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SF-25-076 pyAXEAP1-GUI package is an X-ray emission spectroscopy (XES) data analysis package in the Python language. It can transfer the collected images from a 2D detector into spectra and display XES and RXES data as 3Dimages, 2D images and 1D profiles.
The Arizona Imager/Spectrograph is a set of imaging spectrographs and 2D imagers for space flight. Nine nearly identical spectrographs record wavelengths from 114 to 1090 nm with a resolution of 0.5-1.3 nm. The spatial resolution along the slit is electronically selectable and can reach 192 elements. Twelve passband imagers cover wavelengths in the 160-900-nm range and have fields of view from 2 to 21 deg. The spectrographs and imagers rely on intensified CCD detectors to achieve substantial capability in an instrument of minimum mass and size. By use of innovative coupling techniques only two CCDs are required to record images from 12 imagers, and single CCDs record spectra from pairs of spectrographs. The fields of view of the spectrographs and imagers are coaligned, and all spectra and images can be exposed simultaneously. A scan platform can rotate the sensor head about two orthogonal axes. The Arizona imager/spectrograph is designed for investigations of the interaction between the Space Shuttle and its environment. It is scheduled for flight on a Shuttle subsatellite.
A three-dimensional (3D) x-ray luggage- screening system has been proposed to reduce the fatigue experienced by human inspectors and increase their ability to detect weapons and other contraband. The system and variants thereof could supplant thousands of xray scanners now in use at hundreds of airports in the United States and other countries. The device would be applicable to any security checkpoint application where current two-dimensional scanners are in use. A conventional x-ray luggage scanner generates a single two-dimensional (2D) image that conveys no depth information. Therefore, a human inspector must scrutinize the image in an effort to understand ambiguous-appearing objects as they pass by at high speed on a conveyor belt. Such a high level of concentration can induce fatigue, causing the inspector to reduce concentration and vigilance. In addition, because of the lack of depth information, contraband objects could be made more difficult to detect by positioning them near other objects so as to create x-ray images that confuse inspectors. The proposed system would make it unnecessary for a human inspector to interpret 2D images, which show objects at different depths as superimposed. Instead, the system would take advantage of the natural human ability to infer 3D information from stereographic or stereoscopic images. The inspector would be able to perceive two objects at different depths, in a more nearly natural manner, as distinct 3D objects lying at different depths. Hence, the inspector could recognize objects with greater accuracy and less effort. The major components of the proposed system would be similar to those of x-ray luggage scanners now in use. As in a conventional x-ray scanner, there would be an x-ray source. Unlike in a conventional scanner, there would be two x-ray image sensors, denoted the left and right sensors, located at positions along the conveyor that are upstream and downstream, respectively (see figure). X-ray illumination may be provided by a single source or by two sources. The position of the conveyor would be detected to provide a means of matching the appropriate left- and right-eye images of an item under inspection. The appropriate right- and left-eye images of an item would be displayed simultaneously to the right and left eyes, respectively, of the human inspector, using commercially available stereo display screens. The human operator could adjust viewing parameters for maximum viewing comfort. The stereographic images thus generated would differ from true stereoscopic images by small distortions that are characteristic of radiographic images in general, but these distortions would not diminish the value of the images for identifying distinct objects at different depths.
An automatic threat recognition system and method is disclosed for scanning the x-ray CT image of an article to identify the objects of interest (OOIs) contained within the article, which are otherwise not always quickly apparent or discernable to an individual. The system uses a computer to receive information from two-dimensional (2D) image slices from a reconstructed computed tomography (CT) scan image and to produce a plurality of voxels for each slice of the 2D image. The computer analyzes the voxels to create a likelihood map (LM) representing likelihoods that voxels making up the CT image are associated with a material of interest (MOI). The computer further analyzes the LM to construct neighborhoods of voxels within the LM, and classifies each voxel neighborhood based on its features, thereby decluttering the LM to facilitate the process of connecting voxels of a like MOI together to form segments. The computer classifies each candidate segment based on its features, thereby identifying those segments that correspond to objects of interest.
A desktop-scale, computer-controlled display system, initially developed for NASA and now known as the VolumeViewer(TradeMark), generates three-dimensional (3D) images of 3D objects in a display volume. This system differs fundamentally from stereoscopic and holographic display systems: The images generated by this system are truly 3D in that they can be viewed from almost any angle, without the aid of special eyeglasses. It is possible to walk around the system while gazing at its display volume to see a displayed object from a changing perspective, and multiple observers standing at different positions around the display can view the object simultaneously from their individual perspectives, as though the displayed object were a real 3D object. At the time of writing this article, only partial information on the design and principle of operation of the system was available. It is known that the system includes a high-speed, silicon-backplane, ferroelectric-liquid-crystal spatial light modulator (SLM), multiple high-power lasers for projecting images in multiple colors, a rotating helix that serves as a moving screen for displaying voxels [volume cells or volume elements, in analogy to pixels (picture cells or picture elements) in two-dimensional (2D) images], and a host computer. The rotating helix and its motor drive are the only moving parts. Under control by the host computer, a stream of 2D image patterns is generated on the SLM and projected through optics onto the surface of the rotating helix. The system utilizes a parallel pixel/voxel-addressing scheme: All the pixels of the 2D pattern on the SLM are addressed simultaneously by laser beams. This parallel addressing scheme overcomes the difficulty of achieving both high resolution and a high frame rate in a raster scanning or serial addressing scheme. It has been reported that the structure of the system is simple and easy to build, that the optical design and alignment are not difficult, and that the system can be built by use of commercial off-the-shelf products. A prototype of the system displays an image of 1,024 by 768 by 170 (=133,693,440) voxels. In future designs, the resolution could be increased. The maximum number of voxels that can be generated depends upon the spatial resolution of SLM and the speed of rotation of the helix. For example, one could use an available SLM that has 1,024 by 1,024 pixels. Incidentally, this SLM is capable of operation at a switching speed of 300,000 frames per second. Implementation of full-color displays in future versions of the system would be straightforward: One could use three SLMs for red, green, and blue, respectively, and the colors of the voxels could be automatically controlled. An optically simpler alternative would be to use a single red/green/ blue light projector and synchronize the projection of each color with the generation of patterns for that color on a single SLM.
Segmentation of high-resolution X-ray microcomputed tomography (µCT) images is crucial in digital rock physics (DRP), affecting the characterization and analysis of microscale phenomena in the porous media. The complexity of geological structures and nonideal scanning conditions pose significant challenges to conventional image segmentation approaches. Motivated by the recent increasing popularity of deep learning (DL) techniques in image processing, this work undertakes a comparative study of DL models, specifically U-Net and its variants, for segmenting multiple targets with distinguished features in digital rocks, including discrete fracture networks (DFNs), pore spaces, and solid rock. Particularly, DFNs have a smaller volumetric fraction over others, bringing in a substantial challenge of imbalanced segmentation. The primary focus is to evaluate the architecture and feature enhancement strategies of various DL models, including U-Net, attention U-Net, residual U-Net, U-Net++, and residual U-Net++. The models were designed as 2.5D, utilizing a central 2D image and its two adjacent upper and lower 2D images as input to provide a pseudo-3D context. In addition, because the ground truth of segmentation was unknown for real-world digital rocks, we created a benchmark data set following the inverse operations of segmentation. The data synthesis started from the label images (i.e., solid rock, pore spaces, and DFNs), followed by simulating partial volume blurring, adding random background noise, and introducing ring artifacts to mimic real raw X-ray µCT images. The data set, which included various rock types (i.e., sandstone and artificial data), scanning resolution, and magnitudes of noise and artifacts, was divided into training and testing data sets with a 90% and 10% ratio, respectively. Moreover, in addition to the conventional pixel-wise evaluation metrics, the physics-based metric of the lattice-Boltzmann method (LBM) simulated permeability provided more comprehensive assessments. The results demonstrated that the residual connections, nested architectures, and redesigned skip connections contribute to the model performance and give the residual U-Net++ the highest accuracy. The improvements were mainly on the boundaries and small targets, especially the DFNs, which dominate the interconnectivity and therefore affect the permeability greatly. This study also rigorously evaluated the efficiency and generalization of each model, demonstrating that the sophisticated architectures achieved excellent practicability and maintained robust performance on completely unseen data, ensuring their suitability for diverse and challenging DRP applications.
The 3D Virtual Astromaterials Samples (3DVAS) collection is a multi-year funded project to create a digital database of sixty Apollo Lunar and Antarctic Meteorite samples following non-destructive documentation conservation protocols. After initial image processing, the photos are evaluated and processed using unique structure-from-motion photogrammetric techniques in a high performance modelling software designed to create a 3D model from 2D images: Agisoft Photoscan Pro. Agisoft Photoscan Pro uses image processing algorithms and techniques originating in computer vision to resolve 3D models for accurate and detailed visualization of a subject. The software provides a stepwise process that is tailored per model based on spatial and specular reflectance properties, for example. The process includes: photo alignment, creation of a dense point cloud, mesh, and finally texture. Photo alignment is dependent on model properties. The 3DVAS process requires a special rotation platform with calibrated photogrammetric targets, specific distance rotation protocols, and a contrasting background for alignment and scale accuracy. As a result of the photographic process, alignment will complete with two mirrored hemispheres that, in a sense, represent the 2D images overlapping to create a 3D model. Each dense point cloud is analyzed with provided statistical measures in a gradual selection process to eliminate outliers. The point cloud is reduced to include only data valuable to the final model. When a precise dense point cloud is achieved, a mesh and texture are applied. Each model is scaled with scale bar accuracies within 100 microns. Each sample has its own intimate process for modelling; there is no standard for the parameters required in the final creation of a high resolution model. By processing multiple samples, a skill is gained in practice to allow a close definition of the original sample and will result in the most detailed version of the sample shell. This process completes one-fifth of the 3DVAS protocol for providing accurate digital documentation. Each model shell is merged with X-ray Computed Tomography data to create a full volumetric sample. All 3DVAS data will be served on NASA's Astromaterials Acquisition and Curation website with an early subset of data available in 2019 and the 3D Virtual Astromaterials Samples Collection launch in 2020.
Here, we report an algorithm to identify and correct distorted wavefronts in atomic resolution scanning tunneling microscope images. This algorithm can be used to correct nonlinear in-plane distortions without prior knowledge of the physical scanning parameters, the characteristics of the piezoelectric actuator, or individual atom positions. The 2D image is first defined as a sum of sinusoidal plane waves, where a nonlinear distortion renders a curve for an otherwise ideal linear wavefront. Using the Fourier transforms of local areas of the image, the algorithm generates a wavefront vector field. The identified wavefronts are subsequently linearized for each plane wave without changing lattice orders, giving rise to distortion corrections. Our algorithm is complementary to conventional post-processing algorithms that require prior detection of real space features, which can also be used to correct nonlinear distortions in 2D images acquired by other microscopy techniques.
Here, we report a new machine-learning-based approach to automatically measure the small angle between multiple planar surfaces characterized by white light interferometers. By applying an unsupervised clustering algorithm, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), the multiple surfaces in an interferometer image are automatically identified as distinct surfaces. The angles between every two surfaces are then calculated through the surface fitting. This method can be applied to multiple surfaces regardless of their shapes and locations and significantly simplifies the angle measurement procedure. Using the developed method, we have demonstrated a quick and precise angle measurement for the alignment of 2D Multilayer Laue Lenses (MLLs) for the development of high-resolution x-ray microscopy. This automatic, accurate, and robust small-angle measurement method is compatible with widely used white light interferometers and can be further applied to other metrology applications of interferometer results.
Angular velocity perception is examined for rotations both in depth and in the image plane and the influence of several object properties on this motion parameter is explored. Two major object properties are considered, namely, texture density which determines the rate of edge transitions for rotations in depth, i.e., the number of texture elements that pass an object's boundary per unit of time, and object size which determines the tangential linear velocities and 2D image velocities of texture elements for a given angular velocity. Results of experiments show that edge-transition rate biased angular velocity estimates only when edges were highly salient. Element velocities had an impact on perceived angular velocity; this bias was associated with 2D image velocity rather than 3D tangential velocity. Despite these biases judgements were most strongly determined by the true angular velocity. Sensitivity to this higher order motion parameter appeared to be good for rotations both in depth (y-axis) and parallel to the line of sight (z-axis).
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The effect of displacement uncertainty is examined on 2-dimensional strain, calculated using linear surfaces fitted to the displacement field. A classical engineering error propagation method is used to calculate uncertainty in Green-Lagrangian strain calculations. The derived uncertainty is compared to a Monte Carlo simulation and discrepancies under 2% are seen between these two methods. The effect of virtual strain gauge size, displacement uncertainty, and boundaries on the region of interest on the strain uncertainty are considered. Here, an exponential decay relationship is observed between strain uncertainty and virtual strain gauge size, while a linear relationship is seen between strain and displacement uncertainty. For boundaries in the region of interest, strain uncertainty is affected by the reduced number of points available to perform the regression.
We extract a suitably averaged fluctuating density from the two-dimensional radiographic image of a flow. The X-ray attenuation is given by the Beer–Lambert law which exponentially damps the incident beam intensity by a factor proportional to the density, opacity and thickness of the target. By making reasonable assumptions for the mean density, opacity and effective thickness of the target flow, we estimate the density fluctuation contribution to the attenuation. The extracted density fluctuations averaged across the thickness of the flow in the direction of the beam may be used to form the density–specific-volume correlation $b$. In a statistical description of variable-density turbulence, $b$ quantifies the degree of mixedness. The ability to extract a measure of mixedness from experimental data would be a powerful tool that could be used in the validation of mix models. The scheme proposed is tested for DNS data computed for variable density buoyancy-driven mixing. We quantify the deficits in the extracted value of $b$ due to target thickness, Atwood number and modeled signal noise. This analysis justifies using the proposed scheme to infer the mix parameter from thin targets at moderate to low Atwood numbers. Furthermore, to illustrate how the scheme might be used in a practical problem, we demonstrate its application to a radiographic image of counter-shear flow obtained from experiments at the National Ignition Facility.
The semiconductor-metal junction is one of the most critical factors for high performance electronic devices. In two-dimensional (2D) semiconductor devices, minimizing voltage drop at this junction is of particularly challenging and important. Despite numerous studies on contact resistance in 2D semiconductors, the exact nature of the buried interface under a 3D metal remains elusive and unclear. Herein, we report the direct observation of 2D semiconductor-metal buried interface using recently developed metal-assisted transfer technique to expose the buried interface which is then directly investigated using scanning probe techniques. We characterize the spatially varying electronic and optical properties of this buried interface with < 20 nm resolution. To be specific, potential, conductance and photoluminescence at the buried metal/MoS 2 interface are correlated under variety of metal deposition conditions as well as type of metal contacts. We observe that direct evaporation of Au on MoS 2 induces large strain of ~5% in the MoS 2 which coupled with charge transfer leads to degenerate doping of the MoS 2 underneath the contact. These factors lead to improvement of contact resistance to record values of 138 kΩ μm measured using local conductance probes. This approach was adopted to investigate MoS 2 -In/Au alloy interface, which records lowest contact resistance of 63 kΩ μm. Overall, our results highlight that the MoS 2 /Metal interface is very sensitive to method of contact making and provides a universal strategy to characterize buried contact interfaces involving 2D semiconductors.
High-resolution scanning transmission electron microscopy (HR-STEM) with spherical aberration correction enables researchers to peer into two-dimensional (2D) materials and correlate the material properties with those of single atoms. The maximum intensity of corrected electron beam is confined in the area having sub-angstrom size. Meanwhile, the residual threefold astigmatism of the electron probe implies a triangular shape distribution of the intensity, whereas its tails overlap and thus interact with several atomic species simultaneously. The result is the resonant modulation of contrast that interferes the determination of phase transition of 2D materials. Here, we theoretically reveal and experimentally determine the origin of resonant modulation of contrast and its unintended impact on violating the power-law dependence of contrast on coordination modes between transition metal and chalcogenide atoms. The finding illuminates the correlation between atomic contrast, spatially inequivalent chalcogenide orientation, and residual threefold astigmatism on determining the atomic structure of emerging 2D materials.
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Abstract Motivation BISCAP is a state-of-the-art tool for automatically characterizing biofilm images obtained from Optical Coherence Tomography. Limited availability of other software tools is reported in the field. BISCAP’s first version processes 2D images only. Processing 3D images is a problem of greater scientific relevance since it deals with the entire structure of biofilms instead of their 2D slices. Results Building on the image-processing principles and algorithms proposed earlier for 2D images, these were adapted to the 3D case, and a more general implementation of BISCAP was developed. The primary goal concerns the extension of the initial methodology to incorporate the depth axis in 3D images; multiple improvements were also made to boost computational performance. The calculation of structural properties and visual outputs was extended to offer new insights into the 3D structure of biofilms. BISCAP was tested using 3D images of biofilms with different morphologies, consistently delivering accurate characterizations of 3D structures in a few minutes using standard laptop machines. Low user dependency is required for image analysis. Availability and implementation BISCAP is available from https://github.com/diogonarciso/BISCAP. All images used in the tutorials and the validation examples are available from https://web.fe.up.pt/∼fgm/biscap3d.