Search Results (6,378)

Accurately monitoring soil moisture content (SMC) in the field is crucial for achieving precision irrigation management. Currently, the development of UAV platforms provides a cost-effective method for large-scale SMC monitoring. This study investigates silage corn by employing UAV remote sensing technology to obtain multispectral imagery during the seedling, jointing, and tasseling stages. Field experimental data were integrated, and supervised classification was used to remove soil background and image shadows. Canopy reflectance was extracted using masking techniques, while Pearson correlation analysis was conducted to assess the linear relationship strength between spectral indices and SMC. Subsequently, convolutional neural networks (CNNs), back-propagation neural networks (BPNNs), and partial least squares regression (PLSR) models were constructed to evaluate the applicability of these models in monitoring SMC before and after removing the soil background and image shadows. The results indicated that: (1) After removing the soil background and image shadows, the inversion accuracy of SMC for CNN, BPNN, and PLSR models improved at all growth stages. (2) Among the different inversion models, the accuracy from high to low was CNN, PLSR, BPNN. (3) From the perspective of different growth stages, the inversion accuracy from high to low was seedling stage, tasseling stage, jointing stage. The findings provide theoretical and technical support for UAV multispectral remote sensing inversion of SMC in silage corn root zones and offer validation for large-scale soil moisture monitoring using remote sensing. Full article

This work provides a synthesis of an initial experience in the development of accessible imaging techniques and their implementation on a real case: the analysis of colonial Hispano-American paintings at the Complejo Museográfico Provincial “Enrique Udaondo” (Luján, Buenos Aires). It discusses different aspects related to the possibilities of obtaining, using, and reusing equipment and materials locally, as well as details of the ways of acquiring images for photography on site. It also provides information about the composition and conservation state of selected artworks, complementing image analysis with portable X-ray fluorescence (XRF) data, and reflects on articulated/collaborative work in situ as a methodology for transferring knowledge and skills. The project aims to contribute to strengthening Latin American sustainability by creating accessible non-invasive tools for heritage conservation institutions, highlighting the value of regional capacities to approach heritage studies from collaborative and ethical proposals that promote sovereignty and reduce dependence on external inputs. Full article

Monitoring plantar foot temperatures is essential for assessing foot health, particularly in individuals with diabetes at increased risk of complications. Traditional thermographic imaging measures foot temperatures in unshod individuals lying down, which may not reflect thermal characteristics of feet in shod, active, real-world conditions. These controlled settings limit understanding of dynamic foot temperatures during daily activities. Recent advancements in wearable technology, such as insole-based sensors, overcome these limitations by enabling continuous temperature monitoring. This study leverages a data-driven clustering approach, independent of pre-selected foot regions or models like the angiosome concept, to explore normative thermal patterns in shod feet with insole-based sensors. Data were collected from 27 healthy participants using insoles embedded with 21 temperature sensors. The data were analysed using clustering algorithms, including k-means, fuzzy c-means, OPTICS, and hierarchical clustering. The clustering algorithms showed a high degree of similarity, with variations primarily influenced by clustering granularity. Six primary thermal patterns were identified, with the “butterfly pattern” (elevated medial arch temperatures) predominant, representing 51.5% of the dataset, aligning with findings in thermographic studies. Other patterns, like the “medial arch + metatarsal area” pattern, were also observed, highlighting diverse yet consistent thermal distributions. This study shows that while normative thermal patterns observed in thermographic imaging are reflected in insole data, the temperature distribution within the shoe may better represent foot behaviour during everyday activities, particularly when enclosed in a shoe. Unlike thermal imaging, the proposed in-shoe system offers the potential to capture dynamic thermal variations during ambulatory activities, enabling richer insights into foot health in real-world conditions. Full article

Ultrafast diagnostic technology has caused breakthroughs in fields such as inertial confinement fusion, particle accelerator research, and laser-induced phenomena. As the most widely used tool for ultrafast diagnostic technology, investigating the characteristics of streak cameras in the imaging process and streak tubes’ complex physical processes is significant for its overall development. In this work, the imaging process of a streak camera is modeled and simulated using Geant4-based Monte Carlo simulations. Based on the selected phosphor screen P43 (Gd₂O₂S: Tb) and charged coupled device (CCD) sensor parameters, Monte Carlo simulation models of phosphor screens and CCD sensors(We refer to the sensor parameters of the US company onsemi’s KAF-50100 sensor, but some adjustments are made during the simulation), implemented with the toolkit Geant4, are used to study the electron beam to generate fluorescence on phosphor and photoelectrons on CCD sensors. The physical process of a high-energy electron beam hitting a phosphor screen and imaging on the CCD camera is studied. Meanwhile, merits such as the luminous efficiency of the selected phosphor, spatial resolution of the phosphor screen, and spatial resolution of the selected CCD sensor are analyzed. The simulation results show that the phosphor screen and CCD sensor simulation models can accurately simulate the selected components’ performance parameters with the imaging process’ simulation results precisely reflecting the distribution of output electrons in the streak image tube. References for simulation and device selection in the subsequent research on streak cameras can be provided. Full article

: Background: Quantitative evaluation of myocardial function traditionally relies on parameters such as ejection fraction and strain. Strain, reflecting the relative change in the length of a myocardial segment over the cardiac cycle, has been extensively studied in various cardiac pathologies over the past two decades. However, the absolute length change, or longitudinal displacement, of myocardial segments during the cardiac cycle has received limited attention. This study aims to evaluate longitudinal displacement in two separate groups: healthy athletes and patients with left ventricular dysfunction, providing new insights into myocardial function assessment. Methods: Echocardiographic examinations were performed on 30 healthy football players and 30 patients with left ventricular dysfunction using speckle-tracking imaging analysis. Global and regional peak longitudinal displacement values were calculated and compared with corresponding global and regional peak longitudinal strain measurements. A manual alternative for calculating global longitudinal strain was also proposed. Results: An inverse correlation was found between regional longitudinal displacement and regional longitudinal strain. Longitudinal displacement was maximal in the basal segments and lowest in the apex of the left ventricle, exhibiting a reversed basal-to-apical gradient (17.6 ± 3.5 mm vs. 11.5 ± 2.9 mm vs. 4.22 ± 1.7 mm in basal, mid, and apical segments, respectively; p < 0.000001). Maximal longitudinal displacement was observed in the inferior and posterior walls of the left ventricle. In the 30 patients with left ventricular dysfunction, global longitudinal displacement was significantly lower than in healthy individuals (4.4 ± 1.7 mm vs. 11.7 ± 1.5 mm, p < 0.000001). Global longitudinal displacement and global longitudinal strain showed a strong negative correlation (r = −0.72, p < 0.000001). Manually calculated global longitudinal strain demonstrated good agreement with speckle-tracking-based global longitudinal strain. Conclusions: Peak longitudinal displacement can be used to evaluate both regional and global myocardial function, similarly to peak longitudinal strain. Unlike strain, longitudinal displacement exhibits a reversed basal-to-apical gradient, with the highest values at the base of the left ventricle and the lowest at the apex. Global and regional longitudinal displacement is significantly reduced in patients with left ventricular dysfunction. Global longitudinal strain can be manually calculated using displacement measurements. Further studies are needed to evaluate peak longitudinal displacement in various cardiac pathologies. Full article

With the advancement of non-classical light sources such as single-photon and entangled-photon sources, innovative microscopy based on quantum principles has been proposed for traditional microscopy. This paper introduces the experimental demonstration of a quantum polarization microscopic technique that incorporates a quantum-entangled photon source. Although the point that employs the variation in polarization angle due to reflection or transmission at the sample is similar to classical polarization microscopy, the method for constructing the image contrast is significantly different. The image contrast is constructed by the coincidence count of signal and idler photons. In the case that the coincidence count is recorded from both the signal and idler photons, the photon statistics resemble a thermal state, similar to the blackbody radiation, but with a significantly higher peak intensity in the second-order autocorrelation function at zero delay that is derived from the coincidence count, while, when the coincidence count is taken from either the signal or idler photon only, although the photon state exhibits a thermal state again, the photon statistics become more dispersive and result in a lower peak intensity of the autocorrelation function. These different thermal states can be switched by slightly changing the photon polarization, which is suddenly aroused within a narrow range of the analyzer angle. The autocorrelation function g²(0) at the thermal state exhibits a sensitivity that is three times higher compared to the classical coincidence count rate, and this concept can be effectively utilized to enhance the contrast of the image. One of the key achievements of our proposed method is ensuring a low power of illumination (in the order of Pico-joules) for constructing the image. In addition, the robustness without any precise setup is also favorable for practical use. This polarization microscopic technique can provide a superior imaging technique compared to the classical method, opening a new frontier for research in material sciences, biology, and other fields requiring high-resolution imaging. Full article

Archaeologists and conservation scientists join interdisciplinary projects aiming at the in-depth analysis of artefacts and the resolution of new archaeological issues, overcoming the common limits of mesoscopic observation. The aim of this research is to perform multidisciplinary research, adapting imaging techniques (RTI imaging and 3D photogrammetry) and Raman spectroscopy from their conventional field of application to study and valorise neolithic archaeological findings from Piacentine sites (Emilia-Romagna, Italy). RTI images enable the detection of a comprehensive framework of anthropic and natural traces on the object surfaces to support the hypothesis of the intended usage of artefacts. Combining qualitative and quantitative Raman spectra analysis, the specific lithological characterisation of each fragment is conducted, thereby the understanding of their probable geographic provenance is enhanced. This contributes to the identification of the External Ligurian Units as a possible primary supply area, along with the already known outcrops in the Mont Viso Massif and Voltri Group. Their potential as a powerful instrument for conservation and valorisation has been revealed by 3D models. In fact, they may enrich museum exhibits, enhancing visitors’ experience through interactive engagement and guarantee the examination of artefacts by experts across the globe through online sharing, without the need for transportation and excessive manipulation. Full article

Person re-identification (Re-ID) aims to retrieve all images of the specific person captured by non-overlapping cameras and scenarios. Regardless of the significant success achieved by daytime person Re-ID methods, they will perform poorly due to the degraded imaging quality under low-light conditions. Therefore, some works attempt to synthesize low-light images to explore the challenges in the nighttime, which omits the fact that synthetic images may not realistically reflect the challenges of person Re-ID at night. Moreover, other works follow the “enhancement-then-match” manner, but it is still hard to capture discriminative identity features owing to learning enlarged irrelevant noise for identifying pedestrians. To this end, we propose a novel nighttime person Re-ID method, termed Feature Discovery Transformer (FDT), explicitly capturing the pedestrian identity information hidden in darkness at night. More specifically, the proposed FDT model contains two novel modules: the Frequency-wise Reconstruction Module (FRM) and the Attribute Guide Module (AGM). In particular, to reduce noise disturbance and discover pedestrian identity details, the FRM utilizes the Discrete Haar Wavelet Transform to acquire the high- and low-frequency components for learning person features. Furthermore, to avoid high-frequency components being over-smoothed by low-frequency ones, we propose a novel Normalized Contrastive Loss (NCL) to help the model obtain the identity details in high-frequency components for extracting discriminative person features. Then, to further decrease the negative bias caused by appearance-irrelevant features and enhance the pedestrian identity features, the AGM improves the robustness of the learned features by integrating the auxiliary information, i.e., camera ID and viewpoint. Extensive experimental results demonstrate that our proposed FDT model can achieve state-of-the-art performance on two realistic nighttime person Re-ID benchmarks, i.e., Night600 and RGBNT201rgb datasets. Full article

To address the complex and space-constrained characteristics of underground coal mine roadways, this study proposes an electromagnetic wave reflection model based on the mirror image method. A U-shaped roadway model was designed and a relay node was established at the center of the roadway to simplify calculations. The point normal vector method was used to calculate the equations and boundary ranges of eight reflection planes. The valid reflection paths were determined by calculating the mirror points, counting the number of reflection lines, and evaluating their validity. The sensitivity of the number of valid reflection lines to the positions of the transmitting and receiving points relative to the corners was determined, and the reflected field strength at the receiving point was calculated. Its sensitivity to variables such as the distance between the relay node and the receiving point, antenna transmitting frequency, relative dielectric constant of the roadway walls, and width of the U-shaped roadway was studied. The simulation results showed that the number of valid reflection lines decreased with increasing distance from the transmitting and receiving points to the corners. The horizontal position of the transmitting point has a higher effect on the number of effective reflection lines than the vertical position, while the transmitting and receiving points are favorable for electromagnetic wave propagation when they are located in the center of the roadway. As the distance between the relay node and the receiving point increases, the reflection field strength attenuation at the receiving point will decrease with a larger roadway width, a smaller relative permittivity of the roadway walls, and a lower transmitting frequency of the antenna. Full article

Background/Objectives: Multinodular and vacuolating neuronal tumors (MNVTs) are a type of recently identified benign neuroepithelial tumor with debated malformative or neoplastic origins. This review summarizes their neuroanatomical localization, imaging, histopathology, immunohistochemistry, and diagnostic challenges. Methods: A systematic review of PUBMED/MEDLINE was performed in December 2024. Results: Of 118 screened articles, 39 were eligible, covering 299 patients. MNVTs are often asymptomatic “leave me alone” lesions, discovered incidentally, though nonspecific symptoms (59.9%) and seizures (19.7%) are reported. Immunohistochemistry reveals variable profiles, reflecting complex cellular differentiation. The characteristic “bubble-like” MRI pattern along the subcortical ribbon and superficial white matter is a reliable diagnostic feature. Rare cortical involvement and atypical band-like lesions occur. MRI signal intensity varies. Over a mean follow-up of 36 months, lesions were stable or non-recurrent, with only one case of progression. Conclusion: MVNT imaging mimics other glioneuronal lesions, but reliable diagnostic MRI features include a “bubble-clustered” appearance, lack of cortical involvement, absence of enhancement, and temporal lobe predominance. Hemodynamic and metabolic properties support the diagnosis. Most lesions remain stable, requiring no treatment. Surgical resection is reserved for cases with uncontrolled seizures or atypical locations where the diagnosis is unclear. Full article

Terahertz (THz) is an emerging technology particularly well suited for the non-destructive investigation of inner structures in polymers. To realize its full potential, THz imaging systems adapted to industrial constraints as well as more application studies in areas of interest are needed. In this work, we present a fast and flexible THz imaging system comprising hardware and software and demonstrate its capabilities for the investigation of defects in glass fiber-reinforced polymers (GFRPs), particularly for the detection of drilling-induced delaminations. Measurement data obtained by raster scanning of GFRP samples are gathered in 3D volumetric images. THz images of the drilled holes are then compared to reference images of the same holes obtained from X-ray computed tomography measurements. We show that THz imaging is capable of identifying not only artificial defects in the form of aluminum and Teflon inlays, but also real defects such as delaminations generated by drilling operations, and is suitable for non-destructive testing in industrial conditions. Full article

Nitrogen serves as a critical nutrient influencing the yield and quality of processed tomatoes; however, traditional methods for assessing its levels are both labor-intensive and costly. This study aimed to explore an efficient monitoring approach by analyzing the relationship between leaf nitrogen content (LNC) and canopy spectral reflectance characteristics throughout the growth stages of processed tomatoes at the Laolong River Tomato Base in Changji City, Xinjiang. The experimental design incorporated nine treatments, each with three replicates. LNC data were obtained using a dedicated leaf nitrogen content analyzer, while drones were utilized to capture multispectral images for the extraction of vegetation indices. Through Pearson correlation analysis, the optimal spectral variables were identified, and the relationships between LNC and spectral variables were established using models based on backpropagation (BP), multiple linear regression (MLR), and random forests (RFs). The findings revealed that the manually measured LNC data exhibited two peak values, which occurred during the onset of flowering and fruit setting stages, displaying a bimodal pattern. Among the twelve selected vegetation indices, ten demonstrated spectral sensitivity, passing the highly significant 0.01 threshold, with the Normalized Difference Chlorophyll Index (NDCI) showing the highest correlation during the full bloom stage. The combination of the NDCI and RF model achieved a prediction accuracy exceeding 0.8 during the full bloom stage; similarly, models incorporating multiple vegetation indices, such as RF, MLR, and BP, also reached prediction accuracies exceeding 0.8. Consequently, during the seedling establishment and initial flowering stages (vegetation coverage of <60%), the RF model with multiple vegetation indices was suitable for monitoring LNC; during the full bloom stage (vegetation coverage of 60–80%), both the RF model with the NDCI and the MLR model with multiple indices proved effective; and during the fruit setting and maturation stages (vegetation coverage of >80%), the BP model was more appropriate. This research provides a scientific basis for the cultivation management of processed tomatoes and the optimization of nitrogen fertilization within precision agriculture. It advances the application of precision agriculture technologies, contributing to improved agricultural efficiency and resource utilization. Full article

Background/Objectives: Vascular complications during ovarian cancer surgery are rare but potentially severe. The objective of this review was to underline the need to standardize and optimize the management of these rare complications within an evidence-based framework. Methods: This review included the literature until 29 December 2024 and finally analyzed 17 studies, with 40 vascular complications reported. Results: Complications primarily occurred intraoperatively and involved both arterial and venous systems. Management approaches involved interdisciplinary collaboration, including vascular surgeons and interventional radiologists. Nevertheless, the collected data do not accurately reflect the reality of vascular complications in ovarian cancer surgery, as more than half of the included studies were case reports or research letters. This highlights the lack of standardized guidelines and limited training in vascular surgery for gynecologic oncologists, the importance of preoperative planning, including detailed imaging, risk stratification, and a multidisciplinary approach to mitigate complications. The authors propose an algorithm emphasizing prevention, timely identification, and effective management of vascular injuries alongside postoperative monitoring. Conclusions: The findings stress the need for treatment in high-volume tertiary centers and advocate advanced surgical training, incorporating virtual reality simulations to address vascular complications. Future research should focus on large multicenter studies to establish evidence-based guidelines for managing vascular complications in ovarian cancer surgery. Innovations in technology and education may further improve outcomes, ensuring optimal care for patients undergoing these complex procedures. Full article

Surface albedo is a key variable influencing ground-reflected solar irradiance, which is a vital factor in boosting the energy gains of bifacial solar installations. Therefore, surface albedo is crucial towards estimating photovoltaic power generation of both bifacial and tilted solar installations. Varying across daylight hours, seasons, and locations, surface albedo is assumed to be constant across time by various models. The lack of granular temporal observations is a major challenge to the modeling of intra-day albedo variability. Though satellite observations of surface reflectance, useful for estimating surface albedo, provide wide spatial coverage, they too lack temporal granularity. Therefore, this paper considers a novel approach to temporal downscaling with imaging time series of satellite-sensed surface reflectance and limited high-temporal ground observations from surface radiation (SURFRAD) monitoring stations. Aimed at increasing information density for learning temporal patterns from an image series and using visual redundancy within such imagery for temporal downscaling, we introduce temporally shifted heatmaps as an advantageous approach over Gramian Angular Field (GAF)-based image time series. Further, we propose Multispectral-WaveMix, a derivative of the mixer-based computer vision architecture, as a high-performance model to harness image time series for surface albedo forecasting applications. Multispectral-WaveMix models intra-day variations in surface albedo on a 1 min scale. The framework combines satellite-sensed multispectral surface reflectance imagery at a 30 m scale from Landsat and Sentinel-2A and 2B satellites and granular ground observations from SURFRAD surface radiation monitoring sites as image time series for image-to-image translation between remote-sensed imagery and ground observations. The proposed model, with temporally shifted heatmaps and Multispectral-WaveMix, was benchmarked against predictions from models image-to-image MLP-Mix, MLP-Mix, and Standard MLP. Model predictions were also contrasted against ground observations from the monitoring sites and predictions from the National Solar Radiation Database (NSRDB). The Multispectral-WaveMix outperformed other models with a Cauchy loss of 0.00524, a signal-to-noise ratio (SNR) of 72.569, and a structural similarity index (SSIM) of 0.999, demonstrating the high potential of such modeling approaches for generating granular time series. Additional experiments were also conducted to explore the potential of the trained model as a domain-specific pre-trained alternative for the temporal modeling of unseen locations. As bifacial solar installations gain dominance to fulfill the increasing demand for renewables, our proposed framework provides a hybrid modeling approach to build models with ground observations and satellite imagery for intra-day surface albedo monitoring and hence for intra-day energy gain modeling and bifacial deployment planning. Full article

Severe reflections on the surfaces of smooth objects can result in low dynamic range and uneven illumination in images, which negatively impacts downstream tasks such as defect detection and QR code recognition on images of smooth workpieces. Consequently, this paper proposes a novel approach to real-time high dynamic equalization imaging based on a fully convolutional network, termed Multi-exposure Image Fusion with Multi-dimensional Attention Mechanism and Training Storage Units (MEF-AT). Specifically, this paper innovatively proposes using training storage units, which utilize intermediate results during network training as auxiliary images, to remove uneven illumination and enhance image dynamic range effectively. Furthermore, by integrating a multi-dimensional attention mechanism into the backbone network, the model can more efficiently extract and utilize critical image information. Additionally, this paper introduces a Deep Guided Filter (DGF) with learnable parameters, which upsample the weight maps generated by the network, thus better adapting to complex industrial scenarios and producing higher quality fused images. An image evaluation metric assessing the lighting uniformity is introduced to thoroughly evaluate the proposed method’s performance. Given the lack of an MEF dataset for smooth workpieces, this paper collects a new dataset for multi-exposure fusion tasks on metallic workpieces. Our method takes less than 4 ms to run four 2K images on a GPU 3090. Both qualitative and quantitative experimental results demonstrate our method’s superior comprehensive performance in proprietary industrial and public datasets. Full article