Real-time Tracking Research Articles

Intelligent Bus Tracking System with Attendance based on Facial Recognition

The Intelligent Bus Tracking System is a cutting-edge solution that integrates machine learning tools and face detection technology to revolutionize transportation services within academic institutions. Leveraging the power of artificial intelligence, this system enhances the accuracy, efficiency, and security of bus tracking while introducing innovative features such as facial recognition for identification to Student Attendance.(Saad et al., 2018) The bus tracking system is a comprehensive solution designed to enhance the efficiency and safety of transportation services within academic institutions. This system leverages modern technologies, including GPS and mobile communication to provide real time tracking and monitoring of buses operating within the academic campus. The primary goal is to optimize the transportation experience for students, faculties and staff while also improving overall operational management.(Sharif et al., 2018) Keywords: GPS, Tracking system, Attendance system Face Recognition, Machine Learning,

A thermal infrared target tracking based on multi-feature fusion and adaptive model update

Thermal infrared target tracking has attracted increasing attention from researchers due to its unique imaging advantages. In response to common issues encountered in correlation filtering algorithms during the tracking process, such as background clutter, distractors, and occlusion, a framework for improving infrared target tracking is proposed. This paper proposes to extend multi-channel stepped grayscale features and optimize the feature set, fully leveraging the complementarity between multiple features and enhancing their ability to represent tracking targets. The tracking results are evaluated using the peak energy of the position weighted average, enabling an adaptive model update that improves the algorithm's ability to resist occlusion. The proposed algorithm is evaluated on the LSOTB-TIR and PTB-TIR infrared tracking datasets in terms of precision, success rate, and processing speed. In addition, the algorithm is also experimented on a self-collected dataset. The experimental results indicate that the proposed optimization framework can be integrated into correlation filtering trackers with a certain level of applicability. The improved algorithm demonstrates effective handling of various complex infrared scenarios and interferences, achieving high-precision and real-time tracking of infrared targets.

Implementation on Anti-Theft Mobile Tracking and Monitoring System

The rapid proliferation of mobile devices has led to a significant increase in the cases of mobile theft and unauthorized access to personal data. To address these security concerns, an “Antitheft Mobile Tracking and Monitoring System” is proposed. The application aims to enhance the security of mobile devices by providing users with the means to track and recover their stolen or lost devices. The Antitheft Mobile Tracking and Monitoring System offers a comprehensive solution to combat mobile theft and enhance device security. By combining real-time GPS tracking, image capturing and use of mail protocols to facilitate them has provided it with capabilities with strong authentication measures, the system provides users with effective tools to mitigate the risks associated with mobile device theft. As mobile devices continue to play an integral role in modern life, such security measures are essential to protect user privacy and personal information. The widespread adoption of mobile devices has revolutionized the way we communicate, work, and access information. With this technological advancement, however, comes an unfortunate increase in the incidents of mobile theft and unauthorized access to personal data. The loss of a mobile device not only represents a financial setback but also exposes users to potential breaches of their privacy and security. This real-time tracking capability enables users and authorized personnel, such as law enforcement agencies, to pinpoint the device's location accurately. Key Words: GPS technology, Real-time Notification, SMTP, Authentication.

Towards circular economy: A IoT enabled framework for circular supply chain integration

Nowadays, the Internet of Things (IoT) has evolved beyond mere buzzword status, emerging as a pivotal force that can confer a competitive advantage in the market. By facilitating seamless communication between objects and the internet, IoT catalyzes unprecedented connectivity between machines and objects. An inherent advantage lies in its capacity to facilitate real-time information sharing, enabling efficient management of vital aspects within the realm of supply chain management, including inventory, products, and orders. Therefore, this study investigates the impact of IoT on supply chain performance, with a specific focus on the mediating role of interconnected systems in the dynamics of supply and demand, and the performance of supply chain firms within a sustainable circular economy context. A survey involving 268 manufacturers forms the basis of quantitative data analysis. Partial Least Square Structural Equation Modelling (PLS-SEM) was employed to test the relationships between latent and construct factors. The results highlight the significant influence of IoT capabilities on both supply chain performance and the integration of internet-enabled supply and demand processes. Concurrently, the study reveals a reciprocal positive effect, as supply and demand integration within internet-enabled supply chains contributes to enhanced supply chain performance. Moreover, the integration of internet-enabled supply and demand processes emerges as a crucial mediator, amplifying the relationship between IoT capabilities and supply chain performance. This study provides valuable insights for managers, researchers, and policymakers, fostering a deeper understanding of the vital role that IoT plays in cultivating and enhancing superior supply chain management practices and overall performance. The study highlights the significant benefits of IoT in enhancing supply chain performance through improved real-time tracking, inventory management, and resource optimization. It also emphasizes the crucial role of integrating internet-enabled supply and demand processes as a mediator, boosting overall supply chain efficiency and responsiveness.

No Pain Device: Empowering Personal Safety with an Artificial Intelligence-Based Nonviolence Embedded System

This paper presents the development of a novel anti-violence device titled “no pAIn” (an acronym for Never Oppressed Protected by Artificial Intelligence Nonviolence system), which harnesses the power of artificial intelligence (AI). Primarily designed to combat violence against women, the device offers personal safety benefits for individuals across diverse demographics. Operating autonomously, it necessitates no user interaction post-activation. The AI engine conducts real-time speech recognition and effectively discerns genuine instances of aggression from non-violent disputes or conversations. Facilitated by its Internet connectivity, in the event of detected aggression, the device promptly issues assistance requests with real-time precise geolocation tracking to predetermined recipients for immediate assistance. Its compact size enables discreet concealment within commonplace items like candy wrappers, purpose-built casings, or wearable accessories. The device is battery-operated. The prototype was developed using a microcontroller board (Arduino Nano RP2040 Connect), incorporating an omnidirectional microphone and Wi-Fi module, all at a remarkably low cost. Subsequent functionality testing, performed in debug mode using the Arduino IDE serial monitor, yielded successful results. The AI engine exhibited exceptional accuracy in word recognition, complemented by a robust logic implementation, rendering the device highly reliable in discerning genuine instances of aggression from non-violent scenarios.

Abstract PO5-13-01: An App-Based Approach to Monitor Chemotherapy Symptoms

Abstract Introduction Patient-Reported Outcome Measures (PROM’s) are important tools that can enhance symptom management and patient quality of life in cancer treatment. Despite well-documented benefits, they are not often employed in routine care. This study aims to investigate the feasibility and acceptance of the integration of a dedicated mobile app for real-time symptom tracking and intervention using PROM’s among patients undergoing neoadjuvant or adjuvant chemotherapy for breast cancer. Methods We developed a dedicated mobile app for tracking PROM’s utilizing the NCI PRO-CTCAE to monitor 12 common chemotherapy-related symptoms. We aimed to evaluate patient acceptance and clinical feasibility of the app. Patient responses were directed to a personalized dashboard for designed for the clinical treatment team. We determined self-reported grade 3 or 4 vomiting, diarrhea, constipation, shortness of breath, cough, or peripheral neuropathy and grade 4 mucositis, nausea, or pain as symptoms severe enough to trigger an automatic alert requiring a phone call from our nursing staff for support. Each questionnaire generated up to 1 alert regardless of the number of severe symptoms. Symptomatic patients also received personalized automated instructions and video links for symptom management regardless of the severity of their symptoms. In response to staff feedback during the study, we added an additional assessment to determine whether patients with severe symptoms were interested in direct contact from the clinical staff when alerts were triggered. At baseline patients reported if they felt they were skillful at using smartphone apps. Quality of life, psychological and emotional health measures were collected at baseline, and weeks 6, 12, and 18. Categorical variable were described using percentages and compared using the chi squared test. Statistical analyses were performed by SPSS. Results: At the time of this analysis, 58 patients were recruited and found eligible for the study. All 58 patients self-reported as skilled with the use of smartphone apps. Eighteen patients (31%) withdrew from the study after receiving at least one questionnaire and their data was included up to their point of withdrawal. The different chemotherapy regimens given in the study are shown in table 1. Out of 772 questionnaires sent, 404 were returned, resulting in a 52% completion rate. These 404 completed questionnaires generated 126 (31%) alerts. After including feedback on the interest of direct contact from our nursing staff to manage alarming symptoms, only 23/102 (22.5%) alerts required clinical contact. Twenty-one patients (36%) did not complete even a single questionnaire. For the 37 (64%) patients who completed at least 1 questionnaire the completion rate was 74%. There was no significant difference in the alert rate of completed questionnaires between patients receiving TCHP, TC-AC-Pembro, and AC-T (38%, 38%, 29% p=NS). TC triggered significantly less alerts than AC-T (11% vs 29% p=0.01). Conclusion: The interim results of our ongoing study highlight the potential of a mobile app in real-time monitoring of chemotherapy-related symptoms. Our results suggest symptom monitoring using a dedicated mobile app is both acceptable and feasible for both patients and clinical personnel. The digital approach also seems effective in triggering alerts for severe symptoms, facilitating timely intervention. However, most alerts did not necessitate direct clinical contact, underscoring the role these interventions may play in allowing for patient self-care. However, over a third of patients did not utilize the app at all, emphasizing the need for further research examining the factors influencing the utilization of this technology for symptom monitoring. As the study continues, we hope to gain insights that can simplify and improve patient and clinician interaction with this tool, potentially improving symptom management for adjuvant breast cancer. Table 1 Chemotherapy Regimens DD AC-T dose dense doxorubicin cyclophosphamide paclitaxel, TCHP docetaxel carboplatin trastuzumab pertuzumab, TC-AC-Pembro paclitaxel carboplatin doxorubicin cyclophosphamide pembrolizumab, TC docetaxel cyclophosphamide,DD AC-T weekly dose dense doxorubicin cyclophosphamide paclitaxel weekly, TH paclitaxel trastuzumab, AC-THP doxorubicin cyclophosphamide paclitaxel trastuzumab pertuzumab, AC-TC doxorubicin cyclophosphamide paclitaxel carboplatin Citation Format: Opher Globus, Tal Sella, Amit Itay, Tali Shapira, Raisa Gold, Tami Modiano, Cecilia Oedegaard, Maya Krikli, Michal Bakalenik-Gavry, Beatrice Shaham, Alex Galper, Einav Nili-Gal Yam. An App-Based Approach to Monitor Chemotherapy Symptoms [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-13-01.

1690: Comparing Transformer training strategies for real-time tumor tracking in MRI-guided radiotherapy

1690: Comparing Transformer training strategies for real-time tumor tracking in MRI-guided radiotherapy

1080: The first clinical implementation of real-time 6DoF tumour tracking for liver SABR in the LARK trial

1080: The first clinical implementation of real-time 6DoF tumour tracking for liver SABR in the LARK trial

Real-time railroad track components inspection framework based on YOLO-NAS and edge computing

The demand for efficient track inspection systems in the rapidly evolving rail transportation field is more pronounced than ever. Hence, this study combines deep learning and edge computing for railroad track component inspection, focusing on the YOLO-NAS architecture. Our objective was twofold: to harness the advantages of YOLO-NAS for accurate and high-speed detection while addressing the computational constraints of edge devices. Consequently, YOLO-NAS-S-PTQ model achieved a remarkable balance, with 74.77% mAP and 92.20 FPS, on the NVIDIA Jetson Orin platform. By deploying this model on an edge device and utilizing a multiprocessor pipeline, we observed an inference speed of 60.468 FPS, which was nearly double the rate of its single-threaded counterpart. Field tests further confirmed the efficiency of the model, demonstrating a recall rate of 80.77% and an accuracy of 96.64%. These findings underscore the potential of YOLO-NAS in transforming traditional rail component inspection methods, significantly reducing human interventions and potential errors.

Real-time charged track reconstruction for CLAS12

This paper presents the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use machine learning algorithms to reconstruct tracks, including their momentum and direction, with high accuracy from raw hits of the CLAS12 drift chambers. The reconstruction is performed in real-time, with the rate of data acquisition, and allows for the identification of event topologies in real-time. This approach revolutionizes the Nuclear Physics experiments' data processing, allowing us to identify and categorize the experimental data on the fly, and will lead to a significant reduction in experiment data processing. It can also be used in streaming readout applications leading to more efficient data acquisition and post-processing.

Optimizing Production Scheduling in Manufacturing: Strategies and Technologies for Efficiency

This is manufacturing, efficient production scheduling is crucial for maximizing output and reducing costs. This abstract describes strategies and technologies to achieve this efficiency. It covers aspects like demand forecasting, resource allocation, optimizing task sequencing, and leveraging real-time tracking. By implementing these methods, manufacturers can streamline workflow, minimize idle time, and ensure timely deliveries. This report explores the challenges faced in production scheduling in manufacturing and examines strategies and technologies that can be used to optimize scheduling processes. The report discusses the importance of production scheduling in achieving operational efficiency and meeting customer demands. Various strategies, such as lean manufacturing, just-in- time (JIT) production, and advanced planning and scheduling (APS) systems, are analyzed for their effectiveness in improving production scheduling. Additionally, the report explores technologies such as artificial intelligence (AI), machine learning, and the Internet of Things (IoT) that can enhance production scheduling capabilities. Case studies and examples are used to illustrate how these strategies and technologies have been implemented successfully in real-world manufacturing settings. The report concludes with recommendations for manufacturers looking to optimize their production scheduling processes.

Revolutionizing supermarket checkout: A comprehensive review of Radio Frequency Identification (RFID)-enabled shopping carts for automatic payments and real-time inventory tracking

This paper provides an in-depth exploration of Radio Frequency Identification (RFID)-enabled shopping carts, a novel solution tailored for automatic payments and real-time inventory tracking in supermarkets. Through the integration of RFID technology into shopping carts, these systems update the cart’s contents automatically as products are added, thus optimizing checkout processes and improving operational effectiveness. The review delves into the fundamental technology, advantages, obstacles, and potential uses of RFID-enabled shopping carts in the retail sector, offering insights into implementation factors and avenues for future research.

Solar Tracking Methods: A Comprehensive Survey

Abstract: In the pursuit of maximizing solar energy utilization, solar tracking systems have emerged as indispensable tools for enhancing the efficiency of photovoltaic (PV) systems. This comprehensive survey delves into the multifaceted landscape of solar tracking methods, elucidating the diverse techniques and strategies employed in optimizing solar panel orientation relative to the sun's position Solar tracking systems are classified into two primary categories: single-axis and dual-axis tracking. Single-axis tracking systems adjust solar panels along one axis, typically the azimuth axis, while dual-axis tracking systems dynamically alter both azimuth and elevation angles to accurately follow the sun's apparent motion across the sky.Within the realm of solar tracking, passive and active tracking methods are explored. Passive tracking mechanisms rely on natural phenomena such as gravity or thermal expansion to adjust panel orientation, whereas active tracking systems employ mechanical, electrical, or hydraulic actuators driven by motors or other means to actively adjust panel angles based on real-time tracking algorithms.A pivotal aspect of solar tracking systems is the selection and implementation of tracking algorithms. Various algorithms, including sun position algorithms, maximum power point tracking (MPPT) algorithms, and predictive algorithms, are analyzed for their effectiveness in accurately predicting and optimizing solar panel orientation.Furthermore, this survey scrutinizes the role of sensor technologies in solar tracking, encompassing GPS-based tracking, light-intensity-based tracking, weather-based tracking utilizing meteorological sensors, and horizon tracking techniques. These sensor-based methodologies offer different advantages and trade-offs in terms of accuracy, cost, and reliability. To provide a comprehensive understanding, comparative analyses are conducted, evaluating the efficiency, cost-effectiveness, and energy yield of different tracking methods. These comparative assessments offer valuable insights into the performance characteristics and suitability of various tracking methodologies across diverse applications and environmental conditions.In conclusion, this survey serves as an invaluable resource for researchers, engineers, and policymakers involved in the design, development, and optimization of solar tracking systems for renewable energy applications. By synthesizing the latest advancements and comparative analyses, this survey facilitates informed decision-making and fosters the advancement of sustainable solar energy technologies.

Automatic Home Controlling System for Paralyzed Patient using Eye Gestures

Abstract: The system revolutionizes the lives of paralyzed individuals by harnessing the power of eye-tracking technology for seamless control of home appliances. Utilizing OpenCV for robust and real-time eye tracking, the system enables patients to effortlessly interact with their surroundings by focusing on predefined patterns or commands. A user-friendly interface facilitates the establishment of a connection between eye movements and various household devices, including lights, fans, and entertainment systems. This innovative solution empowers individuals with limited mobility to regain independence, simplifying the management of daily routines and living spaces through intuitive gaze-based commands. By providing a novel avenue for communication and control, the system offers paralyzed patients a renewed sense of autonomy, convenience, and improved quality of life.

Predictive Analysis Application for Academic Student Advancement in Secondary Education Institutions

The study developed a Predictive Analysis Application for Academic Student Advancement at Dr. Josefa Jara Martinez High School (DJJMHS) to manage school forms and data records centrally. This system offers real-time attendance tracking, academic progress reports, prediction, visualization, staff performance monitoring, and report generation. It ensures secure network connections and improved security measures while presenting all functions through a user-friendly interface. Developed using HTML5, CSS3, JavaScript, and Python-MySQL, the system facilitates the issuance and processing of various school forms. Through two testing cycles, it demonstrated functionality and reliability, receiving a "Very Good" rating from 30 respondents. This indicates accurate and dependable access to end users' data and services through the system.

Measuring Vital Signs such as Temperature, BPM, Humidity using different IOT Sensors and Designing a Mobile Application using Firebase

Abstract: This research describes the creation of an Internet of Things-based smart health surveillance system that allows for the real-time data tracking of a person's vital signs. An ESP8266 Node MCU microcontroller is integrated with temperature, humidity, and heart rate sensors in the system to gather information on the user's body temperature, ambient humidity, and heart rate. A specially created Android mobile application shows the gathered data, which is wirelessly sent to a Firebase cloud database. With the use of the mobile app, users can easily create individualized profiles, view vital sign data, and get warnings. Early health issue diagnosis and proactive illness management are aided by successful implementation, which shows that lowcost IoT devices, cloud computing, and mobile technologies may all be used for effective remote health monitoring

Video Processing Based Tracking and Vehicle Identification

Video surveillance and analysis have become integral components of various domains such as security, traffic management, and urban planning. However, effective tracking and identification of vehicles in video streams remain challenging due to environmental factors, occlusions, and complex motion patterns. This research proposes a novel approach leveraging YOLOv5, a state-of-the-art object detection algorithm, for real-time vehicle tracking and identification. By integrating YOLOv5 with advanced video processing techniques, including preprocessing for enhancing video quality and Kalman filtering for object tracking, the proposed system achieves improved accuracy and robustness in diverse scenarios. Experimental results demonstrate the effectiveness of the approach, showcasing high accuracy in vehicle tracking and reliable identification performance. The findings suggest significant potential for practical applications in enhancing video surveillance systems for better security and traffic management. Additionally, avenues for future research are discussed to further enhance the capabilities of video-based vehicle tracking and identification systems

The role of AI and mobile apps in patient-centric healthcare delivery

Patient-centric healthcare is an approach to healthcare that prioritizes the needs, preferences, and values of patients in the delivery of medical services. As part of a patient-centric model, patients take active part in decision-making regarding their own care, while healthcare providers tailor treatments and services to each patient's needs. The rise of artificial intelligence (AI) and mobile applications (apps) has now opened new avenues for enhancing patient engagement and improving healthcare outcomes, so by leveraging AI's sophisticated data analytics and predictive capabilities, healthcare providers are able to gain unprecedented insights into patient health patterns, thereby enabling personalized, and proactive care plans tailored to the patient’s individual needs. Furthermore, mobile apps have become potent tools, giving patients smooth access to healthcare resources, virtual medical consultations, and real-time health tracking, boosting patient independence and enabling them to manage their health with greater ease. The integration of AI and mobile apps into healthcare delivery is revolutionizing patient-centric care by enhancing patient engagement, empowerment, and self-management capabilities. However, addressing concerns around data privacy, security, and digital literacy is crucial for successful implementation. This study outlines a framework for harnessing these technologies to enhance patient outcomes and overall healthcare experiences. It explores the roles AI and mobile apps play in facilitating patient-centric healthcare and examines how the use of these technologies can promote personalized care, boost patient education and self-management, and improve communication between patients and healthcare providers.

Smart Billing System in Shopping Cart

Abstract: The smart billing system embedded within shopping carts scans products as they're placed inside, displaying the total price to be paid. Its primary goal is to reduce time spent on billing and simplify product scanning, aiming to streamline the checkout process and elevate overall shopping experiences. By minimizing errors inherent in manual billing methods, the system enhances efficiency. Leveraging RFID technology, it enables real-time inventory tracking, personalized promotions, and smooth payment options, ensuring a convenient shopping journey. Additionally, the project explores in-cart payment methods and security technologies to monitor customer loyalty, further enhancing efficiency and security in the shopping process.

Improved RSA dynamic cryptographic accumulator-based anonymous batch authentication scheme for Internet of Vehicles

The Internet of Vehicles (IoV) provides various services to vehicles through information sharing to ensure road safety and improve traffic efficiency. However, malicious attackers can challenge the privacy and security systems of the IoV by conducting security attacks. Currently, many schemes use bilinear pairing or elliptic curves to construct authentication systems, ensuring anonymity of vehicle identities and message security. However, these schemes suffer drawbacks such as low computational efficiency and high communication overhead in highly dynamic IoV. Therefore, we propose a new efficient certificateless message authentication scheme. The proposed scheme constructs a lightweight security authentication protocol by improving the RSA dynamic accumulator and combining it with non-interactive discrete logarithm zero-knowledge proofs. In addition, our scheme eliminates the need for bilinear pairing operations, thereby reducing computational overhead. Simultaneously, it enables real-time tracking and revocation of malicious vehicle identities. Security analysis shows that our scheme can be reduced to a secure S-RSA assumption under the random oracle model, meeting various security requirements of the IoV. Performance analysis shows that our scheme diminishes not only the computational overhead but also the communication overhead.