Search Results (16)

Quantitative characterization of permeability in porous media represents a central problem in filtration theory, geosciences, and materials engineering. Standard numerical approaches, including finite element methods and Lattice Boltzmann simulations, typically require extensive domain-specific expertise together with specialized computational software. This motivates the development of computationally simpler and more accessible geometric approaches applicable directly to binary volumetric data. We introduce a novel algorithmic framework for the analysis of porous structures that reformulates permeability-related characterization in terms of discrete geometry and graph-based computation. The method combines parallel raster-grid and graph representations of a binarized three-dimensional CT image. The principal transport-limiting feature of the pore network, interpreted as the minimal constriction governing connectivity, is identified through iterative morphological dilation coupled with a three-dimensional scanline seed-fill procedure. In addition, a dichotomous bisection strategy is proposed to accelerate the determination of the critical bottleneck scale. The proposed methodology was evaluated on five volumetric datasets of size 100 × 100 × 100 voxels obtained from CT-derived porous structures. Experimental results demonstrate that dilation- and erosion-based formulations yield equivalent estimates of the bottleneck parameter in four of the five investigated samples. Furthermore, incorporation of the bisection optimization reduces computational time in three-dimensional experiments by approximately 50% relative to sequential iteration. The presented approach provides a computationally efficient and fully open-source alternative to conventional physics-based permeability solvers for binary porous media. The resulting bottleneck parameter b should be interpreted as a discrete geometric invariant characterizing the pore-network connectivity and minimal transport cross-section. It is not intended to replace the absolute permeability coefficient K appearing in Darcy’s law, but rather to serve as an independent structural descriptor suitable for comparative and topological analysis of porous systems. Full article

To improve the data quality of underwater point clouds acquired by UAV-borne LiDAR bathymetry, a scanline-based sliding window filtering method is proposed based on an analysis of scanline data characteristics. Scanline data of underwater point clouds are first extracted from raw point clouds, and the radius outlier removal algorithm is employed to eliminate outliers. Taking the acquisition time of scanline points as the X-axis and elevation as the Y-axis, a 3D problem is simplified into a 2D representation, and a sliding window is constructed along the scanline. Robust least-squares fitting is applied within the window. The median absolute deviation of the fitting residuals is adopted to calculate the terrain feature values for quantifying the terrain complexity, followed by an adaptive filtering threshold determination according to terrain feature values. Fine filtering of the individual scanlines is performed using a point-by-point sliding window. Experimental results demonstrate that the proposed method is adaptable to various terrain conditions, achieving a noise recall rate ≥ 96%, an overall filtering accuracy ≥99%, and an F1-score ≥ 0.9. Particularly, the precision rate in flat-water areas reached 97.37%. Overall, the proposed filtering method effectively separates noise points while preserving detailed terrain features and supports UAV-borne LiDAR bathymetry for mapping complex shallow-water regions. Full article

Motion blur degrades single-frame imaging when relative motion occurs during sensor exposure; yet, quantitative validation is difficult because ground-truth motion parameters are rarely available in real images. This paper presents an interpretable, measure-first framework for detecting, localizing, and quantifying motion blur in single-frame grayscale images under a validated operating condition of one-dimensional horizontal uniform motion. The method analyzes each image row as a one-dimensional spatial signal, where Movement Artifact denotes the scanline-level imprint of motion blur retained in the legacy algorithm names MAPE and MAQ. The pipeline combines three stages: Movement Artifact Position Estimation (MAPE) using scanline self-similarity, Reference Origin Point Estimation (ROPE) using robust structural trends, and Movement Artifact Quantification (MAQ), which summarizes blur magnitude as an average horizontal spatial displacement after adaptive filtering. The pipeline is evaluated on a controlled empirical dataset of 110 images of a high-contrast marker acquired at known tangential velocities from 0.0 to 1.0 m/s in 0.1 m/s increments (10 images per level). MAPE achieves 70–90% detection rates across velocities, and ROPE localizes reference origins with 97–99% detection. An empirical polynomial mapping from MAQ to velocity attains R² = 0.9900 with RMSE 0.0229 m/s and MAE 0.0221 m/s over 0.0–0.7 m/s, enabling calibrated velocity estimates from blur measurements within the validated regime. An extended additive-noise robustness analysis further shows that severe perturbation can preserve candidate self-similarity responses while progressively destabilizing reference-origin localization and MAQ pairing, thereby clarifying the empirical boundary of the current controlled single-marker regime. The approach is not claimed to generalize to uncontrolled scenes, non-uniform blur, or multi-dimensional and non-rigid motion. Full article

Optical inspection of highly reflective cylindrical components—such as stainless-steel vessels featuring both planar and curvilinear surfaces—presents significant challenges due to complex geometric distortions in single-pass imaging. This study proposes a line-scan imaging framework that integrates synchronized kinematic control with geometry-aware distortion correction. The system addresses shape deformations through three coordinated modules: (1) parametric synchronization between rotational motion and image acquisition ensures full-surface coverage; (2) scanline-specific 1D projective transformations correct perspective distortions on toroidal sidewalls; and (3) adaptive polar coordinate remapping restores radial symmetry on circular bases. Experimental results demonstrate subpixel-level geometric correction accuracy, validating the proposed framework’s effectiveness in eliminating geometric aberrations with low computational complexity and without reliance on data-driven training, while maintaining compatibility with defect detection and quantitative surface analysis of specular cylindrical specimens. Full article

Wafer map defect classification plays a critical role in yield monitoring and root-cause analysis in semiconductor manufacturing. Although recent convolutional neural network (CNN)-based approaches have achieved high classification accuracy, most existing models are evaluated primarily on clean datasets and remain vulnerable to unseen perturbations and representation-level variability at test time. In this paper, we propose a hybrid CNN–echo state network (ESN) architecture that integrates spatial feature extraction with sequential aggregation to enhance robustness under input perturbations. The CNN backbone extracts two-dimensional feature maps, which are converted into ordered sequences using a multidirectional scanline strategy and processed by an ESN reservoir. The resulting sequential representations are combined with CNN features through a class-specific adaptive fusion mechanism. Using the defect-only eight-class version of the WM-811K dataset, we systematically evaluate robustness under multiple perturbation scenarios, with particular focus on the clean train/noisy test (CT-NT) setting. To ensure a controlled robustness evaluation aligned with the binary nature of wafer map data, we introduce binary-consistent die-flip perturbations and additionally employ additive Gaussian perturbations as a representation-level stress test. Under clean-data conditions, the proposed model showed a 0.61 pp improvement in test accuracy compared to the ResNet34-based CNN, with notably larger gains for rare classes and defect types exhibiting strong structural patterns. In the clean train/noisy test scenario, where the model was trained on clean wafer map data and evaluated under controlled test-time perturbations, the accuracy of the CNN baseline dropped to 77.59% at $\sigma$ = 0.10, whereas the proposed hybrid model maintained an accuracy of 87.30%, resulting in an absolute improvement of 9.71 pp. Per-class analysis reveals that the robustness gain is class-dependent, with pronounced improvements for defect types exhibiting clear and repetitive structural patterns, such as Loc and Edge-Ring. Further mechanistic analysis demonstrates that the robustness improvement arises from enhanced representation stability and bounded reservoir dynamics, rather than from changes in CNN feature extraction or training regularization. These results demonstrate that the proposed CNN-ESN hybrid architecture provides meaningful advantages in terms of robustness under noisy evaluation conditions without requiring noise-aware training or prior knowledge of perturbation characteristics. Full article

A vibration-based protocol is presented for identifying barely visible impact damage (BVID) in type-IV composite-overwrapped pressure vessels (COPVs). A 1 kJ hemispherical-tip strike was applied to a fully pressurized vessel, which was subsequently depressurized and characterized by free–free experimental modal analysis over a 168-point grid. The frequency response functions (FRFs) at the impact meridian exhibited distinct peaks near 3.70, 4.34, and 4.90 kHz with larger amplitudes and lower coherence than at the diametrically opposite meridian, indicating local circumferential stiffness loss. A detailed finite element model of the liner, bosses, and carbon/epoxy overwrap was updated by idealizing a cylindrical sub-volume with a 90% reduction in orthotropic stiffness. The pristine and “damaged” numerical modal sets agreed closely (mean frequency error < 2%), and for most of the first 60 modes, the diagonal Modal Assurance Criterion (MAC) remained ≥ 0.90. However, in several nearly degenerate circumferential mode pairs, the diagonal MAC dropped to 0.49–0.88 because the local asymmetry rotated the eigenvectors within a common subspace, showing that classical MAC alone cannot expose such early-stage defects. Radial displacement scan-lines provided the missing spatial resolution. Modes whose antinodal regions intersect the dent showed pronounced local amplitude bulges and slight angular shifts in the peak toward the impact site, whereas modes with a nodal line across the damage were virtually unchanged. The combined use of FRF asymmetry, MAC screening, and scan-line deformation profiling localized the impact to the correct circumferential sector with centimeter-scale resolution along the scan ring, yielding predictive signatures for rapid, non-pressurized in situ assessment of impacted COPVs after depressurization. Full article

This work presents an estimate of the slip activation potential of existing fractures in a remote northern community located on Canadian Shield rocks for geothermal purposes. To accomplish this objective, we analyzed outcrop analogues and recorded geometrical properties of fractures, namely the strike and dip. Then, we estimated the stress regime in the study area through an empirical approach and performed a probabilistic slip tendency analysis. This allowed us to determine the slip probability of the pre-existing fractures at the current state of stress, the orientation of fractures that are most likely to be activated and the fluid pressures needed for the slip activation of pre-existing fractures, which are key aspects for developing Enhanced Geothermal Systems. The results of this simple, yet effective, analysis suggest that at the current state of stress, the pre-existing natural fractures are relatively stable, and an injection pressure of about 12.5 MPa/km could be required to activate the most optimally oriented fractures to slip. An injection of water at this pressure gradient could open the optimally oriented pre-existing fractures and enhance the permeability of the reservoir for geothermal fluid extraction. The information described in this paper provides a significant contribution to the geothermal research underway in remote northern communities. Full article

Hyperspectral sensors mounted on unmanned aerial vehicles (UAV) offer the prospect of high-resolution multi-temporal spectral analysis for a range of remote-sensing applications. However, although accurate onboard navigation sensors track the moment-to-moment pose of the UAV in flight, geometric distortions are introduced into the scanned data sets. Consequently, considerable time-consuming (user/manual) post-processing rectification effort is generally required to retrieve geometrically accurate mosaics of the hyperspectral data cubes. Moreover, due to the line-scan nature of many hyperspectral sensors and their intrinsic inability to exploit structure from motion (SfM), only 2D mosaics are generally created. To address this, we propose a fast, automated and computationally robust georectification and mosaicking technique that generates 3D hyperspectral point clouds. The technique first morphologically and geometrically examines (and, if possible, repairs) poorly constructed individual hyperspectral cubes before aligning these cubes into swaths. The luminance of each individual cube is estimated and normalised, prior to being integrated into a swath of images. The hyperspectral swaths are co-registered to a targeted element of a luminance-normalised orthomosaic obtained using a standard red–green–blue (RGB) camera and SfM. To avoid computationally intensive image processing operations such as 2D convolutions, key elements of the orthomosaic are identified using pixel masks, pixel index manipulation and nearest neighbour searches. Maximally stable extremal regions (MSER) and speeded-up robust feature (SURF) extraction are then combined with maximum likelihood sample consensus (MLESAC) feature matching to generate the best geometric transformation model for each swath. This geometrically transforms and merges individual pushbroom scanlines into a single spatially continuous hyperspectral mosaic; and this georectified 2D hyperspectral mosaic is then converted into a 3D hyperspectral point cloud by aligning the hyperspectral mosaic with the RGB point cloud used to create the orthomosaic obtained using SfM. A high spatial accuracy is demonstrated. Hyperspectral mosaics with a 5 cm spatial resolution were mosaicked with root mean square positional accuracies of 0.42 m. The technique was tested on five scenes comprising two types of landscape. The entire process, which is coded in MATLAB, takes around twenty minutes to process data sets covering around 30 Ha at a 5 cm resolution on a laptop with 32 GB RAM and an Intel^® Core i7-8850H CPU running at 2.60 GHz. Full article

Serious deficiencies in ground characterization, analysis and design at engineering works can occur when working with bimrocks (block-in-matrix rocks) and bimsoils (block-in-matrix soils). Since the 1990s, serious technical problems at engineering works performed in bimrocks/bimsoils spurred practical research, which revealed that the behavior of these geomaterials is directly related to the volumetric block proportions (VBPs). However, the way that VBPs can be confidently and correctly estimated remains an ongoing critical issue that still frustrates designers, contractors and owners. Stereological techniques can be applied to overcome this challenge by inferring 3D block contents from in situ 1D and 2D measurements, but the estimates have often been demonstrated to be erroneous. This paper presents findings from a computer-aided reinvestigation, revalidation and extension of Medley’s work of 1997 and subsequent researchers to provide approachable yet statistically robust methods to limit the uncertainty associated with estimates of 3D VBPs generated from 1D boring/scanline measurements. To this aim, a specialized Matlab code was created and virtual drilling programs were performed through 3D computer-generated bimrock models. Supported by extensive statistical-based investigations, a design chart is provided that updates and extends Medley’s 1999 chart relating uncertainty in estimates of VBP as a function of total boring/scanline lengths. Full article

Fengyun-3E, which is equipped with MicroWave Humidity Sounder 2 (MWHS-2) for detecting both temperature and humidity, is China’s latest polar orbiting meteorological satellite and China’s first satellite in early-morning orbit. The observation bias and observation error characteristics of MWHS-2 are evaluated by using ERA-5 as the background field. The results show that the biases range from −4 to 2 K, and the observation errors are within 1.5 K except for the window channels. A further analysis of the dependence on the scanning angles indicates that observation errors for the window channels and the 118-GHz channels increase with decreasing scanning angles. Observation errors of the window channels and water vapor channels are also latitudinally dependent, and the maximum errors in the high latitudes reach 2.0 K, while those in the tropical regions are approximately 0.8 K. In addition, the observed brightness temperature of FY-3E MWHS-2 is accompanied by striping noises along the track and high-frequency oscillation noises along the scanlines. The noise mitigation results show that both noises are approximately 0.2 K. Full article

Quantitative statistical discontinuity analysis of fractured rock masses is commonly applied in the fields of engineering geology, rock mechanics, slope stability, and hydrogeology. This study presents a workflow for the semiautomatic determination of basic discontinuity parameters, such as spacing, frequency, trace length, and termination, from scanline surveys written for the open-source software Octave. The aim is to provide theoretical background information and scripts for a quick introduction to all interested parties from academia and consulting, in order to promote the use of widely known and accepted statistical evaluation methods. Data from a study site in granodioritic rock are analyzed in the context of the proposed workflow. These test data and all scripts (m-files) used in the study are provided in order to minimize initial training time. The scripts provided herein are kept short and simple, but can be used as a basis for advanced automation of the workflow and presentation of the results. Full article

A research study aimed at the extending the means of estimating ISRM (International Society for Rock Mechanics) geomechanical parameters through non-contact methodologies, in the frame of the remote survey of rock masses, is herein presented. It was conducted by coupling UAV-based photogrammetry and Infrared Thermography. Starting from georeferenced UAV surveys and the definition of rock masses’ RGB point clouds, different approaches for the extraction of discontinuity spatial data were herein compared according to the ISRM subjective and objective discontinuity sampling criteria. These were applied to a survey a window and along a scanline, both defined on the dense point clouds, to simulate a field rock mass survey, although carried out on remotely acquired data. Spatial discontinuity data were integrated via the analysis of dense point clouds built from IRT images, which represents a relatively new practice in remote sensing, and the processing of thermograms. Such procedures allowed the qualitative evaluation of the main geomechanical parameters of tested rock masses, such as aperture, persistence and weathering. Moreover, the novel parameters of Thermal-spacing (T-spacing) and Thermal-RQD (T-RQD) are herein introduced in a tentative attempt at extending the application field of IRT to remote rock mass surveys for practical purposes. The achieved results were validated by field campaign, demonstrating that a remote survey of rock masses can be conducted according to the ISRM procedures even on models built by integrating RGB and IRT photogrammetry. In fact, these two technologies are positively complementary and, besides being feasible, are characterized by a relatively quick and non-contact execution. Thanks to the positive and satisfactory results achieved herein, this research contributes to the implementation of the scientific and technical casuistry on the remote survey of rock masses, which is a technical field offering a wide range of applications. Full article

Quantitative characterization of discontinuities is fundamental to define the mechanical behavior of discontinuous rock masses. Several techniques for the semi-automatic and automatic extraction of discontinuities and their properties from raw or processed point clouds have been introduced in the literature to overcome the limits of conventional field surveys and improve data accuracy. However, most of these techniques do not allow characterizing flat or subvertical outcrops because planar surfaces are difficult to detect within point clouds in these circumstances, with the drawback of undersampling the data and providing inappropriate results. In this case, 2D analysis on the fracture traces are more appropriate. Nevertheless, to our knowledge, few methods to perform quantitative analyses on discontinuities from orthorectified photos are publicly available and do not provide a complete characterization. We implemented scanline and window sampling methods in a digital environment to characterize rock masses affected by discontinuities perpendicular to the bedding from trace maps, thus exploiting the potentiality of remote sensing techniques for subvertical and low-relief outcrops. The routine, named QDC-2D (Quantitative Discontinuity Characterization, 2D) was compiled in MATLAB by testing a synthetic dataset and a real case study, from which a high-resolution orthophoto was obtained by means of Structure from Motion technique. Starting from a trace map, the routine semi-automatically classifies the discontinuity sets and calculates their mean spacing, frequency, trace length, and persistence. The fracture network is characterized by means of trace length, intensity, and density estimators. The block volume and shape are also estimated by adding information on the third dimension. The results of the 2D analysis agree with the input used to produce the synthetic dataset and with the data collected in the field by means of conventional geostructural and geomechanical techniques, ensuring the procedure’s reliability. The outcomes of the analysis were implemented in a Discrete Fracture Network model to evaluate their applicability for geomechanical modeling. Full article

Hyperspectral systems integrated on unmanned aerial vehicles (UAV) provide unique opportunities to conduct high-resolution multitemporal spectral analysis for diverse applications. However, additional time-consuming rectification efforts in postprocessing are routinely required, since geometric distortions can be introduced due to UAV movements during flight, even if navigation/motion sensors are used to track the position of each scan. Part of the challenge in obtaining high-quality imagery relates to the lack of a fast processing workflow that can retrieve geometrically accurate mosaics while optimizing the ground data collection efforts. To address this problem, we explored a computationally robust automated georectification and mosaicking methodology. It operates effectively in a parallel computing environment and evaluates results against a number of high-spatial-resolution datasets (mm to cm resolution) collected using a push-broom sensor and an associated RGB frame-based camera. The methodology estimates the luminance of the hyperspectral swaths and coregisters these against a luminance RGB-based orthophoto. The procedure includes an improved coregistration strategy by integrating the Speeded-Up Robust Features (SURF) algorithm, with the Maximum Likelihood Estimator Sample Consensus (MLESAC) approach. SURF identifies common features between each swath and the RGB-orthomosaic, while MLESAC fits the best geometric transformation model to the retrieved matches. Individual scanlines are then geometrically transformed and merged into a single spatially continuous mosaic reaching high positional accuracies only with a few number of ground control points (GCPs). The capacity of the workflow to achieve high spatial accuracy was demonstrated by examining statistical metrics such as RMSE, MAE, and the relative positional accuracy at 95% confidence level. Comparison against a user-generated georectification demonstrates that the automated approach speeds up the coregistration process by 85%. Full article

Over the last two decades, a wide range of applications have been developed from Light Detection and Ranging (LiDAR) point clouds. Most LiDAR-derived products require the distinction between ground and non-ground points. Because of this, ground filtering its being one of the most studied topics in the literature and robust methods are nowadays available. However, these methods have been designed to work with offline data and they are generally not well suited for real-time scenarios. Aiming to address this issue, this paper proposes an efficient method for ground filtering of airborne LiDAR data based on scan-line processing. In our proposal, an iterative 1-D spline interpolation is performed in each scan line sequentially. The final spline knots of a scan line are taken into account for the next scan line, so that valuable 2-D information is also considered without compromising computational efficiency. Points are labelled into ground and non-ground by analysing their residuals to the final spline. When tested against synthetic ground truth, the method yields a mean kappa value of 88.59% and a mean total error of 0.50%. Experiments with real data also show satisfactory results under visual inspection. Performance tests on a workstation show that the method can process up to 1 million points per second. The original implementation was ported into a low-cost development board to demonstrate its feasibility to run in embedded systems, where throughput was improved by using programmable logic hardware acceleration. Analysis shows that real-time filtering is possible in a high-end board prototype, as it can process the amount of points per second that current lightweight scanners acquire with low-energy consumption. Full article