Real-time Localization Research Articles

A high-speed camera-based measurement system for monitoring and controlling the induced jet break-up fabrication of advanced ceramic breeder pebbles

AbstractThe production of lithium-rich ceramic pebbles is crucial for future fusion reactors, as they are one of the most important components of the tritium-breeding blankets. In order to produce high-quality pebbles, a melt-based fabrication process KALOS (KArlsruhe Lithium OrthoSilicate) has been developed at the Karlsruhe Institute of Technology (KIT), which produces pebbles utilising the break-up of a molten laminar jet. Since the production of the pebbles is very precise (with diameters of hundreds of micrometres) and significantly influenced by process parameters, a system that monitors and regulates the fabrication in real-time is essential. This paper elaborates on a high-speed camera-based measurement system for automatically monitoring and controlling the production process. Experimental proof demonstrates that the presented measurement system can provide the real-time sizes, locations and distance distribution of the molten ceramic droplets accurately. In addition, the system is also designed to enable the control of the pebble production by adjusting a production parameter, i.e. the driving frequency, based on the real-time output of the proposed measurement system.

Enhancing Vehicle Location Prediction Accuracy with Road-Aware Rectification for Multi-Access Edge Computing Applications

In future 6G networks, real-time and accurate vehicular data are key requirements for enhancing the data-driven multi-access edge computing (MEC) applications. Existing estimation techniques to forecast vehicle position aim to meet the real-time data needs but compromise accuracy due to a lack of context awareness. While algorithms such as the Kalman filter improve estimation accuracy by considering certainty-grading and current-state estimate of measurements, they do not include the road context, which is vital for more accurate predictions. Unfortunately, current implementations of linear Kalman filters are not road-aware and struggle to predict a two-dimensional movement accurately. To this end, we propose a significant road-aware rectification-assisted prediction mechanism that enhances the modified Kalman filter predictions by incorporating road awareness. The parameters used for the Kalman filter include vehicle location, angle, speed, and time. In contrast, road-aware location rectification incorporates predicted location and lane shape, increasing the accuracy and precision of vehicle location predictions, reaching up to 99.9%. Performance is evaluated by comparing actual, predicted, and rectified vehicular traces at different speeds. The results demonstrate that the prediction error is approximately 0.005, while the proposed rectification process further reduces the error to 0.001, highlighting the effectiveness of the proposed approach. Overall, results support the idea of provisioning accurate, proactive, and real-time vehicular location data at the edge using a road-aware approach, thereby revolutionizing 6G vehicle location provisioning in MEC.

DELIVERY TRACKING SYSTEM WITH ON-DEMAND LOCATION UPDATE DI PT ANUGRAH HADI ELECTRIC MENGGUNAKAN METODE AGILE

PT Anugrah Hadi Electric, an electrical general supplier in Tangerang, faces challenges in the process of delivering goods to customers in the Jabodetabek area. The company still relies on conventional delivery methods where the owner directly delivers the goods, which causes constraints in time efficiency and communication with customers. This research aims to develop a delivery tracking system with on-demand location update feature to improve operational efficiency and customer satisfaction. Using Agile methodology, the system is developed by integrating Firebase technology for real-time location tracking, Kotlin for mobile application development, Laravel as the backend framework, and MySQL as the database. The system allows customers to monitor delivery status directly through the application, reducing the need for manual communication with the owner. The implementation results show significant improvements in delivery efficiency and customer satisfaction. The system successfully reduces the communication burden on the owner and provides better transparency to customers regarding the status of their shipments. For further development, the addition of automatic notification features, improved data security, and integration with digital payment systems are recommended. Periodic evaluation of the application's performance is also needed to ensure the system continues to meet the needs of the company and customers along with technological developments.

Research on Sports Injury Rehabilitation Detection Based on IoT Models for Digital Health Care.

Physical therapists specializing in sports rehabilitation detection help injured athletes recover from their wounds and avoid further harm. Sports rehabilitators treat not just commonplace sports injuries but also work-related musculoskeletal injuries, discomfort, and disorders. Sensor-equipped Internet of Things (IoT) monitors the real-time location of medical equipment such as scooters, cardioverters, nebulizer treatments, oxygenation pumps, or other monitor gear. Analysis of medicine deployment across sites is possible in real time. Health care delivery based on digital technology to improve access, affordability, and sustainability of medical treatment is known as digital health care. The challenging characteristics of such sports injury rehabilitation for digital health care are playing position, game strategies, and cybersecurity. Hence, in this research, health care IoT-enabled body area networks (HIoT-BAN) have been designed to improve sports injury rehabilitation detection for digital health care. The health care sector may benefit significantly from IoT adoption since it allows for enhanced patient safety; health care investment management includes controlling the hospital's pharmaceutical stock and monitoring the heat and humidity levels. Digital health describes a group of programmers made to aid health care delivery, whether by assisting with clinical decision-making or streamlining back-end operations in health care institutions. A HIoT-BAN effectively predicts the rise in sports injury rehabilitation detection with faster digital health care based on IoT. The research concludes that the HIoT-BAN effectively indicates sports injury rehabilitation detection for digital health care. The experimental analysis of HIoT-BAN outperforms the IoT method in terms of performance, accuracy, prediction ratio, and mean square error rate.

Edge Computing Architecture for the Management of Underwater Cultural Heritage

Underwater cultural heritage (UCH) is a valuable resource that preserves humanity’s historical legacy, offering insights into traditions and civilisations. Despite its significance, UCH faces threats from inadequate regulatory frameworks, insufficient conservation technologies, and climate-induced environmental changes. This paper proposes an innovative platform combining the internet of underwater things and edge computing technologies to enhance UCH’s real-time monitoring, localisation, and management. The platform processes data through a central unit installed on a buoy near heritage sites, enabling efficient data analysis and decision making without relying on cloud connectivity. Integrating acoustic communication systems, LoRa technology, and nonterrestrial networks supports a robust multilayered communication infrastructure for continuous operation, even in remote maritime areas. The platform’s edge node deploys artificial intelligence models for real-time risk assessment, focusing on key environmental parameters to predict and mitigate corrosion rates and climate-related threats. A case study illustrates the system’s capabilities in underwater localisation, demonstrating how edge computing and acoustic triangulation techniques enable precise tracking.

A versatile real-time vision-led runway localisation system for enhanced autonomy

This paper proposes a solution to the challenging task of autonomously landing Unmanned Aerial Vehicles (UAVs). An onboard computer vision module integrates the vision system with the ground control communication and video server connection. The vision platform performs feature extraction using the Speeded Up Robust Features (SURF), followed by fast Structured Forests edge detection and then smoothing with a Kalman filter for accurate runway sidelines prediction. A thorough evaluation is performed over real-world and simulation environments with respect to accuracy and processing time, in comparison with state-of-the-art edge detection approaches. The vision system is validated over videos with clear and difficult weather conditions, including with fog, varying lighting conditions and crosswind landing. The experiments are performed using data from the X-Plane 11 flight simulator and real flight data from the Uncrewed Low-cost TRAnsport (ULTRA) self-flying cargo UAV. The vision-led system can localise the runway sidelines with a Structured Forests approach with an accuracy approximately 84.4%, outperforming the state-of-the-art approaches and delivering real-time performance. The main contribution of this work consists of the developed vision-led system for runway detection to aid autonomous landing of UAVs using electro-optical cameras. Although implemented with the ULTRA UAV, the vision-led system is applicable to any other UAV.

IOT-based Accident Detection and Alert System

Speed is the one of the basic reasons for vehicle accident in India. To reducing the accidents the system is used that is `Accident Detection and Alert System'. This system alert when accident occurred and immediately contact to the emergency unit. Arduino UNO is processor of this system. It control the whole system. The input are Acceleration meter , Gyroscope, Vibration Sensor and output are LCD , Buzzer, GSM module, GPS module .When accident occurred their vibrations are detected by vibration sensor and accelerometer detects sudden changes in speed that may indicate an accident or crash. Gyroscope monitors changes in orientation or rotation of the vehicle, which can be useful for detecting rollovers or sudden swerving. GPS module provides real-time location data, which is useful for sending SMS of accident locations to emergency unit. GSM module get accident location and data send it to emergency unit. And Buzzer get ON and LCD display danger message

Leveraging RTLS to assess clinical space utilisation

Investment in physical space is a key strategic decision for healthcare managers. Understanding the current utilisation of clinical space is a significant input for this decision. Historically, direct observation or analysis of electronic health record (EHR) timestamps has been used to assess ambulatory space utilisation. Manual observation is prohibitively expensive, prospective and infeasible for large spaces. EHR data is limited to electronically captured workflows and to the quality of data capture. Two pilots explored the efficacy of real-time location systems (RTLS) to assess outpatient space utilisation to assist healthcare management professionals to make space allocation decisions. RTLS was shown to be viable, with accuracy that was not different from EHR-derived utilisation. RTLS had the following benefits: (a) more granular information, (b) data coverage in areas without EHR timestamps and (c) real-time assessment of utilisation. RTLS is an option for ambulatory care managers to understand the needed space for clinical operations.

Anomaly Detection System using Machine Learning Algorithms

The "Anomaly Detections Systems" is a advanced web application created to upgrade framework security and execution observing through machine learning strategies. This application, highlighting a strong login and dashboard interface for clients, empowers real-time location and examination of peculiarities inside complex frameworks. By leveraging progressed machine learning calculations, the framework can distinguish abnormal designs and behaviors that go astray from built up standards, subsequently giving early notices of potential issues or breaches. The user-friendly dashboard offers comprehensive bits of knowledge and visualizations, permitting directors to screen framework wellbeing and execution proficiently. Clients can get to point by point reports and real-time alarms, encouraging incite examination and reaction to recognized peculiarities. This proactive approach to framework observing not as it were improving security by recognizing potential dangers some time recently they heighten but moreover makes strides generally framework unwavering quality and execution. The integration of machine learning guarantees nonstop change and adjustment to advancing dangers and operational conditions. This venture underscores the basic part of innovation in keeping up vigorous and secure frameworks, exhibiting the potential of machine learning in proactive framework administration and irregularity location

Automatic Drainage Monitoring and Control System

This paper presents the development and implementation of an automatic monitoring and control system for manholes. The system integrates sensors to monitor temperature, altitude, pressure, and gas levels in drain water. An ultrasonic sensor, controlled by Arduino, detects water levels, triggering a motor to pump out water when a specific threshold is exceeded, thereby preventing clogs. A GPS module provides real-time location data, aiding municipal personnel in efficient management. The system aims to enhance urban drainage maintenance, ensuring prompt response to potential hazards and maintaining optimal functionality of the drainage network.

Intelligent Vehicle Accident Detection System

Road traffic accidents in Nigeria represent a significant public safety concern, with thousands of lives lost annually. The Intelligent Vehicle Accident Detection System (IVADS) addresses this issue by providing a system that detects accidents automatically and instantly notifies emergency responders with real-time location information. IVADS integrates multiple sensors, GPS, and GSM technologies to achieve accurate accident detection and rapid communication. This paper provides a comprehensive review of the system’s design, implementation, testing results, and potential implications for road safety improvements in Nigeria. This work builds upon existing research on accident detection, using innovative sensor integration and machine learning for precise detection.

A Literature Review on Object Detection in Autonomous Driving

Object detection technology is a core component of autonomous driving systems, primarily tasked with the real-time identification and localization of objects in the surrounding environment, such as pedestrians, vehicles, and traffic signs. With the advancement of deep learning, various efficient object detection algorithms have emerged in recent years, such as the YOLO series, RetinaNet, and CenterNet. These algorithms have shown significant improvements in both processing speed and accuracy. However, the complexity and variability of autonomous driving environments still pose severe challenges to object detection. This review summarizes the current research status of object detection algorithms in autonomous driving, focusing specifically on the basic processes and characteristics of YOLO, RetinaNet, and CenterNet. It also discusses their improvements and optimization effects in practical applications. Additionally, this paper reveals the advantages and disadvantages of these three algorithms through a comparative analysis of experimental results, providing direction for future research. Finally, the paper discusses the main challenges faced by current object detection methods in the autonomous driving field and potential solutions, aiming to promote further development and application in this area.

OfferConnect: Location Based Offer Recommendation System

In the digital commerce era, the significance of customized promotions for individual users has reached new heights. This research paper introduces an innovative application designed to transform personalized marketing strategies by incorporating a Location-Based Offer Recommendation System (LBORS). The proposed system not only leverages user location data to dynamically recommend offers from nearby shops but also ensures that customers receive discounts, establishing a win-win situation for both customers and marketers. This approach aims to create a seamless and context-aware shopping experience, enhancing the overall value proposition for users. The system employs a sophisticated algorithm that combines geospatial analytics, user preferences, and historical data to accurately identify and present relevant offers to users based on their current geographical location. By integrating real-time location information, the application ensures that users receive timely and contextually appropriate promotions, thereby maximizing the likelihood of engagement and conversion.

EgoHDM: A Real-time Egocentric-Inertial Human Motion Capture, Localization, and Dense Mapping System

We present EgoHDM, an online egocentric-inertial human motion capture (mocap), localization, and dense mapping system. Our system uses 6 inertial measurement units (IMUs) and a commodity head-mounted RGB camera. EgoHDM is the first human mocap system that offers dense scene mapping in near real-time. Further, it is fast and robust to initialize and fully closes the loop between physically plausible map-aware global human motion estimation and mocap-aware 3D scene reconstruction. To achieve this, we design a tightly coupled mocap-aware dense bundle adjustment and physics-based body pose correction module leveraging a local body-centric elevation map. The latter introduces a novel terrain-aware contact PD controller, which enables characters to physically contact the given local elevation map thereby reducing human floating or penetration. We demonstrate the performance of our system on established synthetic and real-world benchmarks. The results show that our method reduces human localization, camera pose, and mapping accuracy error by 41%, 71%, 46%, respectively, compared to the state of the art. Our qualitative evaluations on newly captured data further demonstrate that EgoHDM can cover challenging scenarios in non-flat terrain including stepping over stairs and outdoor scenes in the wild. Our project page: https://handiyin.github.io/EgoHDM/

Smart Fuel Monitoring for Agricultural Fleet Operations Using Tilt Sensors and Cloud Data Processing

This paper presents a novel Internet of Things (IoT)-based fuel monitoring and tracking system designed to address limitations of traditional fuel gauges and enhance fleet management efficiency. The system leverages an accelerometer and Arduino microcontroller to measure fuel level by detecting tilt angles within the tank. This data, along with real-time location from GPS, is transmitted to a cloud platform for processing and visualization. A mobile application, "Tracer," empowers users with insights into fuel level, location on a map, and distance traveled. Graphical fuel consumption patterns further aid in optimization strategies. This solution offers real-time data for improved fuel efficiency, proactive route planning, and overall fleet management. The proposed system is successfully implemented IOT based technology. This sys in Ada fruit IO cloud issued to provide information about vehicle location, fuel level in tank and distance travelled by the vehicle.

Scene Representations for Robotic Spatial Perception

The ability of a robot to build a persistent, accurate, and actionable model of its surroundings through sensor data in a timely manner is crucial for autonomous operation. While representing the world as a point cloud might be sufficient for localization, denser scene representations are required for obstacle avoidance. On the other hand, higher-level semantic information is often crucial for breaking down the necessary steps to autonomously complete a complex task, such as cooking. So the looming question is, What is a suitable scene representation for the robotic task at hand? This survey provides a comprehensive review of key approaches and frameworks driving progress in the field of robotic spatial perception, with a particular focus on the historical evolution and current trends in representation. By categorizing scene modeling techniques into three main types—metric, metric–semantic, and metric–semantic–topological—we discuss how spatial perception frameworks are transitioning from building purely geometric models of the world to more advanced data structures incorporating higher-level concepts, such as the notion of object instances and places. Special emphasis is placed on approaches for real-time simultaneous localization and mapping, their integration with deep learning for enhanced robustness and scene understanding, and their ability to handle scene dynamicity as some of the hottest topics of interest driving robotics research today. We conclude with a discussion of ongoing challenges and future research directions in the quest to develop robust and scalable spatial perception systems suitable for long-term autonomy.

Mamba-VNPS: A Visual Navigation and Positioning System with State-Selection Space

This study was designed to address the challenges of autonomous navigation facing UAVs in urban air mobility environments without GPS. Unlike traditional localization methods that rely heavily on GPS and pre-mapped routes, Mamba-VNPS leverages a self-supervised learning framework and advanced feature extraction techniques to achieve robust real-time localization without external signal dependence. The results show that Mamba-VNPS significantly outperforms traditional methods across multiple aspects, including localization error. These innovations provide a scalable and effective solution for UAV navigation, enhancing operational efficiency in complex spaces. This study highlights the urgent need for adaptive positioning systems in urban air mobility (UAM) and provides a methodology for future research on autonomous navigation technologies in both aerial and ground applications.

Real-time location of acupuncture points based on anatomical landmarks and pose estimation models.

Precise identification of acupuncture points (acupoints) is essential for effective treatment, but manual location by untrained individuals can often lack accuracy and consistency. This study proposes two approaches that use artificial intelligence (AI) specifically computer vision to automatically and accurately identify acupoints on the face and hand in real-time, enhancing both precision and accessibility in acupuncture practices. The first approach applies a real-time landmark detection system to locate 38 specific acupoints on the face and hand by translating anatomical landmarks from image data into acupoint coordinates. The second approach uses a convolutional neural network (CNN) specifically optimized for pose estimation to detect five key acupoints on the arm and hand (LI11, LI10, TE5, TE3, LI4), drawing on constrained medical imaging data for training. To validate these methods, we compared the predicted acupoint locations with those annotated by experts. Both approaches demonstrated high accuracy, with mean localization errors of less than 5 mm when compared to expert annotations. The landmark detection system successfully mapped multiple acupoints across the face and hand even in complex imaging scenarios. The data-driven approach accurately detected five arm and hand acupoints with a mean Average Precision (mAP) of 0.99 at OKS 50%. These AI-driven methods establish a solid foundation for the automated localization of acupoints, enhancing both self-guided and professional acupuncture practices. By enabling precise, real-time localization of acupoints, these technologies could improve the accuracy of treatments, facilitate self-training, and increase the accessibility of acupuncture. Future developments could expand these models to include additional acupoints and incorporate them into intuitive applications for broader use.

Research progress of organic small probes sensitive to tumor microenvironment

The development, metastasis, and spread of malignant tumors are closely associated with the inherent properties of tumor cells. These tumors often exhibit low pH, low polarity, high viscosity, and hypoxia. Understanding these characteristics is essential for early cancer detection and monitoring treatment effectiveness. Small molecule fluorescent probes have emerged as powerful tools for real-time localization, dynamic detection, and visualization of tumors. In this paper, we review recent progress in the use of these probes to study tumor biology, focusing on their design strategies, response mechanisms, and preclinical applications for detecting pH, viscosity, polarity, and hypoxia.

EXPRESS: Does Location Familiarity Increase Response to Mobile Ads?

Targeting ads based on consumers’ real-time locations has evolved into a practice worth billions of dollars in the advertising industry. Yet, the implications of repeated mobile ad exposure are poorly understood, primarily due to the confounding effects of locational context. This paper seeks to bridge this gap. Using two large datasets comprising more than three million observations from a major European mobile telecommunication company, we investigate how ad repetition and location revisits (i.e., returning to a previously visited location) jointly determine consumer response to mobile display advertising. Our empirical strategy leverages coarsened exact matching combined with a logit model with fixed effects at the consumer-location level. We find that the click rate through ads is more than 26 percent higher at revisited locations compared to locations visited for the first time. However, mobile ad repetition decreases click rates, and this effect is amplified at revisited locations. Our results contribute to the theory and practice of mobile advertising.