Search Results (769)

A laser tracker is an essential tool in the field of precise geometric measurement. Its fundamental operating idea is a dual-axis rotating device that propels the laser beam to continuously align and measure the attitude of a collaborating target. Such systems provide numerous benefits, including a broad measuring range, high precision, outstanding real-time performance, and ease of use. To solve the issue of low beam recovery efficiency in typical laser trackers, this research offers a two-dimensional laser tracking system that incorporates a machine vision module. The system uses a unique off-axis optical design in which the distance measuring and laser tracking paths are independent, decreasing the system’s dependency on optical coaxiality and mechanical processing precision. A tracking head error calibration method based on singular value decomposition (SVD) is introduced, using optical axis point cloud data obtained from experiments on various components for geometric fitting. A complete prototype system was constructed and subjected to accuracy testing. Experimental results show that the proposed system achieves a relative positioning accuracy of less than 0.2 mm (spatial root mean square error (RMSE) = 0.189 mm) at the maximum working distance of 1.5 m, providing an effective solution for the design of high-precision laser tracking systems. Full article

High-altitude long-endurance (HALE) UAVs require navigation payloads that are both fully autonomous and lightweight. This paper presents a full-parameter calibration method for a dual-axis rotational-modulation RINS/CNS integrated system in which the IMU is mounted on a two-axis indexing mechanism and the reconnaissance camera is reused as the star sensor. We establish a unified error propagation model that simultaneously covers IMU device errors (bias, scale, cross-axis/installation), gimbal non-orthogonality and encoder angle errors, and camera exterior/interior parameters (EOPs/IOPs), including Brown–Conrady distortion. Building on this model, we design an error-decoupled calibration path that exploits (i) odd/even symmetry under inner-axis scans, (ii) basis switching via outer-axis waypoints, and (iii) frequency tagging through rate-limited triangular motions. A piecewise-constant system (PWCS)/SVD analysis quantifies segment-wise observability and guides trajectory tuning. Simulation and hardware-in-the-loop results show that all parameter groups converge primarily within the segments that excite them; the final relative errors are typically ≤5% in simulation and 6–$16\%$ with real IMU/gimbal data and catalog-based star pixels. Full article

To enhance imaging quality, singular value decomposition (SVD) has been applied to single-wavelength ghost imaging (GI) or color GI. In this paper, we extend the application of SVD to wavelength-multiplexing ghost imaging (WMGI) for reducing the redundant information in the random measurement matrix corresponding to multi-wavelength modulated speckle fields. The feasibility of this method is demonstrated through numerical simulations and optical experiments. Based on the intensity statistical properties of multi-wavelength speckle fields, we derived an expression for the contrast-to-noise ratio (CNR) to characterize imaging quality and conducted a corresponding analysis. The theoretical results indicate that in SVDWMGI, for the m-wavelength case, the CNR of the reconstructed image is $\sqrt{m}$ times that of single-wavelength GI. Moreover, we carried out an optical experiment with a three-wavelength speckle-modulated light source to verify the method. This approach integrates the advantages of both SVD and wavelength division multiplexing, potentially facilitating the application of GI in long-distance imaging fields such as remote sensing. Full article

Structural valve deterioration (SVD) remains a key limitation in bioprosthetic heart valve (BHV) usage influenced by patient age. A deeper understanding of SVD pathogenesis, particularly of the immune-mediated processes altering current BHV materials, is therefore critical. To this end, commercially available BHV tissues of bovine, porcine, and equine origin were investigated following subcutaneous implantation into α1,3-galactosyltransferase-knockout (Gal KO) mice. We compared the immune responses between adult and aged animals via histological assessments of explants and measurement of serum anti-galactose α1,3-galactose (Gal) and anti-non-Gal antibodies at 2 months post-implantation. In contrast to adult mice, old Gal KO mice did not show increased levels of serum anti-Gal or -non-Gal antibodies after receiving specific BHV tissue (i.e., Freedom-Solo). Instead, a significant decrease in serum anti-Gal IgM was found in old recipients of Freedom-Solo. Furthermore, the overall cellular immune response was attenuated in explants from old mice compared with adults (i.e., ATS 3f and Crown). Nevertheless, the Freedom-Solo (bovine) and the Hancock-II (porcine) tissues still elicited strong cellular immune infiltration in the old cohorts. Therefore, the Gal KO mouse model offers a valuable platform to investigate age-related differences regarding cellular and humoral immune responses to various BHV tissues, contributing to our understanding of SVD. Full article

In the digital era, protecting the integrity and ownership of digital content is increasingly crucial, particularly against unauthorized copying and tampering. Traditional watermarking techniques often struggle to remain robust under various image manipulations, leading to a need for more resilient methods. To address this challenge, we propose a novel watermarking technique that integrates the Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), and Schur matrix factorization to embed a QR code as a watermark into digital images. Our method was rigorously tested across a range of common image attacks, including histogram equalization, salt-and-pepper noise, ripple distortions, smoothing, and extensive cropping. The results demonstrate that our approach significantly outperforms existing methods, achieving high normalized correlation (NC) values such as 0.9949 for histogram equalization, 0.9846 for salt-and-pepper noise (2%), 0.96063 for ripple distortion, 0.9670 for smoothing, and up to 0.9995 under 50% cropping. The watermark consistently maintained its integrity and scannability under all tested conditions, making our method a reliable solution for enhancing digital copyright protection. Full article

Electromagnetic ultrasonic testing technology, owing to its couplant-free, high-temperature-resistant, and non-contact characteristics, exhibits unique advantages for thickness measurement in harsh industrial environments. However, its accuracy is fundamentally limited by inherent constraints in signal bandwidth and low signal-to-noise ratio. To address these challenges, this work proposes an electromagnetic ultrasonic thickness measurement method that integrates Adaptive Denoising with Bayesian Vector Autoregressive (AD-BVAR) spectral extrapolation. The approach employs Particle Swarm Optimization (PSO) and automatically determines the optimal parameters for Variational Mode Decomposition (VMD), followed by integration with Singular Value Decomposition (SVD) to achieve the adaptive denoising of signals. Subsequently, the BVAR model incorporating prior constraints performs robust extrapolation of the effective frequency band spectrum, ultimately achieving high measurement accuracy signal reconstruction. The experimental results demonstrate that on step blocks with thicknesses of 3 mm and 12.5 mm, the proposed method achieved significantly reduced error rates of 0.267% and 0.240%, respectively. This performance markedly surpasses that of the conventional Autoregressive (AR) method, which yielded errors of 0.767% and 0.560% under identical conditions, while maintaining stable performance across different thicknesses. Full article

Nonlinear signal models are widely used in power amplifier predistortion, full-duplex self-interference cancellation, and other scenarios. The parallel Hammerstein (PH) model is a typical nonlinear signal model, but its serial and parallel hybrid architecture brings difficulties in performance analysis and coefficient estimation. This paper focuses on the performance analysis and coefficient estimation of the PH model for nonlinear systems with memory effects, such as power amplifiers. By comparing the PH model with the memory polynomial (MP) model under identical basis functions, we analyze its performance across varying numbers of parallel branches, nonlinear orders, and memory depths. Using singular value decomposition (SVD), we derive a closed-form expression for the PH model’s performance under underdetermined conditions, establishing its relationship to the non-zero singular values of the MP model’s coefficient matrix. Based on this, we propose a coefficient generation method combining SVD and least squares (LS), which directly computes coefficients and assesses performance during execution. Simulations confirm the method’s effectiveness, showing that selecting branches associated with larger singular values achieves near-optimal performance with reduced complexity. Full article

Nitrogen fertilization is extensively applied in agricultural activities to improve food production. However, the applied dose of nitrogen is often higher than that required for the desired productivity level of a given crop. Thus, research on methods that could increase the uptake of nitrogen supplied with fertilizers by plants is of high significance. One way to achieve this goal is to employ a hybrid fertilization technique (a combination of the application of solid fertilizers in the first dose with foliar application of liquid fertilizers in the second and third doses), using reduced doses of nitrogen fertilizers as well as fertilizers enriched with 10% sulfur in the form of thiosulfate. The aim of our study was to assess the productivity resulting from different fertilization treatments and the stability of the resulting yield based on interactions between the method of fertilizer application and environmental conditions. To determine interaction patterns, an additive main effects and multiplicative interaction model was employed. The key finding is that sulfur-enriched fertilizers can significantly increase yield, but at the expense of reduced stability. However, yield stability was more strongly related to meteorological conditions. Understanding of such interactions can help increase the efficiency of selection and accuracy of recommendations for new technologies of crop cultivation. Full article

In this paper, we propose a mechanical fault diagnosis technology for circuit breakers based on the NGO-VMD, aiming to improve the accuracy and efficiency of fault diagnosis. The circuit breaker is a key protection device in power systems, and its operational status is crucial to grid security. This paper introduces the NGO-VMD method to decompose its vibration signals, aiming to improve the accuracy and efficiency of fault diagnosis. Failure to detect and address mechanical faults in circuit breakers can lead to equipment damage, power outages, and even personal injury. Therefore, it is of great significance to develop efficient and accurate mechanical fault diagnosis technology for after converting the mechanical fault signal of the vacuum circuit breaker in the distribution network into the IMF form, the modal information of the vibration signal under different faults of the circuit breaker is effectively extracted, and the singular value decomposition of the IMF signal component is carried out to make the information characteristics contained more obvious, Finally, LSTM is used to achieve precise identification of circuit breaker faults. In this paper, the experimental test is carried out on the basis of the actual vacuum circuit breaker in the distribution network, and the feasibility of the design scheme is verified by comprehensive analysis. The comparison and analysis with other methods can be obtained, and the scheme has the advantages of higher efficiency, stronger stability and more accuracy. Full article

Background: Cancer poses a significant health risk to humans, with prostate cancer (PCa) being the second most common and deadly form among men, following lung cancer. Each year, it affects over a million individuals and presents substantial diagnostic challenges due to variations in tissue appearance and imaging quality. In recent decades, various techniques utilizing Magnetic Resonance Imaging (MRI) have been developed for identifying and classifying PCa. Accurate classification in MRI typically requires the integration of complementary feature types, such as deep semantic representations from Convolutional Neural Networks (CNNs) and handcrafted descriptors like Histogram of Oriented Gradients (HOG). Therefore, a more robust and discriminative feature integration strategy is crucial for enhancing computer-aided diagnosis performance. Objectives: This study aims to develop a multi-stage hybrid learning model that combines deep and handcrafted features, investigates various feature reduction and classification techniques, and improves diagnostic accuracy for prostate cancer using magnetic resonance imaging. Methods: The proposed framework integrates deep features extracted from convolutional architectures with handcrafted texture descriptors to capture both semantic and structural information. Multiple dimensionality reduction methods, including singular value decomposition (SVD), were evaluated to optimize the fused feature space. Several machine learning (ML) classifiers were benchmarked to identify the most effective diagnostic configuration. The overall framework was validated using k-fold cross-validation to ensure reliability and minimize evaluation bias. Results: Experimental results on the Transverse Plane Prostate (TPP) dataset for binary classification tasks showed that the hybrid model significantly outperformed individual deep or handcrafted approaches, achieving superior accuracy of 99.74%, specificity of 99.87%, precision of 99.87%, sensitivity of 99.61%, and F1-score of 99.74%. Conclusions: By combining complementary feature extraction, dimensionality reduction, and optimized classification, the proposed model offers a reliable and generalizable solution for prostate cancer diagnosis and demonstrates strong potential for integration into intelligent clinical decision-support systems. Full article

We develop a shared denominator Carathéodory–Fejér (CF) method for efficiently evaluating linear combinations of $\phi$-functions for matrices whose spectrum lies in the negative real axis, as required in exponential integrators for large stiff ODE systems. This entire family is approximated with a single set of poles (a common denominator). The shared pole set is obtained by assembling a stacked Hankel matrix from Chebyshev boundary data for all target functions and computing a single SVD; the zeros of the associated singular-vector polynomial, mapped via the standard CF slit transform, yield the poles. With the poles fixed, per-function residues and constants are recovered by a robust least squares fit on a suitable grid of the negative real axis. For any linear combination of resolvent operators applied to right-hand sides, the evaluation reduces to one shifted linear solve per pole with a single combined right-hand side, so the dominant cost matches that of computing a single $\phi$-function action. Numerical experiments indicate geometric convergence at a rate consistent withHalphen’s constant, and for highly stiff problems our algorithm outperforms existing Taylor and Krylov polynomial-based algorithms. Full article

We study statistical dependence in the frequency domain using the integral probability metric (IPM) framework. We propose the uniform Fourier dependence measure (UFDM) defined as the uniform norm of the difference between the joint and product-marginal characteristic functions. We provide a theoretical analysis, highlighting key properties, such as invariances, monotonicity in linear dimension reduction, and a concentration bound. For the estimation of the UFDM, we propose a gradient-based algorithm with singular value decomposition (SVD) warm-up and show that this warm-up is essential for stable performance. The empirical estimator of UFDM is differentiable, and it can be integrated into modern machine learning pipelines. In experiments with synthetic and real-world data, we compare UFDM with distance correlation (DCOR), Hilbert–Schmidt independence criterion (HSIC), and matrix-based Rényi’s $\alpha$-entropy functional (MEF) in permutation-based statistical independence testing and supervised feature extraction. Independence test experiments showed the effectiveness of UFDM at detecting some sparse geometric dependencies in a diverse set of patterns that span different linear and nonlinear interactions, including copulas and geometric structures. In feature extraction experiments across 16 OpenML datasets, we conducted 160 pairwise comparisons: UFDM statistically significantly outperformed other baselines in 20 cases and was outperformed in 13. Full article

To improve recognition accuracy, convergence speed, and numerical stability in estimating the road adhesion coefficient for distributed-drive electric vehicles, a nonlinear seven-degree-of-freedom vehicle dynamics model was developed based on a modified Dugoff tire model. Using the Unscented Kalman Filter (UKF) as a foundation, a Square-Root Unscented Kalman Filter (SR-UKF) algorithm was derived through covariance-square-root processing and Singular Value Decomposition (SVD). A co-simulation platform was built with CarSim and Simulink, and a vehicle speed-following model was developed for simulation analysis. The results show that the SR-UKF algorithm for road identification consistently maintains matrix positive definiteness, ensures numerical stability, speeds up convergence, and fully utilizes measurement information. Simulations under various road conditions (high-adhesion, low-adhesion, split-μ, and opposite-μ) and driving scenarios demonstrate that, compared to the traditional UKF, the SR-UKF converges faster and provides higher estimation accuracy, enabling real-time, accurate estimation of the road adhesion coefficient across multiple scenarios. Final results confirm that the SR-UKF exhibits excellent estimation accuracy and robustness on low-adhesion surfaces, confirming its superiority under high-risk conditions. This offers a dependable basis for improving vehicle active safety. Full article

This study develops an updated national Social Accounting Matrix (SAM) for Greece, based on the 2020 Input–Output Table that captures post-crisis structural and macroeconomic transformations, implemented in Python 3, hence producing a reusable, modular code. This methodological approach facilitates multiplier-based policy analysis of how shocks propagate through the Greek economy, and therefore, this study contributes to the literature by addressing the gap in multiplier analysis for this setting. Output, value-added, and income multipliers were estimated using the Moore–Penrose pseudo-inverse via Singular Value Decomposition (SVD). Findings highlighted the substantial role of government transfers in supporting household and firm incomes, largely due to COVID-19 relief measures. This analysis showed that production expansion in energy, construction, and wholesale and retail trade can stimulate broad economic activity, while service-related sectors play a critical role in income generation and equity considerations. At the same time, firms in trade, hospitality, and real estate were heavily affected by the pandemic shock. The findings of this study provide a benchmark for understanding Greece’s economic structure at a critical moment in time (the COVID-19 pandemic). Full article

Background: Endothelial and microvascular dysfunction play a central role in the pathogenesis of both cardiovascular and neurodegenerative disorders. However, whether impaired peripheral endothelial function independently predicts cognitive decline, cerebral small-vessel disease (SVD) progression, or stroke remains uncertain. Methods: We conducted a systematic review and meta-analysis of prospective cohort studies assessing the prognostic value of non-invasive peripheral endothelial tests—flow-mediated dilation (FMD), peripheral arterial tonometry (PAT/EndoPAT), and sublingual microcirculatory imaging—for cognitive or cerebrovascular outcomes. Databases (PubMed, Embase, Scopus, Web of Science) were searched from inception through 30 September 2025. Study quality was appraised using the Newcastle–Ottawa Scale (NOS), and evidence certainty was graded via GRADE. Random-effects models (DerSimonian–Laird or REML) pooled hazard ratios (HRs) using inverse-variance weighting. PROSPERO-registered (CRD42025211876). Results: Fifteen prospective cohorts (n = 13,972 participants; median follow-up 4.3 years) met inclusion criteria. Across all modalities, impaired endothelial or microvascular function predicted cognitive decline, SVD progression, or cerebrovascular events (pooled HR = 1.72, 95% CI 1.38–2.14, p < 0.001; I² = 57%). Subgroup analyses confirmed consistent associations for FMD (HR = 1.59, 95% CI 1.27–1.98) and PAT/EndoPAT (HR = 1.84, 95% CI 1.40–2.41). Evidence certainty was rated moderate-to-high according to GRADE. Conclusions: Peripheral endothelial dysfunction, measured by validated non-invasive techniques, independently predicts future cognitive and cerebrovascular events. These findings support the concept of a vascular–neural continuum, suggesting that endothelial health represents a modifiable biomarker for early neurovascular risk stratification. Routine assessment of endothelial function may help identify high-risk individuals and guide preventive interventions aimed at preserving brain and vascular health. Full article