Active Tracking Research Articles

Comparative Analysis of Polycystic Ovary Syndrome Detection Using Machine Learning Algorithms

INTRODUCTION: Polycystic Ovary Syndrome is a condition in which the ovaries manufacture androgen, seen in small traces, resulting in the production of cysts. Menstrual cycle abnormalities, clinical and/or biochemical hyperandrogenism, and the presence of polycystic ovaries on ultrasound should all be used to diagnose PCOS. PCOS appears to be a multifaceted illness influenced by both genetic and environmental factors and the symptoms include excessive hair on the face and body, weight gain, voice changes, skin type changes, and irregular periods.
 OBJECTIVES: This is the objective of this paper is to identify PCOS in its initial stage.
 METHODS: To address this issue the study proposes a comparison of various machine learning algorithms and optimization techniques Among which GSCV gave the best result of 94% accuracy, followed by TPOT with 91% accuracy. Additionally, we also applied Feature selection methods to eliminate zero-importance features to increase the accuracy of algorithms.
 RESULTS: The main results obtained in this paper This study explored various Feature selection techniques, ML and DL models. It is shown that Grid Search CV and TPOT classifier were best classifiers with 94% and 91% respectively.
 CONCLUSION: These are the conclusions of this paper and this study will explore various DL methodologies and try to find out best optimal results for the PCOS Detection. And also, to develop an PCOS detection application to keep track of menstrual cycles and track activities and symptoms for PCOS.

Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning.

Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues. In addition, GBCA administration increases health care costs. Thus, reducing or eliminating GBCA administration for active lesion detection is important for improved patient safety and reduced healthcare costs. Current state-of-the-art methods for identifying active lesions in brain MRI without GBCA administration utilize data-intensive deep learning methods. To implement nonlinear dimensionality reduction (NLDR) methods, locally linear embedding (LLE) and isometric feature mapping (Isomap), which are less data-intensive, for automatically identifying active lesions on brain MRI in MS patients, without the administration of contrast agents. Fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images were included in the multiparametric MRI dataset used in this study. Subtracted pre- and post-contrast T1-weighted images were labeled by experts as active lesions (ground truth). Unsupervised methods, LLE and Isomap, were used to reconstruct multiparametric brain MR images into a single embedded image. Active lesions were identified on the embedded images and compared with ground truth lesions. The performance of NLDR methods was evaluated by calculating the Dice similarity (DS) index between the observed and identified active lesions in embedded images. LLE and Isomap, were applied to 40 MS patients, achieving median DS scores of 0.74 ± 0.1 and 0.78 ± 0.09, respectively, outperforming current state-of-the-art methods. NLDR methods, Isomap and LLE, are viable options for the identification of active MS lesions on non-contrast images, and potentially could be used as a clinical decision tool.

Live Location Sharing System (L2S2): Empowering Management of Unmanned Aircraft Systems (UAS) Operations

Transparency and confidence-building measures in Unmanned Aircraft Systems (UAS) operations continue to be important topics of discussion in various contexts. Transparency helps build trust and alleviate concerns about the use of UAS, especially in sensitive areas or situations where privacy and security are paramount. Meanwhile, confidence-building measures aim to establish trust and mitigate potential risks associated with UAS operations, particularly in complex UAS operations involving multiple teams. These measures include implementing systems that enable the identification and tracking of UAS operations activities that can be achieved through technologies such as global positioning system (GPS)-based tracking systems. Although there are several UAS operations GPS-based management systems available in the market, it requires a certain initial investment cost that creates a barrier for some parties, particularly smaller organisations or those with limited budgets. Therefore, in this study, we have developed a web-based application called as Live Location Sharing System (L2S2) by leveraging the capabilities of Traccar, a free and open-source GPS tracking system. Through this system, the movement activities of the Malaysian Space Agency (MYSA) UAS operation teams can be monitored in real-time by the mission commander, which has finally empowered the management of UAS operations as a whole.

Solar PV tracking system using arithmetic optimization with dual axis and sensor

Presently, PV system is considered the best renewable energy source for electricity production. In order to maximize energy output in photovoltaic systems, a system for tracking the sun's position and adjusting panel positions was created. Despite the fact that several models for tracking solar radiation have been suggested to improve energy production, it faces challenges in continuous tracking and power consumption. This paper proposes a novel sensor-based solar tracking system with numerical optimization to increase photovoltaic systems' energy output. The initial model was for a two-axis tracking system based on sensors. Solar panel and sun positions are detected by this system using ultraviolet and microelectromechanical sun sensors. To improve tracking movements and photovoltaic energy production, we recommend using solar sensors to construct a novel two-axis solar tracking device. This technology benefits from increased solar radiation and solar energy harvesting capabilities. The main disadvantage of dual-axis active solar tracking systems is that the drive mechanism frequently uses up the output power of the solar panels. As a result, the net power gain of the solar panel is less than its maximum. Further, a control system was developed with proportional integral derivative (PID) controller and arithmetic optimization (AO) to adjust the panel position relative to suns movement. The AO technique tunes the PID controller gains until it reaches its desired level to enhance the production of power. With the use of sensors, the tracking system can use mathematical optimization algorithms and the sun's position to minimize power consumption while still maintaining optimal solar panel positioning. The developed model was designed in the MATLAB software and the outcomes are analyzed. The paper also presents an energy analysis of the system, which evaluates the energy input, output and overall system efficiency. We recommend using solar sensors to create a unique dual-axis solar tracking system. This technology benefits from increased solar radiation and solar energy harvesting capabilities.

Longitudinal changes in device-measured physical activity from childhood to young adulthood: the PANCS follow-up study

BackgroundThere is a lack of longitudinal studies examining changes in device-measured physical activity and sedentary time from childhood to young adulthood. We aimed to assess changes in device-measured physical activity and sedentary time from childhood, through adolescence, into young adulthood in a Norwegian sample of ostensibly healthy men and women.MethodsA longitudinal cohort of 731 Norwegian boys and girls (49% girls) participated at age 9 years (2005–2006) and 15 years (2011–2012), and 258 of these participated again at age 24 years (2019–2021; including the COVID-19 pandemic period). Physical activity and sedentary time were measured using ActiGraph accelerometers. Linear mixed models were used to analyse changes in physical activity and sedentary time and whether low levels of childhood physical activity track, i.e., persist into young adulthood (nchange=721; ntracking=640).ResultsThe most prominent change occurred between the ages of 9 to 15 years, with an increase in sedentary time (150 min/day) and less time spent in light (125 min/day), moderate (16 min/day), and vigorous physical activity (8 min/day). Only smaller changes were observed between the ages of 15 and 24 years. Changes in moderate-to-vigorous physical activity from childhood to young adulthood differed between subgroups of sex, tertiles of body mass index at baseline and tertiles of peak oxygen uptake at baseline. While the tracking models indicated low absolute stability of physical activity from childhood to young adulthood, children in the lowest quartiles of moderate-to-vigorous (OR:1.88; 95%CI: 1.23, 2.86) and total physical activity (OR: 1.87; 95%CI: 1.21, 2.87) at age 9 years were almost 90% more likely to be in these quartiles at age 24 years compared to those belonging to the upper three quartiles at baseline.ConclusionsWe found a substantial reduction in physical activity and increase in time spent sedentary between age 9 and 15 years. Contrary to previous studies, using mainly self-reported physical activity, little change was observed between adolescence and young adulthood. The least active children were more likely to remain the least active adults and could be targeted for early intervention.

Active tracking measures: Providing targets for youth alcohol use and health behavior interventions.

This study seeks to use examine the utility of recently published, developmentally informed measures of parental active tracking for work on youth health behaviors. Information on parental active tracking and health behaviors were collected in six independent survey studies (n's = 157-408). A series of empirical questions regarding parental active tracking were examined. The recently published measures of parental active tracking in high school and college were shown to have sufficient internal consistency, to be well described by single latent factors, to be empirically distinct from helicopter parenting, to be inconsistently linked with youth risk behaviors, and to demonstrate closer linkages with youth substance use when accounting for parental motivations for tracking. The measures of parental active tracking employed provide face valid, psychometrically sound, and empirically supported indices for use examining parental influences during adolescence and young adulthood.

Optimum active power tracking based control of brushless doubly-fed reluctance generator tied to renewable microgrid

Among all the existing wind energy generators (WEG), a brushless doubly-fed reluctance generator (BDFRG) is the better option in terms of maintenance cost and constructional simplicity. For more adaptability, the efficiency of the BDFRG can be improved through proper control mechanisms. Therefore, this paper presents an optimum active power tracking-based control technique for BDFRG for minimum losses. Copper loss of the machine is considered to be the objective function for the proposed optimization technique. However, one issue found to be the major in all the existing controllers is the accuracy of sensor less control of the BDFRG. Therefore, this paper proposes an accurate model reference adaptive system (MRAS) based sensor less mechanism for computation of BDFRG secondary winding flux position. The no sensitivity of the proposed technique to the machine parameter variation and hence the accuracy, catch-on-fly, and non-inclusion of integrator and differentiators and hence less burden on the processor makes the proposed scheme robust. The control scheme is practically implemented with a 2.5 kW BDFRG using MATLAB/Simulink platform.

Recording physiological history of cells with chemical labeling.

Recordings of the physiological history of cells provide insights into biological processes, yet obtaining such recordings is a challenge. To address this, we introduce a method to record transient cellular events for later analysis. We designed proteins that become labeled in the presence of both a specific cellular activity and a fluorescent substrate. The recording period is set by the presence of the substrate, whereas the cellular activity controls the degree of the labeling. The use of distinguishable substrates enabled the recording of successive periods of activity. We recorded protein-protein interactions, G protein-coupled receptor activation, and increases in intracellular calcium. Recordings of elevated calcium levels allowed selections of cells from heterogeneous populations for transcriptomic analysis and tracking of neuronal activities in flies and zebrafish.

Regulation of long-range BMP gradients and embryonic polarity by propagation of local calcium-firing activity

Many amniote vertebrate species including humans can form identical twins from a single embryo, but this only occurs rarely. It has been suggested that the primitive-streak-forming embryonic region emits signals that inhibit streak formation elsewhere but the signals involved, how they are transmitted and how they act has not been elucidated. Here we show that short tracks of calcium firing activity propagate through extraembryonic tissue via gap junctions and prevent ectopic primitive streak formation in chick embryos. Cross-regulation of calcium activity and an inhibitor of primitive streak formation (Bone Morphogenetic Protein, BMP) via NF-κB and NFAT establishes a long-range BMP gradient spanning the embryo. This mechanism explains how embryos of widely different sizes can maintain positional information that determines embryo polarity. We provide evidence for similar mechanisms in two different human embryo models and in Drosophila, suggesting an ancient evolutionary origin.

Active Object Detection and Tracking Using Gimbal Mechanisms for Autonomous Drone Applications

Object recognition, localization, and tracking play a role of primordial importance in computer vision applications. However, it is still an extremely difficult task, particularly in scenarios where objects are attended to using fast-moving UAVs that need to robustly operate in real time. Typically the performance of these vision-based systems is affected by motion blur and geometric distortions, to name but two issues. Gimbal systems are thus essential to compensate for motion blur and ensure visual streams are stable. In this work, we investigate the advantages of active tracking approaches using a three-degrees-of-freedom (DoF) gimbal system mounted on UAVs. A method that utilizes joint movement and visual information for actively tracking spherical and planar objects in real time is proposed. Tracking methodologies are tested and evaluated in two different realistic Gazebo simulation environments: the first on 3D positional tracking (sphere) and the second on tracking of 6D poses (planar fiducial markers). We show that active object tracking is advantageous for UAV applications, first, by reducing motion blur, caused by fast camera motion and vibrations, and, second, by fixating the object of interest within the center of the field of view and thus reducing re-projection errors due to peripheral distortion. The results demonstrate significant object pose estimation accuracy improvements of active approaches when compared with traditional passive ones. More specifically, a set of experiments suggests that active gimbal tracking can increase the spatial estimation accuracy of known-size moving objects, under conditions of challenging motion patterns and in the presence of image distortion.

Path tracking control for autonomous vehicles using hybrid fault-tolerant approach

Signal delay, execution deviation and reference model mismatch are common faults in autonomous vehicles, resulting in reduced path tracking control performance. To improve the path tracking performance of autonomous vehicles under fault conditions, this paper proposes a hybrid fault-tolerant (HFT) path tracking control approach, in which passive fault-tolerant path tracking control and fault observer-based hybrid active fault-tolerant path tracking control are introduced. The probable usual errors in path tracking are first examined and analyzed, together with the vehicle kinematic model and a typical autonomous driving system. Then, a slide-mode-based passive fault-tolerant path tracking controller is designed and integrated with a fault observer to create a hybrid active fault-tolerant controller. Finally, simulations and experiments are conducted, and the results demonstrate that our proposal is effective. After convergence of the fault observations, the lateral tracking error is less than 0.2m and the root mean square of the tracking error is 44% smaller than the LQR approach.

Three Virtual Reality Environments for the Assessment of Executive Functioning Using Performance Scores and Kinematics: An Embodied and Ecological Approach to Cognition.

Virtual reality (VR) is an attractive technology for cognitive assessment, as it provides a more embodied experience compared with typical test situations, such as those using paper and pencil. In addition, VR can immerse individuals in complex situations similar to real-life ones, thereby improving the ecological validity (i.e., face validity) of the assessment. VR also offers improved scoring of tests as it facilitates the tracking of kinematic information and the temporal tracking of activities. This study assesses the correlation between scores on executive function assessments using standard neuropsychological tasks in paper-and-pencil format, on a tablet, and in three immersive VR environments, each designed to involve specific aspects of executive function. This study also aims to assess the correlation between these performance scores and a set of kinematic measures (speed, duration, and distance traveled by the hand) collected in VR. The outcomes, including performance scores and kinematic measures, correlate both with traditional assessment methods (such as paper and pencil, and computerized 2D tests) and with each other, suggesting their potential usefulness in clinical and research contexts. The discussion focuses on the advantages of embodied, situated, and spatialized tests for cognitive assessment and the benefits of kinematic tracking in VR tests for the quality of this assessment.

Aperiodic brain activity tracks temporal fluctuations during sleep: an (i)EEG study

Aperiodic brain activity tracks temporal fluctuations during sleep: an (i)EEG study

Research Paper on Staff Attendance Portal

For every organization to succeed, including academic institutions, governmental bodies, and businesses, attendance management is essential. In order to improve their efficacy, organizations must monitor their internal staff, including staff. Monitoring staff attendance in the midst of the Covid-19 pandemic has proven to be difficult. Calculating the attendance percentage by hand is quite time-consuming and prone to error, which makes it a major issue. To keep track of staff activities and attendance on a daily basis, an efficient Android application for attendance monitoring has been created. This application stores a photograph, along with the time and location of attendance, in a database

Adaptive milliseconds tracking and zooming optics based on a high-speed gaze controller and liquid lenses.

The high-speed gaze and high resolution are critical factors for actual monitoring systems. However, the conventional method cannot track and zoom as fast as expected due to the larger inertia and it results in a low resolution due to the digital zoom. In this paper, we proposed a high-speed tracking and zooming optics that is coaxial designed and with an active tracking unit and an optical zooming unit to overcome the above issues. The tracking unit always tracks the object in the center of view by a pan-tilt mirror controller and a visual feedback tracking algorithm within 4 milliseconds response order. The zooming unit can continuously change the magnification from 1X to 2X by three liquid lenses within milliseconds. Besides, the zooming unit provides a compensation algorithm to achieve accurate zoom and focus.

An Active Track and Hold Circuit With Linearity Enhancement Technique and Its Analysis Using Volterra Series

An Active Track and Hold Circuit With Linearity Enhancement Technique and Its Analysis Using Volterra Series

Modern IT solutions for household waste management in the Arctic region

The Arctic region presents a unique challenge for waste management due to extreme weather conditions and fragile ecosystems. This paper looks at modern IT solutions that can contribute to sustainable and efficient household garbage disposal in the Arctic. We discuss the introduction of smart bins, remote monitoring systems, geographic information systems (GIS) and blockchain technology to optimize waste collection, transport and disposal processes. The integration of modern IT solutions for waste management in the Arctic region offers a promising approach to address the challenges posed by extreme weather conditions and delicate ecosystems. Smart bins equipped with sensors and IoT technology can accurately monitor waste levels and optimize collection routes, reducing unnecessary transportation and minimizing environmental impact. Remote monitoring systems enable real-time tracking of waste disposal activities, ensuring timely interventions and efficient resource allocation. Geographic Information Systems (GIS) play a crucial role in mapping out optimal disposal sites and identifying areas of high waste generation, enabling authorities to plan strategically for waste management. Blockchain technology offers a secure and transparent platform for tracking waste from collection to disposal, enhancing accountability and fostering a circular economy approach. By leveraging these IT solutions, sustainable and efficient household garbage disposal practices can be implemented in the Arctic, safeguarding the pristine environment for future generations.

Reconstructing Signaling Networks Using Biosensor Barcoding.

Understanding how signaling networks are regulated offers valuable insights into how cells and organisms react to internal and external stimuli and is crucial for developing novel strategies to treat diseases. To achieve this, it is necessary to delineate the intricate interactions between the nodes in the network, which can be accomplished by measuring the activities of individual nodes under perturbation conditions. To facilitate this, we have recently developed a biosensor barcoding technique that enables massively multiplexed tracking of numerous signaling activities in live cells using genetically encoded fluorescent biosensors. In this chapter, we detail how we employed this method to reconstruct the EGFR signaling network by systematically monitoring the activities of individual nodes under perturbations.

Advanced Active Player Tracking System in Handball Videos Using Multi-Deep Sort Algorithm with GAN Approach

Advanced Active Player Tracking System in Handball Videos Using Multi-Deep Sort Algorithm with GAN Approach

Bubble flow analysis using multi-phase field method

In simulations of gas-liquid two-phase flows using conventional interface capture methods, we observed that when bubbles come close to each other, they tend to merge numerically, despite experimental evidence indicating that they would repel each other. Given the significant impact of sequential numerical coalescence on flow patterns, it is necessary to regulate the merging behavior of close bubbles. To address this issue, we introduced the Multi-Phase Field (MPF) method, which mitigates bubble coalescence by applying an independent fluid fraction function to each bubble. In this study, we employed the MPF based on the N-phase model [7] to minimize numerical errors associated with surface interactions at triple junction points. Additionally, we implemented the Ordered Active Parameter Tracking (OAPT) method [9] to efficiently store several hundreds of fluid fraction functions. To validate the MPF method, we conducted analysis of turbulent bubbly pipe flows and compared the results against experimental data from Colin et al [12]. The validation results showed reasonable agreements with respect to the bubble distribution and the flow velocity profiles.