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Results for “data reconstruction”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Acquisition of quantitative physiological data and computerized image reconstruction using a single scan TV system

A single-scan radiography system has been interfaced to a minicomputer, and the combined system has been used with a variety of fluoroscopic systems and image intensifiers available in clinical facilities. The system's response range is analyzed, and several applications are described. These include determination of the gray scale for typical X-ray-fluoroscopic-television chains, measurement of gallstone volume in patients, localization of markers or other small anatomical features, determinations of organ areas and volumes, computer reconstruction of tomographic sections of organs in motion, and computer reconstruction of transverse axial body sections from fluoroscopic images. It is concluded that this type of system combined with a minimum of statistical processing shows excellent capabilities for delineating small changes in differential X-ray attenuation.

Baily, N. A.↗
NASA NTRS1976

Simulation of the reconstruction of distorted digital data

Results of simulation runs obtained while varying detector sampling rates and threshold levels for a 20,000 foot equalized cable and a bit rate of 1 Mbs are given. The simulation techniques appear useful for preliminary design of detector levels and sampling rates and for analyzing sensitivity of detector performance as system transfer function parameters are varied.

Zobrist, G. W.↗
NASA NTRS1985

Three-dimensional, transonic rotor flow field reconstructed from holographic interferogram data

Holographic interferometry and computer-assisted tomography (CAT) are used to determine the transonic flow field of a model rotor blade in hover. A pulsed ruby laser records 40 interferograms with a 61 cm-diam view field near the model rotor-blade tip operating at a tip Mach number of 0.90. After digitizing the interferograms and extracting fringe-order functions, the data are transferred to a CAT code. The CAT code then calculates pressure coefficients in several planes above the blade surface. The values from the holography-CAT method compare favorably with previously obtained numerical computations and laser velocimeter measurements at most locations near the blade tip. The results demonstrate the technique's potential for three-dimensional transonic rotor flow studies.

Kittleson, J. K.↗
NASA NTRS1991

Mesospheric KHI signatures in reconstructed broadband rocket probe data

In an accepted method of measuring D region electron density with a rocket-borne dc-mode Langmuir probe, the resulting fine-structure signal is modified by a high-pass filter. While this form of data has been successfully used for power spectral analysis, it fails to reveal important spatial domain features without further processing. It is demonstrated that the broadband signal can be recovered by use of a digital filter that corrects for the on-board high-pass filter effects. The D-region broadband structure so recovered for a daytime equatorial flight shows variations in electron density consistent with a model of Kelvin-Helmholtz billows, and not consistent with inertial range turbulence. The isolated regions of irregularity are about 100 m thick near an altitude of 75 km, imbedded in a region with a staircase-structure electron density profile with about 200-m steps. Suggestive similarities to a numerical model of KHI are to undersea observations of KHI-caused microstructure are shown.

Parker, Jay W.↗
NASA NTRSDate not supplied

Flow field Reconstruction for Inhomogeneous Turbulence using Data and Physics Driven Models

A methodology combining Large Eddy Simulation (LES) trained data and a physics driven wave packet model to obtain a reduced order reconstruction for broadband, three-dimensional, temporally stationary but spatially inhomogeneous, incompressible turbulence. Wake turbulence generated by an axisymmetric dragging disk with a turbulent co-flow serves as the benchmark test case. We begin by studying the proper-orthogonal decomposition of the turbulent fluctuations taken from a high-resolution LES to first identify whether the fields demonstrate a low-rank character. It is argued that the presence of the turbulent co-flow results in a largely broadband character lacking any tonal properties. This is especially true for Strouhal numbers greater than 1 and only a small fraction of energy is contained in the leading order Kelvin-Helmholtz modes. As such reconstructions and reduced order modeling purely relying on data from LES does not appear to be a lucrative solution - contrary to problems with strongly tonal character. To supplement the missing energy from a low order truncated mode expansion, we utilize a physics based super-resolution (enrichment) algorithm that relies on spatio-temporally localized Gabor wave packets whose time evolution is described using a set of ordinary differential equations. The reconstructed flow has single- and two-point correlations that are consistent with the reference high resolution simulation data.

SLS↗
NASA NTRS2021

AIRS Point Spread Function Reconstruction using AIRS and MODIS Data

The purpose of this work is to use data from the Atmospheric Infrared Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) to refine our knowledge of post-launch AIRS point spread functions (PSFs), including suspected changes over the mission. We develop methodology, by deriving mathematical optimization formulation based on variational principles and Sobolev gradient descent, for reconstruction of AIRS spatial response functions. We use the data over the ocean, collected for the duration of a day, to reconstruct a single PSF. We examine the repeatability of our reconstructions by computing PSFs based on data collected during two consecutive days, and also investigating the change in the reconstructions by comparing the reconstructed PSF based on data collected in the beginning and the middle of the mission. We also quantify uncertainties in our reconstruction results.

Vese, Luminita↗
NASA NTRS1980

Data recording and processing for speckle image reconstruction

Image reconstruction from astronomical speckle data using the Knox-Thompson algorithm has now been extensively demonstrated as feasible through analysis and computer simulations. Experimental verification of the technique and its implementation for astronomy places stringent constraints on the recording system and requires complex processing algorithms to handle the many experimental details. In this paper we describe a video recording system specifically designed for the requirements of speckle imaging. This system has been built and tested, yielding high quality image reconstructions from a laboratory-atmospheric simulator for a wide range of input light levels and test objects.

Nisenson, P.↗
NASA NTRS1987

Infrared Astronomical Satellite (IRAS) image reconstruction and restoration

IRAS sky mapping data is being reconstructed as images, and an entropy-based restoration algorithm is being applied in an attempt to improve spatial resolution in extended sources. Reconstruction requires interpolation of non-uniformly sampled data. Restoration is accomplished with an iterative algorithm which begins with an inverse filter solution and iterates on it with a weighted entropy-based spectral subtraction.

Gonsalves, R. A.↗
NASA NTRS1983

U.S. data processing for the IRAS project

The JPL's Scientific Data Analysis System (SDAS), which will process IRAS data and produce a catalogue of perhaps a million infrared sources in the sky, as well as other information for astronomical records, is described. The purposes of SDAS are discussed, and the major SDAS processors are shown in block diagram. The catalogue processing is addressed, mentioning the basic processing steps which will be applied to raw detector data. Signal reconstruction and conversion to astrophysical units, source detection, source confirmation, data management, and survey data products are considered in detail.

Duxbury, J. H.↗
DOE OSTI2022

Systems and methods for point-of-care positron emission tomography

A positron emission tomography (PET) system is provided. The system includes a first detector panel including a first array of detectors, a second detector panel including a second array of detectors, said second detector panel being moveable relative to a point between the first detector panel and the second detector panel, a tracking system configured to detect a position of said second detector panel relative to the first detector panel while imaging a subject, a computing device in communication with the first detector panel, the second detector panel, and the tracking system, the computing device configured to receive coincidence data from the first and second detector panels, receive position data from the tracking system, wherein each coincidence datum of the received coincidence data is associated with a unique position datum of the received position data, and reconstruct a plurality of images based on the received coincidence data and the received position data.

Tai, Yuan-Chuan↗
DOE OSTI2020

Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection

One of the fundamental assumptions of compressive sensing (CS) is that a signal can be reconstructed from a small number of samples by solving an optimization problem with the appropriate regularization term. Two standard regularization terms are the L1 norm and the total variation (TV) norm. We present a comparison of CS reconstruction results based on these two approaches in the context of chemical detection, and we demonstrate that optimization based on the L1 norm outperforms optimization based on the TV norm. Our comparison is driven by CS sampling, reconstruction, and chemical detection in two real-world datasets: the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset and the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset. Both datasets contain the release of a chemical simulant such as glacial acetic acid, triethyl phosphate, and sulfur hexafluoride. For chemical detection we use the adaptive coherence estimator (ACE) and bulk coherence, and we propose algorithmic ACE thresholds to define the presence or absence of a chemical of interest in both un-compressed data cubes and reconstructed data cubes. The un-compressed data cubes provide an approximate ground truth. We demonstrate that optimization based on either the L1 norm or TV norm results in successful chemical detection at a compression rate of 90%, but we show that L1 optimization is preferable. We present quantitative comparisons of chemical detection on reconstructions from the two methods, with an emphasis on the number of pixels with an ACE value above the threshold.

Hyperspectral imagery, L1-norm, total variation, A↗
DOE OSTI2020

Beam Hardening Artifact Reduction in X-Ray CT Reconstruction of 3D Printed Metal Parts Leveraging Deep Learning and CAD Models

Nondestructive evaluation (NDE) of additively manufactured (AM) parts is important for understanding the impacts of various process parameters and qualifying the built part. X-ray computed tomography (XCT) has played a critical role in rapid NDE and characterization of AM parts. However, XCT of metal AM parts can be challenging because of artifacts produced by standard reconstruction algorithms as a result of a confounding effect called “beam hardening.” Beam hardening artifacts complicate the analysis of XCT images and adversely impact the process of detecting defects, such as pores and cracks, which is key to ensuring the quality of the parts being printed. In this work, we propose a novel framework based on using available computer-aided design (CAD) models for parts to be manufactured, accurate XCT simulations, and a deep-neural network to produce high-quality XCT reconstructions from data that are affected by noise and beam hardening. Using extensive experiments with simulated data sets, we demonstrate that our method can significantly improve the reconstruction quality, thereby enabling better detection of defects compared with the state of the art. We also present promising preliminary results of applying the deep networks trained using CAD models to experimental data obtained from XCT of an AM jet-engine turbine blade.

Ziabari, Amir↗
DOE OSTI2025

Sempervirens: A Fast Reconstruction Algorithm for Noisy and Incomplete Binary Matrix Representations of Trees

Applications such as reconstructing cell lineage trees (represented as phylogenetic trees) from single-cell sequencing data require reconstructing a {0,1}-matrix that has many errors and missing entries. We introduce Sempervirens, a very fast matrix reconstruction algorithm for noisy and incomplete matrix representations of phylogenetic trees. Sempervirens uses an iterative maximum-likelihood approach to determine the topology tree represented by the corrupted data. We show that Sempervirens is at least three orders of magnitude faster than other methods on thousand by thousand matrices, with the speed gap widening with larger matrices. We also show that Sempervirens matches state-of-the-art methods in reconstruction accuracy. The speed of Sempervirens enables it to be tractably applied to reconstructing much larger matrices than those that other methods can reconstruct. In addition to experimental results, we justify the algorithm with a mathematical treatment of its subprocedures.

algorithms↗