Search Results (48)

Reference-based super-resolution (RefSR) enhances the detail restoration capability of low-resolution images (LR) by utilizing the details and texture information of external reference images (Ref). This study proposes a RefSR method based on hash adaptive matching and progressive multi-scale dynamic aggregation to improve the super-resolution reconstruction capability. Firstly, to address the issue of feature matching, this chapter proposes a hash adaptive matching module. On the basis of similarity calculation between traditional LR images and Ref images, self-similarity information of LR images is added to assist in super-resolution reconstruction. By dividing the feature space into multiple hash buckets through spherical hashing, the matching range is narrowed down from global search to local neighborhoods, enabling efficient matching in more informative regions. This not only retains global modeling capabilities, but also significantly reduces computational costs. In addition, a learnable similarity scoring function has been designed to adaptively optimize the similarity score between LR images and Ref images, improving matching accuracy. Secondly, in the process of feature transfer, this chapter proposes a progressive multi-scale dynamic aggregation module. This module utilizes dynamic decoupling filters to simultaneously perceive texture information in both spatial and channel domains, extracting key information more accurately and effectively suppressing irrelevant texture interference. In addition, this module enhances the robustness of the model to large-scale biases by gradually adjusting features at different scales, ensuring the accuracy of texture transfer. The experimental results show that this method achieves superior super-resolution reconstruction performance on multiple benchmark datasets. Full article

Background: The most widely used contemporary intraocular lens power calculation formulas are the Kane formula, Barrett Universal II formula, Hill–Radial Basis Function, and Ladas Super Formula, each of which was developed to improve postoperative refractive accuracy. This study aims to conduct a comprehensive comparative analysis of these formulas to evaluate their predictive accuracy across diverse biometric profiles. Methods: A total of 210 eyes that met the inclusion criteria were analyzed in this study. This study was designed as a retrospective observational investigation. The biometric parameters of the intraocular lens were evaluated using the ARGOS optical biometer. Refractive intraocular lens power calculations were performed using the formulas, and the resulting values were systematically compared to assess predictive accuracy. In our research, a parametric approach was adopted by applying ANOVA repeated measures analysis. Multiple measurements were evaluated through homogeneity of covariances. Pairwise comparisons between formula-derived values were conducted using the Bonferroni test to identify significant differences. A paired-sample t-test was used to compare the spherical equivalent levels calculated at the first and last controls. Potential correlations were examined using Pearson correlation analysis. Results: A statistically significant difference was observed between formulas. The differences among the formulas were caused by the values obtained from the Ladas Super Formula being significantly higher than the others. There was a statistically significant positive correlation between the data obtained from the formulas. The spheric equivalent values were similar, with no statistically significant difference. Conclusions: This study reinforces the notion that modern intraocular lens power calculation formulas exhibit a high degree of accuracy and correlation in predicting postoperative refractive outcomes. Full article

Motivated by the need for more accurate radiation environment modeling, this study focuses on identifying and analyzing the drivers behind the sub-relativistic electron flux variations in the inner magnetosphere. We utilize electron flux data between 1 and 500 keV from the Hope and MagEIS instruments on board the RBSP satellites, as well as from the FEEPS instruments on board the MMS spacecrafts, along with solar wind parameters and geomagnetic indices obtained from the OmniWeb2 and SuperMag data services. We calculate the correlation coefficients between these parameters and electron flux. Our analysis shows that substorm activity is a crucial driver of the source electron population (10–100 keV), while also showing that seed electrons (100–400 keV) are not purely driven by substorm events but also from enhanced convection/inward diffusion. By introducing time lags, we observed a delayed response of electron flux to changes in geospace conditions, and we identified specific time lag periods where the correlation is maximum. This work contributes to our broader understanding of the outer belt sub-relativistic electron dynamics and forms the basis for future research. Full article

The short-wave infrared (SWIR) grating imaging spectrometer based on indium gallium arsenide (InGaAs) material inverts the atmospheric methane concentration by measuring the scattered light signals in the sky. This study proposes spectral and radiometric calibration methods for the characteristics of the spectrometer, such as the small-area array, high signal-to-noise ratio, and high spectral resolution. Four spectral response function models, namely, the Gauss, Lorentz, Voigt and super-Gaussian models, were compared during spectral calibration. With a fitting residual of 0.032, the Gauss model was found to be the most suitable spectral response function for the spectrometer. Based on the spectral response function, the spectral range and spectral resolution of the spectrometer were determined to be 1592.4–1677.2 and 0.1867 nm, respectively. In addition, radiometric calibration of the spectrometer was achieved by combining an integrating sphere and linear measuring instrument. Moreover, absolute and relative radiometric calibrations of the spectrometer were performed. The low signal response problem caused by the quantum efficiency of the detector at long wavelength was corrected, and the uncertainty and non-stability uncertainty of absolute radiometric calibration were calculated to be less than 0.2%. Finally, the calibrated spectrometer was used to accurately measure the solar scattering spectrum in the SWIR band, and the solar spectrum was simulated by the radiative transfer model for verification; the measurement error was found to be 5%. Concurrently, a methane sample gas experiment was performed using the integrating-sphere light source, and the measurement error was less than 4%. This fully proves the effectiveness of the spectral and radiometric calibrations of the SWIR spectrometer and strongly guarantees a subsequent, rapid and accurate inversion of atmospheric methane concentration. Full article

To establish a finite element model that accurately represents the dynamic characteristics of actual super high-rise building and improve the accuracy of the finite element simulation results, a finite element model updating method for super high-rise building is proposed based on the response surface method (RSM). Taking a 120 m super high-rise building as the research object, a refined initial finite element model is firstly established, and the elastic modulus and density of the main concrete and steel components in the model are set as the parameters to be updated. A significance analysis was conducted on 16 parameters to be updated including E1–E8, D1–D8, and the first 10 natural frequencies of the structure, and 6 updating parameters are ultimately selected. A sample set of updating parameters was generated using central composite design (CCD) and then applied to the finite element model for calculation. The response surface equations for the first ten natural frequencies were obtained through quadratic polynomial fitting, and the optimal solution of the objective function was determined using a genetic algorithm. The results of the engineering case study indicate that the errors in the first ten natural frequencies of the updated finite element model are all within 5%. The updated model accurately reflects the current situation of the super high-rise building and provides a basis for super high-rise building health monitoring, damage detection, and reliability assessment. Full article

Purpose: To compare the accuracy of seven artificial intelligence (AI)-based intraocular lens (IOL) power calculation formulas in medium-long Caucasian eyes regarding the root-mean-square absolute error (RMSAE), the median absolute error (MedAE) and the percentage of eyes with a prediction error (PE) within ±0.5 D. Methods: Data on Caucasian patients who underwent uneventful phacoemulsification between May 2018 and September 2023 in MW-Med Eye Center, Krakow, Poland and Kyiv Clinical Ophthalmology Hospital Eye Microsurgery Center, Kyiv, Ukraine were reviewed. Inclusion criteria, i.e., complete biometric and refractive data, were applied. Exclusion criteria were as follows: intraoperative or postoperative complications, previous eye surgery or corneal diseases, postoperative BCVA less than 0.8, and corneal astigmatism greater than 2.0 D. Prior to phacoemulsification, IOL power was computed using SRK/T, Holladay1, Haigis, Holladay 2, and Hoffer Q. The refraction was measured three months after cataract surgery. Post-surgery intraocular lens calculations for Hill-RBF 3.0, Kane, PEARL-DGS, Ladas Super Formula AI (LSF AI), Hoffer QST, Karmona, and Nallasamy were performed. RMSAE, MedAE, and the percentage of eyes with a PE within ±0.25 D, ±0.50 D, ±0.75 D, and ±1.00 were counted. Results: Two hundred fourteen eyes with axial lengths ranging from 24.50 mm to 25.97 mm were tested. The Hill-RBF 3.0 formula yielded the lowest RMSAE (0.368), just before Pearl-DGS (0.374) and Hoffer QST (0.378). The lowest MedAE was achieved by Hill-RBF 3.0 (0.200), the second-lowest by LSF AI (0.210), and the third-lowest by Kane (0.228). The highest percentage of eyes with a PE within ±0.50 D was obtained by Hill-RBF 3.0, LSF AI, and Pearl-DGS (86.45%, 85.51%, and 85.05%, respectively). Conclusions: The Hill-RBF 3.0 formula provided highly accurate outcomes in medium-long eyes. All studied AI-based formulas yielded good results in IOL power calculation. Full article

To optimize the acceleration performance of independently driven electric vehicles with four in-wheel motors, this paper proposes an anti-slip regulation (ASR) strategy based on dynamic road surface observer for more efficient tracking of the optimal slip ratio and enhanced vehicle acceleration. The method uses the Unscented Kalman Filter (UKF) observer to estimate vehicle speed and calculate the actual slip ratio, while a fuzzy controller based on the Burckhardt tire model identifies road surfaces. The road’s peak adhesion coefficient and optimal slip ratio curve are fitted using a Back Propagation Neural Network (BPNN) optimized by Particle Swarm Optimization (PSO). The control strategy further refines torque management through an adaptive sliding mode control (ASMC) that integrates adaptive laws and a super-twisting sliding mode approach to track the optimal slip ratio. Joint simulations with MATLAB/Simulink and Carsim on low-adhesion, joint, and split road surfaces demonstrate that the strategy quickly and accurately identifies the optimal slip ratio across various road surfaces. This enables the tire slip ratio to approach the optimal value in minimal time, significantly improving vehicle dynamic performance. Compared to conventional sliding mode controllers, the optimized ASMC reduces chattering and improves control precision. Full article

The quality of fuel depends on its chemical composition, which influences engine performance. Gas chromatography, a cornerstone of global oil and fuel R&D, remains crucial for ensuring the quality of petroleum products and regulatory compliance. Scientists use the most accurate analysis (PIONA) as a tool derived from gas chromatography coupled with mass spectrometry to identify and quantify hydrocarbons that influence resistance to detonation, which is determined by the research octane number (RON). This study introduces the “average carbon number (ACN)”, calculated from the molar chemical composition of commercial gasoline samples sold in Ecuador (Extra gasoline and Súper gasoline). A quantitative comparison of the ACN with techniques applied using standardized international procedures reveals that the ACN characterizes gasoline samples by providing insight into the distribution shape of carbon graphs. A comprehensive statistical analysis demonstrates the potential usefulness of ACN in characterizing fuel composition, highlighting its relevance in broader fuel quality assessments without the need for carbon distribution plots. Full article

Plant height is an important parameter of plant phenotype as one indicator of plant growth. In view of the complexity and scale limitation in current measurement systems, a scaleless method is proposed for the automatic measurement of plant height based on monocular computer vision. In this study, four peppers planted side by side were used as the measurement objects. Two color images of the measurement object were obtained by using a monocular camera at different shooting heights. Binary images were obtained as the images were processed by super-green grayscale and the Otsu method. The binarized images were transformed into horizontal one-dimensional data by the statistical number of vertical pixels, and the boundary points of multiple plants in the image were found and segmented into single-plant binarized images by filtering and searching for valleys. The pixel height was extracted from the segmented single plant image and the pixel displacement of the height was calculated, which was substituted into the calculation together with the reference height displacement to obtain the realistic height of the plant and complete the height measurements of multiple plants. Within the range of 2–3 m, under the light condition of 279 lx and 324 lx, this method can realize the rapid detection of multi-plant phenotypic parameters with a high precision and obtain more accurate plant height measurement results. The absolute error of plant height measurement is not more than ±10 mm, and the absolute proportion error is not more than ±4%. Full article

The magnetoelectric (ME) antennas rely on the mechanical movement of magnetic dipoles, making it possible to break the constraints on physical dimensions decided by the wavelength of the electromagnetic wavelength. The ME antennas achieve super-low frequency (SLF) communications with a smaller size to provide a novel solution for long-range, underwater, and underground communications; navigation over the horizon; and geological exploring. As a result, further theoretical research and optimization of ME antennas have been an open challenge for decades. Here, we report on minitype arrays of acoustically actuated ME antenna and their more rigorous equivalent circuits. These arrays of ME antenna adjust amplitude-frequency response through the mechanical regulation method. The mechanical parameters of ME antennas in the arrays result in regulating amplitude-frequency response, such as working frequency, fractional bandwidth, and intensity of magnetic induction. Our work provides a more accurate theoretical model and diverse array form over state-of-the-art ME antenna arrays. The frequency, fractional bandwidth, and magnetic induction strength of the ME antenna arrays were achieved to be adjustable in the ranges of 84 to 181 Hz, 3.9% to 8.3%, and two to four times, respectively. In addition, we have calculated the attenuation characteristics of ME antennas and their minitype arrays in seawater. The results show that the ME antenna array described in this manuscript is able to enhance the radiation intensity and information-loading capability, which has a positive potential for application in SLF communication systems. Full article

There is a need to investigate the flexural behavior and mechanical properties of super high-performance concrete (SHPC) for a better understanding of its response to compression, tension, and bending. Super-high-performance concrete (SHPC) lies between high-performance concrete (HPC) and ultra-high-performance concrete (UHPC) in strength, durability, and workability and is suitable for sustainable buildings. This paper presents an extensive experimental and analytical study to investigate the effect of the hybridization of micro-polypropylene and macro-steel fibers on the flexural behavior and mechanical properties of super-high-performance concrete (SHPC). The hybridization of both micro-PP fibers and macro-hooked-end ST fibers gathers the benefits of their advantages and offsets their disadvantages. Three types of fibers (micro polypropylene fibers (PP), macro hooked-end steel fiber (ST), and hybrid fiber (PP + ST)) with different fiber content up to 2% were tested to study their effect on the following: (a) the workability of fresh concrete, (b) concrete compressive strength, (c) splitting tensile strength, (d) flexural behavior, including flexural tensile strength and toughness, and (e) the optimum percentage of each of the two fibers, PP and ST, in the hybrid to get the maximum structural and economic benefits of hybridization. Based upon the experimental results and using a statistical program, formulae to calculate both the tensile splitting strength ($f_{sp}$) and the flexural tensile strength in the form of the modulus of rupture ($f_{ctr}$) were obtained. These formulae were able to predict accurately both the splitting tensile strength and modulus of rupture for SHPC with each of the three types of fibers used in this research. Also, they were in very good agreement with the values corresponding to different experimental results of other research, which means the ability to use these equations more generally. In addition, the prediction of the additional ultimate moment provided for all fibers was investigated. This research confirms the structural and the economical efficiency of hybridization in the behavior of SHPC. It was found that the optimum percentage of the fiber volume content for the hybrid of ST and PP is 1%; 0.5% for each of the two kinds. Full article

With the emergence of the Smart City concept, the rapid advancement of urban three-dimensional (3D) reconstruction becomes imperative. While current developments in the field of 3D reconstruction have enabled the generation of 3D products such as Digital Surface Models (DSM), challenges persist in accurately reconstructing shadows, handling occlusions, and addressing low-texture areas in very-high-resolution remote sensing images. These challenges often lead to difficulties in calculating satisfactory disparity maps using existing stereo matching methods, thereby reducing the accuracy of 3D reconstruction. This issue is particularly pronounced in urban scenes, which contain numerous super high-rise and densely distributed buildings, resulting in large disparity values and occluded regions in stereo image pairs, and further leading to a large number of mismatched points in the obtained disparity map. In response to these challenges, this paper proposes a method to refine the disparity in urban scenes based on open-source GIS data. First, we register the GIS data with the epipolar-rectified images since there always exists unignorable geolocation errors between them. Specifically, buildings with different heights present different offsets in GIS data registering; thus, we perform multi-modal matching for each building and merge them into the final building mask. Subsequently, a two-layer optimization process is applied to the initial disparity map based on the building mask, encompassing both global and local optimization. Finally, we perform a post-correction on the building facades to obtain the final refined disparity map that can be employed for high-precision 3D reconstruction. Experimental results on SuperView-1, GaoFen-7, and GeoEye satellite images show that the proposed method has the ability to correct the occluded and mismatched areas in the initial disparity map generated by both hand-crafted and deep-learning stereo matching methods. The DSM generated by the refined disparity reduces the average height error from 2.2 m to 1.6 m, which demonstrates superior performance compared with other disparity refinement methods. Furthermore, the proposed method is able to improve the integrity of the target structure and present steeper building facades and complete roofs, which are conducive to subsequent 3D model generation. Full article

Patterns entered into knitting CAD have thousands or tens of thousands of different colors, which need to be merged by color-separation algorithms. However, for degraded patterns, the current color-separation algorithms cannot achieve the desired results, and the clustering quantity parameter needs to be managed manually. In this paper, we propose a fast and automatic FCM color-separation algorithm based on superpixels, which first uses the Real-ESRGAN blind super-resolution network to clarify the degraded patterns and obtain high-resolution images with clear boundaries. Then, it uses the improved MMGR-WT superpixel algorithm to pre-separate the high-resolution images and obtain superpixel images with smooth and accurate edges. Subsequently, the number of superpixel clusters is automatically calculated by the improved density peak clustering (DPC) algorithm. Finally, the superpixels are clustered using fast fuzzy c-means (FCM) based on a color histogram. The experimental results show that not only is the algorithm able to automatically determine the number of colors in the pattern and achieve the accurate color separation of degraded patterns, but it also has lower running time. The color-separation results for 30 degraded patterns show that the segmentation accuracy of the color-separation algorithm proposed in this paper reaches 95.78%. Full article

Restricted by the ill-posed antenna measurement matrix, the conventional smoothed $L_0$ norm algorithm (SL0) fails to enable direct real aperture radar angular super-resolution imaging. This paper proposes a modified smoothed $L_0$ norm (MSL0) algorithm to address this issue. First, as the pseudo-inverse of the ill-posed antenna measurement matrix is required to set the initial values and calculate the gradient projection, a regularization strategy is employed to relax the ill-posedness. Based on the regularization strategy, the proposed MSL0 algorithm can avoid noise amplification when faced with the ill-posed antenna measurement matrix of real aperture radar. Additionally, to prevent local minima problems, we introduce a hard thresholding operator, based on which the proposed MSL0 algorithm can accurately reconstruct sparse targets. Simulations and experimental results verify the performance of the proposed MSL0 algorithm. Full article

A super multi-view (SMV) near-eye display (NED) effectively provides depth cues for three-dimensional (3D) display by projecting multiple viewpoint or parallax images onto the retina simultaneously. Previous SMV NED have suffered from a limited depth of field (DOF) due to a fixed image plane. In this paper, a holographic SMV Maxwellian display based on depth segmentation is proposed to enhance the DOF. The proposed approach involves capturing a set of parallax images and their corresponding depth maps. According to the depth maps, the parallax images are segmented into N sub-parallax images at different depth ranges. These sub-parallax images are then projected onto N image-recording planes (IRPs) of the corresponding depth for hologram computation. The wavefront at each IRP is calculated by multiplying the sub-parallax images with the corresponding spherical wave phases. Then, they are propagated to the hologram plane and added together to form a DOF-enhanced hologram. The simulation and experimental results are obtained to validate the effectiveness of the proposed method in extending the DOF of the holographic SMV displays, while accurately preserving occlusion. Full article