Location Tracking Research Articles

Route Positioning System for Campus Shuttle Bus Service Using a Single Camera

A route positioning system is a technology that identifies the current route when driving from one stop to the next, commonly found in public transportation systems such as shuttle buses that follow fixed routes. This is especially useful for smaller-scale services, such as shuttle buses, where using expensive technology and sensors for location tracking might not be feasible. Particularly in urban areas with tall buildings or mountainous regions with lots of trees, relying solely on GPS can lead to many errors. Therefore, this paper suggests a cost-effective solution that uses just one camera sensor to accurately determine the location of small-scale transportation services on fixed routes. For this, this paper uses a single-stage detection network that quickly identifies objects and tracks them using a simple algorithm. These detected features are compiled into a “codebook” using the bag-of-visual-words technique. During actual trips, this pre-created codebook is compared with landmarks that the camera sees. This comparison helps to determine the route currently being traveled. To test the effectiveness of this approach, this paper used the route of a shuttle bus on the Gachon University campus, which is similar to a downtown area with tall buildings or a wooded mountainous area. The results showed that the shuttle bus’s route was recognized with an accuracy of 0.60. Areas with distinct features were recognized with an accuracy of 0.99, while stops with simple, nondescript structures were recognized with an accuracy of 0.29. Additionally, applying the SORT algorithm to enhance performance slightly improved the accuracy from 0.60 to 0.61. This demonstrates that our proposed method can effectively perform location recognition using only cameras in small shuttle buses.

Can Digital Democracy Guard Citizens’ Safety? Taking Taiwan’s Battle against covid as an Exemplary Case

Abstract The covid-19 pandemic required swift responses from governments at all levels. Government agencies were faced with the immense task of mitigating the health, social, and economic effects of covid-19. These actions and responses included developing mobile phone location tracking systems and ‘electronic fences’ alongside the use of big data analytics. Whether intentionally or not, this led to questions about the rise of the ‘biosurveillance state’. In this paper, we examine the extent to which digital democracy has emerged as a contested concept in Taiwan. Furthermore, we ask: to what extent is the use of digital surveillance for disease control and prevention justifiable, and to what extent can personal privacy be sacrificed when adopting digital surveillance measures with the aim of securing collective safety? We compare Taiwanese citizens’ concerns about personal privacy with those in other democracies, such as the UK, and those in the EU and North America.

Electrochemical Synthesis of Hollow Nanoparticles via Anodic Transformation of Metastable Core-Shell Precursors.

Recent advances in electrosynthesis of nanomaterials expanded structural and compositional variations accessible by the electrochemical method; however, reliably synthesizable morphological variety fall shy of that available by conventional solvothermal synthesis. In this communication, electrochemical preparation of surfactant-free hollow nanoparticles is demonstrated. By anodic conversion of core-shell precursors with metastable cores, hollowed nickel nanoparticles with uniform dimensions were synthesized and characterized. Implementation of TEM grids as the working electrodes, identical location tracking of the morphological evolution of single particles to anodic stimulus has been demonstrated. The synthesized nanoparticles were employed as catalysts for the alkaline hydrogen evolution reaction and exhibited catalytic rates that compare favorably to the Pt/C benchmark. This marks the first pure electrochemical synthesis of hollow nanoparticles and shall contribute to the structural diversification of electrosynthesized nanomaterials.

The impact of social distancing on trading activity during the COVID‐19 pandemic

AbstractI explore the impact of social distancing on trading activity during the COVID‐19 pandemic using real‐time location tracking data from Facebook users. I find that stocks categorized as “harder‐to‐value,” which are typically smaller, less transparent, less visible, and less profitable, experience decreased abnormal trading with more social distancing in their headquarters counties. In contrast, “easier‐to‐value” stocks show an increase in abnormal trading due to high social distancing in their headquarters counties. These findings indicate that greater firm‐specific information asymmetry arises from increased social distancing within the firm headquarters counties. Although “easier‐to‐value” stocks can use alternative information sources to mitigate the information asymmetry, “harder‐to‐value” stocks fail to do so and face negative impact on trading. Additional analyses using alternative information channels, market sidedness, and liquidity confirm this hypothesis.

A decentralized solid compound storage facility managed by a centralized electronic platform at a growing drug discovery company

Within a growing drug discovery company, scientists acquire (either through in house synthesis or purchase) then store, retrieve, and ship solid compound samples daily between multiple locations. The efficient management and tracking of this entire process to support drug discovery is a significant challenge. This article describes a decentralized and cost-effective inventory facility that simplifies the solid compound storage and retrieval process. Standardized storage cabinets from the market are utilized, providing a cost-effective physical infrastructure. The cabinets can be distributed across storage rooms at multiple sites and arranged into spaces with a variety of dimensions, allowing the system to be retrofitted into existing facilities and scaled up easily. We can provide storage close to work areas at each location, minimizing both unnecessary movement of staff and transportation of substances. We have applied a systematic barcoding method to the compound batch identifier that correlates with its compound location. This simplifies the compound registration process as well as the process of finding and returning compounds. Additionally, a centralized electronic platform has been employed to store, update and track solid compound information, such as properties, location and quantity. Compound shipment may be initiated from different sites, and a centralized electronic platform assists the information retrieval process, ensuring each location possesses up-to-date information. The electronic platform we present streamlines the management of compound registration, location tracking, weight updates and shipment information, facilitating seamless record sharing among all stakeholders. Every step of the process can be tracked in real time by the project team. The platform can be flexibly configured to adapt to an evolving set of storage locations, with all information and processes being audited.

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization.

The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo-Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

IoT Based Smart Helmet for Industry Rutuja Kokate

The safety and well-being of industrial workers has become a primary concern for organizations worldwide. One of the critical aspects of industrial safety is the use of personal protective equipment (PPE), including helmets. This project proposes the development of an IoT-based smart helmet designed to enhance the safety and efficiency of industrial workers. The smart helmet integrates multiple sensors, such as temperature, humidity, motion, and gas sensors, to monitor the worker's environment in real-time. These sensors gather data on potential hazards like extreme temperatures, toxic gases, or sudden impacts. The helmet also includes a communication system that enables seamless interaction with control centers or other team members. The mining industry, a vital contributor to global economic growth and infrastructure development, grapples with inherent safety challenges posed by hazardous environments and unpredictable events. In response to these concerns, this abstract introduces the "Smart Helmet for Air Quality and Hazardous Event Detection" – a groundbreaking technology tailored specifically for mining. This advanced helmet integrates sensors like the MQ3 gas sensor, temperature and humidity sensors, LED lights with automatic controls, and a GPS module. Real-time data from these sensors are transmitted to an IoT platform, enabling continuous monitoring and immediate alerts. Traditional safety measures, including personal protective equipment and periodic monitoring, fall short in addressing the dynamic conditions of mining environments. The Smart Helmet offers a comprehensive solution by providing continuous air quality and environmental monitoring, coupled with automatic lighting and location tracking. This innovation marks a paradigm shift in mining safety, addressing the urgent need for proactive measures to safeguard the well-being of miners in the face of persistent safety challenges. Key Words: Safety, Sensors, IoT, Smart Helmet, Air quality monitoring

Campus Navigation using Augmented Reality and Virtual Reality

With the rapid advancement of Augmented Reality (AR) and Virtual Reality (VR) technologies, there has been a growing interest in leveraging these immersive technologies for enhancing campus navigation experiences. This paper presents a research study focused on designing and implementing a campus navigation system for the Maharashtra Institute of Technology World Peace University (MIT WPU) using AR/VR technologies. The proposed system aims to provide students, faculty, and visitors with an intuitive and immersive wayfinding experience, facilitating navigation across the expansive campus grounds. The core features of the proposed campus navigation system include real-time location tracking, interactive campus maps, personalized route planning, and integration with campus services and facilities information. The AR component overlays digital navigation cues and points of interest onto the user's physical surroundings, enhancing spatial awareness and providing contextual information. The VR component offers an immersive 3D visualization of the campus environment, allowing users to explore virtual replicas of buildings, landmarks, and amenities. The research methodology involves a multi-stage process, beginning with requirements gathering through stakeholder interviews and user surveys to understand the navigation challenges faced by campus users. Based on the gathered requirements, a prototype AR/VR campus navigation application is developed, leveraging state-of-the-art AR/VR development tools and technologies

Rescue Relief Agency Management For Disaster Recovery

In the aftermath of a natural or man-made disaster, the swift and coordinated deployment of rescue agencies is critical to minimizing casualties and maximizing the effectiveness of relief efforts. However, the current landscape of disaster response is often hampered by the absence of a centralized platform for communication and collaboration among various rescue agencies. This lack of a unified system often leads to duplication of efforts, inefficient resource allocation, and delays in providing aid to those in dire need. To address these challenges and enhance the efficiency of disaster response, we propose the development of a comprehensive mobile application that would enable rescue agencies to seamlessly register their information, share real-time location updates, and coordinate their operations effectively. This proposed mobile application would serve as a central hub for rescue agencies to register their information, including their location, contact details, areas of expertise, and available resources. This information could be entered manually or automated using GPS or other location tracking technologies. Once the database is populated, the application would provide a user-friendly interface that allows users to visualize the locations of registered rescue agencies on a map. Additionally, users could filter the results based on specific criteria, such as the type of disaster, the resources available, or the time since the last reported activity. Beyond displaying the locations of rescue agencies, the application would also facilitate communication and collaboration among these organizations. Rescue agencies could send alerts or requests for assistance directly through the application, enabling them to coordinate their efforts and optimize resource utilization. Moreover, the application could facilitate the sharing of critical resources such as medical equipment, transportation, and communication infrastructure, ensuring that these resources are deployed where they are most needed. Overall, the proposed mobile application has the potential to revolutionize disaster response by providing a centralized platform for communication, collaboration, and resource management among rescue agencies. By enabling real-time coordination and efficient resource allocation, this application could significantly enhance the effectiveness of disaster relief efforts, saving lives and minimizing the impact of natural and man-made disasters..

Relationships between Anthropogenic Activities and Distribution of Medium to Large Mammal Species Assemblage in Madi Wildlife Corridor: Implication for Biodiversity Conservation in Uganda

Wildlife corridors between protected areas increase connectivity by integrating populations into single demographic units, thereby increasing gene flows within populations and thus probability of survival. This study assessed the relationship between anthropogenic activities and mammal species assemblage within and around Madi wildlife corridor. The study adopted recce walks along a zig-zag line transect and straight line transect methods of game tracking. Global Position System (GPS) was used to map locations of direct mammal sightings, vocalizations, tracks, dung/droppings, diggings, carcasses, and skeletal remains of mammal species. Similarly, anthropogenic activities such as hunting using bow and arrows, rifles, trapping, tree cutting, charcoal burning, bush burning, cultivation, and settlements that seemed to influence assemblage of the mammals in the area of study were mapped. The result showed that the Madi wildlife corridor still contained diverse mammal species including but not limited to; the African Elephants (Loxodonta Africana sp.), Buffaloes (Syncerus caffer) Leopards (Panthera pardus), Spotted Hyena (Crocuta Crocuta), Northern Giraffe (Giraffa Camelopardalis), Uganda kob (Kobus kob thomasi), Hartebeest (Alcelaphus buselaphus) and Reedbuck (Redunca arundinum), which were comparable to that of the adjacent Murchison Falls National Park (MFNP). The relationship between anthropogenic activities and species assemblage showed a negatively skewed distribution of some of the large mammal species specifically Elephants, Giraffes, and Hippopotamus. The study recommends gazettement of a wildlife corridor between MFNP and East Madi Wildlife Reserve to promote wildlife connectivity between two adjacent ecosystem-protected areas in northern Uganda. As a long-term strategy for wildlife conservation, it is crucial to undertake a systematic assessment and prioritization and demarcation of wildlife corridors and development of a comprehensive action plan for securing them. We further recommend development of a national Wildlife Corridor Regulations to provide a framework for the sustainable conservation of biodiversity

A Time Headway Control Scheme for Virtually Coupled Heavy Haul Freight Trains

Abstract Virtual coupling of railway trains is an emerging technology that has the potential to significantly increase railway operational efficiency by reducing the train following distance from absolute braking distances to relative braking distances. Current research in this topic is mainly focused on passenger trains and uses distance-based headways. This paper studied virtual coupling for heavy haul freight trains and demonstrated that the distance headway scheme was challenging and sometimes impractical for heavy haul trains to achieve virtual coupling. A time-based headway scheme was then proposed to set the follower train to be a certain time behind the schedule of the leader train rather than a distance headway. The time-based headway required the follower train to reproduce the leader train's operational status at the same track location. This also allowed the follower train to copy any optimized train driving strategies from the leader train. Demonstrative simulations were carried out without the consideration of communication errors and train localization errors. The results show that a conventional distance headway simulation had maximum distance and speed errors of 716 m (36%, reference 2 km) and 24 km/h (66%, reference 36 km/h), respectively. A time-based headway simulation reduced the maximum distance and speed errors to 0.07 m (0%, reference 2 km) and 0.1 km/h (9%, reference 1.18 km/h), respectively.

1467: Markerless tracking of tumor location for SBRT of sarcoma and melanoma metastases to the lung

1467: Markerless tracking of tumor location for SBRT of sarcoma and melanoma metastases to the lung

Women Safety System Using ARM Micro controller and Arduino

This abstract presents a comprehensive women's safety device leveraging modern technology for effective emergency response. The device integrates various components including the Neo6M GPS module, module, Arduino Nano microcontroller, push button for emergency alert, , pulse oximeter MAX30100, and temperature sensor LM35. The Arduino Nano serves as the central processing unit, orchestrating data collection, processing, and transmission.Incorporating a Python Flask WebApp and ThingSpeak platform, the device ensures real-time alerts and cloud storage capabilities. The Flask WebApp facilitates user interaction and customization, enabling users to set preferences and receive notifications. ThingSpeak enables seamless data logging and retrieval from the cloud, ensuring data integrity and accessibility.Upon activation, the device initiates location tracking via the GPS module and triggers an emergency alert through the module. Simultaneously, vital signs such as pulse rate and temperature are monitored using the MAX30100 pulse oximeter and LM35 temperature sensor respectively. These data are transmitted to the Flask WebApp and ThingSpeak platform for real-time monitoring and alert generation.This innovative solution aims to provide women with a reliable, multifunctional safety device capable of prompt emergency response and continuous monitoring, ultimately enhancing their safety and well-being in various settings. Key Words: Safety system, ARM micro-controller, Arduino, GPS, Alerting, Web-Application.

IoT Based Street Light Fault Detection and Location Tracking

Abstract: In the era of smart cities, efficient management of public infrastructure is paramount. Street lighting is a fundamental component of urban infrastructure, ensuring safety and security for citizens. However, the conventional methods of monitoring street lights are often inefficient and labor-intensive, leading to delayed detection and resolution of faults. This paper proposes a novel approach for street light fault detection and location tracking leveraging advanced technologies such as Internet of Things (IoT), machine learning, and geographic information systems (GIS). The proposed system consists of a network of IoT-enabled sensors installed on street lights, capable of monitoring various parameters such as luminosity, power consumption, and operational status in real-time, voltage drop, current loss. Through machine learning algorithms, the system intelligently analyzes the sensor data to detect anomalies and potential faults in the street lights. Upon detection of a fault, the system employs location tracking techniques, to precisely pinpoint the faulty street light's location

Bus Navigation and Ticketing Solution

In the busy metropolitan cities people don't have time to invest in waiting for transport. Waiting time for transport in such crowded cities leads to less productivity on a whole. People face this problem in their daily life where they have no idea about the current status of their transport. So, the proposed solution is an android based application that will help the user to check out the Bus Ticket Reservation with Location Tracking System facilitates the passengers to enquire about the bus available on the basis of source and destination, booking of tickets, enquire about the status of the booked ticket, tracking the current position etc. The aim of case study is to design and develop a database maintaining the records of different trains, train status, and passenger. The current location of the bus and also will help the user to know how much time the bus will take to reach the current location of the user. The system will use GPS as the basis for the application and basic android application will be interfacing with the updated database to provide the real-time data to the user.

A System of IoT Devices to Prevent Under-Loading Over-Loading of Cargo Vehicles

Efficient and safe cargo transportation is a cornerstone of modern logistics and the loading of cargo vehicle is a crucial aspect of this process. This project introduces a comprehensive system of IOT Devices designed to prevent both under-loading and overloading of CARGO VEHICLE.The system incorporates an Arduino UNO microcontroller, load cells for load detection, MEMS sensors to act as virtual doors for theft detection, IOT modules for sending SMS alerts and cloud data upload, GPS for location tracking and information sharing and an LCD display for data visualization.

FPGA Implementation of a Pipelined Kalman Filter for Object Tracking in Two Dimensions

In a location tracking system for a moving object, not only accuracy but also real-time processing are important factors. The Kalman filter, as a recursive function, stands out as one of the prominent algorithms for object tracking. It continuously compares measured data with predicted values based on the system characteristics in real time, and then corrects the error of the predicted values while considering the noise of both system and measured data. This paper focuses on designing a hardware-based Kalman filter for object tracking in two dimensions. Following an analysis of the Kalman filter algorithm, the blocks capable of parallel processing are identified and configured to be processed in parallel, effectively reducing data processing time. The clock speed is enhanced by using the pipeline technique. In addition, the time-sharing technique is applied to increase the utilization of hardware resources and reduce the area. Data was processed at 32-bit floating points to uphold accuracy comparable to software-implemented Kalman filters. The proposed Kalman filter architecture is designed using verilog HDL and then simulated in Synopsys VCS/Verdi. And the accuracy is verified by comparing results with a software-based Kalman filter designed using MATLAB. It was implemented using Zynq ZYNQ-7 ZC702 Programmable logic via Xilnix Vivado, and can operate at 33MHz. It takes a total of 44 clocks, or 1.32 us, to process one data. Therefore, it was confirmed that the designed Kalman filter hardware is suitable for real-time processing.

Child Safety and Vitals Tracking using IoT

The basic operation of the proposed child tracking system is that when a violation of child safety is detected, a pulse and temperature sensor in the child module will produce a signal. The safety device monitors the health status of a child using sensors such as temperature sensor, pulse sensor. These sensors produce a signal when violation of child safety is detected. It is incorporated with GPS module to provide location tracking of the device. Parents receive alert through connected devices. Parents receive alerts through connected devices, allowing them to promptly respond to emergencies, track movements, and monitor vital signs. The device can detect the child's approximate location, it can detect the body temperature and the surrounding temperature, and pulse rate of a child.

OCR Based Facilitator for the Visually Challenged

Your proposal for an OCR-based smart book reader catering to the visually challenged fills a crucial need for affordable accessibility solutions, particularly given the global population of approximately 285 million visually impaired individuals, with a majority residing in developing countries. Leveraging a Raspberry Pi 3 board and Tesseract OCR technology, your device converts printed or handwritten text into machine-encoded text, enhanced by Computer Vision libraries like OpenCV for image preprocessing. Beyond text conversion, features such as obstacle detection via a smart stick and an emergency help button exhibit a comprehensive approach to addressing user needs. Integration with an Android app for location tracking adds an extra layer of safety. Challenges in optimizing OCR for handwritten text and potential expansions like language translation or cloud integration hold promise for further enhancing functionality. Overall, your project stands to significantly enhance access to information and safety for visually impaired individuals, particularly in underserved communities. Key Words: OCR-based smart book reader, Visually Challenged accessibility, Raspberry Pi integration, Tesseract OCR technology Computer vision

FINCHILD: The Missing Child Finder Application

"FINCHILD" is an innovative and user-friendly mobile application designed to address the critical issue of missing children by leveraging real-time tracking technology. The application aims to empower parents, guardians, and law enforcement agencies with an effective tool to locate and recover missing children swiftly and efficiently. In a world where child safety is paramount, FINCHILD fills a significant gap by harnessing the power of real-time location tracking, geofencing, and community engagement. The application provides a comprehensive platform that integrates the latest advancements in mobile technology and data analytics to ensure a rapid and coordinated response in the event a child goes missing. The FINCHILD application is a cutting-edge real-time solution designed to address the critical issue of missing children. Leveraging advanced technologies, including GPS tracking, facial recognition, and real-time data analytics, FINCHILD aims to significantly reduce the response time and enhance the efficiency of locating missing children. Key Words: GPS Tracking, Facial Recognition, Real-Time Alerts, Community Engagement.