Search Results (114)

Accurate delineation of landslides from high-resolution optical imagery is important for hazard assessment and infrastructure safety in mountainous regions. However, automatic segmentation remains challenging because landslides vary greatly in scale, morphology, surface appearance, and boundary clarity. These difficulties are particularly evident for old landslides, whose surface characteristics may become less distinct after long-term erosion, vegetation recovery, and surface modification. To establish a consistent baseline, five representative semantic segmentation models were evaluated on the Landslide Recognition along the Sichuan–Tibet Transportation Corridor (LRSTTC) dataset under a unified experimental protocol. DeepLabV3+ showed the strongest overall baseline performance and was selected as the base architecture. We then developed a Boundary-Aware Attention-Enhanced DeepLabV3+ (BAA-DeepLabV3+) framework, in which a Convolutional Block Attention Module (CBAM) is applied after Atrous Spatial Pyramid Pooling (ASPP) for high-level feature refinement, while an auxiliary boundary branch provides additional spatial supervision during training. In the controlled ablation experiment with seed 42, BAA-DeepLabV3+ improved the Intersection over Union (IoU) from 0.3644 to 0.4229 and the Dice coefficient from 0.5341 to 0.5944, with only a negligible increase in model parameters. Across three random seeds, the mean IoU increased from 0.3599 ± 0.0275 to 0.3876 ± 0.0315. External validation on the independent Bijie landslide dataset further improved the mean IoU from 0.7295 to 0.7444, with BAA-DeepLabV3+ outperforming the baseline under all three random seeds. These results indicate that attention-based feature refinement and boundary-aware supervision can work complementarily to improve landslide segmentation while maintaining a favorable balance between segmentation performance and computational efficiency. Full article

The rapid development of quantum technology and single-photon LiDAR places ever-increasing demands on detection systems in terms of bandwidth suppression, wavelength stability, and transmission accuracy. Conventional narrowband filters, however, are falling short in sub-nanometer bandwidth control and high-temperature stability, thereby failing to support high-precision measurements. Consequently, ultra-narrowband optical filters have emerged as a key approach to overcoming these performance bottlenecks. This paper focuses on the core technologies involved in the development of such filters. In the design phase, multiparameter optimization based on a Fabry–Perot (F-P) etalon translates application requirements into fabrication parameters. For substrate fabrication, a sequential process of computer numerical control (CNC) milling, lapping, chemical–mechanical polishing (CMP), and ion-beam polishing, followed by atomic layer deposition (ALD) step formation and ion-beam evaporation coating, is employed to achieve a nanometer-level surface figure and roughness. Temperature control is achieved via a dual-tank hybrid circulation system, maintaining stability within ±0.1 °C. Test results show that the fabricated filter exhibits a full width at a half maximum (FWHM) of 30–60 pm, a free spectral range (FSR) of 260 ± 5 pm, a temperature stability of ≤3 pm/°C, and a peak transmittance of ≥80%. These results preliminarily confirm the device’s excellent overall performance. Full article

Lanthanum oxide (La₂O₃) powder is widely used in optical, catalytic, and ceramic applications, but its flowability is highly sensitive to ambient humidity, and reliable discrete element method (DEM) contact parameters under varying moisture conditions remain unavailable. To address this, the present study introduces coarse-graining theory to reduce computational cost while preserving macroscopic mechanical equivalence, and systematically calibrates the DEM contact parameters of La₂O₃ powder under three controlled humidity levels (5%, 50%, and 95% RH) using the Hertz–Mindlin with JKR contact model. The angle of repose was measured as the macroscopic response. A Plackett–Burman design was employed to screen three significant factors from seven candidate parameters, followed by a steepest ascent test to determine optimal parameter ranges, and a Box–Behnken design to construct response surface models. Quantitative relationships were established between the angle of repose and the particle–particle static friction coefficient, particle–particle rolling friction coefficient, and particle–stainless steel static friction coefficient under each humidity condition. The calibrated parameters were verified against experimental anglef of repose measurements, showing good agreement, with errors of 0.48%, 2.15%, and 1.67% for the three humidity levels, respectively. This work provides reliable DEM parameters specifically for the three tested relative humidity levels (5%, 50%, and 95% RH) under the specific conditioning procedures used, and offers a calibration framework extendable to other moisture-sensitive cohesive powders. Full article

Ultraviolet (UV) radiation is a major environmental factor contributing to photoaging, oxidative stress, inflammation, DNA damage, and photocarcinogenesis. Conventional UV filters, although widely used in sunscreen formulations, are associated with limitations including photoinstability, photocatalytic ROS generation, potential toxicity, and environmental concerns. In recent years, plant-mediated nanomaterials have emerged as promising multifunctional photoprotective systems, combining UV attenuation with antioxidant, anti-inflammatory, and biologically adaptive properties. Plant extracts are increasingly used as reducing and stabilising agents in the green synthesis of metal and metal oxide nanoparticles. Among these, ZnO and TiO₂ serve as established inorganic UV filters, whereas Ag and Au nanoparticles have primarily been investigated for their antioxidant, anti-inflammatory, antimicrobial, and ROS-modulating properties, which may indirectly enhance photoprotection. In parallel, plant-derived organic nanoparticles and herbal nanocomposites have demonstrated enhanced biocompatibility and multifunctional performance. This review critically examines the current landscape of plant-mediated photoprotective nanomaterials, focusing on the mechanistic interplay among optical UV attenuation, reactive oxygen species (ROS) modulation, and cellular signalling regulation. Particular emphasis is placed on structure–function relationships governing nanoparticle size, surface chemistry, bandgap properties, antioxidant behaviour, and biological interactions. The review further discusses translational challenges, including reproducibility, standardisation, scalability, long-term safety, regulatory classification, and limitations in benchmarking. Importantly, current evidence suggests that no single material system simultaneously optimises UV-blocking efficiency, ROS control, biocompatibility, and industrial scalability, highlighting the need for multifunctional hybrid design strategies. Finally, future perspectives involving predictive nanoengineering, computational modelling, machine learning-guided optimisation, and adaptive photoprotective systems are discussed as emerging directions for next-generation sustainable photoprotective technologies. Full article

Corrosion remains one of the principal causes of degradation in metallic structures across a wide range of industrial sectors. Although visual inspection is routinely employed for preliminary corrosion assessment, its effectiveness depends heavily on operator experience and subjective interpretation. This work introduces CorrQuant, a web-based computer vision platform designed to transform qualitative corrosion images into quantitative measurements of corrosion extent and morphology. The proposed methodology processes images acquired with conventional mobile devices and integrates geometric calibration using a reference coin, perspective correction, adaptive image enhancement through Contrast Limited Adaptive Histogram Equalization (CLAHE), multi-descriptor feature extraction, and consensus-based corrosion segmentation. The detected corrosion regions are subsequently quantified to determine corrosion area, surface coverage, spatial distribution, morphological descriptors, and corrosion intensity maps. The methodology was verified using an aluminum specimen with a known corrosion area of 143 mm² under both controlled illumination and optical stress-test conditions. Under standard acquisition conditions, corrosion-area estimation accuracies exceeding 90% were achieved. Additional evaluations under red illumination, fisheye, blur, and kaleidoscope distortions demonstrated that the proposed framework is considerably more sensitive to degradation of local image information than to variations in illumination spectrum. These results demonstrate the robustness of the proposed multi-descriptor voting strategy while defining the operational limits of the platform under challenging image acquisition conditions. Full article

Optical image-based texture and material surface recognition is important for computational imaging, intelligent inspection, and material-reference retrieval, where a system often needs not only a closed-set label but also inspectable evidence and an expandable gallery. Scale-varying micro-textures, repeated surface structures, global layout cues, and visually ambiguous categories make this task difficult under non-controlled imaging conditions. This paper presents Reliability-Aware Global-Local Metric Retrieval (RAGL-MR), which maps images and patches into a shared ArcFace metric space, retrieves multi-scale patch neighbors as local surface evidence, compresses whole-image reference galleries using Multi-Prototype Global Distribution Modeling (MP-GDM), and combines branch scores through validation-selected reliability-aware fusion. The framework is evaluated on public texture/material surface image benchmarks used as proxies for surface image analysis rather than as controlled photonic experiments. On the first five official DTD splits, the final fusion reaches 74.06 ± 0.11 Top-1, 92.61 ± 0.61 Top-5, and 96.11 ± 0.23 Top-10; on MINC-2500, it reaches 85.84 ± 0.25 Top-1, 97.63 ± 0.28 Top-5, and 99.15 ± 0.10 Top-10, with additional checks on FMD and KTH-TIPS2-b. Additional perturbation, calibration, large-gallery, and industrial surface-defect checks further examine robustness, reliability, and practical retrieval behavior. These results indicate that RAGL-MR provides an evidence-aware and gallery-extensible retrieval complement for optical material surface image analysis. Full article

Background: Accurate thyroid volume measurement is essential for diagnosing and monitoring thyroid disorders and for determining radioiodine therapy dosage. The ellipsoid method applied to two-dimensional B-mode ultrasound (Bmode-2D-US) is the most prevalent clinical approach, yet it is known to suffer from high observer dependence and limited accuracy. Tracked three-dimensional ultrasound (3D-US) is a promising radiation-free alternative, but direct comparisons across modalities under controlled conditions remain scarce. Methods: A custom-built anthropomorphic multi-modality phantom was constructed containing six anatomically realistic thyroid lobe samples molded from segmented MRI data, with ground truth volumes verified by the suspension method and 3D surface scanning. Volume measurements were performed by six observers of varying experience using Bmode-2D-US, electromagnetically tracked 3D-US (EM-3D-US), inertial-measurement-unit-tracked 3D-US (IMU-3D-US), and optically tracked 3D-US (Opt-3D-US), as well as computed tomography (CT) and magnetic resonance imaging (MRI). Inter- and intraobserver variability were assessed using modified Bland–Altman analysis. Results: All three 3D-US methods reduced inter- and intraobserver variability compared to Bmode-2D-US, achieving variability comparable to CT and lower than MRI. Mean accuracy was similar across 3D-US, CT, and MRI. Bmode-2D-US showed strong observer dependence, with operator experience having the most pronounced effect on this method. Among tracked methods, EM-3D-US and Opt-3D-US were least sensitive to operator movement quality, while IMU-3D-US showed somewhat higher sensitivity. Conclusions: Tracked 3D-US is a promising radiation-free alternative to conventional Bmode-2D-US for thyroid volumetry, offering improved reproducibility and accuracy across operators of varying experience in this phantom-based evaluation. Full article

Autonomous takeoff and landing (ATOL) remains one of the most challenging tasks for fixed-wing unmanned aerial vehicles (UAVs), particularly in environments where Global Navigation Satellite System (GNSS) signals are degraded or unavailable. This paper presents an LiDAR-assisted terrain-relative navigation framework for the autonomous takeoff and landing of a 5 kg fixed-wing UAV operating under degraded navigation conditions. The proposed architecture integrates a downward-facing LiDAR rangefinder with barometric altitude sensing, INS/GNSS navigation, optical-flow measurements, and airspeed information within a multi-sensor fusion and flight-control framework. The system combines a Pixhawk-based autopilot with a companion-computer architecture responsible for real-time sensor processing, altitude estimation, mission supervision, and MAVLink-based communication. A dedicated filtering strategy and sensor fusion approach enable reliable terrain-relative altitude estimation during critical low-altitude flight phases, while fault-tolerant command-management mechanisms improve operational robustness in the presence of temporary communication losses and sensor disturbances. The proposed framework was validated through Software-in-the-Loop (SITL), Hardware-in-the-Loop (HITL), and real-flight experiments. Experimental results demonstrated stable and repeatable autonomous landing performance. Comparative analyses showed that the LiDAR sensor provided the most accurate and responsive terrain-relative altitude measurements during takeoff, flare, and landing operations, particularly over irregular and vegetation-covered surfaces. In contrast, barometric sensing provided greater long-term stability during cruise flight, highlighting the importance of multi-sensor fusion for reliable altitude estimation throughout the mission profile. The results confirm that LiDAR-based terrain-relative sensing significantly improves autonomous takeoff and landing performance for fixed-wing UAVs operating in GNSS-degraded environments. The proposed architecture offers a practical and low-cost solution for the autonomous takeoff and landing of fixed-wing UAVs operating in GNSS-degraded environments while demonstrating the benefits of integrating LiDAR, inertial, barometric, and GNSS measurements within a unified multi-sensor autonomous flight framework. Full article

The ability to manipulate and probe individual nano-particles, viruses, and organelles with high sensitivity and specificity is an essential part of modern nanoscience and molecular biology. Plasmonic optical tweezers (POT), which use localized surface plasmons to create nanoscale-confined optical fields, have emerged as a powerful platform for trapping and manipulating single nano–bio entities at low optical powers. When combined with surface-enhanced Raman spectroscopy (SERS) from the same plasmonic nanostructures, these platforms offer a unique multi-modal capability: simultaneous optical manipulation and label-free chemical fingerprinting of a single specimen. However, the field faces critical challenges, including low throughput, thermal noise, photothermal damage, and the overwhelming complexity of interpreting dynamic, single-molecule SERS data. This review examines the transformation of plasmonic optical trapping and spectroscopy as they evolve toward autonomous operation and intelligent decision-making. We begin with the fundamental principles that enable these tools to manipulate and probe single viruses, organelles, and nano-particles. Building on this foundation, we explore how computational intelligence is being integrated into the field to address long-standing challenges. This includes the emergence of data-driven methods for designing optimized plasmonic nanostructures, for decoding the complex molecular fingerprints hidden in single-molecule SERS spectra, and for creating feedback-driven systems capable of adaptive, real-time experiment control. By synthesizing these developments, we illustrate a clear trajectory: from manually operated instruments toward fully integrated intelligent nanophotonic laboratories that can autonomously discover and characterize the nano-world. We conclude by discussing the remaining challenges—from data acquisition and model interpretability to the mitigation of photothermal effects—and the most promising pathways toward realizing this transformative vision for virology, cell biology, and nanomedicine. Full article

Steel manufacturers currently rely on optical sensing systems that capture surface images for quality control, but these systems still face practical challenges in industrial environments. Early machine vision methods used handcrafted features, and their accuracy dropped when lighting changed or the background texture became complex. Deep learning improved detection, yet small defects that blend into background noise or share visual patterns with other classes still cause missed detections and false positives on factory floors where hardware resources are tight. GEA-YOLO integrates different refinement strategies into the Backbone, Neck, and training stage. C2DGA replaces C2PSA in the Backbone. Deformable attention adapts to defect structures that deviate from fixed sampling patterns, and a dynamic gate fuses global contextual information with local texture features. EMA modules are inserted into the Neck, where they recalibrate features independently at each scale and reduce the influence of background interference. DetectAux provides auxiliary supervision for hard samples during training. Unlike approaches that introduce attention at a single fixed position, GEA-YOLO places each module at the stage where it can improve the corresponding representation. We evaluated GEA-YOLO on the NEU-DET and GC10-DET datasets. On NEU-DET, the model reached 80.3% mAP@0.5, 2.6 percentage points above YOLOv11s, while keeping a reasonable balance between accuracy and computational cost and maintaining an inference speed of 169.5 FPS. Cross-dataset validation on GC10-DET further confirmed generalization, yielding 74.9% mAP@0.5, 2.9 percentage points above YOLOv11s, showing strong potential for real-time steel surface inspection. Ablation results confirm that each modification fixes a different weakness in the detection pipeline, but the full gain only appears when all three are used together. These results indicate that GEA-YOLO is promising for real-time optical inspection in controlled benchmark settings. Full article

This study presents multi-objective geometric optimization of a modular friction disk cutter for spur gear thermo-friction processing within the ANSYS Workbench 2024 R1. The integrated workflow—Geometry → Steady-State Thermal → Static Structural → Design of Experiments → Response Surface → Response Surface Optimization—enables selection of a rational tool geometry within a single parametric model. Variable dimensions (a, b, c) describe the load-bearing part: a characterizes the transitional thin profile zone, b is the massive supporting part, and c is the intermediate disk thickness controlling thermo-mechanical load transmission. Dimension c most significantly influences equivalent stresses and directional deformation, while maximum temperature depends on combined a and c effects. Based on Response Surface Optimization, the rational solution domain is concentrated near $a\approx 3 \mathrm{m}\mathrm{m}$, $b\approx 10 \mathrm{m}\mathrm{m}$, and $c\approx 4 \mathrm{m}\mathrm{m}$, yielding $P4\approx 33.306 \mathrm{M}\mathrm{P}\mathrm{a}$, $P5\approx 295.93 °\mathrm{C}$, and $P6\approx -5.7488·{10}^{-5} \mathrm{m}$. These values demonstrate a sufficient calculated safety margin within the finite element framework, providing a technically justified direction for prototype manufacturing. Although currently evaluated as purely computational without direct full-scale physical measurements, these results establish a foundation for subsequent experimental validation using thermal imaging and optical deformation analysis. Future research will focus on transient thermo-mechanical modeling with impulse cooling and experimental verification. Full article

Structured illumination (SI) involves projecting controlled spatial light patterns onto a scene or sample and processing the captured response to recover information such as the phase, surface shape, or optical contrast. Three-Step Phase Shifting (TPS), also known as three-phase demodulation (TPD) in the spatial frequency domain imaging (SFDI) field, is a common SI workflow in which three phase-shifted sinusoidal patterns are projected and captured sequentially for one demodulation. To achieve acceptable demodulation quality, all three phase-shifted patterns must be captured correctly. However, achieving this quality introduces a trade-off since each phase must be captured at the correct time, increasing acquisition time and requiring precise projector–camera synchronization. In real-time TPD-based SI, low pattern-generation throughput, synchronization uncertainty, and often bulky desktop implementations remain major limitations. Therefore, this work investigates, designs, and validates a deterministic, low-latency, and portable projection–capture synchronization system for TPD-based SI. First, a Hyperspectral (HS) Python-based SI (HSPy-SI) system, representative of common state-of-the-art (SOTA) implementations, is evaluated. It uses an HS snapshot camera and a fixed-delay desktop synchronizer. Then, the proposed HyperSI system is introduced as a real-time projection–capture synchronizer implemented as a bench-top prototype on heterogeneous embedded platforms: a Single-Board Computer (SBC) and a System-on-Chip (SoC) board. Its core contribution is a tunable parameter, W (Frame Count to Wait), which counts frame-generation interrupts before capture and reduces the delay-search space. Compared with the SOTA, HyperSI achieves over 8× higher pattern-generation throughput, increases polarized acquisition by 7× to nearly 4 FPS, reaches about 12 FPS without polarizers, and reduces waiting time by 88×. Full article

Infrared cameras used in radio telescopes often suffer image degradation in complex optical and thermal environments. Solar radiation, convergent reflected light, and thermal emission from support structures can substantially impair imaging performance. To address this problem, this paper proposes a split reflective ellipsoidal baffle for suppressing infrared imaging degradation. Unlike conventional baffles, which mainly rely on structural occlusion and surface absorption, the proposed design functions as an upstream stray light regulation unit. It also establishes a computational framework integrating ellipsoidal vane geometry, realistic edge microtopography modelling, ray-tracing simulation, and detector plane irradiance response analysis. First, the reflective properties of the ellipsoidal surface are used to construct an off-axis stray light propagation constraint model. Under this model, incident stray radiation is redirected away from the effective imaging path or guided into light-trapping regions between adjacent vanes. Second, a laser confocal microscope is used to capture the true three-dimensional edge morphology of vanes with different materials and machining angles. This strategy addresses the limitations of the conventional 0.02 mm rounded edge approximation, which cannot accurately represent real scattering behaviour. The measured morphologies are then converted into high-fidelity computational models compatible with ray-tracing analysis. Furthermore, stray light suppression performance is evaluated using point source transmittance, detector plane irradiance distribution, and grey scale response in experimental images. Simulation and darkroom experiments show that the proposed baffle suppresses residual stray light more effectively than conventional absorptive baffles. The results demonstrate a computable, manufacturable, and experimentally verifiable strategy for front-end stray light control and baffle optimisation. This strategy can also support image quality enhancement in infrared imaging systems operating under complex optical and thermal environments. Full article

To evaluate the influence of near-field ground scattering on ultra-wideband (UWB) fuze performance, this paper presents an efficient operator-expansion time-domain physical optics (OE-TD-PO) framework. This method extends conventional far-field TD-PO to electrically large, dispersive rough surfaces under near-field excitation. By leveraging the local plane wave approximation (LPA) and time-domain Kirchhoff approximation (KA), the complex scattering process is decomposed into independent element-wise responses, which reduces the coupling between geometry and wave propagation. The scattering physics of each facet are represented using closed-form material and geometric operators. The material operator accounts for frequency-dependent dispersion and polarimetric reflection, while the geometric operator models intra-facet delay spread in the time domain. An excitation-order expansion of the transient dipole radiation formula is introduced to decouple the source waveform from spatial facet loops, yielding radiation, induction, and static components corresponding to the derivative, proportional, and integral terms of the excitation signal. This decoupling reduces computational complexity while preserving physical fidelity. Validated against analytical and numerical benchmarks, the proposed method effectively quantifies terrain-induced ranging biases and initiation reliability, providing a rigorous basis for adaptive error compensation and gain control in UWB fuzes across diverse environments. Full article

Rapid and non-contact surface inspection is essential for quality control in modern production. Optical coherence tomography (OCT) can image a surface without contact, but turning those images into roughness parameters usually requires specialized processing software. This study examined whether a multimodal large language model (LLM) could estimate roughness parameters directly from OCT B-scans as a screening tool. The study was designed as a controlled macro-scale proof of concept using periodic, analytically defined phantoms rather than as validation on stochastic industrial micro-roughness. Five test surfaces with exactly known geometries were designed, 3D-printed, and scanned with a spectral-domain OCT system. For each surface, roughness values were computed from the theoretical shape, extracted from the OCT image using MATLAB, and also estimated by the LLM from the same image. The repeatability of the LLM was checked by running the same prompt ten times per surface. On a sawtooth profile, the LLM estimates varied by 3.8% for Ra, 4.2% for Rq, 3.5% for Rp, 2.8% for Rv, and 3.1% for Rt. Across all five surfaces, the variation in Ra and Rq was around 3–5%, and for Rt, it stayed below 5%. The results show that a generative AI approach can produce repeatable roughness estimates that are useful for comparative screening. This method offers a flexible option for surface comparison and AI-assisted quality control when calibrated measurements are not required. Full article