High-speed Motion Research Articles

A Privacy-Preserving V2I Fast Authentication Scheme in VANETs

Due to the characteristics of high-speed movement of vehicles, authentication between vehicles and roadside units (RSUs) needs to be performed quickly. Vehicles can obtain the authentication information of the relevant RSUs from the certification authority (CA) in advance through route planning. Fast authentication can be performed when the vehicle enters the RSU range. However, in most of the current vehicle-to-infrastructure (V2I) fast authentication schemes, when the vehicle requests the authentication information of an RSU from the CA, the vehicle often needs to provide the CA with the travel path information, which leads to the CA easily obtaining the travel path of the vehicle. In addition, the CA encrypts the private keys of RSUs and sends them to vehicles as authentication keys, and the vehicles can obtain the private key information of RSUs directly after decryption. Once the private keys of RSUs are leaked, vehicular ad hoc networks (VANETs) can be attacked by malicious access. In order to protect the confidentiality of RSU private keys and the route privacy of vehicles, we propose a privacy-preserving V2I fast authentication scheme in VANETs. The scheme realizes the confidentiality of RSU private keys and the route privacy protection of vehicles by improving the oblivious transfer (OT) algorithm. Security analysis proves that our scheme has good privacy and attack resistance. Finally, performance evaluation shows that the proposed scheme performs better in terms of computational overhead and communication overhead compared to related schemes.

Revolutionizing the Application of Automatic Inspection System for Industrial Parts Using AI Machine Vision Technology

This paper addresses the challenges of large data management, prolonged operation times, and low detectionefficiency encountered in automatic detection systems. To overcome these issues, we propose a novel research and application method utilizing AI machine vision technology. The methodology employs the Pulse-Coupled Neural Network (PCNN) algorithm for analyzing machine control points, enhancing system reliability and detection efficiency. Furthermore, a three-stage sliding table mechanism is implemented to facilitate seamless operation restarts. Notably, our approach significantly reduces the time required for key operations, such as feeding, imaging, decision-making, on-site inspection, and re-inspection, all within 5 seconds and 1 meter distance. It supports high-precision dynamic identification, detection, and correction of errors during high-speed movement, thereby enhancing overall system performance. The experimental results demonstrate exceptional accuracy, particularly in detecting small parts measuring 28.87 mm in length and 12.36 mm in width, achieving an impressive precision of 0.04 mm. Additionally, our system boasts meticulous hardware selection, robust software stability, and high-performance capabilities, culminating in improved detection efficiency and accuracy. This research not only contributes novel ideas and results but also holds significant commercial value in industrial applications. Overall, our proposed methodology represents a noteworthy advancement in automatic inspection systems, offering superior performance and reliability compared to existing approaches.

High-Speed Motion Target Real-Time Detection Based on Lightweight Deep Feature Learning Network

High-Speed Motion Target Real-Time Detection Based on Lightweight Deep Feature Learning Network

Cross-modal learning for optical flow estimation with events

Benefiting from the low latency and high dynamic range, event cameras have recently been adopted for Optical Flow (OF) prediction under harsh environments with high-speed motion or extreme lighting conditions. However, the emitted events only respond to brightness changes, mostly at edges with high-intensity contrast, leading to the ill-posedness of OF prediction in non-edge or low-contrast regions. To address this problem, we propose a network to learn OF by exploring the complements between the frame and the event domains. In particular, we first extract cross-modal features from frames and events via a fusion-based encoder and then apply a self-attention module of cross-dimension interaction to suppress disturbances in noisy events. Furthermore, a long-term memory recurrent module is built for local and global temporal information combination and cross-modal feature enhancement. Finally, a CNN decoder is designed to upscale cross-modal features and provide pyramidal results for multi-scale loss computation. Extensive experiments demonstrate that the proposed method can achieve leading performance compared with the state-of-the-art OF models.

Positioning Accuracy and High Speed of Movement on Belt Transmission

Positioning Accuracy and High Speed of Movement on Belt Transmission

Motion segmentation with event camera: [formula omitted]-patches optical flow estimation and Pairwise Markov Random Fields

This paper proposes the N-patches optical flow estimation-based motion compensation for motion segmentation using event camera. The requirement for prior knowledge of the scenario to cluster events into different motion types is addressed by the N-patches optical flow estimation. The framework of motion segmentation combines motion compensation and Pairwise Markov Random Fields, which not only realize the alignment of events in space but also enhance the spatial correlation between events. The proposed approach aims to maximize the contrast of the images of warped events. It does so by minimizing a specially designed energy function that jointly estimates motion parameters and discrete label items. The problem of selecting the optimal value for N∗ when executing N-patches optical flow estimation is discussed in Section 4.2 of the experiments. Finally, the proposed approach is evaluated in several scenes involving high-speed motion, low-light conditions, occlusions, and multiple moving objects. The experimental results show that the proposed approach achieves better segmentation performance than the event-based motion segmentation by motion compensation.

Progress in Spin Logic Devices Based on Domain-Wall Motion.

Spintronics, utilizing both the charge and spin of electrons, benefits from the nonvolatility, low switching energy, and collective behavior of magnetization. These properties allow the development of magnetoresistive random access memories, with magnetic tunnel junctions (MTJs) playing a central role. Various spin logic concepts are also extensively explored. Among these, spin logic devices based on the motion of magnetic domain walls (DWs) enable the implementation of compact and energy-efficient logic circuits. In these devices, DW motion within a magnetic track enables spin information processing, while MTJs at the input and output serve as electrical writing and reading elements. DW logic holds promise for simplifying logic circuit complexity by performing multiple functions within a single device. Nevertheless, the demonstration of DW logic circuits with electrical writing and reading at the nanoscale is still needed to unveil their practical application potential. In this review, we discuss material advancements for high-speed DW motion, progress in DW logic devices, groundbreaking demonstrations of current-driven DW logic, and its potential for practical applications. Additionally, we discuss alternative approaches for current-free information propagation, along with challenges and prospects for the development of DW logic.

In-fiber illumination-detection-synchronized confocal microscopy for high-precision fast axial scanning

Confocal microscopy remains a major workhorse in the 3D surface topography measurement owing to its reliability and compatibility for workpieces with various reflectance, which, however, suffers from slow scanning speed and limited axial resolution. Commercial confocal microscopy primarily relies on the axial movement of a sample or objective lens to create a series of 2D image stacks, and then reconstruct 3D map. Nevertheless, most samples and objective lens are too bulky to scan rapidly, let alone that multiple scans are required to increase the signal-to-noise ratio (SNR). To address this issue, an original approach named illumination-detection-synchronized confocal microscopy was developed, which enabled high-precision fast axial scanning via a compact fiber-multiplexed port with the high-speed synchronous motion of point source and point detection. The approach can effectively improve the peak localization precision while not requiring a high-precision motion stage. Theoretically, via controlling the magnification of the optical system M > 1, the total peak localization precision of axial light intensity response curve can be improved by M3 times. Experiments show that the single-point repeat measurement precision by this approach is improved by 4 times, and the profile measurement precision is enhanced by 2.3 times. Moreover, this approach can effectively avoid collisions between the sample and objective lens, and has the potential to design a low-cost single-point confocal sensor or confocal profilometer.

Highly robust and accurate multi-sensor fusion localization system for complex and challenging scenarios

Highly robust and accurate localization using on-board sensors in the absence of GNSS is a key issue for long-term autonomous navigation of vehicles. However, the limited sensing capabilities of a single sensor cannot resist the effects of feature degradation and high-speed motion in complex and challenging scenarios, which results in the fragile of existing LiDAR-based and visual-based localization algorithms. To solve these problems, we propose a highly robust and accurate localization system by fusing information from multi-sensors. Firstly, we propose a global localization method that leverages deep learning models for processing visual information to provide an accurate initial position for vehicles, which solves the problem of global localization of vehicles in the absence of GNSS. Then, we propose a highly robust and accurate LiDAR-Vision-IMU localization method based on pose graph optimization, which integrates LiDAR-based poses and visual-inertial-based poses. The proposed method detects sensor degradation in real time to dynamically adjust the fusion weights, which solves the problem of low localization performance of a single sensor. Finally, we conduct experiments in complex and challenging scenarios, such as urban, tunnels, and unstructured mountains. Experimental results show that our method can achieve a global localization error of less than 0.5 m in complex scenes. In unstructured mountainous scenarios of length 21.1 km, the root mean square errors (RMSE) of translation and rotation are 0.67 m and 0.048rad, respectively. Compared to most existing algorithms, our method achieves the most robust and accurate localization performance.

High-Speed Motion Planning with Object Acceleration Constraint for Industrial Robots

In this paper, we propose a new approach to efficiently plan trajectories for industrial robots, where total trajectory time is reduced while limiting the robot end-effector acceleration to a safe threshold, which guarantees an object to be transported safely without damage or spills. We also propose to solve the optimization problem in two steps by utilizing both the quadratic programming algorithm and sparse sequential quadratic programming method. The proposed two-step procedure empirically proves that computing time of solving the optimization problem is significantly reduced. Effectiveness of the proposed approach was verified in Drake, where the obtained results are highly promising.

Design and Analysis of 5-DOF Compact Electromagnetic Levitation Actuator for Lens Control of Laser Cutting Machine.

In laser beam processing, the angle or offset between the auxiliary gas and the laser beam axis have been proved to be two new process optimization parameters for improving cutting speed and quality. However, a traditional electromechanical actuator cannot achieve high-speed and high-precision motion control with a compact structure. This paper proposes a magnetic levitation actuator which could realize the 5-DOF motion control of a lens using six groups of differential electromagnets. At first, the nonlinear characteristic of a magnetic driving force was analyzed by establishing an analytical model and finite element calculation. Then, the dynamic model of the magnetic levitation actuator was established using the Taylor series. And the mathematical relationship between the detected distance and five-degree-of-freedom was determined. Next, the centralized control system based on PID control was designed. Finally, a driving test was carried out to verify the five-degrees-of-freedom motion of the proposed electromagnetic levitation actuator. The results show it can achieve a stable levitation and precision positioning with a desired command motion. It also proves that the proposed magnetic levitation actuator has the potential application in an off-axis laser cutting machine tool.

Swift-Eye: Towards Anti-blink Pupil Tracking for Precise and Robust High-Frequency Near-Eye Movement Analysis with Event Cameras.

Eye tracking has shown great promise in many scientific fields and daily applications, ranging from the early detection of mental health disorders to foveated rendering in virtual reality (VR). These applications all call for a robust system for high-frequency near-eye movement sensing and analysis in high precision, which cannot be guaranteed by the existing eye tracking solutions with CCD/CMOS cameras. To bridge the gap, in this paper, we propose Swift-Eye, an offline precise and robust pupil estimation and tracking framework to support high-frequency near-eye movement analysis, especially when the pupil region is partially occluded. Swift-Eye is built upon the emerging event cameras to capture the high-speed movement of eyes in high temporal resolution. Then, a series of bespoke components are designed to generate high-quality near-eye movement video at a high frame rate over kilohertz and deal with the occlusion over the pupil caused by involuntary eye blinks. According to our extensive evaluations on EV-Eye, a large-scale public dataset for eye tracking using event cameras, Swift-Eye shows high robustness against significant occlusion. It can improve the IoU and F1-score of the pupil estimation by 20% and 12.5% respectively, compared with the second-best competing approach, when over 80% of the pupil region is occluded by the eyelid. Lastly, it provides continuous and smooth traces of pupils in extremely high temporal resolution and can support high-frequency eye movement analysis and a number of potential applications, such as mental health diagnosis, behaviour-brain association, etc. The implementation details and source codes can be found at https://github.com/ztysdu/Swift-Eye.

Visual control for robotic 3D printing on a moving platform

In recent years, there has been significant progress in developing specialized 3D printing techniques that cater to various demanding applications. However, the current state of this technology is challenged when it comes to complex in situ printing scenarios, which require a controlled printing platform. The lack of a stable printing platform is a fundamental limitation of its use in in situ applications. To address this issue, we present a novel platform-independent 3D fabrication process that enables printing on platforms with non-cooperative movement. The process overcomes the challenge of high-speed tracking, motion compensation, and real-time printing by developing a closed-loop visual feedback-controlled robotic printing process. The proposed process incorporates a marker-based visual detection and tracking controller setup, which is discussed in detail. The algorithm consists of two loops running asynchronously: a high-speed inner control loop and an outer measurement loop. This setup enables precise and accurate tracking of the printing platform, compensating for any disturbances during the printing process. Our experimental results demonstrate the successful printing of simple linear geometries, even with low-disturbing platform velocities. Moreover, the tracking controllers' ability to handle measurement occlusion is validated, showing the proposed process's robustness and effectiveness. Our work provides a significant step towards enabling 3D printing in complex in situ printing scenarios.

An Integrated C4-Spline Interpolation and Time-Free Global Optimization Methodology Applied to High-Speed Cam Motion Design

The optimal tuning of high-order motion parameters has emerged as a promising strategy for actively controlling the kinematics/dynamics of high-speed cam mechanisms. However, accomplishing this task remains challenging with current low-order interpolation or tuning methods. This study proposes an integrated high-order interpolation and tuning methodology for the optimal construction of high-speed motion curves. Initially, an explicit C4-spline interpolant (C4SI) is developed. This interpolant utilizes four-order continuous (C4) splines to synthesize a high-fidelity motion curve that satisfies the predefined motion constraints up to the fourth order, including dimensionless displacement, velocity, acceleration, jerk, and quirk. Concerning the reduction of motion peaks, a unique C4SI-based global kinematics optimization strategy is designed, using the definite integral of the motion curve (free of the time variable) as the objective function. This facile time-free optimization strategy could yield a simultaneous reduction in multiple motion peaks (up to five), which is currently inaccessible with conventional motion tuning strategies. Concerning the improvement of dynamic characteristics, the C4SI-based time-free global dynamics optimization of variable motion parameters is further performed. The results indicate that the optimized fourth-order motion curve offers minimal high-speed transmission error and residual vibration over the whole rise-dwell-return-dwell cycle, which outperforms the standard motion curves and other low-order counterparts.

Long-Term Coherent Integration Algorithm for High-Speed Target Detection.

Long-term coherent integration (CI) can effectively improve the radar detection capability for high-speed targets. However, the range walk (RW) effect caused by high-speed motion significantly degrades the detection performance. To improve detection performance, this study proposes an improved algorithm based on the modified Radon inverse Fourier transform (denoted as IMRIFT). The proposed algorithm uses parameter searching for velocity estimation, designs a compensation function based on the relationship between velocity and distance walk and Doppler ambiguity terms, and performs CI based on the compensated signal. IMRIFT can achieve RW correction, avoid the blind-speed sidelobe (BSSL) effect caused by velocity mismatch, and improve detection performance, while ensuring low computational complexity. In addition, considering the relationship between energy concentration regions and bandwidth in the 2D frequency domain, a fast method based on IMIRFT is proposed, which can balance computational cost and detection capacity. Finally, a series of comparative experiments are conducted to demonstrate the effectiveness of the proposed algorithm and the fast method.

Pole balancing on the fingertip: model-motivated machine learning forecasting of falls.

Introduction: There is increasing interest in developing mathematical and computational models to forecast adverse events in physiological systems. Examples include falls, the onset of fatal cardiac arrhythmias, and adverse surgical outcomes. However, the dynamics of physiological systems are known to be exceedingly complex and perhaps even chaotic. Since no model can be perfect, it becomes important to understand how forecasting can be improved, especially when training data is limited. An adverse event that can be readily studied in the laboratory is the occurrence of stick falls when humans attempt to balance a stick on their fingertips. Over the last 20 years, this task has been extensively investigated experimentally, and presently detailed mathematical models are available. Methods: Here we use a long short-term memory (LTSM) deep learning network to forecast stick falls. We train this model to forecast stick falls in three ways: 1) using only data generated by the mathematical model (synthetic data), 2) using only stick balancing recordings of stick falls measured using high-speed motion capture measurements (human data), and 3) using transfer learning which combines a model trained using synthetic data plus a small amount of human balancing data. Results: We observe that the LTSM model is much more successful in forecasting a fall using synthetic data than it is in forecasting falls for models trained with limited available human data. However, with transfer learning, i.e., the LTSM model pre-trained with synthetic data and re-trained with a small amount of real human balancing data, the ability to forecast impending falls in human data is vastly improved. Indeed, it becomes possible to correctly forecast 60%-70% of real human stick falls up to 2.35 s in advance. Conclusion: These observations support the use of model-generated data and transfer learning techniques to improve the ability of computational models to forecast adverse physiological events.

Drag reduction of locomotive and practical scene

As an important tool for peoples long-distance travel and transportation, the design accuracy of the high-speed railway must be strictly controlled. In addition, the train design needs to conform to aerodynamics, such as improving the linearity of the train flow to ensure that the gas flows smoothly through every part of the train, or reducing the front pressure to reduce the wind resistance, so as to reduce the problems of train instability, high noise and high energy consumption. This paper will analyse the design concept, flow linearity and how to reduce the resistance of locomotives under high-speed motion of Shinkansen trains in Japan. Because the Shinkansen trains developed in the early and later generations are mainly flat-headed, the study of Shinkansen can understand the significance of flat-headed trains. The practicability and manoeuvrability of ' flat head and 'bullet head' locomotives and the application of locomotives in real life are analysed in detail. Finally, it is concluded that the bullet train has a large turning angle and needs to open up a special road to run smoothly, but the bullet train is faster than the flat head. The flat-head train is highly practical and suitable for driving in the city, and another advantage of the flat-head train is that it can still maintain high stability and safety when carrying many passengers.

面向对海监视的舰船目标跟踪与航迹融合数据集

Real-time trajectory association and trajectory fusion in maritime surveillance pose great challenges and remain hot issues in security, regional situation monitoring, and long-range precision strikes for both military and civilian applications. High-quality datasets play a pivotal role in advancing research in target tracking and fusion technologies within this domain. This paper addresses the data requirements for technological research in target tracking and fusion, as well as the limitations of currently available datasets, including data scarcity, inadequate scene design specificity, uniform data formats, and incomplete data descriptions. We used simulation software to emulate multi-sensor multi-target detection data in complex scenarios, so as to present a dataset tailored for typical maritime surveillance scenarios—targeting ships using 2D radar and Electronic Support Measures (ESM) sensors. The simulation software comprises a scenario generator and a sensor simulator, creating a mature target tracking scenario simulation environment with realistic detection data modeling capabilities. The dataset includes data from 2D radar and ESM sensors, covering typical maritime ship categories, supporting configurations with radiation sources. It is designed for a variety of scenarios such as high-speed motion, dense traffic, multi-sensor data fusion, specific ship detection, and cross-positioning. The dataset contains a total of 368,155 target tracks from 101 ships, spanning a duration of 15,000 seconds. The data format conforms to actual equipment reporting scenarios, while the detection error model accurately reflects real-world conditions. Accuracy assessment and data completeness are ensured through various methods including normality testing of data error, scenario testing of detection rate and false alarm rate, as well as field research. This dataset can provide fundamental data for algorithmic research and validation in the ship's target tracking, trajectory fusion and other related areas.

Экспериментальные исследования динамической работы железнодорожных мостов при воздействии высокоскоростного подвижного состава

The issue of considering the dynamic interaction of the railway rolling stock and the structures of bridge structures is especially important when solving the tasks of increasing the speeds of trains on the existing railway lines, as well as when prescribing high-speed mode on designed bridges. Rational accounting of the parameters of dynamic interaction allows you to perform complete and reliable calculations of elements of bridge structures, which in turn has a beneficial effect on predicting the period and nature of the operation of structures and, because of the economic component of projects. The modern solution to the problem of dynamic interaction is reduced to determining the degree of influence of each component of the unified mechanical system of the bridge-travel. Objective: the purpose of this study is to determine the parameters of dynamic interaction through experimental measurements of stresses, accelerations, and displacements of structures of the bridge structure located on the existing railway line with an organized high-speed traffic movement. Methods: experimental measurement using high-frequency voltage sensors, accelerations and movements with subsequent statistical processing and analysis of the results. Results: graphs and tables displaying a change in the measured values of relative deformations, accelerations, and movements over time. Practical importance: The practical significance of this study is to assess the degree of influence of the components of the dynamic interaction of high-speed rolling composition and the bridge structure, which allows you to form a holistic picture of the nature of the process of dynamic interaction.

An augmented reality-guided biopsy system using a high-speed motion tracking and real-time registration platform.

Biopsies play a crucial role in diagnosis of various diseases including cancers. In this study, we developed an augmented reality (AR) system to improve biopsy procedures and increase targeting accuracy. Our AR-guided biopsy system uses a high-speed motion tracking technology and an AR headset to display a holographic representation of the organ, lesions, and other structures of interest superimposed on real physical objects. The first application of our AR system is prostate biopsy. By incorporating preoperative scans, such as computed tomography (CT) or magnetic resonance imaging (MRI), into real-time ultrasound-guided procedures, this innovative AR-guided system enables clinicians to see the lesion as well as the organs in real time. With the enhanced visualization of the prostate, lesion, and surrounding organs, surgeons can perform prostate biopsies with an increased accuracy. Our AR-guided biopsy system yielded an average targeting accuracy of 2.94 ± 1.04 mm and can be applied for real-time guidance of prostate biopsy as well as other biopsy procedures.