Assumption Of Constant Velocity Research Articles

Improved Transcranial Plane-Wave Imaging With Learned Speed-of-Sound Maps.

Although transcranial ultrasound plane-wave imaging (PWI) has promising clinical application prospects, studies have shown that variable speed-of-sound (SoS) would seriously damage the quality of ultrasound images. The mismatch between the conventional constant velocity assumption and the actual SoS distribution leads to the general blurring of ultrasound images. The optimization scheme for reconstructing transcranial ultrasound image is often solved using iterative methods like full-waveform inversion. These iterative methods are computationally expensive and based on prior magnetic resonance imaging (MRI) or computed tomography (CT) information. In contrast, the multi-stencils fast marching (MSFM) method can produce accurate time travel maps for the skull with heterogeneous acoustic speed. In this study, we first propose a convolutional neural network (CNN) to predict SoS maps of the skull from PWI channel data. Then, use these maps to correct the travel time to reduce transcranial aberration. To validate the performance of the proposed method, numerical, phantom and intact human skull studies were conducted using a linear array transducer (L11-5v, 128 elements, pitch = 0.3 mm). Numerical simulations demonstrate that for point targets, the lateral resolution of MSFM-restored images increased by 65%, and the center position shift decreased by 89%. For the cyst targets, the eccentricity of the fitting ellipse decreased by 75%, and the center position shift decreased by 58%. In the phantom study, the lateral resolution of MSFM-restored images was increased by 49%, and the position shift was reduced by 1.72 mm. This pipeline, termed AutoSoS, thus shows the potential to correct distortions in real-time transcranial ultrasound imaging, as demonstrated by experiments on the intact human skull.

In situ observation of electrical resistivity increase via creep-induced dislocations in n-type PbTe

A decrease in electrical conductivity is measured in situ in n-type PbTe during dislocation-climb creep deformation at 673 K. Transmission electron microscopy reveals the accumulation of high density of immobile dislocations that form stable subgrains which increase in density with increasing strain from 8 to 20%. At a constant applied stress, we conclude the increase in immobile dislocation density is primarily responsible for the continuous increase in the electrical resistivity with strain. With the assumption of constant dislocation velocity at a constant applied stress, the mobile dislocation density is a constant so the increase of immobile dislocation density with strain should dictate the total dislocation density increase during creep deformation. This effect of creep deformation on electrical conductivity, and thermoelectric properties in general, should help design materials and operational conditions to limit such adverse effects on device efficiency while engineering thermal conductivity reductions.

INTEGRATING MOTION PRIORS FOR END-TO-END ATTENTION-BASED MULTI-OBJECT TRACKING

Abstract. Recent advancements in multi-object tracking (MOT) have heavily relied on object detection models, with attention-based models like DEtection TRansformer (DETR) demonstrating state-of-the-art capabilities. However, the utilization of attention-based detection models in tracking poses a limitation due to their large parameter count, necessitating substantial training data and powerful hardware for parameter estimation. Ignoring this limitation can lead to a loss of valuable temporal information, resulting in decreased tracking performance and increased identity (ID) switches. To address this challenge, we propose a novel framework that directly incorporates motion priors into the tracking attention layer, enabling an end-to-end solution. Our contributions include: I) a novel approach for integrating motion priors into attention-based multi-object tracking models, and II) a specific realisation of this approach using a Kalman filter with a constant velocity assumption as motion prior. Our method was evaluated on the Multi-Object Tracking dataset MOT17, initial results are reported in the paper. Compared to a baseline model without motion prior, we achieve a reduction in the number of ID switches with the new method.

Bayesian acoustic emission time-difference method for locating the collision point of sweeping robot shell

The existing source localization based on acoustic emission technology often depends on the assumption of constant wave velocity inside the material. However, this assumption is hardly satisfied in actual engineering. The uncertainty of wave velocity can easily lead to low localization accuracy of sweeping robots. To overcome these deficiencies, a complete probability multi-directional measurement method based on the Bayesian inference mechanism is proposed. In the proposed method, based on the Bayesian probabilistic model, the extracted sensor time difference is subjected to probabilistic inference using the coordinate input model to determine the posterior distribution of the source’s position and wave velocity of the given arrival time. Compared with the traditional time-difference method, the proposed method achieves excellent results and outperforms the standard time-difference method in localization accuracy and anti-interference. In addition, the proposed method can conveniently, quickly, and effectively determine the location of the colliding point without considering the source emission time and wave velocity. The research in this paper provides an effective method for solving the collision localization problem of the sweeping robot shell under the acoustic emission time and wave velocity are unknown.

Decentralized Approach for Translational Motion Estimation with Multistatic Inverse Synthetic Aperture Radar Systems

This paper addresses the estimation of the target translational motion by using a multistatic Inverse Synthetic Aperture Radar (ISAR) system composed of an active radar sensor and multiple receiving-only devices. Particularly, a two-step decentralized technique is derived: the first step estimates specific signal parameters (i.e., Doppler frequency and Doppler rate) at the single-sensor level, while the second step exploits these estimated parameters to derive the target velocity and acceleration components. Specifically, the second step is organized in two stages: the former is for velocity estimation, while the latter is devoted to velocity estimation refinement if a constant velocity model motion can be regarded as acceptable, or to acceleration estimation if a constant velocity assumption does not apply. A proper decision criterion to select between the two motion models is also provided. A closed-form theoretical performance analysis is provided for the overall technique, which is then used to assess the achievable performance under different distributions of the radar sensors. Additionally, a comparison with a state-of-the-art centralized approach has been carried out considering computational burden and robustness. Finally, results obtained against experimental multisensory data are shown confirming the effectiveness of the proposed technique and supporting its practical application.

A simplified mesoscale 3D model for characterizing fibrinolysis under flow conditions

One of the routine clinical treatments to eliminate ischemic stroke thrombi is injecting a biochemical product into the patient’s bloodstream, which breaks down the thrombi’s fibrin fibers: intravenous or intravascular thrombolysis. However, this procedure is not without risk for the patient; the worst circumstances can cause a brain hemorrhage or embolism that can be fatal. Improvement in patient management drastically reduced these risks, and patients who benefited from thrombolysis soon after the onset of the stroke have a significantly better 3-month prognosis, but treatment success is highly variable. The causes of this variability remain unclear, and it is likely that some fundamental aspects still require thorough investigations. For that reason, we conducted in vitro flow-driven fibrinolysis experiments to study pure fibrin thrombi breakdown in controlled conditions and observed that the lysis front evolved non-linearly in time. To understand these results, we developed an analytical 1D lysis model in which the thrombus is considered a porous medium. The lytic cascade is reduced to a second-order reaction involving fibrin and a surrogate pro-fibrinolytic agent. The model was able to reproduce the observed lysis evolution under the assumptions of constant fluid velocity and lysis occurring only at the front. For adding complexity, such as clot heterogeneity or complex flow conditions, we propose a 3-dimensional mesoscopic numerical model of blood flow and fibrinolysis, which validates the analytical model’s results. Such a numerical model could help us better understand the spatial evolution of the thrombi breakdown, extract the most relevant physiological parameters to lysis efficiency, and possibly explain the failure of the clinical treatment. These findings suggest that even though real-world fibrinolysis is a complex biological process, a simplified model can recover the main features of lysis evolution.

Cubature Kalman Filters Model Predictive Static Programming Guidance Method with Impact Time and Angle Constraints Considering Modeling Errors

This paper proposes a CKF-MPSP guidance method for hitting stationary targets with impact time and angle constraints for missiles in the presence of modeling errors. This innovative guidance scheme is composed of three parts: First, the model predictive static programming (MPSP) algorithm is used to design a nominal guidance method that simultaneously satisfies impact time and angle constraints. Second, the cubature Kalman filter (CKF) is introduced to estimate values of the influence of the inevitable modeling errors. Finally, a one-step compensation scheme is proposed to eliminate the modeling errors’ influence. The proposed method uses a real missile dynamics model, instead of a simplified one with a constant-velocity assumption, and eliminates the effects of modeling errors with the compensation scheme; thus, it is more practical. Simulations in the presence of modeling errors are conducted, and the results illustrate that the CKF-MPSP guidance method can reach the target with a high accuracy of impact time and angles, which demonstrates the high precision and strong robustness of the method.

A distributed coordinated path planning algorithm for maritime autonomous surface ship

The anti-collision path planning is vital for establishing the decision support system for Maritime Autonomous Surface Ship (MASS). Numerous methods have been developed to solve the problem of collision avoidance with static and dynamic obstacles in the past two decades. However, most of the studies are conducted with certain limitations, such as the assumption of constant velocity of target ships, the simplification of ship’ dynamic properties during manoeuvering, the insufficient consideration of coordination among different ships' actions, etc. In this article, a distributed coordinated path planning algorithm especially for multi-ship encounter is proposed. Combined with constraints of apparent action, time dimension and ship's dynamic properties, a Three-dimension Generalized Velocity Obstacle (TGVO) algorithm is adopted to generate practical and COLREGs compliant collision-free velocities for vessels. Compared to traditional collision avoidance parameter calculation approach, the impact of ship manoeuvrability on avoidance performance is further discussed and analyzed to improve the accuracy and universality of the optimization solutions. In addition, a distributed multistage decision model aiming at solving the situation including multiple give-way vessels is introduced based on the priority analysis. Finally, simulation experiments show that this proposed scheme can work properly in various maritime environments with high degree of coordination and consistency with navigation practice.

Three-Dimensional Approach Angle Guidance Under Varying Velocity and Field-of-View Limit Without Using Line-of-Sight Rate

In this article, a practical three-dimensional (3-D) guidance is designed to achieve trajectory constrained flight, i.e., arriving at the destination with desired approach angles under limited field-of-view (FOV) and varying velocity. For this goal, the nonlinear engagement model is first transformed to a set of new differential equations in terms of the line-of-sight (LOS) angles. Then, the approach angle constraints are handled by constructing two reference range profiles as cubic polynomials of LOS angles. By solving the unknown coefficients involved in the polynomials, a semianalytical 3-D guidance solution is derived from the second-order range dynamics. To meet the FOV limit and avoid the initial guidance saturation, the achievable approach angle set is determined for capturability analysis by introducing the look angle dynamics with respect to the range profiles. The technique does not involve the model linearization, guidance switching logic, constant velocity assumption, and LOS rate information. Notably, only the LOS angle measurement is required during the guidance process once the initial conditions are provided, which makes it preferable for vehicles equipped with passive angles-only sensors. Extensive numerical simulations and Monte Carlo test are conducted to validate effectiveness and robustness of the proposed technique.

Complex ink flow mechanisms in micro-direct-ink-writing and their implications on flow rate control

Despite its simplicity, low cost, and ability to process a wide range of materials, direct-ink-writing (DIW) is an additive manufacturing process with low resolution and accuracy, in the multiple hundred microns to millimeter range. One of the main sources for this issue is the difficulty with accurately controlling ink flow rate at smaller size scales. Towards addressing this limitation, this paper elucidates complex ink flow mechanisms that renders flow rate control difficult and explores printing implementations to increase flow rate accuracy in direct-ink-writing at the micro scale. To this end, a DIW system utilizing hybrid pressure and velocity-controlled extrusion is used to obtain pressure-flow rate relationships for a water-based sodium carboxymethyl cellulose (NaCMC) solution ink, as a function of printing nozzle diameter (510–100 µm). These studies showed that the transient response of piston velocity-controlled extrusion significantly slows down with decreasing nozzle diameter. For pressure-controlled extrusion, the wall slip increases with decreasing nozzle diameter and the constant slip velocity assumption no longer holds as nozzle size decreases below a certain diameter. To contextualize the influence of such behavior on flow rate accuracy, temperature-controlled parallel plate rheometry was performed on the inks and rheological ink models were accordingly determined. It was shown that the associated flow rate predictions under predicted flow rates due to lack of wall-slip consideration, particularly for smaller nozzle sizes. Lastly, an iterative pressure-controlled DIW implementation was explored to address the accuracy issues for micro-DIW. Our results indicated significant improvement in the transient response and flow rate accuracy for nozzle diameters as small as 100 µm using this approach compared to both of the conventional pressure and velocity control approaches.

InterpolationSLAM: An effective visual SLAM system based on interpolation network

Visual SLAM can be mainly divided into direct method and feature-based method, and these two methods develop relatively independently. In recent years, feature-based SLAM systems have been significantly improved by introducing more robust features, effective matching and optimization frameworks, or other sensors. The introduction of semantic information also promotes the development of direct methods. However, visual SLAM usually assumes that the system satisfies the constant velocity assumption. This assumption may lead to a poor initial pose so that the subsequent optimization falls into a local minimal. Meanwhile, large field of view changes often lead to an increase in the error of feature matching for feature-based method and large illumination changes often lead to an increase of photometric error for direct method, thus negatively influencing SLAM systems. In this paper, we mainly target at feature-based method. In detail, we focus on the number and quality of feature matches, as well as the accuracy of the initial pose, thus an interpolation mechanism for SLAM is proposed. Specifically, we introduce the interpolation network originally used to increase the number of video frames into visual SLAM. First, we point out that the traditional interpolation network evaluation metrics are not suitable for the SLAM systems, and we provide the corresponding evaluation metric. Secondly, we verify that it works both for hand-crafted and deep learning features. Thirdly, in order to verify the effectiveness and transferability of our method, we also apply our method to SLAM systems based on direct method, which proves that our method is also applicable to the direct method. Fourthly, we point out that the interpolation network effectively slows down the pose transformation of the SLAM system by inserting an intermediate frame between the previous frame and the current frame, so that the system can obtain a better initial pose based on the constant velocity assumption. This can also explain why visual-inertial systems can effectively improve the performance of visual SLAM. Finally, to ensure the efficiency of the SLAM system, we provide a turning detection module and propose a method to interpolate only at turnings. Extensive experiments and analyses verify the effectiveness and transferability of the proposed system.

$\mathcal {L}{}_{1}$ Adaptive Path-Following of Small Fixed-Wing Unmanned Aerial Vehicles in Wind

This article proposes an adaptive path-following controller of small fixed-wing unmanned aerial vehicles (UAVs) in the presence of wind disturbances, which explicitly considers that wind speed is time-varying. The main idea was to formulate UAVs path-following as control design for systems with parametric uncertainties and external disturbances. Assuming that there is no prior information on wind, the proposed solution is based on the $\mathcal {L}{}_{1}$ adaptive control, using linearized model dynamics. This approach makes clear statements for performance specifications of the controller and relaxes the common constant wind velocity assumption. This makes the design more realistic and the analysis more rigorous, because in practice wind is usually time varying (windshear, turbulence, and gusting). The path-following controller was demonstrated in flight under wind speed up to $10 \text{ m/s}$, representing 50% of the nominal UAV airspeed.

Prepare for Ludicrous Speed: Marker-based Instantaneous Binocular Rolling Shutter Localization.

We propose a marker-based geometric framework for the high-frequency absolute 3D pose estimation of a binocular camera system by using the data captured during the exposure of a single rolling shutter scanline. In contrast to existing approaches enforcing temporal or motion models among scanlines (e.g. linear motion, constant velocity or small motion assumptions), we strive to determine the pose from instantaneous binocular capture (i.e. without using data from previous scanlines) and achieve drift-free pose estimation. We leverage the projective invariants of a novel rigid planar pattern, to both define a geometric reference as well as to determine 2D-3D correspondences from raw edge detection measurements from individual scanlines. Moreover, to tackle the ensuing multi-view estimation problem, achieve real-time operation, and minimize latency, we develop a pair of custom solvers leveraging our geometric setup. To mitigate sensitivity to noise, we propose a geometrically consistent measurement refinement mechanism. We verify the quality of our solvers by comparing with state of the art general solvers for absolute pose estimation of generalized cameras. Finally, we demonstrate the effectiveness of our proposed approach with an FPGA-based implementation which achieves a localization throughput of 129.6 KHz with a 1.5 μs latency.

Fusing Convolutional Neural Network and Geometric Constraint for Image-Based Indoor Localization

This letter proposes a new image-based localization framework that explicitly localizes the camera/robot by fusing Convolutional Neural Network (CNN) and sequential images’ geometric constraints. The camera is localized using a single or few observed images and training images with 6-degree-of-freedom pose labels. A Siamese network structure is adopted to train an image descriptor network, and the visually similar candidate image in the training set is retrieved to localize the testing image geometrically. Meanwhile, a probabilistic motion model predicts the pose based on a constant velocity assumption. The two estimated poses are finally fused using their uncertainties to yield an accurate pose prediction. This method leverages the geometric uncertainty and is applicable in indoor scenarios predominated by diffuse illumination. Experiments on simulation and real data sets demonstrate the efficiency of our proposed method. The results further show that combining the CNN-based framework with geometric constraint achieves better accuracy when compared with CNN-only methods, especially when the training data size is small.

Research on Trajectory Tracking of Sliding Mode Control Based on Adaptive Preview Time

The preview model is one of the common methods used in trajectory tracking. The traditional fixed preview time is not adaptable to most speeds and road conditions, which not only reduces the tracking accuracy but also reduces the vehicle stability. Therefore, a controller can be designed to determine the adaptive preview time based on an optimization function of the lateral deviation, the road boundary, and the road boundary of the whole vehicle motion response characteristics. Traditional optimal preview control theory predicts the next state of the vehicle by the assumption of constant transverse pendulum angular velocity. In this paper, an expectation-based approach is used to find the ideal steering wheel turning angle based on the adaptive preview time, and a single-point preview model is established. Based on the two-degree-of-freedom dynamics model, a sliding mode controller is designed for control, and the low-pass filters are designed to suppress jitter in the sliding mode controller. Simulation results with different preview times, different speeds and different road adhesion coefficients prove that the controller has a good control effect and has good effectiveness and adaptability to speed and adhesion coefficient.

Using Spherical Harmonics for Navigating in Dynamic and Uncertain Environments

In this paper, we present a novel approach for local motion planning in an unknown environment populated by static and dynamic obstacles. A key component of our planner is the use of harmonic basis functions defined over the unit sphere, known as spherical harmonics (SH), to represent a collision-free space near an agent. In contrast with our previous approach, which only applies to unknown static environments, the approach proposed herein handles dynamic obstacles as well. It is done by efficiently calculating the coefficients of a spherical harmonics approximation of the local collision-free space for each step of a given planning horizon. Our method for approximating the collision-free space with spherical harmonics allows us to plan trajectories in challenging environments (e.g., planning through narrow passages) where other methods for generating search-spaces fail. To accurately approximate the collision-free space for future time steps along a planning horizon, we use a Kalman Filter (KF) to propagate the measured point cloud through time. Our use of the KF allows us to better approximate obstacles moving in space in comparison to inaccurate constant obstacle velocity assumptions used by other methods. Finally, we show the effectiveness of our proposed trajectory planner using two different non-trivial scenarios.

Analysis of the effect of uncertainties in hydrodynamic parameters on the accuracy of the gas flow modulation technique for bubble columns

The gas flow modulation technique has recently been proposed as a novel method for determining the axial gas dispersion coefficient in bubble columns. The approach is based on a marginal sinusoidal modulation of the gas inlet flow rate that acts as a virtual tracer. Axial gas dispersion is then inversely calculated from amplitude damping and phase shift via an analytical solution of the axial dispersion model. The proposed study provides an analysis of the inherent uncertainties related to the assumptions of constant axial gas dispersion coefficient and bubble rise velocity, which are crucial for implementing the method. Besides, the sensitivity of the approach is assessed as a function of the modulation parameters, the bubble rise velocity and the axial gas dispersion coefficient. Eventually, the possibility of tailoring the modulation parameters depending on the expected value of the axial gas dispersion coefficient to increase the sensitivity and to reduce the uncertainty is also assessed.

An Iterative-Optimization-Based Calibration Framework for VIO With Limited Prior Conditions

Most of the visual inertial positioning accuracy is constrained by the calibration accuracy of the sensor, however, there are few navigation systems that take the time offset and extrinsic parameters into account at the same time. In this paper, an iterative-optimization-based calibration framework for VIO (visual-inertial odometry) with limited prior conditions is proposed. In particular, a three-stage calibration method is presented, which includes the time offset estimation stage, the extrinsic and intrinsic parameters calibration stage, and global temporal and IMU parameter optimization stage. In the first stage, the time offset calibration model is proposed, which estimates the time delay by iteratively optimizing the orientation residual equation. In the second stage, based on the assumption of constant angle velocity and linear velocity between two continuous keyframes, an iterative-based coarse-to-fine calibration strategy is presented to calibrate the initial extrinsic and IMU intrinsic parameters. In the third stage, a global optimization-based algorithm is introduced to obtain a more accurate time offset value and IMU intrinsic parameters. What’s more worth mentioning is that the proposed method does not require any prior parameters, which greatly improves the universality of the navigation system. To evaluate the effectiveness and accuracy of the proposed algorithm, the method is tested on both simulation and public dataset, and also compared with classical systems. Abundant experimental results illustrate that the proposed method can effectively estimate the time offset, the IMU intrinsic and extrinsic parameters.

The Effects of Variable and Constant Rupture Velocity on the Generation of High-Frequency Radiation from Earthquakes

A viewpoint exists that generation of seismic high frequencies results from sudden movements of ruptures, experiencing episodes of acceleration/deceleration. We investigated this effect theoretically for (1) the far field of a small near-line source and (2) the near field of a large fault computed exactly from the representation integral. The results indicate that, in both cases, the strength of the high-frequency radiation and the spectral fall-off for ruptures moving with constant positive or negative acceleration, as well as with regularly changing acceleration, are variable: depending on the parameters, they can exceed, be similar to, or fall below the levels produced by constant-velocity propagation. The directivity spectral levels for the scenarios of constant (positive or negative) acceleration, acceleration modulated by an orderly function, or constant velocity are fully controlled by regular interference: a particular high-frequency slope seen is an artifact of the case-specific interference. Randomization of rupture speed suppresses regular interference and creates an appearance of high-frequency generation by irregularity in the velocity, while in reality it is due to the elimination of artifacts. Realistic ruptures will always have a random component in their travel times, depending on fault and material properties, and will always exhibit elevated high-frequency content with respect to an idealized constant-velocity scenario. The conclusion is that interpreting this phenomenon as high-frequency generation by rupture irregularity would be incorrect. The additional $${\omega }^{-1}$$ spectral roll-off, typically attributed to fault finiteness, is a consequence of the constant-velocity assumption. Removing the latter flattens the spectrum, even for a finite fault.

Empirical Investigations of the Instrument Response for Distributed Acoustic Sensing (DAS) across 17 Octaves

ABSTRACT With the potential of high temporal and spatial sampling and the capability of utilizing existing fiber-optic infrastructure, distributed acoustic sensing (DAS) is in the process of revolutionizing geophysical ground-motion measurements, especially in remote and urban areas, where conventional seismic networks may be difficult to deploy. Yet, for DAS to become an established method, we must ensure that accurate amplitude and phase information can be obtained. Furthermore, as DAS is spreading into many different application domains, we need to understand the extent to which the instrument response depends on the local environmental properties. Based on recent DAS response research, we present a general workflow to empirically quantify the quality of DAS measurements based on the transfer function between true ground motion and observed DAS waveforms. With a variety of DAS data and reference measurements, we adapt existing instrument-response workflows typically in the frequency band from 0.01 to 10 Hz to different experiments, with signal frequencies ranging from 1/3000 to 60 Hz. These experiments include earthquake recordings in an underground rock laboratory, hydraulic injection experiments in granite, active seismics in agricultural soil, and icequake recordings in snow on a glacier. The results show that the average standard deviations of both amplitude and phase responses within the analyzed frequency ranges are in the order of 4 dB and 0.167π radians, respectively, among all experiments. Possible explanations for variations in the instrument responses include the violation of the assumption of constant phase velocities within the workflow due to dispersion and incorrect ground-motion observations from reference measurements. The results encourage further integration of DAS-based strain measurements into methods that exploit complete waveforms and not merely travel times, such as full-waveform inversion. Ultimately, our developments are intended to provide a quantitative assessment of site- and frequency-dependent DAS data that may help establish best practices for upcoming DAS surveys.