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NASA NTRS1999

Method and Apparatus for the Portable Identification of Material Thickness and Defects Using Spatially Controlled Heat Application

A method and a portable apparatus for the nondestructive identification of defects in structures. The apparatus comprises a heat source and a thermal imager that move at a constant speed past a test surface of a structure. The thermal imager is off set at a predetermined distance from the heat source. The heat source induces a constant surface temperature. The imager follows the heat source and produces a video image of the thermal characteristics of the test surface. Material defects produce deviations from the constant surface temperature that move at the inverse of the constant speed. Thermal noise produces deviations that move at random speed. Computer averaging of the digitized thermal image data with respect to the constant speed minimizes noise and improves the signal of valid defects. The motion of thermographic equipment coupled with the high signal to noise ratio render it suitable for portable, on site analysis.

Cramer, K. Elliott↗

NASA NTRS2004

Compact Optoelectronic Compass

A compact optoelectronic sensor unit measures the apparent motion of the Sun across the sky. The data acquired by this chip are processed in an external processor to estimate the relative orientation of the axis of rotation of the Earth. Hence, the combination of this chip and the external processor finds the direction of true North relative to the chip: in other words, the combination acts as a solar compass. If the compass is further combined with a clock, then the combination can be used to establish a threeaxis inertial coordinate system. If, in addition, an auxiliary sensor measures the local vertical direction, then the resulting system can determine the geographic position. This chip and the software used in the processor are based mostly on the same design and operation as those of the unit described in Micro Sun Sensor for Spacecraft (NPO-30867) elsewhere in this issue of NASA Tech Briefs. Like the unit described in that article, this unit includes a small multiple-pinhole camera comprising a micromachined mask containing a rectangular array of microscopic pinholes mounted a short distance in front of an image detector of the active-pixel sensor (APS) type (see figure). Further as in the other unit, the digitized output of the APS in this chip is processed to compute the centroids of the pinhole Sun images on the APS. Then the direction to the Sun, relative to the compass chip, is computed from the positions of the centroids (just like a sundial). In the operation of this chip, one is interested not only in the instantaneous direction to the Sun but also in the apparent path traced out by the direction to the Sun as a result of rotation of the Earth during an observation interval (during which the Sun sensor must remain stationary with respect to the Earth). The apparent path of the Sun across the sky is projected on a sphere. The axis of rotation of the Earth lies at the center of the projected circle on the sphere surface. Hence, true North (not magnetic North), relative to the chip, can be estimated from paths of the Sun images across the APS. In a test, this solar compass has been found to yield a coarse estimate of the North (within tens of degrees) in an observation time of about ten minutes. As expected, the accuracy was found to increase with observation time: after a few hours, the estimated direction of the rotation axis becomes accurate to within a small fraction of a degree.

Christian, Carl↗

NASA NTRS1996

Color Image Processing and Object Tracking System

This report describes a personal computer based system for automatic and semiautomatic tracking of objects on film or video tape, developed to meet the needs of the Microgravity Combustion and Fluids Science Research Programs at the NASA Lewis Research Center. The system consists of individual hardware components working under computer control to achieve a high degree of automation. The most important hardware components include 16-mm and 35-mm film transports, a high resolution digital camera mounted on a x-y-z micro-positioning stage, an S-VHS tapedeck, an Hi8 tapedeck, video laserdisk, and a framegrabber. All of the image input devices are remotely controlled by a computer. Software was developed to integrate the overall operation of the system including device frame incrementation, grabbing of image frames, image processing of the object's neighborhood, locating the position of the object being tracked, and storing the coordinates in a file. This process is performed repeatedly until the last frame is reached. Several different tracking methods are supported. To illustrate the process, two representative applications of the system are described. These applications represent typical uses of the system and include tracking the propagation of a flame front and tracking the movement of a liquid-gas interface with extremely poor visibility.

Klimek, Robert B.↗

DOE OSTI2022

Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network.

Neural operators have recently become popular tools for designing solution maps between function spaces in the form of neural networks. Differently from classical scientific machine learning approaches that learn parameters of a known partial differential equation (PDE) for a single instance of the input parameters at a fixed resolution, neural operators approximate the solution map of a family of PDEs [6, 7]. Despite their success, the uses of neural operators are so far restricted to relatively shallow neural networks and confined to learning hidden governing laws. In this work, we propose a novel nonlocal neural operator, which we refer to as nonlocal kernel network (NKN), that is resolution independent, characterized by deep neural networks, and capable of handling a variety of tasks such as learning governing equations and classifying images. Our NKN stems from the interpretation of the neural network as a discrete nonlocal diffusion reaction equation that, in the limit of infinite layers, is equivalent to a parabolic nonlocal equation, whose stability is analyzed via nonlocal vector calculus. The resemblance with integral forms of neural operators allows NKNs to capture long-range dependencies in the feature space, while the continuous treatment of node-to-node interactions makes NKNs resolution independent. The resemblance with neural ODEs, reinterpreted in a nonlocal sense, and the stable network dynamics between layers allow for generalization of NKN’s optimal parameters from shallow to deep networks. This fact enables the use of shallow-to-deep initialization techniques [8]. Our tests show that NKNs outperform baseline methods in both learning governing equations and image classification tasks and generalize well to different resolutions and depths.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2016

Exploration Clinical Decision Support System: Medical Data Architecture

The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear dimension biomarkers that enable image content-based retrieval and criteria assessment. In addition, a data mining engine (DME) is applied to cross-sectional adult surveys for predicting occurrence of renal calculi, ranked by statistical significance of demographics and specific food ingestion. In addition to this precursor space flight algorithm training, the DME will utilize a feature-engineering capability for unstructured clinical text classification health discovery. The ECDS backbone is a proposed multi-tier modular architecture providing data messaging protocols, storage, management and real-time patient data access. Technology demonstrations and success metrics will be finalized in FY16.

Biomedical support↗

DOE OSTI2024

Vision Transformers Explained Series

Since their introduction in 2017 with Attention is All You Need¹, transformers have established themselves as the state of the art for natural language processing (NLP). In 2021, An Image is Worth 16x16 Words successfully adapted transformers for computer vision tasks. Since then, numerous transformer-based architectures have been proposed for computer vision. This article walks through the Vision Transformer (ViT) as laid out in An Image is Worth 16x16 Words.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

AI-enabled high-resolution scanning coherent diffraction imaging

Ptychographic imaging is a powerful means of imaging beyond the resolution limits of typical x-ray optics. Capturing recovered images from raw ptychographic data, however, requires the solution of an inverse problem, namely, phase retrieval. Phase retrieval algorithms are computationally expensive, which precludes real-time imaging. In this work, we propose PtychoNN, an approach to solve the ptychography data inversion problem based on a deep convolutional neural network. We demonstrate how the proposed method can be used to predict real-space structure and phase at each scan point solely from the corresponding far-field diffraction data. Our results demonstrate the practical application of machine learning to recover high fidelity amplitude and phase contrast images of a real sample hundreds of times faster than current ptychography reconstruction packages. Furthermore, by overcoming the constraints of iterative model-based methods, we can significantly relax sampling constraints on data acquisition while still producing an excellent image of the sample. Besides drastically accelerating acquisition and analysis, this capability has profound implications for the imaging of dose sensitive, dynamic, and extremely voluminous samples.

47 OTHER INSTRUMENTATION↗

NASA NTRS2000

Exploiting the Temperature Dependence of Magnetic Susceptibility to Control Convection in Fundamental Studies of Solidification Phenomena

It is well known that convection is a dominant mass transport mechanism when materials are solidified on Earth's surface. This convection is caused by gradients in density (and therefore gravitational force) that are brought about by gradients in temperature, composition or both. Diffusion of solute is therefore dwarfed by convection and the study of fundamental parameters, such as dendrite tip shape and growth velocity in the absence of convection is nearly impossible. Significant experimental work has therefore been carried out in orbiting laboratories with the intent of minimizing convection by minimizing gravity. One of the best known experiments of this kind is the Isothermal Dendritic Growth Experiment (IDGE), supported by NASA. Naturally such experiments are costly and one objective of the present investigation is to develop an experimental method whereby convection can be- halted, in solidification and other experiments, on the surface. A second objective is to use the method to minimize convection resulting from the residual accelerations suffered by experiments in microgravity. The method to be used to minimize convection relies on the dependence of the magnetic susceptibility of a fluid on temperature or composition (whichever is driving convection). All materials experience a force when placed in a magnetic field gradient. The direction and magnitude of that force depend on the magnetic susceptibility of the material. Consequently the force will vary if the susceptibility varies with temperature or composition. With a magnetic field gradient in the right direction (typically upward) and of the right magnitude, this variation in the magnetic force can be made to exactly cancel the variation in the gravitational force. Expressed another way, normal buoyancy is exactly countered by a "magnetic buoyancy". To demonstrate the principle, a solution of MnC12 in water has been used. First the variation of the susceptibility of this paramagnetic solution with temperature and concentration was measured. Then a "cell", containing this solution and 50mm long by 15mm high by 155mm wide, was placed in a superconducting magnet at Marshall Space Flight Center. The magnetic field was measured at various positions within the bore of the magnet using a Hall effect probe. In this way, a position was found where the magnetic field gradient was predominantly upward; the magnitude of the gradient could then be adjusted by adjusting the current of the magnet. The ends of the cell consisted of machined copper blocks maintained at controlled temperatures by circulating water from constant temperature baths. The walls of the cell were of rectangular section glass tubing so that the cell contents could be seen. Velocities arising from thermal gradients within the cell were measured by particle image velocimetry (PIV). Particles used for this purpose were silver-coated hollow glass spheres of micrometers diameter and nearly the same density as the solution. A central vertical plane of the cell was illuminated by a laser beam passing through a cylindrical lens. Digital images of the particles were captured on a CCD camera and fed to a computer so that frame-to-frame movements of particles traveling with the fluid were captured. These images were employed to compute velocity maps using commercial PIV software. In a typical experiment the cold end of the cell was maintained at 10C and the warm end at 30 C. With no current in the magnet, i.e.- with natural -convection allowed to occur, the fluid was observed to circulate with an average speed of approximately 0.3 millimeters per second. It was visually apparent that this circulation was diminished as the current was increased. At currents of approximately 20A the flow was halted, to within the precision of the PIV measurements. At yet higher currents the convection was reversed with the hotter solution sinking and the cooler solution rising. At 40A this reversed convection had speeds averaging 0.43 millimeters per second. The measurements of susceptibility and density allow an estimate of the field gradient necessary to halt convection in the experiment. That estimate was 7.8T (squared) per meter and the convection was observed to halt in the magnet at a current giving 7.21T (squared) per meter from the magnetic field measurements. Calculations of the flow have been carried out using the computational fluid dynamics software FLUENT and show good agreement with the measurements.

Seybert, C. D.↗

DOE OSTI2023

PhytoOracle: Scalable, modular phenomics data processing pipelines

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to ( i ) improve data processing efficiency; ( ii ) provide an extensible, reproducible computing framework; and ( iii ) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1980

Effects of magnification and visual accommodation on aimpoint estimation in simulated landings with real and virtual image displays

Twenty professional pilots observed a computer-generated airport scene during simulated autopilot-coupled night landing approaches and at two points (20 sec and 10 sec before touchdown) judged whether the airplane would undershoot or overshoot the aimpoint. Visual accommodation was continuously measured using an automatic infrared optometer. Experimental variables included approach slope angle, display magnification, visual focus demand (using ophthalmic lenses), and presentation of the display as either a real (direct view) or a virtual (collimated) image. Aimpoint judgments shifted predictably with actual approach slope and display magnification. Both pilot judgments and measured accommodation interacted with focus demand with real-image displays but not with virtual-image displays. With either type of display, measured accommodation lagged far behind focus demand and was reliably less responsive to the virtual images. Pilot judgments shifted dramatically from an overwhelming perceived-overshoot bias 20 sec before touchdown to a reliable undershoot bias 10 sec later.

Randle, R. J.↗

NASA NTRS2006

Compressing Image Data While Limiting the Effects of Data Losses

ICER is computer software that can perform both lossless and lossy compression and decompression of gray-scale-image data using discrete wavelet transforms. Designed for primary use in transmitting scientific image data from distant spacecraft to Earth, ICER incorporates an error-containment scheme that limits the adverse effects of loss of data and is well suited to the data packets transmitted by deep-space probes. The error-containment scheme includes utilization of the algorithm described in "Partitioning a Gridded Rectangle Into Smaller Rectangles " (NPO-30479), NASA Tech Briefs, Vol. 28, No. 7 (July 2004), page 56. ICER has performed well in onboard compression of thousands of images transmitted from the Mars Exploration Rovers.

Kiely, Aaron↗

NASA NTRS2008

Vision System Measures Motions of Robot and External Objects

A prototype of an advanced robotic vision system both (1) measures its own motion with respect to a stationary background and (2) detects other moving objects and estimates their motions, all by use of visual cues. Like some prior robotic and other optoelectronic vision systems, this system is based partly on concepts of optical flow and visual odometry. Whereas prior optoelectronic visual-odometry systems have been limited to frame rates of no more than 1 Hz, a visual-odometry subsystem that is part of this system operates at a frame rate of 60 to 200 Hz, given optical-flow estimates. The overall system operates at an effective frame rate of 12 Hz. Moreover, unlike prior machine-vision systems for detecting motions of external objects, this system need not remain stationary: it can detect such motions while it is moving (even vibrating). The system includes a stereoscopic pair of cameras mounted on a moving robot. The outputs of the cameras are digitized, then processed to extract positions and velocities. The initial image-data-processing functions of this system are the same as those of some prior systems: Stereoscopy is used to compute three-dimensional (3D) positions for all pixels in the camera images. For each pixel of each image, optical flow between successive image frames is used to compute the two-dimensional (2D) apparent relative translational motion of the point transverse to the line of sight of the camera. The challenge in designing this system was to provide for utilization of the 3D information from stereoscopy in conjunction with the 2D information from optical flow to distinguish between motion of the camera pair and motions of external objects, compute the motion of the camera pair in all six degrees of translational and rotational freedom, and robustly estimate the motions of external objects, all in real time. To meet this challenge, the system is designed to perform the following image-data-processing functions: The visual-odometry subsystem (the subsystem that estimates the motion of the camera pair relative to the stationary background) utilizes the 3D information from stereoscopy and the 2D information from optical flow. It computes the relationship between the 3D and 2D motions and uses a least-mean-squares technique to estimate motion parameters. The least-mean-squares technique is suitable for real-time implementation when the number of external-moving-object pixels is smaller than the number of stationary-background pixels.

Talukder, Ashit↗

DOE OSTI2021

Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering

Scanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively thick specimen than the conventional transmission electron microscopy, whose resolution is limited by the chromatic aberration of image forming lenses, and thus, the STEM mode has been employed frequently for computed electron tomography based three-dimensional (3D) structural characterization and combined with analytical methods such as annular dark field imaging or spectroscopies. However, the image quality of STEM is severely suffered by noise or artifacts especially when rapid imaging, in the order of millisecond per frame or faster, is pursued. Here we demonstrate a deep-learning-assisted rapid STEM tomography, which visualizes 3D dislocation arrangement only within five-second acquisition of all the tilt-series images even in a 300 nm thick steel specimen. The developed method offers a new platform for various in situ or operando 3D microanalyses in which dealing with relatively thick specimens or covering media like liquid cells are required.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Assessment of Mechanical Draft Cooling Tower Thermal Emissions from Visual Images of Plumes

Using a one-dimensional code, we computed the power (enthalpy discharge rate) of a twelve-cell mechanical draft cooling tower (MDCT) using over two hundred visible condensed water vapor plume volume measurements derived from images, weather data, and tower operating conditions. The plume images were simultaneously captured by multiple stationary digital cameras surrounding the cooling tower. An analysis technique combining structure from motion (SfM), a neural-network-based image segmentation algorithm, and space carving was used to quantify the volumes. Afterwards, the power output was computed using novel techniques in the one-dimensional code that included cooling tower exhaust plume adjacency effects implemented with a modified version of the entrainment function, weather data averaged from eleven stations, and fan operations at the times when plume volumes were measured. The model was then compared with the averaged observed power output, and it validated well with an average error ranging from 6 to 12%, depending on the meteorological data used in the simulations. This methodology can possibly determine power plant fuel consumption rates by applying visible imagery.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Preface to the Special Issue on Signal Processing, Data Acquisition and Front-end Systems for Radiation Detection and Measurements

This special issue of the Journal of Signal Processing Systems focuses on advancement of signal processing and data-acquisition for radiation detection and measurements. The seven articles included cover diverse topics within the scope of radiation detection ranging from understanding of fundamental fission process, instrumentation, and application of radiation interaction (e.g., tomography and imaging). The articles focus on signal processing for fission spectrometer, neutron and gamma-ray computed tomography and artificial intelligence methods, dynamic neutron imaging system, pulse-pileup correction in x-ray spectroscopy and calibration methods in radiation measurement.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

NASA NTRS2000

Diffractive Optical Elements for Spectral Imaging

Diffractive optical elements fabricated on flat and non-flat substrates frequently act as dispersive elements in imaging spectrometers. We describe the design and electron-beam fabrication of blazed and computer-generated-hologram gratings for slit and tomographic imaging spectrometers.

diffractive optics gratings spectral imaging imagi↗

NASA NTRS2000

Diffractive Optical Elements for Spectral Imaging

diffractive optics gratings spectral imaging imagi↗

DOE OSTI2022

SDRD Image Data Delivery 1/4

This is a collection of radiographic image data collected at the SEALab test bed at Los Alamos operations.

97 MATHEMATICS AND COMPUTING↗