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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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NASA NTRS1985

The great flare of 1982 June 6

The great soft X-ray (SXR) flare (X12) of the past solar maximum was observed by Hinotori and by Big Bear Solar Observatory (BBSO) on June 6, 1982. Hinotori data consist of hard X-ray (HXR) and SXR images in the rise and decay of the flare, high-resolution soft X-ray spectra throughout the flare, and HXR and gamma-ray data. The BBSO data include films of H-alpha, H-alpha blue wing, D3 and longitudinal magnetic field, as well as video tapes of continuum. Images in HXR, SXR, H-alpha, D3 and the continuum are compared and SXR spectra analyzed. The flare resulted from extended motion of a large spot shearing the magnetic field. D3 and white-light images exhibit a progression from fast flashes to two ribbons, while both HXR and SXR are centered on the optical kernels. The continuum emission shows the same temporal behavior as the HXR at 160 keV. In its early phases, the Fe XXV line was double-peaked, and a decreasing blueshifted (up to 400 km/sec) component was observed, from which the evaporation rate of chromospheric material was estimated. It is suggested that this upflow is adequate to supply the coronal cloud. Flare energetics are discussed in detail, and it is concluded that a significant amount of energy was deposited in the corona, and that nonthermal electrons are the major energy input.

Tanaka, K.↗

NASA NTRS1992

Reduction of blocking effects for the JPEG baseline image compression standard

Transform coding has been chosen for still image compression in the Joint Photographic Experts Group (JPEG) standard. Although transform coding performs superior to many other image compression methods and has fast algorithms for implementation, it is limited by a blocking effect at low bit rates. The blocking effect is inherent in all nonoverlapping transforms. This paper presents a technique for reducing blocking while remaining compatible with the JPEG standard. Simulations show that the system results in subjective performance improvements, sacrificing only a marginal increase in bit rate.

Zweigle, Gregary C.↗

NASA NTRS2001

Radarsat Antarctic Mapping Project: Antarctic Imaging Campaign 2

The Radarsat Antarctic Mapping Project is a collaboration between NASA and the Canadian Space Agency to map Antarctica using synthetic aperture radar (SAR). The first Antarctic Mapping Mission (AMM-1) was successfully completed in October 1997. Data from the acquisition phase of the 1997 campaign have been used to achieve the primary goal of producing the first, high-resolution SAR image map of Antarctica. The limited amount of data suitable for interferometric analysis have also been used to produce remarkably detailed maps of surface velocity for a few selected regions. Most importantly, the results from AMM-1 are now available to the general science community in the form of various resolution, radiometrically calibrated and geometrically accurate image mosaics. The second Antarctic imaging campaign occurred during the fall of 2000. Modified from AMM-1, the satellite remained in north looking mode during AMM-2 restricting coverage to regions north of about -80 degrees latitude. But AMM-2 utilized for the first time RADARSAT-1 fine beams providing an unprecedented opportunity to image many of Antarctica's fast glaciers whose extent was revealed through AMM-1 data. AMM-2 also captured extensive data suitable for interferometric analysis of the surface velocity field. This report summarizes the science goals, mission objectives, and project status through the acquisition phase and the start of the processing phase. The reports describes the efforts of team members including Alaska SAR Facility, Jet Propulsion Laboratory, Vexcel Corporation, Goddard Space Flight Center, Wallops Flight Facility, Ohio State University, Environmental Research Institute of Michigan, White Sands Facility, Canadian Space Agency Mission Planning and Operations Groups, and the Antarctic Mapping Planning Group.

Source record↗

NASA NTRS2009

Low-Complexity Lossless and Near-Lossless Data Compression Technique for Multispectral Imagery

This work extends the lossless data compression technique described in Fast Lossless Compression of Multispectral- Image Data, (NPO-42517) NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26. The original technique was extended to include a near-lossless compression option, allowing substantially smaller compressed file sizes when a small amount of distortion can be tolerated. Near-lossless compression is obtained by including a quantization step prior to encoding of prediction residuals. The original technique uses lossless predictive compression and is designed for use on multispectral imagery. A lossless predictive data compression algorithm compresses a digitized signal one sample at a time as follows: First, a sample value is predicted from previously encoded samples. The difference between the actual sample value and the prediction is called the prediction residual. The prediction residual is encoded into the compressed file. The decompressor can form the same predicted sample and can decode the prediction residual from the compressed file, and so can reconstruct the original sample. A lossless predictive compression algorithm can generally be converted to a near-lossless compression algorithm by quantizing the prediction residuals prior to encoding them. In this case, since the reconstructed sample values will not be identical to the original sample values, the encoder must determine the values that will be reconstructed and use these values for predicting later sample values. The technique described here uses this method, starting with the original technique, to allow near-lossless compression. The extension to allow near-lossless compression adds the ability to achieve much more compression when small amounts of distortion are tolerable, while retaining the low complexity and good overall compression effectiveness of the original algorithm.

Xie, Hua↗

NASA NTRS2004

Compact Microscope Imaging System with Intelligent Controls

The figure presents selected views of a compact microscope imaging system (CMIS) that includes a miniature video microscope, a Cartesian robot (a computer- controlled three-dimensional translation stage), and machine-vision and control subsystems. The CMIS was built from commercial off-the-shelf instrumentation, computer hardware and software, and custom machine-vision software. The machine-vision and control subsystems include adaptive neural networks that afford a measure of artificial intelligence. The CMIS can perform several automated tasks with accuracy and repeatability . tasks that, heretofore, have required the full attention of human technicians using relatively bulky conventional microscopes. In addition, the automation and control capabilities of the system inherently include a capability for remote control. Unlike human technicians, the CMIS is not at risk of becoming fatigued or distracted: theoretically, it can perform continuously at the level of the best human technicians. In its capabilities for remote control and for relieving human technicians of tedious routine tasks, the CMIS is expected to be especially useful in biomedical research, materials science, inspection of parts on industrial production lines, and space science. The CMIS can automatically focus on and scan a microscope sample, find areas of interest, record the resulting images, and analyze images from multiple samples simultaneously. Automatic focusing is an iterative process: The translation stage is used to move the microscope along its optical axis in a succession of coarse, medium, and fine steps. A fast Fourier transform (FFT) of the image is computed at each step, and the FFT is analyzed for its spatial-frequency content. The microscope position that results in the greatest dispersal of FFT content toward high spatial frequencies (indicating that the image shows the greatest amount of detail) is deemed to be the focal position.

McDowell, Mark↗

DOE OSTI2022

Summary: SEE4GEO

The seismoelectric effects technique (SEE) is a new and innovative approach for geothermal subsurface imaging and monitoring at reservoir scale. The objective of this project is to assess SEE in terms of data acquisition, cost and quality, and to determine its capability in comparison with classical imaging and monitoring techniques, particularly decoupled seismic and electromagnetic methods. This will be achieved by (1) development of a fast, true 3D numerical package, handling SEE imaging and subsurface properties characterization, including resistivity and permeability, (2) laboratory experiments performed in a controlled environment to define optimal deployment design, data quality, and inform field deployment, and (3) field surveys to ultimately test and draw lessons for practical use of SEE technology. There is a relatively extensive body of work in the literature on SEE, and members of this consortium have been involved in theoretical and numerical development of SEE modeling as well as laboratory experiments. Nevertheless, to our knowledge few, if any, documented efforts have been specifically targeting the use of SEE for geothermal subsurface imaging and monitoring. The strength and originality of our proposal rely on an integrated approach leveraging numerical, laboratory and field experiments, to properly document the practical use of SEE. Through this process, SEE in-hand technology for the geothermal industry will be able to progress from a TRL 1 to TRL 3.

58 GEOSCIENCES↗

DOE OSTI2022

Seismoelectric Effects for Geothermal Resources Assessment and Monitoring (SEE4GEO)

58 GEOSCIENCES↗

DOE OSTI2020

Extracting Optical Bandgaps from Luminescence Images of Perovskite Solar Cells

We report a camera-based method to capture optical bandgap images of perovskite solar cells (PSCs). It is fast and non-destructive. It can also provide micron-scale spatial resolution. This imaging technique utilizes well-defined and relatively symmetrical band-to-band luminescence spectra emitted from perovskite materials. Their spectra can be fitted with Gaussian functions whose peak locations are similar to absorption thresholds and thus represent the material optical bandgaps. We apply the technique to resolve relative variations in optical bandgaps across various PSCs, and to show optical bandgap inhomogeneity within the same device due to material degradation and impurities. Degradation and impurities were found to both cause optical bandgap shifts inside the materials. Our results are independently confirmed with photoluminescence and absorption spectroscopy.

14 SOLAR ENERGY↗

NASA NTRS2003

Use of Multi-Resolution Wavelet Feature Pyramids for Automatic Registration of Multi-Sensor Imagery

The problem of image registration, or alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times, and that would provide sub-pixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the band-pass wavelets obtained from the Steerable Pyramid due to Simoncelli perform better than two types of low-pass pyramids when the images being registered have relatively small amount of nonlinear radiometric variations between them. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.

Zavorin, Ilya↗

NASA NTRS2003

Distal airways in humans: dynamic hyperpolarized 3He MR imaging--feasibility

Dynamic hyperpolarized helium 3 (3He) magnetic resonance (MR) imaging of the human airways is achieved by using a fast gradient-echo pulse sequence during inhalation. The resulting dynamic images show differential contrast enhancement of both distal airways and the lung periphery, unlike static hyperpolarized 3He MR images on which only the lung periphery is seen. With this technique, up to seventh-generation airway branching can be visualized. Copyright RSNA, 2003.

NASA Discipline Life Sciences Technologies↗

NASA NTRS2005

Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery

The problem of image registration, or the alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast, and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times and that would provide subpixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the bandpass wavelets obtained from the steerable pyramid due to Simoncelli performs best in terms of accuracy and consistency, while the low-pass wavelets obtained from the same pyramid give the best results in terms of the radius of convergence. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.

Evaluation Studies↗

NASA NTRS1987

The Penn State astronomical image processing system

The needs of modern astronomy for image processing set demanding standards in simultaneously requiring fast computation speed, high-quality graphic display, large data storage, and interactive response. An innovative image processing system was designed, integrated, and used; it is based on a supermicro architecture which is tailored specifically for astronomy, which provides a highly cost-effective alternative to the traditional minicomputer installation. The paper describes the design rationale, equipment selection, and software developed to allow other astronomers with similar needs to benefit from the present experience.

Truax, Ryland J.↗

NASA NTRS1991

The Mees CCD imaging spectrograph

The Mees CCD (MCCD0 instrument is an imaging spectroscopy device which uses the 25-cm coronagraph telescope and the 3.0-m Coude spectrograph at Mees Solar Observatory on Haleakala, Maui. The instrument works with resolving power up to about 200,000 with significant throughput from 3934 A (Ca II K) to about 10,000 A. A fast guiding active mirror stabilizes the image during observations. A rapidly writing magnetic tape storage system allows observations to be recorded at 256 kbytes/s. Currently, the MCCD is used for imaging spectroscopy of solar flares at 6563 A (H-alpha), and velocity measurements of umbral oscillations; future plans include emission line studies of active region coronae, and photospheric studies of solar oscillations.

Penn, Matthew J.↗

NASA NTRS2012

Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System

Modern hyperspectral imaging systems are able to acquire far more data than can be downlinked from a spacecraft. Onboard data compression helps to alleviate this problem, but requires a system capable of power efficiency and high throughput. Software solutions have limited throughput performance and are power-hungry. Dedicated hardware solutions can provide both high throughput and power efficiency, while taking the load off of the main processor. Thus a hardware compression system was developed. The implementation uses a field-programmable gate array (FPGA). The implementation is based on the fast lossless (FL) compression algorithm reported in Fast Lossless Compression of Multispectral-Image Data (NPO-42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26, which achieves excellent compression performance and has low complexity. This algorithm performs predictive compression using an adaptive filtering method, and uses adaptive Golomb coding. The implementation also packetizes the coded data. The FL algorithm is well suited for implementation in hardware. In the FPGA implementation, one sample is compressed every clock cycle, which makes for a fast and practical realtime solution for space applications. Benefits of this implementation are: 1) The underlying algorithm achieves a combination of low complexity and compression effectiveness that exceeds that of techniques currently in use. 2) The algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. 3) Hardware acceleration provides a throughput improvement of 10 to 100 times vs. the software implementation. A prototype of the compressor is available in software, but it runs at a speed that does not meet spacecraft requirements. The hardware implementation targets the Xilinx Virtex IV FPGAs, and makes the use of this compressor practical for Earth satellites as well as beyond-Earth missions with hyperspectral instruments.

Aranki, Nazeeh I.↗

DOE OSTI2021

Tutorial: Machine Learning and Artificial Intelligence in Batteries

Machine learning (ML) promises to compress the time needed to characterize battery performance, lifetime and safety. By coupling ML with physical models and metrics, that learning can bridge across materials, chemistries and cell designs. This tutorial will discuss the most popular ML techniques and resources and review recent work in the electrochemical literature. Applications include materials discovery, image recognition for quantitative microscopy analysis, fast charge algorithm development and life prediction.

47 OTHER INSTRUMENTATION↗

NASA NTRS1991

Comparison of different fractal dimension measuring algorithms for RE-TM M-O films

Noise in magneto-optical recording devices is discussed. In general, it appears that either the divider technique or amplitude spectrum technique may be used interchangeably to measure the fractal dimension (D) in the domain wall structure of ideal images. However, some caveats must be observed for best results. The divider technique is attractive for its simplicity and relatively modest computation requirements. However, it is sensitive to noise, in that noise pixels that touch the domain boundary are interpreted as being part of the boundary, skewing the measurement. Also, it is not useful in measuring nucleation-dominated films or domains that have significant amounts of structure within the interior of the domain wall. The amplitude spectrum method is more complex, and less intuitive than the divider method, and somewhat more expensive to implement computationally. However, since the camera noise tends to be white, the noise can be avoided in the measurement of D by avoiding that portion of the curve that is flat (due to the white noise) when the least squares line is fit to the plot. Also, many image processing software packages include a Fast Fourier Transformation (FFT) facility, while the user will most likely have to write his own edge extraction routine for the divider method. The amplitude spectrum method is a true two dimensional technique that probes the interior of the domain wall, and in fact, can measure arbitrary clusters of domains. It can also be used to measure grey-level images, further reducing processing steps needed to threshold the image.

Bernacki, Bruce E.↗

NASA NTRS2010

Spotlight-Mode Synthetic Aperture Radar Processing for High-Resolution Lunar Mapping

During the 2008-2009 year, the Goldstone Solar System Radar was upgraded to support radar mapping of the lunar poles at 4 m resolution. The finer resolution of the new system and the accompanying migration through resolution cells called for spotlight, rather than delay-Doppler, imaging techniques. A new pre-processing system supports fast-time Doppler removal and motion compensation to a point. Two spotlight imaging techniques which compensate for phase errors due to i) out of focus-plane motion of the radar and ii) local topography, have been implemented and tested. One is based on the polar format algorithm followed by a unique autofocus technique, the other is a full bistatic time-domain backprojection technique. The processing system yields imagery of the specified resolution. Products enabled by this new system include topographic mapping through radar interferometry, and change detection techniques (amplitude and coherent change) for geolocation of the NASA LCROSS mission impact site.

SAR↗

NASA NTRS2009

A Real-Time Microwave Camera at K-Band (24 GHz)

Nondestructive testing (NDT) community has been moving towards effective and robust inspection systems that can provide real-time information about materials, geometrical, structural and mechanical characteristics of composite materials/structures. Moreover, in many applications it is desired to have an image of the structure, after which the information contained in the image is correlated to the above characteristics. Microwave signals penetrate inside of dielectric composite structures and their interaction with the interior of the structure renders critical information for NDT purposes. Subsequently, this information (i.e., magnitude and phase or reflected signal) may be used to produce an image of the interior of the structure revealing potential flaws or anomalies. Image processing and reconstruction techniques may also be incorporated to produce high-resolution images (i.e., synthetic-aperture, back-propagation, etc.). There are several different approaches for designing areal-time microwave camera system. One approach is based on modulated scatterer technique (MST), which is used to tag scattered electric field in a discrete two-dimensional (2D) spatial domain (e.g. a retina) resulting in the 2D magnitude and phase distribution of the scattered electric field which is required for producing an image of a material or structure under inspection. The ability to rapidly modulate resonant slot antennas in such a retina along with using receivers with fast responses provide for real-time image production capability. Design issue and criteria become more challenging at higher frequencies and for a relatively large retina size. This paper presents the basic design and challenges for a microwave camera with a retina size of 6" by 6" operating at a frequency of 24 GHz.

Ghasr, M. T.↗