Real-time Tracking Research Articles

Visualizing Electrochemical CO2 Conversion via the Emerging Scanning Electrochemical Microscope: Fundamentals, Applications and Perspectives.

With the rapid development and maturity of electrochemical CO2 conversion involving cathodic CO2 reduction reaction (CO2RR) and anodic oxygen evolution reaction (OER), conventional ex situ characterizations gradually fall behind in detecting real-time products distribution, tracking intermediates, and monitoring structural evolution, etc. Nevertheless, advanced in situ techniques, with intriguing merits like good reproducibility, facile operability, high sensitivity, and short response time, can realize in situ detection and recording of dynamic data, and observe materials structural evolution in real time. As an emerging visual technique, scanning electrochemical microscope (SECM) presents local electrochemical signals on various materials surface through capturing micro-current caused by reactants oxidation and reduction. Importantly, SECM holds particular potentials in visualizing reactive intermediates at active sites and obtaining instantaneous morphology evolution images to reveal the intrinsic reactivity of active sites. Therefore, this review focuses on SECM fundamentals and its specific applications toward CO2RR and OER, mainly including electrochemical behavior observation on local regions of various materials, target products and onset potentials identification in real-time, reaction pathways clarification, reaction kinetics exploration under steady-state conditions, electroactive materials screening and multi-techniques coupling for a joint utilization. This review undoubtedly provides a leading guidance to extend various SECM applications to other energy-related fields.

Unleashing the Power of Django for Construction Materials Rental

In the realm of construction equipment rental, efficient management of resources and seamless access to construction materials are paramount for project success. This paper explores the utilization of Django, a high-level Python web framework, to revolutionize the construction equipment rental industry. By harnessing the power of Django, this study proposes a comprehensive platform that streamlines the process of renting construction equipment and accessing necessary materials. The platform integrates features such as user-friendly interfaces, robust inventory management systems, real-time tracking, and secure payment gateways to enhance user experience and optimize operational efficiency. Through a combination of Django's flexibility, scalability, and rich ecosystem of plugins, this solution aims to address the challenges faced by construction companies in managing equipment rentals and material procurement. Furthermore, the paper discusses the technical implementation of Django within the context of construction equipment rental, highlighting its potential to transform traditional workflows and propel the industry towards digital innovation. This study specify the adoption of Django in construction equipment rental has the potential to unleash a new era of efficiency, transparency, and productivity, thereby driving the industry towards greater success and competitiveness in the modern era

Construction Worker Hiring Management System

The Integrated Construction Management System (ICMS) is a sophisticated web-based application developed to optimize the management processes associated with construction projects. Leveraging a technology stack that includes HTML, CSS, Bootstrap, PHP, and MySQL, this system aims to streamline communication, collaboration, and resource management within construction teams, ultimately contributing to the successful and efficient completion of projects. One of the primary features of ICMS is its robust user authentication and authorization system. The application implements a secure login mechanism that authenticates users while assigning distinct access levels based on their roles within the construction project. This ensures that project managers, architects, engineers, and other stakeholders have tailored access to relevant information and functionalities. The project dashboard serves as a centralized hub, providing a comprehensive overview of the project's status, milestones, and critical updates. Through real-time tracking of project progress, ICMS facilitates effective decision-making by offering stakeholders immediate insights into the current state of affairs. This dynamic approach extends to task and resource management, where tasks can be created, assigned, and tracked in real time, ensuring optimal workforce utilization

BLINDS EYE: Advancing Navigation for the Visually Impaired with Arduino Uno

Blinds Eye Advancing Navigation for the Visually Impaired with Arduino Uno. Navigating their surroundings independently, visually impaired individuals encounter challenges with traditional aids. This advanced blind navigation system, integrating an Arduino Uno for processing, a GPS module for real-time tracking, and a panic button for emergencies, presents a groundbreaking solution. Ultrasonic and IR sensors detect obstacles, issuing audio alerts and adjusting routes. An RF module aids in locating a misplaced smart stick. Overcoming limitations of existing solutions, the system's audio output provides crucial information, ensuring safer and more efficient independent travel for the visually impaired." Key Words: Arduino Uno, Blind navigation, Visually impaired, GPS module, Ultrasonic sensors, Infrared sensors, RF module, Assistive technology

An Image Analysis of River-Floating Waste Materials by Using Deep Learning Techniques

Plastic pollution in the ocean is a severe environmental problem worldwide because rivers carry plastic waste from human activities, harming the ocean’s health, ecosystems, and people. Therefore, monitoring the amount of plastic waste flowing from rivers and streams worldwide is crucial. In response to this issue of river-floating waste, our present research aimed to develop an automated waste measurement method tailored for real rivers. To achieve this, we considered three scenarios: clear visibility, partially submerged waste, and collective mass. We proposed the use of object detection and tracking techniques based on deep learning architectures, specifically the You Only Look Once (YOLOv5) and Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT). The types of waste classified in this research included cans, cartons, plastic bottles, foams, glasses, papers, and plastics in laboratory flume experiments. Our results demonstrated that the refined YOLOv5, when applied to river-floating waste images, achieved high classification accuracy, with 88% or more for the mean average precision. The floating waste tracking using DeepSORT also attained F1 scores high enough for accurate waste counting. Furthermore, we evaluated the proposed method across the three different scenarios, each achieving an 80% accuracy rate, suggesting its potential applicability in real river environments. These results strongly support the effectiveness of our proposed method, leveraging the two deep learning architectures for detecting and tracking river-floating waste with high accuracy.

Real-time and Intelligent Moving Targets Tracking based on UAV Remote Sensing Video Camera and Brain-like Computing Chips

Abstract. Moving target tracking technology based on Unmanned Aerial Vehicles (UAV) is widely used in many fields such as automatic inspection and emergency response. The existing moving target tracking methods usually have the problems of large computation and low tracking efficiency. Limited by the computing power of the UAV platform, real-time tracking and analysis of multiple targets based on the video data collected by UAV platform is a difficult task. In this paper, we proposed a novel Target Specific Filtering Tracking with Memory (TSFMTrack) method designed for UAV-based real-time tracking tasks, which involves a Tracklet Filtering Module (TFM) for capturing object appearance features and a Tracklet Matching Module (TMM) for bounding box association in each frame. By experimental comparison with other State-Of-The-Art (SOTA) methods on popular MOT and UAV tracking datasets, the TSFMTrack have shown obvious advantages in accuracy, computational efficiency and reliability. Furthermore, we deployed the TSFMTrack on the brain-inspired chip Lynchip KA200, the experimental results have shown that the TSFMTrack is effective on edge computational platform and suitable for UAV real-time tracking tasks.

Gradient projection-based trajectory tracking control for automatic guided vehicle

In order to solve the problems of long operation time for solving the trajectory tracking controller and low real-time tracking. This paper proposes an improved model predictive control algorithm. By introducing the gradient projection method (GPM), the projection direction is used as the fastest descent direction of the objective function, which is used to reduce the number of iterations of the operation. Meanwhile, this paper adds constraints and relaxation factors to improve the trajectory tracking accuracy and prevent the controller operations from falling into local optimal solutions. Simulation by Simulink, the improved model predictive control algorithm reduces the average operation time by 24.74% and the single maximum operation time by 36.04%, effectively improving the real-time trajectory tracking performance. Finally, the verification experiment of trajectory tracking control is carried out by actual vehicle, proving the method’s effectiveness.

Flexible piezoelectric pressure sensor based on BaTiO3-CNT/RTV composite film with filter paper microstructures for monitoring hand motions

Flexible piezoelectric sensors are widely used in various applications such as physiological signal monitoring and human-computer interaction. The present study introduces a BaTiO3-CNT/RTV piezoelectric sensor fabricated using a filter paper template. It incorporates micro-scale fiber stacking and a 1% CNT doping in the microstructure, resulting in a notable enhancement of sensor sensitivity, increasing it from 0.07 V N−1 to 0.69 V N−1, representing an almost tenfold improvement. Furthermore, the study investigates the influence of affecting factors like the flexible substrate of the sensing film, thickness, and mass fractions of various materials on the output voltage. The sensor exhibits superior characteristics such as good repeatability under 5000 cyclic loads, high elongation at break, fast response (80 ms) and recovery times (90 ms), and good linearity. It also demonstrates outstanding sensitivity (12 mV/10°) when monitoring different finger bending states, enabling real-time, sensitive, and reliable hand motion tracking. This sensor holds promising prospects for future developments in the fields of intelligent grasping and sign language translation.

Label-free metabolic optical biomarkers track stem cell fate transition in real time.

Tracking stem cell fate transition is crucial for understanding their development and optimizing biomanufacturing. Destructive single-cell methods provide a pseudotemporal landscape of stem cell differentiation but cannot monitor stem cell fate in real time. We established a metabolic optical metric using label-free fluorescence lifetime imaging microscopy (FLIM), feature extraction and machine learning-assisted analysis, for real-time cell fate tracking. From a library of 205 metabolic optical biomarker (MOB) features, we identified 56 associated with hematopoietic stem cell (HSC) differentiation. These features collectively describe HSC fate transition and detect its bifurcate lineage choice. We further derived a MOB score measuring the "metabolic stemness" of single cells and distinguishing their division patterns. This score reveals a distinct role of asymmetric division in rescuing stem cells with compromised metabolic stemness and a unique mechanism of PI3K inhibition in promoting ex vivo HSC maintenance. MOB profiling is a powerful tool for tracking stem cell fate transition and improving their biomanufacturing from a single-cell perspective.

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.

PREFERENCE OF INNOVATIVE AND TECHNOLOGICAL INTEGRATION OF MUTUAL FUND INVESTMENT TO THE INVESTOR IN THE CHENNAI CITY

In today's investment landscape, the infusion of innovation and technology within mutual fund offerings has become increasingly indispensable, especially for investors in Chennai. This study aims to explore the preferences of Chennai-based investors towards innovative and technologically advanced mutual fund investment solutions. Through a comprehensive survey methodology, the research seeks to discern investor sentiments and expectations concerning the integration of technology in mutual fund platforms. Specifically, it investigates the extent to which investors prioritize user-friendly interfaces, personalized investment choices, and real-time tracking functionalities. By pinpointing these preferences, financial institutions and fund managers can tailor their offerings to meet the distinct requirements of Chennai investors, thus fostering heightened engagement and participation in the mutual fund market. This investigation underscores the imperative of embracing innovation to enrich the investor journey and propel mutual fund adoption in Chennai, addressing the evolving needs and expectations of investors in the digital era.

Implementation of Vehicle Detection and Tracking Model Using YOLO and DeepSORT for Controlling Traffic Rule Violation

The field of computer vision has advanced significantly in the last several years, especially in the areas of object tracking and detection. This research offers a thorough analysis of the use of a vehicle tracking and detection model that makes use of DeepSORT (Deep Simple Online and Realtime Tracking) for tracking and YOLOv8 (You Only Look Once version 8) for detection. By combining these state-of-the-art methods, it will be possible to track and identify vehicles in a variety of situations in real-time, improving surveillance and traffic management as well as autonomous vehicle navigation. This project intends to develop computer vision applications in the transportation and surveillance sectors by means of a thorough examination and experimentation.

Review on Human Detection in Catastrophe Scenarios Using UAV and Deep Learning Techniques

In catastrophe scenarios, such as natural disasters or humanitarian crises, rapid and accurate detection of human presence is critical for effective response and rescue operations. Unmanned Aerial Vehicles (UAVs) equipped with computer vision algorithms have emerged as a promising solution for remote sensing and human detection in such scenarios. This survey paper provides an overview of the state-of-the-art techniques for human detection using UAVs, with a focus on the integration of You Only Look Once (YOLO) object detection and DeepSORT (Deep Simple Online and Realtime Tracking) tracking algorithms.

An efficient visual servo tracker for herd monitoring by UAV

It is a challenging and meaningful task to carry out UAV-based livestock monitoring in high-altitude (more than 4500 m on average) and cold regions (annual average – 4 °C) on the Qinghai Tibet Plateau. The purpose of artificial intelligence (AI) is to execute automated tasks and to solve practical problems in actual applications by combining the software technology with the hardware carrier to create integrated advanced devices. Only in this way, the maximum value of AI could be realized. In this paper, a real-time tracking system with dynamic target tracking ability is proposed. It is developed based on the tracking-by-detection architecture using YOLOv7 and Deep SORT algorithms for target detection and tracking, respectively. In response to the problems encountered in the tracking process of complex and dense scenes, our work (1) Uses optical flow to compensate the Kalman filter, to solve the problem of mismatch between the target bounding box predicted by the Kalman filter (KF) and the input when the target detection in the current frame is complex, thereby improving the prediction accuracy; (2) Using a low confidence trajectory filtering method to reduce false positive trajectories generated by Deep SORT, thereby mitigating the impact of unreliable detection on target tracking. (3) A visual servo controller has been designed for the Unmanned Aerial Vehicle (UAV) to reduce the impact of rapid movement on tracking and ensure that the target is always within the field of view of the UAV camera, thereby achieving automatic tracking tasks. Finally, the system was tested using Tibetan yaks on the Qinghai Tibet Plateau as tracking targets, and the results showed that the system has real-time multi tracking ability and ideal visual servo effect in complex and dense scenes.

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

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.