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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Contour Error Map Algorithm

The contour error map (CEM) algorithm and the software that implements the algorithm are means of quantifying correlations between sets of time-varying data that are binarized and registered on spatial grids. The present version of the software is intended for use in evaluating numerical weather forecasts against observational sea-breeze data. In cases in which observational data come from off-grid stations, it is necessary to preprocess the observational data to transform them into gridded data. First, the wind direction is gridded and binarized so that D(i,j;n) is the input to CEM based on forecast data and d(i,j;n) is the input to CEM based on gridded observational data. Here, i and j are spatial indices representing 1.25-km intervals along the west-to-east and south-to-north directions, respectively; and n is a time index representing 5-minute intervals. A binary value of D or d = 0 corresponds to an offshore wind, whereas a value of D or d = 1 corresponds to an onshore wind. CEM includes two notable subalgorithms: One identifies and verifies sea-breeze boundaries; the other, which can be invoked optionally, performs an image-erosion function for the purpose of attempting to eliminate river-breeze contributions in the wind fields.

Merceret, Francis↗
NASA NTRS2005

Multichannel Brain-Signal-Amplifying and Digitizing System

An apparatus has been developed for use in acquiring multichannel electroencephalographic (EEG) data from a human subject. EEG apparatuses with many channels in use heretofore have been too heavy and bulky to be worn, and have been limited in dynamic range to no more than 18 bits. The present apparatus is small and light enough to be worn by the subject. It is capable of amplifying EEG signals and digitizing them to 22 bits in as many as 150 channels. The apparatus is controlled by software and is plugged into the USB port of a personal computer. This apparatus makes it possible, for the first time, to obtain high-resolution functional EEG images of a thinking brain in a real-life, ambulatory setting outside a research laboratory or hospital.

Gevins, Alan↗
NASA NTRS2012

Investigation into Cloud Computing for More Robust Automated Bulk Image Geoprocessing

Geospatial resource assessments frequently require timely geospatial data processing that involves large multivariate remote sensing data sets. In particular, for disasters, response requires rapid access to large data volumes, substantial storage space and high performance processing capability. The processing and distribution of this data into usable information products requires a processing pipeline that can efficiently manage the required storage, computing utilities, and data handling requirements. In recent years, with the availability of cloud computing technology, cloud processing platforms have made available a powerful new computing infrastructure resource that can meet this need. To assess the utility of this resource, this project investigates cloud computing platforms for bulk, automated geoprocessing capabilities with respect to data handling and application development requirements. This presentation is of work being conducted by Applied Sciences Program Office at NASA-Stennis Space Center. A prototypical set of image manipulation and transformation processes that incorporate sample Unmanned Airborne System data were developed to create value-added products and tested for implementation on the "cloud". This project outlines the steps involved in creating and testing of open source software developed process code on a local prototype platform, and then transitioning this code with associated environment requirements into an analogous, but memory and processor enhanced cloud platform. A data processing cloud was used to store both standard digital camera panchromatic and multi-band image data, which were subsequently subjected to standard image processing functions such as NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index), band stacking, reprojection, and other similar type data processes. Cloud infrastructure service providers were evaluated by taking these locally tested processing functions, and then applying them to a given cloud-enabled infrastructure to assesses and compare environment setup options and enabled technologies. This project reviews findings that were observed when cloud platforms were evaluated for bulk geoprocessing capabilities based on data handling and application development requirements.

Brown, Richard B.↗
NASA NTRSDate not supplied

Mapping Ocean-Reflected Radio Frequency Interference for the GPM Microwave Imager Using Normalized Retrieval Cost Function

By adapting an approach originally developed for WindSat, we identify regions where GMI is affected by radio frequency interference from ocean-reflected geostationary satellite emissions. Around North America, K-band emissions from geostationary satellites affect the GMI 18.7-GHz brightness temperatures, resulting in increases in the retrieval cost function. The method does not detect any 10.7-GHz RFI reflected of the ocean surrounding Europe. This study, which depends on the physically-based forward model for non-raining ocean retrievals, confirms results from a previous study that employed an empirical methodology.

Ian S. Adams↗
DOE OSTI2022

Determinants of synapse diversity revealed by super-resolution quantal transmission and active zone imaging

Neural circuit function depends on the pattern of synaptic connections between neurons and the strength of those connections. Synaptic strength is determined by both postsynaptic sensitivity to neurotransmitter and the presynaptic probability of action potential evoked transmitter release (P r ). Whereas morphology and neurotransmitter receptor number indicate postsynaptic sensitivity, presynaptic indicators and the mechanism that sets P r remain to be defined. To address this, we developed QuaSOR, a super-resolution method for determining P r from quantal synaptic transmission imaging at hundreds of glutamatergic synapses at a time. We mapped the P r onto super-resolution 3D molecular reconstructions of the presynaptic active zones (AZs) of the same synapses at the Drosophila larval neuromuscular junction (NMJ). We find that P r varies greatly between synapses made by a single axon, quantify the contribution of key AZ proteins to P r diversity and find that one of these, Complexin, suppresses spontaneous and evoked transmission differentially, thereby generating a spatial and quantitative mismatch between release modes. Transmission is thus regulated by the balance and nanoscale distribution of release-enhancing and suppressing presynaptic proteins to generate high signal-to-noise evoked transmission.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

A new programming metaphor for image processing procedures

Most image processing systems, besides an Application Program Interface (API) which lets users write their own image processing programs, also feature a higher level of programmability. Traditionally, this is a command or macro language, which can be used to build large procedures (scripts) out of simple programs or commands. This approach, a legacy of the teletypewriter has serious drawbacks. A command language is clumsy when (and if! it attempts to utilize the capabilities of a multitasking or multiprocessor environment, it is but adequate for real-time data acquisition and processing, it has a fairly steep learning curve, and the user interface is very inefficient,. especially when compared to a graphical user interface (GUI) that systems running under Xll or Windows should otherwise be able to provide. ll these difficulties stem from one basic problem: a command language is not a natural metaphor for an image processing procedure. A more natural metaphor - an image processing factory is described in detail. A factory is a set of programs (applications) that execute separate operations on images, connected by pipes that carry data (images and parameters) between them. The programs function concurrently, processing images as they arrive along pipes, and querying the user for whatever other input they need. From the user's point of view, programming (constructing) factories is a lot like playing with LEGO blocks - much more intuitive than writing scripts. Focus is on some of the difficulties of implementing factory support, most notably the design of an appropriate API. It also shows that factories retain all the functionality of a command language (including loops and conditional branches), while suffering from none of the drawbacks outlined above. Other benefits of factory programming include self-tuning factories and the process of encapsulation, which lets a factory take the shape of a standard application both from the system and the user's point of view, and thus be used as a component of other factories. A bare-bones prototype of factory programming was implemented under the PcIPS image processing system, and a complete version (on a multitasking platform) is under development.

Smirnov, O. M.↗
NASA NTRS1992

Enhancing images with Intensity-Dependent Spread functions

The theory of Intensity-Dependent Spread functions (IDS), a model of the human visual system proposed by Cornsweet (1985), is applied to image enhancement. An artificial image is examined which illustrates the characteristics of IDS processing and shows how the theoretical results translate into visual effects. Examples of realistic scenes that have been enhanced by IDS are presented. The system is shown to be particularly useful for bringing out detail in regions of low-contrast images. IDS can be readily implemented on a parallel computer.

Reese, Greg↗
DOE OSTI2024

Image Deconvolution and Point-spread Function Reconstruction with STARRED: A Wavelet-based Two-channel Method Optimized for Light-curve Extraction

We present starred, a point-spread function (PSF) reconstruction, two-channel deconvolution, and light-curve extraction method designed for high-precision photometric measurements in imaging time series. An improved resolution of the data is targeted rather than an infinite one, thereby minimizing deconvolution artifacts. In addition, starred performs a joint deconvolution of all available data, accounting for epoch-to-epoch variations of the PSF and decomposing the resulting deconvolved image into a point source and an extended source channel. The output is a high-signal-to-noise-ratio, high-resolution frame combining all data and the photometry of all point sources in the field of view as a function of time. Of note, starred also provides exquisite PSF models for each data frame. We showcase three applications of starred in the context of the imminent LSST survey and of JWST imaging: (i) the extraction of supernovae light curves and the scene representation of their host galaxy; (ii) the extraction of lensed quasar light curves for time-delay cosmography; and (iii) the measurement of the spectral energy distribution of globular clusters in the "Sparkler," a galaxy at redshift z = 1.378 strongly lensed by the galaxy cluster SMACS J0723.3-7327. starred is implemented in jax, leveraging automatic differentiation and graphics processing unit acceleration. This enables the rapid processing of large time-domain data sets, positioning the method as a powerful tool for extracting light curves from the multitude of lensed or unlensed variable and transient objects in the Rubin-LSST data, even when blended with intervening objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

PET Imaging of Leg Arteries for Determining the Input Function in PET/MRI Brain Studies Using a Compact, MRI-Compatible PET System

In this work, we used a compact, high-resolution, and magnetic resonance imaging (MRI)-compatible positron emission tomography (PET) camera (VersaPET) to assess the feasibility of measuring the image-derived input function (IDIF) from arteries in the leg with the ultimate goal of enabling fully quantitative PET brain imaging without blood sampling. We used this approach in five 18 F-FDG PET/MRI brain studies in which the input function was also acquired using the gold standard of serial arterial blood sampling. After accounting for partial volume, dispersion, and calibration effects, we compared the metabolic rates of glucose (MRglu) quantified from VersaPET IDIFs in 80 brain regions to those using the gold standard and achieved a bias and variability of <5% which is within the range of reported test-retest values for this type of study. We also achieved a strong linear relationship (R 2 >0.97) against the gold standard across regions. The results of this preliminary study are promising and support further studies to optimize methods, validate in a larger cohort, and extend to the modeling of other radiotracers.

47 OTHER INSTRUMENTATION↗
NASA NTRS2018

Super Resolution of Remote Sensing Images Using Edge-Directed Radial Basis Functions

Edge-Directed Radial Basis Functions (EDRBF) are used to compute super resolution(SR) image from a given set of low resolution (LR) images differing in subpixel shifts. The algorithm is tested on remote sensing images and compared for accuracy with other well-known algorithms such as Iterative Back Projection (IBP), Maximum Likelihood (ML) algorithm, interpolation of scattered points using Nearest Neighbor (NN) and Inversed Distance Weighted (IDW) interpolation, and Radial Basis Functin(RBF) . The accuracy of SR depends on various factors besides the algorithm (i) number of subpixel shifted LR images (ii) accuracy with which the LR shifts are estimated by registration algorithms (iii) and the targeted spatial resolution of SR. In our studies, the accuracy of EDRBF is compared with other algorithms keeping these factors constant. The algorithm has two steps: i) registration of low resolution images and (ii) estimating the pixels in High Resolution (HR) grid using EDRBF. Experiments are conducted by simulating LR images from a input HR image with different sub-pixel shifts. The reconstructed SR image is compared with input HR image to measure the accuracy of the algorithm using sum of squared errors (SSE). The algorithm has outperformed all of the algorithms mentioned above. The algorithm is robust and is not overly sensitive to the registration inaccuracies.

Mareboyana, Manohar M.↗
NASA NTRS2010

Spatial Standard Observer

The present invention relates to devices and methods for the measurement and/or for the specification of the perceptual intensity of a visual image. or the perceptual distance between a pair of images. Grayscale test and reference images are processed to produce test and reference luminance images. A luminance filter function is convolved with the reference luminance image to produce a local mean luminance reference image . Test and reference contrast images are produced from the local mean luminance reference image and the test and reference luminance images respectively, followed by application of a contrast sensitivity filter. The resulting images are combined according to mathematical prescriptions to produce a Just Noticeable Difference, JND value, indicative of a Spatial Standard Observer. SSO. Some embodiments include masking functions. window functions. special treatment for images lying on or near border and pre-processing of test images.

Watson, Andrw B.↗
NASA NTRS2012

Spatial Standard Observer

The present invention relates to devices and methods for the measurement and/or for the specification of the perceptual intensity of a visual image, or the perceptual distance between a pair of images. Grayscale test and reference images are processed to produce test and reference luminance images. A luminance filter function is convolved with the reference luminance image to produce a local mean luminance reference image. Test and reference contrast images are produced from the local mean luminance reference image and the test and reference luminance images respectively, followed by application of a contrast sensitivity filter. The resulting images are combined according to mathematical prescriptions to produce a Just Noticeable Difference, JND value, indicative of a Spatial Standard Observer, SSO. Some embodiments include masking functions, window functions, special treatment for images lying on or near borders and pre-processing of test images.

Watson, Andrew B.↗
NASA NTRS1981

Use of imaging radar for geology and archeology

Imaging radar is shown to be a useful sensor for geological analysis as a standal one sensor in clouded regions or as a complementary data source with visible NIR systems. Radar image tone is a function of the radar system parameters (imaging geometry, frequency, polarization) and a function of the target (local slope, electrical properties, and surface roughness). Substantial topographic texture enhancement can be achieved for large scale features by using specular returns associated with steep-incidence radars or shadows associated with grazing-incidence systems. Texture enhancement also allows radar to image lineaments and archeological features, such as canals and causeways. Future multispectral radars may achieve better discrimination of subresolution structures. Seasat radar images of several geographic locations are provided.

Daily, M.↗
NASA NTRS1984

Edge analyzing properties of center/surround response functions in cybernetic vision

The ability of center/surround response functions to make explicit high resolution spatial information in optical images was investigated by performing convolutions of two dimensional response functions and image intensity functions (mainly edges). The center/surround function was found to have the unique property of separating edge contrast from shape variations and of providing a direct basis for determining contrast and subsequently shape of edges in images. Computationally simple measures of contrast and shape were constructed for potential use in cybernetic vision systems. For one class of response functions these measures were found to be reasonably resilient for a range of scan direction and displacements of the response functions relative to shaped edges. A pathological range of scan directions was also defined and methods for detecting and handling these cases were developed. The relationship of these results to biological vision is discussed speculatively.

Jobson, D. J.↗
NASA NTRS1991

Spatial acquisition and tracking for deep-space optical communication packages

The problem of acquiring and tracking a sun-lit earth, which poses a rather stringent demand on the design of the pointing acquisition and tracking subsystem for a deep-space optical communication package, is addressed. The acquisition subsystem must be capable of locating and tracking the receiver location based on a sun-lit earth image that is blurred by the point spread function of the imaging optics and spatially quantized by the detector pixels. Spatial tracking must be performed at bandwidths in excess of 1 kHz to effectively reject platform jitters. A simple solution of locating the earth limb shows a great potential for providing the necessary acquisition and tracking accuracy. By using separate acquisition and tracking detectors, the system can be implemented without extensive technology development. The slow bandwidth required for the acquisition process can be readily accomplished with an available CCD array, and an analog tracking loop can be implemented using a quadrant tracker and calibrated centroid tracking without the excessive computing overhead of the array detector-based spatial tracker.

Chen, Chien-Chung↗
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↗