Search Results (275)

The Standard Model (SM) fails to explain many problems (neutrino masses, dark matter, and matter–antimatter asymmetry, among others) that may be resolved with new particles beyond the SM. No observation of such new particles may be explained either by their exceptionally high mass or by considerably small coupling to SM particles. The latter case implies relatively long lifetimes. Such long-lived particles (LLPs) then to have signatures different from those of SM particles. Searches in the “central region” are covered by the LHC general purpose experiments. The forward small angle region far from the interaction point (IP) is unexplored. Such particles are expected to have the energy as large as E = $\mathcal{O}$(1 TeV) and Lorentz time dilation factor $\gamma =E/m\approx {10}^2$–${10}^3$ (with m the particle mass) hence long enough decay distances. A new class of specialized LHC detectors dedicated to LLP searches has been proposed for the forward regions. Among these experiments, FASER is already operational, and FACET is under consideration at a location 100 m from the LHC IP5 (the CMS detector intersection). However, some features of FACET require a specially enlarged beam pipe, which cannot be implemented for LHC Run 4. In this study, we explore a simplified version of the proposed detector PREFACE compatible with the standard LHC beam pipe in the HL-LHC Run 4. Realistic Geant4 simulations are performed and the background is evaluated. An initial analysis of the physics potential with the PREFACE geometry indicates that several significant channels could be accessible with sensitivities comparable to FACET and other LLP searches. Full article

Due to diverse lighting conditions and photo environments, shadows are almost ubiquitous in images, especially document images captured with mobile devices. Shadows not only seriously affect the visual quality and readability of a document but also significantly hinder image processing. Although shadow removal research has achieved good results in natural scenes, specific studies on document images are lacking. To effectively remove shadows in document images, the dark illumination correction network is proposed, which mainly consists of two modules: shadow detection and illumination correction. First, a simplified shadow-corrected attention block is designed to combine spatial and channel attention, which is used to extract the features, detect the shadow mask, and correct the illumination. Then, the shadow detection block detects shadow intensity and outputs a soft shadow mask to determine the probability of each pixel belonging to shadow. Lastly, the illumination correction block corrects dark illumination with a soft shadow mask and outputs a shadow-free document image. Our experiments on five datasets show that the proposed method achieved state-of-the-art results, proving the effectiveness of illumination correction. Full article

To address the concerns of contrast deterioration, detail loss, and color distortion in images produced under haze conditions in scenarios such as intelligent driving and remote sensing detection, an algorithm for image defogging that combines Retinex theory and the dark channel prior is proposed in this paper. The method involves building a two-stage optimization framework: in the first stage, global contrast enhancement is achieved by Retinex preprocessing, which effectively improves the detail information regarding the dark area and the accuracy of the transmittance map and atmospheric light intensity estimation; in the second stage, an a priori compensation model for the dark channel is constructed, and a depth-map-guided transmittance correction mechanism is introduced to obtain a refined transmittance map. At the same time, the atmospheric light intensity is accurately calculated by the Otsu algorithm and edge constraints, which effectively suppresses the halo artifacts and color deviation of the sky region in the dark channel a priori defogging algorithm. The experiments based on self-collected data and public datasets show that the algorithm in this paper presents better detail preservation ability (the visible edge ratio is minimally improved by 0.1305) and color reproduction (the saturated pixel ratio is reduced to about 0) in the subjective evaluation, and the average gradient ratio of the objective indexes reaches a maximum value of 3.8009, which is improved by 36–56% compared with the classical DCP and Tarel algorithms. The method provides a robust image defogging solution for computer vision systems under complex meteorological conditions. Full article

Dark web traffic classification is an important research direction in cybersecurity; however, traditional classification methods have many limitations. Although deep learning architectures like CNN and LSTM, as well as multi-structural fusion frameworks, have demonstrated partial success, they remain constrained by shallow feature representation, localized decision boundaries, and poor generalization capacity. To improve the prediction accuracy and classification precision of dark web traffic, we propose a novel dark web traffic classification model integrating multi-channel image deep learning and a three-dimensional convolutional neural network (3D-CNN). The proposed framework leverages spatial–temporal feature fusion to enhance discriminative capability, while the 3D-CNN structure effectively captures complex traffic patterns across multiple dimensions. The experimental results show that compared to common 2D-CNN and 1D-CNN classification models, the dark web traffic classification method based on multi-channel image visual features and 3D-CNN can improve classification by 5.1% and 3.3% while maintaining a smaller total number of parameters and feature recognition parameters, effectively reducing the computational complexity of the model. In comparative experiments, 3D-CNN validates the model’s superiority in accuracy and computational efficiency compared to state-of-the-art methods, offering a promising solution for dark web traffic monitoring and security applications. Full article

Drone-view object detection models operating under low-light conditions face several challenges, such as object scale variations, high image noise, and limited computational resources. Existing models often struggle to balance accuracy and lightweight architecture. This paper introduces ELS-YOLO, a lightweight object detection model tailored for low-light environments, built upon the YOLOv11s framework. ELS-YOLO features a re-parameterized backbone (ER-HGNetV2) with integrated Re-parameterized Convolution and Efficient Channel Attention mechanisms, a Lightweight Feature Selection Pyramid Network (LFSPN) for multi-scale object detection, and a Shared Convolution Separate Batch Normalization Head (SCSHead) to reduce computational complexity. Layer-Adaptive Magnitude-Based Pruning (LAMP) is employed to compress the model size. Experiments on the ExDark and DroneVehicle datasets demonstrate that ELS-YOLO achieves high detection accuracy with a compact model. Here, we show that ELS-YOLO attains a mAP@0.5 of 74.3% and 68.7% on the ExDark and DroneVehicle datasets, respectively, while maintaining real-time inference capability. Full article

The underwater environment severely degrades image quality by absorbing and scattering light. This causes significant challenges, including non-uniform illumination, low contrast, color distortion, and blurring. These degradations compromise the performance of critical underwater applications, including water quality monitoring, object detection, and identification. To address these issues, this study proposes a detail-oriented hybrid framework for underwater image enhancement that synergizes the strengths of traditional image processing with the powerful feature extraction capabilities of unsupervised deep learning. Our framework introduces a novel multi-scale detail enhancement unit to accentuate structural information, followed by a Latent Low-Rank Representation (LatLRR)-based simplification step. This unique combination effectively suppresses common artifacts like oversharpening, spurious edges, and noise by decomposing the image into meaningful subspaces. The principal structural features are then optimally combined with a gamma-corrected luminance channel using an unsupervised MU-Fusion network, achieving a balanced optimization of both global contrast and local details. The experimental results on the challenging Test-C60 and OceanDark datasets demonstrate that our method consistently outperforms state-of-the-art fusion-based approaches, achieving average improvements of 7.5% in UIQM, 6% in IL-NIQE, and 3% in AG. Wilcoxon signed-rank tests confirm that these performance gains are statistically significant (p < 0.01). Consequently, the proposed method significantly mitigates prevalent issues such as color aberration, detail loss, and artificial haze, which are frequently encountered in existing techniques. Full article

Enhancement and restoration algorithms are widely used in the exploration of coral reefs for improving underwater images. However, by selecting an improvement algorithm based on image quality metrics, image processing key factors such as the execution time are not considered. In response to this issue, herein is presented a novel method built on multicriteria decision analysis that evaluates the processing time and feature point increase with respect to the original image. To set the Decision Matrix (DM), both the processing time and keypoint increase criteria of the evaluated algorithms are normalized. The criteria weights in the DM are set in accordance with the application, and the quantitative metric used to select the best alternative is the highest Weighted Sum Method (WsuM) score. In this work, the DM of six scenarios is shown, since the setting of weights could completely change the decision. For this research’s target application of generating underwater photomosaics, the Dark Channel Prior (DCP) algorithm emerged as the most suitable under a weighting scheme of 75% for processing time and 25% for keypoint increase. This proposal represents a solution for evaluating improvement algorithms in applications where computational efficiency is critical. Full article

Visual target detection under adverse weather conditions presents a fundamental challenge for autonomous driving, particularly in achieving all-weather operational capabilities. Unlike existing reviews that concentrate on individual technical domains such as image restoration or detection robustness, this review introduces an innovative “restoration–detection” collaborative framework. This paper systematically examines state-of-the-art methods for degraded image recovery and improvement of detection model robustness, encompassing from traditional, physically driven approaches as well as contemporary deep learning paradigms. A comprehensive overview and comparative analysis are provided to elucidate these advancements. Regarding the recovery of degraded images, traditional methods demonstrate advantages in interpretability within specific scenarios, such as those based on dark channel prior. In contrast, deep learning methods have achieved significant breakthroughs in modeling complex degradations and enhancing cross-domain generalization through a data-driven paradigm. In the field of enhancing detection robustness, traditional improvement techniques that utilize anisotropic filtering, alongside deep learning methods such as SSD, R-CNN, and the YOLO series, contribute to perceptual stability through feature optimization and end-to-end learning approaches, respectively. This paper summarizes 11 types of mainstream public datasets, examining their multimodal annotation system and addressing issues related to discrepancies. Furthermore, it provides an extensive evaluation of algorithm performance using PSNR, SSIM, mAP, among others. It has been identified that significant bottlenecks persist in dynamic weather coupling modeling, multimodal heterogeneous data fusion, and the efficiency of edge deployment. Future research should focus on establishing a physically guided hybrid learning architecture, developing techniques for dynamic and adaptive timing calibration, and designing a flexible multimodal fusion framework to overcome the limitations associated with complex environment perception. This paper serves as a systematic reference for both the theoretical development and practical implementation of automatic driving vision detection technology under severe weather conditions. Full article

In deformation measurements, processing overexposed images poses challenges due to the welding process or metal reflection. To track the deformation surface, an Adaptive Brightness Global Digital Image Correlation method is proposed. First, the effective range is determined based on the extent of image overexposure. Second, an improved Dark Channel Prior method is employed to adjust the brightness of overexposed images. Third, by calculating the parameter results of Finite Element Partitioning, Adaptive Brightness Global Digital Image Correlation can be utilized to conduct deformation measurements. The proposed method can adjust both the image brightness and Finite Element Partitioning for Global Digital Image Correlation. The experimental results demonstrate that the improved dark channel method modifies the image brightness without altering its brightness distribution. The modified image can significantly increase the Mean Intensity Gradient within different partitions. This method overcomes the difficulty in measuring the weld deformation during the welding process and can achieve dynamic deformation measurement using overexposed images. Finally, the evolution processes of unstable deformation and angular deformation in the whole welding field are obtained, which can assist in optimizing the welding process. Full article

This study introduces a comprehensive physical modeling framework for phototransistors based on quasi-two-dimensional transition metal dichalcogenides, with a particular emphasis on MoS₂. By integrating electromagnetic simulations of optical absorption with semiconductor transport calculations, the model captures both dark and photocurrent behaviors across diverse operating conditions. For 20 nm MoS₂ films, the model reproduces the experimental transfer characteristics with a threshold voltage accuracy better than 0.1 V and achieves quantitative agreement with photocurrent and dark current values across the full range of gate voltages, with the worst-case deviation not exceeding a factor of seven. Additionally, the model captures a three-order-of-magnitude increase in the photocurrent as the MoS₂ thickness varies from 4 nm to 40 nm, reflecting the strong thickness dependence observed experimentally. A key insight from the study is the critical role of defect states, including traps, impurities, and interfacial imperfections, in governing the dark current and photocurrent under channel pinch-off conditions (Vg < −1.0 V). The model successfully replicates the qualitative trends observed in experimental devices, highlighting how small variations in film thickness, doping levels, and contact geometries can significantly influence device performance, in agreement with published experimental data. These findings underscore the importance of precise defect characterization and optimization of material and structural parameters for 2D-material-based phototransistors. The proposed modeling framework serves as a powerful tool for the design and optimization of next-generation phototransistors, facilitating the integration of 2D materials into practical electronic and optoelectronic applications. Full article

Nickel-based single-crystal superalloy turbine blades are typically manufactured via investment casting followed by a well-established heat treatment process, resulting in a uniform microstructure composed of thin γ channels and cubic-shaped γ’. However, the region near the corner of the aerofoil/platform of the blade exhibits a distinct contrast compared to the surrounding area. High-resolution scanning electron microscopy (SEM) reveals significant degradation of the γ and γ’ phases in the dark contrast region. In this area, the γ’ phase no longer maintains its characteristic cubic morphology and appears partially dissolved or even melted. Although the regularity of the γ/γ’ microstructure is disrupted, the region is still composed of irregular-shaped γ and γ’ phases. Based on these microstructural observations, a possible formation mechanism of the abnormal microstructure is discussed. Although the blades are not exposed to conventional creep conditions during casting and heat treatment, residual stress accumulated during casting may be relieved at elevated temperatures during the heat treatment process. The synergistic effect of stress, temperature, and time may contribute to the formation of the observed abnormal microstructure. Full article

This research addresses visibility challenges in surveillance systems under foggy conditions through a novel synthetic fog generation method leveraging the GridNet dehazing architecture. Our approach uniquely reverses GridNet, originally developed for fog removal, to synthesize realistic foggy images. The proposed Fog Generator Model incorporates perceptual and dark channel consistency losses to enhance fog realism and structural consistency. Comparative experiments on the O-HAZY dataset demonstrate that dehazing models trained on our synthetic fog outperform those trained on conventional methods, achieving superior Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) scores. These findings confirm that integrating high-performance dehazing networks into fog synthesis improves the realism and effectiveness of fog removal solutions, offering significant benefits for real-world surveillance applications. Full article

The acoustic scattering of targets in shallow-sea waveguides exhibits complex features such as multipath propagation and intricate echo components, with its acoustic field properties remaining incompletely understood. This study employs a hybrid method combining normal modes and scattering functions to numerically model the acoustic scattering of targets in waveguide channels. We analyze the coupling mechanisms of multipath acoustic waves and derive precise predictive formulas for the bright–dark interference fringe patterns in range–frequency spectra based on the physical mechanisms governing acoustic field interference. By tracking the peak trajectories of these interference fringes in range–frequency spectra, we investigate the variations of the waveguide invariant with frequency, range, and depth, revealing statistical patterns of the waveguide invariant in target–waveguide coupled scattering fields under different water depths. The results demonstrate that, under constant channel conditions, waveguide properties exhibit a weak correlation with target material characteristics. In shallow water environments, waveguide invariant values display broader distributions with higher probability density peaks, whereas increasing water depth progressively narrows the distribution range and monotonically reduces the peak magnitudes of the probability density function. Experimental validation via scaled elastic target echo testing confirms the observed trends of waveguide invariant variation with water depth. Full article

In fog, rain, snow, haze, and other complex environments, environmental objects photographed by imaging equipment are prone to image blurring, contrast degradation, and other problems. The decline in image quality fails to satisfy the requirements of application scenarios such as video surveillance, satellite reconnaissance, and target tracking. Aiming at the shortcomings of the traditional dark channel prior algorithm in video defogging, this paper proposes a method to improve the guided filtering algorithm to refine the transmittance image and reduce the halo effect in the traditional algorithm. Meanwhile, a gamma correction method is proposed to recover the defogged image and enhance the image details in a low-light environment. The parallel symmetric pipeline design of the FPGA is used to improve the system’s overall stability. The improved dark channel prior algorithm is realized through the hardware–software co-design of ARM and the FPGA. Experiments show that this algorithm improves the Underwater Image Quality Measure (UIQM), Average Gradient (AG), and Information Entropy (IE) of the image, while the system is capable of stably processing video images with a resolution of 1280 × 720 @ 60 fps. By numerically analyzing the power consumption and resource usage at the board level, the power consumption on the FPGA is only 2.242 W, which puts the hardware circuit design in the category of low power consumption. Full article

Zoharite (IMA 2017-049), (Ba,K)₆ (Fe,Cu,Ni)₂₅S₂₇, and gmalimite (IMA 2019-007), ideally K₆□Fe²⁺₂₄S₂₇, are two new sulfides of the djerfisherite group. They were discovered in an unusual gehlenite–wollastonite paralava with pyrrhotite nodules located in the Hatrurim pyrometamorphic complex, Negev Desert, Israel. Zoharite and gmalimite build grained aggregates confined to the peripheric parts of pyrrhotite nodules, where they associate with pentlandite, chalcopyrite, chalcocite, digenite, covellite, millerite, heazlewoodite, pyrite and rudashevskyite. The occurrence and associated minerals indicate that zoharite and gmalimite were formed at temperatures below 800 °C, when sulfides formed on external zones of the nodules have been reacting with residual silicate melt (paralava) locally enriched in Ba and K. Macroscopically, both minerals are bronze in color and have a dark-gray streak and metallic luster. They are brittle and have a conchoidal fracture. In reflected light, both minerals are optically isotropic and exhibit gray color with an olive tinge. The reflectance values for zoharite and gmalimite, respectively, at the standard COM wavelengths are: 22.2% and 21.5% at 470 nm, 25.1% and 24.6% at 546 nm, 26.3% and 25.9% at 589 nm, as well as 27.7% and 26.3% at 650 nm. The average hardness for zoharite and for gmalimite is approximately 3.5 of the Mohs hardness. Both minerals are isostructural with owensite, (Ba,Pb)₆(Cu,Fe,Ni)₂₅S₂₇. They crystallize in cubic space group Pm$\overline{3}$m with the unit-cell parameters a = 10.3137(1) Å for zoharite and a = 10.3486(1) Å for gmalimite. The calculated densities are 4.49 g·cm⁻³ for the zoharite and 3.79 g·cm⁻³ for the gmalimite. The primary structural units of these minerals are M₈S₁₄ clusters, composed of MS₄ tetrahedra surrounding a central MS₆ octahedron. The M site is occupied by transition metals such as Fe, Cu, and Ni. These clusters are further connected via the edges of the MS₄ tetrahedra, forming a close-packed cubic framework. The channels within this framework are filled by anion-centered polyhedra: SBa₉ in zoharite and SK₉ in gmalimite, respectively. In the M₈S₁₄ clusters, the M atoms are positioned so closely that their d orbitals can overlap, allowing the formation of metal–metal bonds. As a result, the transition metals in these clusters often adopt electron configurations that reflect additional electron density from their local bonding environment, similar to what is observed in pentlandite. Due to the presence of shared electrons in these metal–metal bonds, assigning fixed oxidation states—such as Fe²⁺/Fe³⁺ or Cu⁺/Cu²⁺—becomes challenging. Moreover, modeling the distribution of mixed-valence cations (Fe²⁺/³⁺, Cu⁺/²⁺, and Ni²⁺) across the two distinct M sites—one located in the MS₆ octahedron and the other in the MS₄ tetrahedra—often results in ambiguous outcomes. Consequently, it is difficult to define an idealized end-member formula for these minerals. Full article