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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Apparatus and method for determining microscale interactions based on compressive sensors such as crystal structures

Techniques for determining values for a metric of microscale interactions include determining a mesoscale metric for a plurality of mesoscale interaction types, wherein a value of the mesoscale metric for each mesoscale interaction type is based on a corresponding function of values of the microscale metric for the plurality of the microscale interaction types. A plurality of observations that indicate the values of the mesoscale metric are determined for the plurality of mesoscale interaction types. Values of the microscale metric are determined for the plurality of microscale interaction types based on the plurality of observations and the corresponding functions and compressed sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

UQ Toolkit v 2.0

The Uncertainty Quantification (UQ) Toolkit is a software library for the characterizaton and propagation of uncertainties in computational models. For the characterization of uncertainties, Bayesian inference tools are provided to infer uncertain model parameters, as well as Bayesian compressive sensing methods for discovering sparse representations of high-dimensional input-output response surfaces, and also Karhunen-Loève expansions for representing stochastic processes. Uncertain parameters are treated as random variables and represented with Polynomial Chaos expansions (PCEs). The library implements several spectral basis function types (e.g. Hermite basis functions in terms of Gaussian random variables or Legendre basis functions in terms of uniform random variables) that can be used to represent random variables with PCEs. For propagation of uncertainty, tools are provided to propagate PCEs that describe the input uncertainty through the computational model using either intrusive methods (Galerkin projection of equations onto basis functions) or non-intrusive methods (perform deterministic operation at sampled values of the random values and project the obtained results onto basis functions).

Source record↗
DOE OSTI2016

UQ Toolkit v. 3.0

The Uncertainty Quantification (UQ) Toolkit is a software library for the characterization and propagation of uncertainties in computational models. This library provides Bayesian inference tools to infer uncertain model parameters, Bayesian compressive sensing methods for discovering sparse representations of high-dimensional input-output response surfaces, methods for constructing Karhunen-Loève representations of stochastic processes, and global sensitivity analysis tools used to compute Sobol indices in order to characterize the importance of uncertain inputs or the interactions between them. The basis for many of these methods relies on representing random variables with Polynomial Chaos expansions (PCEs). This library implements several spectral basis function types (e.g. Hermite basis functions in terms of Gaussian random variables or Legendre basis functions in terms of uniform random variables) that can be used to represent random variables with PCEs. For the propagation of uncertainty, tools are provided to propagate PCEs that describe the input uncertainty through the computational model using either intrusive methods (Galerkin projection of equations onto basis functions) or non-intrusive methods (perform deterministic operation at sampled values of the random values and project the obtained results onto basis functions).

Sargsyan, Khachik↗
DOE OSTI2017

EDGE COMPUTING AND CONTEXTUAL INFORMATION FOR THE INTERNET OF THINGS SENSORS

Interpreting sensor data require knowledge about sensor placement and the surrounding environment. For a single sensor measurement, it is easy to document the context by visual observation, however for millions of sensors reporting data back to a server, the contextual information needs to be automatically extracted from either data analysis or leveraging complimentary data sources. Data layers that overlap spatially or temporally with sensor locations, can be used to extract the context and to validate the measurement. To minimize the amount of data transmitted through the internet, while preserving signal information content, two methods are explored; computation at the edge and compressed sensing. We validate the above methods on wind and chemical sensor data (1) eliminate redundant measurement from wind sensors and (2) extract peak value of a chemical sensor measuring a methane plume. We present a general cloud based framework to validate sensor data based on statistical and physical modeling and contextual data extracted from geospatial data.

03 NATURAL GAS↗
DOE OSTI2017

Random On-Board Pixel Sampling (ROPS) X-Ray Camera

Recent advances in compressed sensing theory and algorithms offer new possibilities for high-speed X-ray camera design. In many CMOS cameras, each pixel has an independent on-board circuit that includes an amplifier, noise rejection, signal shaper, an analog-to-digital converter (ADC), and optional in-pixel storage. When X-ray images are sparse, i.e., when one of the following cases is true: (a.) The number of pixels with true X-ray hits is much smaller than the total number of pixels; (b.) The X-ray information is redundant; or (c.) Some prior knowledge about the X-ray images exists, sparse sampling may be allowed. Here we first illustrate the feasibility of random on-board pixel sampling (ROPS) using an existing set of X-ray images, followed by a discussion about signal to noise as a function of pixel size. Next, we describe a possible circuit architecture to achieve random pixel access and in-pixel storage. The combination of a multilayer architecture, sparse on-chip sampling, and computational image techniques, is expected to facilitate the development and applications of high-speed X-ray camera technology.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

EDGE COMPUTING AND CONTEXTUAL INFORMATION FOR THE INTERNET OF THINGS SENSORS

Interpreting sensor data require knowledge about sensor placement and the surrounding environment. For a single sensor measurement, it is easy to document the context by visual observation, however for millions of sensors reporting data back to a server, the contextual information needs to be automatically extracted from either data analysis or leveraging complimentary data sources. Data layers that overlap spatially or temporally with sensor locations, can be used to extract the context and to validate the measurement. To minimize the amount of data transmitted through the internet, while preserving signal information content, two methods are explored; computation at the edge and compressed sensing. We validate the above methods on wind and chemical sensor data (1) eliminate redundant measurement from wind sensors and (2) extract peak value of a chemical sensor measuring a methane plume. We present a general cloud based framework to validate sensor data based on statistical and physical modeling and contextual data extracted from geospatial data.

Klein, Levente↗
DOE OSTI2018

Electron beam masks for compressive sensors

Transmission microscopy imaging systems include a mask and/or other modulator situated to encode image beams, e.g., by deflecting the image beam with respect to the mask and/or sensor. The beam is modulated/masked either before or after transmission through a sample to induce a spatially and/or temporally encoded signal by modifying any of the beam/image components including the phase/coherence, intensity, or position of the beam at the sensor. For example, a mask can be placed/translated through the beam so that several masked beams are received by a sensor during a single sensor integration time. Images associated with multiple mask displacements are then used to reconstruct a video sequence using a compressive sensing method. Another example of masked modulation involves a mechanism for phase-retrieval, whereby the beam is modulated by a set of different masks in the image plane and each masked image is recorded in the diffraction plane.

36 MATERIALS SCIENCE↗
DOE OSTI2019

TEM phase contrast imaging with image plane phase grating

Transmission microscopy imaging systems include a mask and/or other modulator situated to encode image beams, e.g., by deflecting the image beam with respect to the mask and/or sensor. The beam is modulated/masked either before or after transmission through a sample to induce a spatially and/or temporally encoded signal by modifying any of the beam/image components including the phase/coherence, intensity, or position of the beam at the sensor. For example, a mask can be placed/translated through the beam so that several masked beams are received by a sensor during a single sensor integration time. Images associated with multiple mask displacements are then used to reconstruct a video sequence using a compressive sensing method. Another example of masked modulation involves a mechanism for phase-retrieval, whereby the beam is modulated by a set of different masks in the image plane and each masked image is recorded in the diffraction plane.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Compressive transmission microscopy

Transmission microscopy imaging systems include a mask and/or other modulator situated to encode image beams, e.g., by deflecting the image beam with respect to the mask and/or sensor. The beam is modulated/masked either before or after transmission through a sample to induce a spatially and/or temporally encoded signal by modifying any of the beam/image components including the phase/coherence, intensity, or position of the beam at the sensor. For example, a mask can be placed/translated through the beam so that several masked beams are received by a sensor during a single sensor integration time. Images associated with multiple mask displacements are then used to reconstruct a video sequence using a compressive sensing method. Another example of masked modulation involves a mechanism for phase-retrieval, whereby the beam is modulated by a set of different masks in the image plane and each masked image is recorded in the diffraction plane.

42 ENGINEERING↗
DOE OSTI2019

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.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2022

Functional soft materials and methods of making and using thereof

Disclosed are functional materials for use in additive manufacturing (AM). The functional material can comprise an elastomeric composition (e.g., a silicone composite) for use in, for example, direct ink writing. The elastomeric composition can include and elastomeric resin, and a magnetic nanorod filler dispersed within the elastomeric resin. Nanorod characteristics (e.g., length, diameter, aspect ratio) can be selected to create 3D-printed constructs with desired mechanical properties along different axes. Furthermore, since nickel nanorods are ferromagnetic, the spatial distribution and orientation of nanorods within the continuous phase can be controlled with an external magnetic field. This level of control over the nanostructure of the material system offers another degree of freedom in the design of functional parts and components with anisotropic properties. Magnetic fields can be used to remotely sense compression of the constructs, or alternatively, control the stiffness of these materials.

Berkland, Cory↗
DOE OSTI2015

Beam hardening correction using iterative total variation (ITV)-based algorithm in CBCT reconstruction

Recently, beam hardening reduction is required to produce high-quality reconstructions of X-ray cone-beam computed tomography (CBCT) system for medical applications. This paper introduces the iterative total variation (ITV) for filtered-backprojection suffering from the serious beam hardening problems. Feldkamp, Davis, and Kress (FDK) reconstruction algorithm for CBCT system is widely used reconstruction technique. FDK reconstruction algorithm could be realized by generating the weighted projection data, filtering the projection images, and back-projecting the filtered projection data into the volume. However, FDK algorithm suffers from the beam hardening artifacts by X-ray attenuation coefficients. Recently, total variation (TV) method for compressed sensing (CS) has been particularly useful in exploiting the prior knowledge of minimal variation in the X-ray attenuation characteristics across object or human body. But a practical implementation of this method still remains a challenge. The main problem is the iterative nature of solving the TV-based CS formulation, which generally requires multiple iterations of forward and backward projections of a large dataset in clinically or industrially feasible time frame. In this paper, we propose ITV method after FDK reconstruction for reducing the beam hardening artifacts. The beam hardening problems are reduced by the ITV method to promote sparsity inherent in the X-ray attenuation characteristics. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Functional soft materials and methods of making and using thereof

Disclosed are functional materials for use in additive manufacturing (AM). The functional material can comprise an elastomeric composition (e.g., a silicone composite) for use in, for example, direct ink writing. The elastomeric composition can include an elastomeric resin, and a magnetic nanorod filler dispersed within the elastomeric resin. Nanorod characteristics (e.g., length, diameter, aspect ratio) can be selected to create 3D-printed constructs with desired mechanical properties along different axes. Furthermore, since nickel nanorods are ferromagnetic, the spatial distribution and orientation of nanorods within the continuous phase can be controlled with an external magnetic field. This level of control over the nanostructure of the material system offers another degree of freedom in the design of functional parts and components with anisotropic properties. Magnetic fields can be used to remotely sense compression of the constructs, or alternatively, control the stiffness of these materials.

Berkland, Cory↗
NASA NTRS1994

The development of lossless data compression technology for remote sensing applications

Lossless data compression has been studied for many NASA missions to achieve the benefit of increased science return, reduced onboard memory requirement, station contact time and communication bandwidth. This paper first addresses the requirement for onboard applications and provides rational for the selection of the Rice algorithm among other available techniques. A top-level description of the Rice algorithm will be given, along with some new capabilities already implemented in both software and hardware VLSI forms. The paper then addresses systems issues important for onboard implementation including sensor calibration, error propagation and data packetization. The latter part of the paper provides several case study examples drawn from a broad spectrum of science instruments including the thematic mapper, x-ray telescope, gamma-ray spectrometer, and acousto-optical spectrometer.

Yeh, Pen-Shu↗