SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Technique rapidly diagnoses diabetes mellitus. Photographs of "whites" of patients' eyes scanned by computerized image analyzer programmed to quantify density of small blood vessels in conjuctiva. Comparison with data base of known normal and diabetic patients facilitates rapid diagnosis.
The rapid advancement of generative models has made the detection of AI-generated images a critical challenge for both research and society. Recent works have shown that most state-of-the-art fake image detection methods overfit to their training data and catastrophically fail when evaluated on curated hard test sets with strong distribution shifts. In this work, we argue that it is more principled to learn a tight decision boundary around the real image distribution and treat the fake category as a sink class. To this end, we propose SimLBR, a simple and efficient framework for fake image detection with Latent Blending Regularization (LBR). Our method significantly improves cross-generator generalization, achieving up to +24.85% accuracy and +69.62% recall on the challenging Chameleon benchmark. SimLBR is also highly efficient, training orders of magnitude faster than existing approaches. Furthermore, we emphasize the need for reliability-oriented evaluation in fake image detection, introducing risk-adjusted metrics and worst-case estimates to better assess model robustness. All the code and models are availabe at: https://github.com/mvrl/SimLBR
Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspects of materials structure from imaging data. While unsupervised methods for clustering and classification are widely used for these tasks, their performance can be sensitive to hyperparameter selection in the analysis workflow. In this study, the effects of descriptors and hyperparameters are explored on the capability of unsupervised ML methods to distill local structural information, exemplified by the discovery of polarization and lattice distortion in Sm − dopped BiFeO 3 (BFO) thin films. It is demonstrated that a reward-driven approach can be used to optimize these key hyperparameters across the full workflow, where rewards are designed to reflect domain wall continuity and straightness, ensuring that the analysis aligns with the material's physical behavior. This approach allows the discovery of local descriptors that are best aligned with the specific physical behavior, providing insight into the fundamental physics of materials. The reward driven workflow is further extended to disentangle structural factors of variation via an optimized variational autoencoder (VAE). Lastly, the importance of well-defined rewards is explored as a quantifiable measure of the success of the workflow.
Proposed charge-coupled device (CCD) imaging array of photodetectors has features of back-side-illuminated CCD and interline-transfer CCD. Photoelectrons generated near back surface and swept by electric fields toward front into collecting potential wells, not directly opposite that location. By suitable timing of control signals, charges from multiple image frames stored in rapid succession. Useful in imaging rapid transient phenomena as chemically reacting flows, with interframe intervals of microseconds.
During the 1975, 1976, and 1977, NOAA's National Environmental Satellite Service and NASA's Goddard Space Flight Center conducted a cooperative program to determine the optimum resolution and frequency of satellite images for deriving winds to study and forecast tropical cyclones. Rapid scan images were obtained at 7.5 minute interval from SMS-2 for hurricane Eloise and cyclone Caroline, and at 3 minute intervals from GOES-1 for tropical storms Belle, Holly, and Anita. Cloud motions were derived from these images using the Atmospheric and Oceanographic Information Processing System. Winds that were derived from the movement of upper and lower tropospheric level clouds using rapid scan data were compared with the 15 and 30 minute interval data. Greater than 10 (5) times as many clouds could be tracked to obtain winds using 3 and 7.5 minute rapid scan images as when using 15 or 30 minute interval images. A few bright areas within the central dense overcast which appeared to be moving with the winds at low levels were tracked.
The rapidly increasing use of electronics in high-radiation environments and the continued evolution in transistor architectures and materials demand improved methods to characterize the potential damaging effects of radiation on device performance. Here, electron-beam-induced current is used to map hot-carrier transport in model metal-oxide semiconductor field-effect transistors irradiated with a 300 KeV focused He + beam as a localized line spanning across the gate and bulk Si. By correlating the damage to the electronic properties and combining these results with simulations, the contribution of spatially localized radiation damage on the device characteristics is obtained. Furthermore, this identified damage, caused by the He + beam, is attributed to localized interfacial P b centers and delocalized positive fixed-charges, as surmised from simulations. Comprehension of the long-term interaction and mobility of radiation-induced damage are key for future design of rad-hard devices.
Advanced manufacturing (AM) processes such as laser powder bed fusion (LPBF) are increasingly capable of fabricating components with useful and unprecedented mechanical properties by incorporating complex internal bracing structures. From the standpoint of quality control and assessment, however, internally complex assemblies present significant build-verification challenges. Here we propose a hybrid approach to the inspection involving the application of computer-aided speckle interferometry (CASI) and morphological image processing as a rapid, inexpensive, and facile method for AM quality control. The described methodology has low capital equipment costs, is full-field and non-contact, can be used in an industrial setting, and has very low requirements in terms of operator training and expertise. Consisting primarily of the combination of image processing software with a simple optical system of variable sensitivity, the method is shown to be effective for inspection of a titanium honeycomb component subjected to differential pressure. Results are compared to those achieved with computed tomography (CT), immersion ultrasound testing (UT), and optical holographic interferometry. Here we propose several possible processing strategies for automated quality assessment based on this powerful hybrid approach.
Light-sheet fluorescence microscopy has greatly improved the speed and overall photostability of optically sectioning cellular and multi-cellular specimens. Similar gains have also been conferred by light-sheet Raman imaging; these schemes, however, rely on diffraction limited Gaussian beams that hinder the uniformity and size of the imaging field-of-view, and, as such, the resulting throughput rates. Here, we demonstrate that a digitally scanned Airy beam increases the Raman imaging throughput rates by more than an order of magnitude than conventional diffraction-limited beams. Overall, this, spectrometer-less, approach enabled 3D imaging of microparticles with high contrast and 1 µm axial resolution at 300 msec integration times per plane and orders of magnitude lower irradiation density than coherent Raman imaging schemes. We detail the apparatus and its performance, as well as its compatibility with fluorescence light-sheet and quantitative-phase imaging towards rapid and low phototoxicity multimodal imaging.
As the data volume of astronomical imaging surveys rapidly increases, traditional methods for image anomaly detection, such as visual inspection by human experts, are becoming impractical. We introduce a machine-learning-based approach to detect poor-quality exposures in large imaging surveys, with a focus on the DECam Legacy Survey (DECaLS) in regions of low extinction (i.e., E ( B − V ) < 0.04 ). Our semi-supervised pipeline integrates a vision transformer (ViT), trained via self-supervised learning (SSL), with a k-Nearest Neighbor (kNN) classifier. We train and validate our pipeline using a small set of labeled exposures observed by surveys with the Dark Energy Camera (DECam). A clustering-space analysis of where our pipeline places images labeled in good and bad categories suggests that our approach can efficiently and accurately determine the quality of exposures. Applied to new imaging being reduced for DECaLS Data Release 11, our pipeline identifies 780 problematic exposures, which we subsequently verify through visual inspection. Being highly efficient and adaptable, our method offers a scalable solution for quality control in other large imaging surveys.
Photorefractive image-processing apparatus generates images of rapidly moving objects and suppresses images of stationary and slowly moving objects. Resembles apparatus described in article, "Improved Interferometric Photorefractive Optical Processor", NPO-17773. Principle of operation similar, except system includes two video cameras and instead of transparencies, electro-optical spatial light modulators used to impress image information from video camera on laser beams.
Prototype special-purpose charge-coupled device (CCD) designed to detect two 1,024 x 1,024-pixel images in rapid succession. Readout performed slowly to minimize noise. CCD operated in synchronism with pulsed laser, stroboscope, or other pulsed source of light to form pairs of images of rapidly moving objects.
Abstract The proper biogenesis, morphogenesis, and dynamics of subcellular organelles are essential to their metabolic functions. Conventional techniques for identifying, classifying, and quantifying abnormalities in organelle morphology are largely manual and time-consuming, and require specific expertise. Deep learning has the potential to revolutionize image-based screens by greatly improving their scope, speed, and efficiency. Here, we used transfer learning and a convolutional neural network (CNN) to analyze over 47,000 confocal microscopy images from Arabidopsis wild-type and mutant plants with abnormal division of one of three essential energy organelles: chloroplasts, mitochondria, or peroxisomes. We have built a deep-learning framework, DeepLearnMOR (Deep Learning of the Morphology of Organelles), which can rapidly classify image categories and identify abnormalities in organelle morphology with over 97% accuracy. Feature visualization analysis identified important features used by the CNN to predict morphological abnormalities, and visual clues helped to better understand the decision-making process, thereby validating the reliability and interpretability of the neural network. This framework establishes a foundation for future larger-scale research with broader scopes and greater data set diversity and heterogeneity.
Pattern recognition invariant to image rotations of up to 75 deg, using a single filter, has been demonstrated for binary synthetic discriminant function (BSDF) optical filters, suggesting their use in directed graph-arranged data bases which can be rapidly traversed by means of a filter-plane programmable spatial light modulator (SLM). The filter data base is arranged as a tree structure in which the root node filters are invariant to over 60 deg rotation, while the leaves are 5-deg invariant. Results are presented from experiments with BSDFs designed to recognize in-plane-rotated views of a Space Shuttle Orbiter. Using a magnetooptic SLM that is driven at 350 Hz in the filter plane, orientation identifications requiring less than 30 msec have been achieved after sequencing through only 10 BSDFs.
K-means clustering analysis is applied to frequency-domain thermoreflectance (FDTR) hyperspectral image data to rapidly screen the spatial distribution of thermophysical properties at material interfaces. Performing FDTR while raster scanning a sample consisting of 8.6 μm of doped-silicon (Si) bonded to a doped-Si substrate identifies spatial variation in the subsurface bond quality. Routine thermal analysis at select pixels quantifies this variation in bond quality and allows assignment of bonded, partially bonded, and unbonded regions. Performing this same routine thermal analysis across the entire map, however, becomes too computationally demanding for rapid screening of bond quality. To address this, K-means clustering was used to reduce the dimensionality of the dataset from more than 20 000 pixel spectra to just K = 3 component spectra. The three component spectra were then used to express every pixel in the image through a least-squares minimized linear combination providing continuous interpolation between the components across spatially varying features, e.g., bonded to unbonded transition regions. Fitting the component spectra to the thermal model, thermal properties for each K cluster are extracted and then distributed according to the weighting established by the regressed linear combination. Thermophysical property maps are then constructed and capture significant variation in bond quality over 25 μm length scales. The use of K-means clustering to achieve these thermal property maps results in a 74-fold speed improvement over explicit fitting of every pixel.
Phase retrieval, the problem of recovering lost phase information from measured intensity alone, is an inverse problem that is widely faced in various imaging modalities ranging from astronomy to nanoscale imaging. The current process of phase recovery is iterative in nature. As a result, the image formation is time consuming and computationally expensive, precluding real-time imaging. Here, we use 3D nanoscale X-ray imaging as a representative example to develop a deep learning model to address this phase retrieval problem. We introduce 3D-CDI-NN, a deep convolutional neural network and differential programing framework trained to predict 3D structure and strain, solely from input 3D X-ray coherent scattering data. Our networks are designed to be “physics-aware” in multiple aspects; in that the physics of the X-ray scattering process is explicitly enforced in the training of the network, and the training data are drawn from atomistic simulations that are representative of the physics of the material. We further refine the neural network prediction through a physics-based optimization procedure to enable maximum accuracy at lowest computational cost. 3D-CDI-NN can invert a 3D coherent diffraction pattern to real-space structure and strain hundreds of times faster than traditional iterative phase retrieval methods. Our integrated machine learning and differential programing solution to the phase retrieval problem is broadly applicable across inverse problems in other application areas.
Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages ( Dozor and DIALS ), demonstrating that this is an appropriate framework to support future extensions and improvements.
Images processed rapidly in rugged, compact device. Conceptual integrated optical image processor performs complicated analog calculations, including Fourier transforms, convolutions, correlations, and matrix multiplications. Compact and rugged on account of monolithic structure, processor faster than equivalent all-electronic devices because calculations require only time for light to travel across device; processing speed limited by electronic input and output circuits.
A novel 2D microwave imaging system at 24 GHz based on MST techniques. Enhanced sensitivity and SNR by utilizing PIN diode-loaded resonant slots. Specific slot and array design to increase transmission and reduce cross -coupling. Real-time imaging at a rate in excess of 30 images per second. Reflection as well transmission mode capabilities. Utility and application for electric field distribution mapping related to: Nondestructive Testing (NDT), imaging applications (SAR, Holography), and antenna pattern measurements.