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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

The Impact of Stellar Variability on the Detection of Transiting Earth-like Planets

Stellar variability represents a key limitation on the detectability of weak transit signatures of small, Earth-size planets with space-based missions such as NASA's Kepler Mission or ESA's PLATO Mission. The expectations for the performance of the Kepler Mission in the face of solar-like variability were predicated on observations of the Sun with the Variability of Solar Irradiance and Gravity Oscillations (VIRGO) instrument aboard NASA and ESA's joint Solar and Heliospheric Observatory (SOHO) mission, which was launched in December 1995. Analyses of SOHO data indicated that solar-like variability would contribute approx.10 ppm to Kepler's noise budget at 6.5 ours, a typical grazing transit timescale. This proved to be optimistic as the typical 12th mag G2V star in the Kepler field of view exhibited 20 ppm of stellar variability-related noise at 6.5 hours, making the detection of true Earth-Sun analogs more difficult. While the consequent increase in the total noise at 6.5 hours from 20 ppm to 30 ppm could have been overcome by extending the mission to a total duration of 7 to 8 years. Unfortunately, a second reaction wheel failed in May 2013, ending the Kepler primary mission. Nevertheless, Kepler proved to be a veritable cornucopia of science results, both for exoplanets and for astrophysics. The phenomenal photometric precision and continuous observations required in order to identify small, rocky transiting planets enables the study of a large range of phenomena contributing to stellar variability for many thousands of solar-like stars in Kepler's field of view in exquisite detail. These effects range from less than 1 ppm acoustic oscillations on timescales from a few minutes and longward, to flares on timescales of hours, to spot-induced modulation on timescales of days to weeks to activity cycles on timescales of months to years. Kepler discovered over 2600 validated and confirmed exoplanets, and measured the pressure-mode oscillations of over 15000 stars over the course of its mission and the reconstituted K2 mission. We present the adaptive, wavelet-based matched filter used for both the Kepler and the TESS missions, and discuss the connections between this detection algorithm and fundamental detection theory, which allows the detector to function as a noise characterization engine. This provides a dynamical measurement of the photometric noise at transit timescales and thus, a key performance metric for missions like PLATO and Kepler. We give an overview of the stellar variability we see across the full range of spectral types observed by Kepler, from the cool, small red M stars to the hot, large late A stars, both in terms of amplitude as well as timescale.

transit surveys↗
NASA NTRS2019

Time Series Analysis in the Search for Other Worlds Through Transit Photometry

The Kepler Mission launched in June 2009 to commence NASA's first mission to search for potentially habitable, Earth-size planets orbiting Sun-like stars. Kepler discovered explanets via the transit method: searching for minute (100 ppm) drops in brightness lasting 1 - 13 hours corresponding to occasions where the planet crosses the face of its host star from Kepler's point of view. The exquisite precision required to carry out the Kepler mission (20 ppm in 6.5 hours) pushed astronomical time series analysis to the limits, and motivated the development of novel algorithmic approaches. Transit signatures of rocky planets are often dwarfed by the intrinsic stellar variability, which is not white noise, and often is non-stationary, and by instrumental systematic effects, which can include transients and electronic artifacts. Surmounting this challenging regime of weak, temporally compact, periodic signals in observation noise with strong systematics and other sources of variability motivated the development of 1) an overcomplete, non-decimated, wavelet-based matched filter to jointly estimate the properties of the non-stationary, non-white observation noise process, and 2) a multi-scale, maximum a posteriori (msMAP) approach to identifying and removing instrumental systematic effects. After over nine years of observations, the Kepler spacecraft finally ran out of fuel in November 2018, ending its data collection activities. Over 2300 planets were discovered by Kepler in its primary mission, and over 355 have been discovered by K2, the repurposed mission that followed Kepler's primary mission after the loss of a second reaction wheel in May 2013. We have ported the Kepler science pipeline for the Transiting Exoplanet Survey Satellite (TESS) Mission, which began science observations in July 2019, and report initial results and performance of the modified science pipeline.The Kepler and TESS Missions are supported by NASA's Science Mission Directorate.

transit surveys↗
NASA NTRS2018

The Impact of Stellar Variability on the Detection of Transiting Earth-Like Planets

Stellar variability represents a key limitation on the detectability of weak transit signatures of small, Earth-size planets with space-based missions such as NASA's Kepler Mission or ESA's PLATO Mission. The expectations for the performance of the Kepler Mission in the face of solar-like variability were predicated on observations of the Sun with the Variability of Solar Irradiance and Gravity Oscillations (VIRGO) instrument aboard NASA and ESA's joint Solar and Heliospheric Observatory (SOHO) mission, which was launched in December 1995. Analyses of SOHO data indicated that solar-like variability would contribute ~10 ppm to Kepler's noise budget at 6.5 ours, a typical grazing transit timescale. This proved to be optimistic as the typical 12th mag G2V star in the Kepler field of view exhibited 20 ppm of stellar variability-related noise at 6.5 hours, making the detection of true Earth-Sun analogs more difficult. While the consequent increase in the total noise at 6.5 hours from 20 ppm to 30 ppm could have been overcome by extending the mission to a total duration of 7 to 8 years. Unfortunately, a second reaction wheel failed in May 2013, ending the Kepler primary mission. Nevertheless, Kepler proved to be a veritable cornucopia of science results, both for exoplanets and for astrophysics. The phenomenal photometric precision and continuous observations required in order to identify small, rocky transiting planets enables the study of a large range of phenomena contributing to stellar variability for many thousands of solar-like stars in Kepler's field of view in exquisite detail. These effects range from <1 ppm acoustic oscillations on timescales from a few minutes and longward, to flares on timescales of hours, to spot-induced modulation on timescales of days to weeks to activity cycles on timescales of months to years. Kepler discovered over 2600 validated and confirmed exoplanets, and measured the pressure-mode oscillations of over 15000 stars over the course of its mission and the reconstituted K2 mission. We present the adaptive, wavelet-based matched filter used for both the Kepler and the TESS missions, and discuss the connections between this detection algorithm and fundamental detection theory, which allows the detector to function as a noise characterization engine. This provides a dynamical measurement of the photometric noise at transit timescales and thus, a key performance metric for missions like PLATO and Kepler. We give an overview of the stellar variability we see across the full range of spectral types observed by Kepler, from the cool, small red M stars to the hot, large late A stars, both in terms of amplitude as well as timescale.

exoplanets↗
NASA NTRS1999

An Application of Rotation- and Translation-Invariant Overcomplete Wavelets to the Registration of Remotely Sensed Imagery

A wavelet-based image registration approach has previously been proposed by the authors. In this work, wavelet coefficient maxima obtained from an orthogonal wavelet decomposition using Daubechies filters were utilized to register images in a multi-resolution fashion. Tested on several remote sensing datasets, this method gave very encouraging results. Despite the lack of translation-invariance of these filters, we showed that when using cross-correlation as a feature matching technique, features of size larger than twice the size of the filters are correctly registered by using the low-frequency subbands of the Daubechies wavelet decomposition. Nevertheless, high-frequency subbands are still sensitive to translation effects. In this work, we are considering a rotation- and translation-invariant representation developed by E. Simoncelli and integrate it in our image registration scheme. The two types of filters, Daubechies and Simoncelli filters, are then being compared from a registration point of view, utilizing synthetic data as well as data from the Landsat/ Thematic Mapper (TM) and from the NOAA Advanced Very High Resolution Radiometer (AVHRR).

LeMoigne, Jacqueline↗
NASA NTRS1999

An Application of Rotation- and Translation-Invariant Overcomplete Wavelets to the registration of Remotely Sensed Imagery

A wavelet-based image registration approach has previously been proposed by the authors. In this work, wavelet coefficient maxima obtained from an orthogonal wavelet decomposition using Daubechies filters were utilized to register images in a multi-resolution fashion. Tested on several remote sensing datasets, this method gave very encouraging results. Despite the lack of translation-invariance of these filters, we showed that when using cross-correlation as a feature matching technique, features of size larger than twice the size of the filters are correctly registered by using the low-frequency subbands of the Daubechies wavelet decomposition. Nevertheless, high-frequency subbands are still sensitive to translation effects. In this work, we are considering a rotation- and translation-invariant representation developed by E. Simoncelli and integrate it in our image registration scheme. The two types of filters, Daubechies and Simoncelli filters, are then being compared from a registration point of view, utilizing synthetic data as well as data from the Landsat/ Thematic Mapper (TM) and from the NOAA Advanced Very High Resolution Radiometer (AVHRR).

LeMoigne, Jacqueline↗
NASA NTRS2010

Multi-Stage System for Automatic Target Recognition

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an artificial neural network classifier. The multi-stage system allows tuning the detection sensitivity and the identification specificity individually in each stage. It is easier to achieve optimized ATR operation based on its specific goal. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar and video image datasets.

Chao, Tien-Hsin↗
NASA NTRS2009

Neural Network Target Identification System for False Alarm Reduction

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

ATR↗
DOE OSTI2024

Waveform retrieval for ultrafast applications based on convolutional neural networks

Electric field waveforms of light carry rich information about dynamical events on a broad range of timescales. The insight that can be reached from their analysis, however, depends on the accuracy of retrieval from noisy data. In this article, we present a novel approach for waveform retrieval based on supervised deep learning. We demonstrate the performance of our model by comparison with conventional denoising approaches, including wavelet transform and Wiener filtering. The model leverages the enhanced precision obtained from the nonlinearity of deep learning. The results open a path toward an improved understanding of physical and chemical phenomena in field-resolved spectroscopy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1998

Arctic Sea Ice Motion from Wavelet Analysis of Satellite Data

Wavelet analysis of DMSP SSM/I (Special Sensor Microwave/Imager) 85 GHz and 37 GHz radiance data, SMMR (Scanning Multichannel Microwave Radiometer) 37 GHz, and NSCAT (NASA Scatterometer) 13.9 GHZ data can be used to obtain daily sea ice drift information for both the northern and southern polar regions. The derived maps of sea ice drift provide both improved spatial coverage over the existing array of Arctic Ocean buoys and better temporal resolution over techniques utilizing data from satellite synthetic aperture radars (SAR). Examples of derived ice-drift maps in the Arctic illustrate large-scale circulation reversals within a period of a couple weeks. Comparisons with ice displacements derived from buoys show good quantitative agreement. NASA Scatterometer (NSCAT) 13.9 GHZ data have been also used for wavelet analysis to derive sea-ice drift. First, the 40' incidence-angle, sigma-zero (surface roughness) daily map of whole Arctic region with 25 km of pixel size from satellite's 600 km swath has been constructed. Then, the similar wavelet transform procedure to SSM/I data can be applied. Various scales of wavelet transform and threshold have been tested. By overlaying , neighbor filtering, and block-averaging the results of multiscale wavelet transforms, the final sea ice drift vectors are much smooth and representative to the sea ice motion. This wavelet analysis procedure is robust and can make a major contribution to the understanding of ice motion over large areas at relatively high temporal resolutions. The results of wavelet analysis of SSM/I and NSCAT images and buoy data can be merged by some data fusion techniques and will help to improve our current knowledge of sea ice drift and related processes through the data assimilation of ocean-ice numerical model.

Liu, Antony K.↗
NASA NTRS2003

Macromolecular Topography Leaps into the Digital Age

A low-cost, real-time digital topography system is under development which will replace x-ray film and nuclear emulsion plates. The imaging system is based on an inexpensive surveillance camera that offers a 1000x1000 array of 8 im square pixels, anti-blooming circuitry, and very quick read out. Currently, the system directly converts x-rays to an image with no phosphor. The system is small and light and can be easily adapted to work with other crystallographic equipment. Preliminary images have been acquired of cubic insulin at the NSLS x26c beam line. NSLS x26c was configured for unfocused monochromatic radiation. Six reflections were collected with stills spaced from 0.002 to 0.001 degrees apart across the entire oscillation range that the reflections were in diffracting condition. All of the reflections were rotated to the vertical to reduce Lorentz and beam related effects. This particular CCD is designed for short exposure applications (much less than 1 sec) and so has a relatively high dark current leading to noisy raw images. The images are processed to remove background and other system noise with a multi-step approach including the use of wavelets, histogram, and mean window filtering. After processing, animations were constructed with the corresponding reflection profile to show the diffraction of the crystal volume vs. the oscillation angle as well as composite images showing the parts of the crystal with the strongest diffraction for each reflection. The final goal is to correlate features seen in reflection profiles captured with fine phi slicing to those seen in the topography images. With this development macromolecular topography finally comes into the digital age.

Lovelace, J.↗
NASA NTRS1976

On the response of superpressure balloons to displacements from equilibrium density level

The response of a superpressure balloon to an initial displacement from its constant-density floating level is examined. An approximate solution is obtained to the governing vertical equation of motion for constant-density superpressure balloons. This solution is used to filter out neutrally buoyant oscillations in balloon records despite the nonlinear behavior of the balloon. The graph depicting the pressure data after deconvolution between the raw pressure data and the normalized balloon wavelet shows clearly the strong filtering-out of the neutral buoyancy oscillations.

Levanon, N.↗
DOE OSTI2021

Data Challenges for Structural Health Monitoring of Electrical Machines [Slides]

Induction motors (ID) consist of a stator and a rotor. Induction motor slip decreases with decreasing load. Induction motor slip decreases with decreasing load. This talk will focus on removing closely coupled power grid frequencies in an effort to observe the true dynamic response signature of induction motors. Using the time difference between the zeros, we found the frequency as it changes over time for each of the 3 phases. The frequencies of the 3 phases were averaged to reduce the effects of sidebands on the instantaneous frequency. The frequencies of the 3 phases were averaged to reduce the effects of sidebands on the instantaneous frequency. The Kalman filter rapidly synchronizes with the expected output. The sidebands were recreated using spectral subtraction. The frequency domain shows the reduction in magnitude of the power grid frequency the Kalman output was subtracted out. Kalman filtering also reveals important data about the sidebands in healthy motors. The Continuous Wavelet Transform is an alternative to a Bandpass IIR filter used in the analysis of the acceleration data. Analysis of the acceleration data reveals 4 sidebands around the slip frequency.

42 ENGINEERING↗
NASA NTRS2019

High-Order Finite-Difference Nonlinear Filter Methods for Subsonic Turbulence Simulation with Stochastic Forcing

Numerical stability of high-order filter schemes developed by Yee & Sjogreen is tested on three-dimensional turbulence simulations with stochastic forcing and their performance is compared with that of TVD and WENO schemes. The best­ performing filter method employs an eighth-order central base scheme with the Kennedy & Gruber skew-symmetric splitting of the inviscid flux derivative, a wavelet-based local flow sensor, a nonlinear filter utilizing the dissipative portion of seventh-order \VENO scheme, and an explicit third - or fourth-order Runge­-Kutta time integration. We show that the filter scheme is more computational]y efficient and provides a wider spectral bandwidth compared to the seventh-order WENO scheme. The method also demonstrates robust long-time integration for moderately compressible turbulence. In contrast, the fifth- and seventh­ order WENO schemes show non-trivial evolution of the velocity and density power spectra. over a. few dozen dynamical times, where both TVD and filter schemes recover a so lid statistically stationary turbulent state

Kritsuk, Alexei G.↗
NASA NTRS2004

A Foreign Object Damage Event Detector Data Fusion System for Turbofan Engines

A Data Fusion System designed to provide a reliable assessment of the occurrence of Foreign Object Damage (FOD) in a turbofan engine is presented. The FOD-event feature level fusion scheme combines knowledge of shifts in engine gas path performance obtained using a Kalman filter, with bearing accelerometer signal features extracted via wavelet analysis, to positively identify a FOD event. A fuzzy inference system provides basic probability assignments (bpa) based on features extracted from the gas path analysis and bearing accelerometers to a fusion algorithm based on the Dempster-Shafer-Yager Theory of Evidence. Details are provided on the wavelet transforms used to extract the foreign object strike features from the noisy data and on the Kalman filter-based gas path analysis. The system is demonstrated using a turbofan engine combined-effects model (CEM), providing both gas path and rotor dynamic structural response, and is suitable for rapid-prototyping of control and diagnostic systems. The fusion of the disparate data can provide significantly more reliable detection of a FOD event than the use of either method alone. The use of fuzzy inference techniques combined with Dempster-Shafer-Yager Theory of Evidence provides a theoretical justification for drawing conclusions based on imprecise or incomplete data.

Turso, James A.↗
NASA NTRS2002

Distant Cluster Hunting: A Comparison of X-Ray and Optical Cluster Detection Techniques and Catalogs from the ROSAT Optical X-Ray Survey - II

We present and analyze the optical and X-ray catalogs of moderate-redshift cluster candidates from the ROSA TOptical X-Ray Survey, or ROXS. The survey covers the sky area contained in the fields of view of 23 deep archival ROSA T PSPC pointings, 4.8 square degrees. The cross-correlated cluster catalogs were con- structed by comparing two independent catalogs extracted from the optical and X-ray bandpasses, using a matched-filter technique for the optical data and a wavelet technique for the X-ray data. We cross-identified cluster candidates in each catalog. As reported in Paper 1, the matched-filter technique found optical counter- parts for at least 60% (26 out of 43) of the X-ray cluster candidates; the estimated redshifts from the matched filter algorithm agree with at least 7 of 1 1 spectroscopic confirmations (Az 5 0.10). The matched filter technique. with an imaging sensitivity of ml N 23, identified approximately 3 times the number of candidates (155 candidates, 142 with a detection confidence >3 u) found in the X-ray survey of nearly the same area. There are 57 X-ray candidates, 43 of which are unobscured by scattered light or bright stars in the optical images. Twenty-six of these have fairly secure optical counterparts. We find that the matched filter algorithm, when applied to images with galaxy flux sensitivities of mI N 23, is fairly well-matched to discovering z 5 1 clusters detected by wavelets in ROSAT PSPC exposures of 8000-60,000 s. The difference in the spurious fractions between the optical and X-ray (30%) and IO%, respectively) cannot account for the difference in source number. In Paper I, we compared the optical and X-ray cluster luminosity functions and we found that the luminosity functions are consistent if the relationship between X-ray and optical luminosities is steep (Lx o( L&f). Here, in Paper 11, we present the cluster catalogs and a numerical simulation of the ROXS. We also present color-magnitude plots for several of the cluster candidates, and examine the prominence of the red sequence in each. We find that the X-ray clusters in our survey do not all have a prominent red sequence. We conclude that while the red sequence may be a distinct feature in the color-magnitude plots for virialized massive clusters, it may be less distinct in lower mass clusters of galaxies at even moderate redshifts. Multiple, complementary methods of selecting and defining clusters may be essential, particularly at high redshift where all methods start to run into completeness limits, incomplete understanding of physical evolution, and projection effects.

Donahue, Megan↗
NASA NTRS1997

Wavelet Analysis of Satellite Images for Coastal Watch

The two-dimensional wavelet transform is a very efficient bandpass filter, which can be used to separate various scales of processes and show their relative phase/location. In this paper, algorithms and techniques for automated detection and tracking of mesoscale features from satellite imagery employing wavelet analysis are developed. The wavelet transform has been applied to satellite images, such as those from synthetic aperture radar (SAR), advanced very-high-resolution radiometer (AVHRR), and coastal zone color scanner (CZCS) for feature extraction. The evolution of mesoscale features such as oil slicks, fronts, eddies, and ship wakes can be tracked by the wavelet analysis using satellite data from repeating paths. Several examples of the wavelet analysis applied to various satellite Images demonstrate the feasibility of this technique for coastal monitoring.

Liu, Antony K.↗
NASA NTRS1996

Wavelet-Based Grid Generation

Wavelets can provide a basis set in which the basis functions are constructed by dilating and translating a fixed function known as the mother wavelet. The mother wavelet can be seen as a high pass filter in the frequency domain. The process of dilating and expanding this high-pass filter can be seen as altering the frequency range that is 'passed' or detected. The process of translation moves this high-pass filter throughout the domain, thereby providing a mechanism to detect the frequencies or scales of information at every location. This is exactly the type of information that is needed for effective grid generation. This paper provides motivation to use wavelets for grid generation in addition to providing the final product: source code for wavelet-based grid generation.

HIGH PASS FILTERS↗