Search Results (751)

Structural analysis using X-rays and electron beams has long provided the average arrangement of atoms and molecules—that is, “structural information”—with high precision. By contrast, static measurements cannot directly yield dynamic information on how a material changes over time; instead, information on motion is convolved into a single numerical value such as the B-factor (atomic displacement parameter). Taking this limitation as its starting point, this review surveys the recent trend of introducing a time axis into measurements to observe material dynamics directly. First, we outline the technological foundations that have made the transition from static to time-resolved measurement possible. It rests on the dramatic shortening of exposure times, enabled by the increased brilliance of X-ray and electron sources and by advances in detection technology such as direct photon-counting detectors. Next, we survey dynamic measurement techniques, including time-resolved X-ray crystallography, coherent X-ray scattering, neutron scattering, and time-resolved electron microscopy. We also point out the essential limitation that most of them still return ensemble or volume averages. Building on this, we systematically describe diffracted X-ray tracking (DXT), diffracted X-ray blinking (DXB), small-angle X-ray blinking (SAXB), transmitted X-ray blinking (TXB), and electron-beam molecular dynamics (EBMD), which use gold nanocrystals and gold nanoparticles as motion probes. We distinguish throughout between methods that follow individual objects—DXT and EBMD, which yield trajectories of single labeled molecules or single particles—and methods that analyze intensity fluctuations arising from many contributors within one pixel or illuminated volume—DXB, SAXB and TXB. The latter are not single-molecule measurements; rather, they replace a global ensemble average by a spatially localized statistical one, retaining local heterogeneity that a bulk measurement would average away. Finally, we discuss the implementation and prospects of the large-volume data analysis—principal component analysis, Bayesian inference, machine learning, and autonomous measurement—needed to handle the explosively increasing amount of information that the time axis introduces. We close with the outlook that time-resolved measurement incorporating AI and big-data analysis will become established as a new measurement platform that complements and extends conventional static structural analysis. Full article

Recent developments in particle physics require cost-effective pixel detectors capable of operating under increased energy and luminosity conditions foreseen in future collider experiments. In response, monolithic CMOS pixel sensors incorporating modern readout architectures have emerged, combining high-rate capability with substantial radiation tolerance. To optimize the performance of these sensors for application in tracking detectors, a comprehensive characterization has been done focusing on threshold and noise behavior as a function of front-end DAC tuning parameters. The effect of radiation damage has been investigated using high-intensity X-ray irradiation, followed by a detailed comparison of sensor performance before and after irradiation. The threshold distribution is observed to be uniform across the pixel matrix. Irradiation introduces a systematic shift in the threshold, with a larger impact at low-threshold configurations, while overall uniformity is preserved. In contrast, the noise remains largely stable across the parameter space. The correlation between threshold and noise is used to identify optimal operating regions, demonstrating that stable, efficient performance can be achieved across quite a wide range of configurations. These results confirm the robustness of the sensor under irradiation and its suitability for operation in radiation environments relevant to future high-energy physics experiments. Full article

Region-level weakly supervised hyperspectral target detection (HTD) using multiple-instance learning (MIL) reduces annotation costs but encounters challenges such as bag label ambiguity, boundary over-smoothing, and test-time computational latency. To address these issues, we propose Hyper-VMIL, a spatial–spectral topology-regularized variational hypergraph network. Hyper-VMIL formulates latent target localization as variational inference over dual-path hypergraphs: a boundary-aware spatial hypergraph modeling geometric patch continuity and a dynamic spectral-manifold hypergraph capturing non-local material similarity. Node-adaptive gating dynamically balances spatial and spectral evidence to mitigate over-smoothing near target boundaries. Furthermore, a confidence-aware continuous posterior refinement (CTPR) mechanism reduces the confirmation bias associated with conventional hard pseudo-label binarization. Finally, a teacher–student distillation strategy transfers contextual topology into a lightweight single-spectrum student detector. Benchmark experiments on simulated ASTER and airborne MUUFL Gulfport and Avon datasets show that Hyper-VMIL achieves competitive performance against 15 baseline methods. Notably, Hyper-VMIL supports dual inference modes: Context Mode provides improved detection accuracy (+4.6% average NAUC over VMIL-ECM on MUUFL), while Pixel Mode enables single-spectrum inference ($1.25\mathsf{\mu }\mathrm{s}$ single-instance latency and an amortized streaming throughput of $0.015\mathsf{\mu }\mathrm{s}$ per pixel) suitable for onboard real-time deployment. Full article

Image restoration lies between camera acquisition and geometric estimation, but pixel improvements may not transfer to motion estimates. We evaluated directional-kernel Richardson–Lucy (RL) deblurring under kernel-length and direction controls. The restoration analysis covered 3071 paired GoPro, RealBlur-J, and RealBlur-R images. An exploratory feature analysis used a fixed 155-image subset with Oriented FAST and Rotated BRIEF (ORB), scale-invariant feature transform (SIFT), two geometry models, ten random directions, NAFNet, and Restormer. A separate task analysis used ten red–green–blue plus depth (RGB-D) sequences from the Technical University of Munich (TUM) benchmark, synthetic 20 ms exposures, and fixed RGB-D perspective-n-point odometry. Estimated directions contained information relative to random angles, yet the tested global RL branches remained below Blur Input on average. Changes in sequence-mean absolute trajectory error (ATE) RMSE ranged from +0.004 to +0.051 m for ORB and from −0.008 to +0.036 m for SIFT. Seeds were averaged within each sequence before inference. No tested branch achieved a robust ATE improvement across both detectors. Pixel, raw-feature, normalized-feature, geometry-state, and trajectory endpoints produced different method rankings. These findings motivate endpoint-specific evaluation. The task experiment does not validate naturally blurred long-exposure video, a deployed simultaneous localization and mapping system, or sensor hardware. Full article

Small objects in unmanned aerial vehicle (UAV) imagery often occupy only a few pixels, making their responses vulnerable to downsampling, cluttered backgrounds, and cross-scale feature misalignment. Most detector improvements handle backbone representation, encoder context modeling, and neck fusion as separate design choices, so the frequency structure of tiny targets and the geometric consistency of multi-scale features remain underused. FSA-DETR addresses this gap with a frequency-aware and structure-aligned design built on Real-Time Detection Transformer (RT-DETR). Its central idea is to treat UAV small-object detection as a frequency–structure representation problem: high-frequency target cues are enhanced in the backbone and encoder, while local structural orientation is aligned during cross-scale fusion. The design combines a Cross-stage Spectral-Aware Multi-scale Block, a Wavelet-enhanced Intra-scale Transformer Encoder, and a Spectral Orientation-Aligned Fusion module. On VisDrone2019, FSA-DETR achieves 21.5% AP and 37.5% AP50, improving the RT-DETR baseline by 1.6% and 2.4%, respectively, while reducing the computational cost to 48.8 GFLOPs and the parameter count to 15.04 M. The small-object metric APs increases by 1.3%, and the broader size-stratified gains indicate improved sensitivity to aerial targets across multiple scales. On AI-TOD, FSA-DETR obtains 22.5% AP and 51.9% AP50, suggesting that the same frequency–structure design transfers to extremely tiny objects. Under the reported benchmark protocol, explicitly coupling frequency-sensitive perception with structure-aligned fusion improves UAV small-object detection while keeping the complete model smaller than the RT-DETR baseline used in this study; this suggests that separating spectral discrimination from geometric alignment is a useful design pattern for UAV detectors. Full article

Integrated qiaopi office–residence heritage preserves the material setting of remittance-letter operations together with domestic, educational, and ritual activities. In Lingnan’s hot–humid and disaster-prone environment, condition records need to be repeatable, spatially traceable, and continuously updatable. Taking Jingzu Jiashu and Mingde Jiashu, two former qiaopi office sites in Chaoshan, as case studies, this research develops an evidence-traceable digital conservation workflow integrating multi-source documentation; an adopted YOLOv8 surface-deterioration baseline; qualitative Grad-CAM visualization; structured deterioration records; and semi-automatic, human-confirmed Revit/HBIM association. UAV and terrestrial photography, mobile LiDAR/scanning, handheld measurement, measured drawings, point-cloud and reality-based products, and geometric models were organized into case-specific HBIM environments. The adopted deterioration dataset comprised 362 original images at 512 × 512 pixels and 2024 bounding-box annotations for five visually identifiable categories: spalling, staining, plants, saltpetering, and crack. The original images were divided into 253 training, 72 validation, and 37 independent-test images, while augmentation was restricted to the training subset, increasing the training pool to 1600 images. The previously established YOLOv8 baseline achieved a Precision of 0.85, Recall of 0.72, mAP50 of 0.83, and mAP50–95 of 0.58. Grad-CAM heatmaps were used as qualitative aids to examine model-emphasized image regions. Retained detections associated with Jingzu Jiashu and Mingde Jiashu were converted into versioned records containing source-image identifiers, deterioration classes, detector confidence, survey information, spatial references, verification states, and revision histories. Candidate spatial associations were generated through case identifiers, façade or space zones, element identifiers, and available spatial evidence, while final M1–M3 associations required human confirmation. By preserving source provenance, spatial uncertainty, and record histories, the workflow provides an auditable information basis for routine inspection, post-event review, maintenance prioritization, repair interpretation, and resilience-oriented preventive conservation. The workflow supports screening-level deterioration recognition and information management but does not provide causal diagnosis, structural assessment, exact affected-area measurement, building-independent generalization, or automatic repair recommendations. Full article

Aircraft fuselage corrosion inspection requires both image-domain recognition and metric physical-surface localization for maintenance execution. This study develops a camera-calibrated vision measurement framework that combines PWDE-YOLOv8n-based corrosion perception, original-image coordinate restoration, lens-distortion compensation, ray-based surface mapping, and detector-center bias correction. The perception dataset comprised 2143 images and 5941 corrosion annotations and was partitioned at the physical-specimen, acquisition-session, or source-group level into 1500 training images, 429 validation images, and 214 independent detector-test images. Detailed physical localization was evaluated on a six-image metrology cohort acquired in six sessions, containing 21 corrosion boxes and 84 axial coordinates. A six-fold leave-one-image-out procedure was adopted; in each fold, the center-shift parameters were estimated from the other five images and applied unchanged to the held-out image. The proposed method achieved a mean absolute axial error of 0.641 mm (95% image-cluster bootstrap confidence interval: 0.571–0.708 mm), an RMSE of 0.809 mm, a maximum error of 3.262 mm, and a projected physical-plane bounding-box IoU of 87.12%. The expanded uncertainty of the manually established reference coordinates was 0.374 mm at k = 2, and Monte Carlo propagation produced a mean absolute error of 0.656 mm with a 95% interval of 0.618–0.693 mm. The proposed method reduced the MAE by 97.91% relative to local pixel-to-millimeter scaling and by 70.58% relative to conventional calibrated camera mapping, while producing accuracy comparable to planar homography mapping. Within ρ ≥ 1500 mm, W ≤ 150 mm, and θ ≤ 20°, the estimated curvature-induced additional axial error did not exceed 0.683 mm. A separate ten-image deployment evaluation produced a mean axial error of 2.448 mm and an average processing time of 53.35 ms/image. Full article

Aircraft often manifest as highly aspect/pose-sensitive, disjointed blobs made of pixels with fluctuating levels of brightness in classic grayscale SAR images, which makes the aircraft annotation task challenging even for human experts. As more and more high-resolution colorized SAR images acquired by the latest commercial imaging modes are released for open access, both the academia and the industry started to notice the benefits of color-coded SAR images. However, since the large-scale datasets in the area of SAR aircraft detection feature grayscale SAR images, research on discrimination feature learning in color space for SAR aircraft detection is very limited. To embrace the opportunity brought by the new generation of highly informative colored SAR images, we propose the Phase-Aware Clustering Enhanced Detector (PACE-Det), which consists of three main components: the Phase-Orientation Color Encoder (POCE) module, the core detection network, and the Multi-Space Clustering Constraint (MSCC) module. The front-end POCE module generates pseudo-color SAR images based on Phase Congruency (PC). The color representations are fed into the core detection network for feature extraction, where the phase-aware alignment loss is introduced in addition to the classification and regression losses in the standard object detection task. The initial predictions generated by the core detection network are further refined by the contextual information extracted by the post-processing MSCC module based on the image segmentation result in $L^{\ast }{a}^{\ast }{b}^{\ast }$ color space, where anisotropic objects like aircraft and isotropic scatterers like impervious surfaces exhibit distinct spatial distributions and color features. Experiments based on the Composite SAR Aircraft Dataset (CSAD), which is constructed by fusing airport scenes with SAR aircraft target patches, show that the proposed PACE-Det achieves a ${\mathrm{mAP}}_{75}$ of 0.948 and ${\mathrm{mAP}}_{90}$ of 0.561, which are higher than many state-of-the-art networks. Full article

Ecological resilience is a critical indicator of forest ecosystem stability and the capacity to respond to disturbance. Under intensifying climate change and human activities, accurately evaluating forest ecological resilience is important for ecosystem restoration and sustainable management. This study developed a satellite time−series−based framework for assessing ecological resilience from the complementary perspectives of resistance and recovery. Taking the Qianshan region, a typical forest area in the northeastern forest belt, as a case study, MODIS Normalized Difference Vegetation Index (NDVI) time−series data from 2005 to 2024 were analyzed. The Breaks For Additive Season and Trend (BFAST) algorithm was used to detect vegetation breakpoints, after which ecological resistance and recovery were quantified using breakpoint magnitude and post−disturbance NDVI growth rate. The optimal−parameter−based geographical detector (OPGD) was further applied to identify the spatial drivers of resistance and recovery and their interaction effects. Approximately 21% of the pixels in the Qianshan region experienced at least one breakpoint during the study period, and more than 80% of the disturbed pixels contained only one detected breakpoint. More than 70% of the disturbed pixels subsequently exhibited vegetation recovery, and most recovered pixels had normalized recovery values between 0.40 and 1.00. In contrast, ecological resistance was generally low and varied substantially among land−cover types. Forests exhibited higher resistance but lower recovery, whereas grasslands and croplands showed lower resistance but stronger post−disturbance recovery. Among the individual factors, precipitation and slope had relatively high explanatory power for the spatial differentiation of recovery. Factor interactions substantially enhanced explanatory power, with the interaction between precipitation and elevation exerting the strongest influence on resistance and the interaction between precipitation and slope exerting the strongest influence on recovery. Although mining density had relatively limited explanatory power at the regional scale, mining activities caused non−negligible localized impacts, particularly in open−pit mining areas. The proposed framework provides a practical basis for long−term monitoring, ecological restoration, and differentiated forest management in disturbance−prone regions. Full article

Hydropower-generator rotors contain numerous closely spaced bolted joints, making full-coverage contact instrumentation impractical, while single-view vision methods are vulnerable to missing or ambiguous evidence during rotation. This study proposes a detector-guided multi-view visual monitoring framework that separates region-of-interest (ROI) localization from explicit geometric interpretation. On a simulated hydropower-generator rotor platform operating at 15 rpm, YOLO-family detectors localize candidate ${bolt}_{top}$, ${bolt}_{side}$, and ${star}_{marker}$ regions. Quality-retained top-view ROIs yield the image-space angular indicator ${\theta }_{img}$ from the relative orientation of nut-side and disk-side anti-loosening lines; side-view ROIs yield the pixel-space thread-exposure indicator $L_{px}$ from exposed-thread endpoints and, when marker geometry is sufficiently visible, an auxiliary angular cue. A star-shaped reference marker organizes accepted frame-level observations into approximate rotation intervals, while hierarchical checks of ROI completeness, endpoint availability, image quality, geometric plausibility, and temporal membership retain both usable evidence and explicit rejection reasons. YOLO11n achieved precision 0.9987, recall 1.0000, mAP50 0.9950, and mAP50–95 0.7798 for laboratory ROI localization. After geometric screening, evidence availability was 16.7% for the top-view branch and 76.3% for the side-view branch. In a supplementary 169-image operational field subset, the principal ROI model achieved precision 0.9782, recall 0.9942, mAP50 0.9946, and mAP50–95 0.7948. The field results support appearance-level localization under complex rotor-bolt conditions, and the framework provides a traceable, reliability-aware basis for organizing, screening, and interpreting multi-view evidence in hydropower-generator rotors and similar rotating structures. Full article

Detecting naval vessels at extreme distances using infrared search and track (IRST) systems presents severe physical challenges, notably the complete loss of geometric texture and the non-linear submersion of weak target signals within high-dynamic-range sea clutter. Traditional pure data-driven convolutional neural networks (CNNs) rely heavily on visual appearances and suffer from critical feature blind spots under such extreme physical degradation. To overcome this, we propose a Physics-Aware Deep Coupling Network that shifts the detection paradigm from appearance-based feature extraction to physics-guided attribute recognition. Our method deconstructs the degraded infrared signal into three complementary physical domains: an adaptive radiation energy mapping, corresponding to the energy domain, to rescue weak targets; a bio-inspired spatial saliency filtering mechanism, corresponding to the frequency domain, to maximize the signal-to-clutter ratio; and a PSF-coherent gradient topology framework, corresponding to the gradient domain, to discriminate genuine point targets from chaotic sun glints and island edges. These processed priors, alongside the raw image, are integrated into a 4-channel tensor and fused via a Cross-Domain Attention Module, ensuring deep network coupling. To evaluate this architecture, we conduct extensive experiments on the real-world Maritime-SIRST dataset. Since the original dataset provides only pixel-level segmentation masks, we generate axis-aligned bounding-box detection labels from these masks and retrain both the proposed method and a suite of state-of-the-art YOLO detectors under a unified detection paradigm. Extensive benchmarking demonstrates that our physics-aware methodology consistently outperforms these detectors, achieving a mAP50 of 0.923 and an F1 score of 89.92%, thus providing a highly interpretable and robust solution for maritime domain awareness under extreme physical constraints. Full article

Thermal drone inspections have become a practical solution for monitoring large photovoltaic plants, but defect detection in infrared imagery remains challenging because apparent anomalies can be influenced by shadows, vegetation, reflections, background structures, and the limitations of color-based thermal visualization. In this work, we propose a defect detection approach for photovoltaic modules that integrates real temperature metadata directly into a Faster R-CNN detector as a fourth input channel. Instead of relying only on the conventional thermal rendering produced by the camera, the model is trained with both the visual thermal image and a temperature-derived representation constructed from the corresponding per-pixel radiometric values. The method was evaluated on a dataset collected from real inspections performed across heterogeneous environments, comprising 16,052 images and 39,595 annotated defects distributed across four classes: spot, diode, multiple spots, and open circuit. Under the same data volume and training process, the temperature-augmented model outperformed the standard model, improving macro-averaged precision from 0.832 to 0.874, recall from 0.871 to 0.903, and F1-score from 0.851 to 0.887. Qualitative comparisons further show improved detection of subtle defects and a reduction in false positives caused by thermally misleading background patterns. These results support the value of incorporating physically meaningful thermal information into deep-learning pipelines for photovoltaic defect inspection. Full article

The Cryogenic Anticoincidence (CryoAC) detector is a critical subsystem designed to reduce the particle background for the X-ray Integral Field Unit (X-IFU) instrument onboard the NewAthena space observatory, the next ESA X-ray Large mission. Advancing this technology to Technology Readiness Level 5 (TRL5) requires a unified validation spanning both cleanroom microfabrication repeatability and mK low temperature operational performance. This work presents the complete development cycle of the Demonstration Model 1.2 (DM 1.2), which is aimed at completing the TRL5 demonstration path featured by all the critical technologies operating simultaneously. First, single-process verification protocols were established for Iridium pulsed laser deposition, Reactive Ion Etching (RIE), and deep silicon trenching via the Bosch process. Second, three identical single-pixel prototypes were fabricated and subjected to mK characterization. Four-wire resistance measurement results confirmed a 2/3 production yield against strict design targets (T_C ~100 mK). Finally, functional testing at a bath temperature of 50 mK using a VTT FAB4 SQUID readout demonstrated excellent performance, including a low-energy threshold of ~0.6 keV, a pixel power dissipation of 5.15 nW, and an energy resolution ΔE_FWHM = 735 eV at 6 keV. These combined achievements successfully validate the entire manufacturing and operational baseline against all primary space mission requirements. This paper has to be considered as a review of the CryoAC technology path toward the TRL5 achievement; main findings will be reported and discussed. Details are relegated to other papers. Full article

Manual visual diagnosis of apple diseases suffers from low efficiency, strong subjectivity and poor scalability for large commercial orchards. Existing research mainly targets diseases on single plant organs, whereas full-growth-cycle detection has to cope with extreme multi-scale differences among lesions. For instance, Valsa canker on tree trunks leads to extensive cortical necrosis, while early-stage anthracnose on fruits appears as tiny spots spanning only a few pixels. These significant scale gaps necessitate robust spatial feature aggregation and anti-noise ability to resist complex background interference. Aiming to achieve rapid and precise detection of diseases on multiple apple organs including leaves, fruits, trunks and branches, this work presents an enhanced YOLOv11 model equipped with the Information Integration Attention (IIA) module. The IIA module is embedded into the key fusion layers of the backbone and neck networks. It strengthens the extraction of fine-grained lesion features, recovers spatial location information via a bidirectional attention mechanism, and suppresses noise induced by uneven lighting and intricate backgrounds. To guarantee stable convergence on low-resource computing devices, a tailored training scheme is designed. Experimental results on a seven-category dataset with 7406 images demonstrate that YOLOv11-IIA reaches a precision of 0.763, a recall of 0.819, mAP@50 of 0.857 and mAP@50-95 of 0.699, which achieves clear performance improvements over the original YOLOv11 (mAP@50 improved from 0.485 to 0.857) and other attention-augmented detectors. The model operates stably on an NVIDIA GTX 1050 4GB GPU with an inference speed of 16 FPS for 640 × 640 input images; comprehensive quantitative computational metrics including parameter count, FLOPs and memory consumption will be fully measured in subsequent UAV deployment experiments. The proposed method provides a reliable technical reference for intelligent apple disease monitoring in smart orchard systems. Full article

Deploying object detection models on low-power embedded devices requires a careful balance between detection accuracy, runtime performance, and energy efficiency, especially for aerial images that contain small objects. We present a hardware-measured evaluation of Edge-TPU-compatible detectors on an MCU-class NXP i.MX RT1176 (bare-metal FreeRTOS, Cortex-M7) driving a Coral Edge TPU over an internal USB 2.0 link. We benchmark quantized YOLOv5-based detectors at input resolutions from 256 to 1024 px, reporting the accuracy (size-stratified AP_S on the VisDrone person split) together with the measured per-frame and per-pixel energy, sampled at 100 Hz with an automatic window and N ≥ 5 repetitions. We use the energy per pixel as a resolution-normalized view, not as a new metric, to show where the analytical model fails. The measured per-pixel energy departs from the compute (MAC)-based model because the inference is transfer-bound: it is dominated by the instruction stream and input activations re-sent over USB on every invoke, while the parameters stay cached on-chip. An Edge TPU clock sweep and a compiler-byte analysis agree independently that about 79% of each invocation is the USB transfer. This gives concrete design rules (a lower clock is more energy-efficient; ReLU is preferred to SiLU), and shows that the USB 2.0 ceiling is architecturally fundamental for the MCU class. Full article