Alignment Errors Research Articles

Accurate and Robust Rotation-Invariant Estimation for High-Precision Outdoor AR Geo-Registration

Geographic registration (geo-registration) is a crucial foundation for augmented reality (AR) map applications. However, existing methods encounter difficulties in aligning spatial data with the ground surface in complex outdoor scenarios. These challenges make it difficult to accurately estimate the geographic north orientation. Consequently, the accuracy and robustness of these methods are limited. To overcome these challenges, this paper proposes a rotation-invariant estimation method for high-precision geo-registration in AR maps. The method introduces several innovations. Firstly, it improves the accuracy of generating heading data from low-cost hardware by utilizing Real-Time Kinematic GPS and visual-inertial fusion. This improvement contributes to the increased stability and precise alignment of virtual objects in complex environments. Secondly, a fusion method combines the true-north direction vector and the gravity vector to eliminate alignment errors between geospatial data and the ground surface. Lastly, the proposed method dynamically combines the initial attitude relative to the geographic north direction with the motion-estimated attitude using visual-inertial fusion. This approach significantly reduces the requirements on sensor hardware quality and calibration accuracy, making it applicable to various AR precision systems such as smartphones and augmented reality glasses. The experimental results show that this method achieves AR geo-registration accuracy at the 0.1-degree level, which is about twice as high as traditional AR geo-registration methods. Additionally, it exhibits better robustness for AR applications in complex scenarios.

Frequency-dependent reflection of a misaligned beam by a Fabry–Pérot cavity

Gravitational-wave detectors, such as Virgo, LIGO, and KAGRA, are modified Michelson interferometers, with a system of coupled Fabry–Pérot cavities, to increase its sensitivity and bandwidth. In order to control the detector, several radio frequency sidebands, not resonant in the kilometric arm cavities but resonant in the central cavities of the interferometer, are added to the carrier frequency to extract longitudinal and alignment error signals. Misalignment of the laser in the Fabry–Pérot cavities causes sensitivity degradation through different mechanisms and results in non-superposition of carrier and sidebands. These relative misalignment between fields at different frequency contain clues to optimally align the interferometer, but the question of the direction of a reflected beam by a Fabry–Pérot cavity, as a function of the state of resonance of the incoming electromagnetic field, is neither straightforward nor intuitive. While numerical optical simulations used in the gravitational-wave detector community are able to answer the question, they do not give a qualitative and handy understanding of the observed phenomenon, useful for the commissioning and operation of the detectors. In this Letter, we present a model based on Gaussian beam first-order modal expansion to calculate analytically how misalignment on the input beam in a Fabry–Pérot cavity translates into misalignment of the reflected and circulating beams. We find a strong dependence not only on the beam resonance condition but also on the mirror geometry. Finally, we checked the consistency of our model by comparing its predictions with existing numerical simulators.

A self-adaptive high precision gripper for shape variant components: Towards higher reliability and efficiency of a cobotic cell

With continuously shrinking batch sizes and increasing order numbers the handling of different object variants becomes a key parameter for the automatization of assembly processes in Industry 4.0. Many compliant and underactuated grasping devices that exploit hierarchical principles, including whipple trees, tendon-pulley systems, pin arrays, fluid differentials, and air pressure, just to name a few, are subject to research. However, these often bionically inspired systems are mostly used for humanoid applications, such as prosthesis, dexterous hands, and crop harvesting. While they can achieve a significant grasp robustness, their precision is often too low for industrial manufacturing processes. Therefore, this paper proposes a deterministic grasping system with one level of adaptation to grasp a great number of object variants in a cobotic assembly cell. This grasping system not only adapts to different shapes but also compensates initial position and alignment errors and guarantees a predefined centring within the grasp. The technology has been tested intensively in the laboratory and subsequently demonstrated in the cobotic cell, where it decreased the machine breakdown time due to pick and place failures by 90%.

A Graph Fusion Approach for Cross-Lingual Machine Reading Comprehension

Although great progress has been made for Machine Reading Comprehension (MRC) in English, scaling out to a large number of languages remains a huge challenge due to the lack of large amounts of annotated training data in non-English languages. To address this challenge, some recent efforts of cross-lingual MRC employ machine translation to transfer knowledge from English to other languages, through either explicit alignment or implicit attention. For effective knowledge transition, it is beneficial to leverage both semantic and syntactic information. However, the existing methods fail to explicitly incorporate syntax information in model learning. Consequently, the models are not robust to errors in alignment and noises in attention. In this work, we propose a novel approach, which jointly models the cross-lingual alignment information and the mono-lingual syntax information using a graph. We develop a series of algorithms, including graph construction, learning, and pre-training. The experiments on two benchmark datasets for cross-lingual MRC show that our approach outperforms all strong baselines, which verifies the effectiveness of syntax information for cross-lingual MRC.

Dense optic nerve head deformation estimated using CNN as a structural biomarker of glaucoma progression.

To present a new structural biomarker for detecting glaucoma progression based on structural transformation of the optic nerve head (ONH) region over time. Dense ONH deformation was estimated using deep learning methods namely DDCNet-Multires, FlowNet2, and FlowNetCorrelation, and legacy computational methods namely the topographic change analysis (TCA) and proper orthogonal decomposition (POD) methods. A candidate biomarker was estimated as the average magnitude of deformation of the ONH and evaluated using longitudinal confocal scans of 12 laser treated and 12 contralateral normal eyes of 12 primates from the LSU Experimental Glaucoma Study (LEGS); and 36 progressing eyes and 21 longitudinal normal eyes from the UCSD Diagnostic Innovations in Glaucoma Study (DIGS). Area under the ROC curve (AUC) was used to assess the diagnostic accuracy of the biomarker. AUROC (95% CI) for LEGS were: 0.83 (0.79, 0.88) for DDCNet-Multires; 0.83 (0.78, 0.88) for FlowNet2; 0.83 (0.78, 0.88) for FlowNet-Correlation; 0.94 (0.91, 0.97) for POD; and 0.86 (0.82, 0.91) for TCA methods. For DIGS: 0.89 (0.80, 0.97) for DDCNet-Multires; 0.82 (0.71, 0.93) for FlowNet2; 0.93 (0.86, 0.99) for FlowNet-Correlation; 0.86 (0.76, 0.96) for POD; and 0.86 (0.77, 0.95) for TCA methods. Lower diagnostic accuracy of the learning-based methods for LEG study eyes were due to image alignment errors in confocal sequences. Deep learning methods trained to estimate generic deformation were able to estimate ONH deformation from image sequences and provided a higher diagnostic accuracy. Our validation of the biomarker using ONH sequences from controlled experimental conditions confirms the diagnostic accuracy of the biomarkers observed in the clinical population. Performance can be further improved by fine-tuning these networks using ONH sequences.

An Installation Angle Error Calibration Method in an Ultra-Short Baseline System Based on a Dual Transponder

Abstract The installation error of an acoustic transceiver array is one of the important error sources in an ultra-short baseline (USBL) system. In a USBL system with a positioning accuracy of 0.5%, an installation error angle of 1° will lead to a positioning error of 1.7% times the slant distance. In this paper, a dual transponder-based installation angle error calibration method for USBL is proposed. First, the positioning errors induced by various installation angles are deduced and analysed using the linear measurement of seafloor targets. Then, an iterative algorithm is proposed that estimates the rolling alignment error, pitching alignment error, and heading alignment error, in that order. The simulation and experienced results show that, after three iterations, the estimates of the three alignment errors can converge quickly, all of the estimates converge to within 0.001° and the estimated values are very close to the true values. The horizontal positioning error caused by the installation error angle can be reduced by nearly 75%. The method has good effectiveness and robustness, and can greatly improve the positioning accuracy of the USBL system.

PEDS13: Systemic force evaluation of Magnetic Bearings Design for a Pediatric LVAD Study

Study: A miniature magnetic levitation bearing system (maglev) was proposed for an implantable pediatric LVAD. The maglev bearing system is comprised passive radial bearings made of permanent magnet (PM) rings, and an active axial bearing. The study evaluates the passive bearing’s ability to radially stabilize the rotor within a moveable range of radial and axial displacements, and characterizes the axial forces produced. Methods: A stacked pair of concentric PM rings was placed on each side of the rotor and stator for the radial bearings. The PM rings were placed with like poles facing each other. The outer PM rings and stator were mounted on a six-axis force sensor attached to an xyz micrometer driven stage to set the position, and a rotational stage for alignment. The rotor and inner PM rings were inserted in the stator and held in place by both ends. The forces produced were measured at a combination of radial (0mm to 0.35mm with 0.05mm steps) and axial (0mm to 0.25mm with 0.05mm steps) displacements away from the center for a combination of 48 points. The measurements were taken three times and the average was taken. A finite element simulation was conducted by combining previously validated models of the motor, and the PM ring bearings to calculate radial and axial displacement forces. Results: As radial displacement increases, bearing force towards the center increases. Axial displacement decreases the force magnitude, but the direction towards the center remains. This shows the bearing’s ability to radially stabilize the rotor at a range of different radial and axial displacement combinations. As you increase axial displacement, the axial force away from the center increases. The radial position has little effect on axial force. This study quantifies the passive bearing’s axial forces to use in future work of developing the active axial bearing. The simulation results are in accordance with the experimental results. Variation is likely due to rotor-stator alignment error, holder manufacturing tolerances, and rotor rod flexing.Figure 1. Experimental Force Test SetupFigure 2. Axial and radial forces produced. Points are experimental results, dashed lines are simulation results.

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

Automatic Alignment Method of Underwater Charging Platform Based on Monocular Vision Recognition

To enhance the crypticity and operational efficiency of unmanned underwater vehicle (UUV) charging, we propose an automatic alignment method for an underwater charging platform based on monocular vision recognition. This method accurately identifies the UUV number and guides the charging stake to smoothly insert into the charging port of the UUV through target recognition. To decode the UUV’s identity information, even in challenging imaging conditions, an encryption encoding method containing redundant information and an ArUco code reconstruction method are proposed. To address the challenge of underwater target location determination, a target location determination method was proposed based on deep learning and the law of refraction. The method can determine the two-dimensional coordinates of the target location underwater using the UUV target spray position. To meet the real-time control requirements and the harsh underwater imaging environment, we proposed a target recognition algorithm to guide the charging platform towards the target direction. The practical underwater alignment experiments demonstrate the method’s strong real-time performance and its adaptability to underwater environments. The final alignment error is approximately 0.5548 mm, meeting the required alignment accuracy and ensuring successful alignment.

A ray tracing‐assisted device discovery approach for low terahertz communications

AbstractFuture wireless communication systems at low terahertz frequencies (0.1–1 THz) suffer from high attenuation losses, and therefore, highly directive antennas are foreseen. As highly directive antennas have narrow beams, the alignment of the transmitter and the receiver is a crucial step in such systems. A device discovery approach using results from ray tracing (RT) combined with an iterative fine tuning is presented by the authors. A summary of previous investigations emphasises the benefits in terms of performance compared to an iterative approach. Furthermore, it indicates strong dependency of the RT output on the accuracy of the input data, that is, the environmental model. The authors extend the previous works by evaluating the impact of antenna characteristics. It is shown that the sidelobes lead to an error in the final alignment due to the superposition of the received power via multipath components, and equations to calculate the maximum error are given. Besides, the impact is evaluated with respect to sidelobe suppression. Here, equations to calculate the maximum error are given and it is shown that the sidelobes do not affect the alignment if the gain of the sidelobe is more than 30 dB lower than that of the mainlobe.

A VHR Bi-Temporal Remote-Sensing Image Change Detection Network Based on Swin Transformer

Change detection (CD), as a special remote-sensing (RS) segmentation task, faces challenges, including alignment errors and illumination variation, dense small targets, and large background intraclass variance in very high-resolution (VHR) remote-sensing images. Recent methods have avoided the misjudgment caused by illumination variation and alignment errors by increasing the ability of global modeling, but the latter two problems have still not been fully addressed. In this paper, we propose a new CD model called SFCD, which increases the feature extraction capability for small targets by introducing a shifted-window (Swin) transformer. We designed a foreground-aware fusion module to use attention gates to trim low-level feature responses, enabling increased attention to the changed region compared to the background when recovering the changed region, thus reducing background interference. We evaluated our model on two CD datasets, LEVIR-CD and CDD, and obtained F1 scores of 91.78 and 97.87, respectively. The experimental results and visual interpretation show that our model outperforms several previous CD models. In addition, we adjusted the parameters and structure of the standard model to develop a lightweight version that achieves an accuracy beyond most models with only 1.55 M parameters, further validating the effectiveness of our design.

Deep Compressed Super-Resolution Imaging with DMD Alignment Error Correction

In the field of compressed imaging, many attempts have been made to use the high-resolution digital micromirror array (DMD) in combination with low-resolution detectors to construct imaging systems by collecting low-resolution compressed data to reconstruct high-resolution images. However, the difficulty of achieving micrometer-level alignment between DMD devices and detectors has resulted in significant reconstruction errors. To address this issue, we proposed a joint input generative adversarial network with an error correction function that simulates the degradation of image quality due to alignment errors, designed an optical imaging system, and incorporated prior imaging system knowledge in the data generation process to improve the training efficiency and reconstruction performance. Our network achieved the ability to reconstruct 4× high-resolution images with different alignment errors and performed outstanding reconstruction in real-world scenes. Compared to existing algorithms, our method had a higher peak signal-to-noise ratio (PSNR) and better visualization results, which demonstrates the feasibility of our approach.

Rigid waist shift: A new method for local coupling corrections in the LHC interaction regions

Successful operation of large-scale particle accelerators depends on the precise correction of magnet field or alignment errors present in the machine. In the Large Hadron Collider (LHC), transverse linear coupling has been shown to have a significant impact on the beam dynamics. However, current measurement methods are not sufficient for precise local coupling measurement at the interaction point. In this paper, an approach to determine interaction region local coupling corrections with a rigid waist shift based on correlated global variables such as the closest tune approach $|{C}^{\ensuremath{-}}|$ is presented. The validity of the method is demonstrated through simulations and experimental measurements taken during the LHC Run 3 commissioning in 2022, where determined corrections were applied and led to a measured luminosity increase of 9.7% and 3.5% at the ATLAS and CMS detectors, respectively.

All-Parameter System-Level Calibration for SINS/CNS Based on Global Observability Analysis

Before using the strap-down inertial navigation system (SINS)/ celestial navigation system (CNS) integrated navigation system, the calibration of its error parameters is a necessary process to improve the system accuracy. In this paper, an all-parameter system-level calibration method has been proposed, which utilizes Kalman filtering for simultaneously estimating the bias, scale factor, misalignments of inertial measurement unit (IMU), and installation errors of the star sensor. The observation has been constructed using the inertial navigation output information and the starlight vector measured by the star sensor. The observability of the error parameters has been analyzed theoretically, and the optimal excitation conditions for each error parameter are obtained. The effects of the angle between the starlight vector and the optical axis of the star sensor, and the attitude of the IMU, on the estimation accuracy of the installation error of the star sensor are also given. Finally, a 10-order rotation scheme has been designed and all the error parameters can be optimally excited. The simulation and experimental results demonstrate that all the error parameters have better stability and repeatability, when compared with the traditional method, and the initial alignment error of 3.4" is better than 13.1" of the traditional method. Moreover, the installation error of the star sensor can be estimated with a high precision only when the angle between the starlight vector and the optical axis of the star sensor exceeds 1°. It is shown that the proposed method can effectively improve the navigation performance of the SINS/CNS integrated navigation system.

A novel approach for determining instantaneous centers of rotation of the mandible with an intraoral scanner: A preliminary study.

Recording and reproducing mandibular movements have been of key importance in the practice of dentistry for over a century. Recently, it has become possible to use digital technologies for these tasks. This study presents a preliminary method to try to identify the mandibular instantaneous centres of rotation based solely on intraoral scanners. The dentitions of four participants were scanned, multiple inter-occlusal registrations and buccal scans were performed in closed and opened positions. Blender software was used to align the meshes during the post-scan digital workflow. Bite alignment accuracy was assessed and then improved with a strict exclusion protocol. An automated algorithm was used to find rotations between closed stage and open stage meshes. Our exclusion protocol reduced the bite alignment error significantly (p = 0.001) and the root-mean-square error value of the meshes decreased from 0.09 mm (SD = 0.15) to 0.03 mm (SD = 0.017). However, the remaining translational error caused an unexpectedly large shift in the axis of rotation (mean = 1.35 mm, SD = 0.77) with a 41.83: 1 ratio. As found in other studies, our results showed even a small amount of error during registration can shift the axis of rotation a large amount. This phenomenon will compromise the results of common pantographic methods which assume a rotation axis of the condyle. It also adds valuable information to the concept of instantaneous centers of rotation by revealing their true characteristics.

Insights Regarding Optometric Findings of CHARGE Syndrome in a Pediatric Low Vision Clinic.

CHARGE, named for common findings-coloboma, heart defects, atresia of choanae, retardation of growth and development, genital hypoplasia, and ear anomalies-is a frequent etiology of deaf-blindness. A retrospective review in a pediatric low vision clinic presented the opportunity to investigate ocular findings in this syndrome with variable clinical presentations. This retrospective study reviewed ocular findings and visual function measures from low vision evaluations of patients with CHARGE syndrome, which may influence their multidisciplinary management. A retrospective chart review was conducted by three examiners of 60 patients presenting with CHARGE syndrome at a pediatric low vision clinic. Visual acuity and contrast sensitivity were obtained using standard measures. Ocular alignment and cycloplegic refractive error measurements were recorded. Refractive findings were analyzed using vector analysis. Anterior and posterior segment findings were recorded. Patients ranged in age from 1 to 29 years and were followed up for a mean of 4.3 years. Best-corrected visual acuity ranged from no light perception to 20/20 Snellen equivalent. Characteristics of strabismus, occurring in 82% of patients, were reported. Contrast sensitivity was reduced in 52% of patients. Chorioretinal colobomas were reported in 88% of patients. The most common ocular findings included nystagmus (43%), microphthalmia (27%), iris coloboma (27%), and facial nerve palsy (23%). Refractive vector analysis revealed significant myopic progression of the spherical equivalent with age and a tendency for with-the-rule astigmatism and minimal obliquity. This retrospective review of a relatively large sample size for this rare condition outlined the most common ocular manifestations of CHARGE syndrome. Decreased visual acuity, myopic refractive error, strabismus, and reduced contrast sensitivity were common. Thus, careful optometric evaluation in this population is required, as these findings must be considered in appropriate clinical and habilitative management.

Quantification of congruence among gene trees with polytomies using overall success of resolution for phylogenomic coalescent analyses.

Gene-tree-inference error can cause species-tree-inference artefacts in summary phylogenomic coalescent analyses. Here we integrate two ways of accommodating these inference errors: collapsing arbitrarily or dubiously resolved gene-tree branches, and subsampling gene trees based on their pairwise congruence. We tested the effect of collapsing gene-tree branches with 0% approximate-likelihood-ratio-test (SH-like aLRT) support in likelihood analyses and strict consensus trees for parsimony, and then subsampled those partially resolved trees based on congruence measures that do not penalize polytomies. For this purpose we developed a new TNT script for congruence sorting (congsort), and used it to calculate topological incongruence for eight phylogenomic datasets using three distance measures: standard Robinson-Foulds (RF) distances; overall success of resolution (OSR), which is based on counting both matching and contradicting clades; and RF contradictions, which only counts contradictory clades. As expected, we found that gene-tree incongruence was often concentrated in clades that are arbitrarily or dubiously resolved and that there was greater congruence between the partially collapsed gene trees and the coalescent and concatenation topologies inferred from those genes. Coalescent branch lengths typically increased as the most incongruent gene trees were excluded, although branch supports typically did not. We investigated two successful and complementary approaches to prioritizing genes for investigation of alignment or homology errors. Coalescent-tree clades that contradicted concatenation-tree clades were generally less robust to gene-tree subsampling than congruent clades. Our preferred approach to collapsing likelihood gene-tree clades (0% SH-like aLRT support) and subsampling those trees (OSR) generally outperformed competing approaches for a large fungal dataset with respect to branch lengths, support and congruence. We recommend widespread application of this approach (and strict consensus trees for parsimony-based analyses) for improving quantification of gene-tree congruence/conflict, estimating coalescent branch lengths, testing robustness of coalescent analyses to gene-tree-estimation error, and improving topological robustness of summary coalescent analyses. This approach is quick and easy to implement, even for huge datasets.

Geometric shape measurement and its application in bridge construction based on UAV and terrestrial laser scanner

Oblique photography and laser scanning techniques are increasingly being used to measure the geometric shapes of structures. Optimizing the flight path of unmanned aerial vehicles (UAVs) in data acquisition can make the collected image data more complete with muti-view details and improve the utilization of image data in 3D reconstruction. This paper proposed a convenient UAV flight planning approach for oblique photography based on feature waypoints along with an efficient framework for automatically extracting bridge alignment from point cloud data. The application on an under-construction long multi-span girder bridge demonstrated the effectiveness of the optimized UAV flight planning method for collecting multi-view bridge images and the superiority of the rebuilt 3D model compared to commonly used flight strategies. Taking the total station data as references, the errors of the bridge alignment obtained from the point cloud collected by the frequently adopted terrestrial laser scanners (TLS) and UAV devices, respectively, achieve 4.41 mm and 10.97 mm in 142 m measured length. The analysis of the original and eliminated-slope bridge alignments illustrates the potential for using the suggested method in general large-scale bridge constructions.

Virtual Joint Motion Simulator Accurately Predicts Effects of Femoral Component Malalignment during TKA.

Component alignment accuracy during total knee arthroplasty (TKA) has been improving through the adoption of image-based navigation and robotic surgical systems. The biomechanical implications of resulting component alignment error, however, should be better characterized to better understand how sensitive surgical outcomes are to alignment error. Thus, means for analyzing the relationships between alignment, joint kinematics, and ligament mechanics for candidate prosthesis component design are necessary. We used a digital twin of a commercially available joint motion simulator to evaluate the effects of femoral component rotational alignment. As anticipated, the model showed that an externally rotated femoral component results in a knee which is more varus in flexion, with lower medial collateral ligament tension compared to a TKA knee with a neutrally aligned femoral implant. With the simulation yielding logical results for this relatively simple test scenario, we can have more confidence in the accuracy of its predictions for more complicated scenarios.

Magnetic alignment technology for wafer bonding

PurposeWafer bonding is a key process for 3 D advanced packaging of integrated circuits. It requires very high accuracy for the wafer alignment. To solve the problems of large movement stroke, position calibration error and low production efficiency in optical alignment, this paper aims to propose a new wafer magnetic alignment technology (MAT) which is based on tunnel magneto resistance effect. MAT can realize micro distance alignment and reduces the design and manufacturing difficulty of wafer bonding equipment.Design/methodology/approachThe current methods and existing problems of wafer optical alignment are introduced, and the mechanism and realization process of wafer magnetic alignment are proposed. Micro magnetic column (MMC) marks are designed on the wafer by the semiconductor manufacturing process. The mathematical model of the space magnetic field of the MMC is established, and the magnetic field distribution of the MMC alignment is numerically simulated and visualized. The relationship between the alignment accuracy and the MMC diameter, MMC remanence, MMC thickness and sensor measurement height was studied.FindingsThe simulation analysis shows that the overlapping double MMCs can align the wafer with accuracy within 1 µm and can control the bonding distance within the micrometer range to improve the alignment efficiency.Originality/valueMagnetic alignment technology provides a new idea for wafer bonding alignment, which is expected to improve the accuracy and efficiency of wafer bonding.