Search Results (947)

This study aims to address the challenges posed by uneven energy amplitude and a low signal-to-noise ratio (SNR) in the total focus imaging of coarse-crystalline elastic anisotropic materials. A novel method for acoustic field correction vector-coherent total focus imaging, based on the materials’ properties, is proposed. To demonstrate the effectiveness of this method, a test specimen, an austenitic stainless steel nozzle weld, was employed. Seven side-drilled hole defects located at varying positions and depths, each with a diameter of 2 mm, were examined. An ultrasound simulation model was developed based on material backscatter diffraction results, and the scattering attenuation compensation factor was optimized. The acoustic field correction function was derived by combining acoustic field directivity with diffusion attenuation compensation. The phase coherence weighting coefficients were calculated, followed by image reconstruction. The results show that the proposed method significantly improves imaging amplitude uniformity and reduces the structural noise caused by the coarse crystal structure of austenitic stainless steel. Compared to conventional total focus imaging, the detection SNR of the seven defects increased by 2.34 dB to 10.95 dB. Additionally, the defect localization error was reduced from 0.1 mm to 0.05 mm, with a range of 0.70 mm to 0.88 mm. Full article

In this study, the effects of post-weld heat treatment (PWHT) on residual stress distribution and fatigue crack propagation (FCP) behavior in linear friction welded (LFW) Ti-6Al-4V joints were investigated. Microstructural evolution in the weld center zone (WCZ), thermomechanically affected zone (TMAZ), heat-affected zone (HAZ), and base metal (BM) was characterized using scanning electron microscropy (SEM) and electron backscatter diffraction (EBSD). Mechanical properties were evaluated via Vickers hardness testing and digital image correlation (DIC)-based tensile testing. Residual stresses before and after PWHT were measured using the contour method. The LFW process introduced significant residual stresses, with tensile stresses up to 709.2 MPa in the WCZ, resulting in non-uniform fatigue crack growth behavior. PWHT at 650 °C and 750 °C effectively reduced these stresses. After PWHT, fatigue cracks propagated uniformly across the weld region, enabling reliable determination of crack growth rates. The average crack growth rates of the heat-treated specimens were comparable to those of the base metal, confirming that PWHT, particularly at 750 °C, stabilizes the fatigue crack path and relieves internal stress. Full article

Marine oil spills cause significant environmental damage worldwide. Marine radar imagery is used for oil spill detection. However, the rapid attenuation of backscatter intensity with increasing distance limits detectable coverage. A multi-stage image enhancement framework integrating background clutter fitting subtraction, Multi-Scale Retinex, and Gamma correction is proposed. Experimental results using marine radar images sampled in the oil spill incident in Dalian 2010 are used to demonstrate the significant improvements. Compared to Contrast-Limited Adaptive Histogram Equalization and Partially Overlapped Sub-block Histogram Equalization, the proposed method enhances image contrast by 24.01% and improves the measurement of enhancement by entropy by 17.11%. Quantitative analysis demonstrates 95% oil spill detection accuracy through visual interpretation, while significantly expanding detectable coverage for oil extraction. Full article

This study intends to understand the effect of annealing behavior on the microstructure and mechanical and magnetic properties of cold-rolled extra-low-carbon steel. Deformed steel samples are annealed at temperature ranges of 200–690 °C followed by air-cooling. As part of this study, Magnetic Hysteresis loop (MHL) and Barkhausen emission (MBE) measurements are carried out for non-destructive evaluation (NDE) of the mechanical properties that are altered during annealing, viz. recovery and recrystallization. At low annealing temperature ranges 200 < T < 550 °C, the recovery causes no substantial variations in microstructure, hardness value from 191–185 HV, and tensile strength 456–452 MPa, while magnetic coercivity decreases from 293–275 A/m for cold-rolled annealed steels. The microstructural changes due to recovery and recrystallization are examined using transmission electron microscopy and orientation imaging microscopy (OIM) through electron backscattered diffraction (EBSD). Recrystallization is found after annealing at T > 550 °C, confirmed by the lowering of the microstructural KAM value from 0.81° to 0.65° and a hardness drop from 190.02 to 98 HV for cold-rolled extra-low-carbon steel. Full article

High-sensitivity fiber scintillator detectors are the key to achieving high signal-to-noise ratio and high contrast imaging in X-ray Compton backscattering technology. We established a simulation model of wavelength-shifting fiber (WSF) scintillator detectors based on Geant4. The influences of ray source energy, detection area, number of WSFs, and coupling mechanism on detection efficiency were simulated. By using the epoxy resin coupling method, the transmission efficiency between the WSF and scintillator was increased from 4.56% to 19.79%. Based on the simulation data, we developed an X-ray WSFs scintillator detector, built an X-ray backscattering imaging experimental system, obtained high-contrast backscattering images, and verified the performance of the detector. Full article

Accurate and timely flood mapping is critical for informing emergency response and risk mitigation during extreme weather events. This study presents a synthetic aperture radar (SAR)-based approach for rapid flood extent mapping using Sentinel-1 imagery, demonstrated for Tropical Storm Imelda (17–21 September 2019) in southeastern Texas. Dual-polarization Sentinel-1 SAR data (VH and VV) were processed by computing the VH/VV backscatter ratio, and the resulting ratio image was classified using a supervised Random Forest classifier to delineate water and land. All Sentinel-1 images underwent radiometric calibration, speckle noise filtering, and terrain correction to ensure precision in flood delineation. The Random Forest classifier achieved an overall flood mapping accuracy exceeding 94%, with Cohen’s kappa coefficients of approximately 0.75–0.80, demonstrating the approach’s reliability in distinguishing transient floodwaters from permanent water bodies. The spatial distribution of flooding was strongly influenced by topography and land cover. Analysis of Shuttle Radar Topography Mission (SRTM) digital elevation data revealed that low-lying, flat terrain was most vulnerable to inundation; correspondingly, the land cover types most affected were hay/pasture, cultivated land, and emergent wetlands. Additionally, urban areas with low-intensity development experienced extensive flooding, attributed to impervious surfaces exacerbating runoff. A strong, statistically significant correlation (R² = 0.87, p < 0.01) was observed between precipitation and flood extent, indicating that heavier rainfall led to greater inundation; accordingly, the areas with the highest rainfall totals (e.g., Jefferson and Chambers counties) experienced the most extensive flooding, as confirmed by SAR-based change detection. The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. Harnessing globally available Sentinel-1 data with near-real-time processing and a robust classifier, this approach provides a scalable solution for rapid flood monitoring. These findings underscore the potential of SAR-based flood mapping under adverse weather conditions, thereby contributing to improved disaster preparedness and resilience in flood-prone regions. Full article

Mueller matrix polarimetry has great potential for tissue detection and clinical diagnosis due to its ability to provide rich microstructural information accurately. However, in practical in vivo tissue imaging based on backscattering Mueller matrix polarimetry, specular reflection is often inevitable, leading to overexposed regions and the following inaccurate polarization information acquisition of tissues. In this study, we probe the influence of specular reflection and overexposure on backscattering Mueller matrix polarimetry for tissue imaging and sensing. We investigate in detail the differentiation of polarization behaviors between the specular reflection and non-specular reflection tissue regions using a 3 × 3 backscattering Mueller matrix measurement. Then, we obtain the vertical projection profiles to further quantify the Mueller matrix elements of porcine liver tissue in different specular reflection regions. Finally, we calculate the polarization feature parameters derived from a 3 × 3 Mueller matrix and analyze their behavior in overexposed regions. Based on the quantitative analysis and comparisons, we obtain a group of polarization feature parameters with strong immunity to the specular reflection process. This study offers a strategy for selecting the polarization parameters during in vivo polarimetric imaging applications, provides valuable references for further eliminating the characterization errors induced by specular reflection, and may contribute to the advancement of quantitative tissue polarimetric imaging and sensing. Full article

Knowledge of surface water extent is critical for ecological and disaster monitoring. However, surface water extraction from optical satellite imagery is challenging due to the impact of weather. Synthetic Aperture Radar (SAR) can penetrate cloud cover and has significant advantages for surface water mapping, but the classification accuracy might be limited by SAR’s inherent properties and land cover, which have similar backscatter to surface water. This study finds that the accuracy of surface water extraction at the Prairie Pothole Region (PPR) can be improved by combining interferometric coherence and backscatter for machine learning classification. This study performs time-series analysis on surface water and land to investigate their discrimination at different seasonal periods. The accuracy improvement of this method on Sentinel-1 images reached 10% during the seasons of fall and winter, where the combination of backscatter and coherence was proven to be efficient for separating water and land. Hence, our approaches of combining backscatter and coherence provide new insights for surface water extraction from SAR images in future studies. Full article

The microstructure of cement paste is governed by the hydration of its major component, tricalcium silicate (C₃S). Quantitative analysis of C₃S microstructural images is critical for elucidating the microstructure-property correlation in cementitious systems. Existing image segmentation methods rely on image contrast, leading to a struggle with multi-phase segmentation in regions with close grayscale intensities. Therefore, this study proposes a weighted K-means clustering method that integrates intensity gradients, texture variations, and spatial coordinates for the quantitative analysis of hydrated C₃S microstructure. The results indicate the following: (1) The deep convolutional neural network with guided filtering demonstrates superior performance (mean squared error: 53.52; peak signal-to-noise ratio: 26.35 dB; structural similarity index: 0.8187), enabling high-fidelity preservation of cementitious phases. In contrast, wavelet denoising is effective for pore network analysis but results in partial loss of solid phase information. (2) Unhydrated C₃S reflects optimal boundary clarity at intermediate image relative resolutions (0.25–0.56), while calcium hydroxide peaks at 0.19. (3) Silhouette coefficients (0.70–0.84) validate the robustness of weighted K-means clustering, and the Clark–Evans index (0.426) indicates CH aggregation around hydration centers, contrasting with the random CH distribution observed in Portland cement systems. Full article

Floods caused by extreme weather events critically impact human and natural systems. Remote sensing can be a very useful tool in mapping these impacts. However, processing and analyzing satellite imagery covering extensive periods is computationally intensive and time-consuming, especially when data from different sensors need to be integrated, hampering its operational use. To address this issue, the present study focuses on mapping flooded areas and analyzing the impacts of the 2023 Storm Daniel flood in the Thessaly region (Greece), utilizing Earth Observation and GIS methods. The study uses multiple Sentinel-1, Sentinel-2, and Landsat 8/9 satellite images based on backscatter histogram statistics thresholding for SAR and Modified Normalized Difference Water Index (MNDWI) for multispectral images to delineate the extent of flooded areas triggered by the 2023 Storm Daniel in Thessaly region (Greece). Cloud computing on the Google Earth Engine (GEE) platform is utilized to process satellite image acquisitions and track floodwater evolution dynamics until the complete drainage of the area, making the process significantly faster. The study examines the usability and transferability of the approach to evaluate flood impact through land cover, linear infrastructure, buildings, and population-related geospatial datasets. The results highlight the vital role of the proposed approach of integrating remote sensing and geospatial analysis for effective emergency response, disaster management, and recovery planning. Full article

The mineralized cover of chiton (Polyplacophora) soft tissue consists of aragonite, developed as shell-plates, girdle-scales, and girdle-spicules. This study characterizes crystallographic aspects of the girdle-spicules of the species Ischnochiton rissoi, Rhyssoplax olivacea, Acanthopleura vaillantii, and Acanthopleura spinosa. Spicule crystal arrangements and texture variations are described. Different misorientations between the spicule crystals are shown and are discussed with respect to the physical properties of the biomaterial. Characterization was performed with electron backscattered diffraction (EBSD), as well as with laser confocal and Field emission scanning electron microscopy (FE-SEM) imaging. All investigated species had porous spicules and distinct structural characteristics. Spicule crystal co-orientation strength was strongly increased for R. olivacea and I. rissoi, and it was almost random for A. vaillantii. R. olivacea, I. rissoi. A. spinosa spicule crystal texture was axial, whereas A. vaillantii spicule crystals were almost untextured. For all species investigated, spicule aragonite was twinned, as demonstrated with the strong 63°/64° peak in the misorientation angle distribution diagram, indicating a {110}-twin relationship. R. olivacea and I. rissoi spicules consisted of few twinned crystals and twin boundaries; A. vaillantii and A. spinosa spicules showed an abundance of twinned crystals and twin boundaries. We observed a difference in spicule dimension, morphology, arrangement on the girdle, and crystal organization for the investigated species, but always the generation of twinned aragonite. Full article

Advanced packaging represents a crucial technological evolution aimed at overcoming limitations posed by Moore’s Law, driving the semiconductor industry from two-dimensional toward three-dimensional integrated structures. The increasing complexity and miniaturization of electronic devices have significantly heightened the challenges associated with failure analysis during process development. The focused ion beam–scanning electron microscope (FIB-SEM), characterized by its high processing precision and exceptional imaging resolution, has emerged as a powerful solution for the fabrication, defect localization, and failure analysis of micro- and nano-scale devices. This paper systematically reviews the innovative applications of FIB-SEM in the research of core issues, such as through-silicon-via (TSV) defects, bond interfacial failures, and redistribution layer (RDL) electromigration. Additionally, the paper discusses multimodal integration strategies combining FIB-SEM with advanced analytical techniques, such as high-resolution three-dimensional X-ray microscopy (XRM), electron backscatter diffraction (EBSD), and spectroscopy. Finally, it provides a perspective on the emerging applications and potential of frontier technologies, such as femtosecond-laser-assisted FIB, in the field of advanced packaging analysis. Full article

This study explores the relationship between nutrient concentration (NC) and epidermal thickness (d) of the leaves of hydroponically grown Brassica rapa and its attenuation coefficients (m) using portable Time-Domain Optical Coherence Tomography (TD-OCT), which is a non-invasive imaging technique that uses low-coherence interferometry to generate axial scans of plants’ leaves by measuring the time delay and intensity of backscattered light. The portable TD-OCT system in this study has an axial and lateral resolution of 7 m and 3 m, respectively, a scanning depth of 12 mm, and a 1310 nm Super Luminescent Diode (SLD). Several studies suggest that the differences in d and m are related to nutritional, physiological, and anatomical status. The study used the Kratky method, a simple non-circulating hydroponic system, to cultivate Brassica rapa with varying NC (25%, 50%, 75%, 100% (control), and 125%). Each treatment group used two plants. The TD-OCT sample probe was placed on a fixed holder and was oriented vertically so that light was directed downward onto the leaf’s surface to obtain the depth profile (A-scan). The distance between the probe and the leaf was adjusted to obtain the optimum interference signal. Five averaged A-scans were obtained per leaf on the 7th, 18th, and 21st days post nutrient exposure. The logarithm of the averaged A-scan is linearly fitted to extract m. The results showed a positive correlation between NC and m, which suggests that plants produce more chlorophyll and develop denser cells and increase m. There was no correlation obtained between NC and d. The study demonstrates the potential of TD-OCT as a non-destructive tool for assessing plant health and monitoring growth dynamics in hydroponic systems and m as a sensitive indicator of plant health as compared to d. The continued exploration of TD-OCT applications in agriculture can contribute to improving crop management strategies and promoting sustainable food production practices. Full article

The characterization of soil attribute variability often requires dense sampling grids, which can be economically unfeasible. A possible solution is to perform targeted sampling based on previously collected data. The objective of this research was to develop a method for mapping soil attributes based on Management Zones (MZs) delineated from Sentinel-1 radar data. Sentinel-1 images were used to create time profiles of six indices based on VV (vertical–vertical) and VH (vertical–horizontal) backscatter in two agricultural fields. MZs were delineated by analyzing indices and VV/VH backscatter bands individually through two approaches: (1) fuzzy k-means clustering directly applied to the indices’ time series and (2) dimensionality reduction using deep-learning autoencoders followed by fuzzy k-means clustering. The best combination of index and MZ delineation approaches was compared with four soil attribute mapping methods: conventional (single composite sample), high-density uniform grid (one sample per hectare), rectangular cells (one composite sample per cell of 5 to 10 hectares), and random cells (one composite sample per cell of varying sizes). Leave-one-out cross-validation evaluated the performance of each sampling method. Results showed that combining the VV/VH index and autoencoders for MZ delineation provided more accurate soil attribute estimates, outperforming the conventional, random cells, and often the rectangular cell method. In conclusion, the proposed methodology presents scalability potential, as it does not require prior calibration and was validated on soil types commonly found across Brazil’s agricultural regions, making it suitable for integration into digital platforms for broader application in precision agriculture. Full article

Previous work has demonstrated quantitative ultrasound (QUS) analysis techniques for extracting features and texture features from ultrasound radiofrequency data which can be used to distinguish between benign and malignant breast masses. It is desirable that there be good agreement between estimates of such features acquired using different ultrasound devices. Handheld ultrasound imaging systems are of particular interest as they are compact, relatively inexpensive, and highly portable. This study investigated the agreement between QUS parameters and texture features estimated from clinical ultrasound images of breast tumors acquired using two different ultrasound scanners: a traditional cart-based system and a wireless handheld ultrasound system. The 28 patients who participated were divided into two groups (benign and malignant). The reference phantom technique was used to produce functional estimates of the normalized power spectra and backscatter coefficient for each image. Root mean square differences of feature estimates were calculated for each cohort to quantify the level of feature variation attributable to tissue heterogeneity and differences in system imaging parameters. Cross-system statistical testing using the Mann–Whitney U test was performed on benign and malignant patient cohorts to assess the level of feature estimate agreement between systems, and the Bland–Altman method was employed to assess feature sets for systematic bias introduced by differences in imaging method. The range of p-values was 1.03 × 10⁻⁴ to 0.827 for the benign cohort and 3.03 × 10⁻¹⁰ to 0.958 for the malignant cohort. For both cohorts, all five of the primary QUS features (MBF, SS, SI, ASD, AAC) were found to be in agreement at the 5% confidence level. A total of 13 of the 20 QUS texture features (65%) were determined to exhibit statistically significant differences in the sample medians of estimates between systems at the 5% confidence level, with the remaining 7 texture features being in agreement. The results showed a comparable magnitude of feature variation between tissue heterogeneity and system effects, as well as a moderate level of statistical agreement between feature sets. Full article