Distortion Measure Research Articles

Chebyshev Parameterization for Woven Fabric Modeling

Distortion-minimizing surface parameterization is an essential step for computing 2D pieces necessary to fabricate a target 3D shape from flat material. Garment design and textile fabrication are a prominent application example. Common distortion measures quantify length, angle or area preservation in an isotropic manner, so that when applied to woven textile fabrication, they implicitly assume fabric behaves like paper, which is inextensible in all directions and does not permit shearing. However, woven fabric differs significantly from paper: it exhibits anisotropy along the yarn directions and allows for some degree of shearing. We propose a novel distortion energy based on Chebyshev nets that anisotropically penalizes shearing and stretching. Our energy formulation can be used as an optimization objective for surface parameterization and is simple to minimize via a local-global algorithm. We demonstrate its advantages in modeling nets or woven fabric behavior over the commonly used isotropic distortion energies.

Mapping and assessment of harmonic voltage levels for railway traction supply stations in Sweden

Assessing harmonic distortion measurements in the electric railway power systems (ERPS) requires evaluating the time-varying behavior, interactions, and performance in different time scales. This paper aims to map and assess harmonic voltage levels in 13 traction converter stations for the Swedish railway power supply system, with findings that have direct practical implications. For that, measurements from the public and railway grid sides for 69 weeks are analyzed. Statistical values are explored for the harmonic voltage spectra and total harmonic distortion (THD) variation. The public grid side measurements are investigated using 95th percentile weekly values, and performance is evaluated by comparing the recommended planning levels of IEC 61,000–3–6. The intraweek variation complements the information about the time-varying behavior of the THD. The 95th percentile, minimum daily values, and intraday variation are explored to understand the time-based behavior since there are no reference limits from standards for comparison, looking to the railway grid side. Extended analysis is placed on the railway grid side to highlight some aspects of measurement time-aggregation based on 10-min values, and time-series trend analysis is used to confirm traffic planning impact. Discussion and findings regarding railway operation, the technology deployed at the traction converter station, time-varying behavior, traffic planning impact, measurement time-aggregation, and spectra patterns were presented.

Open-source hardware and software for the measurement, characterization, reporting, and correction of geometric distortion in MRI.

Geometric distortion is a serious problem in MRI, particularly in MRI guided therapy. A lack of affordable and adaptable tools in this area limits research progress and harmonized quality assurance. To develop and test a suite of open-source hardware and software tools for the measurement, characterization, reporting, and correction of geometric distortion in MRI. An open-source python library was developed, comprising modules for parametric phantom design, data processing, spherical harmonics, distortion correction, and interactive reporting. The code was used to design and manufacture a distortion phantom consisting of 618 oil filled markers covering a sphere of radius 150mm. This phantom was imaged on a CT scanner and a novel split-bore 1.0 T MRI magnet. The CT images provide distortion-free dataset. These data were used to test all modules of the open-source software. All markers were successfully extracted from all images. The distorted MRI markers were mapped to undistorted CT data using an iterative search approach. Spherical harmonics reconstructed the fitted gradient data to 1.0±0.6% of the input data. High resolution data were reconstructed via spherical harmonics and used to generate an interactive report. Finally, distortion correction on an independent data set reduced distortion inside the DSV from 5.5±3.1 to 1.6±0.8mm. Open-source hardware and software for the measurement, characterization, reporting, and correction of geometric distortion in MRI have been developed. The utility of these tools has been demonstrated via their application on a novel 1.0 T split bore magnet.

Diminishing residual stress and distortion by in-situ rolling tensioning to increase fatigue performance of friction stir welded AA2024-T3 joints

In this research, local mechanical tensioning treatment in the form of in-situ rolling tensioning (ISRT) was applied during friction stir welding of AA2024-T3 sheets. Two types of roller configurations were used. First, a single roller located at the rear of the tool which passed over the weld region and secondly, two rollers were located next to the weld zone symmetrically. Subsequently, several experiments comprising residual stress, distortion and fatigue crack growth (FCG) measurements were carried out combined with microstructure, texture, hardness and tensile tests. Results demonstrated that a single roller ISRT effectively diminished residual stress in the nugget zone (NZ) from + 11.7 MPa to −45.3 MPa accompanied by better weld FCG resistance. Apart from residual stress reduction, the improved weld fatigue performance was likely correlated with the modifications of weld microstructure and texture due to rolling tensioning.

Development of an Inconsistent Responding Scale for the Big Five Inventory-2

The Big Five Inventory − 2 (BFI-2) is a commonly used self-report assessment of normal personality trait domains (Extraversion, Agreeableness, Conscientiousness, Negative Emotionality, Open-Mindedness) and facets. To date, however, no direct measures of response distortion have been developed for it to identify potentially invalid responses. Such distortions (e.g., careless or random responding) can adversely impact data quality. The current study developed and provided initial validation data for an inconsistent responding scale within the BFI-2 to identify careless responders using two large undergraduate samples and a community sample. To create the scale, we first identified highly correlated BFI-2 item pairs in one undergraduate sample (N = 1,461) and then computed a total score by summing the absolute differences of these item pairs. This scale, the Detection of Response Inconsistency Procedure (DRIP), differentiated randomly generated and genuine data and generally correlated as expected with personality domains and other inconsistent responding scales across samples. The DRIP also incrementally predicted random data beyond a composite of items with exceptionally high or low base rates of endorsement from the Comprehensive Infrequency/Frequency Item Repository. We provide recommendations for DRIP cut scores that can detect careless responding while balancing sensitivity and specificity.

Advancements on the use of Filtered Rayleigh Scattering (FRS) with Machine learning methods for flow distortion in Aero-Engine intakes

In-flight measurements of aerodynamic quantities are a requirement to ensure the correct scaling of Reynolds and Mach number and for the airworthiness certification of an aircraft. The ability to obtain such measurement is subject to several challenges such as instrument installation, environment, type of measurand, and spatial and temporal resolution. Given expected, more frequent use of embedded propulsion systems in the near future, the measurement technology needs to adapt for the characterization of multi-type flow distortion in complex flow, to assess the operability of air-breathing propulsion systems. To meet this increasing demand for high-fidelity experimental data, the Filtered Rayleigh Scattering (FRS) method is identified as a promising technology, as it can provide measurements of pressure, temperature and 3D velocities simultaneously, across a full Aerodynamic Interface Plane (AIP). Τhis work demonstrates the application of a novel FRS instrument, to assess the flow distortion in an S-duct diffuser, in a ground testing facility. A comparison of FRS results with Stereo-Particle Image Velocimetry (S-PIV) measurements reveals good agreement of the out of plane velocities, within 3.3% at the AIP. Furthermore, the introduction of machine learning methods significantly accelerates the processing of the FRS data by up to 200 times, offering a substantial prospect towards real time data analysis. This study demonstrates the further development of the FRS technique, with the ultimate goal of inlet flow distortion measurements for in-flight environments.

Redshift-space distortions corner interacting dark energy

Despite the fact that the ΛCDM model has been highly successful over the last few decades in providing an accurate fit to a broad range of cosmological and astrophysical observations, different intriguing tensions and anomalies emerged at various statistical levels. Given the fact that the dark energy and the dark matter sectors remain unexplored, the answer to some of the tensions may rely on modifications of these two dark sectors. This manuscript explores the important role of the growth of structure in constraining non-standard cosmologies. In particular, we focus on the interacting dark energy (IDE) scenario, where dark matter and dark energy interact non-gravitationally. We aim to place constraints on the phenomenological parameters of these alternative models, by considering different datasets related to a number of cosmological measurements, to achieve a complementary analysis. A special emphasis is devoted to redshift-space distortion measurements (RSD), whose role in constraining beyond the standard paradigm models has not been recently highlighted. These observations indeed have a strong constraining power, rendering all parameters to their ΛCDM canonical values, and therefore leaving little room for the IDE models explored here.

An acoustic bellows-type round window transducer for middle-ear implants

BackgroundThis study describes the development of output devices for round window middle-ear. To overcome the problems of output devices that apply sound pressure directly to the round window, an acoustic bellows-type round window transducer was implemented by combining a small bellows, acoustic tube, and balanced armature driver. MethodsThe output characteristics of the proposed acoustic bellows-type round window transducer were confirmed through bench tests and distortion measurements. To compare the vibration transmission characteristics of the proposed transducer with those of sound pressure stimulation devices, an experiment was performed using four human temporal bones. FindingsThe average output magnitude of the acoustic bellows-type round window transducer was equivalent to sound pressure levels of 92, 96, and 108 dB for frequency ranges of <1, 1–2, and > 2 kHz, respectively. The results showed that the proposed transducer delivered vibration consistently without reducing the sound pressure level due to leakage, unlike the sound pressure stimulation device. InterpretationTherefore, the acoustic bellows-type round window transducer is a more stable and suitable output device for round window middle-ear implants than a sound pressure stimulation device. It is expected to overcome the limitations of sound pressure stimulation devices and to contribute to new technical solutions in the field of round window middle-ear implants development.

Reduction in residual stress and distortion of thin-walled inconel 718 specimens fabricated by selective laser melting: Experiment and numerical simulation

Residual stresses were induced in the fabrication of thin-walled Inconel 718 specimens through selective laser melting (SLM) due to rapid heating and cooling, which yield distortion and have a detrimental impact on their mechanical performance. Reducing the process-induced residual stress and distortion is of great importance for practical applications. For achieving this aim, accurate measurement of residual stress and distortion is required. In the present work, residual stresses of SLMed samples were measured through hole drilling method (HDM), taking into account their anisotropic features. The distortion induced by the release of residual stress after manufacturing was measured using digital image correlation (DIC). The effect of thickness of thin-walled samples on residual stress and distortion was investigated. The results indicate that by reducing the thickness of sample, the residual stress could be reduced, whereas the distortion was increased. Furthermore, it was revealed that by manufacturing a pair of side samples in one printing job, the distortion and residual stress of the thin-walled component fabricated by SLM can be evidently reduced. The obtained results were clarified by using thermal-mechanical simulations of SLM.

Measurement precision bounds on aberrated single-molecule emission patterns.

Single-molecule localization microscopy (SMLM) has revolutionized the study of biological phenomena by providing exquisite nanoscale spatial resolution. However, optical aberrations induced by sample and system imperfections distort the single-molecule emission patterns (i.e. PSFs), leading to reduced precision and resolution of SMLM, particularly in three-dimensional (3D) applications. While various methods, both analytical and instrumental, have been employed to mitigate these aberrations, a comprehensive analysis of how different types of commonly encountered aberrations affect single-molecule experiments and their image formation remains missing. In this study, we addressed this gap by conducting a quantitative study of the theoretical precision limit for position and wavefront distortion measurements in the presence of aberrations. Leveraging Fisher information and Cramér-Rao lower bound (CRLB), we quantitively analyzed and compared the effects of different aberration types, including index mismatch aberrations, on localization precision in both biplane and astigmatism 3D modalities as well as 2D SMLM imaging. Furthermore, we studied the achievable wavefront estimation precision from aberrated single-molecule emission patterns, a pivot step for successful adaptive optics in SMLM through thick specimens. This analysis lays a quantitative foundation for the development and application of SMLM in whole-cells, tissues and with a large field of view, providing in-depth insights into the behavior of different aberration types in single-molecule imaging and thus generating theoretical guidelines for developing highly efficient aberration correction strategies and enhancing the precision and reliability of 3D SMLM.

Super‐Resolution of Histopathological Frozen Sections via Deep Learning Preserving Tissue Structure

Histopathology plays a pivotal role in medical diagnostics. In contrast to preparing permanent sections for histopathology, a time‐consuming process, preparing frozen sections is significantly faster and can be performed during surgery, where the sample scanning time should be optimized. Super‐resolution techniques allow imaging of histopathalogical samples in lower magnification, thus sparing scanning time. Herein, a new approach is presented to super‐resolution of histopathological frozen sections, with focus on achieving better distortion measures, rather than pursuing photorealistic images that may compromise critical diagnostic information. Our deep‐learning architecture focuses on learning the error between interpolated images and real images; thereby generating high‐resolution images while preserving critical image details, which reduces the risk of diagnostic misinterpretation. This is done by leveraging the loss functions in the frequency domain and assigning higher weights to the reconstruction of complex, high‐frequency components. In comparison with existing methods, significant improvements are obtained in terms of distortion metrics, improving the pathologist's clinical decisions. This approach has a great potential to provide faster frozen‐section imaging, with less scanning, speeding up intraoperative decisions, while preserving the high‐resolution details in the imaged sample.

Constraining holographic dark energy and analyzing cosmological tensions

We investigate cosmological constraints on the holographic dark energy (HDE) using the state-of-the-art cosmological datasets: Planck CMB angular power spectra and weak lensing power spectra, Atacama Cosmology Telescope (ACT) temperature power spectra, baryon acoustic oscillation (BAO) and redshift-space distortion (RSD) measurements from six-degree-field galaxy survey and Sloan Digital Sky Survey (DR12 & DR16) and the Cepheids-Supernovae measurement from SH0ES team (R22). We also examine the HDE model and ΛCDM with and without Neff (effective number of relativistic species) being treated as a free parameter. We find that the HDE model can relieve the tensions of H0 and S8 to certain degrees. With “Planck+ACT+BAO+RSD” datasets, the constraints are H0=69.70±1.39km s−1Mpc−1 and S8=0.823±0.011 in HDE model, which brings down the Hubble tension down to 1.92σ confidence level (C.L.) and the S8 tension to (1−2)σ C.L. By adding the R22 data, their values are improved as H0=71.86±0.93km s−1Mpc−1 and S8=0.813±0.010, which further brings the Hubble tension down to 0.85σ C.L. and relieves the S8 tension. We also quantify the goodness-of-fit of different models with Akaike information criterion (AIC) and Bayesian information criterion (BIC), and find that the HDE agrees with the observational data better than the ΛCDM and other extended models (treating Neff as free for fitting).

Perceptual point cloud qality assessment for immersive metaverse experience

Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task. Firstly, because point cloud is formed by unstructured 3D points that makes the topology more complex. Second, the quality impairment generally involves both geometric attributes and color properties, where the measurement of the geometric distortion becomes more complex. We proposed a perceptual point cloud quality assessment model that follows the perceptual features of Human Visual System (HVS) and the intrinsic characteristics of the point cloud. The point cloud is first pre-processed to extract the geometric skeleton keypoints with graph filtering-based re-sampling, and local neighboring regions around the geometric skeleton keypoints are constructed by K-Nearest Neighbors (KNN) clustering. For geometric distortion, the Point Feature Histogram (PFH) is extracted as the feature descriptor, and the Earth Mover's Distance (EMD) between the PFHs of the corresponding local neighboring regions in the reference and the distorted point clouds is calculated as the geometric quality measurement. For color distortion, the statistical moments between the corresponding local neighboring regions are computed as the color quality measurement. Finally, the global perceptual quality assessment model is obtained as the linear weighting aggregation of the geometric and color quality measurement. The experimental results on extensive datasets show that the proposed method achieves the leading performance as compared to the state-of-the-art methods with less computing time. Meanwhile, the experimental results also demonstrate the robustness of the proposed method across various distortion types. The source codes are available at https://github.com/llsurreal919/PointCloudQualityAssessment.

Development of Tools for the Direct Measurement of Shear Stress and Shear Strain within a Soil Mass

Abstract This article describes the development of sensing tools designed and applied to the direct measurement of shear stresses and shear strain within a soil mass. The importance in the development of these tools is in their ability to measure shear stresses and shear distortions within soil without any a priori information of the stress and strains applied at the boundaries of the system. These tools may be applied to element testing, testing of physical models, and field applications. The sensors were used in the testing of a sand in a large simple shear apparatus, under both constant height and constant vertical pressure conditions. Testing demonstrated that shear stresses smaller than 0.1 kPa are easily resolved. Shear strains smaller than 10−5 (abs) were reliably measured. Measurement of shear stress and shear distortion within the soil mass allows for direct determination of the shear stiffness of the soil. Shear stiffness measured in this fashion is significantly greater than that determined from the globally measured shear stress and shear strain. The stiffness degradation curve illustrates a constant shear stiffness over the range of 10−5 through 5·10−4.

Measurement of nonlinear and Doppler distortion in Distributed Mode Loudspeakers

Distributed Mode Loudspeakers (DMLs), as other electroacoustic transducers with electrodynamic excitation, are susceptible to the occurrence of nonlinear distortion. The literature on nonlinear distortion in DMLs is scarce. The vibrations of the DML panel are very small in amplitude, thus the coil excursion of the exciter is low, which is a favorable condition for low nonlinear distortion. The scope of this paper covers investigation of harmonic and intermodulation distortion, occurring in the DML. The harmonic distortion was examined with two methods and the intermodulation distortion – with three, including one author’s proposed method. The result indicate, that nonlinear distortion in DMLs may be a bigger problem than those in diaphragm loudspeakers.

A Low-Bit-Rate Image Semantic Communication System Based on Semantic Graph

In the progress of research in the field of semantic communication, most efforts have been focused on optimizing the signal-to-noise ratio (SNR), while the design and optimization of the bit rate required for transmission have been relatively neglected. To address this issue, this study introduces an innovative low-bit-rate image semantic communication system model, which aims to reconstruct images through the exchange of semantic information rather than traditional symbol transmission. This model employs an image feature extraction and optimization reconstruction framework, achieving visually satisfactory and semantically consistent reconstruction performance at extremely low bit rates (below 0.03 bits per pixel (bpp)). Unlike previous methods that used pixel accuracy as the standard for distortion measurement, this research introduces multiple perceptual metrics to train and evaluate the proposed image semantic encoding model, aligning more closely with the fundamental purpose of semantic communication. Experimental results demonstrate that, compared to WebP, JPEG, and deep learning-based image codecs, our model not only provides a more visually pleasing reconstruction effect but also significantly reduces the required bit rate while maintaining semantic consistency.

Highly adaptive single-shot surface shape distortion measurement method for high-power lasers

Highly adaptive single-shot surface shape distortion measurement method for high-power lasers

82‐1: Distinguished Paper: Geometric Distortion on Video See‐Through Head‐Mounted Displays

We evaluate the geometric distortion on video see‐through (VST) head‐mounted displays (HMDs) with single‐camera and dualcamera design. We present the experimental setup and metrology for geometric calibration and VST geometric distortion measurement. Physical geometric distortion is compared to the distortion of digital content on a VST HMD. We investigate the impacts of camera misalignment and depth distance on VST geometric distortion with angular‐based distortion correction.

Non-local sparse attention based swin transformer V2 for image super-resolution

In single image super resolution tasks, distortion measurement (such as PSNR, SSIM) and perceptual quality (such as PI, NIQE) are contradictory, and methods that perform well in perceptual quality often perform poorly in distortion measurement, and vice versa. In this article, we propose a method of balancing the two, which is divided into three stages. Firstly, this article proposes an image super-resolution model NLSAV2 that focuses on PSNR and SSIM metrics. The entire NLSAV2 consists of three modules: shallow feature extraction, deep feature extraction, and high-quality image reconstruction. In the shallow feature extraction module, non-local sparse attention is used to identify the most abundant feature information in mapping input from low-dimensional space to high-dimensional space, and the deep feature extraction module mainly consists of residual Swin Transformer V2 Block. Then, NLSAV2 is used as a generator and a relative discriminator is introduced to further train the model, which is called NLSAV2-GAN. The experimental results indicate that NLSAV2 and NLSAV2-GAN exhibit advantages in distortion measurement and perceptual quality respectively. Finally, network interpolation and image interpolation strategies are used to continuously adjust the reconstruction style and smoothness to achieve a balance between the two.

Optimum image alignment setting selection for structure-from-motion photogrammetry using Remotely Piloted Aircraft Systems (RPAS) to support coral habitat classification

Coral reefs provide a range of ecological services and support highly diverse coastal ecosystem. However, the extent of living corals has been reduced by half globally as a result of anthropogenic natural stressors. Continuous monitoring by deploying accessible remote sensing (RS) methods to map coral habitats is necessary for effective conservation and management strategies. Recently, there has been an increased use of Remotely Piloted Aircraft Systems (RPAS) for coral reef RS. Image misalignment is considered a key problem in the Structure from Motion (SfM) workflow. Combinations of image alignment settings (IAS) and key point limit (KPL) optimizations aim to improve quality of the sparse cloud while lowering SfM reconstruction uncertainty, increasing projection accuracy, and ultimately improving coral habitat classification accuracy. In this study, the orthoimages were produced from a total of 25 IAS (lowest, low, medium, high and highest) and KPM (5k, 10k, 20k, 40k, and 60k) combinations using Agisoft™ Metashape software. Measurements of geometric distortion, efficiency, and completeness were used to evaluate these orthoimages with three visible bands. Results that satisfied the requirements for both geometric quality, spectral accuracy, processing efficiency identified the optimum alignment methods needed for routinely monitoring and mapping of coral reefs of Pulau Bidong. The SfM-image alignment techniques chosen for this study could produce the coral mapping extent, a higher number of tie points and matches, better image alignment success and coral habitat classification accuracy, less processing time and memory usage and no geometric distortions. The development of the methodology for the optimum parametrization of RPAS multispectral imagery would be beneficial to researchers studying coral reefs, marine sciences, and RS data analysts. It would provide them reliable methods for evaluating the quality of orthoimages and faster processing methods to achieve coastal RS objectives.