Search Results (1,240)

Time-of-flight (ToF) image sensors must operate across a wide span of reflected-light intensities, from weak diffuse reflections to extremely strong retroreflections. We present a signal-intensity range-extension technique that linearizes the linear–logarithmic (Lin–Log) pixel response for short-pulse multi-tap indirect ToF (iToF) sensors. Per-pixel two-region (2R) and three-region (3R) models covering the linear, transition, and logarithmic regimes are derived and used to recover a near-linear signal. Compared with a two-region approach that does not linearize the transition region, the 3R method substantially improves linearity near the knee point if extremely high linearity is required. Experiments with a six-tap iToF imager validate the approach. Depth imaging shows that linearization with common parameters reduces average error but leaves pixel-wise deviations, whereas pixel-wise 3R linearization yields accurate and stable results. Range measurements with a retroreflective target moved from 1.8–13.0 m in 0.20 m steps and achieved centimeter-level resolution and reduced the linearity-error bound from ±6.7%FS to ±1.5%FS. Residual periodic deviations are attributed to small pulse-width mismatches between the illumination and demodulation gates. These results demonstrate that Lin–Log pixels, combined with pixel-wise three-region linearization, enable robust ToF sensing over an extended dynamic range suitable for practical environments with large reflectance variations. Full article

High-throughput phenotypic acquisition and analysis allow us to accurately quantify trait expressions, which is essential for developing intelligent breeding strategies. However, there is still much potential to explore in the field of high-throughput phenotyping for edible fungi. In this study, we developed a portable multi-phenotypic acquisition system for Pleurotus eryngii using RGB and RGB-D cameras. We developed an innovative Unet-based semantic segmentation model by integrating the ASPP structure with the VGG16 architecture. This allows for precise segmentation of the cap, gills and stem of the fruiting body. By leveraging depth images from RGB-D cameras, we can effectively collect phenotypic information about Pleurotus eryngii. By combining K-means clustering with Lab color space thresholds, we are able to achieve more precise automatic classification of Pleurotus eryngii cap colors. Moreover, AlexNet is utilized to classify the shapes of the fruiting bodies. The Aspp-VGGUnet network demonstrates remarkable performance with a mean Intersection over Union (mIoU) of 96.47% and a mean pixel accuracy (mPA) of 98.53%. These results reflect respective improvements of 3.03% and 2.23% compared to the standard Unet model, respectively. The average error in size phenotype measurement is just 0.15 ± 0.03 cm. The accuracy for cap color classification reaches 91.04%, while fruiting body shape classification achieves 97.90%. The proposed multi-phenotype acquisition system reduces the measurement time per sample from an average of 76 s (manual method) to about 2 s, substantially increasing data acquisition throughput and providing robust support for scalable phenotyping workflows in breeding research. Full article

Offshore wind turbine support structures are in harsh and unsteady marine environments, and their dynamic characteristics could change gradually after long-term service. To better understand the status and improve remaining life estimation, it is essential to conduct in situ measurement and update the numerical models of these support structures. In this paper, the modal properties of a 5.5 MW offshore wind turbine were first identified by a widely used operational modal analysis technique, frequency-domain decomposition, given the acceleration data obtained from eight sensors located at four different heights on the tower. Then, a finite element model was created in MATLAB R2020a and a set of model parameters including scour depth, foundation stiffness, hydrodynamic added mass and damping coefficients was updated in a Bayesian inference frame. It is found that the posterior distributions of most parameters significantly differ from their prior distributions, except for the hydrodynamic added mass coefficient. The predicted natural frequencies and damping ratios with the updated parameters are close to those values identified with errors less than 2%. But relatively large differences are found when comparing some of the predicted and identified mode shape coefficients. Specifically, it is found that different combinations of the scour depth and foundation stiffness coefficient can reach very similar modal property predictions, meaning that model updating results are not unique. This research demonstrates that the Bayesian inference framework is effective in constructing a more accurate model, even when confronting the inherent challenge of non-unique parameter identifiability, as encountered with scour depth and foundation stiffness. Full article

Three-dimensional (3D) printing enables accurate implant pre-shaping in orbital reconstruction but is costly and time-consuming. Naked-eye stereoscopic displays (NEDs) enable virtual implant modeling without fabrication. This study aimed to compare the reproducibility and accuracy of NED-based virtual reality (VR) pre-shaping with conventional 3D-printed models. Two surgeons pre-shaped implants for 11 unilateral orbital floor fractures using both 3D-printed and NED-based VR models with identical computed tomography data. The depth, area, and axis dimensions were measured, and reproducibility and agreement were assessed using intraclass correlation coefficients (ICCs), Bland–Altman analysis, and shape similarity metrics—Hausdorff distance (HD) and root mean square error (RMSE). Intra-rater ICCs were ≥0.80 for all parameters except depth in the VR model. The HD and RMSE reveal no significant differences between 3D (2.64 ± 0.85 mm; 1.02 ± 0.42 mm) and VR (3.14 ± 1.18 mm; 1.24 ± 0.53 mm). Inter-rater ICCs were ≥0.80 for the area and axes in both modalities, while depth remained low. Between modalities, no significant differences were found; HD and RMSE were 2.95 ± 0.94 mm and 1.28 ± 0.49 mm. The NED-based VR pre-shaping achieved reproducibility and dimensional agreement comparable to 3D printing, suggesting a feasible cost- and time-efficient alternative for orbital reconstruction. These preliminary findings suggest that NED-based preshaping may be feasible; however, larger studies are required to confirm whether VR can achieve performance comparable to 3D-printed models. Full article

Purpose: The purpose of this work was to conduct a comprehensive literature review of the challenges encountered in ocular biometry and intraocular lens (IOL) power calculations in patients with keratoconus undergoing cataract surgery and to evaluate the performance of various biometric techniques and IOL power calculation formulas in this population. Methods: A comprehensive literature search was conducted in PubMed for studies published until October 2025. Keywords included “keratoconus”, “biometry”, “IOL power calculation”, “cataract surgery”, “keratometry”, and related terms. Studies evaluating the repeatability of biometric measurement, the accuracy of IOL formulas, and surgical outcomes in keratoconus patients were included. Study quality was assessed using standardized criteria, including study design, measurement standardization, and statistical appropriateness. Results: Twenty studies comprising 1596 eyes with keratoconus were analyzed. Biometric challenges include reduced keratometry repeatability (especially with K > 55 D), altered anterior-to-posterior corneal curvature ratios, anterior chamber depth, unreliable corneal power measurements, and tear film instability affecting measurement consistency. Keratoconus-specific formulas (Barrett’s True-K for keratoconus and Kane’s formula for keratoconus) demonstrated superior accuracy compared to standard formulas. The Barrett True-K formula with predicted posterior corneal astigmatism showed median absolute errors of 0.10–0.35 D across all severity stages, with 39–72% of eyes within ±0.50 D of target refraction. Traditional formulas (excluding SRK/T) produced hyperopic prediction errors that increased with disease severity. Swept-source optical coherence tomography biometry with total keratometry measurements improved prediction accuracy, particularly in severe keratoconus. Conclusions: IOL power calculation in keratoconus remains challenging due to multiple biometric measurement errors. Keratoconus-specific formulas significantly improve refractive outcomes compared to standard formulas. The use of total keratometry and swept-source OCT biometry, as well as the incorporation of posterior corneal power measurements, enhances accuracy. A multimodal approach combining advanced biometry devices with keratoconus-specific formulas is recommended for optimal outcomes. Full article

SWMM (Stormwater Management Model) is one of the most widely used computation tools in urban water resources management. Traditionally, the choice of rainfall data for calibrating the SWMM model has been arbitrary, lacking clarity on the most suitable rainfall types. In addition, the simplification in the SWMM hydrological module of the rainfall–runoff process, coupled with measurement errors, introduces a high level of uncertainty in the calibration. This study investigates the influences of rainfall types on the highly uncertain SWMM model calibration by implementing the Bayesian inference theory. A Bayesian SWMM calibration framework was established, in which an advanced DREAM(zs) (Differential Evolution Adaptive Metropolis, Version ZS) sampling method was used. The investigation focused on eight key hydrological parameters of SWMM. The impact of rainfall types was analyzed using nine rainfall intensities and three rainfall patterns. Results show that rainfall events equivalent to a one-year return period (R5, 42.70 mm total depth) or higher generally yield the most accurate parameters, with posterior distribution standard deviations reduced by 40–60% compared to low-intensity rainfalls. Notably, three parameters (impervious area percentage [Imperv], storage depth of impervious area [S-imperv], and Manning’s coefficient of impervious area [N-imperv]) demonstrated consistent accuracy irrespective of rainfall intensity, with a coefficient of variation below 0.05 for Imperv and S-imperv across all rainfall intensities. Furthermore, it was found that rainfall events with double peaks resulted in more satisfactory calibration compared to single or triple peaks, reducing the standard deviation of the Width parameter from 168.647 (single-peak) to 110.789 (double-peak). The findings from this study could offer valuable insights for selecting appropriate rainfall events before SWMM model calibration for more accurate predictions when it comes to urban non-point pollution control strategies and watershed management. Full article

Zero-depth nanopores present a promising solution to the challenges associated with ultrathin membranes used in solid-state resistive pulse sensors for DNA sequencing. Most existing fabrication methods are either complex or lack the nanoscale precision required. In this study, we introduce a cost-effective approach that combines PDMS molding at the intersection of silicon micro-blades with an innovative high-resolution nano-positioning technique. These blades are created through photolithography and a two-step KOH wet etching process, allowing for the formation of sub-50 nm 3D rhombic zero-depth nanopores featuring large vertex angles. To address the limitations of SEM imaging—such as dielectric charging and deformation of PDMS membranes under electron beam exposure—we devised a finite element model (FEM) that correlates electrical conductance with pore size and electrolyte concentration. This model aligns closely with experimental data, yielding a mean absolute percentage error of 3.69%, thereby enabling real-time indirect sizing of the nanopores based on the measured conductance. Additionally, we identified a critical channel length beyond which pore resistance becomes negligible, facilitating a linear relationship between conductance and pore diameter. The nanopores produced using this method exhibited a 2.4-fold increase in conductance compared to earlier designs, highlighting their potential for high-precision DNA sequencing applications. Full article

The quality of scanned topographic maps—including parameters such as image compression, scanning resolution, and bit depth—may strongly influence the performance of deep learning models for contour vectorization. In this study, we investigate this dependence by training eight U-Net models on the same map data but under varying input quality conditions. Each model is trained to segment contour lines from the raster input, followed by a postprocessing pipeline that converts segmented output into vector contours. We systematically compare the models with respect to topological error metrics (such as contour intersections and dangling ends) in the resulting vector output and overlay metrics of matched contour segments within given tolerance. Our experiments demonstrate that while the input data quality indeed matters, moderate lowering of quality parameters doesn’t introduce significant practical tradeoff, while storage and computational requirements remain low. We discuss implications for the preparation of archival map scans and propose guidelines for choosing scanning settings when the downstream goal is automated vectorization. Our results highlight that deep learning methods, though resilient against reasonable compression, remain measurably sensitive to degradation in input fidelity. Full article

Water depth in shallow marine environments is a fundamental parameter for oceanographic research and coastal engineering applications. High-resolution satellite imagery and long-term medium-resolution imagery offer significant potential for detailed bathymetric mapping and monitoring spatiotemporal variations in bathymetry. However, most of these images contain only three visible bands (blue, green, and red), making bathymetric mapping from such images challenging in practical applications. For the empirical approach, high-quality in situ depth calibration data, which are essential for establishing a reliable empirical bathymetric model, are either unavailable or excessively expensive. For the physics-based approach, images containing only three visible bands can be problematic in accurately deriving depths. To address this limitation, this study proposes a novel semi-analytical-empirical hybrid model for water depth retrieval. The core of the proposed method is the integration of a semi-analytical model with a physics-based dual-band model. This integration quantifies the relative depth relationships among pixels and uses them as a physical constraint. Through this constraint, the method identifies physically reliable depth estimates from the multiple numerical solutions of the semi-analytical model for a subset of shallow-water pixels, which then serve as an in situ–free calibration dataset. This dataset is subsequently used to determine the empirically based optimal retrieval model, which is finally applied to generate the complete bathymetric map. The results from four typical coral reef regions—Buck Island, Yongxing Island, Kaneohe Bay, and Yongle Atoll—demonstrated that the proposed model achieved root-mean-square errors (RMSE) of 0.98–1.62 m, mean absolute errors (MAE) of 0.73–1.13 m, and coefficients of determination (R²) of 0.91–0.95 in comparison to in situ measurements. Compared to both the physics-based dual-band model and the L-S model (i.e., the bathymetry mapping approach combining Log-ratio and Semi-analytical models), the proposed model reduced the RMSE by 9–55%, reduced the MAE by 4–56%, and improved the R² by 0.01–0.29. Additionally, the accuracy of the proposed model surpasses that of both the physics-based dual-band model and the L-S model across all depth intervals, particularly in deeper depth waters (>15 m). This study offers a robust solution for bathymetric mapping in areas lacking in situ depth data and contributes significantly to advancing optical remote sensing techniques for underwater topography detection. Full article

This study proposes an automated, visual–geometric fusion method for measuring pupillary height (PH) and interpupillary distance (PD), aiming to replace manual measurements while balancing accuracy, efficiency, and cost accessibility. To this end, a two-layer Ensemble of Regression Tree (ERT) is used to coarsely localize facial landmarks and the pupil center, which is then refined via direction-aware ray casting and edge-side-stratified RANSAC followed by least-squares fitting; in parallel, an RC-BlendMask instance-segmentation module extracts the lowest rim point of the spectacle lens. Head pose and lens-plane depth are estimated with the Perspective-n-Point (PnP) algorithm to enable pixel-to-millimeter calibration and pose gating, thereby achieving 3D quantification of PH/PD under a single-camera setup. In a comparative study with 30 participants against the Zeiss i.Terminal2, the proposed method achieved mean absolute errors of 1.13 mm (PD), 0.73 mm (PH-L), and 0.89 mm (PH-R); Pearson correlation coefficients were r = 0.944 (PD), 0.964 (PH-L), and 0.916 (PH-R), and Bland–Altman 95% limits of agreement were −2.00 to 2.70 mm (PD), −0.84 to 1.76 mm (PH-L), and −1.85 to 1.79 mm (PH-R). Lens segmentation performance reached a Precision of 97.5% and a Recall of 93.8%, supporting robust PH extraction. Overall, the proposed approach delivers measurement agreement comparable to high-end commercial devices on low-cost hardware, satisfies ANSI Z80.1/ISO 21987 clinical tolerances for decentration and prism error, and is suitable for both in-store dispensing and tele-dispensing scenarios. Full article

The deep formations of the Yingqiong Basin are situated in a high-temperature and high-pressure (HTHP) environment, characterized by a narrow formation pressure window and consequently high operational risks. Accurate prediction of Equivalent Circulating Density (ECD) is crucial for wellbore stability control and well control safety during the drilling of extended-reach wells (ERWs) in this block. Existing calculation methods fail to account for the errors in well depth and true vertical depth (TVD) measurements caused by drill string buckling, which affect the ECD calculation. Therefore, to achieve precise ECD control, this study addresses the HTHP characteristics of ERWs in the Yingqiong Basin. It takes into consideration the variations in drilling fluid performance parameters, the influence of cuttings, and the well depth/TVD measurement errors induced by drill string buckling. On this basis, the traditional ECD calculation model is modified, and a set of ECD calculation models tailored to ERWs in the Yingqiong Basin is established. This model aims to meet the requirements for fine ECD control in drilling operations within the block and reduce operational risks. By comparing the error rates between the prediction results of the traditional ECD calculation model and those of the proposed model in this study, using the on-site measured ECD data from Well LD10-X-X in the Yingqiong Basin, the results demonstrate that for HTHP ERWs in the Yingqiong Basin, incorporating the well depth and TVD measurement errors caused by drill string buckling can enhance the accuracy of ECD prediction. Full article

To eliminate the risk of damage to buried pipelines during excavation, a survey in advance or on the spot is necessary. Here we propose a wireless rotable magnetic sensor array to detect underground ferromagnetic pipelines. It consists of several sensing nodes placed on a rail, which can rotate automatically or manually. We adopted rotating rather than translating the array since translation is difficult on uneven or muddy ground. Moreover, we could judge the existence and orientation of a pipeline by simply checking the periodic variation of measured data without resorting to complex inversion algorithms. Field experiments showed that the equipment could provide a decimeter-level locating accuracy for both the horizontal offset and buried depth, and a strike angle error of a few degrees, which meet general engineering application requirements. Full article

Background/Objectives: Accurate patient weight estimation is critical for safe and effective drug dosing in emergency and critical care settings. Inaccurate estimates exceeding a 10% deviation from true weight can result in significant dosing errors in time-sensitive treatments such as thrombolysis for stroke or urgent sedation. In situations where direct weight measurement is impractical, reliable alternative estimation methods are essential. Methods: We propose a three-dimensional (3D) depth-camera system that employs a convolutional neural network (CNN) pipeline to automatically estimate total body weight (TBW), ideal body weight (IBW), and lean body weight (LBW) from volumetric features derived from a single supine patient image. Our approach was evaluated in a prospective pilot study to assess feasibility and accuracy. CNNs were selected because of their ability to extract spatial features from complex image data, outperforming regression and tree-based models in preliminary comparisons. Results: The results demonstrated that our 3D camera system was more accurate than conventional techniques, including clinician visual estimation (Mean Absolute Percentage Error [MAPE]: 12%), tape-based methods (±8.5%), and anthropometric formulas (±9.2%), achieving a mean error of ±5.4%. Conclusions: Future work will extend this technology to pediatric populations, support integration with automated dosing systems, and explore prehospital applications to further reduce medication errors and enhance patient safety. Full article

Repeatability accuracy is a key performance metric for robotic arms. To address limitations in existing monocular vision-based measurement methods, this study proposes a likelihood-based pose estimation approach. Our method first obtains initial pose estimates through optimized likelihood estimation, then iteratively refines depth information. By modeling the statistical characteristics of multiple observed poses, we derive a global theoretical pose. Within this framework, two-dimensional feature points are backprojected into three-dimensional space to form an observed point cloud. The error between this observed cloud and the theoretical feature point cloud is computed using the Iterative Closest Point (ICP) algorithm, enabling accurate quantification of repeatability accuracy. Based on 30 repeated trials at each of five target poses, the proposed method achieved repeatability positioning accuracy of 0.0115 mm, 0.0121 mm, 0.0068 mm, 0.0162 mm, and 0.0175 mm at the five poses, respectively, with a mean value of 0.0128 mm and a standard deviation of 0.0038 mm across the poses. Compared with two existing monocular vision-based methods, it demonstrates superior accuracy and stability, achieving average accuracy improvements of 0.79 mm and 1.06 mm, respectively, and reducing the standard deviation by over 85%. Full article

Magnetic inversion through three-dimensional (3D) susceptibility reconstruction can effectively identify the deep extension characteristics and structural variations in faults. Therefore, the reliability of inversion results from magnetic anomaly data is a key issue that must be addressed in fault detection and quantitative evaluation of fault activity. In recent years, deep neural network-driven magnetic data inversion methods have rapidly become a research focus in the field of geophysical magnetic data inversion. However, existing methods primarily rely on convolutional neural networks (CNNs), whose inherent local feature extraction capabilities limit their ability to model the spatial continuity of large-scale subsurface magnetic structures. Moreover, the general lack of prior physical constraints in these network models often leads to unreliable inversion results. To address these limitations, this paper proposes a physics-informed multi-scale deep learning inversion method for magnetic anomaly data. The method designs a dual-stream Transformer-CNN fusion module (TCFM). It leverages the self-attention mechanism in Transformers to model global susceptibility correlations while efficiently capturing local geological features through CNN convolutional operations. This enables collaborative modeling of multi-scale subsurface magnetic structures, significantly enhancing inversion accuracy. Furthermore, by incorporating deep physical priors, we design a depth-aware weighted loss function. By strengthening optimization constraints in deep regions, it effectively improves the vertical resolution of inversion models for deep magnetic structures. Comparative experiments with U-Net++ and Transformer demonstrate that the proposed method achieves smaller errors and higher inversion accuracy. Applied to measured aeromagnetic data from the Dandong region of China, the method yields reliable inversion results. Variations in magnetic susceptibility within these results successfully delineate the spatial distribution of fault zones, providing a geophysical basis for regional seismic hazard monitoring and assessment. Full article