Automatic Measurement Procedure Research Articles

High-Precision Visual Servoing for the Neutron Diffractometer STRESS-SPEC at MLZ.

With neutron diffraction, the local stress and texture of metallic components can be analyzed non-destructively. For both, highly accurate positioning of the sample is essential, requiring the measurement at the same sample location from different directions. Current sample-positioning systems in neutron diffraction instruments combine XYZ tables and Eulerian cradles to enable the accurate six-degree-of-freedom (6DoF) handling of samples. However, these systems are not flexible enough. The choice of the rotation center and their range of motion are limited. Industrial six-axis robots have the necessary flexibility, but they lack the required absolute accuracy. This paper proposes a visual servoing system consisting of an industrial six-axis robot enhanced with a high-precision multi-camera tracking system. Its goal is to achieve an absolute positioning accuracy of better than 50μm. A digital twin integrates various data sources from the instrument and the sample in order to enable a fully automatic measurement procedure. This system is also highly relevant for other kinds of processes that require the accurate and flexible handling of objects and tools, e.g., robotic surgery or industrial printing on 3D surfaces.

Automated superconducting qubit characterization platform based on a modified 3D printer

Josephson Junctions are important components in superconducting qubits. It introduces anharmonicity to the energy level spacings of the qubit which allow us to identify two unique quantum energy states for computing. It is difficult to fabricate multiple junctions within the same desired parameter range. Characterisation of the junctions is, therefore, a necessary step after fabrication. In particular, the critical current of the junctions is determined by measuring their normal state resistance. This is done via two-point or four-point resistance measurement at a manual probe station which is a time-consuming process, especially for wafer-scale fabrication. This bottleneck can be circumvented by automation with object detection. The base of the automated probe station is a 3D printer modified with multiple Arduino Uno microcontrollers and motorized linear stages. The automation process is achieved via auto-alignment of the probes and an automatic measurement procedure. As a result, the fully automated process will take about 27–29[Formula: see text]s to measure the resistance of one junction which saves 28%–51% of the time compared to the manual probe station and can be unsupervised. Due to the reuse of a commercial 3D printer, the cost of this system is 800 SGD which is much less than comparable commercial solutions.

Automated ultrasound measurements of lateral abdominal muscles under controlled breathing phases

Background and objectivesA breathing phase during ultrasound measurements of the lateral abdominal muscles (LAMs) are usually indirectly controlled by visual inspection of the position of the transversus abdominis (TrA) muscle. This is due to the lack of devices to directly control airflow that are connected to the ultrasound in order to automatically and simultaneously freeze ultrasound images at the programmed breathing phase. Such indirect control may be related with potential measurement error because LAMs are respiratory muscles. Thus, the aim of this study was to present a newly developed and automatic measurement procedure to directly control airflow and at the same time automatically collect ultrasound images at the programed breathing phase. Additionally, it was decided to compare LAMs measurements obtained manually by the examiner and with an external device controlling the peak phase of tidal inspiration and expiration and compare the elasticity and thickness measurements between tidal inspiration and expiration in young participants. MethodsThe study was carried out on 10 healthy youth. The thickness and shear modulus were measured by an Aixplorer ultrasound scanner. The measurements were obtained manually by the examiner and with a newly developed external device controlling the peak phases of tidal inspiration and expiration. ResultsA significant difference in external/internal oblique thickness between the expiration and inspiration phases depended on the measurement procedure. The TrA thickness was similar during inspiration and expiration. During inspiration, the TrA shear modulus was higher than during expiration, and the TrA shear modulus depended on the measurement procedure. ConclusionAlthough the raw LAMs thickness and external/internal oblique thickness/shear modulus data were similar, the measurement procedure may affect the interpretation of the results. The TrA shear modulus is the most vulnerable to errors related to the measurement procedure. Construction of this study device controlling airflow and automatically collecting ultrasound images at the selected breathing phase seems to be promising in future studies considering measurements of respiratory muscles in a strictly defined breathing phase.

Assessment of a Vision-Based Technique for an Automatic Van Herick Measurement System

The adoption of Artificial Intelligence methods within the instrumentation and measurements field is nowadays an attractive research area. On the one hand, making machines learn from data how to perform an activity, rather than hard code sequential instructions, is a convenient and effective solution in many modern research areas. On the other hand, AI allows for the compensation of inaccurate or not complete models of specific phenomena or systems. In this context, this paper investigates the possibility to exploit suitable Machine Learning techniques in a vision-based ophthalmic instrument to perform automatic Anterior Chamber Angle (ACA) measurements. In particular, two CNN–based networks have been identified to automatically classify acquired images and select the ones suitable for the Van–Herick procedure. Extensive clinical trials have been conducted by clinicians, from which a realistic and heterogeneous image dataset has been collected. The measurement accuracy of the proposed instrument is derived by extracting measures from the images of the aforementioned dataset, as well as the system performances have been assessed with respect to differences in patients’ eye color. Currently, the ACA measurement procedure is performed manually by appropriately trained medical personnel. For this reason, Machine Learning and Vision–Based techniques may greatly improve both test objectiveness and diagnostic accessibility, by enabling an automatic measurement procedure.

AI-based mobile application to fight antibiotic resistance

Antimicrobial resistance is a major global health threat and its development is promoted by antibiotic misuse. While disk diffusion antibiotic susceptibility testing (AST, also called antibiogram) is broadly used to test for antibiotic resistance in bacterial infections, it faces strong criticism because of inter-operator variability and the complexity of interpretative reading. Automatic reading systems address these issues, but are not always adapted or available to resource-limited settings. We present an artificial intelligence (AI)-based, offline smartphone application for antibiogram analysis. The application captures images with the phone’s camera, and the user is guided throughout the analysis on the same device by a user-friendly graphical interface. An embedded expert system validates the coherence of the antibiogram data and provides interpreted results. The fully automatic measurement procedure of our application’s reading system achieves an overall agreement of 90% on susceptibility categorization against a hospital-standard automatic system and 98% against manual measurement (gold standard), with reduced inter-operator variability. The application’s performance showed that the automatic reading of antibiotic resistance testing is entirely feasible on a smartphone. Moreover our application is suited for resource-limited settings, and therefore has the potential to significantly increase patients’ access to AST worldwide.

Robot Calibration combining Kinematic Model and Neural Network for enhanced Positioning and Orientation Accuracy

Traditionally, the calibration of robots is pursued either using model-based or model-free methods. Only a few attempts to combine both approaches were reported, particularly the combination of geometric calibration and artificial neural network (ANN). The latter was mostly used to compensate the positioning error, however. This paper introduces an ANN for compensation of residual positioning as well as orientation error. Moreover, the ANN compensation can be applied with or without prior geometric calibration. An automatic measurement procedure was developed and nearly 14 000 robot poses were measured using a laser tracker. Five-fold cross validation on the training data was applied to find the best parameters of the ANN. These tests indicate that better accuracy is achievable by combining geometric calibration and ANN. Applying this combination on the test data reduced the maximum/average position error to 6.28 %/4.26 % and the maximum/average orientation error to 7.41 %/3.34 % of the original values (obtained without calibration).

A powerful method for measuring fish size of small-scale fishery catches using ImageJ

Measuring fish length is useful for exploring size structure of catches and fishing effects on marine resources. However manually measuring large numbers of individuals requires time-consuming manipulations, which are hardly operational in the context of tropical small-scale fisheries. To overcome this limitation, we developed a method to automatically measure total fish length using digital images and an analytical procedure using the ImageJ software. For testing this procedure, a total of 180 fishes (3 to 50 cm TL) from 19 families, presenting two color patterns and two caudal fin types, were obtained from small-scale fishers in southwestern Madagascar in 2018. We took standardized images of the fishes using a locally made, cost-effective camera-equipped device. The accuracy of fish size estimates and the time taken for measurements were compared across the ImageJ automatic procedure, manual measurements made on-screen using ImageJ, and measurements using an ichthyometer, the latter providing the reference fish length. The ImageJ automatic measurement procedure reached 98.4% accuracy while the measurement time was reduced by 57% and 40% compared to the measures using an ichthyometer and ImageJ on-screen tools, respectively. We conclude that the automatic measurement procedure provides very effective results for fish <50 cm TL. This method is relevant for governmental, non-governmental, and research organizations that investigate size-related fishing and management impacts on fish community structure and dynamics.

Orientation measurement of graphically simulated nanoscale electrospun fibrous structures using particle-based modeling and 2-D metaball fitting

PurposeThe purpose of this paper is to provide an efficient tool for simulating electrospinning process in virtual 3D space and optimizing experimental parameters. The fiber orientation from virtual or real electrospinning process can be easily measured using the image analysis technique. Using the semi-implicit Euler integration, the time integration can be more fast and stable, which enabled optimization of the electrospinning process. Also boundary conditions can be easily adopted during conjugate gradient matrix solving step.Design/methodology/approachTo simulate the electrospinning process, the authors have adopted a particle-based modeling technique using the molecular dynamics theory, which is known to be suitable for modeling materials with nonlinear and nonhomogeneous behavior such as fibers or fabrics. Gravitational, tensional, and electrostatical forces and their Jacobians were carefully defined and chosen to maintain the stability of the governing equation. Preconditioned conjugate gradient method was used to solve the matrix iteratively with boundary conditions. The 2-D metaball fitting technique, which was applied in the previous research (Sul et al., 2009) on experimental nanofiber scanning electron microscopy images, was utilized with virtual nanofiber images. A staircase function and a new shading language were proposed to automatically calculate the orientation and radius distribution of the graphically simulated electrospun fiber structures. The automatic measurement procedure was verified via graphically designed virtual replica images. Also the orientation tendency acquired from the simulation was compared with that of experimental data.FindingsSimulation result of fiber orientation showed linear relationship with the collecting drum speed. Use of particle-based method generated a simple system to simulate electrospinning process.Originality/valueThe semi-implicit Euler integration was applied to the electrospinning process and the final linear system was numerically stable to solve.

Network design and quality checks in automatic orientation of close-range photogrammetric blocks.

Due to the recent improvements of automatic measurement procedures in photogrammetry, multi-view 3D reconstruction technologies are becoming a favourite survey tool. Rapidly widening structure-from-motion (SfM) software packages offer significantly easier image processing workflows than traditional photogrammetry packages. However, while most orientation and surface reconstruction strategies will almost always succeed in any given task, estimating the quality of the result is, to some extent, still an open issue. An assessment of the precision and reliability of block orientation is necessary and should be included in every processing pipeline. Such a need was clearly felt from the results of close-range photogrammetric surveys of in situ full-scale and laboratory-scale experiments. In order to study the impact of the block control and the camera network design on the block orientation accuracy, a series of Monte Carlo simulations was performed. Two image block configurations were investigated: a single pseudo-normal strip and a circular highly-convergent block. The influence of surveying and data processing choices, such as the number and accuracy of the ground control points, autofocus and camera calibration was investigated. The research highlights the most significant aspects and processes to be taken into account for adequate in situ and laboratory surveys, when modern SfM software packages are used, and evaluates their effect on the quality of the results of the surface reconstruction.

Verification of clinical precision after computer-aided reconstruction in craniomaxillofacial surgery

Computer-aided surgery (CAS) has proved to be useful in reconstructive craniomaxillofacial surgery. Preoperative creation of virtual models by segmentation of the computerized tomography (CT) dataset and mirroring of the unaffected side allows for precise planning of complex reconstructive procedures. The aim of this study was to evaluate the accuracy of the preoperative planning and the postoperative result regarding the skeletal reconstruction. In a first step, the symmetry of unaffected human skulls and faces were evaluated by 20 midface CT data of skulls and 20 surface-scan data of healthy individuals. By mirroring and adjusting the original and mirrored datasets using a 3-dimensional modeling software, an automatic measurement procedure could evaluate the mean and the maximal modulus of the distances between both datasets. In a second step, 18 consecutive cases were selected which had been treated with CAS support. Group 1 consisted of orbital floor and/or medial wall fractures (n = 12), group 2 consisted of zygomatic bone fractures (n = 4), and group 3 included 2 patients who were treated by secondary orbital reconstruction including reosteotomy of the zygomatic bone (n = 2). To verify the surgical result, the preoperative CT dataset including the virtual planning and the postoperative CT dataset were compared by using image fusion. Additionally, postoperative surface scans and the clinical symptoms of the patients were evaluated. No differences between the skull and face symmetry were found. Mean values for distances considering the skull symmetry were 0.83 mm for male and 0.71 mm for female and for the face symmetry 0.65 mm for male and 0.76 mm for female. Comparing the preoperative planning with the postoperative outcome, a mean accuracy of 1.49-4.12 mm with maximum modulus of 2.49-6.00 mm was achieved. Orbital true-to-original reconstructions and the secondary reconstructions were more precise than the reposition of the zygomatic bones. The postoperative acquired surface scans resulted in mean distances from 0.89 to 1.784 mm. Despite these deviations, all patients demonstrated satisfying clinical outcome. The natural asymmetry in humans influences the accuracy of preoperative planning procedure, when the mirroring tool is used. The accuracy transforming the preoperative planning to the surgical reconstruction using CAS depends on location, surgical approach, and matter of reconstruction.

Initiation fracture toughness of HSLA steel through automatic measurement of stretch zone

Stretch zones are formed due to crack tip blunting in ductile materials before the initiation of a crack. Using special edge detection filters, an automatic measurement procedure based on image analysis (IA) for estimating stretch zone dimensions has been proposed. The in house developed software based on the proposed technique requires only fractographic images to automatically define stretch zone boundaries. Employing the proposed technique, stretch zone measurements were carried out on a high strength low alloy (HSLA) steel. The initiation fracture toughness of this HSLA steel has been estimated using these stretch zone dimensions. A comparison of measured stretch zone dimensions with the crack tip blunting region of the fracture resistance curve revealed that the blunting behaviour is non-linear and the data range normally considered for constructing the blunting line is inadequate to represent this non-linearity for these steels. Prediction of initiation fracture toughness (Ji) in high strength, high toughness materials therefore requires blunting line estimation from a modified data range. An upper bound blunting line data range is proposed for accurate prediction of Δacr (SZW) and Ji. The results of this study indicate that the IA technique can be used effectively for accurate determination of SZW, SZD, Ji, and JQ. It is also noted that for materials with non-linear blunting behaviour, SZW is a better parameter than SZD for predicting Ji and JQ.