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Copyright of International Journal of Engineering Transactions C: Aspects is the property of International Journal of Engineering (IJE) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background/Objectives: The early identification of neurodevelopmental disorders (NDDs) in infants is crucial for effective intervention and improved long-term outcomes. Recent evidence indicates a correlation between deficits in spontaneous movements in newborns and the likelihood of developing NDDs later in life. This study aims to address this aspect by employing a marker-less Artificial Intelligence (AI) approach for the automatic assessment of infants' movements from single-camera video recordings. Methods: A total of 74 high-risk infants were selected from the Italian Network for Early Detection of Autism Spectrum Disorders (NIDA) database and closely observed at five different time points, ranging from 10 days to 24 weeks of age. Automatic motion tracking was performed using deep learning to capture infants' body landmarks and extract a set of kinematic parameters. Results: Our findings revealed significant differences between infants later diagnosed with NDD and typically developing (TD) infants in three lower limb features at 10 days old: 'Median Velocity', 'Area differing from moving average', and 'Periodicity'. Using a Support Vector Machine (SVM), we achieved an accuracy rate of approximately 85%, a sensitivity of 64%, and a specificity of 100%. We also observed that the disparities in lower limb movements diminished over time points. Furthermore, the tracking accuracy was assessed through a comparative analysis with a validated semi-automatic algorithm (Movidea), obtaining a Pearson correlation (R) of 93.96% (88.61–96.60%) and a root mean square error (RMSE) of 9.52 pixels (7.29–12.37). Conclusions: This research highlights the potential of AI movement analysis for the early detection of NDDs, providing valuable insights into the motor development of infants at risk. [ABSTRACT FROM AUTHOR]

With the wide application of UAVs in modern intelligent warfare as well as in civil fields, the demand for C-UAS technology is increasingly urgent. Traditional detection methods have many limitations in dealing with "low, slow, and small" targets. This paper presents a pure laser automatic tracking system based on Geiger-mode avalanche photodiode (Gm-APD). Combining the target motion state prediction of the Kalman filter and the adaptive target tracking of Camshift, a Cam–Kalm algorithm is proposed to achieve high-precision and stable tracking of moving targets. The proposed system also introduces two-dimensional Gaussian fitting and edge detection algorithms to automatically determine the target's center position and the tracking rectangular box, thereby improving the automation of target tracking. Experimental results show that the system designed in this paper can effectively track UAVs in a 70 m laboratory environment and a 3.07 km to 3.32 km long-distance scene while achieving low center positioning error and MSE. This technology provides a new solution for real-time tracking and ranging of long-distance UAVs, shows the potential of pure laser approaches in long-distancelow, slow, and small target tracking, and provides essential technical support for C-UAS technology. [ABSTRACT FROM AUTHOR]

Background: Automated 4D flow MRI valvular flow quantification without time‐consuming manual segmentation might improve workflow. Purpose: Compare automated valve segmentation (AS) to manual (MS), and manually corrected automated segmentation (AMS), in corrected atrioventricular septum defect (c‐AVSD) patients and healthy volunteers, for assessing net forward volume (NFV) and regurgitation fraction (RF). Study Type: Retrospective. Population: 27 c‐AVSD patients (median, 23 years; interquartile range, 16–31 years) and 24 healthy volunteers (25 years; 12.5–36.5 years). Field strength/Sequence: Whole‐heart 4D flow MRI and cine steady‐state free precession at 3T. Assessment: After automatic valve tracking, valve annuli were segmented on time‐resolved reformatted trans‐valvular velocity images by AS, MS, and AMS. NFV was calculated for all valves, and RF for right and left atrioventricular valves (RAVV and LAVV). NFV variation (standard deviation divided by mean NFV) and NFV differences (NFV difference of a valve vs. mean NFV of other valves) expressed internal NFV consistency. Statistical Tests: Comparisons between methods were assessed by Wilcoxon signed‐rank tests, and intra/interobserver variability by intraclass correlation coefficients (ICCs). P < 0.05 was considered statistically significant, with multiple testing correction. Results: AMS mean analysis time was significantly shorter compared with MS (5.3 ± 1.6 minutes vs. 9.1 ± 2.5 minutes). MS NFV variation (6.0%) was significantly smaller compared with AMS (6.3%), and AS (8.2%). Median NFV difference of RAVV, LAVV, PV, and AoV between segmentation methods ranged from −0.7–1.0 mL, −0.5–2.8 mL, −1.1–3.6 mL, and − 3.1–‐2.1 mL, respectively. Median RAVV and LAVV RF, between 7.1%–7.5% and 3.8%–4.3%, respectively, were not significantly different between methods. Intraobserver/interobserver agreement for AMS and MS was strong‐to‐excellent for NFV and RF (ICC ≥0.88). Data Conclusion: MS demonstrates strongest internal consistency, followed closely by AMS, and AS. Automated segmentation, with or without manual correction, can be considered for 4D flow MRI valvular flow quantification. Level of Evidence: 3 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

Background: The harmonious operation of many insect societies depends upon colony-wide dissemination of a non-volatile pheromone produced by a single queen, which informs workers of her presence. This represents a major challenge in large colonies. Honeybee colonies, which can exceed 60,000 bees, are believed to solve this challenge using 'messenger' workers that actively relay the queen pheromone throughout the hive. However, little is known about the structure and effectiveness of the underlying relay network or the biology of messaging. Results: Here, we combine automated tracking with modelling to address these outstanding questions. We find that both queen movement and worker messaging play fundamental roles in queen pheromone dissemination. Fine-grained analyses of worker behaviour confirmed the existence of active messaging, as physical contacts with the queen caused workers to move faster and straighter, thereby accelerating pheromone transmission. Finally, we show that messaging follows a stereotypical developmental trajectory, resulting in an age-dependent hierarchical relay network, with the most intense messaging observed between three and five days of age, when workers undergo a suite of physiological changes associated with queen rearing. Conclusions: These results suggest that the individuals that contribute most to advertising the presence of the queen are also the ones that control queen production. [ABSTRACT FROM AUTHOR]

The human pupil changes size in response to processing demands or cognitive (work)load and emotional processing. Therefore, it is important to test if automatic tracking of cognitive load by pupil-size measurement is possible under conditions of varying levels of emotion-related processing. Here, we investigated this question in an experiment simulating a highly relevant applied context in which cognitive load and emotional processing can vary independently: a clinical interview. Our participants conducted a live clinical interview via computer monitor with a confederate as an interviewee. We used eye-tracking and automatic extraction of participants' pupil size to monitor cognitive load (single vs. dual tasks, between participants), while orthogonally varying the emotional content of the interviewee's answers (neutral vs. negative, between participants). We ensured participants' processing of the verbal content of the interview by asking all participants to report on the content of the interview in a subsequent memory test and by asking them to discriminate if the answers of the interviewee referred to only herself or to somebody else (too). In the dual-task condition, participants had to monitor additionally if the facial emotional expressions of the interviewee matched the content of her verbal responses. Results showed that pupil-size extraction reliably discriminated between high and low cognitive load, albeit to a lower degree under negative emotional content conditions. This was possible with an algorithmic online measure of cognitive load as well as with a conventional pupil-size measure, providing proof of the external validity of the algorithm/online measure. [ABSTRACT FROM AUTHOR]

High-voltage overhead power lines serve as the carrier of power transmission and are crucial to the stable operation of the power system. Therefore, it is particularly important to detect and remove foreign objects attached to transmission lines, as soon as possible. In this context, the widespread promotion and application of smart robots in the power industry can help address the increasingly complex challenges faced by the industry and ensure the efficient, economical, and safe operation of the power grid system. This article proposes a bionic-based UAV pose estimation and target perception strategy, which aims to address the lack of pattern recognition and automatic tracking capabilities of traditional power line inspection UAVs, as well as the poor robustness of visual odometry. Compared with the existing UAV environmental perception solutions, the bionic target perception algorithm proposed in this article can efficiently extract point and line features from infrared images and realize the target detection and automatic tracking function of small multi-rotor drones in the power line scenario, with low power consumption. [ABSTRACT FROM AUTHOR]

Copyright of Afyon Kocatepe University Journal of Science & Engineering / Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi is the property of Afyon Kocatepe University, Faculty of Science & Literature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Steel Pipe is the property of Steel Pipe Magazine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An automatic solar tracking system is an approach for optimizing the generation of solar power and modifying the angles and direction of a solar panel by considering changes in the position and path of the sun. The performance status of an automatic solar tracking system depends on various factors, including its design, location, and maintenance or repairs. The solar energy from the sun that the Earth intercepts is approximately 1.8 × 1011 MW, which is thousands of times greater than the intensity at which the Earth now uses all other commercially available energy sources combined. Currently, research into automatic solar trackers is on the rise, as solar energy is abundant in nature, but its use in a highly efficient way is still lacking. This paper provides a detailed literature review and highlights some key advancements and challenges associated with state-of-the-art automatic solar tracking systems. The performance of the dual-axis photovoltaic tracking system outperforms that of the stationary systems by more than 27% based on the overall system efficiency. Under diverse weather conditions, the efficiency of the scheduled-based solar tracking systems was enhanced by 4.2% compared with that of the light-dependent resistor-based solar trackers. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Calculating others' visual perspective automatically is a pivotal ability in human social communications. In the dot-perspective task, the ability is shown as a consistency effect: adults respond more slowly to judge the number of discs that they can see when a computer-generated avatar sees fewer discs. The implicit mentalizing account attributes the effect to relatively automatic tracking of others' visual perspective. However, the submentalizing account attributes the effect to domain-general attentional orienting. Accordingly, the current study focuses on elucidating the ongoing implicit mentalizing vs. submentalizing debate. The review tried to shed light on the debate regarding level-1 visual perspective taking and its potential relationship between the uncanny valley effect. Future research may focus on new manipulations of uncanny valley effect to further uncover the relationship between uncanny valley effect and level-1 visual perspective taking. This may provide new insight into the debate and the processing mechanisms of level-1 visual perspective-taking and uncanny valley effect, which may be beneficial for AI development. [ABSTRACT FROM AUTHOR]

Introduction: Intelligent vehicles and autonomous driving have been the focus of research in the field of transport, but current autonomous driving models have significant errors in lateral tracking that cannot be ignored. Methods: In view of this, this study innovatively proposes a lateral trajectory algorithm for intelligent vehicles based on improved radial basis function (RBF). The algorithm first models the lateral trajectory behaviour of the car based on the pre-scanning steering theory, and then proposes an improved RBF network model to compensate for the error of the lateral trajectory model and further improve the accuracy. Results: According to the simulation test results, after 20 iterations, the proposed algorithm always shows the highest accuracy with the same number of iterations. When the number of iterations reaches 370, the accuracy of the algorithm is stable at 88%. In addition, the bending test shows that the proposed algorithm performs best at low speeds with an overall error of 0.028 m, which is a higher accuracy compared to the algorithm without neural network compensation. Discussion: The maximum error of the proposed algorithm does not exceed 0.04 m in complex continuous curved terrain, which is safe within the normal road width. Overall, the lateral tracking algorithm proposed in this research has better lateral tracking capability compared to other improved algorithms of the same type. The research results are of some significance to the field of lateral tracking of automatic driving, which provides new ideas and methods for the field of lateral tracking of automatic driving technology and helps to promote the overall development of automatic driving technology. By reducing the lateral tracking error, the driving stability and safety of the self-driving car can be improved, creating favourable conditions for the wide application of the self-driving technology. [ABSTRACT FROM AUTHOR]

Soft X‐ray spectroscopy is an important technique for measuring the fundamental properties of materials. However, for measurements of samples in the sub‐millimetre range, many experimental setups show limitations. Position drifts on the order of hundreds of micrometres during thermal stabilization of the system can last for hours of expensive beam time. To compensate for drifts, sample tracking and feedback systems must be used. However, in complex sample environments where sample access is very limited, many existing solutions cannot be applied. In this work, we apply a robust computer vision algorithm to automatically track and readjust the sample position in the dozens of micrometres range. Our approach is applied in a complex sample environment, where the sample is in an ultra‐high vacuum chamber, surrounded by cooled thermal shields to reach sample temperatures down to 2.5 K and in the center of a superconducting split coil. Our implementation allows sample‐position tracking and adjustment in the vertical direction since this is the dimension where drifts occur during sample temperature change in our setup. The approach can be easily extended to 2D. The algorithm enables a factor of ten improvement in the overlap of a series of X‐ray absorption spectra in a sample with a vertical size down to 70 µm. This solution can be used in a variety of experimental stations, where optical access is available and sample access by other means is reduced. [ABSTRACT FROM AUTHOR]

Background/Objectives: Radial artery tracking (RAT) in the short-axis view is a pivotal step for ultrasound-guided radial artery catheterization (RAC), which is widely employed in various clinical settings. To eliminate disparities and lay the foundations for automated procedures, a pilot study was conducted to explore the feasibility of U-Net and its variants in automatic RAT. Methods: Approved by the institutional ethics committee, patients as potential RAC candidates were enrolled, and the radial arteries were continuously scanned by B-mode ultrasonography. All acquired videos were processed into standardized images, and randomly divided into training, validation, and test sets in an 8:1:1 ratio. Deep learning models, including U-Net and its variants, such as Attention U-Net, UNet++, Res-UNet, TransUNet, and UNeXt, were utilized for automatic RAT. The performance of the deep learning architectures was assessed using loss functions, dice similarity coefficient (DSC), and Jaccard similarity coefficient (JSC). Performance differences were analyzed using the Kruskal–Wallis test. Results: The independent datasets comprised 7233 images extracted from 178 videos of 135 patients (53.3% women; mean age: 41.6 years). Consistent convergence of loss functions between the training and validation sets was achieved for all models except Attention U-Net. Res-UNet emerged as the optimal architecture in terms of DSC and JSC (93.14% and 87.93%), indicating a significant improvement compared to U-Net (91.79% vs. 86.19%, p < 0.05) and Attention U-Net (91.20% vs. 85.02%, p < 0.05). Conclusions: This pilot study validates the feasibility of U-Net and its variants in automatic RAT, highlighting the predominant performance of Res-UNet among the evaluated architectures. [ABSTRACT FROM AUTHOR]

In the domain of maritime surveillance, the continuous tracking and monitoring of vessels are imperative for the early detection of potential threats. The Automatic Identification System (AIS) database, which collects vessel movement data over time, including timestamps and other motion details, plays a crucial role in real-time maritime monitoring. However, it frequently exhibits irregular intervals of data collection and intricate, intersecting trajectories, underscoring the importance of analyzing long-term temporal patterns for effective vessel tracking. While Kalman Filters and other physics-based models have been employed to tackle these issues, their effectiveness is limited by their inability to capture long-term dependence and non-linearity in the historical data. This paper introduces a novel approach that leverages Long Short-Term Memory (LSTM), a type of recurrent neural network, renowned for its proficiency in recognizing patterns over extended periods. Recognizing the strengths and limitations of the LSTM model, we propose a hybrid machine-learning algorithm that integrates LSTM with a physics-based model. This combination harnesses the physical laws governing vessel movements alongside data driven pattern mining, thereby enhancing the predictive accuracy of vessel locations. To assess the performance of standalone and hybrid models, various scenarios with different levels of complexity are generated. Furthermore, to simulate real-world data loss conditions often encountered in maritime tracking, temporal data gaps are randomly introduced into the scenarios. The competing approaches are then evaluated using both with time gap and without time gap conditions. Our results show that, although the LSTM model performs better than the physics-based model, the hybrid model consistently outperforms both standalone models across all scenarios. Furthermore, while data gaps negatively impact the accuracy of all models, the performance reduction is minimal for the physics-infused model. In summary, this study not only demonstrates the potential of combining data-driven and physics-based approaches but also sets a new benchmark for maritime vessel tracking. [ABSTRACT FROM AUTHOR]

[Objective] Current target tracking systems and algorithms for unmanned aerial vehicle (UAV) detection, designed for fixed optoelectronic equipment with a single field of view, perform well for non-moving targets but fall short in tracking accuracy and real-time requirements for small, moving UAV targets. To facilitate accurate and expeditious tracking of "low, slow, and small" UAVs in complex urban low-altitude environments, this paper proposes an automatic anti-UAV tracking system that incorporates the DSST(discriminative scale space tracker)-KCF(kernel correlation filter) algorithm. [Methods] The system comprises two principal components: The dual-light image acquisition unit and the display control unit. The gimbal camera, which is equipped with infrared and visible light cameras, handles image acquisition. The computer-based display control unit processes real-time images, calculates gimbal control parameters, and displays UAV images. These units are interconnected via gigabit Ethernet for efficient commands and image uploads and downloads. The system initially captures low-altitude images through the dual-optical unit, which are processed in real time using the fusion DSST-KCF algorithm. The proposed algorithm combines the high real-time performance of the KCF algorithm with the high performance of the DSST algorithm to achieve high real-time performance and target scale adaptation. It includes position and scale filters. The position filter uses a cyclic matrix to train the position filter, which subsequently predicts the target position in the subsequent frame. The scale filter, trained on a loop matrix data set, extracts features from the target position center at 33 scales to determine the tracking frame size. Finally, the DSST-KCF-gimbal control strategy maintains the UAV centered in the field of view, ensuring continuous and effective tracking of the target. This algorithm balances multi-scale variations while simultaneously enhancing real-time performance. [Results] The proposed system was tested for real-time UAV tracking in a campus environment. Results demonstrate that the improved algorithm enhances the detection success rate by 31.51% on average and tracking accuracy by 43% on average compared to the original KCF algorithm. By integrating this enhanced algorithm with gimbal control, the tracking system achieves autonomous and stable tracking in real-world conditions. It also maintains stable tracking at a distance of 2.3 km, an altitude of 100 m, and a velocity of 5 m/s, each frame is processed in less than 20 ms. [Conclusions] The test results demonstrate the effectiveness of the proposed system in tracking UAVs across both complex and simple backgrounds, offering high real-time performance and adaptability to different environments. It is also particularly well-suited for tracking small, moving targets in complex urban low-altitude scenarios. [ABSTRACT FROM AUTHOR]

To study the influence of speed factors on the stability of a tractor automatic navigation system, combined with the neural network control theory, the authors proposed a dual-objective joint sliding mode control method based on lateral position deviation and heading angle deviation, using a back propagation neural network to establish a two-wheel tractor-path dynamics model and a straight-line path tracking deviation model. The overall system simulation was carried out using Matlab/Simulink, and the reliability of the control method was verified. The experimental results showed that when the tractor was tracked with the automatic control of a linear path under the condition of variable speed, the maximum deviation of the lateral position deviation was 12.7 cm, and the average absolute deviation was kept within 4.88 cm; the maximum deviation of the heading angle deviation was 5°, and the average absolute deviation was kept within 2°; the maximum value of the actual rotation angle was 3.13°, and the standard deviation of the fluctuation was within 0.84°. Under the conditions of constant speed and variable speed, using the joint sliding mode control method designed by the authors, the dual-objective joint control of lateral position deviation and heading angle deviation could be realized, the controlled overshoot was small, the controlled deviation was small after reaching a stable state, and the adaptability to speed factors was strong, which basically could meet the accuracy requirements of farmland operations. [ABSTRACT FROM AUTHOR]

Mongolian extratropical cyclones (ETCs) play a crucial role in the weather and climate over East Asia, and the use of datasets with higher spatiotemporal resolutions can improve the understanding of Mongolian ETCs. Based on an automatic detection and tracking algorithm and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) dataset, the characteristics of the frequency, lifetime, moving distance, moving speed, and intensity of Mongolian ETCs in spring during 1979–2021 are investigated. The results show that 92.6% of the Mongolian ETCs are locally formed. There were two decadal changes in the Mongolian ETC frequency in approximately 1993 and 2004, showing a "high–low–high" variation. In terms of ETC passages, there is an increasing trend from central Mongolia to Northeast China and a decreasing trend east of Lake Baikal, indicating that the Mongolian ETC tracks are located more southward. Based on the standard deviation ellipse method and K-means clustering, the Mongolian ETCs are clustered into three categories, namely, southern track (ST, accounting for 53.4% of Mongolian ETCs) ETCs, northern track (NT, accounting for 43.8%) ETCs, and long track (LT, accounting for 2.8%) ETCs. The ST ETCs have a significant increasing trend, while the NT ETCs show a significant decreasing trend. Moreover, the other properties of Mongolian ETCs also show significant decadal variability. The sequences of lifetime, moving distance, moving speed and intensity exhibit decadal changes in the 1990s, with longer lifetime and moving distance, faster moving speed, and stronger intensity after the abrupt change. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Both micro‐ and macrostructural white matter (WM) abnormalities, particularly those related to axonal degeneration, are associated with cognitive decline in adults with Down syndrome (DS) prior to a diagnosis of Alzheimer disease. Neurofilament light chain (NfL) is a support protein within myelinated axons released into blood following axonal damage. In this study we investigated cross‐sectional relationships between WM microstructural changes as measured by diffusion tensor imaging (DTI) and plasma NfL concentration in adults with DS without dementia. METHODS: Thirty cognitively stable (CS) adults with DS underwent diffusion‐weighted MRI scanning and plasma NfL measurement. DTI measures of select WM tracts were derived using automatic fiber tracking, and associations with plasma NfL were assessed using Spearman correlation coefficients. RESULTS: Higher Plasma NfL was associated with greater altered diffusion measures of select tracts. DISCUSSION: Early increases in plasma NfL may reflect early white matter microstructural changes prior to dementia in DS. Highlights: The onset of such WM changes in DS has not yet been widely studied.WM microstructural properties correlated with plasma neurofilament light chain (NfL).NfL may reflect early, selective WM changes in adults with DS at high risk of developing AD. [ABSTRACT FROM AUTHOR]

Animal collective behavior produces group‐level patterns that emerge from individual‐level interactions among members. Such sophisticated behavioral coordination can be observed across various taxa, indicating evolutionary convergence. Among the most striking examples are two social insect lineages: ants and termites. Ants and termites evolved socially independently, inhabit terrestrial and subterranean environments, and face a similar collective task of maintaining their colonies with many individuals. This results in the convergent evolution of collective phenomena, such as constructing complex nest structures or movement coordination based on chemical and tactile cues. However, although the striking similarity attracts the most attention, little research has focused on how ants and termites differ in collective behaviors at individual and group levels. Here, we review the similarities and distinctions of collective behaviors in ants and termites. Even with similarities in group‐level patterns, rich diversity exists in mechanisms of behavioral coordination and the functions of collective patterns. Comparative analysis of collective behavior is challenging, but recent advances in automatic movement tracking methodologies have greatly expanded the potential for generating further insights. Finally, we conclude that comparative collective behavioral analysis can enhance our ability to understand biodiversity and provide alternative solutions for collective problem‐solving in many fields. [ABSTRACT FROM AUTHOR]

The aim of this work is to propose a new technique for automatic fault tracking from 3D seismic data using the 2D Continuous Wavelet Transform (CWT) method combined with artificial intelligence. Time slices of the variance attribute, derived from the 3D seismic data and chosen by the user, are analysed using the 2D CWT with the 2D Mexican Hat as an analysing wavelet, and the maxima of the modulus of the 2D CWT are mapped for the full range of scales. The ensemble of mapped maxima for the set of time slices is filtered using a Convolutional Neural Network machine. Machine training is performed with a supervised mode using the manually tracked faults as a desired output. Application to real data shows the efficiency and robustness of the proposed method, which can greatly help seismic interpreters in avoiding manual fault tracking, a difficult and time-consuming task. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem that the current arc suppression method of coil cannot achieve full compensation. First, the calculation model of the full compensation parameters of the controllable voltage source is established by analyzing the grounding fault arc suppression principle of the independent controllable voltage source and the parallel arc suppression coil. Second, by analyzing the arc suppression characteristics of controllable voltage source, a PI automatic regulation method based on phase deviation‐fault residual voltage control is proposed. The method uses fault residual voltage and the change of output voltage amplitude of controllable voltage source to control the adjustment direction and fault residual voltage to control the output voltage amplitude of controllable voltage source. The simulation results show that when the capacitive current of the distribution network is 24.47 A, the fault residual voltage is 2.5 V and the residual current is 446 mA after compensation by the independent controllable voltage source. When the capacitive current is 40.8 A and the deharmonic degree of arc suppression coil is −9%, the fault residual voltage and residual current are 2.2 V and 60 mA after compensated by the parallel arc suppression coil of the controlled voltage source. The proposed PI control method exhibits good stability, resulting in low fault residual voltage, and outperforms traditional control methods, enhancing the arc extinction effect of ground faults. [ABSTRACT FROM AUTHOR]

This study introduces new insights into the climatology of South Atlantic (SAt) cyclones by employing a novel cyclone life cycle detection method, the CycloPhaser. Utilizing the minimum relative vorticity series and its derivative at the cyclone centre, the program effectively identifies distinct phases in the cyclone life cycle. Cyclone tracks are obtained through the analysis of relative vorticity at 850 hPa, using the ERA5 dataset. The study identified six main cyclone life cycle patterns from the analysis of 28,458 systems. The predominant cyclone type, accounting for approximately 60% of the analysed systems, exhibited a four‐phase configuration: incipient, intensification, mature and decay. Detailed statistics for each developmental phase and the overall life cycle are presented, offering valuable comparisons and new insights while corroborating previous research findings. Key genesis regions in the SAt are identified, along with track density maps that reveal distinct preferences in cyclone developmental cycle. The main outcome of this study is the demonstration that the automated classification procedure enables the analysis of cyclones' life cycles to be conducted promptly and with low computing costs, facilitating the comprehensive study of cyclone behaviour with high efficiency. [ABSTRACT FROM AUTHOR]

Polar lows are small, high-latitude, intense maritime cyclones and frequently have severe impacts on the ocean such as strong winds, high waves and heavy rainfall. They are difficult to observe and forecast due to their short lifetime (<48 hours), small horizontal scales (200 ~ 1000 km), and the sparse synoptic observing network that exists in the subarctic and Arctic oceans. Previous studies have identified and monitored polar lows by visual analysis of visible and thermal infrared imagery from satellites. However, this manual inspection method is subjective, time-consuming and inevitably involves errors in polar low detections. In this study, we present an automatic objective procedure which we demonstrate by detecting polar lows using spaceborne active synthetic aperture radar (SAR) and passive microwave radiometer observations. Based on the marker-controlled watershed segmentation method and the morphological image thinning algorithm, the centre locations of polar lows are determined using RADARSAT-2 and Sentinel-1A high-resolution SAR images and total atmospheric water vapour content fields from radiometers (AMSR2, SSM/I, GMI, and WindSat). Furthermore, the trajectories of polar lows are constructed, using detected centres from multi-temporal SAR and radiometer observations. Polar low detections are confirmed by high surface wind speeds from SAR, scatterometer, and radiometer data, the presence of cloud vortex signatures visible in MODIS, AVHRR, and VIIRS thermal infrared imagery, as well as the difference between the sea surface temperature and the air temperature at 500 hPa. These results show that the proposed methods have potential to automatically detect and track polar lows from multisensor data. We also estimate the characteristic parameters of detected polar lows. The diameters, translation speeds, and distances travelled are 189 km and 225 km, 8 m/s and 4.9 m/s, and 318 km and 263 km, respectively. [ABSTRACT FROM AUTHOR]

With the rapid development of urban rail transit, the existing track detection has some problems such as low efficiency and insufficient detection coverage, so an intelligent and automatic track detection method based on UAV is urgently needed to avoid major safety accidents. At the same time, the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of devices. As a result, the Dispersed Computing (DCOMP) architecture enables collaborative computing between devices in the Internet of Everything (IoE), promotes low-latency and efficient cross-wide applications, and meets users' growing needs for computing performance and service quality. This paper focuses on examining the resource allocation challenge within a dispersed computing environment that utilizes UAV inspection tracks. Furthermore, the system takes into account both resource constraints and computational constraints and transforms the optimization problem into an energy minimization problem with computational constraints. The Markov Decision Process (MDP) model is employed to capture the connection between the dispersed computing resource allocation strategy and the system environment. Subsequently, a method based on Double Deep Q-Network (DDQN) is introduced to derive the optimal policy. Simultaneously, an experience replay mechanism is implemented to tackle the issue of increasing dimensionality. The experimental simulations validate the efficacy of the method across various scenarios. [ABSTRACT FROM AUTHOR]

Autonomous-rail Rapid Transit (ART) tram is a new type of multiple-articulated rubber-tire transit that utilizes intelligent perception, path tracking, and trajectory following control technologies to eliminate reliance on physical railway tracks. The adoption of power batteries, hydrogen energy, wheel-edge motor drive, and other technologies has comprehensively realized the dual advantages of large-capacity rail transportation, which is punctual, high volume, energy-saving, and environmentally friendly, as well as the flexibility and low comprehensive cost of traditional bus operations. This has created a brand-new urban rail transit model. This article first introduces the ART tram systems architecture, operating principles, applicable scenarios. Secondly, it introduces the core subsystems of ART tram vehicle structure, electrical system, and energy storage system. Thirdly, it focuses on analyzing the structure composition and control principles of the Automatic All-Wheel Steering System, which includes two key core subsystems: path tracking control subsystems and trajectory following control subsystems. Then, a horizontal comparison is made between the performance advantages and disadvantages of ART and other transportation systems, and the application status of ART tram is summarized. Finally, some common issues related to the development of ART tram are discussed, and a development plan for future ART systems is proposed to better integrate ART tram into urban transportation and meet people's demands for intelligent, comfortable, fast, and environmentally friendly urban public transportation.

Evaluation of left ventricular (LV) function in critical care patients is useful for guidance of therapy and early detection of LV dysfunction, but the tools currently available are too time-consuming. To resolve this issue, we previously proposed a method for the continuous and automatic quantification of global LV function in critical care patients based on the detection and tracking of anatomical landmarks on transesophageal heart ultrasound. In the present study, our aim was to improve the performance of mitral annulus detection in transesophageal echocardiography (TEE). We investigated several state-of-the-art networks for both the detection and tracking of the mitral annulus in TEE. We integrated the networks into a pipeline for automatic assessment of LV function through estimation of the mitral annular plane systolic excursion (MAPSE), called autoMAPSE. TEE recordings from a total of 245 patients were collected from St. Olav's University Hospital and used to train and test the respective networks. We evaluated the agreement between autoMAPSE estimates and manual references annotated by expert echocardiographers in 30 Echolab patients and 50 critical care patients. Furthermore, we proposed a prototype of autoMAPSE for clinical integration and tested it in critical care patients in the intensive care unit. Compared with manual references, we achieved a mean difference of 0.8 (95% limits of agreement: –2.9 to 4.7) mm in Echolab patients, with a feasibility of 85.7%. In critical care patients, we reached a mean difference of 0.6 (95% limits of agreement: –2.3 to 3.5) mm and a feasibility of 88.1%. The clinical prototype of autoMAPSE achieved real-time performance. Automatic quantification of LV function had high feasibility in clinical settings. The agreement with manual references was comparable to inter-observer variability of clinical experts. [ABSTRACT FROM AUTHOR]

The rapidly developed Unmanned Aerial Vehicles (UAV) and artificial intelligence technology has prompted the real-time and accurate observation measurements of tidal bore, the basis of which is tidal bore propagation velocity. In this article, we construct a tidal observation system framework based on UAV and computer vision in order to obtain the tidal bore propagating velocity datasets. Firstly, we focus on the identification of tidal headlines based on the Sobel edge detection, the improved Otsu image segmentation algorithm and the edge connection algorithm with an accuracy of 91%. And then, the detected tidal headlines could be used to control the flight parameters of UAV in order to stably track tidal bore on the specified route with the deviation range below 0.5, and finally to acquire the tidal bore propagation velocity datasets. Comparing with the propagation velocity of the tidal line measured on site, the error of the results is maintained within 0.1 m/s, which demonstrates the effectiveness of our proposed observation method. [ABSTRACT FROM AUTHOR]

Adiabatic calorimeter experiments have quantified the sulfuric acid‐catalyzed reaction of ethylene oxide (EO) with water to form ethylene glycol (EG) and higher glycols. Conditions were selected relevant to industrial safety, such as a scenario of reverse flow of acid from a scrubber to an EO tank. Concentrations of 25–95 wt% EO with acidified water of pH 3.0 down to pH –0.7 were examined. In some experiments, EG was also added to the mix. The best fit apparent reaction rate expression exhibits a 1.5 order in EO concentration, third order in water concentration, nearly first order in acid concentration, and an activation energy of 76,500 kJ/kmol. The reaction model provides an adequate fit of adiabatic self‐heat rate versus temperature, pressure rate versus temperature, pressure versus time, and temperature versus time data for a large number of APTAC (Automatic Pressure Tracking Adiabatic Calorimeter) experiments. The model builds on one previously developed for the neutral reactions of EO with water and EGs. A case study involving a sulfuric acid‐contaminated EO railcar is presented. The reaction model can help quantify the behavior of EO contaminated with sulfuric acid solution below about 120°C. Above this temperature, other reaction pathways not characterized in this study become more prominent. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Automatic tracking of three-dimensional (3D) human motion pose has the potential to provide corresponding technical support in various fields. However, existing methods for tracking human motion pose suffer from significant errors, long tracking times and suboptimal tracking results. To address these issues, an automatic tracking method for 3D human motion pose using contrastive learning is proposed. By using the feature parameters of 3D human motion poses, threshold variation parameters of 3D human motion poses are computed. The golden section is introduced to transform the threshold variation parameters and extract the features of 3D human motion poses by comparing the feature parameters with the threshold of parameter variation. Under the supervision of contrastive learning, a constraint loss is added to the local–global deep supervision module of contrastive learning to extract local parameters of 3D human motion poses, combined with their local features. After normalizing the 3D human motion pose images, frame differences of the background image are calculated. By constructing an automatic tracking model for 3D human motion poses, automatic tracking of 3D human motion poses is achieved. Experimental results demonstrate that the highest tracking lag is 9%, there is no deviation in node tracking, the pixel contrast is maintained above 90% and only 6 sub-blocks have detail loss. This indicates that the proposed method effectively tracks 3D human motion poses, tracks all the nodes, achieves high accuracy in automatic tracking and produces good tracking results. [ABSTRACT FROM AUTHOR]

Background: The curved planar reformation (CPR) technique is one of the most commonly used methods in clinical practice to locate coronary arteries in medical images. Purpose: The artery centerline is the cornerstone for the generation of the CPR image. Here, we describe the development of a new fully automatic artery centerline tracker with the aim of increasing the efficiency and accuracy of the process. Methods: We propose a COronary artery Centerline Tracker (COACT) framework which consists of an ostium point finder (OPFinder) model, an intersection point detector (IPDetector) model and a set of centerline tracking strategies. The output of OPFinder is the ostium points. The function of the IPDetector is to predict the intersections of a sample sphere and the centerlines. The centerline tracking process starts from two ostium points detected by the OPFinder, and combines the results of the IPDetector with a series of strategies to gradually reconstruct the coronary artery centerline tree. Results: Two coronary CT angiography (CCTA) datasets were used to validate the models. Dataset1 contains 160 cases (32 for test and 128 for training) and dataset2 contains 70 cases (20 for test and 50 for training). The results show that the average distance between the ostium points predicted by the OPFinder and the manually annotated ostium points was 0.88 mm, which is similar to the differences between the results obtained by two observers (0.85 mm). For the IPDetector, the average overlap of the predicted and ground truth intersection points was 97.82% and this is also close to the inter‐observer agreement of 98.50%. For the entire coronary centerline tree, the overlap between the results obtained by COACT and the gold standard was 94.33%, which is slightly lower than the inter‐observer agreement, 98.39%. Conclusions: We have developed a fully automatic centerline tracking method for CCTA scans and achieved a satisfactory result. The proposed algorithms are also incorporated in the medical image analysis platform TIMESlice (https://slice‐doc.netlify.app) for further studies. [ABSTRACT FROM AUTHOR]

Modal tracking plays a vital role in structural health monitoring since changes in modal parameters help us understand a structure's dynamic characteristics and may reflect the potential deterioration of structural performance. Although numerous modal parameter estimation (MPE) methods exist, it is not guaranteed that an MPE process will exclude all spurious modes and not lose any physical modes every time over a long-term monitoring period. Relatively large damping of a structure, poor data quality, and significant changes in structural modal parameters may make the estimated modal parameters spurious, missing, or misclassified. It makes long-term modal tracking semiautomated or manual, which constrains timely downstream applications such as anomaly detection, condition assessment, and decision making. This research aims to propose a long-term continuous automatic modal tracking algorithm based on Bayesian inference even when the modal parameters, damping, and data quality change significantly. Bayesian inference is used to determine the physical modes from the results of existing MPE methods. Both the modes identified from the most recent response set and the modal probability model from multiple previous response sets are considered in the Bayesian model to better determine the physical modes from the results of MPE. Moreover, the proposed algorithm requires only three extra hyperparameters compared to general modal tracking algorithms, and they can be quickly determined by a grid search method. The performance of the proposed algorithm is verified by a numerical example and a real-world civil structure Z24 Bridge benchmark. [ABSTRACT FROM AUTHOR]

This paper introduces the main technical characteristics and possible applications of Modern Ships Tracking and Detections systems for maritime applications. As is evident, the Global Maritime Distress and Safety System (GMDSS) network is designed by integrated radio and satellite systems for ships safety, distress alert and Search and Rescue (SAR) communications. However, this system is not enough effective to provide a real tracking and detection system of ships for every day navigation aids, positions of adjacent ships, for enhanced safety and security and for more effective collision avoidance of ships in very critical weather and awareness conditions. The Modern Space Tracking and Detection Systems and Equipment are usually using VHF, UHF and SHF-band integrated or autonomous onboard mobiles space Communication, Navigation and Surveillance (CNS) equipment that are introduced in the following sectors. [ABSTRACT FROM AUTHOR]

The supercell storm that occurred in southwestern Germany on June 23, 2021, had an exceptionally long lifetime of 7.5 hr, travelled a distance of nearly 190 km, and produced large amounts of hail. During that summer, the Swabian MOSES field campaign was held in that area, and several hydro‐meteorological measurements are available, as the storm passed directly over the main observation site. We present hindcasts of this event with the Icosahedral Non‐hydrostatic model using two horizontal grid spacings (i.e., 2 km, 1 km) with a single‐moment and an advanced double‐moment microphysics scheme. Numerical results show that all 2 km model realizations do not simulate convective precipitation at the correct location and time. For the 1 km grid spacing, changes in aerosol concentration resulted in large changes in convective precipitation. Only the 1 km run assuming a low cloud condensation nuclei (CCNs) concentration is able to realistically capture the storm, whereas no supercell is simulated in the more polluted scenarios. Observed aerosol particle concentrations indicate that CCNs values were the lowest of the month, which suggests that the low aerosol concentration is a reasonable assumption. The thermodynamic structure of the pre‐convective environment, as well as other observations, showed the best agreement to this model run as well, indicating that the good representation of the supercell was obtained for the right reason. The automatic tracking of individual clouds revealed that more convective cells with longer lifetimes are simulated at finer resolution. We also find a negative aerosol–precipitation effect that is not only due to a reduced collision–coalescence process, but also to weaker cold‐rain processes. These findings demonstrate the benefits of using an aerosol‐aware double‐moment microphysics scheme for convective‐scale predictability and that the use of different CCNs concentrations can determine whether a supercell is successfully simulated or not. [ABSTRACT FROM AUTHOR]

The article focuses on the control of an omni-tracked vehicle in a symmetrical fully overlapping track system. The vehicle in question is equipped with four independently controlled tracks. The links of each crawler are equipped with a single rolling roller, fixed at an angle to the direction of the vehicle main axis. The authors propose mathematical description to determine the direction and speed of movement of a single pair of omni-tracks with oppositely arranged rolling rollers. Numerical tests were carried out, the results of which were compared with the mathematical model. The numerical studies were then subjected to experimental verification, using a full-scale prototype. A dynamic direction correction algorithm was also proposed with its effectiveness proved experimentally. [ABSTRACT FROM AUTHOR]

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A parked vehicle damaged by a hit-and-run can only be repaired at the expense of the owner, unless the fleeing vehicle is identified and the driver apprehended. Identifying the fleeing vehicle involves using a video investigation method that searches for perpetrators through CCTV footage of the crime scene. When the length of the recorded video is long, the investigation may require an extended amount of time from the investigator, resulting in an added burden on their daily work. Some commercial companies are using object recognition and tracking technology to detect hit-and-run incidents; however, detecting small movements of a vehicle during a minor collision still remains a challenge. Therefore, there is a need for a system that can detect small movement in a vehicle in a lengthy video. Automatic recognition and tracking require a sufficient amount of training dataset. However, such a dataset for hit-and-run incidents is not publicly available. One of the reasons behind this scarcity is that it may violate personal information protection acts. On the other hand, instead of using real accident videos, we could use actors to simulate such accident scenes. Although this may be feasible, creating such a dataset would require substantial costs. In this paper, we describe a new dataset for hit-and-run incidents. We collected 833 hit-and-run videos by recreating a parking lot using miniaturized cars. This dataset has been made publicly available through Kaggle. We used three-dimensional convolution neural network, which is frequently used in the field of action recognition, to detect small movements of vehicles during hit-and-run incidents. In addition, the proportion of the area that surrounds the target vehicle to the min-max box of the vehicle itself and the length of the input frame are varied to compare the accuracy. As a result, we were able to achieve better accuracy by using the lowest proportion and the shortest input frame. [ABSTRACT FROM AUTHOR]

The global population is progressively entering an aging phase, with population aging likely to emerge as one of the most-significant social trends of the 21st Century, impacting nearly all societal domains. Addressing the challenge of assisting vulnerable groups such as the elderly and disabled in carrying or transporting objects has become a critical issue in this field. We developed a mobile Internet of Things (IoT) device leveraging Ultra-Wideband (UWB) technology in this context. This research directly benefits vulnerable groups, including the elderly, disabled individuals, pregnant women, and children. Additionally, it provides valuable references for decision-makers, engineers, and researchers to address real-world challenges. The focus of this research is on implementing UWB technology for precise mobile IoT device localization and following, while integrating an autonomous following system, a robotic arm system, an ultrasonic obstacle-avoidance system, and an automatic leveling control system into a comprehensive experimental platform. To counteract the potential UWB signal fluctuations and high noise interference in complex environments, we propose a hybrid filtering-weighted fusion back propagation (HFWF-BP) neural network localization algorithm. This algorithm combines the characteristics of Gaussian, median, and mean filtering, utilizing a weighted fusion back propagation (WF-BP) neural network, and, ultimately, employs the Chan algorithm to achieve optimal estimation values. Through deployment and experimentation on the device, the proposed algorithm's data preprocessing effectively eliminates errors under multi-factor interference, significantly enhancing the precision and anti-interference capabilities of the localization and following processes. [ABSTRACT FROM AUTHOR]

In this paper, a chained architecture of virtual track train (VTT), gantry virtual track train (G-VTT), was proposed. The distributed virtual driving model (DVD model) for G-VTT was proposed to realise the automatic tracking of paths with arbitrary ommon curves. The core of the DVD model is the cascade modular wheel steering angle (WSA) control algorithm based on the optimal lateral acceleration (OLA) of tracking points. A local tracking objective function based on 2-norm was established to find the OLA in the preview window, thereby the optimal local trajectory was realised. The cascade structure of the algorithm gives the DVD model the advantages of modularity and high scalability, facilitating the cascade control of an n-unit G-VTT. The proposed control strategy was verified by co-simulation of G-VTT dynamics model and the DVD model. Results show that the DVD model can effectively improve the path-following ability and small-curve-passing capacity of the G-VTT with remarkable reduction of the lateral deviation (up to about 99.16%) and turning passageway width (about 59.71%); Besides, the yaw and anti-jackknifing stability of the train are also significantly improved, and the DVD model is well adaptable to speed, curve radius, and payload. [ABSTRACT FROM AUTHOR]

Purpose: MR‐guided cardiac catheterization procedures currently use passive tracking approaches to follow a gadolinium‐filled catheter balloon during catheter navigation. This requires frequent manual tracking and repositioning of the imaging slice during navigation. In this study, a novel framework for automatic real‐time catheter tracking during MR‐guided cardiac catheterization is presented. Methods: The proposed framework includes two imaging modes (Calibration and Runtime). The sequence starts in Calibration mode, in which the 3D catheter coordinates are determined using a stack of 10–20 contiguous saturated slices combined with real‐time image processing. The sequence then automatically switches to Runtime mode, where three contiguous slices (acquired with partial saturation), initially centered on the catheter balloon using the Calibration feedback, are acquired continuously. The 3D catheter balloon coordinates are estimated in real time from each Runtime slice stack using image processing. Each Runtime stack is repositioned to maintain the catheter balloon in the central slice based on the prior Runtime feedback. The sequence switches back to Calibration mode if the catheter is not detected. This framework was evaluated in a heart phantom and 3 patients undergoing MR‐guided cardiac catheterization. Catheter detection accuracy and rate of catheter visibility were evaluated. Results: The automatic detection accuracy for the catheter balloon during the Calibration/Runtime mode was 100%/95% in phantom and 100%/97 ± 3% in patients. During Runtime, the catheter was visible in 82% and 98 ± 2% of the real‐time measurements in the phantom and patients, respectively. Conclusion: The proposed framework enabled real‐time continuous automatic tracking of a gadolinium‐filled catheter balloon during MR‐guided cardiac catheterization. [ABSTRACT FROM AUTHOR]