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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.

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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Mapped Landmark Algorithm for Precision Landing

A report discusses a computer vision algorithm for position estimation to enable precision landing during planetary descent. The Descent Image Motion Estimation System for the Mars Exploration Rovers has been used as a starting point for creating code for precision, terrain-relative navigation during planetary landing. The algorithm is designed to be general because it handles images taken at different scales and resolutions relative to the map, and can produce mapped landmark matches for any planetary terrain of sufficient texture. These matches provide a measurement of horizontal position relative to a known landing site specified on the surface map. Multiple mapped landmarks generated per image allow for automatic detection and elimination of bad matches. Attitude and position can be generated from each image; this image-based attitude measurement can be used by the onboard navigation filter to improve the attitude estimate, which will improve the position estimates. The algorithm uses normalized correlation of grayscale images, producing precise, sub-pixel images. The algorithm has been broken into two sub-algorithms: (1) FFT Map Matching (see figure), which matches a single large template by correlation in the frequency domain, and (2) Mapped Landmark Refinement, which matches many small templates by correlation in the spatial domain. Each relies on feature selection, the homography transform, and 3D image correlation. The algorithm is implemented in C++ and is rated at Technology Readiness Level (TRL) 4.

Johnson, Andrew↗
DOE OSTI2025

FORECASTOR – II. Simulating galaxy surveys with the Cosmological Advanced Survey Telescope for Optical and UV Research

The Cosmological Advanced Survey Telescope for Optical and UV Research (CASTOR) is a planned flagship space telescope, covering the blue-optical and UV part of the spectrum. Here, we introduce the CASTOR image simulator, a python GalSim package-based script capable of generating mock CASTOR images from an input catalogue. We generate example images from the CASTOR Wide, Deep, and Ultra-Deep surveys using simulated lightcones from the Santa Cruz semi-analytic model. We make predictions for the performance of these surveys by comparing galaxies that are extracted from each image using Source Extractor to the input catalogue. We find that the Wide, Deep, and Ultra-Deep surveys will be 75 per cent complete for point sources down to $\sim 27$, 29, and 30 mag, respectively, in the UV, u, and g filters, with the UV-split and u-split filters reaching a shallower depth. With a large area of $\sim 2200$ deg$^2$, the Wide survey will detect hundreds of millions of galaxies out to $z\sim 4$, mostly with $M_\ast \gtrsim 10^{9}\,{\rm M}_{\odot }$. The Ultra-Deep survey will probe to $z\sim 5$, detecting galaxies with $M_\ast \gtrsim 10^{7}{\rm M}_{\odot }$. These galaxy samples will enable precision measurements of the distribution of star formation in the cosmic web, connecting the growth of stellar mass to the assembly of dark matter haloes over two thirds of the history of the Universe, and other core goals of CASTOR’s legacy surveys. These image simulations and the tools developed to generate them will be a vital planning tool to estimate CASTOR’s performance and iterate the telescope and survey designs prior to launch.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1989

Imaging spectrometry in the post-EOS era

It is argued that the next generation of imaging spectrometers will need greater capabilities and more flexibility than the High Resolution Imaging Spectrometer (HIRIS) and the Moderate Resolution Imaging Spectrometer (MODIS), which were designed for the first Earth Observation System platform. The emerging technology that represents a major advance in the scientific utility of imaging spectrometry is the acoustooptic tunable filters, (AOTFs). AOTFs are compact robust solid-state crystals which provide rapid (millisecond) electronically-controlled spectral tunability. This paper discusses the evolving requirements of imaging spectrometers and examines the basic system parameters of the HIRIS and the MODIS concepts involving the use of AOTFs, which will satisfy these requirements.

Duval, Valerie G.↗
NASA NTRS1973

Cartographic evaluation of ERTS orbit and attitude data

The author has identified the following significant results. Without the required RBV images, increased attention has been directed toward evaluating the geometric quality of MSS images. A line scan anomaly was identified and analyzed. Successive generations of images have been checked for variations in geometric distortion; it has been consistent. Some recent MSS images have about 250 m rms of relative positional accuracy although earlier images were generally over 300 m. Efforts are continuing to isolate systematic errors in MSS images but present results are inconclusive.

Mcewen, R. B.↗
DOE OSTI2023

Evaluating generative networks using Gaussian mixtures of image features

We develop a measure for evaluating the performance of generative networks given two sets of images. A popular performance measure currently used to do this is the Fréchet Inception Distance (FID). However, FID assumes that images featurized using the penultimate layer of Inception follow a Gaussian distribution. This assumption allows FID to be easily computed, since FID uses the 2-Wasserstein distance of two Gaussian distributions fitted to the featurized images. However, we show that Inception features of the ImageNet dataset are not Gaussian; in particular, each marginal is not Gaussian. To remedy this problem, we model the featurized images using Gaussian mixture models (GMMs) and compute the 2-Wasserstein distance restricted to GMMs. We define a performance measure, which we call WaM, on two sets of images by using inception (or another classifier) to featurize the images, estimate two GMMs, and use the restricted 2-Wasserstein distance to compare the GMMs. We experimentally show the advantages of WaM over FID, including how FID is more sensitive than WaM to image perturbations. By modelling the non-Gaussian features obtained from inception as GMMs and using a GMM metric, we can more accurately evaluate generative network performance.

machine learning, genrative adversarial networks↗
NASA NTRS2012

Next-Generation Microshutter Arrays for Large-Format Imaging and Spectroscopy

A next-generation microshutter array, LArge Microshutter Array (LAMA), was developed as a multi-object field selector. LAMA consists of small-scaled microshutter arrays that can be combined to form large-scale microshutter array mosaics. Microshutter actuation is accomplished via electrostatic attraction between the shutter and a counter electrode, and 2D addressing can be accomplished by applying an electrostatic potential between a row of shutters and a column, orthogonal to the row, of counter electrodes. Microelectromechanical system (MEMS) technology is used to fabricate the microshutter arrays. The main feature of the microshutter device is to use a set of standard surface micromachining processes for device fabrication. Electrostatic actuation is used to eliminate the need for macromechanical magnet actuating components. A simplified electrostatic actuation with no macro components (e.g. moving magnets) required for actuation and latching of the shutters will make the microshutter arrays robust and less prone to mechanical failure. Smaller-size individual arrays will help to increase the yield and thus reduce the cost and improve robustness of the fabrication process. Reducing the size of the individual shutter array to about one square inch and building the large-scale mosaics by tiling these smaller-size arrays would further help to reduce the cost of the device due to the higher yield of smaller devices. The LAMA development is based on prior experience acquired while developing microshutter arrays for the James Webb Space Telescope (JWST), but it will have different features. The LAMA modular design permits large-format mosaicking to cover a field of view at least 50 times larger than JWST MSA. The LAMA electrostatic, instead of magnetic, actuation enables operation cycles at least 100 times faster and a mass significantly smaller compared to JWST MSA. Also, standard surface micromachining technology will simplify the fabrication process, increasing yield and reducing cost.

Moseley, Samuel↗
DOE OSTI2021

Mimicking of corruption in images

Methods and systems are provided to generate an uncorrupted version of an image given an observed image that is a corrupted version of the image. In some embodiments, a corruption mimicking (“CM”) system iteratively trains a corruption mimicking network (“CMN”) to generate corrupted images given modeled images, updates latent vectors based on differences between the corrupted images and observed images, and applies a generator to the latent vectors to generate modeled images. The training, updating, and applying are performed until modeled images that are input to the CMN result in corrupted images that approximate the observed images. Because the CMN is trained to mimic the corruption of the observed images, the final modeled images represented the uncorrupted version of the observed images.

Anirudh, Rushil↗
DOE OSTI2021

Computational optical physical unclonable function

A system or method for encryption of data includes a light source, a random optical element and a light detection element. The light source is arranged to transmit an input data signal to the random optical element. The light source is incident on the random optical element such that the input data signal is randomly scattered by the random optical element to generate an image at on the detector disposed at an output of the random optical element. The image received by the detector is applied to a compressive sensing algorithm to generate a transfer function. The transfer function defines a relationship between the input data signal and the image to enable estimation and reconstruction of the input data signal.

Birch, Gabriel Carlisle↗
DOE OSTI2023

Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties

Here we introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-tuned properties. Denoising diffusion probabilistic models are generative models that use diffusion-based dynamics to gradually denoise images and generate realistic synthetic samples. By learning the reverse of a Markov diffusion process, we design an artificial intelligence to efficiently manipulate the topology of microstructures to generate a massive number of prototypes that exhibit constitutive responses sufficiently close to designated nonlinear constitutive behaviors. To identify the subset of microcstructures with sufficiently precise fine-tuned properties, a convolutional neural network surrogate is trained to replace high-fidelity finite element simulations to filter out prototypes outside the admissible range. Results of this study indicate that the denoising diffusion process is capable of creating microstructures of fine-tuned nonlinear material properties within the latent space of the training data. More importantly, this denoising diffusion algorithm can be easily extended to incorporate additional topological and geometric modifications by introducing high-dimensional structures embedded in the latent space. Numerical experiments are conducted on the open-source mechanical MNIST data set (Lejeune, 2020). Consequently, this algorithm is not only capable of performing inverse design of nonlinear effective media, but also learns the nonlinear structure–property map to quantitatively understand the multiscale interplay among the geometry, topology, and their effective macroscopic properties.

42 ENGINEERING↗
NASA NTRS1990

Helmet-Mounted Visual Display For Flight Simulation

Helmet-mounted visual display system provides pilot with broad range of visual information for flight simulation. Offers nearly unlimited field of regard. Optical fibers transmit wide-angle images in response to motions of head. Two "pancake" lenses mounted on lightweight helmet. Cable of optical fibers carries images to each lens. "Light-valve" projectors deliver computer-generated binocular images to cables.

Cook, Anthony M.↗
NASA NTRS1991

Randomization of dust-ejecta motions and the observed morphology of cometary heads

The computer code that has been used over years to study the dust-coma morphology of comets and to generate their synthetic images is upgraded by introducing two categories of random perturbations into the motions of dust particles ejected from discrete sources of activity on the nucleus surface. The new technique is applied to three types of morphological features: multiple concentric halos, a system of spiral jets, and a sunward fan-shaped coma. The results indicate that inclusion of random perturbations substantially enhances the capability for generating synthetic images that faithfully simulate the observed appearance of dust comets. By increasing the magnitude of perturbations in the synthetic images beyond a certain limit, it is possible to 'erase' morphological features characteristic of the ejection process. This implies that the lack of distinct morphology in the head of a comet cannot be interpreted as the absence of discrete sources of activity on the nucleus surface. The effects of varying random perturbations on the appearance of the computer-generated images are investigated in detail and the suspected major role that imperfect flow collimation (or flow divergence) in dust jets plays with regard to the observed coma morphology is discussed.

Sekanina, Zdenek↗
DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗
DOE OSTI2025

A case study in contrastive learning information combination: Application to technical forensics of additive manufacturing filament source identification

Combination of information from disparate data sources into a single decision is a core challenge in many fields, including the field of technical forensics. Technical forensics (TF) utilizes technical characterization of questioned samples to determine properties of that sample; these properties are then used to infer information of forensic interest, such as provenance, age, or attribution. TF is utilized in traditional forensic applications, such as the attribution of material fragments from an explosive, and in nuclear forensic applications, such as the attribution of actinides which have been interdicted out of regulatory control. The challenge of combining information from disparate sources, described alternately by many terms including “Data Fusion” and “Data Integration”, is exacerbated in the technical forensics domain due to at least two factors: the challenge of interpreting each information source singularly, and the relatively small data set sizes available. Extensive literature exists attempting to combine technical forensics information sources, both in manual and automated processes. These attempts are often bespoke to the specific information sources (such as the bi-, tri-, or quad-isotope chart (Moody, Grant, and Hutcheon 2005)), with some emerging examples of simple early- and late- fusion (, respectively). Simultaneous to the information combination efforts described in the previous paragraph, the field of natural language processing attempted (and largely succeeded) in combining information from multiple non-technical information sources. The ecosystem of “multi-modal” language models, which can take text and images as input, and generate text and images as output, became large and diverse by 2025 (Khan et al. 2025). In a generalized sense, many of these methods are trained by learning neural networks which can convert raw text or images into a vector of numbers describing the text or image, hereafter called “embeddings” and the neural networks performing the conversion are called “embedders”. By using a separate embedder for text and images, finding coincident text and images (such as images with their captions), and optimizing the parameters of the embedders such that the embeddings for the text and the image are similar, the field has found a bridge between text and images (Girdhar et al. 2023). It is the contention of the authors of this report that this insight is not limited to text and images but instead can be extended to any modality which can be found coincidently. The subject of the rest of this report is the application of this method to example multi-modal technical forensic data. Some details about the data used in this report are not appropriate for this report, and are included in a companion report (PNNL-38669).

36 MATERIALS SCIENCE↗
DOE OSTI2023

When Spectral Modeling Meets Convolutional Networks: A Method for Discovering Reionization-era Lensed Quasars in Multiband Imaging Data

Over the last two decades, around 300 quasars have been discovered at z ≳ 6, yet only one has been identified as being strongly gravitationally lensed. We explore a new approach—enlarging the permitted spectral parameter space, while introducing a new spatial geometry veto criterion—which is implemented via image-based deep learning. We first apply this approach to a systematic search for reionization-era lensed quasars, using data from the Dark Energy Survey, the Visible and Infrared Survey Telescope for Astronomy Hemisphere Survey, and the Wide-field Infrared Survey Explorer. Our search method consists of two main parts: (i) the preselection of the candidates, based on their spectral energy distributions (SEDs), using catalog-level photometry; and (ii) relative probability calculations of the candidates being a lens or some contaminant, utilizing a convolutional neural network (CNN) classification. The training data sets are constructed by painting deflected point-source lights over actual galaxy images, to generate realistic galaxy–quasar lens models, optimized to find systems with small image separations, i.e., Einstein radii of θ E ≤ 1''. Visual inspection is then performed for sources with CNN scores of P lens > 0.1, which leads us to obtain 36 newly selected lens candidates, which are awaiting spectroscopic confirmation. These findings show that automated SED modeling and deep learning pipelines, supported by modest human input, are a promising route for detecting strong lenses from large catalogs, which can overcome the veto limitations of primarily dropout-based SED selection approaches.

High-redshift galaxies↗
NASA NTRS1976

Recent developments at JPL in the application of digital image processing techniques to astronomical images

Several techniques of a digital image-processing nature are illustrated which have proved useful in visual analysis of astronomical pictorial data. Processed digital scans of photographic plates of Stephans Quintet and NGC 4151 are used as examples to show how faint nebulosity is enhanced by high-pass filtering, how foreground stars are suppressed by linear interpolation, and how relative color differences between two images recorded on plates with different spectral sensitivities can be revealed by generating ratio images. Analyses are outlined which are intended to compensate partially for the blurring effects of the atmosphere on images of Stephans Quintet and to obtain more detailed information about Saturn's ring structure from low- and high-resolution scans of the planet and its ring system. The employment of a correlation picture to determine the tilt angle of an average spectral line in a low-quality spectrum is demonstrated for a section of the spectrum of Uranus.

Lorre, J. J.↗
NASA NTRS1981

Structure of the St. Francois Mountains and surrounding lead belt, S. E. Missouri: Inferences from thermal IR and other data sets

Day-IR, day-visible, and night-IR image data sets were analyzed. All three images were contrast enhanced, using a linear stretch. The night-IR image was destriped using a box-car filtering approach. Apparent thermal inertia images were generated from the data. The enhanced data and the apparent thermal inertia image were also registered and overlayed onto shaded relief images depicting topography and onto a colored version of the Missouri geologic map. The combination of the apparent thermal inertial image and the shaded relief map proved to have the greatest discriminability in terms of portraying linear features. The reason seems to be that the vegetation canopy on the Ozark Plateau is different topography, tend to emphasize subtle topographic effects.

Arvidson, R. E.↗
NASA NTRS1989

Optical processing for distributed sensors in control of flexible spacecraft

A recent potential of distributed image processing is discussed. Applications in the control of flexible spacecraft are emphasized. Devices are currently being developed at NASA and in universities and industries that allow the real-time processing of holographic images. Within 5 years, it is expected that, in real-time, one may add or subtract holographic images at optical accuracy. Images are stored and processed in crystal mediums. The accuracy of their storage and processing is dictated by the grating level of laser holograms. It is far greater than that achievable using current analog-to-digital, pixel oriented, image digitizing and computing techniques. Processors using image processing algebra can conceptually be designed to mechanize Fourier transforms, least square lattice filters, and other complex control system operations. Thus, actuator command inputs derived from complex control laws involving distributed holographic images can be generated by such an image processor. Plans are revealed for the development of a Conjugate Optics Processor for control of a flexible object.

Montgomery, Raymond C.↗
NASA NTRS1994

Video Event Trigger

Video event trigger (VET) processes video image data to generate trigger signal when image shows significant change like motion or appearance, disappearance, change in color, change in brightness, or dilation of object. System aids in efficient utilization of image-data-storage and image-data-processing equipment in applications in which many video frames show no changes and are wasteful to record and analyze all frames when only relatively few frames show changes of interest. Applications include video recording of automobile crash tests, automated video monitoring of entrances, exits, parking lots, and secure areas.

Williams, Glenn L.↗