Hybrid Motion Research Articles

Hybrid Attention and Motion Constraint for Anomaly Detection in Crowded Scenes

Crowds often appear in surveillance videos in public places, from which anomaly detection is of great importance to public safety. Since the abnormal cases are rare, variable and unpredictable, autoencoders with encoder and decoder structures using only normal samples have become a hot topic among various approaches for anomaly detection. However, since autoencoders have excessive generalization ability, they can sometimes still reconstruct abnormal cases very well. Recently, some researchers construct memory modules under normal conditions and use these normal memory items to reconstruct test samples during inference to increase the reconstruction errors for anomalies. However, in practice, the errors of reconstructing normal samples with the memory items often increase as well, which makes it still difficult to distinguish between normal and abnormal cases. In addition, the memory-based autoencoder is usually available only in the specific scene where the memory module is constructed and almost loses the prospect of cross-scene applications. We mitigate the overgeneralization of autoencoders from a different perspective, namely, by reducing the prediction errors for normal cases rather than increasing the prediction errors for abnormal cases. To this end, we propose an autoencoder based on hybrid attention and motion constraint for anomaly detection. The hybrid attention includes the channel attention used in the encoding process and spatial attention added to the skip connection between the encoder and decoder. The hybrid attention is introduced to reduce the weight of the feature channels and regions representing the background in the feature matrix, which makes the autoencoder features more focused on optimizing the representation of the normal targets during training. Furthermore, we introduce motion constraint to improve the autoencoder’s ability to predict normal activities in crowded scenes. We conduct experiments on real-world surveillance videos, UCSD, CUHK Avenue, and ShanghaiTech datasets. The experimental results indicate that the prediction errors of the proposed method for frequent normal crowd activities are smaller than those of other approaches, which increases the gap between the prediction errors for normal frames and the prediction errors for abnormal frames. In addition, the proposed method does not depend on a specific scene. Therefore, it balances good anomaly detection performance and strong cross-scene capability.

Photocatalysis dramatically influences motion of magnetic microrobots: Application to removal of microplastics and dyes

Micromachines gain momentum in the applications for environmental remediation. Magnetic components have been used to functionalize light–responsive micromachines to achieve efficient magnetic microrobots with photodegradation activity for decomposition of environmental pollutants. However, the influence of photocatalyst itself on the trajectory of micromotors in conjunction with magnetic motion was never considered. In this work, light-powered catalysis and transversal rotating magnetic field have been independently and simultaneously applied over Fe3O4@BiVO4 microrobots to investigate the dynamics of their hybrid motion. Light exposure of microrobots results in the production of reactive oxygen species (ROS) which power the microrobots, in addition to magnetic powered motion, and have a strong influence on the magnetic trajectories, resulting in an unexpected alteration of the direction of the motion of the microrobots. We have subsequently applied such magnetic/light powered micromachines for removal of microplastics in cigarette filter residues, one of the major contributors to the microplastic pollution, and dyes via photocatalysis. Such dual orthogonal propulsion modes act independently on the motion of the micromachines; and they also bring additional functionality as photodegradation agents. Hence, the dual magnetic/photocatalytic microrobots shall find a variety of catalytic applications in different fields.

Error modelling and motion reliability analysis of a multi-DOF redundant parallel mechanism with hybrid uncertainties

Accuracy is one of the most important properties of mechanisms. However, the inherent uncertainties will cause an error between the actual motion and the desired motion, leading to a motion reliability problem. The error model and motion reliability of a type of multi-DOF (degrees of freedom) redundant parallel mechanism (MDRPM) with hybrid uncertainties are studied. Firstly, the error model of the mechanism is established and verified. Then, the motion inside the joints is analyzed, and the joint clearance is regarded as an interval variable, while the other variables are treated as random variables. On this basis, a hybrid motion reliability method based on the quasi-Monte Carlo simulation method (QMCSM) is developed for a mechanism with hybrid uncertainties. Compared with the traditional simulation method, the amount of computation required by the QMCSM is significantly reduced, and it is more suitable for solving the large-scale motion reliability problem of the MDRPM. A numerical simulation and experiments are performed. The simulation results show that the feasibility and adaptability of the motion reliability method in this paper are different. The experimental results demonstrate that the error model is effective, so the motion reliability analysis based on the error model is reliable.

Neural Motion Planning for Autonomous Parking

This paper presents a hybrid motion planning strategy that combines a deep generative network with a conventional motion planning method. Existing planning methods such as A* and Hybrid A* are widely used in path planning tasks because of their ability to determine feasible paths even in complex environments; however, they have limitations in terms of efficiency. To overcome these limitations, a path planning algorithm based on a neural network, namely the neural Hybrid A*, is introduced. This paper proposes using a conditional variational autoencoder (CVAE) to guide the search algorithm by exploiting the ability of CVAE to learn information about the planning space given the information of the parking environment. An efficient expansion strategy is utilized based on a distribution of feasible trajectories learned in the demonstrations. The proposed method effectively learns the representations of a given state, and shows improvement in terms of computational time and the number of node expanded related to algorithm performance.

Hybrid Motion Model for Multiple Object Tracking in Mobile Devices

For an intelligent transportation system, multiple object tracking (MOT) is more challenging from the traditional static surveillance camera to mobile devices of the Internet of Things (IoT). To cope with this problem, previous works always rely on additional information from multivision, various sensors, or precalibration. Only based on a monocular camera, we propose a hybrid motion model to improve the tracking accuracy in mobile devices. First, the model evaluates camera motion hypotheses by measuring optical flow similarity and transition smoothness to perform robust camera trajectory estimation. Second, along the camera trajectory, smooth dynamic projection is used to map objects from image to world coordinate. Third, to deal with trajectory motion inconsistency, which is caused by occlusion and interaction of long time interval, tracklet motion is described by the multimode motion filter for adaptive modeling. Fourth, in tracklets association, we propose a spatiotemporal evaluation mechanism, which achieves higher discriminability in motion measurement. Experiments on MOT15, MOT17, and KITTI benchmarks show that our proposed method improves the trajectory accuracy, especially in mobile devices and our method achieves competitive results over other state-of-the-art methods.

A Deep-Learning-Based Approach for Aircraft Engine Defect Detection

Borescope inspection is a labour-intensive process used to find defects in aircraft engines that contain areas not visible during a general visual inspection. The outcome of the process largely depends on the judgment of the maintenance professionals who perform it. This research develops a novel deep learning framework for automated borescope inspection. In the framework, a customised U-Net architecture is developed to detect the defects on high-pressure compressor blades. Since motion blur is introduced in some images while the blades are rotated during the inspection, a hybrid motion deblurring method for image sharpening and denoising is applied to remove the effect based on classic computer vision techniques in combination with a customised GAN model. The framework also addresses the data imbalance, small size of the defects and data availability issues in part by testing different loss functions and generating synthetic images using a customised generative adversarial net (GAN) model, respectively. The results obtained from the implementation of the deep learning framework achieve precisions and recalls of over 90%. The hybrid model for motion deblurring results in a 10× improvement in image quality. However, the framework only achieves modest success with particular loss functions for very small sizes of defects. The future study will focus on very small defects detection and extend the deep learning framework to general borescope inspection.

Hybrid Motion Representation Learning for Prediction From Raw Sensor Data

Motion prediction from raw LiDAR sensor data has drawn increasing attention and led to a surge of studies following two main paradigms. One paradigm is global motion paradigm, which simultaneously detects objects from point clouds and predicts the trajectories of each object in the future. The other paradigm is local motion paradigm, which directly performs dense motion prediction pointwisely. We observe that global motion prediction can benefit from local motion representation, since it contains rich local displacement contexts that are not explicitly exploited in global motion prediction. Correspondingly, local motion prediction can benefit from global motion representation, since it provides object contexts to improve prediction consistency inside an object. However, the complement of these two motion representations has not fully explored in the literature. To this end, we propose Hybrid Motion Representation Learning (HyMo), a unified framework to address the problem of motion prediction by making the best of both global and local motion cues. We have conducted extensive experiments on nuScenes dataset. The experimental results demonstrate that the learned hybrid motion representation achieves state-of-the-art performance on both global and local motion prediction tasks.

Research on a New Rehabilitation Robot for Balance Disorders.

The treatment of patients with balance disorders is an urgent problem to be solved by the medical community. The causes of balance disorders are diverse. An aging population, traffic accidents, stroke, genetic diseases and so on are all possible factors. It has brought great pain and inconvenience to patients and their families. At present, there are two main types of assisted rehabilitation training robots for patients with balance disorders: exoskeleton robots and end robots. The exoskeleton robot is generally installed on the outside of the patient's body to follow their movement, which can support the weight of the body and provide power support to help the patient train and recover lower limb ability. The use of end robots is usually to secure the patient's foot to the motion platform and control the pedal to drive the lower limbs to conduct gait training. Such passive training is more suitable for patients with severe disorders. The patient has low awareness of active participation. This paper focuses on research on end rehabilitation training robots for balance disorders. In this paper, a robotic system for rehabilitation training of patients with balance disorders is invented. The robot body is a 9 degree of freedom (DOF) redundant series-parallel hybrid motion platform. Two sets of motion platforms with symmetrical mirror images are used together to simulate different motion modes of the human body and drive the human body to move. Each set of motion platforms is composed of a 6-DOF vestibular parallel device and a 3-DOF proprioception parallel device. It has the advantages of DOF decoupling and fast response, proposing a new structural form for the design of proprioceptive and vestibular simulation platforms. The robot's functional level can be divided into a vestibular sense module and a proprioception module according to the structure. The two modules can work independently to achieve different functions or work together to achieve complex motion and multisensory fusion. This robot is a redundant mechanism device with 9 DOFs. Through a reasonable distribution of DOF and motion, the robot's working space can be increased, and the robot's flexibility and motion performance can be improved. In this paper, a trajectory tracking control algorithm for vestibular and proprioceptive simulation is proposed, which can provide unlimited body sense training for patients within the robot's limited motion range.

Motion analysis and stability optimization for metamorphic robot reconfiguration

AbstractMetamorphic robots are a new type of unmanned vehicle that can reconfigure and morph between a car mode and a biped walking machine mode. Such a vehicle is superior in trafficability because it can drive at high speeds on its wheels on structured pavement and walk on its legs on unstructured pavement. An engineering prototype of a metamorphic robot was proposed and designed based on the characteristics of wheeled–legged hybrid motion, and reconfiguration planning of the robot was conducted. A kinematics model of the reconfiguration process was established using the screw theory for metamorphic robots. To avoid component impact during the rapid global reconfiguration and achieve smoothness of the reconfiguration process, a rotation rule for each rotating joint was designed and the kinematics model was used to simulate and validate the motion of the system’s end mechanism (front frame) and the entire robot system. Based on the kinematics model and the rotation rules of the rotating joints, a zero-moment point (ZMP) calculation model of the entire robot mechanism in the reconfiguration process was established, and the stability of the reconfiguration motions was evaluated based on the ZMP motion trajectory. The foot landing position was optimized to improve the robot’s stability during the reconfiguration. Finally, the smoothness and stability of the reconfiguration motion were further validated by testing the prototype of the metamorphic robot.

Vision-Based Structural Modal Identification Using Hybrid Motion Magnification

As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have attracted much attention from the research community. Among these technologies, Eulerian video magnification has a unique capability of analyzing modal responses and visualizing modal shapes. To reduce the noise interference and improve the quality and stability of the modal shape visualization, this study proposes a hybrid motion magnification framework that combines linear and phase-based motion processing. Based on the assumption that temporal variations can represent spatial motions, the linear motion processing extracts and manipulates the temporal intensity variations related to modal responses through matrix decomposition and underdetermined blind source separation (BSS) techniques. Meanwhile, the theory of Fourier transform profilometry (FTP) is utilized to reduce spatial high-frequency noise. As all spatial motions in a video are linearly controllable, the subsequent phase-based motion processing highlights the motions and visualizes the modal shapes with a higher quality. The proposed method is validated by two laboratory experiments and a field test on a large-scale truss bridge. The quantitative evaluation results with high-speed cameras demonstrate that the hybrid method performs better than the single-step phase-based motion magnification method in visualizing sound-induced subtle motions. In the field test, the vibration characteristics of the truss bridge when a train is driving across the bridge are studied with a commercial camera over 400 m away from the bridge. Moreover, four full-field modal shapes of the bridge are successfully observed.

Airline Point-of-Care System on Seat Belt for Hybrid Physiological Signal Monitoring.

With a focus on disease prevention and health promotion, a reactive and disease-centric healthcare system is revolutionized to a point-of-care model by the application of wearable devices. The convenience and low cost made it possible for long-term monitoring of health problems in long-distance traveling such as flights. While most of the existing health monitoring systems on aircrafts are limited for pilots, point-of-care systems provide choices for passengers to enjoy healthcare at the same level. Here in this paper, an airline point-of-care system containing hybrid electrocardiogram (ECG), breathing, and motion signals detection is proposed. At the same time, we propose the diagnosis of sleep apnea-hypopnea syndrome (SAHS) on flights as an application of this system to satisfy the inevitable demands for sleeping on long-haul flights. The hardware design includes ECG electrodes, flexible piezoelectric belts, and a control box, which enables the system to detect the original data of ECG, breathing, and motion signals. By processing these data with interval extraction-based feature selection method, the signals would be characterized and then provided for the long short-term memory recurrent neural network (LSTM-RNN) to classify the SAHS. Compared with other machine learning methods, our model shows high accuracy up to 84-85% with the lowest overfit problem, which proves its potential application in other related fields.

Cooperative collision avoidance in multirobot systems using fuzzy rules and velocity obstacles

AbstractCollision avoidance is critical in multirobot systems. Most of the current methods for collision avoidance either require high computation costs (e.g., velocity obstacles and mathematical optimization) or cannot always provide safety guarantees (e.g., learning-based methods). Moreover, they cannot deal with uncertain sensing data and linguistic requirements (e.g., the speed of a robot should not be large when it is near to other robots). Hence, to guarantee real-time collision avoidance and deal with linguistic requirements, a distributed and hybrid motion planning method, named Fuzzy-VO, is proposed for multirobot systems. It contains two basic components: fuzzy rules, which can deal with linguistic requirements and compute motion efficiently, and velocity obstacles (VOs), which can generate collision-free motion effectively. The Fuzzy-VO applies an intruder selection method to mitigate the exponential increase of the number of fuzzy rules. In detail, at any time instant, a robot checks the robots that it may collide with and retrieves the most dangerous robot in each sector based on the predicted collision time; then, the robot generates its velocity in real-time via fuzzy inference and VO-based fine-tuning. At each time instant, a robot only needs to retrieve its neighbors’ current positions and velocities, so the method is fully distributed. Extensive simulations with a different number of robots are carried out to compare the performance of Fuzzy-VO with the conventional fuzzy rule method and the VO-based method from different aspects. The results show that: Compared with the conventional fuzzy rule method, the average success rate of the proposed method can be increased by 306.5%; compared with the VO-based method, the average one-step decision time is reduced by 740.9%.

A hybrid motion planning framework for autonomous driving in mixed traffic flow

As a core part of an autonomous driving system, motion planning plays an important role in safe driving. However, traditional model- and rule-based methods lack the ability to learn interactively with the environment, and learning-based methods still have problems in terms of reliability. To overcome these problems, a hybrid motion planning framework (HMPF) is proposed to improve the performance of motion planning, which is composed of learning-based behavior planning and optimization-based trajectory planning. The behavior planning module adopts a deep reinforcement learning (DRL) algorithm, which can learn from the interaction between the ego vehicle (EV) and other human-driven vehicles (HDVs), and generate behavior decision commands based on environmental perception information. In particular, the intelligent driver model (IDM) calibrated based on real driving data is used to drive HDVs to imitate human driving behavior and interactive response, so as to simulate the bidirectional interaction between EV and HDVs. Meanwhile, trajectory planning module adopts the optimization method based on road Frenet coordinates, which can generate safe and comfortable desired trajectory while reducing the solution dimension of the problem. In addition, trajectory planning also exists as a safety hard constraint of behavior planning to ensure the feasibility of decision instruction. The experimental results demonstrate the effectiveness and feasibility of the proposed HMPF for autonomous driving motion planning in urban mixed traffic flow scenarios.

P74. During activities of daily living two-level motion preservation and hybrid constructs may be protective of adjacent segments

BACKGROUND CONTEXT In lumbar spine motion preservation, either two-level motion preservation or motion preservation device adjacent to fusion (hybrid construct), are alternatives to two-level fusion when treating a symptomatic segment next to a level that has only mild radiographic changes. By preserving motion, this strategy may theoretically protect the adjacent mobile segments. PURPOSE This work investigated the protective effect on L3-L4 of a L4-S1 hybrid construct and a L4-S1 two-level total joint replacement (TJR). STUDY DESIGN/SETTING Biomechanical cadaveric study. OUTCOME MEASURES Segmental motion. METHODS Segmental motion data were collected in human cadaveric spine specimens (T10-S1, N=8, 56±5 years) during simulation of standing flexion and the transition from standing to sitting. To determine the protective effect, we determined the change in motion at L3-L4 during activities of daily living when the segment was adjacent to a hybrid L4-S1 construct (a motion-preserving total joint replacement (TJR) at L4-L5 with fusion at L5-S1) and a two-level TJR (L4-S1). Change was calculated using displacement-control analysis. L3-L4 motion before surgery was the reference value. RESULTS Forward flexion while standing: mean L3-L4 motion with an L4-S1 hybrid construct was a tenth of a degree less than intact L3-L4 motion (Hybrid 4.8° (1.6°) vs 4.9° (1.2°), p>0.05; data is mean (standard deviation)). Adjacent to an L4-S1 TJR, the average L3-L4 motion was 1.2 degrees less than intact. This reduction in L3-L4 motion over intact was statistically significant (L4-S1 TJR 3.7° (1.2°), p=0.006). Transition from standing to sitting: L3-L4 motion for the same reduction in sacral slope (or comparable sitting posture) was not significantly different from intact with hybrid and 2-level motion preservation constructs (Intact 3.8°(1.2°), hybrid 4.0°(1.3°), L4-S1 TJR, 2.7°(1.1°), p>0.05). On average, L3-L4 motion was 0.2 degrees higher with the L4-S1 hybrid construct and 1.1 degrees lower with the two-level L4-S1 TJR. CONCLUSIONS The hybrid and two-level total joint replacement constructs did not exhibit the increased proximal segment motion during forward bending and sitting activities that may lead to junctional breakdown over time. Preserving motion using a hybrid construct or two-level total joint replacement may be an alternative surgical option to improve patient comfort and function during activities of daily living while potentially decreasing the need for adjacent level surgery. FDA DEVICE/DRUG STATUS MOTUS (Investigational/Not Approved) In lumbar spine motion preservation, either two-level motion preservation or motion preservation device adjacent to fusion (hybrid construct), are alternatives to two-level fusion when treating a symptomatic segment next to a level that has only mild radiographic changes. By preserving motion, this strategy may theoretically protect the adjacent mobile segments. This work investigated the protective effect on L3-L4 of a L4-S1 hybrid construct and a L4-S1 two-level total joint replacement (TJR). Biomechanical cadaveric study. Segmental motion. Segmental motion data were collected in human cadaveric spine specimens (T10-S1, N=8, 56±5 years) during simulation of standing flexion and the transition from standing to sitting. To determine the protective effect, we determined the change in motion at L3-L4 during activities of daily living when the segment was adjacent to a hybrid L4-S1 construct (a motion-preserving total joint replacement (TJR) at L4-L5 with fusion at L5-S1) and a two-level TJR (L4-S1). Change was calculated using displacement-control analysis. L3-L4 motion before surgery was the reference value. Forward flexion while standing: mean L3-L4 motion with an L4-S1 hybrid construct was a tenth of a degree less than intact L3-L4 motion (Hybrid 4.8° (1.6°) vs 4.9° (1.2°), p>0.05; data is mean (standard deviation)). Adjacent to an L4-S1 TJR, the average L3-L4 motion was 1.2 degrees less than intact. This reduction in L3-L4 motion over intact was statistically significant (L4-S1 TJR 3.7° (1.2°), p=0.006). Transition from standing to sitting: L3-L4 motion for the same reduction in sacral slope (or comparable sitting posture) was not significantly different from intact with hybrid and 2-level motion preservation constructs (Intact 3.8°(1.2°), hybrid 4.0°(1.3°), L4-S1 TJR, 2.7°(1.1°), p>0.05). On average, L3-L4 motion was 0.2 degrees higher with the L4-S1 hybrid construct and 1.1 degrees lower with the two-level L4-S1 TJR. The hybrid and two-level total joint replacement constructs did not exhibit the increased proximal segment motion during forward bending and sitting activities that may lead to junctional breakdown over time. Preserving motion using a hybrid construct or two-level total joint replacement may be an alternative surgical option to improve patient comfort and function during activities of daily living while potentially decreasing the need for adjacent level surgery.

End-to-End Neural Video Coding Using a Compound Spatiotemporal Representation

Recent years have witnessed rapid advances in learnt video coding. Most algorithms have solely relied on the vector-based motion representation and resampling (e.g., optical flow based bilinear sampling) for exploiting the inter frame redundancy. In spite of the great success of adaptive kernel-based resampling (e.g., adaptive convolutions and deformable convolutions) in video prediction for uncompressed videos, integrating such approaches with rate-distortion optimization for inter frame coding has been less successful. Recognizing that each resampling solution offers unique advantages in regions with different motion and texture characteristics, we propose a hybrid motion compensation (HMC) method that adaptively combines the predictions generated by these two approaches. Specifically, we generate a compound spatiotemporal representation (CSTR) through a recurrent information aggregation (RIA) module using information from the current and multiple past frames. We further design a one-to-many decoder pipeline to generate multiple predictions from the CSTR, including vector-based resampling, adaptive kernel-based resampling, compensation mode selection maps and texture enhancements, and combines them adaptively to achieve more accurate inter prediction. Experiments show that our proposed inter coding system can provide better motion-compensated prediction and is more robust to occlusions and complex motions. Together with jointly trained intra coder and residual coder, the overall learnt hybrid coder yields the state-of-the-art coding efficiency in low-delay scenario, compared to the traditional H.264/AVC and H.265/HEVC, as well as recently published learning-based methods, in terms of both PSNR and MS-SSIM metrics.

Hybrid Force and Motion Control of a Three-Dimensional Flexible Robot Considering Measurement Noises

This work addresses the end-effector trajectory-tracking force and motion control of a three-dimensional three-link robot considering measurement noises. The last two links of the manipulator are considered as structurally flexible. An absolute coordinate approach is used while obtaining the dynamic equations to avoid complex dynamic equations. In this approach, each link is modeled as if there is no connection between the links. Then, joint connections are expressed as constraint equations. After that, these constraint equations are used in dynamic equations to decrease the number of equations. Then, the resulting dynamic equations are transformed into a form which is suitable for controller design. Furthermore, the dynamic equations are divided as pseudostatic equilibrium and deviation equations. The control torques resulting from the pseudostatic equilibrium and the elastic deflections are obtained easily as the solution of algebraic equations. On the other hand, the control torques corresponding to the deviations are obtained without any linearization. Encoders, strain gauges, position sensors and force and moment sensors are required for measurements. Low pass filters are considered for the sensors. For the crossover frequencies of the sensors, low and high values are chosen to observe the filtering effect on the robot output.

Simulative Evaluation of a Joint-Cartesian Hybrid Motion Mapping for Robot Hands Based on Spatial In-Hand Information.

Two sub-problems are typically identified for the replication of human finger motions on artificial hands: the measurement of the motions on the human side and the mapping method of human hand movements (primary hand) on the robotic hand (target hand). In this study, we focus on the second sub-problem. During human to robot hand mapping, ensuring natural motions and predictability for the operator is a difficult task, since it requires the preservation of the Cartesian position of the fingertips and the finger shapes given by the joint values. Several approaches have been presented to deal with this problem, which is still unresolved in general. In this work, we exploit the spatial information available in-hand, in particular, related to the thumb-finger relative position, for combining joint and Cartesian mappings. In this way, it is possible to perform a large range of both volar grasps (where the preservation of finger shapes is more important) and precision grips (where the preservation of fingertip positions is more important) during primary-to-target hand mappings, even if kinematic dissimilarities are present. We therefore report on two specific realizations of this approach: a distance-based hybrid mapping, in which the transition between joint and Cartesian mapping is driven by the approaching of the fingers to the current thumb fingertip position, and a workspace-based hybrid mapping, in which the joint–Cartesian transition is defined on the areas of the workspace in which thumb and fingertips can get in contact. The general mapping approach is presented, and the two realizations are tested. In order to report the results of an evaluation of the proposed mappings for multiple robotic hand kinematic structures (both industrial grippers and anthropomorphic hands, with a variable number of fingers), a simulative evaluation was performed.

First-Order Ocean Surface Cross Section for Shipborne Bistatic HFSWR: Derivation and Simulation

A bistatic high-frequency surface wave radar (HFSWR) with both receiving and transmitting stations placed on different ships (platforms) is a new radar system and referred to as shipborne bistatic HFSWR. In this paper, a first-order ocean surface cross section of shipborne bistatic HFSWR was derived. The first-order cross-section models for three different cases, i.e., ships moving with uniform, periodic, and hybrid motion states, respectively, are presented. The corresponding first-order Doppler spectra were simulated, and the spread width of the first-order spectrum was investigated. The simulation results show that the characteristics of the first-order spectrum are similar to those of a shore-based bistatic HFSWR when the transmitting and receiving platforms move in opposite directions. The first-order spectral spread width in the case of platforms with opposite directions is much smaller than that in the case of platforms with the same direction. This finding is useful for reducing HFSWR first-order spectrum spread due to platform motion, thus improving the target detection performance of the shipborne bistatic HFSWR. In addition, periodic oscillation motion of both platforms will cause complex motion-induced peaks in the first-order spectrum, which may be detrimental to target detection and ocean remote sensing. These results have important implications for the application of shipborne bistatic HFSWR.

Hybrid motion artifact detection and correction approach for functional near-infrared spectroscopy measurements.

.SignificanceFunctional near-infrared spectroscopy (fNIRS) is a promising optical neuroimaging technique, measuring the hemodynamic signals from the cortex. However, improving signal quality and reducing artifacts arising from oscillation and baseline shift (BS) are still challenging up to now for fNIRS applications.AimConsidering the advantages and weaknesses of the different algorithms to reduce the artifact effect in fNIRS signals, we propose a hybrid artifact detection and correction approach.ApproachFirst, distinct artifact detection was realized through an fNIRS detection strategy. Then the artifacts were divided into three categories: BS, slight oscillation, and severe oscillation. A comprehensive correction was applied through three main steps: severe artifact correction by cubic spline interpolation, BS removal by spline interpolation, and slight oscillation reduction by dual-threshold wavelet-based method.ResultsUsing fNIRS data acquired during whole night sleep monitoring, we compared the performance of our approach with existing algorithms in signal-to-noise ratio (SNR) and Pearson’s correlation coefficient (). We found that the proposed method showed improvements in performance in SNR and with strong stability.ConclusionsThese results suggest that the new hybrid artifact detection and correction method enhances the viability of fNIRS as a functional neuroimaging modality.

Force control of a space robot in on-orbit servicing operations

Force control provided by a small-scaled free-flying space robot on a floating or spinning target will be required for future on-orbit servicing missions, such as detumbling a target, performing an assembly or a maintenance operation. Regarding to different contact geometries for a space robot interacting with a spinning target, this paper addresses the contact model considering its multi-dimensional characteristics. A hybrid motion and force controller is developed to acquire desired contact forces and space robot configuration despite the floating feature of the system. On this basis, the control strategy to implement the operations of three typical phases, namely approaching phase, contact phase and post-contact phase, is proposed. The simulations have been performed to verify the control approaches and visualize the contact scenarios.