Search Results (11,196)

Passive seismic interferometry is a technique that reconstructs virtual seismic records using ambient noise, such as random noise or microseisms. The ambient noise in passive seismic data contains rich information, with surface waves being useful for the inversion of shallow subsurface structures, while body waves are employed for deep-layer inversion. However, due to the low signal-to-noise ratio in actual passive seismic data, different types of seismic waves mix together, making them difficult to distinguish. This issue not only affects the dispersion measurements of surface waves but also interferes with the imaging accuracy of reflected waves. Therefore, it is crucial to extract the target waves from passive source data. In practical passive seismic data, body wave noise and surface wave noise often overlap in frequency bands, making it challenging to separate them effectively using conventional methods. The synchrosqueezed continuous wavelet transform, as a high-resolution time–frequency analysis method, can effectively capture the variations in frequency of passive seismic data. This study performs time–frequency analysis of passive seismic data using synchrosqueezed continuous wavelet transform. It combines wavelet thresholding and Gaussian filtering to separate body wave noise from surface wave noise. Furthermore, wavelet cross-correlation is applied to separately obtain high-quality virtual seismic records for both surface waves and body waves. Full article

Among geological disasters, landslides are a common and extremely destructive disaster. Their rapid identification is crucial for disaster analysis and response. However, traditional methods of landslide recognition mainly rely on visual interpretation and manual recognition of remote sensing images, which are time-consuming and susceptible to subjective factors, thereby limiting the accuracy and efficiency of recognition. To overcome these limitations, for high-resolution remote sensing images, this method first uses online equalization sampling and enhancement strategy to sample high-resolution remote sensing images to ensure data balance and diversity. Then, it adopts an encoder–decoder structure, where the encoder is a visual attention network (Van) that focuses on extracting discriminative features of different scales from landslide images. The decoder consists of a pyramid pooling module (PPM) and feature pyramid network (FPN), combined with a convolutional block attention module (CBAM) module. Through this structure, the model can effectively integrate features of different scales, achieving precise positioning and recognition of landslide areas. In addition, this study introduces a sliding window algorithm based on Gaussian fusion as a post-processing method, which optimizes the prediction of landslide edge in high-resolution remote sensing images and ensures the context reasoning ability of the model. In the validation set, this method achieved a significant landslide recognition effect with a Dice score of 84.75%, demonstrating high accuracy and efficiency. This result demonstrates the importance and effectiveness of the research method in improving the accuracy and efficiency of landslide recognition, providing strong technical support for analysis and response to geological disasters. Full article

With the development and application of spaceborne Synthetic Aperture Radar (SAR), higher resolution and a wider swath have become significant demands. However, as the resolution increases and the swath widens, the two-dimensional (2D) spatial variation between different targets in the scene and the radar becomes very pronounced, severely affecting the high-precision focusing and high-quality imaging of spaceborne SAR. In previous studies on the correction of two-dimensional spatial variation in spaceborne SAR, either the models were not accurate enough or the computational efficiency was low, limiting the application of corresponding algorithms. In this paper, we first establish a slant range model and a signal model based on the zero-Doppler moment according to the spaceborne SAR geometry, thereby significantly reducing the impact of azimuth spatial variation in two-dimensional spatial variation. Subsequently, we propose a Curve-Sphere Model (CUSM) to describe the ground observation geometry of spaceborne SAR, and based on this, we establish a more accurate theoretical model and quantitative description of two-dimensional spatial variation. Next, through modeling and simulation, we conduct an in-depth analysis of the impact of two-dimensional spatial variation on spaceborne SAR imaging, obtaining corresponding constraints and thresholds and concluding that in most cases, only one type of azimuth spatial variation needs to be considered, thereby greatly reducing the demand and difficulty of two-dimensional spatial variation correction. Relying on these, we propose a two-dimensional spatial variation correction method that combines range blocking and azimuth nonlinear chirp scaling processing and analyze its scalability to be applicable to more general cases. Finally, the effectiveness and applicability of the proposed method are validated through both simulation experiments and real data experiments. Full article

In this study, we investigated the influence of pre-fire tree mortality on fire behavior. Although other studies have focused on the environmental factors affecting wildfire, the influence of pre-fire tree mortality has not been explored in detail. We used high-spatial-resolution (1.6 m) airborne multispectral orthoimages to detect and map pre-fire dead trees in a portion of the San Bernardino Mountains, where the ‘Old Fire’ burned in 2003, and assessed whether spatial patterns of fire intensity and burn severity coincide with patterns of tree mortality. Dead trees were mapped through a hybrid deep learning classification and manual editing approach and facilitated with Google Earth Pro historical images. Apparent thermal infrared (TIR) brightness temperature captured during the Old Fire was derived from maximum digital number values from FireMapper airborne thermal infrared imagery (7 m) as a measure of fire intensity. Burn severity was analyzed using normalized burn ratio maps derived from pre- and post-fire Landsat 5 satellite imagery (30 m). Pre-fire dead trees were prevalent with 192 dead trees and 108 live trees per ha, with most dead trees clustered near the northwestern part of the study area east of Lake Arrowhead. The degree of spatial correspondence among dead tree density, fire intensity, and burn severity was analyzed using graphical and statistical analyses. The results revealed a significant but weak spatial association of dead trees with fire intensity (R² = 0.31) and burn severity (R² = 0.14). The findings revealed that areas impacted by pre-fire tree mortality were subject to higher fire intensity, followed by severe burn effects, though other biophysical factors also influenced these fire behavior variables. These results contradict a previous study that found no effect of tree mortality on the behavior of the Old Fire. Full article

This review paper provides a comprehensive examination of millimeter-wave radar technologies in automotive systems, reviewing their advancements through signal processing innovations. The evolution of radar systems, from conventional platforms to mmWave technologies, has significantly enhanced capabilities such as high-resolution imaging, real-time tracking, and multi-object detection. Signal processing advancements, including constant false alarm rate detection, multiple-input–multiple-output systems, and machine learning-based techniques, are explored for their roles in improving radar performance under dynamic and challenging environments. The integration of mmWave radar with complementary sensing technologies such as LiDAR and cameras facilitates robust environmental perception essential for advanced driver-assistance systems and autonomous vehicles. This review also calls attention to key challenges, including environmental interference, material penetration, and sensor fusion, while addressing innovative solutions such as adaptive signal processing and sensor integration. Emerging applications of joint communication–radar systems further presents the potential of mmWave radar in autonomous driving and vehicle-to-everything communications. By synthesizing recent developments and identifying future directions, this review stresses the critical role of mmWave radar in advancing vehicular safety, efficiency, and autonomy. Full article

After the subsidence phase that followed the 1982–1984 bradyseismic crisis, a gradual ground uplift at Campi Flegrei caldera resumed in 2005, while volcanic-tectonic earthquakes have steadily increased in frequency and intensity since 2018, with a significant intensification observed since 2023. This rise in seismic activity enabled a new tomographic study using data collected from 2020 to June 2024. In this work, 4161 local earthquakes (41,272 P-phases and 14,683 S-phases) were processed with the tomoDDPS code, considering 388,166 P and 107,281 S differential times to improve earthquake locations and velocity models. Compared to previous tomographic studies, the 3D velocity models provided higher-resolution images of the central caldera’s structure down to ~4 km depth. Additionally, separate inversions of the two 2020–2022 (moderate seismicity) and 2023–2024 (intense seismicity) datasets identified velocity variations ranging from 5% to 10% between these periods. These changes observed in 2023–2024 support the existence of two pressurized sources at different depths. The first, located at 3.0–4.0 km depth beneath Pozzuoli and offshore, may represent either a magma intrusion enriched in supercritical fluids or an accumulation of pressurized, high-density fluids—a finding that aligns with recent ground deformation studies and modeled source depths. Additionally, the upward migration of magmatic fluids interacting with the geothermal system generated a secondary, shallower pressurized source at approximately 2.0 km depth beneath the Solfatara-Pisciarelli area. Overall, these processes are responsible for the recent acceleration in uplift, increased seismicity and gases from the fumarolic field, and changes in crustal elastic properties through stress variations and fluid/gas migration. Full article

Pest infestations remain a critical threat to global agriculture, significantly compromising crop yield and quality. While accurate pest detection forms the foundation of precision pest management, current approaches face two primary challenges: (1) the scarcity of comprehensive multi-scale, multi-category pest datasets and (2) performance limitations in detection models caused by substantial target scale variations and high inter-class morphological similarity. To address these issues, we present three key contributions: First, we introduce Insect25—a novel agricultural pest detection dataset containing 25 distinct pest categories, comprising 18,349 high-resolution images. This dataset specifically addresses scale diversity through multi-resolution acquisition protocols, significantly enriching feature distribution for robust model training. Second, we propose GC-Faster RCNN, an enhanced detection framework integrating a hybrid attention mechanism that synergistically combines channel-wise correlations and spatial dependencies. This dual attention design enables more discriminative feature extraction, which is particularly effective for distinguishing morphologically similar pest species. Third, we implement an optimized training strategy featuring a cosine annealing scheduler with linear warm-up, accelerating model convergence while maintaining training stability. Experiments have shown that compared with the original Faster RCNN model, GC-Faster RCNN has improved the average accuracy mAP0.5 on the Insect25 dataset by 4.5 percentage points, and mAP0.75 by 20.4 percentage points, mAP0.5:0.95 increased by 20.8 percentage points, and the recall rate increased by 16.6 percentage points. In addition, experiments have also shown that the GC-Faster RCNN detection method can reduce interference from multiple scales and high similarity between categories, improving detection performance. Full article

MircroRNA (miRNA) exhibits abnormal expression in many cancer diseases, and the detection and analysis of miRNA are significant for the early diagnosis of diseases and research on miRNA functions. In this work, we construct a UV-triggered DNAzyme (UTD) nanosensor for the early detection of miRNA in tumor cells. As the nanodevice was delivered into cells and irradiated by UV light, the controllable imaging of miRNA in living cells was achieved. This method effectively avoids false signal issues, providing a new strategy for high-spatiotemporal-resolution imaging of miRNA in living cells. Full article

Residual stresses in multilayer thin coatings represent a complex multiscale phenomenon arising from the intricate interplay of multiple factors, including the number and thickness of layers, material properties of the layers and substrate, coefficient of thermal expansion (CTE) mismatch, deposition technique and growth mechanism, as well as process parameters and environmental conditions. A multiscale approach to residual stress measurement is essential for a comprehensive understanding of stress distribution in such systems. To investigate this, two AlGaN/GaN multilayer coatings with distinct layer architectures were deposited on sapphire substrates using metalorganic vapor phase epitaxy (MOVPE). High-resolution X-ray diffraction (HRXRD) was employed to confirm their epitaxial growth and structural characteristics. Focused ion beam (FIB) cross-sectioning and transmission electron microscopy (TEM) lamella preparation were performed to analyze the coating structure and determine layer thickness. Residual stresses within the multilayer coatings were evaluated using two complementary techniques: High-Resolution Scanning Transmission Electron Microscopy—Graphical Phase Analysis (HRSTEM-GPA) and Focused Ion Beam—Digital Image Correlation (FIB-DIC). HRSTEM-GPA enables atomic-resolution strain mapping, making it particularly suited for investigating interface-related stresses, while FIB-DIC facilitates microscale stress evaluation. The residual strain values obtained using the FIB-DIC and HRSTEM-GPA methods were −3.2 × 10⁻³ and −4.55 × 10⁻³, respectively. This study confirms that residual stress measurements at different spatial resolutions are both reliable and comparable at the required coating depths and locations, provided that a critical assessment of the characteristic scale of each method is performed. Full article

Breast magnetic resonance imaging (MRI) is considered the most effective method for detecting breast cancer due to its high sensitivity. Yet multiple factors limit its widespread use, including high direct and indirect costs, a prolonged acquisition time with consequent patient discomfort, and a lack of trained radiologists. During the last decade, new strategies have been followed to increase the availability of breast MRI, including the omission of non-essential sequences to generate abbreviated MRI protocols (AB-MRIs) aimed at reducing the acquisition time with the potential of improving the patient’s experience and accommodating a higher number of MRI examinations per day. An alternative method is ultrafast MRI (UF-MRI), a novel technique that gathers kinetic data within the first minute after contrast injection, offering high temporal resolution. This enables the analysis of early contrast wash-in curves, showing promising outcomes. In this study, we reviewed the role of UF-MRI in breast imaging and detailed how the integration of this new approach with radiomics and mathematical models might further improve diagnostic accuracy and even have a prognostic role, a fundamental characteristic in the modern scenarios of personalized medicine. In addition, possible clinical applications and advantages of UF-MRI will be discussed. Full article

Wheat spike detection holds significant importance for agricultural production as it enhances the efficiency of crop management and the precision of operations. This study aims to improve the accuracy and efficiency of wheat spike detection, enabling efficient crop monitoring under resource-constrained conditions. To this end, a wheat spike dataset encompassing multiple growth stages was constructed, leveraging the advantages of MobileNet and ShuffleNet to design a novel network module, SeCUIB. Building on this foundation, a new wheat spike detection network, LGWheatNet, was proposed by integrating a lightweight downsampling module (DWDown), spatial pyramid pooling (SPPF), and a lightweight detection head (LightDetect). The experimental results demonstrate that LGWheatNet excels in key performance metrics, including Precision, Recall, and Mean Average Precision (mAP50 and mAP50-95). Specifically, the model achieved a Precision of 0.956, a Recall of 0.921, an mAP50 of 0.967, and an mAP50-95 of 0.747, surpassing several YOLO models as well as EfficientDet and RetinaNet. Furthermore, LGWheatNet demonstrated superior resource efficiency with a parameter count of only 1,698,529 and GFLOPs of 5.0, significantly lower than those of competing models. Additionally, when combined with the Slicing Aided Hyper Inference strategy, LGWheatNet further improved the detection accuracy of wheat spikes, especially for small-scale targets and edge regions, when processing large-scale high-resolution images. This strategy significantly enhanced both inference efficiency and accuracy, making it particularly suitable for image analysis from drone-captured data. In wheat spike counting experiments, LGWheatNet also delivered exceptional performance, particularly in predictions during the filling and maturity stages, outperforming other models by a substantial margin. This study not only provides an efficient and reliable solution for wheat spike detection but also introduces innovative methods for lightweight object detection tasks in resource-constrained environments. Full article

Thermal heterogeneity of rivers is essential to support freshwater biodiversity. Salmon behaviorally thermoregulate by moving from patches of warm water to cold water. When implementing river restoration projects, it is essential to monitor changes in temperature and thermal heterogeneity through time to assess the impacts to a river’s thermal regime. Lightweight sensors that record both thermal infrared (TIR) and multispectral data carried via unoccupied aircraft systems (UASs) present an opportunity to monitor temperature variations at high spatial (<0.5 m) and temporal resolution, facilitating the detection of the small patches of varying temperatures salmon require. Here, we present methods to classify and filter visible wetted area, including a novel procedure to measure canopy cover, and extract and correct radiant surface water temperature to evaluate changes in the variability of stream temperature pre- and post-restoration followed by a high-intensity fire in a section of the river corridor of the South Fork McKenzie River, Oregon. We used a simple linear model to correct the TIR data by imaging a water bath where the temperature increased from 9.5 to 33.4 °C. The resulting model reduced the mean absolute error from 1.62 to 0.35 °C. We applied this correction to TIR-measured temperatures of wetted cells classified using NDWI imagery acquired in the field. We found warmer conditions (+2.6 °C) after restoration (p < 0.001) and median absolute deviation for pre-restoration (0.30) to be less than both that of post-restoration (0.85) and post-fire (0.79) orthomosaics. In addition, there was statistically significant evidence to support the hypothesis of shifts in temperature distributions pre- and post-restoration (KS test 2009 vs. 2019, p < 0.001, D = 0.99; KS test 2019 vs. 2021, p < 0.001, D = 0.10). Moreover, we used a Generalized Additive Model (GAM) that included spatial and environmental predictors (i.e., canopy cover calculated from multispectral NDVI and photogrammetrically derived digital elevation model) to model TIR temperature from a transect along the main river channel. This model explained 89% of the deviance, and the predictor variables showed statistical significance. Collectively, our study underscored the potential of a multispectral/TIR sensor to assess thermal heterogeneity in large and complex river systems. Full article

Building extraction—needed for urban planning and monitoring—is affected by the misalignment between labels and off-nadir remote sensing imagery. A computer vision approach to teacher–student learning between large–noisy and small–clean data has been introduced as a solution, but with limited accuracy and efficiency. This paper proposes fine-tuning-based transfer learning (FTL) to adapt a pre-trained model from a noisy source to a clean target dataset, improving segmentation accuracy in off-nadir images. A standardized experimental framework is developed with three new building datasets containing large–noisy and small–clean image–label pairs of multiple spatial resolutions. These datasets cover a range of building types, from low-rise to skyscrapers. Additionally, this paper presents one of the most extensive benchmarking efforts in teacher–student learning for building extraction from off-nadir images. Results demonstrate that FTL outperforms the existing methods with higher F1 scores—0.943 (low-rise), 0.868 (mid-rise), 0.912 (high-rise), and 0.697 (skyscrapers)—and higher computational efficiency. A notable gain in mean difference is observed in taller buildings from complex urban environments. The proposed method, datasets, and benchmarking framework provide a robust foundation for accurate building extraction and broader remote sensing applications. Full article

Background/Objectives: A crucial factor for a successful cochlear implant (CI) outcome is an intact auditory nerve (AN). The integrity of the AN can be tested during implantation by measuring electrical auditory brainstem responses (eABR) via the CI. A method that does not require a CI is the use of the auditory nerve test stimulator (ANTS) from MED-EL (Innsbruck, Austria). The aim of the current study was to investigate the cases tested with the ANTS at our clinic and to describe the hearing results following CI for the cases who were implanted with a CI. Methods: All patients underwent preoperative magnetic resonance imaging (MRI) and high-resolution computed tomography (HRCT) to rule out cochlear malformation or retrocochlear pathology. In this study, we described all cases from when we began using the ANTS in 2011. Results: Five patients were tested intraoperatively: three adults with long-term deafness prior to CI and two children with no detectable AN. Three of the five patients were implanted with a CI. All implanted patients in this study could hear with their CIs, even though the speech perception results were limited. Conclusions: The ANTS can be used as a method to assess cochlear nerve function during implantation. The eABR results from the ANTS and the implanted CI were comparable for all cases in our study. Minor changes in waveform latencies were found between ANTS and CI stimulation and may be explained by the insertion depth of the electrode used for stimulation. Full article

Synthetic aperture radar interferometry (InSAR) has the advantages of a wide monitoring range, high density, high accuracy, and is not limited by weather conditions, providing a new technical means for landslide research. On 21 August 2021, a landslide occurred in Zhonghai Village, Hanyuan County, Ya’an City, Sichuan Province, China, resulting in nine deaths. For the research area, the Small Baseline Subsets InSAR (SBAS-InSAR) technique was used to extract the spatiotemporal evolution characteristics before the landslide occurred (from 16 January 2019 to 22 May 2020), and the height difference before and after the landslide occurrence was extracted using unmanned aerial vehicle photogrammetry, high-resolution remote sensing images, and digital elevation model data. By analyzing seismic activity, human activities, and rainfall in the study area, the main causes of landslides were discussed. This study not only reduces the losses caused by landslide disasters but also provides a scientific basis and technical support for local governments’ disaster prevention and mitigation work. Full article