Tracking Capability Research Articles

Measuring Health: Wearables in Fitness Tracking, Stress Relief, and Sleep Management

Introduction In today's fast-paced world, the quest for better health has led to the rise of wearable devices. These innovative tools, from smartwatches to activity trackers, offer real-time insights into our physical activity, stress levels, and sleep patterns. By seamlessly integrating into our daily lives, wearables empower individuals to take control of their health and well-being like never before. Aim of the study The present review aims to provide the readers with a broader knowledge of the impact of wearables on health. Materials and methods This article provides a foundation for understanding the significance and potential of wearable devices in promoting health and wellness. Comprehensive literature searches were performed across the main electronic databases of PubMed and Google Scholar for studies published in the English language about use and validation of wearables. Results Wearable devices are effective tools in promoting physical activity, managing stress, and monitoring sleep. With advanced sensors and algorithms, these devices enable users to track their health progress in real-time and provide personalized strategies for improvement. By combining fitness tracking, communication, and stress management capabilities, these devices possess the capacity to empower individuals in maintaining a healthy lifestyle and have become a potential option at the point where technology and personal health meet. As a result, wearable devices have tremendous potential to transform healthcare and enhance quality of life.

Flexible head-following motion planning for scalable and bendable continuum robots

Continuum robots, which are characterized by high length-to-diameter ratios and flexible structures, show great potential for various applications in confined and irregular environments. Due to the combination of motion modes, the existence of multiple solutions, and the presence of complex obstacle constraints, motion planning for these robots is highly challenging. To tackle the challenges of online and flexible operation for continuum robots, we propose a flexible head-following motion planning method that is suitable for scalable and bendable continuum robots. Firstly, we establish a piecewise constant curvature (PCC) kinematic model for scalable and bendable continuum robots. The article proposes an adaptive auxiliary points model and a method for updating key nodes in head-following motion to enhance the precise tracking capability for paths with different curvatures. Additionally, the article integrates the strategy for adjusting the posture of local joints of the robot into the head-following motion planning method, which is beneficial for achieving safe obstacle avoidance in local areas. The article concludes by presenting the results of multiple sets of motion simulation experiments and prototype experiments. The study demonstrates that the algorithm presented in this paper effectively navigates and adjusts posture to avoid obstacles, meeting the real-time demands of online operations. The average time for a single-step solution is 4.41×10−5 s, and the average tracking accuracy for circular paths is 7.8928 mm.

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.

Gas rudder 2DOF FOPID position control based on CSFLA optimisation

Aiming at the problems of parameter variety, rigid-flexible coupling and external unknown factors interference in the process of spacecraft propulsion, a two-degree-of-freedom fractional-order PID (2DOF FOPID) controller based on a coevolutionary shuffled frog leaping algorithm (CSFLA) is designed for the high-precision control of the gas rudder servo control system. The anti-interference and target tracking capabilities of 2DOF PID is combined with the advantage of FOPID, which is insensitive to the parameters of the controlled object, to improve the performance of the control system as a whole. CSFLA is formed by integrating the self-learning evolution of elite populations and the interactive learning strategy among populations into the SFLA to enhance the global searching ability and searching accuracy of SFLA, and CSFLA is used to optimize the parameters of the 2DOF FOPID controller. The simulation results show that the designed CSFLA-2DOF FOPID controller has the advantages of fast response speed, small overshoot, strong tracking performance and strong anti-disturbance ability, which improves the dynamic and static performance and anti-disturbance ability of the gas rudder servo control system.

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

An MRI-Conditional Flexible Endoscopic Robot with a Hydraulic Tendon-Driven Actuation System

This paper presents the conceptualization, mathematical modeling, and experimental validation of a magnetic resonance imaging (MRI)-conditional, flexible endoscopic robot for MRI-guided neurosurgery. The robotic assembly encompasses a straight rigid shaft, a tendon-driven steerable tip with two degrees of freedom, and a compact hybrid actuation system made of nonmagnetic modules. The robot’s small diameter, approximately 3 mm, allows for robot navigation through narrow cavities, such as nasal passages and the densely packed anatomy of the skull base. By filling the robot with deionized water, the MRI contrast of the robot can be improved, therefore enabling intra-operative tracking of the robot’s movement. In this paper, we present the design and fabrication process of the robotic system, kinematic analysis of the robot, and experimental studies to validate our model, characterize the robot, and test the feedback robot control. Furthermore, we demonstrate the MRI compatibility of the robot, along with the intra-operative tracking capability.

Implementation of Real-Time Bus Tracking and Passenger Information System for MSRTC Mr. Vikrant Talole, Mr. Prasad Sonavane, Miss. Snehal Saykar

The "Real-Time Bus Tracking and Passenger Information System for Maharashtra State Road Transport Corporation (MSRTC)" is a cutting-edge technological solution designed to revolutionize the public transportation experience. In a world where efficiency, accuracy, and real-time information are paramount, this system stands as a beacon of innovation. The system offers real-time tracking capabilities, allowing passengers to monitor the exact location of MSRTC buses in real-time through intuitive mobile applications and web interfaces. Leveraging GPS technology, this feature not only ensures accurate tracking but also enables precise predictions of bus arrival times at designated stops. Passengers gain access to comprehensive route information, including stops, schedules, and landmarks, enhancing their ability to plan journeys effectively. The Real Time Bus Tracking and Passenger Information System for MSRTC stands as a testament to the power of innovation, ushering in a new era of smart public transportation.

Decoupled incremental nonlinear dynamic inversion control for aircraft autonomous landing with ground-effect

Due to motion coupling and increased model dependence, the use of nonlinear controllers in real-time applications for aircraft is extremely constrained. This explains why proportional, derivative, and integral (PID) controls are frequently used in practice. However, fast changes in the commanded path and the requirement for high accuracy and tracking performance limit use of PID in landing. This paper presents a design of a decoupled incremental nonlinear dynamic inversion (INDI) controller for performing autonomous landing of a fixed-wing aircraft. The investigation is carried out using the ground effect model and is compared without it. A carrot chasing guidance algorithm is implemented for waypoint navigation to generate the command for the proposed controller's outer loop. The navigational state estimate issue has been resolved using the Cubature Kalman Filter (CKF) approach. Two decoupled CKF algorithms named complementary Cubature Kalman Filter (CCKF) and Integrated Cubature Kalman Filter (I-CKF) are proposed to estimate aircraft navigational states comprised of attitudes, navigational position in terms of latitude, longitude altitudes, and North-East-down velocities. The mathematical model for flare and approach trajectory is designed and commanded to follow the same by the proposed INDI controller. The effectiveness of INDI have been evaluated in a simulation environment by landing the aircraft in the same runway position at arbitrary wind conditions. The performance of the proposed INDI controller is juxtaposed with a conventional proportional integrator and derivative (PID) based landing controller. The simulation findings demonstrate that the INDI has a competitive edge over traditional PID approaches due to its robust performance, less dependence on model dynamics, and fast command tracking capability required during the flare phase.

Model-based trajectory tracking of a compliant continuum robot.

Introduction: Compliant mechanisms, especially continuum robots, are becoming integral to advancements in minimally invasive surgery due to their ability to autonomously navigate natural pathways, significantly reducing collision severity. A major challenge lies in developing an effective control strategy to accurately reflect their behavior for enhanced operational precision. Methods: This study examines the trajectory tracking capabilities of a tendon-driven continuum robot at its tip. We introduce a novel feedforward control methodology that leverages a mathematical model based on Cosserat rod theory. To mitigate the computational challenges inherent in such models, we implement an implicit time discretization strategy. This approach simplifies the governing equations into space-domain ordinary differential equations, facilitating real-time computational efficiency. The control strategy is devised to enable the robot tip to follow a dynamically prescribed trajectory in two dimensions. Results: The efficacy of the proposed control method was validated through experimental tests on six different demand trajectories, with a motion capture system employed to assess positional accuracy. The findings indicate that the robot can track trajectories with an accuracy within 9.5%, showcasing consistent repeatability across different runs. Discussion: The results from this study mark a significant step towards establishing an efficient and precise control methodology for compliant continuum robots. The demonstrated accuracy and repeatability of the control approach significantly enhance the potential of these robots in minimally invasive surgical applications, paving the way for further research and development in this field.

Differentiable simulation of a liquid argon time projection chamber

Liquid argon time projection chambers (LArTPCs) are widely used in particle detection for their tracking and calorimetric capabilities. The particle physics community actively builds and improves high-quality simulators for such detectors in order to develop physics analyses in a realistic setting. The ability of these simulators to mimic real, measured data is limited by the modeling of the physical detectors used for data collection. This modeling can be improved by performing dedicated calibration measurements. Conventional approaches calibrate individual detector parameters or processes one at a time. However, the impact of detector processes is entangled, making this a poor description of the underlying physics. We introduce a differentiable simulator that enables a gradient-based optimization, allowing for the first time a simultaneous calibration of all detector parameters. We describe the procedure of making a differentiable simulator, highlighting the challenges of retaining the physics quality of the standard, non-differentiable version while providing meaningful gradient information. We further discuss the advantages and drawbacks of using our differentiable simulator for calibration. Finally, we provide a starting point for extensions to our approach, including applications of the differentiable simulator to physics analysis pipelines.

Superhydrophobic Triboelectric Structural Materials Enabled by Hierarchical Spatial Assembly

AbstractNon‐contact intelligent sensing technology is a key player in the data acquisition and environmental monitoring framework of the Internet of Everything, laying the groundwork for the realization of highly integrated intelligent network systems. However, environmental factors continue to be a major challenge in the process of real‐time monitoring and feedback of objects, constraining the stability and accuracy of the sensing signals. In this study, a hierarchical spatial assembly strategy is implemented to effectively integrate an intermediate energy storage layer with superhydrophobic surfaces (CA = 162 ± 2.5°), resulting in the development of a moisture‐resistant triboelectric structural materials with stable monitoring and tracking capabilities. The moisture‐resistant self‐powered sensor constructed from this triboelectric structural material operates effectively in extreme humidity conditions (99% RH) with a full‐scale output retention rate of 95.2%, and is capable of accurately sensing human activity from the distance of 3 m. Importantly, the sensor can also effectively detect vehicle states during operation and perform real‐time monitoring of the vehicle's surrounding environment. This study not only overcomes the long‐standing challenges of accuracy and stability in high humidity for sensors but also advances the development of sustainable, interconnected intelligent systems for the Internet of Everything in extreme environmental conditions.

Prediction Horizon-Varying Model Predictive Control (MPC) for Autonomous Vehicle Control

The prediction horizon is a key parameter in model predictive control (MPC), which is related to the effectiveness and stability of model predictive control. In vehicle control, the selection of a prediction horizon is influenced by factors such as speed, path curvature, and target point density. To accommodate varying conditions such as road curvature and vehicle speed, we proposed a control strategy using the proximal policy optimization (PPO) algorithm to adjust the prediction horizon, enabling MPC to achieve optimal performance, and called it PPO-MPC. We established a state space related to the path information and vehicle state, regarded the prediction horizon as actions, and designed a reward function to optimize the policy and value function. We conducted simulation verifications at various speeds and compared them with an MPC with fixed prediction horizons. The simulation demonstrates that the PPO-MPC proposed in this article exhibits strong adaptability and trajectory tracking capability.

Antie-theft Device Tracking System

This project's goal is to develop an Android application that offers SMS-based location tracking capabilities for Android devices. We can also follow the images of the individual who took our Android phone. This function supports Android OS and sends and receives emails and SMS in response to calls. With simple SMS commands, you can track your Android device. Tracing a location: We can use basic SMS commands to locate the misplaced phone. In a theft gadget, the camera can be remotely manipulated to take pictures. It is said of mobile gadgets that they are portable and that a person can carry them from one place to another. Mobile device theft occurs frequently, and the current system watches users' walking patterns and movements.

Utilizing natural language processing and large language models in the diagnosis and prediction of infectious diseases: A systematic review

Natural Language Processing (NLP) and Large Language Models (LLMs) hold largely untapped potential in infectious disease management. This review explores their current use and uncovers areas needing more attention. This analysis followed systematic review procedures, registered with the Prospective Register of Systematic Reviews. We conducted a search across major databases including PubMed, Embase, Web of Science, and Scopus, up to December 2023, using keywords related to NLP, LLM, and infectious diseases. We also employed the Quality Assessment of Diagnostic Accuracy Studies-2 tool for evaluating the quality and robustness of the included studies. Our review identified 15 studies with diverse applications of NLP in infectious disease management. Notable examples include GPT-4's application in detecting urinary tract infections and BERTweet's use in Lyme Disease surveillance through social media analysis. These models demonstrated effective disease monitoring and public health tracking capabilities. However, the effectiveness varied across studies. For instance, while some NLP tools showed high accuracy in pneumonia detection and high sensitivity in identifying invasive mold diseases from medical reports, others fell short in areas like bloodstream infection management. This review highlights the yet-to-be-fully-realized promise of NLP and LLMs in infectious disease management. It calls for more exploration to fully harness AI's capabilities, particularly in the areas of diagnosis, surveillance, predicting disease courses, and tracking epidemiological trends.

Chaos-Based NMPC as a Novel MPPT Approach for Organıc Photovoltaıcs

Environmental degradation, alongside the dire consequences of climate change such as the melting of glaciers and rising sea levels, highlight the severe challenges associated with reliance on fossil fuels. As such, the shift towards sustainable and clean renewable energy sources, like photovoltaic systems, is becoming increasingly critical. This paper introduces a novel approach leveraging a chaotic-based nonlinear model predictive control to maximize the power output from organic photovoltaic cells. This method is distinguished by its rapid tracking capabilities and its effectiveness in enhancing the distribution network's performance under fault conditions. Utilizing a feedback-driven recursive control strategy, this approach efficiently predicts and adjusts to the optimal operating state, thereby minimizing its cost function. It comprises two primary phases: estimating the reference point and subsequently adjusting the operating point in alignment with this reference. In this process, the Lagrange function plays a pivotal role in optimizing the estimator's performance, while a chaotic neural network model predictive controller manages the boost converter's operation. Implementing this chaos-based nonlinear model predictive controller has successfully reduced overvoltage incidents by more than 1.3%. Notably, in the absence of such control methods, the penetration of OPV panels leads to voltage fluctuations beyond acceptable limits. The findings demonstrate that, alongside a decrease in network losses, there is an increase in the capacity of distribution feeders and a notable enhancement in the system's overall efficiency.

Vehicle Theft Detection and Tracking Based on GSM and GPS

Abstract: The increasing incidence of vehicle theft poses a significant challenge to vehicle owners and law enforcement agencies worldwide. This paper presents a comprehensive solution for addressing this issue through the integration of Global System for Mobile Communications (GSM) and Global Positioning System (GPS) technologies. The proposed system aims to enhance the security of vehicles by providing real-time theft detection and efficient tracking capabilities. The GSM module facilitates communication between the vehicle and a centralized monitoring system, allowing forseamless data transmission. The system leverages the widespread coverage and reliability of GSM networks to ensure that alerts are promptly delivered to relevant stakeholders, irrespective of the vehicle's location. Furthermore, the GPS technology enables real-time tracking of the stolen vehicle, assisting law enforcement agencies in swiftly responding to the incident. The system's tracking feature provides continuous updates on the vehicle's location, allowing for efficient recovery operations. Additionally, the integration of geofencing capabilities allows users to define virtual boundaries, triggering alerts if the vehicle deviates from the predefined geographical area. The proposed system offers a multi-layered security approach, combining GPS-based tracking with GSMenabled theft detection, making it a robust and effective solution for combating vehicle theft. The integration of these technologies not only enhances the chances of recovering stolen vehicles but also acts as a deterrent, discouraging potential thieves. In conclusion, the Vehicle Theft Detection and Tracking System presented in this paper showcases a technologically advanced and practical solution to mitigate the impact of vehicle theft. The integration of GSM and GPS technologies provides a comprehensive and reliable framework that addresses the security concerns associated with vehicle ownership in today's dynamic environment.

Leveraging System Browsers for Enhanced Mobile Ad Conversions

The growing shift toward mobile-based browsing has led advertisers to explore new ways to optimize user engagement and increase ad conversions. Leveraging system browsers as a tool for enhancing mobile ad conversions has emerged as a promising strategy in digital marketing. Unlike in-app browsers, system browsers offer a more familiar and seamless browsing experience, reducing friction in the customer journey. By utilizing native browsers, marketers can access advanced tracking capabilities, maintain session integrity, and deliver personalized content, leading to higher conversion rates. Additionally, system browsers are often more secure and less prone to technical limitations, enhancing trust and reducing bounce rates. This paper explores the impact of system browsers on mobile ad performance, highlighting their role in improving user experience, reducing page load times, and supporting multi-tab browsing. With consumers increasingly prioritizing privacy and security, leveraging native browsers also allows marketers to stay compliant with data protection regulations while still tracking user behavior through approved mechanisms. The study presents insights into how system browsers can drive more meaningful engagement, reduce abandonment rates, and ultimately contribute to better advertising return on investment (ROI). By integrating system browsers into mobile ad campaigns, marketers can unlock new opportunities to enhance both user satisfaction and conversion outcomes in an increasingly mobile-first world.

Object Detection and Tracking with YOLO and the Sliding Innovation Filter.

Object detection and tracking are pivotal tasks in machine learning, particularly within the domain of computer vision technologies. Despite significant advancements in object detection frameworks, challenges persist in real-world tracking scenarios, including object interactions, occlusions, and background interference. Many algorithms have been proposed to carry out such tasks; however, most struggle to perform well in the face of disturbances and uncertain environments. This research proposes a novel approach by integrating the You Only Look Once (YOLO) architecture for object detection with a robust filter for target tracking, addressing issues of disturbances and uncertainties. The YOLO architecture, known for its real-time object detection capabilities, is employed for initial object detection and centroid location. In combination with the detection framework, the sliding innovation filter, a novel robust filter, is implemented and postulated to improve tracking reliability in the face of disturbances. Specifically, the sliding innovation filter is implemented to enhance tracking performance by estimating the optimal centroid location in each frame and updating the object's trajectory. Target tracking traditionally relies on estimation theory techniques like the Kalman filter, and the sliding innovation filter is introduced as a robust alternative particularly suitable for scenarios where a priori information about system dynamics and noise is limited. Experimental simulations in a surveillance scenario demonstrate that the sliding innovation filter-based tracking approach outperforms existing Kalman-based methods, especially in the presence of disturbances. In all, this research contributes a practical and effective approach to object detection and tracking, addressing challenges in real-world, dynamic environments. The comparative analysis with traditional filters provides practical insights, laying the groundwork for future work aimed at advancing multi-object detection and tracking capabilities in diverse applications.

Cloud Security Using Fine-Grained Efficient Information Flow Tracking

This study provides a comprehensive review and comparative analysis of existing Information Flow Tracking (IFT) tools which underscores the imperative for mitigating data leakage in complex cloud systems. Traditional methods impose significant overhead on Cloud Service Providers (CSPs) and management activities, prompting the exploration of alternatives such as IFT. By augmenting consumer data subsets with security tags and deploying a network of monitors, IFT facilitates the detection and prevention of data leaks among cloud tenants. The research here has focused on preventing misuse, such as the exfiltration and/or extrusion of sensitive data in the cloud as well as the role of anonymization. The CloudMonitor framework was envisioned and developed to study and design mechanisms for transparent and efficient IFT (eIFT). The framework enables the experimentation, analysis, and validation of innovative methods for providing greater control to cloud service consumers (CSCs) over their data. Moreover, eIFT enables enhanced visibility to assess data conveyances by third-party services toward avoiding security risks (e.g., data exfiltration). Our implementation and validation of the framework uses both a centralized and dynamic IFT approach to achieve these goals. We measured the balance between dynamism and granularity of the data being tracked versus efficiency. To establish a security and performance baseline for better defense in depth, this work focuses primarily on unique Dynamic IFT tracking capabilities using e.g., Infrastructure as a Service (IaaS). Consumers and service providers can negotiate specific security enforcement standards using our framework. Thus, this study orchestrates and assesses, using a series of real-world experiments, how distinct monitoring capabilities combine to provide a comparatively higher level of security. Input/output performance was evaluated for execution time and resource utilization using several experiments. The results show that the performance is unaffected by the magnitude of the input/output data that is tracked. In other words, as the volume of data increases, we notice that the execution time grows linearly. However, this increase occurs at a rate that is notably slower than what would be anticipated in a strictly proportional relationship. The system achieves an average CPU and memory consumption overhead profile of 8% and 37% while completing less than one second for all of the validation test runs. The results establish a performance efficiency baseline for a better measure and understanding of the cost of preserving confidentiality, integrity, and availability (CIA) for cloud Consumers and Providers (C&P). Consumers can scrutinize the benefits (i.e., security) and tradeoffs (memory usage, bandwidth, CPU usage, and throughput) and the cost of ensuring CIA can be established, monitored, and controlled. This work provides the primary use-cases, formula for enforcing the rules of data isolation, data tracking policy framework, and the basis for managing confidential data flow and data leak prevention using the CloudMonitor framework.

Comparative Analysis of Force and Eddy Current Position Sensing Approaches for a Magnetic Levitation Platform with an Exceptional Hovering Distance

This paper provides a comparative analysis between a force sensor and an eddy current sensor, focusing on their usability to determine the position of a circular levitating permanent magnet (PM) mover within an axially symmetric magnetic levitation platform (MLP) with an exceptionally large air gap. The sensors enable closed-loop control, which is essential for accurately and stably maintaining the mover’s radial position. For the considered MLP, a change in radial position in principle results in a tilting of the mover, i.e., a deviation from the parallel alignment relative to the stator. As both the radial position and the tilting angle affect the sensors’ (force and eddy current) output voltage, an observer must deduce the radial position from the output sensor’s voltage, requiring a comprehensive MLP dynamic model and calibration of the models for both sensing approaches. The paper discusses the advantages and weaknesses of each sensor concept, exploring operational principles and performance in levitation tests. The force sensor exhibits versatility, proving functional across various application scenarios, such as when the mover is sealed in a conductive, non-magnetic chamber. In contrast, due to its high-frequency operation, the eddy current sensor is more straightforward to characterize, simplifying its behavior relative to the mover’s slower dynamics. Measurements are conducted to validate the models, showing the eddy current sensor’s robustness against disturbances and imperfections in the MLPs and its immunity to cross-axis interference. Conclusively, in levitation experiments where the mover is vertically distanced at 104 mm from the stator, the eddy current sensor achieves a position tracking precision about ten times better and a signal-to-noise ratio (SNR) ten times higher compared to the off-the-shelf force sensor, confirming its better performance and reliability; however, it cannot be used in applications where conductive objects are present in the air gap. Furthermore, additional experiments are conducted on the MLP using the eddy current sensor to show the controller’s robustness and dynamic reference tracking capability, with and without a payload.