Real-time Tracking Research Articles

Design and Development of an Automated Forklift with IOT Weight System

This paper presents the design and development of an automated forklift system with integrated IoT-based weight monitoring and RFID-based pallet identification, aimed at enhancing efficiency and safety in industrial material handling. The forklift autonomously identifies, loads, and transports pallets using RFID technology, ensuring accurate pallet selection and handling. The IOT- based weight system, monitored via the Blynk app, enables real-time load tracking and overload alerts, safe operation. Path-following capabilities are achieved using IR sensors, while ultrasonic sensors provide effective obstacle detection, allowing precise navigation within medium scale industrial environments. By minimizing human intervention, the forklift system optimizes productivity, and offers a scalable solution for medium- scale industries. Results demonstrate the forklift’s effectiveness in automating complex tasks in industrial settings, contributing to improved workflow and operational reliability.

A Stable Multi-Object Tracking Method for Unstable and Irregular Maritime Environments

In this study, an improved stable multi-object simple online and real-time tracking (StableSORT) algorithm that was specifically designed for maritime environments was proposed to address challenges such as camera instability and irregular object motion. Specifically, StableSORT integrates a buffered IoU (B-IoU) and an observation-adaptive Kalman filter (OAKF) into the StrongSORT framework to improve tracking accuracy and robustness. A dataset was collected along the southern coast of Korea using a small autonomous surface vehicle to capture real-world maritime conditions. On this dataset, StableSORT achieved a 2.7% improvement in HOTA, 4.9% in AssA, and 2.6% in IDF1 compared to StrongSORT, and it significantly outperformed ByteTrack and OC-SORT by 84% and 69% in HOTA, respectively. These results underscore StableSORT’s ability to maintain identity consistency and enhance tracking performance under challenging maritime conditions. The ablation studies further validated the contributions of the B-IoU and OAKF modules in maintaining identity consistency and tracking accuracy under challenging maritime conditions.

Automated Transport Management System: Route Optimization and Attendance Tracking for Institutions

Educational institutions face significant challenges in managing student transportation, such as inefficient route planning, manual attendance tracking, and the lack of real-time communication. These issues contribute to delays, operational inefficiencies, and safety concerns, leaving students, parents, and administrators with limited access to critical information about transportation logistics. Existing literature highlights advancements in transportation management through IoT, RFID, GPS, and AI, revealing both the potential of these technologies and their limitations in addressing hardware dependencies, network constraints, and manual interventions. Notable research includes IoT-based systems for bus tracking and attendance, Wi-Fi-enabled attendance solutions, and multiagent routing optimization algorithms, which underscore the effectiveness of real-time tracking and automation but reveal gaps in scalability, cost-effectiveness, and hardware reliability. To bridge these gaps, this project introduces the Automated Transport Management System (ATMS), a comprehensive platform designed to enhance efficiency, safety, and communication in student transportation. ATMS integrates GPS for real-time route visualization, barcode-based automated attendance logging, and proactive notifications accessible through a user-friendly web interface. This approach minimizes manual errors, reduces wait times, and ensures reliable service even in areas with variable connectivity, providing an efficient and flexible solution for educational institutions. By continuously evolving its features, ATMS aims to establish a new standard in transportation management, promoting student safety and operational effectiveness.

Integration and Optimization of Route Planning and Inventory Management in Supply Chain Transaction Systems: A Comprehensive Analysis

This comprehensive article analysis explores the integration and optimization of route planning and inventory management within supply chain transaction systems, examining their transformative impact across various industries. The article investigates the evolution of modern logistics operations, emphasizing the crucial role of advanced technologies, blockchain frameworks, and integrated systems in enhancing operational efficiency. Through a detailed examination of core components including algorithmic foundations, real-time tracking systems, demand forecasting mechanisms, and unified data architectures, this article presents a holistic view of contemporary supply chain optimization. The article analysis encompasses multiple sectors, including retail, e-commerce, and manufacturing, demonstrating how integrated systems have revolutionized traditional approaches to inventory management and route optimization. By exploring performance metrics, return on investment, and future trends, this article provides valuable insights into the implementation strategies and scalability considerations essential for modern supply chain systems. The article highlights the significance of cross-functional optimization and cloud-based solutions in achieving operational excellence while maintaining high levels of customer satisfaction and cost efficiency.

Flexible Photoacoustic Device Integrating Electroluminescence, Piezoelectric Vibration, and Pressure Sensing.

Marine organisms rely on the integration of light, sound, and pressure sensing for essential activities such as communication, hunting, and predator evasion. Current bioinspired devices, however, are limited by independent sensing modules, leading to complex designs and reduced adaptability. This study addresses these challenges by developing a multifunctional flexible photoacoustic device (MFPAD) that integrates electroluminescence, piezoelectric vibration, and pressure sensing into a single structure. Using airflow-assisted electrospinning, the device's fibrous layers were fabricated to optimize mechanical properties and enable synchronized responses to luminescence, sound, and pressure. Evaluations showed that MFPAD is highly sensitive to pressure changes, with corresponding light emissions, and efficient sound output modulated by input voltage and frequency. The device also enabled real-time posture tracking in robotic fish using an image recognition algorithm based on luminescence. Additionally, deep learning models allowed for the reconstruction of missing key points during motion. These findings demonstrate the promising potential of MFPAD for advancing bioinspired systems with multimodal sensing capabilities, offering a scalable solution for complex environments.

Smart Inventory Management System Using RFID Technology

The Smart Inventory Management System using RFID technology is a cutting-edge solution designed to streamline inventory processes, enhance accuracy, and improve operational efficiency. By integrating RFID (Radio Frequency Identification) technology, the system enables real-time tracking and management of inventory items, eliminating the need for manual data entry and reducing human errors. The system leverages RFID tags and readers to automate inventory identification, monitoring, and auditing. Key features include instant stock updates, reduced operational costs, enhanced security, and improved decision-making through data analytics. This innovative approach is particularly beneficial for industries such as retail, logistics, and manufacturing, where efficient inventory management is critical. The implementation of this system demonstrates the potential of RFID technology in revolutionizing inventory management practices and achieving greater productivity.

Molecular Engineering of Ultrabright Biomimetic NanoGhost for Site-Selective Tumor Imaging and Biodistribution.

Optically active ultrabright imaging agents are shown to delineate tumor location with deep tissue visualization in pre noclinical tumor models. NanoGhosts (NGs) particles are reconstructed from the cell membrane and integrated with organic fluorophores to attain ultra-brightness for solid tumor imaging. Moreover, the integration of amphiphilic and lipophilic molecules reveals structural characteristics of NGs (≈70nm), which also alter their brightness. Upon intravenous administration (10 mgkg-1 single dose), these ultrabright NGs (778 MESF) enable the high-resolution of tumor site and real-time tracking of vital organs with high-contrast fluorescence signals. Engineered biomimetic NGs demonstrates better resolution and tissue penetration as compared to the clinically approved indocyanine green (ICG). High precision in tumor detection (0.5h) and strong tumor retention (24h which is further up to 30th day) without affecting healthy tissues ensure the future scope of NGs in early-stage cancer imaging. These findings suggest that these NGs mimic the biological characteristics of native cells, enabling them to evade immune clearance and target the solid tumor naturally.

High-Throughput Single-Cell Analysis of Vaccinia Virus Infection.

Here, we describe a robust and flexible method for analyzing the infection of single cells with a fluorescent-reporter virus, with real-time tracking of infected cells through microscopy. We subsequently generate quantitative data from the resulting time-lapse microscopy, and harness these data to generate biologically meaningful parameters at scale. Our methodology offers a practical solution for researchers seeking to understand the complexities and variability of virus-host interactions at a granular level.

Systems of care that improve outcomes for people with hepatic encephalopathy.

Hepatic encephalopathy (HE) is a critical neuropsychiatric complication of liver cirrhosis with a significant impact on patient quality of life and survival. The global prevalence of cirrhosis and associated HE necessitates a comprehensive understanding of the condition and effective systems of care to optimize outcomes. This review addresses the epidemiology, classification, diagnosis, and management of HE, with an emphasis on systems of care that improve outcomes for people with HE. Current diagnostic challenges include differentiating cognitive deficits attributable to HE from those caused by other etiologies, highlighting the need for accurate diagnostic methods. Traditional psychometric tests, while valuable for diagnosing covert HE (CHE), are limited in their ability to predict overt HE (OHE) due to various confounding factors. As a result, non-psychometric tools have been developed to provide outcome-based predictions aligned with the clinical course of HE. The management of HE includes addressing precipitating factors, pharmacologic interventions to reduce ammonia levels, and supportive care, with lactulose and rifaximin playing a central role. Preventive strategies with the use of remote monitoring in the outpatient management of HE, integrating technology for real-time tracking of therapy compliance and symptom evolution, could contribute to reducing hospital readmissions and improving patient care.

Integrating Solar Induced Fluorescence with High Throughput Plant Screening for Advanced Phenotyping of plants

There is an urgent need to address the escalating impacts of climate change, particularly the exacerbation of drought conditions, which pose significant threats to global food security and agricultural sustainability. Innovative solutions are imperative, and one such solution involves integrating advanced technologies like the "PlantArray" system to monitor and enhance plant physiological responses to water scarcity. The "PlantArray" system enables the precise measurement of critical whole-plant physiological traits such as transpiration rate, canopy stomatal conductance (Gs canopy), and growth rate with an exceptional spatiotemporal resolution. Augmenting this system with photosynthesis measurements offers an additional layer of information, facilitating a more focused interpretation of the system parameters. To overcome the limitations of single-leaf photosynthesis measurement techniques, this study employs a remote sensing approach to rapidly scan numerous samples at multiple time points, revealing insights into drought stress responses of S. licopersicum lines. An ultra-spectral spectroradiometer mounted on a mobile cart was positioned above an experimental matrix comprising drought-stressed S. licopersicum obsolete and mutagenic lines. Our findings reveal that the vegetation index PRI exhibited greater sensitivity to drought stress compared to other vegetation and photosynthesis remote sensing indices. Photosynthesis indices demonstrated increased sensitivity to daily biomass accumulation and served as predictors of final plant yield. Interestingly, Solar-Induced Fluorescence (SIF) parameters, solely indicative of photosynthesis-emitted fluorescence, exhibited no correlation with stress levels or final biomass production. This study articulates the potential to monitor plant responses to agricultural stressors through real-time physiological tracking across complete diel cycles, thereby enriching our understanding of plant-environment interactions. Ultimately, this integrated system shows promise in screening and developing crop cultivars with ideal physiological and photosynthetic traits, vital to cultivating resilient crops in extreme droughts and weather conditions.

Real-Time Gaze Tracking via Head-Eye Cues on Head Mounted Devices

Real-Time Gaze Tracking via Head-Eye Cues on Head Mounted Devices

Intelligent characterization and robustness quantification of frozen soil strength images using a multi-module fusion strategy

In frozen wall engineering, traditional detection methods are difficult to implement for real-time monitoring of strength parameters, due to their intermittent nature. This makes it difficult to provide timely warnings and effective responses to potential risks of frozen walls, highlighting the urgent need for a method that can continuously and accurately monitor the strength of frozen walls. An image data-driven intelligent identification method for frozen soil strength based on convolutional neural networks is proposed. By capturing images of cured samples from multiple angles and conducting uniaxial compressive strength tests, the collected strength data were divided into 12 categories, creating an image dataset for deep learning model training. A Resnet-34 deep learning model combined with global attention and downsampling showed excellent performance in comparison with a series of basic models, with an accuracy of 93.3 % and no overfitting. The accuracy of the deep learning model was assessed using adversarial attack and defense strategies, serving as an indicator of robustness. The improved model exhibited better robustness in comparative analyses. Using SHapley Additive exPlanations (SHAP) value analysis, the feature extraction process of the convolutional neural network in recognizing frozen soil strength was investigated and clarified, confirming the model's ability to identify and extract key features in frozen soil images, such as particle size, texture, cracks, and ice crystal distribution patterns. This technology offers a cutting-edge method for real-time tracking of frozen wall conditions and early disaster warnings, significantly enhancing the safety and success rate of construction projects under freezing conditions.

The role of artificial intelligence and machine learning in optimizing U.S. healthcare supply chain management

This paper aims to review the role of Artificial Intelligence and Machine Learning in managing the healthcare supply chain in the United States. Healthcare supply chains face several challenges such as fragmentation, lack of real-time visibility, and inventory management issues. However, there are solutions with the help of AI and machine learning, including the availability of predictive analytics to improve demand forecasting, optimization algorithms for inventory and logistics, and automated quality control. Application areas demand forecasting, supplier selection, logistics optimization, quality control, and real-time tracking. The applications of AI in healthcare supply chains have the potential to improve the healthcare supply chain in terms of reduced costs, increased efficiency, optimized decision-making, and better patient outcomes. However, the implementation experience has shown several challenges such as data quality, privacy concerns, regulatory compliance, and workforce adaptation within organizations. Successful implementations in various health organizations in the US give valuable insights into how AI could be well implemented. The future presents several opportunities for supply chain optimization with the rise of blockchain and Internet of Things (IoT) integration. For healthcare supply chains to adopt AI, organizations should have specific AI plans, start with pilot projects in high-impact areas, invest in data infrastructure, and ensure strong leadership support. As AI becomes increasingly critical for competitive advantage, it has the potential to create more resilient, efficient, and patient-centric supply chains in the US healthcare system.

IoT-Enabled Cross-Platform Applications for Real-Time Logistics Monitoring

Internet of Things (IoT)The integration of Internet of Things (IoT) technology and cross-platform mobile applications has revolutionized real-time logistics monitoring, enabling unprecedented visibility, efficiency, and proactive management of supply chain operations. This article explores the key components of IoT-enabled logistics monitoring, including IoT sensors, data integration, real-time tracking, automated notifications, predictive maintenance, and proactive supply chain management. The benefits of implementing these solutions are highlighted, such as reduced risks, optimized routes, improved operational efficiency, and enhanced customer satisfaction. The article also discusses the challenges associated with IoT implementation, including data integration, connectivity, security, and privacy, and provides strategies for overcoming these obstacles. Through real-world case studies, the tangible impact of IoT-enabled cross-platform applications in logistics is demonstrated, showcasing the potential for significant improvements in cost savings, asset utilization, and overall supply chain performance. As the IoT landscape continues to evolve, the adoption of these technologies will become increasingly critical for logistics providers looking to maintain a competitive edge in the market.

Nanoparticle-enhanced near-infrared fluorescence probes as a new frontier in cancer imaging

Recent advancements in nanotechnology have significantly enhanced the development of near-infrared fluorescence (NIRF) probes, positioning them as powerful tools in cancer imaging. This paper explores the unique advantages of nanoparticles incorporating NIR dyes, such as indocyanine green (ICG) and DiR, which exhibit deep tissue penetration and minimal background autofluorescence. The enhanced permeability and retention (EPR) effect facilitates selective accumulation in tumor tissues, enabling sophisticated imaging and precision-targeted drug delivery systems. This review highlights the remarkable potential of NIRF imaging techniques in molecular diagnostics, emphasizing their ability to differentiate malignant tissues at a molecular level. Additionally, we discuss various NIRF dye classifications, including cyanine and BODIPY-based probes, along with the development of multifunctional agents that enhance imaging specificity and therapeutic effectiveness. The integration of advanced targeting capabilities, including the use of antibodies and small molecules, further improves the precision of these imaging agents. While challenges remain regarding the pharmacokinetics and potential toxicity of nanoparticle-based probes, their capacity for real-time tumor tracking and the promise of multimodal imaging approaches underscore their transformative role in cancer diagnostics and treatment. By advancing the field of theranostics, nanoparticle-enhanced NIRF probes pave the way for personalized medicine and improved patient outcomes in oncology.

Revolutionizing library systems with advanced automation: A blueprint for efficiency in academic resource management

The advent of advanced automation technologies has the potential to revolutionize library systems, particularly within academic institutions, where effective resource management is essential. This paper outlines a comprehensive blueprint for integrating automation into library operations to enhance efficiency, accessibility, and user engagement. Key technologies discussed include Radio Frequency Identification (RFID), Artificial Intelligence (AI), and Machine Learning (ML), which streamline processes such as cataloging, circulation, and inventory management. The implementation of RFID facilitates self-service checkouts and real-time inventory tracking, significantly reducing the time spent on manual tasks, thereby allowing library staff to concentrate on enhancing user experiences and curating resources strategically. Furthermore, AI-driven chatbots provide personalized recommendations, improving user satisfaction and ensuring that patrons receive tailored support. This blueprint emphasizes the role of automated digital repositories and intelligent content indexing systems, which accelerate cataloging and ensure timely access to scholarly materials. By automating information retrieval processes, libraries can reduce wait times and improve the overall accessibility of resources. Data analytics are also pivotal, enabling libraries to monitor usage patterns, optimize resource allocation, and forecast future demands, ensuring alignment with academic priorities. Moreover, the paper addresses challenges related to automation implementation, including financial constraints, data privacy concerns, and staff training requirements, proposing solutions such as gradual implementation and interdepartmental collaboration. Ultimately, the transition to advanced automation not only enhances operational efficiency but also boosts academic productivity by making resources more accessible and user-centered. This strategic move toward a digital-first library environment signifies a profound transformation in academic libraries, promoting an agile approach to resource management. As institutions increasingly adopt these technologies, they position themselves at the forefront of educational innovation, effectively integrating academic resources into the digital learning landscape.

Work Sync 360

To optimize project and task management in businesses, WorkSync 360 is a stand-alone internet solution that doesn't require any additional tools. It offers a user-friendly interface for keeping track of, arranging, and enhancing work processes. Timely job distribution, deadline control, and real-time progress tracking are critical components that enhance productivity and teamwork. With its news feed, discussion boards, and tools for involving coworkers, WorkSync 360 acts as a safe haven for internal communication, encouraging transparency and information sharing. Task dependencies, connected calendars, file sharing, and other essential technologies are combined by WorkSync 360 to centralize project management operations. Teams are able to improve teamwork, achieve operational efficiency, and effectively manage projects in a single digital environment.

Navify: Smart Shopping Application

The Smart Shopping Application project aims to enhance the shopping experience in large retail stores by integrating indoor navigation and personalized assistance. It employs an Indoor Positioning System (IPS) using Bluetooth Low Energy (BLE) beacons/RFID for real-time shopper tracking and optimized pathfinding. This project bridges the gap between the convenience of e-commerce and traditional shopping, offering a seamless, efficient, and satisfying shopping experience for customers. The paper outlines the system's Architecture, implementation, and future scope.

AI-enabled intelligent inventory and supply chain optimization platform for SMEs

An AI-enabled intelligent inventory and supply chain optimization platform offers SMEs a transformative solution to streamline operations, improve forecasting accuracy, and reduce costs. SMEs often face challenges in managing inventory efficiently, forecasting demand, and maintaining strong supplier relationships, largely due to limited resources and fragmented data. This platform integrates data from multiple sources such as sales, ERP, and supplier systems into a centralized system, enabling comprehensive visibility across inventory and supply chain functions. The platform employs machine learning algorithms to analyze historical data, identify trends, and predict demand patterns, resulting in more accurate forecasting and optimized stock levels. With predictive analytics, SMEs can set dynamic reorder points, minimizing overstock and stockouts while maintaining optimal safety stock. Additionally, the platform provides real-time tracking of shipments and supplier performance, allowing SMEs to monitor logistics in real-time and make informed, proactive decisions. Intelligent automation tools further enhance productivity by automating reorder processes, handling order generation, and reducing manual intervention in restocking. Benefits of this AI-driven platform include increased operational efficiency, reduced inventory holding and ordering costs, and enhanced customer satisfaction through timely order fulfillment. By optimizing stock levels and supply chain flows, SMEs can adapt quickly to market fluctuations and demand shifts, gaining a competitive edge. While challenges remain, such as data integration and employee training, the platform’s scalable design allows SMEs to start small and expand as needed. As AI technology advances, SMEs adopting these intelligent systems will position themselves for sustainable growth, improved resilience, and higher customer satisfaction in a rapidly evolving market landscape.

Development of Artificial Intelligent-Based Methodology to Prepare Input for Estimating Vehicle Emissions

Machine learning has significantly advanced traffic surveillance and management, with YOLO (You Only Look Once) being a prominent Convolutional Neural Network (CNN) algorithm for vehicle detection. This study utilizes YOLO version 7 (YOLOv7) combined with the Kalman-based SORT (Simple Online and Real-time Tracking) algorithm as one of the models used in our experiments for real-time vehicle identification. We developed the “ISTraffic” dataset. We have also included an overview of existing datasets in the domain of vehicle detection, highlighting their shortcomings: existing vehicle detection datasets often have incomplete annotations and limited diversity, but our “ISTraffic” dataset addresses these issues with detailed and extensive annotations for higher accuracy and robustness. The ISTraffic dataset is meticulously annotated, ensuring high-quality labels for every visible object, including those that are truncated, obscured, or extremely small. With 36,841 annotated examples and an average of 32.7 annotations per image, it offers extensive coverage and dense annotations, making it highly valuable for various object detection and tracking applications. The detailed annotations enhance detection capabilities, enabling the development of more accurate and reliable models for complex environments. This comprehensive dataset is versatile, suitable for applications ranging from autonomous driving to surveillance, and has significantly improved object detection performance, resulting in higher accuracy and robustness in challenging scenarios. Using this dataset, our study achieved significant results with the YOLOv7 model. The model demonstrated high accuracy in detecting various vehicle types, even under challenging conditions. The results highlight the effectiveness of the dataset in training robust vehicle detection models and underscore its potential for future research and development in this field. Our comparative analysis evaluated YOLOv7 against its variants, YOLOv7x and YOLOv7-tiny, using both the “ISTraffic” dataset and the COCO (Common Objects in Context) benchmark. YOLOv7x outperformed others with a mAP@0.5 of 0.87, precision of 0.89, and recall of 0.84, showing a 35% performance improvement over COCO. Performance varied under different conditions, with daytime yielding higher accuracy compared to night-time and rainy weather, where vehicle headlights affected object contours. Despite effective vehicle detection and counting, tracking high-speed vehicles remains a challenge. Additionally, the algorithm’s deep learning estimates of emissions (CO, NO, NO2, NOx, PM2.5, and PM10) were 7.7% to 10.1% lower than ground-truth.