Search Results (72)

Synthetic Aperture Radar (SAR) imagery is inherently degraded by multiplicative speckle noise, rendering traditional superpixel methods—which rely on hard assignment and uniform initialization—suboptimal for boundary preservation. This study proposes a complexity-aware superpixel generation framework featuring differentiable soft-assignment optimization. The approach employs an F-LGRP (Fusion of Local Gradient Pattern Representation) feature descriptor that fuses regional gradient statistics via Gaussian filtering to suppress speckle, coupled with a complexity-driven recursive quadtree initialization strategy yielding non-uniform seed density. A U-Net architecture predicts soft pixel–superpixel association maps within a 9-neighborhood constraint, supervised by a multi-objective loss integrating edge information reconstruction and boundary feature reconstruction. Comprehensive evaluations on simulated and real SAR images (WHU-OPT-SAR and Munich) demonstrate that the proposed method achieves state-of-the-art performance across Boundary Recall, Undersegmentation Error, Compactness, and Achievable Segmentation Accuracy compared to SLIC, SNIC, Mean-Shift, PILS, and SSN. Validation on downstream segmentation tasks further confirms superior accuracy and computational efficiency, establishing the framework as an effective solution for end-to-end SAR image analysis. Full article

Chaotic signals commonly exhibit nonlinear and nonstationary characteristics, while noise contamination reduces signal interpretability and degrades subsequent feature extraction and dynamical analysis. To improve the stability of mode-boundary determination and mitigate reconstruction distortion, this paper proposes a hybrid denoising framework that integrates feature mode decomposition (FMD), amplitude-aware permutation entropy (AAPE), dual-tree complex wavelet transform (DTCWT), and Savitzky–Golay (SG) filtering. First, the noisy signal is decomposed into multiple mode components using FMD. Then, the AAPE of each mode is calculated to adaptively distinguish high-frequency noise-dominant modes from non-high-frequency modes. For the high-frequency noise-dominant modes, improved logarithmic threshold shrinkage is applied to the magnitudes of DTCWT complex coefficients to suppress random noise and reduce threshold-induced bias. For the non-high-frequency modes, SG filtering is employed to further attenuate residual noise while preserving local waveform structures. Finally, the processed modes are reconstructed to obtain the denoised signal. Experiments on a simulated Lorenz chaotic signal and a real-world sunspot time series demonstrate that, across different noise levels, AAPE provides more stable mode partitioning than ApEn, CC, and CMSE. Moreover, under Gaussian white noise, Poisson noise, and uniform noise, the proposed method generally achieves a higher output signal-to-noise ratio (SNR) and a lower root mean square error (RMSE) than WT, CEEMD, EEMD, CEEMDAN+LMS, and VMD, while also yielding better performance in phase-space reconstruction and temporal-detail recovery. These results verify the effectiveness and practical applicability of the proposed method for chaotic signal denoising. Full article

To enhance the positioning accuracy of the inertial measurement unit (IMU)-based pedestrian localization, this study proposes an adaptive ensemble extended Kalman filter (EnEKF) that incorporates a distance constraint (DC). This study first introduces a dual foot-mounted IMU-based pedestrian localization system that employs two IMUs to measure the target human’s position. Second, an augmented data fusion model is developed by incorporating attitude quaternions from the inertial navigation system (INS) into the conventional INS error-state vector. Based on this new data fusion model, a DC-based EnEKF is designed. In this method, the EnEKF employs ensemble factors to address nonlinear and non-Gaussian characteristics inherent in the data fusion process. Then, the colored measurement noise (CMN) is considered, and the method is modified to form an EnEKF under CMN (cEnEKF). Moreover, the DC is employed to further restrict the INS-derived position estimates of the left and right feet obtained from the EnEKF algorithm. Finally, validation in two real-world scenarios confirms the effectiveness and superior performance of the proposed approach. Full article

With the high penetration of power electronic devices, modern power systems exhibit complex fractional-order dynamic characteristics. Addressing this, along with the prevalent issues of multi-modal non-Gaussian noise, outliers, and sudden load changes, a fractional-order adaptive generalized cross correlation entropy unscented Kalman filter (FO-AGCCE-UKF) method is proposed in this paper. First, acknowledging that traditional integer-order models overlook the cumulative effects of historical states, a fractional-order (FO) discrete-time state-space model is constructed based on the Grünwald–Letnikov definition. This model accurately characterizes the long-memory and non-locality properties of power systems, thereby improving modeling accuracy during transient processes. Second, to mitigate the impact of non-Gaussian noise and outliers, the generalized cross correlation entropy (GCCE) criterion is adopted to replace the traditional mean square error (MSE) criterion. Combined with statistical linearization techniques, a novel recursive filtering framework is derived to enhance robustness against heavy-tailed noise. Furthermore, to address the time-varying and unknown statistical properties of process and measurement noise, an adaptive update mechanism for noise covariance matrices is introduced, which corrects noise parameters online based on innovation sequences. Simulation experiments and comparative analysis on multiple power systems of different scales demonstrate that the proposed method not only exhibits superior anti-interference capability in mixed-Gaussian noise environments but also achieves a faster convergence speed and higher tracking accuracy during dynamic events such as sudden load changes. Full article

In adaptive optics systems, high spatial resolution detection is a core prerequisite for achieving accurate wavefront correction. High spatial resolution wavefront measurement based on the traditional Shack-Hartmann technique is limited by the density of the microlens array. In contrast, off-axis digital holography technology is applied in wavefront measurement systems of adaptive optics systems due to its advantages of high spatial resolution, non-contact measurement, and full-field measurement. However, during the demodulation of its interference fringes, the accurate extraction of the complex amplitude of the +1st-order diffraction order directly determines the precision of wavefront reconstruction. Traditional frequency-domain filtering methods suffer from drawbacks such as reliance on manual threshold setting, poor adaptability to irregular spectra, and localization deviations caused by multi-region interference, making it difficult to meet the dynamic application requirements of adaptive optics. To address these issues, this study proposes a spectrum extraction method based on the Canny operator for adaptive edge extraction and centroid localization. The method first locks the rough range of the +1st-order spectrum through multi-stage peak screening, then achieves complete segmentation of spectrum spots by combining adaptive histogram equalization with edge closing and filling, resolves centroid indexing errors via maximum connected component screening, and ultimately accomplishes accurate extraction through Gaussian window filtering. Simulation experimental results show that, in comparison with two classical spectrum filtering methods, the centroid estimation error of the proposed method remains below 0.245 pixels under different noise intensity conditions. Moreover, the root mean square error of the residual wavefront corresponding to the reconstructed wavefront of the proposed method is reduced by 89.0% and 87.2% compared with those of the two classical methods, respectively. We further carried out measurement experiments based on a self-developed atmospheric turbulence test bench. The experimental results demonstrate that the proposed method exhibits higher-precision spectral centroid localization capability, which provides a reliable technical support for the high-precision measurement of dynamic distortion induced by atmospheric turbulence. Full article

Many denoising methods model noise as Gaussian noise. However, the realistic noise captured by camera devices does not satisfy Gaussian distribution. Hence, those methods do not perform well when being applied to real-world image denoising tasks. In this work, we indicate that the spatial correlation in noise and the variation of noise intensity are the main factors that impact the performance of Gaussian noise-based methods, and accordingly propose an extension of the method based on the weighted, mixed non-convex ${\mathsf{\ell }}_p$ norm. The proposed method first strengthens the intensity of the noise pattern in the original denoising result through the Guided Filter, then removes the over-amplified frequency in the local area by the proposed regularization term. We prove that the optimal solution can be achieved through the sub-gradient-based iterative optimization scheme, and further reduce the computational cost by optimizing the initial values. Numerical experiments show that the proposed extending method can balance well texture preservation and noise removal, and the PSNR of the extending method’s results are greatly improved, even outperforming the recently proposed realistic noise removal methods which also include deep learning based methods. Full article

This study investigates the horizontal positioning accuracy of a low-cost, multi-frequency GNSS receiver operating in static mode using a newly released PPP-RTK correction service delivering localized corrections. To the authors’ knowledge, this represents one of the first performance evaluations of this service, which optimizes correction data based on the approximate receiver location. The results are compared against those from the previous version of the service, which provided non-localized corrections. Analyses were conducted in both the time and frequency domains, employing robust statistical tools to characterize error behavior. The localized service achieved a mean horizontal error of approximately 0.020 m and a 95% Circular Error Probable (CEP95) of 0.046 m, in line with its declared performance. By contrast, the earlier non-localized service yielded a mean horizontal error of approximately 0.074 m and a CEP95 of 0.124 m under comparable static conditions, confirming the significant improvement achieved by localized corrections. Spectral and wavelet analyses revealed a dominant 33 mHz harmonic in the positioning error, corresponding to the 30 s update period of atmospheric corrections, indicating a periodic influence arising from the correction stream. Continuous wavelet analysis further identified intervals in which this harmonic was absent, during which positioning accuracy improved markedly (CEP95 reduced to 0.019 m). To properly address the non-Gaussian nature of the error distribution, bias-corrected and accelerated (BCa) bootstrap methods were applied to estimate confidence intervals. Overall, the results demonstrate the benefits of localized corrections, while emphasizing the importance of accounting for the temporal structure of correction data in PPP-RTK performance assessments. Future developments will focus on kinematic scenarios and adaptive filtering strategies to mitigate periodic errors induced by correction updates. Full article

In the field of remote sensing, tracking highly maneuvering targets is challenging due to its rapidly changing patterns and uncertainties, particularly under non-Gaussian noise conditions. In this paper, we consider the problem of tracking highly maneuvering targets without using preset parameters in non-Gaussian noise. We propose a multi-domain intelligent state estimation network (MIENet). It consists of two main models to estimate the key parameter for the Unscented Kalman Filter, enabling robust tracking of highly maneuvering targets under various intensities and distributions of observation noise. The first model, called a fusion denoising model (FDM), is designed to eliminate observation noise by enhancing multi-domain feature fusion. The second model, called a parameter estimation model (PEM), is designed to estimate key parameters of target motion by learning both global and local motion information. Additionally, we design a physically constrained loss function (PCLoss) that incorporates physics-informed constraints and prior knowledge. We evaluate our method on radar trajectory simulation and real remote sensing video datasets. Simulation results on the LAST dataset demonstrate that the proposed FDM can reduce the root mean square error (RMSE) of observation noise by more than 60%. Moreover, the proposed MIENet consistently outperforms the state-of-the-art state estimation algorithms across various highly maneuvering scenes, achieving this performance without requiring adjustment of noise parameters under non-Gaussian noise. Furthermore, experiments conducted on the real-world SV248S dataset confirm that MIENet effectively generalizes to satellite video object tracking tasks. Full article

Short fibre-reinforced adhesives (SFRAs) are increasingly used in wind turbine blades to enhance stiffness and fatigue resistance, yet their heterogeneous microstructure poses significant challenges for predictive modelling. This study presents a fully automated digital workflow that integrates micro-computed tomography (µCT), image processing, and finite element modelling (FEM) to investigate the mechanical response of SFRAs. Our aim is also to establish a computational foundation for data-driven modelling and future AI surrogates of adhesive joints in wind turbine blades. High-resolution µCT scans were denoised and segmented using a hybrid non-local means and Gaussian filtering pipeline combined with Otsu thresholding and convex hull separation, enabling robust fibre identification and orientation analysis. Two complementary modelling strategies were employed: (i) 2D slice-based FEM models to rapidly assess microstructural effects on stress localisation and (ii) 3D voxel-based FEM models to capture the full anisotropic fibre network. Linear elastic simulations were conducted under inhomogeneous uniaxial extension and torsional loading, revealing interfacial stress hotspots at fibre tips and narrow ligaments. Fibre clustering and alignment strongly influenced stress partitioning between fibres and the matrix, while isotropic regions exhibited diffuse, matrix-dominated load transfer. The results demonstrate that image-based FEM provides a powerful route for structure–property modelling of SFRAs and establish a scalable foundation for digital twin development, reliability assessment, and integration with physics-informed surrogate modelling frameworks. Full article

High-reliability localization is essential for underground mining autonomous vehicle, as inaccurate positioning triggers collision risks and limits deployment in safety-critical environments. Underground mining localization faces unique challenges: kilometer-scale signal-free tunnels restrict traditional technologies, while wheel slippage-induced non-Gaussian noise and geometric-degraded tunnel localization failures further reduce accuracy—issues existing methods cannot address simultaneously. To resolve these bottlenecks, this study develops a scenario-adapted, self-correcting positioning system for underground autonomous vehicles, fusing multi-source onboard sensor data to suppress slip noise and ensure feature-deficient environment robustness. We propose a three-stage cascaded filtering system: it first fuses LiDAR, IMU, wheel speed, and steering angle data for a self-contained framework, then adds two dedicated modules for core challenges. For wheel slippage noise, an anti-slip prior estimation algorithm integrates kinematic models with IMU data, plus a low-adhesion mine surface-tailored slip compensation mechanism to ensure reliable state estimation and eliminate slip deviations. For geometrically degraded tunnel failures, an anti-degradation algorithm uses point cloud degradation-derived regularization constraints and regularized Kalman filtering to enable stable positioning updates. Experiments show that the system achieves sub-meter accuracy and full-area coverage underground, with improved performance under severe wheel slip and in feature-deprived zones. This work fills the gap in high-reliability, self-contained localization for kilometer-scale underground mining vehicles and provides a safety-oriented paradigm for autonomous vehicle scaling, aligning with critical scenario driving safety demands. Full article

The application prospects of photorealistic 3D reconstruction are broad in smart cities, cultural heritage preservation, and related domains. However, existing methods face persistent challenges in balancing reconstruction accuracy, computational efficiency, and robustness, particularly in complex scenes characterized by reflective surfaces, vegetation, sparse viewpoints, or large-scale structures. In this study, an enhanced 3D Gaussian Splatting (3DGS) framework that integrates three key innovations is proposed: (i) a depth-aware regularization module that leverages metric depth priors from the pre-trained Depth-Anything V2 model, enabling geometrically informed optimization through a dynamically weighted hybrid loss; (ii) a gradient-driven adaptive densification mechanism that triggers Gaussian adjustments based on local gradient saliency, reducing redundant computation; and (iii) a neighborhood density-based floating artifact detection method that filters outliers using spatial distribution and opacity thresholds. Extensive evaluations are conducted across four diverse datasets—ranging from architectures, urban scenes, natural landscapes with water bodies, and long-range linear infrastructures. Our method achieves state-of-the-art performance in both reconstruction quality and efficiency, attaining a PSNR of 34.15 dB and SSIM of 0.9382 on medium-sized scenes, with real-time rendering speeds exceeding 170 FPS at a resolution of 1600 × 900. It demonstrates superior generalization on challenging materials such as water and foliage, while exhibiting reduced overfitting compared to baseline approaches. Ablation studies confirm the critical contributions of depth regularization and gradient-sensitive adaptation, with the latter improving training efficiency by 38% over depth supervision alone. Furthermore, we analyze the impact of input resolution and depth model selection, revealing non-trivial trade-offs between quantitative metrics and visual fidelity. While aggressive downsampling inflates PSNR and SSIM, it leads to loss of high-frequency detail; we identify 1/4–1/2 resolution scaling as an optimal balance for practical deployment. Among depth models, Vitb achieves the best reconstruction stability. Despite these advances, memory consumption remains a challenge in large-scale scenarios. Future work will focus on lightweight model design, efficient point cloud preprocessing, and dynamic memory management to enhance scalability for industrial applications. Full article

Indoor localization based on range measurements in wireless sensor networks involves nonlinear measurement models and is susceptible to non-Gaussian noise, which is associated with complex indoor environments. While particle filters (PFs) are well-suited to such systems, they suffer from sample impoverishment, whereby a diminishing sample diversity leads to failures under various conditions. Hence, this paper proposes a novel hybrid localization algorithm that combines a PF, a finite impulse response (FIR) filter, and an artificial neural network. In the proposed algorithm, the PF serves as the main filter for localization because it performs excellently in nonlinear, non-Gaussian systems under normal operation. The neural network is trained to classify whether the system is operating normally or experiencing a failure, based on estimation results from the PF. If a PF failure is detected by the network, the assisting FIR filter is activated to recover the PF from failures. The localization accuracy and reliability of the proposed neural network-aided hybrid particle/FIR filter are confirmed via comparisons with existing algorithms. Full article

Early printed books, particularly incunabula, are invaluable archives of the beginnings of modern educational systems. However, their complex layouts, antique typefaces, and page degradation caused by bleed-through and ink fading pose significant challenges for automatic transcription. In this work, we present a modular pipeline that addresses these problems by combining modern layout analysis and language modeling techniques. The pipeline begins with historical layout-aware text segmentation using Kraken, a neural network-based tool tailored for early typographic structures. Initial optical character recognition (OCR) is then performed with Kraken’s recognition engine, followed by post-correction using a fine-tuned ByT5 transformer model trained on manually aligned line-level data. By learning to map noisy OCR outputs to verified transcriptions, the model substantially improves recognition quality. The pipeline also integrates a preprocessing stage based on our previous work on bleed-through removal using robust statistical filters, including non-local means, Gaussian mixtures, biweight estimation, and Gaussian blur. This step enhances the legibility of degraded pages prior to OCR. The entire solution is open, modular, and scalable, supporting long-term preservation and improved accessibility of cultural heritage materials. Experimental results on 15th-century incunabula show a reduction in the Character Error Rate (CER) from around 38% to around 15% and an increase in the Bilingual Evaluation Understudy (BLEU) score from 22 to 44, confirming the effectiveness of our approach. This work demonstrates the potential of integrating transformer-based correction with layout-aware segmentation to enhance OCR accuracy in digital humanities applications. Full article

In this paper, for the needs of high-precision and high-continuity localization in complex environments, a modeling method based on time-varying noise and outlier noise is proposed, and variational Bayesian strong tracking filtering is used for 5G/INS fusion localization. A hierarchical progressive fault detection mechanism is proposed to detect IMU rationality faults and consistency faults in 5G observation information. The main contributions are reflected in the following two aspects: first, by innovatively introducing Pearson VII-type distribution for noise modeling, dynamically adjusting the tail thickness characteristics of the probability density function through its shape parameter, and effectively capturing the distribution law of extreme values in the observation data. Afterward, this article combined the variational Bayesian strong tracking filtering algorithm to construct a robust state estimation framework, significantly improving the localization accuracy and continuity in non-Gaussian noise environments. Second, a hierarchical progressive fault detection mechanism is designed. A wavelet fault detection method based on a hierarchical voting mechanism is adopted for IMU data to extract the abrupt features of the observed data and quickly identify faults. In addition, a dual-channel consistency detection model with dynamic fault-tolerant management was constructed. Sudden and gradual faults were quickly detected through a dual-channel pre-check, and then, the fault source was identified through AIME. Based on the fault source detection results, corresponding compensation mechanisms were adopted to achieve robust continuous localization. Full article

Accurate glioma categorization using magnetic resonance (MR) imaging is critical for optimal treatment planning. However, the uneven and diffuse nature of glioma borders makes manual classification difficult and time-consuming. To address these limitations, we provide a unique strategy that combines image decomposition and local texture feature extraction to improve classification precision. The procedure starts with a Gaussian filter (GF) to smooth and reduce noise in MR images, followed by non-subsampled Laplacian Pyramid (NSLP) decomposition to capture multi-scale image information, making glioma borders more visible, TV-L1 normalization to handle intensity discrepancies, and local binary patterns (LBPs) to extract significant texture features from the processed images, which are then fed into a range of supervised machine learning classifiers, such as support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), AdaBoost, and LogitBoost, which have been trained to distinguish between low-grade (LG) and high-grade (HG) gliomas. According to experimental findings, our proposed approach consistently performs better than the state-of-the-art glioma classification techniques, with a higher degree of accuracy in differentiating LG and HG gliomas. This method has the potential to significantly increase diagnostic precision, enabling doctors to make better-informed and efficient treatment choices. Full article