Tracking Method Research Articles

Abstract TP365: Understanding neutrophil recruitment and lifespan following ischemia/reperfusion using a novel tracking method

Introduction: Polymorphonuclear leukocytes (PMNs) are among the first leukocytes recruited to the infarct following reperfusion in ischemic stroke. Although transendothelial migration (TEM) blockade in preclinical studies was found to reduce infarct size, TEM blockade in clinical trials has been unsuccessful. Studying PMN recruitment and lifespan in ischemic stroke may offer insight into these failures in clinical trials and advance therapeutic development. Methods: PMNs were briefly pulse-labeled with 5-Ethynyl-2’-deoxyuridine (EdU) to determine PMN lifespan and migration pattern following ischemia/reperfusion in stroke. Large vessel occlusion and reperfusion were simulated using the transient middle cerebral artery occlusion model (tMCAO, 90 minutes of ischemia followed by reperfusion). Infarct size was determined via triphenyl tetrazolium chloride staining. The percentage of EdU+ PMN in circulation was assessed each day following tMCAO and compared to the percentage of EdU+ PMNs in the ischemic hemisphere at sacrifice by flow cytometry. The position of PMN in coronal sections was examined by wide field fluorescence microscopy. Results: Large cortical ischemic strokes involving over 20% of cerebral volume were induced in mice. A majority of PMN recruitment and extravasation was localized to the cortical surface of the infarct at early time points (12 and 24 h post stroke), contrasting other vascular beds where there is PMN migration deep into the infarcted tissue by 24 h. EdU+ PMNs persisted in the ischemic hemisphere until 96 h post stroke despite their clearance from circulation at least 48 h prior, indicating PMN can survive for days in the infarct. Additionally, the percentage of EdU+ PMN in the ischemic hemisphere was similar to the percentage of EdU+ PMN in circulation at 24 h post stroke, suggesting PMN recruitment centers around 24 h. Conclusions: Our findings demonstrate PMN recruitment and infiltration post stroke evolves over several days. This data suggests that the majority of PMNs are recruited to the infarct within the first 48 hours and likely can persist within the infarct for days. Modulating the timing of PMN labeling via EdU can lead to a better understanding of PMN spatiotemporal infiltration and persistence and will help inform future therapeutic interventions targeting leukocytes.

Energy Efficiency Improvements in Wind Energy Systems via Sensorless Soft-Switching Control

This study implements a series resonant converter (SRC) and pulse density modulation (PDM) power control strategies to minimize switching losses and improve the efficiency of an off-grid, small-scale wind energy conversion system (WECS). Additionally, the maximum power point tracking (MPPT) method was employed to further reduce costs and increase system reliability. The "perturb and observe" (P&O) MPPT technique was utilized, enabling operation at the maximum power point (MPP) without the need for wind speed data or an aerodynamic model of the turbine. The speed and power data required for the P&O algorithm were derived from the three-phase generator variables using the double second-order generalized integrator frequency-locked loop (DSOGI-FLL) algorithm. The performance of a 1.5 kW WECS was analyzed through simulations conducted in Powersim (PSIM), and the results are presented. The analysis revealed that the designed system achieved an average MPPT efficiency of 97%. Furthermore, the efficiency of the resonant converter was measured to be 90% for the lowest wind speed and 93% for other wind speeds. These findings demonstrate that the proposed system offers a significant improvement in overall energy conversion efficiency, ensuring reliable and cost-effective operation of small-scale WECS, with an average system efficiency of approximately 90-93% across varying wind conditions.

3D tracking method based on digital holographic microscopy

Abstract 3D tracking is an important technique for investigating biophysical processes, which provides abundant information for cell identification and characterization. In this paper, we present an effective and practical 3D tracking approach based on digital holographic microscopy (DHM) with auto-focusing reconstruction and a dynamical light-scattering filtering algorithm. An off-axis DHM system was established to capture the holograms of flowing samples. Utilizing reliable auto-focusing, the flowing samples were reconstructed. To improve the resolution and contrast, the principal component analysis filtering method was applied to eliminate the background, speckle noise and interference fringes. Combining the 2D positions of the flowing samples in the focal plane with the depth positions obtained from auto-focusing, the 3D trajectory and velocity of the flowing samples were obtained. The results demonstrated that the method could achieve simultaneous measurement of size and velocity of objects while satisfying signal-to-noise ratio and resolution, providing a foundation for high-throughput cell classification and multi-parameter characterization.

A contribution to 3D tracking of deformable bubbles in swarms using temporal information

Reliable Lagrangian 3D tracking of individual bubble swarm members allows a deeper understanding of hydrodynamic bubble–bubble interactions and their collective rise. For multi-view measurements, we have recently developed such a tracking method (Hessenkemper in Int J Multiph Flow 179:104932, 2024), which is able to track deformable bubbles with low to moderate view obstruction through the bubbles to each other. In the present work, we aim to further enhance the 3D tracking performance by additionally incorporating 2D temporal information in the form of previously established 2D tracks in each camera view. The new 3D tracking method is able to disambiguate cross-view object associations at each time step by using the 2D track information accumulated over time. In addition, the temporal information from multiple 2D domains is used in two post-processing steps to improve the completeness of established 3D trajectories. Compared to the previous 3D tracking method, the extended 3D tracking framework shows noticeable improvements in tracking ability, accuracy, and completeness of trajectories.Graphical abstract

Extreme migratory connectivity and apparent mirroring of non-breeding grounds conditions in a severely declining breeding population of an Afro-Palearctic migratory bird

Understanding the distribution of breeding populations of migratory animals in the non-breeding period (migratory connectivity) is important for understanding their response to environmental change. High connectivity (low non-breeding population dispersion) may lower resilience to climate change and increase vulnerability to habitat loss within their range. Very high levels of connectivity are reportedly rare, but this conclusion may be limited by methodology. Using multiple tracking methods, we demonstrate extremely high connectivity in a strongly declining, peripheral breeding population of a long-distance migrant, the Common Nightingale in the UK. Non-breeding population dispersion is lower than for previously tracked populations of this and other species and likely lower than can usually be detected by light-level geolocation, the main tracking method for small bodied species. Extremely low levels of population mixing were also detected, so any impacts on this population on the non-breeding grounds are unlikely to be shared with more distant breeding populations, corresponding to the observed patterns of European population change. According to a species distribution model using independent field data, this population’s non-breeding grounds had lower suitability than others and likely declined before the period we were able to assess. These results support hypotheses that climatic and habitat-related deterioration of non-breeding grounds contributes to population declines in peripheral and high-connectivity breeding populations of long-distance migrants, including the one studied here.

A Study of Knowledge Graph and IoT Integration-Based Anomalous Event Tracking Method for Digital Platforms

In digital platforms, abnormal events involve multiple data sources and complex information types, and the difficulty of tracking them increases due to the similarity and interaction between components, operations, and user behavior. Therefore, in order to achieve precise tracking and efficient processing of abnormal events, and thereby improve the stability and security of the platform, a digital platform abnormal event tracking method based on a knowledge graph is proposed. First, using data mining and association rule techniques, abnormal event data in the digital platform are effectively collected and integrated. Subsequently, the data are input into a model that integrates residual atrous convolutional neural networks and conditional random fields to achieve precise identification of key entities. On the basis of entity recognition, the correlation between entities is extracted and a knowledge graph architecture for abnormal events is constructed, providing a solid foundation for subsequent deep analysis. Through a visual interface, the knowledge graph of abnormal events can be intuitively displayed, making it easy for users to quickly understand the full picture of the event. At the same time, the knowledge graph subgraph matching algorithm is adopted, combined with flow graph indexing and optimal matching sequence, to achieve accurate tracking and recognition of abnormal events. The experimental results show that this method can effectively track abnormal events in the digital platform. The first detection time is relatively short, with a mishandling time of 8.3[Formula: see text]s and data duplication of 7.9[Formula: see text]s. The continuous tracking time is long, with a security vulnerability of 50[Formula: see text]min. The false alarm rate is low, with the highest being 2.1% for data duplication, and the false miss rate is also low, with the highest being 0.8% for mishandling. This method can identify the number of abnormal events, which helps to understand the stability and health status of the platform. By timely and effectively preventing abnormal events, the frequency of their occurrence can be reduced, and the overall security and stability of the platform can be improved.

Improved particle filter algorithm combined with culture algorithm for collision Caenorhabditis elegans tracking

In order to address the issue of tracking errors of collision Caenorhabditis elegans, this research proposes an improved particle filter tracking method integrated with cultural algorithm. The particle filter algorithm is enhanced through the integration of the sine cosine algorithm, thereby facilitating uninterrupted tracking of the target C. elegans. Furthermore, the cultural algorithm is employed to facilitate recognition of the target C. elegans following a collision. In addition, this method integrates the concepts of down-sample and marking to reduce the average processing time of the image. Ultimately, the experiment was conducted on two strains of C. elegans of six ages. The experimental results demonstrate that the proposed method can accurately identify the target worm in the post-collision stage. The proposed method has the potential to be utilized in the field of worm tracking, offering a novel method into the acquisition of collision C. elegans behavior.

Rapid prospective motion correction using free induction decay and stationary field probe navigators at 7T.

MR-based FID navigators (FIDnavs) do not require gradient pulses and are attractive for prospective motion correction (PMC) due to short acquisition times and high sampling rates. However, accuracy and precision are limited and depend on a separate calibration measurement. Besides FIDnavs, stationary NMR field probes are also capable of measuring local, motion-induced field changes. In this work, a linear model is calibrated between field probe data and motion parameters analog to FIDnav calibration and both tracking methods are compared and combined for PMC. FIDnavs and field probe navigators were implemented in a fast 3D-EPI sequence and calibrated by a linear model to realignment motion parameters of the 3D-EPI time series. A workflow was established to correct head motion prospectively by FIDnavs, field probe navigators or a combination of both. Large motions were instructed to test the accuracy and the impact on image quality in EPI data. In a group of five subjects, FIDnavs demonstrated approximately doubled accuracy and precision in comparison with field probe navigators for large motions, especially nodding motions were tracked less accurately by field probes. A combination of both methods could not improve the accuracy consistently. Motion artifacts in high-resolution data were reduced similarly by both PMC methods, although artifacts remained due to susceptibility-induced B0 changes. Stationary field probe navigators can be calibrated equivalently as FIDnavs and enable rapid PMC of large and fast motions. Although they reveal decreased accuracy, their contrast-independence facilitates the potential insertion into many sequences.

Wavelength-Dependent Calcium Signaling Response to Photobiomodulation in Pancreatic Cells

Diabetes mellitus is a metabolic disorder that is rapidly growing across the world. Our laboratory has recently demonstrated that photobiomodulation (PBM) can couple to its metabolic pathways by modulating calcium dynamics in islet cells, including α- and β-cells. Using computer vision algorithms, changes in PBM-induced calcium dynamics can be verified, and, more importantly, this led us to propose hypotheses that will likely advance our understanding of photostimulatory effects in islet cells. In our previous paper, we determined changes in calcium spiking in response to PBM at 810 nm by manually segmenting the cells and the calcium spiking patterns. We have since developed a computer vison pipeline to automate cell segmentation and subsequent image analyses. By using automated methods for segmentation, registration, tracking, and statistical analysis, we were able to improve the accuracy of previously observed changes in calcium spiking in response to PBM in both cell types. Moreover, this pipeline was applied to elucidate the wavelength-dependent modulation of calcium dynamics at 1064 nm. The extent of increase in calcium spiking appears to have been overestimated by manual analysis, and the machine learning pipeline was able to capture and segment nearly 3-fold more cells, suggesting improved accuracy in the analysis of calcium spiking in islet cells. Detailed calcium analysis also indicates a biphasic dose response among α- and β-cells in response to PBM therapy at different wavelengths. The current findings offer a novel hypothesis and may facilitate the use of translational PBM as a potential therapy for diabetes mellitus.

Worldwide epidemiology of paediatric multiple sclerosis: data from the Multiple Sclerosis International Federation Atlas of MS, third edition

BackgroundLimited data are available on the global rates of paediatric multiple sclerosis. Here, we report on the estimated worldwide prevalence of paediatric MS.MethodsWe included paediatric prevalence data in 2020–2022 (Multiple...

Research on Automatic Tracking and Size Estimation Algorithm of “Low, Slow and Small” Targets Based on Gm-APD Single-Photon LIDAR

In order to solve the problem of detecting, tracking and estimating the size of “low, slow and small” targets (such as UAVs) in the air, this paper designs a single-photon LiDAR imaging system based on Geiger-mode Avalanche Photodiode (Gm-APD). It improves the Mean-Shift algorithm and proposes an automatic tracking method that combines the weighted centroid method to realize target extraction, and the principal component analysis (PCA) method of the adaptive rotating rectangle is realized to fit the flight attitude of the target. This method uses the target intensity and distance information provided by Gm-APD LiDAR. It addresses the problem of automatic calibration and size estimation under multiple flight attitudes. The experimental results show that the improved algorithm can automatically track the targets in different flight attitudes in real time and accurately calculate their sizes. The improved algorithm is stable in the 1250-frame tracking experiment of DJI Elf 4 UAV with a flying speed of 5 m/s and a flying distance of 100 m. Among them, the fitting error of the target is always less than 2 pixels, while the size calculation error of the target is less than 2.5 cm. This shows the remarkable advantages of Gm-APD LiDAR in detecting “low, slow and small” targets. It is of practical significance to comprehensively improve the ability of UAV detection and C-UAS systems. However, the application of this technology in complex backgrounds, especially in occlusion or multi-target tracking, still faces certain challenges. In order to realize long-distance detection, further optimizing the field of view of the Gm-APD single-photon LiDAR is still a future research direction.

Efficient Path Following Control Design for Tractor With Multiple Trailers Systems Utilizing Movement Rules and Passive Multi‐Steering Angles

ABSTRACTThis paper proposes a straightforward and robust control strategy for a tractor with multiple trailers system (TMTS, for short) to effectively follow identical paths. Hereto, the control design is structured into four steps for path‐tracking precision. Firstly, a suitable passive steering angle is assigned to each trailer, granting it some steering capability to follow the moving path of its direct leader. Secondly, movement rules of the TMTS with passive multi‐steering angles are explored in‐depth for the first time, facilitating the convenient deduction of kinematic and dynamic models. Thirdly, a desired movement task of the TMTS is reduced to a tracking control by the tractor through the implementation of suitable passive steering angles. Additionally, by virtue of a curvature tracking method, cumulative location errors could be greatly reduced by tracking the relative curvature. Fourthly, a combination of feedback linearization, optimal control, and integral sliding mode control methods is employed to design two driven torques for the tractor to precisely follow the geometric path. Consequently, both the tractor and all trailers are capable of simultaneously following an identical path. In the end, numerical simulations are presented to demonstrate the effectiveness, feasibility, and robustness of the resulting control strategy for practical applications.

JLEDNet: a nighttime UAV tracking method through joint low-light image enhancement using hybrid attention transformer and denoising

JLEDNet: a nighttime UAV tracking method through joint low-light image enhancement using hybrid attention transformer and denoising

Remote Sensing Target Tracking Method Based on Super-Resolution Reconstruction and Hybrid Networks

Remote Sensing Target Tracking Method Based on Super-Resolution Reconstruction and Hybrid Networks

Real-time CBCT imaging and motion tracking via a single arbitrarily-angled x-ray projection by a joint dynamic reconstruction and motion estimation (DREME) framework

Objective.Real-time cone-beam computed tomography (CBCT) provides instantaneous visualization of patient anatomy for image guidance, motion tracking, and online treatment adaptation in radiotherapy. While many real-time imaging and motion tracking methods leveraged patient-specific prior information to alleviate under-sampling challenges and meet the temporal constraint (<500 ms), the prior information can be outdated and introduce biases, thus compromising the imaging and motion tracking accuracy. To address this challenge, we developed a frameworkdynamicreconstruction andmotionestimation (DREME) for real-time CBCT imaging and motion estimation, without relying on patient-specific prior knowledge.Approach.DREME incorporates a deep learning-based real-time CBCT imaging and motion estimation method into a dynamic CBCT reconstruction framework. The reconstruction framework reconstructs a dynamic sequence of CBCTs in a data-driven manner from a standard pre-treatment scan, without requiring patient-specific prior knowledge. Meanwhile, a convolutional neural network-based motion encoder is jointly trained during the reconstruction to learn motion-related features relevant for real-time motion estimation, based on a single arbitrarily-angled x-ray projection. DREME was tested on digital phantom simulations and real patient studies.Main Results.DREME accurately solved 3D respiration-induced anatomical motion in real time (∼1.5 ms inference time for each x-ray projection). For the digital phantom studies, it achieved an average lung tumor center-of-mass localization error of 1.2 ± 0.9 mm (Mean ± SD). For the patient studies, it achieved a real-time tumor localization accuracy of 1.6 ± 1.6 mm in the projection domain.Significance.DREME achieves CBCT and volumetric motion estimation in real time from a single x-ray projection at arbitrary angles, paving the way for future clinical applications in intra-fractional motion management. In addition, it can be used for dose tracking and treatment assessment, when combined with real-time dose calculation.

Time-varying formation tracking for multi-agent systems based on prescribed-time control

In this paper, a time-varying formation tracking method based on prescribed-time control is presented for second-order multi-agent systems. The control objective is that the formation system forms and maintains the desired time-varying configuration within an arbitrarily specified time. Addressing the problem that the existing prescribed-time control theorem may lead to divergence of system caused by infinite control input, a novel prescribed-time control theorem based on a novel time-varying scale function is proposed. Different from the existing prescribed-time control results, the proposed theorem introduces convergence time, lead time and prescribed time to avoid the occurrence of the infinite control input. Meanwhile, it can estimate the actual convergence time and has faster convergence rate. Based on proposed theorem, a distributed prescribed-time observer is designed to estimate leader’s states for each follower (suppose that only a few followers can obtain the states of leader) and a prescribed-time formation tracking controller is designed with the estimations of the observer to track leader. Both the observer and controller achieve the prescribed-time stability. Finally, a simulation example is proposed to verify the efficiency of the main results.

View adaptive multi-object tracking method based on depth relationship cues

Multi-object tracking (MOT) tasks face challenges from multiple perception views due to the diversity of application scenarios. Different views (front-view and top-view) have different imaging and data distribution characteristics, but the current MOT methods do not consider these differences and only adopt a unified association strategy to deal with various occlusion situations. This paper proposed View Adaptive Multi-Object Tracking Method Based on Depth Relationship Cues (ViewTrack) to enable MOT to adapt to the scene's dynamic changes. Firstly, based on exploiting the depth relationships between objects by using the position information of the bounding box, a view-type recognition method based on depth relationship cues (VTRM) is proposed to perceive the changes of depth and view within the dynamic scene. Secondly, by adjusting the interval partitioning strategy to adapt to the changes in view characteristics, a view adaptive partitioning method for tracklet sets and detection sets (VAPM) is proposed to achieve sparse decomposition in occluded scenes. Then, combining pedestrian displacement with Intersection over Union (IoU), a displacement modulated Intersection over Union method (DMIoU) is proposed to improve the association accuracy between detection and tracklet boxes. Finally, the comparison results with 12 representative methods demonstrate that ViewTrack outperforms multiple metrics on the benchmark datasets. The code is available at https://github.com/Hamor404/ViewTrack.

PnP-epipolar optimization approach for VSLAM using robust pose prediction

Abstract Visual Simultaneous Localization and Mapping (VSLAM) is easily influenced by aggressive motion and low overlap images. Based on the challenge, we propose a VSLAM system with the robust tracking method. The feature tracking model is constructed by the robust pose prediction and PnP-Epipolar optimization. Firstly, as conventional VSLAM systems struggle with aggressive motion, we improve the constant acceleration prediction to estimate a dependable initial pose. The pose prediction takes into account the variation of the rotation axis, providing a more reliable initial guess for the pose optimization. Secondly, a novel global feature matching with BoW acceleration and map-point projection is performed to increase the number of matches. The matching method considers the points without map-point observation. Thirdly, according to the matches, we constitute the PnP-Epipolar constraint to optimize the pose with low overlap images. Finally, we compare our algorithm with several state-of-the-art VSLAM systems on KITTI dataset and EuRoC MAV dataset. The experimental results indicate that the proposed VSLAM reduces the RMSE by about 30% on average. For some sequences containing aggressive angle turning scene, the RMSE reduction even reaches 50%.

Rapid Video Analysis for Contraction Synchrony of Human Induced Pluripotent Stem Cells-Derived Cardiac Tissues.

The contraction behaviors of cardiomyocytes (CMs), especially contraction synchrony, are crucial factors reflecting their maturity and response to drugs. A wider field of view helps to observe more pronounced synchrony differences, but the accompanied greater computational load, requiring more computing power or longer computational time. We proposed a method that directly correlates variations in optical field brightness with cardiac tissue contraction status (CVB method), based on principles from physics and photometry, for rapid video analysis in wide field of view to obtain contraction parameters, such as period and contraction propagation direction and speed. Through video analysis of human induced pluripotent stem cell (hiPSC)-derived CMs labeled with green fluorescent protein (GFP) cultured on aligned and random nanofiber scaffolds, the CVB method was demonstrated to obtain contraction parameters and quantify the direction and speed of contraction within regions of interest (ROIs) in wide field of view. The CVB method required less computation time compared to one of the contour tracking methods, the Lucas-Kanade (LK) optical flow method, and provided better stability and accuracy in the results. This method has a smaller computational load, is less affected by motion blur and out-of-focus conditions, and provides a potential tool for accurate and rapid analysis of cardiac tissue contraction synchrony in wide field of view without the need for more powerful hardware.

Energy Dissipation during Crack Growth in Rubbers with Mullins Softening

The modulus and toughness of rubbers can be improved by incorporating fillers into the rubber matrix. Filled rubbers exhibit a strain‐induced softening phenomenon known as the Mullins effect. Upon deformation, filled rubbers can dissipate strain energy, which results in enhanced resistance to crack growth. An in‐depth understanding of the energy dissipation accompanying crack growth is important for the predictive modeling of rubber fracture. Herein, an experimental study on the fracture toughness of filled and unfilled rubbers with a focus on characterizing the energy dissipation caused by the Mullins effect during crack growth is presented. A particle tracking method is used to measure the nonlinear deformation fields in notched rubber specimens when subjected to monotonic tensile loading. Using the measured deformation fields and an independently calibrated constitutive model, the evolution of stress fields during crack growth is quantitatively evaluated, based on which the energy dissipation and intrinsic toughness are determined with the assumption of steady‐state crack growth. It is found that the filled rubber exhibits more energy dissipation during crack growth than the unfilled rubber. More importantly, the intrinsic toughness of the filled rubber (5000–8000 J m−2) is also much higher than that of the unfilled rubber (150–300 J m−2).