Precise Positioning Research Articles

Target control point position calibration based on coordinate measuring machine and global bundling optimization algorithm.

The target-based coordinate measurement system is primarily composed of a target, a camera, and a computer, relying on image processing to achieve coordinate measurement. It finds extensive applications in spatial positioning and precision measurement, such as industrial manufacturing, robot navigation, and shipbuilding. The accuracy of the target-based coordinate measurement system is highly dependent on the calibration precision of control point positions. A new method based on the calibration of control point positions using a traditional coordinate measuring machine is proposed in this paper. This method does not require the individual calculation of spatial positions for each control point; instead, it utilizes a global bundle algorithm to obtain the positions of all the control points within the field of view, thereby simplifying the calibration process. The simulation results indicate that the key factor influencing the accuracy of visual calibration is the motion range of the measured target within the field of view, and it is positively correlated with calibration accuracy. For a large-sized target, a method of stitching control points within a near-sighted distance is proposed, which achieves higher accuracy compared to full-field calibration without requiring all the control points to be present within the field of view. Experiments demonstrate that using the proposed calibration method, the standard deviations of measurement accuracy of the target-based coordinate measurement system in the x, y, and z directions are 0.01, 0.01, and 0.08mm, respectively.

Beidou Receiver Based on Anti-Jamming Antenna Arrays with Self-Calibration for Precise Relative Positioning

Beidou Receiver Based on Anti-Jamming Antenna Arrays with Self-Calibration for Precise Relative Positioning

Robot-Assisted Ocular Tumour Radiotherapy Positioning and Tracking System.

Precise dose position distribution is crucial for ocular proton therapy. A non-invasive eye positioning and tracking system with novel structure is designed to reduce eye movement and facilitate precise dose by guiding the direction of patients' gaze. The system helps to achieve gaze guidance by controlling the light source fixed on two turntables above the patient's face. Tracking of the eye is achieved by cameras attached to the end of a 6DOFs robotic arm to capture the image reflected from a mirror above the patient's face. After all operation steps, the accuracy of the robotic arm is 0.18mm (SD 0.25mm) and the accuracy of the turntables is 0.01° (SD 0.02°). The EPTS is tested to be remotely controlled in real time with sufficient precision and repeatability. The system is expected to improve the safety and efficiency of ocular proton therapy.

A Compact Wireless Permanent Magnet Synchronous Motor System With Precise Speed and Position Control

A Compact Wireless Permanent Magnet Synchronous Motor System With Precise Speed and Position Control

LEO satellite clock modeling and its benefits for real-time LEO PPP timing

In the kinematic Precise Orbit Determination (POD) process for low earth orbit (LEO) satellites, a significant correlation exists between the orbital and clock parameters. Here, a LEO satellite clock model is proposed to enhance the Precise Point positioning (PPP) timing and time synchronization during the LEO orbit/clock determination. The GRACE-FO satellite was utilized to achieve PPP timing and time transfer for LEO satellites with the real-time (RT) products provided by three analysis centers, namely Centre National d’Etudes Spatiales (CNES), GMV Aerospace and Defense (GMV), and Wuhan Technical University (WHU). For the frequency stability of the PPP timing for LEO satellites, the short-term stability (1280 s) is about 10−13 level, and the long-term stability (10,240 s) is about 10−14 level. Compared with the traditional PPP method (Scheme1), the LEO satellite clock model (Scheme2) is capable of significantly reducing the noise, regardless of whether it is PPP timing or PPP time synchronization. The frequency stability of Scheme 1 shows an improvement in short-term stability (1280 s) by about 8 %-72 % and the max improvement of the long-term stability (10240 s) by approximately 39 %, the result also shows a better improvement in LEO time synchronization.

Improving PPP positioning and troposphere estimates using an azimuth-dependent weighting scheme

Asymmetric troposphere modeling is crucial in Precise Point Positioning (PPP). The functional model of the asymmetric troposphere has been thoroughly studied, while the stochastic model lacks discussion. Currently, there is no suitable stochastic model for asymmetric tropospheric conditions, potentially degrading the positioning accuracy and the reliability of Zenith Total/Wet Delay (ZTD/ZWD) estimates. This paper introduces an Azimuth-Dependent Weighting (ADW) scheme that utilizes information from asymmetric mapping functions to adaptively weight Global Navigation Satellite System (GNSS) observations affected by azimuth-dependent errors. The concept of ADW has been validated using Numerical Weather Prediction data and International GNSS Service data. The results indicate that ADW effectively improves the coordinate repeatability of the PPP solution by approximately 10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10\\%$$\\end{document} in the horizontal and 20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$20\\%$$\\end{document} in the vertical direction. Additionally, ADW appears to be capable to improve the ZWD estimates during the PPP convergence period and yields smoother ZWD estimates. Consequently, it is recommended to adopt this new weighting scheme in PPP applications when an asymmetric mapping functions is employed.

Enhancing BDS-3 PPP-B2b real-time positioning performance by tightly integrating MEMS IMU and LiDAR in GNSS-Degraded environment

Currently, BDS-3 can provide satellite-based real-time precise point positioning (PPP) service via its PPP-B2b signal. However, in a complex Global Navigation Satellite System (GNSS)-degraded environment, the BDS-3 PPP-B2b positioning performance is severely influenced. A tightly coupled PPP/microelectromechanical system inertial measurement unit (MEMS IMU)/Light Detection and Ranging (LiDAR) combination is proposed, in which the original pseudorange and carrier observations, IMU measurements, and LiDAR planar features are fused via an extended Multi-State Constraint Kalman Filter (MSCKF). Moreover, the Galileo satellites are further utilized using the broadcast ephemerides to increase the number of available satellites. And to improve the real-time computing efficiency, a planar feature tracking model based on ground segmentation is adopted. The model consists of point cloud segmentation, planar feature extraction and fusion, and data correlation. To evaluate the effectiveness of the proposed method, the experiment is conducted at both a campus and a park. The result shows that approximately sub-meter-level positioning accuracy can be achieved using tightly coupled BDS-3 PPP-B2b, IMU, and LiDAR, while the positioning errors of BDS-3 PPP-B2b and BDS-3 PPP-B2b/IMU are both larger than 5 m. Furthermore, the MEMS IMU and LiDAR measurements show the capability of bridging the gaps in the GNSS data, which enables continuous and stable positioning. Moreover, compared to the state-of-the-art A-LOAM, LEGO-LOAM, and LIO-SAM frameworks, absolute trajectory errors of less than 1.0 and 2.5 m are achieved in the campus and park, respectively, using the proposed algorithm, with improvements of more than 3.40 and 1.58 times.

Simulations of Dedicated LEO-PNT Systems for Precise Point Positioning: Methodology, Parameter Analysis, and Accuracy Evaluation

Simulations of Dedicated LEO-PNT Systems for Precise Point Positioning: Methodology, Parameter Analysis, and Accuracy Evaluation

How ATP and dATP Act as Molecular Switches to Regulate Enzymatic Activity in the Prototypical Bacterial Class Ia Ribonucleotide Reductase.

Class Ia ribonucleotide reductases (RNRs) are allosterically regulated by ATP and dATP to maintain the appropriate deoxyribonucleotide levels inside the cell for DNA biosynthesis and repair. RNR activity requires precise positioning of the β2 and α2 subunits for the transfer of a catalytically essential radical species. Excess dATP inhibits RNR through the creation of an α-β interface that restricts the ability of β2 to obtain a position that is capable of radical transfer. ATP breaks the α-β interface, freeing β2 and restoring enzyme activity. Here, we investigate the molecular basis for allosteric activity regulation in the well-studied Escherichia coli class Ia RNR through the determination of six crystal structures and accompanying biochemical and mutagenesis studies. We find that when dATP is bound to the N-terminal regulatory cone domain in α, a helix unwinds, creating a binding surface for β. When ATP displaces dATP, the helix rewinds, dismantling the α-β interface. This reversal of enzyme inhibition requires that two ATP molecules are bound in the cone domain: one in the canonical nucleotide-binding site (site 1) and one in a site (site 2) that is blocked by phenylalanine-87 and tryptophan-28 unless ATP is bound in site 1. When ATP binds to site 1, histidine-59 rearranges, prompting the movement of phenylalanine-87 and trytophan-28, and creating site 2. dATP hydrogen bonds to histidine-59, preventing its movement. The importance of site 2 in the restoration of RNR activity by ATP is confirmed by mutagenesis. These findings have implications for the design of bacterial RNR inhibitors.

Management of bone loss in anterior shoulder instability.

Bone defects are frequently observed in anterior shoulder instability. Over the last decade, knowledge of the association of bone loss with increased failure rates of soft-tissue repair has shifted the surgical management of chronic shoulder instability. On the glenoid side, there is no controversy about the critical glenoid bone loss being 20%. However, poor outcomes have been described even with a subcritical glenoid bone defect as low as 13.5%. On the humeral side, the Hill-Sachs lesion should be evaluated concomitantly with the glenoid defect as the two sides of the same bipolar lesion which interact in the instability process, as described by the glenoid track concept. We advocate adding remplissage to every Bankart repair in patients with a Hill-Sachs lesion, regardless of the glenoid bone loss. When critical or subcritical glenoid bone loss occurs in active patients (> 15%) or bipolar off-track lesions, we should consider anterior glenoid bone reconstructions. The techniques have evolved significantly over the last two decades, moving from open procedures to arthroscopic, and from screw fixation to metal-free fixation. The new arthroscopic techniques of glenoid bone reconstruction procedures allow precise positioning of the graft, identification, and treatment of concomitant injuries with low morbidity and faster recovery. Given the problems associated with bone resorption and metal hardware protrusion, the new metal-free techniques for Latarjet or free bone block procedures seem a good solution to avoid these complications, although no long-term data are yet available.

A robotic fish processing line enhanced by machine learning

This paper presents the design of a comprehensive automatic fish processing line utilizing machine learning algorithms. The processing line encompasses several essential steps, including fish identification by type, fish sorting by size, fish orientation based on shape, and fish cutting at the optimal chopping points. The primary objective of this design is not just automation but also maximizing economic benefits by preserving the maximum amount of fish meat during the cutting process, achieved through the application of machine learning algorithms. To accomplish these goals, we employ a combination of transfer learning and convolutional neural networks to establish criteria for actions across all stages of automatic fish processing. At the heart of the processing station is a conveyor belt equipped with numerous sensors and lenses. Positioned along this conveyor belt are two robotic arms, responsible for precise positioning and cutting operations, all guided by the machine learning algorithms. To provide a visual representation of these design concepts, we have created a 3D SolidWorks model.

A novel cross-receptive field fusion cascade network with adaptive mask update for transfer health state diagnosis of manipulators

Manipulators, particularly planar parallel manipulators, are widely employed in high-end precision equipment to conduct precise positioning and operation tasks due to their advantages of high stiffness, high precision, and high load. Moreover, they are also frequently exposed to changeable working circumstances, which significantly cause inconsistent health state data distribution. Although transfer learning can successfully offset the above distribution discrepancies, it remains unclear how to identify and quantify the source domain knowledge’s contribution to the transfer process. To overcome these challenges, a novel transfer health state diagnosis framework, named cross-receptive field fusion cascade network with adaptive mask update (CFFCN-AMU), is developed and employed for manipulators. Specifically, a unique cross-receptive field fusion cascade module (CFFCM), in which the receptive field self-evaluator and channel attention mechanism are jointly designed, is constructed initially to achieve adaptive extraction and fusion of cascaded features. Subsequently, in the target domain fine-tuning stage, an adaptive mask update (AMU) strategy is implemented to evaluate the contribution of source domain knowledge and selectively guide the parameter updating process. Finally, some mechanistic model-driven cross-working condition transfer scenarios are investigated. Multiple sets of excellent transfer diagnosis results fully illustrate the transferability and superiority of the constructed CFFCN-AMU model.

Automatic High-Throughput Injection System for Zebrafish Larvae Based on Precise Positioning of Injection Target

Automatic High-Throughput Injection System for Zebrafish Larvae Based on Precise Positioning of Injection Target

A 2-DOF piezoelectric platform for cross-scale semiconductor inspection

To address the challenge of achieving extensive travel and high precision in semiconductor inspection, this study proposes a novel 2-DOF cross-scale piezoelectric positioning platform. In semiconductor inspection, the platform utilizes elliptical, stick-slip, and direct-push drive modes to meet the motion requirements at millimeter, micrometer, and nanometer scales. By applying defined electrical signals to the piezoelectric units, the platform can achieve high-speed continuous mode (HCM) for the millimeter scale, low-speed stepping mode (LSM) for the micrometer scale, and high-precision positioning mode (HPM) for the nanometer scale. Theoretical analysis and simulations were performed to design the flexible stator of the platform, and its dynamic characteristics were analyzed. A prototype was fabricated, assembled, and experimentally tested to investigate the mechanical performance of the proposed platform. The results show that the prototype successfully realizes cross-scale motion in the three modes: achieving a maximum no-load speed of 62.47 mm/s in HCM, a low-speed stepping motion of 14.62 μm/s in LSM, and high-precision positioning with a resolution of 25 nm within a range of ±21 μm in HPM. Through the flexible switching and cooperation of the three drive modes, the platform can quickly approach the target at millimeter speed, further approach with micrometer step motion, and finally achieve nanometer precision positioning. Finally, the positioning platform was successfully applied to inspect semiconductor devices for defect inspection. This study explores a novel cross-scale driving method for piezoelectric positioning platforms, which provides a new approach for precision manipulation research related to semiconductor component inspection.

Achieving solar sail orbital maintenance with adjustable ballast masses in the ERTBP

An innovative method for orbital maintenance in the Sun-Earth Elliptic Restricted Three-Body Problem (ERTBP) is introduced. The concept arose from observing that Natural Periodic (NP) orbits in the ERTBP exhibit deviations from their periodic behavior over time. These NP orbits are derived solely from accurately specified initial orbital conditions. Hence, a strategy for orbital maintenance becomes essential for these missions. This approach utilizes NP orbits as the designated reference trajectory to be tracked by the spacecraft. It assumes a solar sail equipped with two ballast masses and suggests a Fully Coupled Orbit-Attitude (FCOA) model. In this comprehensive model, both orbital and attitude states mutually influence each other, enabling orbital adjustments through attitude control. Therefore, the first step involves developing the governing equations for this FCOA model incorporating two ballast masses within the ERTBP. The strategy includes dividing the NP orbit period into τ equal time intervals. During each interval, adjusting the velocity of the ballast mass using a 7-mode electrical motor facilitates precise attitude control, establishing a framework for orbital maintenance. Maintaining constant velocity within each interval simplifies achieving the desired ballast mass positions. The adjustment of ballast mass velocity is proposed to be conducted using a hybrid meta-heuristic Invasive Weed Optimization/Particle Swarm Optimization (IWO/PSO). The analysis examines how increasing the mass of the ballast masses enhances tracking performance, underscoring the critical role of mass in optimizing tracking capabilities. This highlights the significance of meticulous structural design in achieving superior outcomes for orbital maintenance missions.

Neural Network-Based Ambiguity Resolution for Precise Attitude Estimation With GNSS Sensors

Neural Network-Based Ambiguity Resolution for Precise Attitude Estimation With GNSS Sensors

Precise baseline determination for InSAR formation-flying satellites based on spaceborne BDS-3 and GPS observations

The precise baseline determination (PBD) is a crucial technique for spaceborne Interferometric Synthetic Aperture Radar (InSAR) formation-flying satellites to successfully carry out scientific missions. The Chinese InSAR formation T-A and T-B satellites are equipped with the multi-GNSS receiver that can track Global Positioning System (GPS) and BeiDou satellite navigation System (BDS-3) signals for PBD. First, we evaluate the data quality of spaceborne GPS and BDS-3 observations and carry out the absolute precise orbit determination (POD) with single-satellite ambiguity fixed, respectively. The result shows that a three-dimensional (3D) POD consistency of 1.48 cm (RMS) between them is reached. Then, the baseline products are obtained based on GPS and BDS-3 observations, respectively. The PBD of GPS-based is assessed via internal consistency checks and external baseline validations, Internally, the overlap comparison is sub-millimeter. Externally, comparisons with external agency is 1.36 mm in 3D. Taking the baseline results based on GPS as a reference, the difference shows that a consistency of PBD result based on BDS-3 is 0.91 mm in 3D. Finally, we perform the GPS + BDS-3 multi-GNSS data fusion for PBD experiment. The result shows that combining multi-GNSS data can not only increase the position precision and obtain a higher overlapping accuracy, but also improve the internal consistency of baseline products.

Patient-based multilevel transcriptome exploration highlights relevant chemokines and chemokine receptor axes in glioblastoma

Chemokines and their receptors form a complex interaction network, crucial for precise leukocyte positioning and trafficking. In cancer, they promote malignant cell proliferation and survival but are also critical for immune cell infiltration in the tumor microenvironment. Glioblastoma (GBM) is the most common and lethal brain tumor, characterized by an immunosuppressive TME, with restricted immune cell infiltration. A better understanding of chemokine-receptor interactions is therefore essential for improving tumor immunogenicity. In this study, we assessed the expression of all human chemokines in adult-type diffuse gliomas, with particular focus on GBM, based on patient-derived samples. Publicly available bulk RNA sequencing datasets allowed us to identify the chemokines most abundantly expressed in GBM, with regard to disease severity and across different tumor subregions. To gain insight into the chemokines–receptor network at the single cell resolution, we explored GBmap, a curated resource integrating multiple scRNAseq datasets from different published studies. Our study constitutes the first patient–based handbook highlighting the relevant chemokine–receptor crosstalks, which are of significant interest in the perspective of a therapeutic modulation of the TME in GBM.

Pan-genome analysis of GT64 gene family and expression response to Verticillium wilt in cotton

BackgroundThe GT64 subfamily, belonging to the glycosyltransferase family, plays a critical function in plant adaptation to stress conditions and the modulation of plant growth, development, and organogenesis processes. However, a comprehensive identification and systematic analysis of GT64 in cotton are still lacking.ResultsThis study used bioinformatics techniques to conduct a detailed investigation on the GT64 gene family members of eight cotton species for the first time. A total of 39 GT64 genes were detected, which could be classified into five subfamilies according to the phylogenetic tree. Among them, six genes were found in upland cotton. Furthermore, investigated the precise chromosomal positions of these genes and visually represented their gene structure details. Moreover, forecasted cis-regulatory elements in GhGT64s and ascertained the duplication type of the GT64 in the eight cotton species. Evaluation of the Ka/Ks ratio for similar gene pairs among the eight cotton species provided insights into the selective pressures acting on these homologous genes. Additionally, analyzed the expression profiles of the GT64 gene family. Overexpressing GhGT64_4 in tobacco improved its disease resistance. Subsequently, VIGS experiments conducted in cotton demonstrated reduced disease resistance upon silencing of the GhGT64_4, may indicate its involvement in affecting lignin and jasmonic acid biosynthesis pathways, thus impacting cotton resistance. Weighted Gene Co-expression Network Analysis (WGCNA) revealed an early immune response against Verticillium dahliae in G. barbadense compared to G. hirsutum. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) analysis indicated that some GT64 genes might play a role under various biotic and abiotic stress conditions.ConclusionsThese discoveries enhance our knowledge of GT64 family members and lay the groundwork for future investigations into the disease resistance mechanisms of this gene in cotton.

Adjacent Neuronal Fascicle Guides Motoneuron 24 Dendritic Branching and Axonal Routing Decisions through Dscam1 Signaling.

The formation and precise positioning of axons and dendrites are crucial for the development of neural circuits. Although juxtracrine signaling via cell-cell contact is known to influence these processes, the specific structures and mechanisms regulating neuronal process positioning within the central nervous system (CNS) remain to be fully identified. Our study investigates motoneuron 24 (MN24) in the Drosophila embryonic CNS, which is characterized by a complex yet stereotyped axon projection pattern, known as 'axonal routing.' In this motoneuron, the primary dendritic branches project laterally toward the midline, specifically emerging at the sites where axons turn. We observed that Scp2-positive neurons contribute to the lateral fascicle structure in the ventral nerve cord (VNC) near MN24 dendrites. Notably, the knockout of the Down syndrome cell adhesion molecule (Dscam1) results in the loss of dendrites and disruption of proper axonal routing in MN24, while not affecting the formation of the fascicle structure. Through cell-type specific knockdown and rescue experiments of Dscam1, we have determined that the interaction between MN24 and Scp2-positive fascicle, mediated by Dscam1, promotes the development of both dendrites and axonal routing. Our findings demonstrate that the holistic configuration of neuronal structures, such as axons and dendrites, within single motoneurons can be governed by local contact with the adjacent neuron fascicle, a novel reference structure for neural circuitry wiring.Significance Summary We uncover a key neuronal structure serving as a guiding reference for neural circuitry within the Drosophila embryonic CNS, highlighting the essential role of an adjacent axonal fascicle in precisely coordinating axon and dendrite positioning in motoneuron 24 (MN24). Our investigation of cell-cell interactions between motoneurons and adjacent axonal fascicles-crucial for initiating dendrite formation, soma mislocation, and axonal pathfinding in MN24-emphasizes the neuronal fascicle's significance in neural circuit formation through Dscam1-mediated inter-neuronal communication. This enhances our understanding of the molecular underpinnings of motoneuron morphogenesis in Drosophila Given the occurrence of analogous axon fascicle formations within the vertebrate spinal cord, such structures may play a conserved role in the morphogenesis of motoneurons via Dscam1 across phyla.