smart sensing.

Download or read book Handbook of Augmented and Virtual Reality written by Sumit Badotra and published by Walter de Gruyter GmbH & Co KG. This book was released on 2023-08-21 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Augmented and Virtual Reality are revolutionizing present and future technologies: these are the fastest growing and most fascinating areas of technologies at present. This book aims to provide insight into the theory and applications of Augmented and Virtual Reality to multiple technologies such as IoT (Internet of Things), ML (Machine Learning), AI (Artificial Intelligence), Healthcare and Education.

Download or read book Machine Learning based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment written by Xiaochun Wang and published by Springer. This book was released on 2019-08-12 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research.

Download or read book A Machine Learning Based Overlay Technique for Improving the Mechanism of Road Traffic Prediction Using Global Positioning System written by Amar Deep Pandey and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Global Positioning System (GPS)-based road traffic prediction is one of the predominating technology in the modern technological era, which facilitates smooth navigation and reduces mobility time. Google Maps is used worldwide for traffic congestion and delay prediction which relies upon the GPS location of the individual's smartphone to predict traffic congestion and delay by stored data and current GPS locations. However, this method sometimes malfunctions due to the uneven distribution of passengers in different vehicle types on the roadway as there are far more passengers in buses as compared with trucks, if few buses are present in the traffic stream then it will show congestion and delay in traffic. So, it is hard to correctly predict the congestion and delay in traffic without using classified vehicle count as the ratio of the area occupied by the vehicle on the roadway and the number of passengers in it is unevenly distributed for different vehicle types. Google Maps have some limitations as it does not incorporate details regarding the classified vehicle count and categories of vehicles as there are distinct categories of vehicles operating on the roadways, with varying sizes, speeds, and passenger capacities. Thus, it would be beneficial to overlay the information of GPS localization, using Google Maps, with classified vehicle count and vehicle categories to estimate better road traffic congestion and delay. Thus the augmentation of Google Maps is required by integrating the classified traffic volume count with categories of vehicles, the present work envisages the same. For the present study, two mid-sized Indian cities in the state of Uttar Pradesh (Varanasi and Gorakhpur) were selected due to the diverse nature of mixed road traffic. For classified vehicle count data, video recording was carried out by using video recording cameras at various sites in both cities. The data of classified vehicles for both directions of traffic streams were manually counted by project staff from the video recordings and GPS coordinates were also integrated with datasets. Subsequently, various other hand-crafted features were extracted before training the machine learning-based forecasting models (ARIMA and SVM) for traffic volume prediction for a specified GPS location. The classified road traffic vehicle count was predicted using previously observed values, thereby helping in making a good decision about route selection and traffic management. Further, this work annotates the forecasted data overlay with GPS value as per the traffic condition to build a XGBoost-based classification model. The build classifier can classify the road conditions in real-time. The rigorous experimental results and real-world evaluation depicted the effectiveness of the proposed technique on the collected dataset.

Download or read book Dynamic Neural Networks for Robot Systems Data Driven and Model Based Applications written by Long Jin and published by Frontiers Media SA. This book was released on 2024-07-24 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural network control has been a research hotspot in academic fields due to the strong ability of computation. One of its wildly applied fields is robotics. In recent years, plenty of researchers have devised different types of dynamic neural network (DNN) to address complex control issues in robotics fields in reality. Redundant manipulators are no doubt indispensable devices in industrial production. There are various works on the redundancy resolution of redundant manipulators in performing a given task with the manipulator model information known. However, it becomes knotty for researchers to precisely control redundant manipulators with unknown model to complete a cyclic-motion generation CMG task, to some extent. It is worthwhile to investigate the data-driven scheme and the corresponding novel dynamic neural network (DNN), which exploits learning and control simultaneously. Therefore, it is of great significance to further research the special control features and solve challenging issues to improve control performance from several perspectives, such as accuracy, robustness, and solving speed.

Download or read book Artificial Intelligence for Sustainable Development written by Anandakumar Haldorai and published by Springer Nature. This book was released on with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Present and Future of Indoor Navigation written by Laura Ruotsalainen and published by Artech House. This book was released on 2023-11-30 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Present and Future of Indoor Navigation provides a complete overview of the latest indoor navigation technologies, algorithms, and systems. It begins by discussing various types of sensors that can be used for indoor navigation, such as accelerometers, gyroscopes, barometers, magnetometers, and cameras. It covers the numerous algorithms that can be used to compute the navigation solution, including Kalman filtering, particle filtering, and machine learning. Also, it discusses the system implementation considerations for indoor navigation, such as infrastructure, data fusion, and security. The book’s focus is on present technologies and algorithms, as well as providing a look into the future possibilities for indoor navigation, making it a great resource for a wide audience. This includes researchers, engineers, and students who are interested in indoor navigation. It is also a valuable resource for anyone who wants to learn more about the latest technologies and algorithms for indoor navigation.

Download or read book Wireless Indoor Localization written by Chenshu Wu and published by Springer. This book was released on 2018-08-22 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and in-depth understanding of wireless indoor localization for ubiquitous applications. The past decade has witnessed a flourishing of WiFi-based indoor localization, which has become one of the most popular localization solutions and has attracted considerable attention from both the academic and industrial communities. Specifically focusing on WiFi fingerprint based localization via crowdsourcing, the book follows a top-down approach and explores the three most important aspects of wireless indoor localization: deployment, maintenance, and service accuracy. After extensively reviewing the state-of-the-art literature, it highlights the latest advances in crowdsourcing-enabled WiFi localization. It elaborated the ideas, methods and systems for implementing the crowdsourcing approach for fingerprint-based localization. By tackling the problems such as: deployment costs of fingerprint database construction, maintenance overhead of fingerprint database updating, floor plan generation, and location errors, the book offers a valuable reference guide for technicians and practitioners in the field of location-based services. As the first of its kind, introducing readers to WiFi-based localization from a crowdsourcing perspective, it will greatly benefit and appeal to scientists and researchers in mobile and ubiquitous computing and related areas.

Download or read book Recent Advances in Big Data Machine and Deep Learning for Precision Agriculture written by Muhammad Fazal Ijaz and published by Frontiers Media SA. This book was released on 2024-02-19 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computational Science ICCS 2021 written by Maciej Paszynski and published by Springer Nature. This book was released on 2021-06-09 with total page 722 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six-volume set LNCS 12742, 12743, 12744, 12745, 12746, and 12747 constitutes the proceedings of the 21st International Conference on Computational Science, ICCS 2021, held in Krakow, Poland, in June 2021.* The total of 260 full papers and 57 short papers presented in this book set were carefully reviewed and selected from 635 submissions. 48 full and 14 short papers were accepted to the main track from 156 submissions; 212 full and 43 short papers were accepted to the workshops/ thematic tracks from 479 submissions. The papers were organized in topical sections named: Part I: ICCS Main Track Part II: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis of Complex Social Systems; Computational Collective Intelligence; Computational Health Part IV: Computational Methods for Emerging Problems in (dis-)Information Analysis; Computational Methods in Smart Agriculture; Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems Part V: Computer Graphics, Image Processing and Artificial Intelligence; Data-Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences; Multiscale Modelling and Simulation Part VI: Quantum Computing Workshop; Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainty; Teaching Computational Science; Uncertainty Quantification for Computational Models *The conference was held virtually.

Download or read book ICT Analysis and Applications written by Simon Fong and published by Springer Nature. This book was released on 2022-01-07 with total page 944 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes new technologies and discusses future solutions for ICT design infrastructures, as reflected in high-quality papers presented at the 6th International Conference on ICT for Sustainable Development (ICT4SD 2021), held in Goa, India, on 5–6 August 2021. The book covers the topics such as big data and data mining, data fusion, IoT programming toolkits and frameworks, green communication systems and network, use of ICT in smart cities, sensor networks and embedded system, network and information security, wireless and optical networks, security, trust, and privacy, routing and control protocols, cognitive radio and networks, and natural language processing. Bringing together experts from different countries, the book explores a range of central issues from an international perspective.

Download or read book Computer Aided Algorithms Based on Mathematics and Machine Learning for Integrated GPS and INS Land Vehicle Navigation Systems written by Deepak Bhatt and published by . This book was released on 2014 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: An integrated navigation system consisting of INS and GPS is usually preferred due to the reduced dependency on GPS-only navigator in an area prone to poor signal reception or affected by multipath. The performance of the integrated system largely depends upon the quality of the Inertial Measurement Unit (IMU) and the integration methodology. Considering the restricted use of high grade IMU and their associated price, low-cost IMUs are becoming the preferred choice for civilian navigation purposes. MEMS based inertial sensors have made possible the development of civilian land vehicle navigation as it offers small size and low-cost. However, these low-cost inertial sensors possess high inherent sensor errors such as biases, drift, noises etc. As a result, the accuracy of the integrated system degrades rapidly in a GPS denied environment. Thus, an accurate in-lab calibration and modeling of inertial sensor errors become mandatory before being deployed. This dissertation introduces a Support Vector Regression (SVR) based IMU error modeling approach for improving the low-cost navigation system accuracy. A low-cost MEMS based IMU offered by cloud cap technology, Crista IMU is used to evaluate the SVR based error modeling approach effectiveness. Alternatively, the IMU derived navigation solution and GPS data is fused to output the more reliable navigation solution and model the errors in the inertial navigation solution simultaneously. This fusion and error modeling continues during the GPS signal availability. In the case of GPS outages, the developed error model is utilized to improve the integrated navigation system accuracy. Thus, in a continued effort to improve the standalone low-cost IMU derived navigation solution reliability during GPS outages, an intelligent technique utilizing neural networks and a hybrid of mathematics and support vector based fusion algorithms are proposed fusing INS and GPS data in an open and closed loop fashion. The performance of the proposed techniques and algorithm is evaluated using real field test data utilizing low-cost MEMS IMU, Crossbow IMU 300CC-100 and a Novatel OEM GPS receiver. The test results demonstrated the improved positioning accuracy in comparison to existing techniques and showed a substantial reduction in standalone Inertial Navigation System (INS) position error drift during GPS outages. Further, a feasibility of statistical based approaches consisting of Cubist, Random Forest and Support Vector Regression is evaluated for a low-cost INS and GPS integrated system. Through experimental demonstration, Random forest regression was found to be a suitable candidate for INS and GPS data fusion as it offers the least training time and ability to tuned the parameter automatically.

Download or read book Machine Learning for Complex and Unmanned Systems written by Jose Martinez-Carranza and published by CRC Press. This book was released on 2024-02-21 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights applications that include machine learning methods to enhance new developments in complex and unmanned systems. The contents are organized from the applications requiring few methods to the ones combining different methods and discussing their development and hardware/software implementation. The book includes two parts: the first one collects machine learning applications in complex systems, mainly discussing developments highlighting their modeling and simulation, and hardware implementation. The second part collects applications of machine learning in unmanned systems including optimization and case studies in submarines, drones, and robots. The chapters discuss miscellaneous applications required by both complex and unmanned systems, in the areas of artificial intelligence, cryptography, embedded hardware, electronics, the Internet of Things, and healthcare. Each chapter provides guidelines and details of different methods that can be reproduced in hardware/software and discusses future research. Features Provides details of applications using machine learning methods to solve real problems in engineering Discusses new developments in the areas of complex and unmanned systems Includes details of hardware/software implementation of machine learning methods Includes examples of applications of different machine learning methods for future lines for research in the hot topic areas of submarines, drones, robots, cryptography, electronics, healthcare, and the Internet of Things This book can be used by graduate students, industrial and academic professionals to examine real case studies in applying machine learning in the areas of modeling, simulation, and optimization of complex systems, cryptography, electronics, healthcare, control systems, Internet of Things, security, and unmanned systems such as submarines, drones, and robots.