Search Results (50)

Tabletop 3D display is an emerging display form that enables multiple users to share viewing around a central tabletop, making it promising for the application of collaborative work. However, achieving an ideal ring-shaped viewing zone with a large radial viewing angle remains a challenge for most current tabletop 3D displays. This paper presents a tabletop 3D display based on a panoramic annular lens array to realize a large radial viewing angle. Each panoramic annular lens in the array is designed with a block-structured panoramic front unit and a relay lens system, enabling the formation of a ring-shaped viewing zone and increasing the radial angle of the outgoing light. Additionally, the diffusion characteristics of the optical diffusing screen component are analyzed under large angles of incidence after light passes through the panoramic annular lens array. Then, a method for generating the corresponding elemental image array is presented. The results of the simulation experiments demonstrate that the viewing range is improved to −78.4–−42.2° and 42.6–78.9°, resulting in a total radial viewing angle of up to 72.5°, and the proposed 3D display can present a 360° viewable 3D image with correct perspective and parallax. Full article

Panoramic images provide distinct advantages in odometry applications, which are largely due to their extensive field of view and higher information density captured in a single frame. Traditional odometry methods often rely on mapping panoramic images onto the planar structure for feature tracking. However, this process introduces uneven distortions of features, which diminish the accuracy of feature tracking and odometry, particularly in scenarios involving large displacements. In this work, we address this challenge by introducing a novel approach, named spherical congruence projection (SCP), that maps panoramic images onto a spherical structure and projects the spherical pixels onto a two-dimensional data format while preserving the spherical pixel topology. SCP effectively eliminates the distortion across the panoramic image. Additionally, we present the optical flow odometry on the panoramic image in the spherical structure and integrate it with the proposed SCP method for the first time. The experimental results in public and custom-built datasets demonstrate that the proposed SCP-based odometry method reliably tracks features and maintains accurate odometry performance, even in fast-moving scenarios. Full article

In the laser powder bed fusion (LPBF) process, the surface quality of intermediate layers impacts interlayer bonding and part forming quality. Due to the complex dynamic process inherent in LPBF, current monitoring methods struggle to achieve high-quality in situ online monitoring, which limits the in-depth understanding of the evolution mechanisms of the surface morphology of LPBF intermediate layers. This paper employs an optimized coaxial optical imaging method to monitor key LPBF processes and analyzes the intermediate layer surface morphology evolution mechanism considering heat, force, and mass transfer. Results indicate that LPBF intermediate layer surfaces are influenced by energy density, melt pool behavior, and previous layer morphology, forming complex topological structures. At a low energy density, insufficient powder melting causes balling, extended by subsequent melt pools to form a reticulated structure and local large-scale protrusions. Heat accumulation at a high energy density promotes melt pool expansion, reduces melt track overlap, and effectively eliminates defects from previous layers via remelting, with spatter becoming the main defect. Additionally, the melt pool wettability on the part contours captures external powder, forming unique, overhanging contour protrusions. This paper enhances understanding of LPBF intermediate layer surface morphology formation mechanisms and provides a theoretical basis for optimizing surface quality. Full article

Aerial optical cameras are the primary method for capturing high-resolution images to produce large-scale mapping products. To improve aerial photography efficiency, multiple cameras are often used in combination to generate large-format virtual central projection images. This paper presents a high-precision method for directly transforming raw images obtained from a dual-camera system mounted at an oblique angle into virtual central projection images, thereby enabling the construction of low-cost, large-format aerial camera systems. The method commences with an adaptive sub-block in the overlapping regions of the raw images to extract evenly distributed feature points, followed by iterative relative orientation to improve accuracy and reliability. A global projection transformation matrix is constructed, and the sigmoid function is employed as a weighted distance function for image stitching. The results demonstrate that the proposed method produces more evenly distributed feature points, higher relative orientation accuracy, and greater reliability. Simulation analysis of image overlap indicates that when the overlap exceeds 7%, stitching accuracy can be better than 1.25 μm. The aerial triangulation results demonstrate that the virtual central projection images satisfy the criteria for the production of 1:1000 scale mapping products. Full article

Additive manufacturing (AM) has become a key element of Industry 4.0, particularly the extrusion AM (EAM) of thermoplastic materials, which is recognized as the most widely used technology. Fused Filament Fabrication (FFF), however, depends on expensive commercially available filaments, making pellet extruder-based EAM techniques more desirable. Large-format EAM systems could benefit from printing lightweight objects with reduced material use and lower power consumption by utilizing hollow rather than solid extrudates. In this study, a custom extruder head was designed and an EAM system capable of extruding inflatable hollow extrudates from a variety of materials was developed. By integrating a co-axial nozzle-needle system, a thermoplastic shell was extruded while creating a hollow core using pressurized nitrogen gas. This method allows for the production of objects with gradient part density and varied mechanical properties by controlling the inflation of the hollow extrudates. The effects of process parameters— such as extrusion temperature, extrusion speed, and gas pressure were investigated—using poly-lactic acid (PLA) and styrene-ethylene-butylene-styrene (SEBS) pellets. The preliminary tests identified the optimal range of these parameters for consistent hollow extrudates. We then varied the parameters to determine their impact on the dimensions of the extrudates, supported by analyses of microscopic images taken with an optical microscope. Our findings reveal that pressure is the most influential factor affecting extrudate dimensions. In contrast, variations in temperature and extrusion speed had a relatively minor impact, whereas changes in pressure led to significant alterations in the extrudate’s size and shape. Full article

With the demand for energy gravimetric and volumetric density in electrical vehicles, lithium-ion batteries are undergoing a trend toward larger formats, along with maximized cell-to-pack efficiency. Current battery thermal management systems and battery modeling, relying on point measurement (thermocouples/thermistors), face challenges in providing comprehensive characterization for larger batteries and extensive monitoring across the pack. Here, we proposed a novel Rayleigh-scattering-based distributed optical fiber sensor to deliver thermal images of a large prismatic cell. Using an optical fiber of 1 mm diameter wrapped around the cell, the optical sensor delivered over 400 unique measurement locations at 3 mm spatial resolution. During a 1.0 C charge, the optical-measured maximum temperature difference was 8.2 °C, while point-like thermocouples, located at the cell front surface and rear surface center, only had a 0.8 °C maximum temperature difference. Moreover, the all-surface-covered optical sensor identified hotspot generation around the vicinity of the tabs, highlighting the essential role of tabs. The maximum temperature on the negative current tab reached 113.9 °C during a 1.5 C discharge, while the hottest spot on the cell surface was only 52.1 °C. This was further validated by the operando thermal image in both the time domain and the spatial domain, facilitating a detailed analysis of the thermal-behavior-like heat generation on the current tabs, transmission through the surface, and dissipation to the cell bottom. Full article

The problem is that existing aircraft detection datasets rarely simultaneously consider the diversity of target features and the complexity of environmental factors, which has become an important factor restricting the effectiveness and reliability of aircraft detection algorithms. Although a large amount of research has been devoted to breaking through few-sample-driven aircraft detection technology, most algorithms still struggle to effectively solve the problems of missed target detection and false alarms caused by numerous environmental interferences in bird-eye optical remote sensing scenes. To further aircraft detection research, we have established a new dataset, Aircraft Detection in Complex Optical Scene (ADCOS), sourced from various platforms including Google Earth, Microsoft Map, Worldview-3, Pleiades, Ikonos, Orbview-3, and Jilin-1 satellites. It integrates 3903 meticulously chosen images of over 400 famous airports worldwide, containing 33,831 annotated instances employing the oriented bounding box (OBB) format. Notably, this dataset encompasses a wide range of various targets characteristics including multi-scale, multi-direction, multi-type, multi-state, and dense arrangement, along with complex relationships between targets and backgrounds like cluttered backgrounds, low contrast, shadows, and occlusion interference conditions. Furthermore, we evaluated nine representative detection algorithms on the ADCOS dataset, establishing a performance benchmark for subsequent algorithm optimization. The latest dataset will soon be available on the Github website. Full article

Snow avalanches, one of the most severe natural hazards in mountainous regions, pose significant risks to human lives, infrastructure, and ecosystems. As climate change accelerates shifts in snowfall and temperature patterns, it is increasingly important to improve our ability to monitor and predict avalanches. This review explores the use of remote sensing technologies in understanding key geomorphological, geobotanical, and meteorological factors that contribute to avalanche formation. The primary objective is to assess how remote sensing can enhance avalanche risk assessment and monitoring systems. A systematic literature review was conducted, focusing on studies published between 2010 and 2025. The analysis involved screening relevant studies on remote sensing, avalanche dynamics, and data processing techniques. Key data sources included satellite platforms such as Sentinel-1, Sentinel-2, TerraSAR-X, and Landsat-8, combined with machine learning, data fusion, and change detection algorithms to process and interpret the data. The review found that remote sensing significantly improves avalanche monitoring by providing continuous, large-scale coverage of snowpack stability and terrain features. Optical and radar imagery enable the detection of crucial parameters like snow cover, slope, and vegetation that influence avalanche risks. However, challenges such as limitations in spatial and temporal resolution and real-time monitoring were identified. Emerging technologies, including microsatellites and hyperspectral imaging, offer potential solutions to these issues. The practical implications of these findings underscore the importance of integrating remote sensing data with ground-based observations for more robust avalanche forecasting. Enhanced real-time monitoring and data fusion techniques will improve disaster management, allowing for quicker response times and more effective policymaking to mitigate risks in avalanche-prone regions. Full article

Additive manufacturing (AM) has emerged as one of the core components of the fourth industrial revolution, Industry 4.0. Among others, the extrusion AM (EAM) of thermoplastic materials has been named as the most widely adopted technology. Fused filament fabrication (FFF) relies on the commercial availability of expensive filaments; hence, pellet extruder-based EAM techniques are desired. Large-format EAM systems would benefit from the ability to print lightweight objects with less materials and lower power consumption, which is possible with the use of hollow extrudates rather than solid extrudates to print objects. In this work, we designed a custom extruder head and developed an EAM system that allows the extrusion of inflatable hollow extrudates of a relatively wide material choice. By incorporating a co-axial nozzle–needle system, a thermoplastic shell was extruded while the hollow core was generated by using pressurized nitrogen gas. The ability to print using hollow extrudates with controllable inflation allows us to print objects with gradient part density with different degrees of mechanical properties. In this article, the effect of different process parameters, namely, extrusion temperature, extrusion speed, and gas pressure, were studied using poly-lactic acid (PLA) pellets. Initially, a set of preliminary tests was conducted to identify the maximum and minimum ranges of these parameters that result in consistent hollow extrudates. Finally, the parameters were varied to understand how they affect the core diameter and shell thickness of the hollow extrudates. These findings were supported by analyses of microscopic images taken under an optical microscope. Full article

In high-resolution remote sensing images, there are areas with weak textures such as large building roofs, which occupy a large number of pixels in the image. These areas pose a challenge for traditional semantic segmentation networks to obtain ideal results. Common strategies like downsampling, patch cropping, and cascade models often sacrifice fine details or global context, resulting in limited accuracy. To address these issues, a novel semantic segmentation framework has been designed specifically for large-format high-resolution remote sensing images by aggregating global and local features in this paper. The framework consists of two branches: one branch deals with low-resolution downsampled images to capture global features, while the other branch focuses on cropped patches to extract high-resolution local details. Also, this paper introduces a feature aggregation module based on the Transformer structure, which effectively aggregates global and local information. Additionally, to save GPU memory usage, a novel three-step training method has been developed. Extensive experiments on two public datasets demonstrate the effectiveness of the proposed approach, with an IoU of 90.83% on the AIDS dataset and 90.30% on the WBDS dataset, surpassing state-of-the-art methods such as DANet, DeepLab v3+, U-Net, ViT, TransUNet, CMTFNet, and UANet. Full article

Clean-burning oxygenated and synthetic fuels derived from renewable power, so-called e-fuels, are a promising pathway to decarbonize compression–ignition engines. Polyoxymethylene dimethyl ethers (PODEs or OMEs) are one candidate of such fuels with good prospects. Their lack of carbon-to-carbon bonds and high concentration of chemically bound oxygen effectively negate the emergence of polycyclic aromatic hydrocarbons (PAHs) and even their precursors like acetylene (C₂H₂), enabling soot-free combustion without the soot-NO_x trade-off common for diesel engines. The differences in the spray combustion process for OMEs and diesel-like reference fuels like n-dodecane and their potential implications on engine applications include discrepancies in the observed ignition delay, the stabilized flame lift-off location, and significant deviations in high-temperature flame morphology. For CFD simulations, the accurate modeling and prediction of these differences between OMEs and n-dodecane proved challenging. This study investigates the spray combustion process of an OME_{3 − 5} mixture and n-dodecane with advanced optical diagnostics, Reynolds-Averaged Navier–Stokes (RANS), and Large-Eddy Simulations (LESs) within a constant-volume vessel. Cool-flame and high-temperature combustion were measured simultaneously via high-speed (50 kHz) imaging with formaldehyde (CH₂O) planar laser-induced fluorescence (PLIF) representing the former and line-of-sight OH* chemiluminescence the latter. Both RANS and LES simulations accurately describe the cool-flame development process with the formation of CH₂O. However, CH₂O consumption and the onset of high-temperature reactions, signaled by the rise of OH* levels, show significant deviations between RANS, LES, and experiments as well as between n-dodecane and OME. A focus is set on the quality of the simulated results compared to the experimentally observed spatial distribution of OH*, especially in OME fuel-rich regions. The influence of the turbulence modeling is investigated for the two distinct ambient temperatures of 900 K and 1200 K within the Engine Combustion Network Spray A setup. The capabilities and limitations of the RANS simulations are demonstrated with the initial cool-flame propagation and periodic oscillations of CH₂O formation/consumption during the quasi-steady combustion period captured by the LES. Full article

The transport and dispersion (T&D) of catalyst particles seeded by weather modification aircraft is crucial for assessing their weather modification effects. This study investigates the capabilities of the Chinese geostationary weather satellite FY-4A for identifying the physical response of cloud seeding with AgI-based catalysts and continuously monitoring its evolution for a weather event that occurred on 15 December 2019 in Henan Province, China. Satellite measurements are also used to verify an operational catalyst T&D algorithm. The results show that FY-4A exhibits a remarkable capability of identifying the cloud-seeding tracks and continuously tracing their evolution for a period of over 3 h. About 60 min after the cloud seeding, the cloud crystallization track became clear in the FY-4A tri-channel composite cloud image and lasted for about 218 min. During this time period, the cloud track moved with the cloud system about 153 km downstream (northeast of the operation area). An operational catalyst T&D model was run to simulate the cloud track, and the outputs were extensively compared with the satellite observations. It was found that the forecast cloud track closely agreed with the satellite observations in terms of the track widths, morphology, and movement. Finally, the FY-4A measurements show that there were significant differences in the microphysical properties across the cloud track. The effective cloud radius inside the cloud track was up to 15 μm larger than that of the surrounding clouds; the cloud optical thickness was about 30 μm smaller; and the cloud-top heights inside the cloud track were up to 1 km lower. These features indicate that the cloud-seeding catalysts led to the development of ice-phase processes within the supercooled cloud, with the formation of large ice particles and some precipitation sedimentation. Full article

Photocatalysis is considered as simple, green, and the best strategy for elimination of hazardous organic contaminants from wastewater. Herein, new broad spectrum photocatalysts based on pure and Sm-doped CuO/ZnO/CuMn₂O₄ ternary composites were simply prepared by co-precipitation approach. The X-ray diffraction results proved the formation of a composite structure. The transmission electron microscope (TEM) images displayed that most particles have a spherical shape with average mean sizes within 26–29 nm. The optical properties of both samples signified that the addition of Sm ions significantly improves the harvesting of the visible light spectrum of CuO/ZnO/CuMn₂O₄ ternary composites. The photocatalytic study confirmed that 97% of norfloxacin and 96% of methyl green pollutants were photo-degraded in the presence of the Sm-doped CuO/ZnO/CuMn₂O₄ catalyst after 50 and 40 min, respectively. The total organic carbon analysis revealed the high mineralization efficiency of the Sm-doped CuO/ZnO/CuMn₂O₄ catalyst to convert the norfloxacin and methyl green to carbon dioxide and water molecules. During three cycles, this catalyst presented a high removal efficiency for norfloxacin and methyl green contaminants. As a dielectric energy storage material, the Sm-doped CuO/ZnO/CuMn₂O₄ ternary composite has large dielectric constant values, mainly at low frequencies, with low dielectric loss compared to a pure CuO/ZnO/CuMn₂O₄ composite. Full article

Background: Hidradenitis suppurativa (HS) is a relapsing–remitting inflammatory disease characterized by the progression of asymptomatic nodules to deep-seated lesions and fistula formation that leads to suppuration and scarring. Optical coherence tomography angiography (OCTA) is a new non-invasive imaging technique that carefully analyzes retinal microvasculature networks with high-resolution imaging. Recent studies have demonstrated that retinal vessel density and retinal perfusion reflect systemic inflammatory responses. This study’s aim was to analyze OCTA-derived retinal microvasculature parameters to understand if patients affected by HS and without any relevant ocular or systemic comorbidities showed impaired retinal vascular function and morphology. Method: We performed a case–control study of HS patients and age- and sex-matched control cohort. A total of 20 eyes from 10 HS patients and 30 eyes from 15 healthy controls were analyzed, and OCTA-derived microvasculature parameters were compared between groups. Results: OCTA images showed that HS patients, compared to healthy controls, were typically characterized by higher values of the foveal avascular zone (FAZ) both in the superficial capillary plexus (SCP) and in the deep capillary plexus (DCP), and by lower values of vessel density (VD)-SCP, VD-DCP, and vessel length density (VLD)-SCP in the foveal region. These findings partially reflect changes that have been demonstrated in diabetic patients that could be induced by a protracted metabolic or systemic inflammatory dysregulation. Conclusions: In conclusion, OCTA enables large-scale, non-invasive visual screening and follow-up of the retinal vasculature features, providing a new strategy for the prevention and monitoring of visual changes in HS patients. Full article

Cloud formations often obscure optical satellite-based monitoring of the Earth’s surface, thus limiting Earth observation (EO) activities such as land cover mapping, ocean color analysis, and cropland monitoring. The integration of machine learning (ML) methods within the remote sensing domain has significantly improved performance for a wide range of EO tasks, including cloud detection and filtering, but there is still much room for improvement. A key bottleneck is that ML methods typically depend on large amounts of annotated data for training, which are often difficult to come by in EO contexts. This is especially true when it comes to cloud optical thickness (COT) estimation. A reliable estimation of COT enables more fine-grained and application-dependent control compared to using pre-specified cloud categories, as is common practice. To alleviate the COT data scarcity problem, in this work, we propose a novel synthetic dataset for COT estimation, which we subsequently leverage for obtaining reliable and versatile cloud masks on real data. In our dataset, top-of-atmosphere radiances have been simulated for 12 of the spectral bands of the Multispectral Imagery (MSI) sensor onboard Sentinel-2 platforms. These data points have been simulated under consideration of different cloud types, COTs, and ground surface and atmospheric profiles. Extensive experimentation of training several ML models to predict COT from the measured reflectivity of the spectral bands demonstrates the usefulness of our proposed dataset. In particular, by thresholding COT estimates from our ML models, we show on two satellite image datasets (one that is publicly available, and one which we have collected and annotated) that reliable cloud masks can be obtained. The synthetic data, the newly collected real dataset, code and models have been made publicly available. Full article