Direction Errors Research Articles

Estimation of direction and zero errors of satellite laser terminals in low-light conditions based on machine learning

In the assembly, launch, and on-orbit operation of satellite optical communication terminals, small deviations are difficult to avoid, which can lead to pointing errors and challenges to the establishment of optical communication links. To estimate the pointing errors of on-orbit satellite terminals, a calibration algorithm is developed based on lunar surface imagery. First, a feature extraction algorithm for low-light images is employed to process consecutive frames of low-light images to obtain a lunar surface feature map. Then, by combining the feature map and error estimation model, predictions of direction errors and zero errors were achieved. The ground validation results demonstrate the effectiveness and feasibility of the proposed on-orbit error estimation algorithm under low-signal-to-noise-ratio conditions.

High-resolution spatiotemporal forecasting of the European crane migration

In this paper we present three different models to forecast bird migration. They are species-specific individual-based models that operate on a high spatiotemporal resolution (kilometres, 15 min-hours), as an addition to radar-based migration forecast models that currently exist. The models vary in complexity, and use GPS-tracked location, flying direction and speed, and/or wind data to forecast migration speed and direction. Our aim is to quantitatively evaluate the forecasting performance and assess which metrics improve forecasts at different ranges. We test the models through cross-validation using GPS tracks of common cranes during spring and autumn migration. Our results show that recordings of flight speed and direction improve the accuracy of forecasts on the short range (<2 h). Adding wind data at flight altitude results in consistent improvements of the forecasts across the entire range, particularly in the predicted speed. Direction forecasts are less affected by adding wind data because cranes mostly compensate for wind drift during migration. Migration in spring is more difficult to forecast than in autumn, resulting in larger errors in flight speed and direction during spring. We further find that a combination of flight behaviours – thermal soaring, gliding, and flapping – complicates the forecasts by inducing variance in flight speed and direction. Fitting those behaviours into flight optimisation models proves to be challenging, and even results in significant biases in speed forecasts in spring. We conclude that flight speed is the most difficult parameter to forecast, whereas flight direction is the most critical for practical applications of these models. Such applications could e.g., be prevention of bird strikes in aviation or with wind turbines, and public engagement with bird migration.

Analysis of orientation errors in triaxial fluxgate sensors and research on their calibration methods

Abstract. Three-axis magnetic flux gate sensors are widely used in Chinese geomagnetic observatories, but due to their directional errors it is necessary to study error correction methods to improve measurement accuracy. Firstly, the mechanism of directional errors produced by three-axis magnetic flux gate sensors is analyzed, followed by the development of measurement tools for conducting directional error measurement experiments on the high-precision three-axis magnetic flux gate sensors of the Chinese FGM-01 series. Experimental results show that correcting the Z-axis and D-axis directional errors is essential. The observation data after error correction, whether in terms of the standard deviation of its all-day baseline values or the relative difference magnitude with the reference instrument, significantly decrease, demonstrating the clear correction effect and proving the effectiveness of this correction method.

Balancing Efficiency and Accuracy: Enhanced Visual Simultaneous Localization and Mapping Incorporating Principal Direction Features

In visual Simultaneous Localization and Mapping (SLAM), operational efficiency and localization accuracy are equally crucial evaluation metrics. We propose an enhanced visual SLAM method to ensure stable localization accuracy while improving system efficiency. It can maintain localization accuracy even after reducing the number of feature pyramid levels by 50%. Firstly, we innovatively incorporate the principal direction error, which represents the global geometric features of feature points, into the error function for pose estimation, utilizing Pareto optimal solutions to improve the localization accuracy. Secondly, for loop-closure detection, we construct a feature matrix by integrating the grayscale and gradient direction of an image. This matrix is then dimensionally reduced through aggregation, and a multi-layer detection approach is employed to ensure both efficiency and accuracy. Finally, we optimize the feature extraction levels and integrate our method into the visual system to speed up the extraction process and mitigate the impact of the reduced levels. We comprehensively evaluate the proposed method on local and public datasets. Experiments show that the SLAM method maintained high localization accuracy after reducing the tracking time by 24% compared with ORB SLAM3. Additionally, the proposed loop-closure-detection method demonstrated superior computational efficiency and detection accuracy compared to the existing methods.

Implementation of an exact completing method of generation for face-milled spiral bevel gears with uniform depth taper

AbstractThis paper proposes an exact completing method of generation of face-milled spiral bevel gears with uniform depth taper. This method ensures localized bearing contact without kinematic transmission errors in both directions of rotation. Both sides of the tooth are generated simultaneously using the same set of basic machine tool settings. A tip relief on the tooth surfaces is the only required microgeometry modification to achieve a smooth transition of load between consecutive pairs of teeth. An analytical approach to determine the basic machine tool settings and select the grinding cutter parameters has been also developed. The selection of the nominal cutter radius follows the recommendations of Information Sheet AGMA 22849-A12. The basis for alignment error compensation has been also established. Tooth contact and stress analyses demonstrate the advantages and limitations of the proposed method, making it suitable for applications with higher power demands for the drive side than for the coast side.

LQR-based control strategy for improving human-robot companionship and natural obstacle avoidance

In the dynamic and unstructured environment of human–robot symbiosis, companion robots require natural human–robot interaction and autonomous intelligence through multimodal information fusion to achieve effective collaboration. Nevertheless, the control precision and coordination of the accompanying actions are not satisfactory in practical applications. This is primarily attributed to the difficulties in the motion coordination between the accompanying target and the mobile robot. This paper proposes a companion control strategy based on the Linear Quadratic Regulator (LQR) to enhance the coordination and precision of robot companion tasks. This method enables the robot to adapt to sudden changes in the companion target’s motion. Besides, the robot could smoothly avoid obstacles during the companion process. Firstly, a human–robot companion interaction model based on nonholonomic constraints is developed to determine the relative position and orientation between the robot and the companion target. Then, an LQR-based companion controller incorporating behavioral dynamics is introduced to simultaneously avoid obstacles and track the companion target’s direction and velocity. Finally, various simulations and real-world human–robot companion experiments are conducted to regulate the relative position, orientation, and velocity between the target object and the robot platform. Experimental results demonstrate the superiority of this approach over conventional control algorithms in terms of control distance and directional errors throughout system operation. The proposed LQR-based control strategy ensures coordinated and consistent motion with target persons in social companion scenarios.

Separated spacecraft attitude algorithm research based on star sensor multi-information fusion

Faced with the problem of poor anti-interference ability of star map recognition technology, in order to improve its anti-interference ability and achieve higher attitude measurement accuracy, this article proposes a star map recognition method based on binary search trees. Star sensors and gyroscopes are combined to solve the problem of the poor real-time performance of star sensors, optimizing the Extended Kalman Filtering (EKF) algorithm. This improves attitude output accuracy and formulates corresponding attitude measurement schemes considering the situation where the star sensor is not visible. The results showed that the improved star pattern recognition algorithm had better adaptability and robustness than the rasterization algorithm, with a high recognition success rate of 95.6% when the star sensor field of view was 3°*3°, while the rasterization algorithm cannot recognize small fields of view. The recognition success rate of the algorithm was 98.6% when the standard deviation of magnitude noise was 2.0 pixels, which was 15.4% higher than the rasterization algorithm. Unlike the EKF algorithm, the optimized EKF algorithm had a higher attitude output accuracy, with an average error of about 0.000 211°; compared to before optimization, it has increased by 93.43%. In the attitude measurement scheme, the relative attitude angle measurement error was small, and the maximum attitude measurement error in the rolling angle direction was 1.8 × 10−3°. This paper adopts a method to achieve high-precision satellite attitude measurement with good adaptability, which can be applied in complex space missions.

Retrospective analysis of treatment-positioning accuracy and dose error in boron neutron capture therapy using a sitting-position treatment system for head and neck cancer

The neutron beam in boron neutron capture therapy (BNCT) exhibits poor directionality and significantly decreasing neutron flux with increasing distance. Therefore, the treatment site must be close to the irradiation aperture. Some patients with head and neck cancer may benefit from a sitting-position setup. The study aim was to evaluate the treatment-positioning accuracy and dose error in sitting patients receiving BNCT. Thirty-two patients with head and neck cancer who underwent sitting-position BNCT at Southern Tohoku BNCT Research Center were included in the study. Horizontal (ΔX) and vertical (ΔY) errors were defined as the displacement between the treatment planning system (TPS) digital reconstructed radiograph and the pre-treatment X-ray image. Using in-house software, image matching was performed. The beam-axial directional (ΔZ) error was compared with the parameters entered into the TPS and the actual pre-treatment measured values. The translational-position error was reflected in the TPS’s patient coordinate system with respect to the reference plan. Re-dose calculations were performed to evaluate the effect of positional error on tumor and normal-tissue doses. The [ΔX, ΔY, ΔZ] DRR-CR mean ± 1SD were − 0.40 ± 2.0, 0.30 ± 2.3, and − 1.4 ± 1.5 mm, respectively. The Dmean and D98% tumor-dose errors were 1.22 % ± 1.44 % and 0.99 % ± 1.63 %, respectively. The D2% pharyngeal and oral mucosal-dose errors were 0.98 % ± 1.91 % and 1.21 % ± 1.78 %, respectively. The tumor- and normal-tissue dose errors were typically < 5 %. High-precision treatment was feasible in sitting-positioned BNCT.

Fuel-Efficient and Fault-Tolerant CubeSat Orbit Correction via Machine Learning-Based Adaptive Control

The increasing deployment of CubeSats in space missions necessitates the development of efficient and reliable orbital maneuvering techniques, particularly given the constraints on fuel capacity and computational resources. This paper presents a novel two-level control architecture designed to enhance the accuracy and robustness of CubeSat orbital maneuvers. The proposed method integrates a J2-optimized sequence at the high level to leverage natural perturbative effects for fuel-efficient orbit corrections, with a gated recurrent unit (GRU)-based low-level controller that dynamically adjusts the maneuver sequence in real-time to account for unmodeled dynamics and external disturbances. A Kalman filter is employed to estimate the pointing accuracy, which represents the uncertainties in the thrust direction, enabling the GRU to compensate for these uncertainties and ensure precise maneuver execution. This integrated approach significantly enhances both the positional accuracy and fuel efficiency of CubeSat maneuvers. Unlike traditional methods, which either rely on extensive pre-mission planning or computationally expensive control algorithms, our architecture efficiently balances fuel consumption with real-time adaptability, making it well-suited for the resource constraints of CubeSat platforms. The effectiveness of the proposed approach is evaluated through a series of simulations, including an orbit correction scenario and a Monte Carlo analysis. The results demonstrate that the integrated J2-GRU system significantly improves positional accuracy and reduces fuel consumption compared to traditional methods. Even under conditions of high uncertainty, the GRU-based control layer effectively compensates for errors in thrust direction, maintaining a low miss distance throughout the maneuvering period. Additionally, the GRU’s simpler architecture provides computational advantages over more complex models such as long short-term memory (LSTM) networks, making it more suitable for onboard CubeSat implementations.

SWiLoc: Fusing Smartphone Sensors and WiFi CSI for Accurate Indoor Localization.

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone's direction and the user's actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (Smartphone and WiFi-based Localization), an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user's walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system's applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 m in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

Adversarial image-to-image model to obtain highly detailed wind fields from mesoscale simulations in urban environments

We propose a conditional Generative Adversarial Network (cGAN) that can produce detailed local wind fields in urban areas, comparable in level of detail to those from Computational Fluid Dynamics (CFD) simulations, that are generated from coarser Numerical Weather Prediction (NWP) data.In our approach, the cGAN is trained using NWP data as input and CFD as targets. Both CFD and NWP data are presented to the network as images, using an image-to-image model based on Pix2Pix to transform coarse meteorological conditions into detailed local wind fields.The methodology is tested in a residential district in a large Spanish city, Zaragoza. The model predictions show significant agreement with the actual CFD results, while reducing the computational time from eight hours to seconds. Feature engineering of image channels effectively reduces the model error, especially in the wind direction, achieving a mean absolute error in the wind speed of 0.35m/s and a wind direction error of 27.0°.

Trajectory planning and automatic docking of LNG five-axis loading arm

PurposeIn response to the challenges posed by the conventional manual flange docking method in the LNG (Liquefied Natural Gas) loading process, such as low positioning accuracy, constraints on production efficiency and safety hazards, this study analyzed the LNG five-axis loading arm’s main functions and structural characteristics.Design/methodology/approachAn automated solution for the joints of the LNG loading arm was designed. The forward kinematic model of the LNG loading arm was established using the Denavit–Hartenberg (D-H) parameter method, and its workspace was analyzed. The Newton–Raphson iteration method was employed to solve the inverse kinematics of the LNG loading arm, facilitating trajectory planning. The relationship between the target position and the joint variables was established to verify the stability of the arm’s motion. Flange center identification was achieved using the Hough transform function. Based on the ROS platform, combined with Gazebo and Rviz, an experimental simulation of automatic docking of the LNG loading arm was conducted.FindingsThe docking errors in the XYZ directions were all less than 0.8 mm, meeting the required docking accuracy. Moreover, the motion performance of the loading arm during docking was smooth and free of abrupt changes, validating its capability to accomplish the automatic docking task.Originality/valueThe proposed trajectory planning and automatic docking scheme can be used for the rapid filling of LNG filling arms and LNG tankers to improve the efficiency of LNG transportation. In guiding the docking, the proposed automatic docking scheme is an accurate and efficient way to improve safety.

A reliable ensemble forecasting modeling approach for complex time series with distributionally robust optimization

Accurate ensemble forecast results provide strong support for management decisions. While the existing ensemble forecasting model ignores the requirement of directional accuracy making its prediction direction is usually error-prone. To address this issue, we develop a convex directional prediction error measure and subsequently construct a reliable ensemble forecasting model that minimizes the horizontal prediction error with the bounded directional prediction error. Mathematically, we provided the proper range of the required directional error level. Furthermore, we find the classical ensemble forecasting model is a special case of our study when the directional error level is larger than the upper bound we gave in this study. To deal with the possible deterioration of the individual model over time, we also considered its worst possible prediction performance by introducing the distributionally robust optimization (DRO) technique into the proposed reliable ensemble forecasting model. Technically, we showed that the DRO-based reliable ensemble forecasting model is convex and can be reformulated into a second-order cone problem which can be readily solved by off-the-shelf solvers. Finally, the effectiveness of the proposed reliable ensemble forecasting model and the DRO-based reliable ensemble forecasting model were validated on three different datasets, e.g., the exchange rate, the oil price, and the country risk index. To sum up, we construct a reliable ensemble forecasting model to simultaneously control the horizontal prediction error and directional prediction error of ensemble forecasting, and thus enhance the reliability of ensemble forecasting.

Replicating Visuo-Motor Remapping in the Laparoscopic Environment

Laparoscopy, a minimally invasive surgical technique, requires precise visuo-motor coordination. Because the surgical field is viewed through a camera which may need to be placed to one side of the would-be direct line of sight, rotational distortion is a concern. This study investigates adaptation to rotational distortions by replicating previous computerized two-dimensional perceptual-motor experiments, and measurement methods, using a non-virtual low-fidelity laparoscopic training simulator. This replication was chosen for the opportunity to observe the best-established adaptation mechanisms (gradual and axis-inversion) and carryover effects. To our knowledge, this is the first time that circular statistics are used to analyze directional error data collected in the laparoscopic environment. Results strongly indicated that gradual adaptation occurs with distortions below 90°, and there is leading evidence that axis-inversion occurs in distortions above 90°. This confirms previous observations of task performance decrement in the laparoscopic environment and provides clarification for new research directions.

Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations.

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC (R² = 0.37, F² = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging.

Oculomotor behaviors in youth with an eating disorder: findings from a video-based eye tracking task

BackgroundThe oculomotor circuit spans many cortical and subcortical areas that have been implicated in psychiatric disease. This, combined with previous findings, suggests that eye tracking may be a useful method to investigate eating disorders. Therefore, this study aimed to assess oculomotor behaviors in youth with and without an eating disorder.MethodsFemale youth with and without an eating disorder completed a structured task involving randomly interleaved pro-saccade (toward at a stimulus) and anti-saccade (away from stimulus) trials with video-based eye tracking. Differences in saccades (rapid eye movements between two points), eye blinks and pupil were examined.ResultsYouth with an eating disorder (n = 65, Mage = 17.16 ± 3.5 years) were compared to healthy controls (HC; n = 65, Mage = 17.88 ± 4.3 years). The eating disorder group was composed of individuals with anorexia nervosa (n = 49), bulimia nervosa (n = 7) and other specified feeding or eating disorder (n = 9). The eating disorder group was further divided into two subgroups: individuals with a restrictive spectrum eating disorder (ED-R; n = 43) or a bulimic spectrum eating disorder (ED-BP; n = 22). In pro-saccade trials, the eating disorder group made significantly more fixation breaks than HCs (F(1,128) = 5.33, p = 0.023). The ED-BP group made the most anticipatory pro-saccades, followed by ED-R, then HCs (F(2,127) = 3.38, p = 0.037). Groups did not differ on rate of correct express or regular latency pro-saccades. In anti-saccade trials, groups only significantly differed on percentage of direction errors corrected (F(2, 127) = 4.554, p = 0.012). The eating disorder group had a significantly smaller baseline pupil size (F(2,127) = 3.60, p = 0.030) and slower pro-saccade dilation velocity (F(2,127) = 3.30, p = 0.040) compared to HCs. The ED-R group had the lowest blink probability during the intertrial interval (ITI), followed by ED-BP, with HCs having the highest ITI blink probability (F(2,125) = 3.63, p = 0.029).ConclusionsThese results suggest that youth with an eating disorder may have different oculomotor behaviors during a structured eye tracking task. The oculomotor behavioral differences observed in this study presents an important step towards identifying neurobiological and cognitive contributions towards eating disorders.

Developing a methodology for user‐oriented verification of polar low forecasts

AbstractPolar lows exhibit features with very sharp weather contrasts. In weather forecasting, a small misplacement of areas with hazardously high wind speeds can have fatal impacts for people living in polar regions. Therefore, a novel application of spatial verification methods for objective metrics of size, shape, and location of areas with hazardous weather is tested. To separate the effect of errors in polar low location and direction of motion from errors relative to the polar low centre, surface wind fields from the limited‐area weather forecasting model Applications of Research to Operations at Mesoscale‐Arctic and Copernicus Climate Change Service Arctic Regional Reanalysis are centred at the polar low centre and rotated according to the direction of background flow surrounding the polar low. Then the possibilities of the features‐based verification methods SAL (structure, amplitude, location) and MODE (Method for Object‐based Diagnostic Evaluation) are explored using a test case from October 2019. The study demonstrates that the methodology can provide valuable information about forecast performance. MODE is a flexible method with metrics that focus on characteristics of individual objects and can be adapted to questions at hand. For example, a measure of storm eye size was added. SAL, on the other hand, provides effective summary metrics for the full domain and proved particularly useful for evaluation of the overall distribution of wind speed. To evaluate the number of correctly or incorrectly identified areas with harsh weather rather than their details about their shape, contingency scores are more suitable. Applied to a larger dataset, this methodology can assess performance as a function of forecast length, as well as geographical area, and the type of polar low. The methodology can also be applied to other types of low‐pressure systems, such as extratropical cyclones.

Robust optimization and assessment of dynamic trajectory and mixed-beam arc radiotherapy: a preliminary study

Objective. Dynamic trajectory radiotherapy (DTRT) and dynamic mixed-beam arc therapy (DYMBARC) exploit non-coplanarity and, for DYMBARC, simultaneously optimized photon and electron beams. Margin concepts to account for set-up uncertainties during delivery are ill-defined for electron fields. We develop robust optimization for DTRT&DYMBARC and compare dosimetric plan quality and robustness for both techniques and both optimization strategies for four cases. Approach. Cases for different treatment sites and clinical target volume (CTV) to planning target volume (PTV) margins, m , were investigated. Dynamic gantry-table-collimator photon paths were optimized to minimize PTV/organ-at-risk (OAR) overlap in beam’s-eye-view and minimize potential photon multileaf collimator (MLC) travel. For DYMBARC plans, non-isocentric partial electron arcs or static fields with shortened source-to-surface distance (80 cm) were added. Direct aperture optimization (DAO) was used to simultaneously optimize MLC-based intensity modulation for both photon and electron beams yielding deliverable PTV-based DTRT&DYMBARC plans. Robust-optimized plans used the same paths/arcs/fields. DAO with stochastic programming was used for set-up uncertainties with equal weights in all translational directions and magnitude δ such that m= 0.7δ . Robust analysis considered random errors in all directions with or without an additional systematic error in the worst 3D direction for the adjacent OARs. Main results. Electron contribution was 7%–41% of target dose depending on the case and optimization strategy for DYMBARC. All techniques achieved similar CTV coverage in the nominal (no error) scenario. OAR sparing was overall better in the DYMBARC plans than in the DTRT plans and DYMBARC plans were generally more robust to the considered uncertainties. OAR sparing was better in the PTV-based than in robust-optimized plans for OARs abutting or overlapping with the target volume, but more affected by uncertainties. Significance. Better plan robustness can be achieved with robust optimization than with margins. Combining electron arcs/fields with non-coplanar photon trajectories further improves robustness and OAR sparing.

Differential-geometry-based multi-dimensional joint position-velocity estimation using Crab pulsar profile distortion

The traditional orbit determination method based on pulsar profile distortion can determine the six elements of the orbit. However, the estimation accuracies of these methods are limited and the computational load of a six-dimensional search is huge. To solve this problem, the differential-geometry-based Multi-dimensional Joint Position-Velocity Estimation (MJPVE) using Crab pulsar profile distortion is proposed in this paper. Firstly, through theoretical analysis, it is found that the pulsar profile distortion caused by the initial state error in some joint position-velocity directions is very small. In other words, the accuracies of estimation in these directions are very low. Namely, the search dimension can be reduced, which in turn greatly reduces the computational load. Then, we construct the chi-squared function of the pulsar profile with respect to the estimation error in joint position-velocity direction and use differential geometry to find the joint position-velocity directions corresponding to different degrees of distortion. Finally, we utilize the grid search based on directory folding in these joint position-velocity directions corresponding to large degrees of distortion to obtain the joint position-velocity estimation. The experimental results show that compared with the grouping bi-chi-squared inversion method, MJPVE has high precision and extensive navigation information.

Saccade Latency and Metrics in the Interleaved Pro- and Anti-Saccade Task in Open Skill Sports Athletes.

Evidence has demonstrated that athletes exhibit superior cognitive performance associated with executive control. In the oculomotor system, this function has been examined using the interleaved pro-saccade and anti-saccade task (IPAST), wherein participants, prior to target appearance, are instructed to either automatically look at the peripheral target (pro-saccade) or suppress the automatic response and voluntarily look in the opposite direction (anti-saccade). While the IPAST has provided much insight into sensorimotor and inhibitory processing, it has yet to be performed in athletes. Moreover, limited research has examined saccade metrics in athletes. Here, we examined saccade latency and movement kinematics in the IPAST among athletes (N = 40) and nonathletes (NON) (N = 40). Higher direction error rates were obtained in the anti-saccade compared to the pro-saccade condition, with no differences between athletes and NONnoted. Significantly faster saccade latencies were observed in athletes compared to NON in both conditions, in addition to faster pro-saccades compared to anti-saccades. Furthermore, athletes showed significantly higher frequencies and faster latencies of express saccades compared to NON in correct pro-saccades. Additionally, athletes exhibited significantly faster latencies of express saccades compared to NON in erroneous anti-saccades. Differences in saccade metrics between athletes and NON were not seen. Overall, these findings demonstrate that athletes display altered saccade performance likely associated with sensorimotor and preparatory processing, highlighting the potential of using IPAST to objectively investigate sensorimotor and cognitive functions in athletes.