Alignment Errors Research Articles

Estimation and performance analysis of multiple incident beam misalignment in spatial diversity based FSO transmissions

A novel approximated misalignment model of an alignment error with multiple incident beams in the position detection of the pointing, acquisition, and tracking (PAT) system is developed for spatial diversity based free-space optical (FSO) communication system. A centroid algorithm used in position detection of the incident beam to align transceivers for the PAT system can make additional pointing errors when multiple beams are incident on the position detector. The centroid algorithm measures the alignment errors through the ratio of the segmented received powers from the position detector. Therefore, the distant arrangement of transmitters and the atmospheric fading affect the alignment error, and the pointing loss of FSO communication increases. We propose a statistical misalignment model with multiple incident beams and analyze the transmission performance using this model to achieve power-efficient FSO communication. The optimum beam width with the misalignment model can reduce the transmit power required to satisfy system demands and outage probability with redundant power for reliable FSO communication.

Overview of Neural Network

Neural network is a machine learning method evolved from the idea of having look of the human brain, a data processing system which includes large number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks have emerged in the past few years as an area of unusual opportunity for research, development and application to a variety of real world problems. Indeed, neural networks exhibit characteristics and capabilities not provided by any other technology. Here are some of the examples human handwriting, reading typewritten text, compensating for alignment errors in robots, modeling complex systems that cannot be modelled mathematically, and predicting whether proposed loans will be good or fail. This paper presents a brief tutorial on neural networks and briefly describes several applications.

Reduced Calibration Strategy Using a Basketball for RGB-D Cameras

RGB-D cameras produce depth and color information commonly used in the 3D reconstruction and vision computer areas. Different cameras with the same model usually produce images with different calibration errors. The color and depth layer usually requires calibration to minimize alignment errors, adjust precision, and improve data quality in general. Standard calibration protocols for RGB-D cameras require a controlled environment to allow operators to take many RGB and depth pair images as an input for calibration frameworks making the calibration protocol challenging to implement without ideal conditions and the operator experience. In this work, we proposed a novel strategy that simplifies the calibration protocol by requiring fewer images than other methods. Our strategy uses an ordinary object, a know-size basketball, as a ground truth sphere geometry during the calibration. Our experiments show comparable results requiring fewer images and non-ideal scene conditions than a reference method to align color and depth image layers.

A note of quantitative diagnosis of compound errors in trough concentration system

In practice, there are multiple optical errors existing in parabolic trough concentrator, which respectively have a unique effect on the flux distribution on the wall of the absorber, causing it to deviate from the ideal operating condition. Diagnosis for multiple errors is difficult. The effect of combined optical errors on the flux distribution can be modelled using convolution operations. In this study, the flux patterns under multiple types of optical errors are traversed and combined, and the actual flux distribution profiles of the parabolic trough concentrator measured by a camera-based method are compared to identify the Gaussian slope error, gravity deformation error and alignment error of the concentrator. This indicates that it is feasible to invert the flux profile on the absorber wall and diagnose the types and values of the corresponding optical errors of the concentrator.

Accuracy of a novel accelerometer-based navigation (Naviswiss) for total hip arthroplasty in the supine position

BackgroundThis study aimed to determine the accuracy of acetabular cup insertion using a novel accelerometer-based navigation system in total hip arthroplasty (THA).MethodsA single-surgeon study was conducted in which 62 prospective patients with navigation and 42 retrospective patients without navigation in a supine position were compared. Absolute values for errors of radiographic inclination and anteversion were calculated. Navigation error was also calculated. Factors that affected absolute value of navigation error in cup alignment were determined.ResultsIn the navigation group, mean absolute errors for radiographic inclination and anteversion were 4.1° and 4.3°, respectively. In the control group, mean absolute errors were 6.6° in inclination (p < 0.01) and 5.9° in anteversion (p = 0.04). Mean absolute values of navigation error were 2.8° in inclination and 2.8°in anteversion. Factors affecting navigation errors were not found.ConclusionThis novel accelerometer-based navigation system significantly increased the accuracy of cup placement during THA in the supine position.

Impact of sample misalignment on grazing incidence x-ray diffraction patterns and the resulting unit cell determination.

Grazing incidence x-ray diffraction (GIXD) is a frequently used tool for the crystallographic characterization of thin films in terms of polymorph identification and determination of the crystallographic lattice parameters. Even full structure solutions are possible. To obtain highly accurate diffraction patterns, the thin film sample has to be aligned carefully with the center of the goniometer, which allows a defined incidence of the primary x-ray beam relative to the sample surface. This work studies the effect of misalignment of a thin film sample on the acquired diffraction pattern. Three potential types of misalignments are considered: the deviation of the sample surface from the center of the goniometer, an error in the incidence angle of the primary beam, and an inclination of the goniometer rotation axis from the normal of the substrate surface. The consequence of these types of sample misalignments is the shift of diffraction peaks toward specific directions in reciprocal space. Mathematical equations are given that relate the error in positions of Bragg peaks for each type of sample misalignment. Experiments with intentionally misaligned samples confirm the given formulas. In a subsequent step, the errors in the peak positions are translated to systematic errors in the estimation of the unit cell parameters. Depending on the type of misalignment, some alignment errors can be reduced or even corrected; in particular, azimuthal sample rotations prove to be advantageous in these cases. The results in this work improve the quality of GIXD measurements, in general, enabling deeper analysis like the full structure solution from the GIXD pattern on everyday basis.

Fabrication of soft x-ray monolithic Wolter mirror based on surface scanning measurement using touch probe.

The monolithic Wolter mirror is an ideal optical device for focusing soft x rays to a submicron-sized spot, with the advantages of high efficiency, large acceptance, achromaticity, and robustness to alignment error. The fabrication process for this type of mirror has not been established because of the difficulty in highly accurate figure measurement of free-form surfaces with small radii of curvature and steep profiles. In this study, we employed tactile scanning measurement for surface characterization to fabricate a high-precision Wolter mirror. First, it was demonstrated that the touch probe measurement did not leave scratches on the raw surface of the mirror substrate. Next, the measurement capability of the surface profiler was assessed, and the data analysis conditions were determined. Finally, the Wolter mirror was fabricated through repeated figure correction based on the tactile measurement, and the figure error of the final surface was evaluated. Wave-optical simulations that used this error as reference suggested that the size of the beam focused by the mirror was equivalent to the theoretical value at 1000 eV. The reflected image with uniform intensity distribution obtained at SPring-8 also revealed the effectiveness of the present fabrication approach based on tactile measurement.

Reproducibility of Manual Transfer of the Clinical Natural Head Position: Influence on the Soft Tissue and Hard Tissue Position of 3-Dimensional Virtual Surgical Planning

The purpose of this study was to assess the reproducibility of manually transferring the clinical natural head position (NHP) to the 3-dimensional (3D) virtual surgical planning and its subsequent influence on the soft tissue and maxillary hard tissue position. A retrospective cohort study was set up. The study population consisted of subjects who underwent bimaxillary osteotomies between 2016 and 2020 at the Department of Oral and Maxillofacial Surgery in Radboud University Medical Centre (Nijmegen, the Netherlands). Cone beam computed tomography scans, dentition data, and clinical photographs were acquired 4weeks before surgery. Two attempts (NHP1 and NHP2) were performed by a single examiner to manually transfer the NHP. 3D transformation matrices were used to quantify the transferred NHP in 3 degrees of freedom (pitch, roll, and yaw). Landmarks and surface-based matching were used to quantify the influence on the soft tissue and hard tissue positions in 6 degrees of freedom. The primary outcome variable was the reproducibility of manually aligning the NHP. The secondary and tertiary outcome variables were the effect of the reproducibility of the manually aligned NHP on the soft tissue and hard tissue displacements in the 3D virtual surgical planning. The study population consisted of 109 subjects: 37 males (33.9%) and 72 females (66.1%) with a mean age of 29.1±10.3years (range, 17.0 to 59.0). The manual transfer of pitch alignment (2.24±1.64⁰; 95% confidence interval [CI], 1.93 to 2.55) was significantly less reproducible than the roll (0.56±0.44⁰; 95% CI, 0.48 to 0.64; P<.001) and yaw (0.67±0.92⁰; 95% CI, 0.50 to 0.85; P<.001). Subsequently, this alignment error influenced the position of the maxilla (incisal point) and soft tissue menton by 0.85±0.86mm and 1.01±1.00mm vertically and 0.78±1.10mm and 0.80±1.18mm sagittally. The present study demonstrated that the manual transfer of the NHP from the clinical situation to the virtual environment influenced the soft tissue and hard tissue position and that a more reproducible method of transferring the clinical NHP is recommended.

Uncertainty Propagation and Assessment of Five-Axis On-Machine Measurement Error

Abstract As an important technology of adaptive machining, on-machine measurement (OMM) is always used to measure the shape of the to-be-cut surface. Due to the multi-source errors in an OMM system, the confidence of the measurement accuracy becomes particularly important before planning the next cutting procedure. OMM is implemented by multi-components involving machine tools, fixture, and on-machine probe. Each component introduces uncertainty of error into the measurement result. The positioning error, alignment error, and eccentricity error are investigated in terms of the expectation, uncertainty, and probability density function. By the kinematics of the components participated in the measurement, these errors are accumulated to affect the measurement results. Thus, the uncertainty of the final measurement result is determined by the accumulation of uncertainty of error sources. In order to evaluate the uncertainty of measurement error, an uncertainty propagation model involving positioning error of five-axis machine tools, alignment error of workpiece, and eccentricity error of the stylus tip is established in terms of the kinematic chain of OMM. Then, the uncertainty of the measurement result in vector form is obtained. Finally, the simulation and the experiment about the impeller are employed to validate the effectiveness and repeatability of the uncertainty propagation model for five-axis OMM.

A point cloud registration-based calibration algorithm for robot offline programming automatic loading in aero-grinding applications

PurposeDuring the process of the robotic grinding and polishing operations on aero-engine blades, the key problem of calibration error lies in fixture error and uneven margin. To solve this problem, this paper aims to propose a novel method to achieve rapid online calibration of the workpiece coordinate system through laser-based measurement techniques.Design/methodology/approachThe authors propose a calibration strategy based on point cloud registration algorithm. The main principle is presented as follows: aero blade mounted on clamping end-effector is hold by industry robot, the whole device is then scanned by a 3D laser scanner to obtain its surface point cloud, and a fast segmentation method is used to acquire the point cloud of the workpiece. Combining Super4PCS algorithm with trimmed iterative closest point, we can align the key points of the scanned point cloud and the sampled points of the blade model, thus obtaining the translation and rotation matrix for calculating the workpiece coordinate and machining allowance. The proposed calibration strategy is experimentally validated, and the positioning error, as well as the margin distribution, is finally analyzed.FindingsThe experimental results show that the algorithm can well accomplish the task of cross-source, partial data and similar local features of blade point cloud registration with high precision. The total time spent on point cloud alignment of 100,000 order of magnitude blade is about 4.2 s, and meanwhile, the average point cloud alignment error is reduced to below 0.05 mm.Originality/valueAn improved point cloud registration method is proposed and introduced into the calibration process of a robotic system. The online calibration technique improves the accuracy and efficiency of the calibration process and enhances the automation of the robotic grinding and polishing system.

Heading-sensitive azimuth error analysis and scheme modification for the multi-position alignment of a fiber-optic gyro strapdown inertial navigation system.

As two-position is the most widely used scheme of multi-position alignment, azimuth error analysis regarding the whole alignment procedure as an entity is explicitly discussed. A formula to calculate an equivalent north accelerometer bias drift rate is developed. The effecting extent of each inertial measurement error is also theoretically deduced and validated through simulation. It is pointed out that the main error sources causing heading-sensitive azimuth error are accelerometer triad non-orthogonality and lever arm error. In a Kalman filter alignment algorithm, affected by the equivalent accelerometer bias change rate, extra azimuth error emerges from the mistake estimation of fiber-optic gyroscope drift. A three-sequence scheme and a reciprocating slow-rotation scheme are proposed to achieve the most inertial measurement error self-compensation. Theoretical error comparison and a turntable four-orientation alignment test show the superiority of the reciprocating slow-rotation scheme over the other two schemes. The heading-sensitive azimuth alignment error is reduced from 0.2268° to better than 0.0015° through scheme modification.

CorAl: Introspection for robust radar and lidar perception in diverse environments using differential entropy

Robust perception is an essential component to enable long-term operation of mobile robots. It depends on failure resilience through reliable sensor data and pre-processing, as well as failure awareness through introspection, for example the ability to self-assess localization performance. This paper presents CorAl: a principled, intuitive, and generalizable method to measure the quality of alignment between pairs of point clouds, which learns to detect alignment errors in a self-supervised manner. CorAl compares the differential entropy in the point clouds separately with the entropy in their union to account for entropy inherent to the scene. By making use of dual entropy measurements, we obtain a quality metric that is highly sensitive to small alignment errors and still generalizes well to unseen environments. In this work, we extend our previous work on lidar-only CorAl to radar data by proposing a two-step filtering technique that produces high-quality point clouds from noisy radar scans. Thus, we target robust perception in two ways: by introducing a method that introspectively assesses alignment quality, and by applying it to an inherently robust sensor modality. We show that our filtering technique combined with CorAl can be applied to the problem of alignment classification, and that it detects small alignment errors in urban settings with up to 98% accuracy, and with up to 96% if trained only in a different environment. Our lidar and radar experiments demonstrate that CorAl outperforms previous methods both on the ETH lidar benchmark, which includes several indoor and outdoor environments, and the large-scale Oxford and MulRan radar data sets for urban traffic scenarios. The results also demonstrate that CorAl generalizes very well across substantially different environments without the need of retraining.

Target alignment method of inertial confinement fusion facility based on position estimation

Target alignment technology is one of the most critical technologies in laser fusion experiments and is an important technology related to the success of laser fusion experiments. In this study, by combining the open-loop and closed-loop errors of the target alignment, the Kalman state observer is used to estimate the position of the target, which improves the observation precision of the target alignment. Then the optimized result is used to guide the alignment of the target. This method can greatly optimize the target alignment error and reduce uncertainty. With the improvement of the target alignment precision, it will greatly improve the reliability and repeatability of the experiments’ results, thereby improving the success rate of the experiments.

Effects of SNR-Dependent Beam Alignment Errors on Millimeter-Wave Cellular Networks

At millimeter-wave (mm-wave) frequencies, narrow beamwidth and inaccurate channel estimation result in beam alignment errors, which degrade the achievable signal-to-noise ratio (SNR) and rate. The extent of beam misalignment depends on the various link parameters, however, prior works ignored such a dependence and considered oversimplified array patterns. This paper formulates beam alignment errors for the uniform linear array at the base station as a function of the link parameters by using the Cramér Rao lower bound of angle-of-arrival estimates and proposes a tractable array pattern approximation to better reflect the effects of such alignment errors. Utilizing stochastic geometry and the statistics of the Student’s <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -distribution, a novel and robust analytical technique is developed to obtain the downlink SNR and rate coverage probabilities due to beam misalignment, assuming a single antenna at the user. We show that beam alignment errors significantly affect the achievable SNR and rate for non line-of-sight (NLOS)/distant users whereas the effect is negligible for LOS/nearby users. It is also illustrated that the effects of alignment errors cannot be mitigated only by increasing the number of antennas. We also show that our proposed array pattern approximation better captures the effects of beam alignment errors as compared to the widely used flat-top pattern.

Coverage analyses on directional transmissions ultra-dense networks with imperfect beam alignment

Blockage and imperfect beam alignment are two principal difficulties in high-frequency bands directional transmissions. In this paper, the coverage performance of downlink directional transmissions in ultra-dense networks is analyzed, with the consideration of beam alignment error and link blockage through stochastic geometry. Numerical experiments demonstrate that narrower beam leads to higher coverage probability with perfect beam alignment, but it is not the case with imperfect beam alignment. Therefore, the optimal beamwidth that maximize the coverage probability is characterized and a closed-form approximation of the optimal beamwidth is derived under imperfect beam alignment, accordingly. Furthermore, the optimal beamwidth is a monotonically increasing function of the standard deviation of the beam alignment error, and a monotonically decreasing function of the beamwidth of correspondent communication end, indicating that the beamwidth of the communication pairs ought to be jointly designed.

Precise retinal shape measurement by alignment error and eye model calibration

Precise retinal shape measurement by alignment error and eye model calibration

Rain Station Network Analysis in the Sampean Watershed: Comparison of Variations in Data Aggregation

The lack of rainfall-runoff accuracy is important for some applications. The choice of data aggregation that affects the estimation results is important at the level of accuracy. Some commonly used aggregations are daily, ten days, and monthly rainfall. This study aimed to compare the results of the estimation of the effect of data aggregation and to analyze the density of the rain gauge network in the Sampean watershed. The evaluation of the rain station network is carried out through the Kagan calculation. Rainfall data are from the rainfall data records for 20 years at 33 rain gauge stations. Measurement of the performance of aggregation variations using the relationship between the correlation value of rainfall with the distance between station locations. Station network positioning is assessed from alignment errors and interpolation errors. The results showed differences in the correlation and estimation values ​​in the variation of data aggregation.The greater interval can increase the effectiveness of deployment with minimum error. Based on Kagan's analysis, there is an uneven distribution of gauge stations in the Sampean watershed eventhough the average and interpolation error in the monthly rainfall is less than 5%. It is this inequality that causes gauge stations to be inefficient.&#x0D; Keywords : Rain gauge network; correlation; Kagan; data aggregation&#x0D; Copyright (c) 2022 Geosfera Indonesia and Department of Geography Education, University of Jember&#x0D; This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

Optimization of English Machine Translation by Deep Neural Network under Artificial Intelligence.

To improve the function of machine translation to adapt to global language translation, the work takes deep neural network (DNN) as the basic theory, carries out transfer learning and neural network translation modeling, and optimizes the word alignment function in machine translation performance. First, the work implements a deep learning translation network model for English translation. On this basis, the neural machine translation model is designed under transfer learning. The random shielding method is introduced to implement the language training model, and the machine translation is slightly adjusted as the goal of transfer learning, thereby improving the semantic understanding ability in translation performance. Meanwhile, the work design introduces the method of word alignment optimization and optimizes the performance of word alignment in the transformer system by using word corpus. The experimental results show that the proposed method reduces the average alignment error rate by 8.1%, 24.4%, and 22.1% in EnRo (English-Roman), EnGe (English-German), and EnFr (English-French), respectively, compared with the previous algorithms. Compared with the designed optimization method, the word alignment error rate is lower than that of traditional methods. The modeling and optimization method is feasible, which can effectively solve the problems of insufficient information utilization, large parameter scale, and difficult storage in the process of machine translation. Additionally, it provides a feasible idea and direction for the optimization and improvement in neural machine translation (NMT) system.

Multi-face detection and alignment using multiple kernels

Multi-face detection and alignment techniques under unlimited environment are challenging issues. In recent years, the demand for face detection and alignment techniques has increased in many areas, including automatic drive and security. However, some mainstream algorithms, such as the Multi-task Cascaded Convolutional Networks (MTCNN) algorithm cannot transfer to multi-face detection and alignment, which introduces new multi-size, multi-resolution, and multi-angle challenges. This paper proposes an improved algorithm—Multi-face-MTCNN for precise original face detection and alignment algorithm when there is an overlapping face scenario. We design two new network structures: Pixelfusion-MTCNN and Twoconv-MTCNN. Moreover, we propose new data augmentation method and optimized detection process which are applied in the Multi-face-MTCNN algorithm. The limitations associated with single-scale kernel size in MTCNN are solved to obtain a satisfactory performance. Compared to the MTCNN algorithm, experimental analysis of the FDDB dataset show that 1.766% improves Multi-face-MTCNN. Meanwhile, on the WIDER FACE verification benchmark, respectively, with regards to the three sub-datasets, the proposed algorithm’s performance is improved by 3.426%, 2.776%, and 21.576%. Besides, average alignment error of the left eye, right eye, nose, left mouth, and right mouth is performed on the proposed algorithm.

Comparison of Visual Outcomes Between Toric Intraocular Lenses and Clear Corneal Incisions to Correct Astigmatism in Image-Guided Cataract Surgery.

PurposeTo compare the astigmatism correction effects of toric intraocular lenses (IOL) and clear corneal incisions during image-guided cataract surgery.MethodsAll patients with regular corneal astigmatism of 0.75–1.5 D underwent cataract surgery and astigmatism correction using the Callisto eye image-guided system. One group had implantation of an AcrySof toric IOL. Another group had implantation of aspheric IOL with 3.0 mm single clear corneal incision (SCCI) on the steep axis. Uncorrected and best-corrected spectacle visual acuity, refraction, and toric IOL axis were evaluated at 1, 4, and 12 weeks postoperatively.ResultsSixty-eight eyes of 68 patients were included. The mean residual refractive cylinder was 0.34 ± 0.40 D in the toric group and 0.64 ± 0.57 D in the SCCI group. There were no significant differences in residual refractive cylinder, spherical equivalent, uncorrected distance visual acuity (UDVA), and best-corrected spectacle visual acuity (BCSVA) between groups. The percentage of the residual cylinder within ± 0.50 D was 75 and 56% for toric and SCCI cases, respectively (p > 0.1). The mean surgical induced astigmatism vector was 0.61 ± 0.29 D in the SCCI group and 1.04 ± 0.38 D in the toric group. The mean magnitude of error was negative (−0.54 ± 0.48 D) and the correction index was <1.0 (p < 0.05) in SCCI group. At 3 months, all toric IOL alignment errors were within 5 degrees from the intended axis.ConclusionsBoth toric IOL and SCCI can correct low and medium astigmatism effectively with the help of a precise image-guided system.