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At least 1,279 records · Page 71
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NASA NTRS2014

Remote Sensing of Cloud Top Height from SEVIRI: Analysis of Eleven Current Retrieval Algorithms

The role of clouds remains the largest uncertainty in climate projections. They influence solar and thermal radiative transfer and the earth's water cycle. Therefore, there is an urgent need for accurate cloud observations to validate climate models and to monitor climate change. Passive satellite imagers measuring radiation at visible to thermal infrared (IR) wavelengths provide a wealth of information on cloud properties. Among others, the cloud top height (CTH) - a crucial parameter to estimate the thermal cloud radiative forcing - can be retrieved. In this paper we investigate the skill of ten current retrieval algorithms to estimate the CTH using observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). In the first part we compare ten SEVIRI cloud top pressure (CTP) data sets with each other. The SEVIRI algorithms catch the latitudinal variation of the CTP in a similar way. The agreement is better in the extratropics than in the tropics. In the tropics multi-layer clouds and thin cirrus layers complicate the CTP retrieval, whereas a good agreement among the algorithms is found for trade wind cumulus, marine stratocumulus and the optically thick cores of the deep convective system. In the second part of the paper the SEVIRI retrievals are compared to CTH observations from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) instruments. It is important to note that the different measurement techniques cause differences in the retrieved CTH data. SEVIRI measures a radiatively effective CTH, while the CTH of the active instruments is derived from the return time of the emitted radar or lidar signal. Therefore, some systematic differences are expected. On average the CTHs detected by the SEVIRI algorithms are 1.0 to 2.5 kilometers lower than CALIOP observations, and the correlation coefficients between the SEVIRI and the CALIOP data sets range between 0.77 and 0.90. The average CTHs derived by the SEVIRI algorithms are closer to the CPR measurements than to CALIOP measurements. The biases between SEVIRI and CPR retrievals range from −0.8 kilometers to 0.6 kilometers. The correlation coefficients of CPR and SEVIRI observations vary between 0.82 and 0.89. To discuss the origin of the CTH deviation, we investigate three cloud categories: optically thin and thick single layer as well as multi-layer clouds. For optically thick clouds the correlation coefficients between the SEVIRI and the reference data sets are usually above 0.95. For optically thin single layer clouds the correlation coefficients are still above 0.92. For this cloud category the SEVIRI algorithms yield CTHs that are lower than CALIOP and similar to CPR observations. Most challenging are the multi-layer clouds, where the correlation coefficients are for most algorithms between 0.6 and 0.8. Finally, we evaluate the performance of the SEVIRI retrievals for boundary layer clouds. While the CTH retrieval for this cloud type is relatively accurate, there are still considerable differences between the algorithms. These are related to the uncertainties and limited vertical resolution of the assumed temperature profiles in combination with the presence of temperature inversions, which lead to ambiguities in the CTH retrieval. Alternative approaches for the CTH retrieval of low clouds are discussed.

Hamann, U.↗
NASA NTRS2010

The JWST/NIRCam Coronagraph Flight Occulters

The NIRCam instrument on the James Webb Space Telescope will have a Lyot coronagraph for high contrast imaging of extrasolar planets and circumstellar disks at lambda = 2 - 5 micrometers. Half-tone patterns are used to create graded-transmission image plane masks. These are generated using electron beam lithography and reactive ion etching of a metal layer on an antireflection coated sapphire substrate. We report here on the manufacture and evaluation of the flight occulters.

extrasolar planets↗
NASA NTRS2016

Motion Estimation System Utilizing Point Cloud Registration

A system and method of estimation motion of a machine is disclosed. The method may include determining a first point cloud and a second point cloud corresponding to an environment in a vicinity of the machine. The method may further include generating a first extended gaussian image (EGI) for the first point cloud and a second EGI for the second point cloud. The method may further include determining a first EGI segment based on the first EGI and a second EGI segment based on the second EGI. The method may further include determining a first two dimensional distribution for points in the first EGI segment and a second two dimensional distribution for points in the second EGI segment. The method may further include estimating motion of the machine based on the first and second two dimensional distributions.

Chen, Qi↗
NASA NTRS2015

Contribution of Phenological and Physiological Variations on Northern Vegetation Productivity Changes over Last Three Decades

Plant phenology and maximum photosynthetic state determine spatiotemporal variability of gross primary productivity (GPP) of vegetation. Recent warming induced impacts accelerate shifts of phenology and physiological status over Northern vegetated land. Thus, understanding and quantifying these changes are very important. Here, we investigate 1) how vegetation phenology and physiological status (maximum photosynthesis) are evolved over last three decades and 2) how such components (phenology and physiological status) contribute on inter-annual variation of the GPP during the last three decades. We utilized both long-term remotely sensed (GIMMS (Global Inventory Modeling and Mapping Studies), NDVI3g (Normalized Difference Vegetation Index 3rd generation) and MODIS (Moderate Resolution Imaging Spectroradiometer)) to extract larger scale phenology metrics (growing season start, end and duration); and productivity (i.e., growing season integrated vegetation index, GSIVI) to answer these questions. For evaluation purpose, we also introduced field-measured phenology and productivity datasets (e.g., FLUXNET) and possible remotely-sensed and modeled metrics at continental and regional scales. From this investigation, we found that onset of the growing season has advanced by 1.61 days per decade and the growing season end has delayed by 0.67 days per decade over the circumpolar region. This asymmetric extension of growing season results in a longer growing-season trend (2.96 days per decade) and widespread increasing vegetation-productivity trend (2.96 GSIVI per decade) over Northern land. However, the regionally-diverged phenology shift and maximum photosynthetic state contribute differently characterized productivity, inter-annual variability and trend. We quantified that about 50 percent, 13 percent and 6.5 percent of Northern land's inter-annual variability are dominantly controlled by the onset of the growing season, the end of the growing season and the maximum photosynthetic state, respectively. Productivity characterization over the other approximately 30 percent region has been driven by these co-dominant drivers. Our study clearly shows that regionally different contribution of phenological and physiological components on characterizing vegetation production over the last three decades.

productivity↗
NASA NTRS2017

Programmable Spectral Source and Design Tool for 3D Imaging Using Complementary Bandpass Filters

An endoscopic illumination system for illuminating a subject for stereoscopic image capture, includes a light source which outputs light; a first complementary multiband bandpass filter (CMBF) and a second CMBF, the first and second CMBFs being situated in first and second light paths, respectively, where the first CMBF and the second CMBF filter the light incident thereupon to output filtered light; and a camera which captures video images of the subject and generates corresponding video information, the camera receiving light reflected from the subject and passing through a pupil CMBF pair and a detection lens. The pupil CMBF includes a first pupil CMBF and a second pupil CMBF, the first pupil CMBF being identical to the first CMBF and the second pupil CMBF being identical to the second CMBF, and the detection lens includes one unpartitioned section that covers both the first pupil CMBF and the second pupil CMBF.

Shahinian, Hrayr Karnig↗
NASA NTRS1995

High Resolution UV Spectroscopy of H2 and N2 Applied to Observations of the Planets by Spacecraft

The next generation of high resolution UV imaging Spacecraft are being prepared for studying the airglow and aurora of the Earth, the other terrestrial planets and the Jovian planets. To keep pace with these technological improvements we have developed a laboratory program to provide electron impact collision cross sections of the major molecular planetary gases. This program is described in detail.

Spectroscopy↗
NASA NTRS2018

Predicting Rapid Fire Growth (Flashover) Using Generative Adversarial Networks

A flashover occurs when a fire spreads very rapidly through crevices due to intense heat. Flashovers present one of the most frightening and challenging fire phenomena to those who regularly encounter them: firefighters. Firefighters’ safety and lives often depend on their ability to predict flashovers before they occur. Typical pre-flashover fire characteristics include dark smoke, high heat, and rollover (“angel fingers”) and can be quantified by color, size, and shape. Using a color video stream from a firefighter’s body camera, we applied generative adversarial neural networks for image enhancement. The neural networks were trained to enhance very dark fire and smoke patterns in videos and monitor dynamic changes in smoke and fire areas. Preliminary tests with limited flashover training videos showed that we predicted a flashover as early as 55 seconds before it occurred.

Lu, Thomas↗
NASA NTRSDate not supplied

Impact histories inferred from exogenic boulders on asteroids Ryugu and Bennu

Rubble-pile asteroids, such as (162173) Ryugu and (101955) Bennu, are formed as a result of catastrophic disruption of a parent body and re-accumulation of the fragments by self-gravity. Therefore, reaccumulated rubble piles could include mixtures of materials from both the parent body and its catastrophic impactor, as they did in the case of 2008 TC3 and the Almahata Sitta meteorites (Jenniskens et al. 2009). Indeed, six unusually bright, basaltic, meter-scale boulders were recently identified on Bennu’s dark surface (DellaGiustina et al. 2021), and their close spectral matches to the Howardite-Eucrite-Diogenite (HED) meteorites and Vesta family members indicate that they originated from asteroid (4) Vesta. In parallel, bright exogenic anhydrous-silicate–rich materials were found on Ryugu (Tatsumi et al. 2021). The bright boulders on Ryugu are consistent with ordinary chondrite meteorites, based on their albedo and weak or even absent absorption band at 2 µm. These exogenous materials on the surfaces of rubble-pile asteroids could be a key to constraining their specific impact conditions and collisional evolution. In this study, we report further study of the visible spectrophotometry and morphology of exogenic boulders on Ryugu and Bennu using two multi-band cameras: the telescopic Optical Navigation Camera (ONC-T) onboard the Hayabusa2 spacecraft, and MapCam onboard the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) spacecraft, respectively. For Ryugu, we first detected objects with peak reflectance brighter by a factor of >1.5 than the surrounding area. Tatsumi et al. (2021) found two spectral groups among the bright boulders: S-type with an absorption near 1 µm and C/X-type with a flat spectrum. That study found 6 S-types and 15 C/X-types, and in this work we additionally find one S-type and ~70 C/X-types. We found that S-type bright boulders follow two parallel trends consistent with space weathering of ordinary chondrites. Fragments from projectiles suggest that Ryugu may not have formed directly from the original parent body, i.e., it is from a second or higher generation of collisional descendants (Sugimoto et al. in revision). For Bennu, we directly surveyed absorption near 1 µm, which is indicative of anhydrous silicates. We propose 77 objects of exogenic origin including 6 boulders previously reported by DellaGiustina et al. (2021). We find that proposed exogenic objects follow two mixing trends with respect to the average spectrum of Bennu (Tatsumi and Popescu et al. submitted). One trend can be explained by mixing with HED as discussed in DellaGiustina et al. (2021), and the other trend can be explained by mixing with another, olivine-rich composition. Near-infrared spectra also support the possibility of multiple compositions among exogenic objects on Bennu. Thus, similar to Ryugu, Bennu could represent a second or higher generation. Using high-resolution images, we found that more than half of the exogenic objects that we identified on Bennu have breccia-like or inclusion-like morphologies. We do not observe a correlation between morphology and spectral shape. Direct comparison of reflectance spectra of exogenic object spectra between Ryugu and Bennu suggests a compositional difference. Furthermore, the number density of exogenic objects > 0.5 m on Bennu is ~40 times that on Ryugu (Tatsumi and Popescu et al. submitted). Bennu exhibits much more contamination from exogenic materials than Ryugu, which likely reflects differences in impact conditions, such as impact velocity and impactor size.

E. Tatsumi↗
NASA NTRS2022

Electromagnetic signatures from supermassive binary black holes approaching merger

We present fully relativistic predictions for the electromagnetic emission produced by accretion disks surrounding spinning and nonspinning supermassive binary black holes on the verge of merging. We use the code Bothros to post-process data from 3D General Relativistic Magnetohydrodynamic (GRMHD) simulations via ray-tracing calculations. These simulations model the dynamics of a circumbinary disk and the mini-disks that form around two equal-mass black holes orbiting each other at an initial separation of 20 gravitational radii, and evolve the system for more than 10 orbits in the inspiral regime. We model the emission as the sum of thermal blackbody radiation emitted by an optically thick accretion disk and a power-law spectrum extending to hard X-rays emitted by a hot optically thin corona. We generate time-dependent spectra, images, and light curves at various frequencies to investigate intrinsic periodic signals in the emission, as well as the effects of the black hole spin. We find that prograde black hole spin makes mini-disks brighter since the smaller ISCO angular momentum demands more dissipation before matter plunges to the horizon. However, compared to mini-disks in larger separation binaries with spinning black holes, our mini-disks are less luminous: unlike those systems, their mass accretion rate is lower than in the circumbinary disk, and they radiate with lower efficiency because their inflow times are shorter. Compared to a single black hole system matched in mass and accretion rate, these binaries have spectra noticeably weaker and softer in the UV. Finally, we discuss the implications of our findings for the potential observability of these systems.

Eduardo M. Gutiérrez↗
NASA NTRS2022

Contribution of Spicules to Solar Coronal Emission

Recent high-resolution imaging and spectroscopic observations have generated renewed interest in spicules' role in explaining the hot corona. Some studies suggest that some spicules, often classified as type II, may provide significant mass and energy to the corona. Here we use numerical simulations to investigate whether such spicules can produce the observed coronal emission without any additional coronal heating agent. Model spicules consisting of a cold body and hot tip are injected into the base of a warm (0.5 MK) equilibrium loop with different tip temperatures and injection velocities. Both piston- and pressure-driven shocks are produced. We find that the hot tip cools rapidly and disappears from coronal emission lines such as Fe xii 195 and Fe xiv 274. Prolonged hot emission is produced by preexisting loop material heated by the shock and by thermal conduction from the shock. However, the shapes and Doppler shifts of synthetic line profiles show significant discrepancies with observations. Furthermore, spatially and temporally averaged intensities are extremely low, suggesting that if the observed intensities from the quiet Sun and active regions were solely due to type II spicules, one to several orders of magnitude more spicules would be required than have been reported in the literature. This conclusion applies strictly to the ejected spicular material. We make no claims about emissions connected with waves or coronal currents that may be generated during the ejection process and heat the surrounding area.

Shanwlee Sow Mondal↗
NASA NTRSDate not supplied

TPS Certification by Analysis: Model-Driven Characterization of Properties and Failure in Woven Thermal Protection Systems

Woven, ablative thermal protection system (TPS) materials provide a robust option for aggressive (re)entries and thus have been baselined for the upcoming Mars Sample Return (MSR) mission’s Earth Entry System (EES). The reliability requirements for MSR-EES necessitate understanding of material property variability, which could be significant given the complex structure and anisotropic nature of properties in TPS weaves, as well as the response to potential impact with micrometeoroids or orbital debris during the EES re-entry. The TPS Certification by Analysis effort within the Entry Systems Modeling project seeks to provide computational models and analyses that support the certification against such material-based risks. For the present talk, focus will be given to the characterization of baseline woven TPS material properties and mechanical failure limits, which entails (1) use of computational techniques (e.g., machine learning) to interpret computed tomography images of the weave to generate representative structural models and (2) application of multiscale material modeling approaches to characterize thermomechanical and failure properties.

Justin B. Haskins↗
NASA NTRSDate not supplied

Automated Detection and Analysis of Resident Space Objects with the 1.3-Meter Eugene Stansbery-Meter Class Autonomous Telescope

Optical telescopes dedicated to the detection of orbital debris employ large-area detectors that generate a large number of images each night. Such surveys require automated data analysis pipelines that process the images and detect moving objects. We present an overview of the data analysis pipeline employed by the 1.3-meter Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT) on Ascension Island, operated by NASA’s Orbital Debris Program Office. The pipeline enfolds the astrometric and photometric calibration of the images, star-trail removal, object detection, correlation over multiple sequential image frames, and orbital parameter estimation. The performance of the pipeline was investigated by means of Monte-Carlo simulations in which simulated object tracks were inserted into ES-MCAT images and then processed by the pipeline. This technique allows one to confidently estimate the completeness for the detection of resident space objects as a function of apparent magnitude and angular velocity. This paper discusses these techniques and provides examples using actual data.

Paul Hickson↗
NASA NTRS2023

Automated Detection and Analysis of Resident Space Objects with the 1.3-Meter Eugene Stansbery-Meter Class Autonomous Telescope

Optical telescopes dedicated to the detection of orbital debris employ large-area detectors that generate a large number of images each night. Such surveys require automated data analysis pipelines that process the images and detect moving objects. We present an overview of the data analysis pipeline employed by the 1.3-meter Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT) on Ascension Island, operated by NASA’s Orbital Debris Program Office. The pipeline enfolds the astrometric and photometric calibration of the images, star-trail removal, object detection, correlation over multiple sequential image frames, and orbital parameter estimation. The performance of the pipeline was investigated by means of Monte-Carlo simulations in which simulated object tracks were inserted into ES-MCAT images and then processed by the pipeline. This technique allows one to confidently estimate the completeness for the detection of resident space objects as a function of apparent magnitude and angular velocity. This paper discusses these techniques and provides examples using actual data.

Paul Hickson↗
DOE OSTI2024

Machine Learning for DUNE Supernova Trigger

One of the major scientific goals of the Deep Underground Neutrino Experiment (DUNE) is to detect and measure the neutrino flux from galactic core-collapse supernovae. These neutrinos, which exist in the low energy range of up to a few tens of MeV and are responsible for carrying away over 99% of the gravitational binding energy of the supernova, provide an opportunity to study the end of life evolution of massive stars, as well as unique properties and interactions of neutrinos. Because galactic supernovae are expected to occur only on the timespan of every few decades, it is crucial that DUNE is able to detect supernova neutrino interactions when they occur. However, detecting these supernova interactions requires sifting through a large amount of data, and DUNE detectors require a trigger to signal when supernova neutrino events occur. Machine learning provides a potential approach to creating this trigger. This project generates ADC and ground truth images of neutrino interactions in a LArTPC detector as simulated by the Model of Argon Reaction Low Energy Yields (MARLEY) to be used for machine learning. The eventual goal of this work is to facilitate DUNE s detection of supernova neutrino interactions by building a machine learning pipeline to train the trigger algorithm.

Damish, Stephanie↗
DOE OSTI2024

Machine Learning for DUNE Supernova Trigger

One of the major scientific goals of the Deep Underground Neutrino Experiment (DUNE) is to detect and measure the neutrino flux originating from galactic core-collapse supernovae. These neutrinos provide an opportunity to study the end of life evolution of massive stars, and reveal information about the structure of core-collapse that is not visible in observations of the electromagnetic spectrum. Because of the rarity of these events, it is crucial that DUNE is able to detect supernova neutrino interactions when they occur. However, this will require sifting through a large quantity of data, motivating the development of a trigger algorithm to identify significant events and discard irrelevant data. Machine learning provides a potential approach to building this trigger. This project generates ADC and ground truth images of simulated neutrino interactions in a LArTPC detector to be used for machine learning, and uses them to train a sparse Convolutional Neural Network (C NN). The performance of this model when applied to the task of pixel classification based on interaction type is examined. This project found that the sparse CNN approach has the potential to have high accuracy in pixel classification, meaning it may be highly relevant to the development of a supernova neutrino trigger for the DUNE far detector.

Damish, S.↗
NASA NTRS2009

Hardware Implementation of a Bilateral Subtraction Filter

A bilateral subtraction filter has been implemented as a hardware module in the form of a field-programmable gate array (FPGA). In general, a bilateral subtraction filter is a key subsystem of a high-quality stereoscopic machine vision system that utilizes images that are large and/or dense. Bilateral subtraction filters have been implemented in software on general-purpose computers, but the processing speeds attainable in this way even on computers containing the fastest processors are insufficient for real-time applications. The present FPGA bilateral subtraction filter is intended to accelerate processing to real-time speed and to be a prototype of a link in a stereoscopic-machine- vision processing chain, now under development, that would process large and/or dense images in real time and would be implemented in an FPGA. In terms that are necessarily oversimplified for the sake of brevity, a bilateral subtraction filter is a smoothing, edge-preserving filter for suppressing low-frequency noise. The filter operation amounts to replacing the value for each pixel with a weighted average of the values of that pixel and the neighboring pixels in a predefined neighborhood or window (e.g., a 9 9 window). The filter weights depend partly on pixel values and partly on the window size. The present FPGA implementation of a bilateral subtraction filter utilizes a 9 9 window. This implementation was designed to take advantage of the ability to do many of the component computations in parallel pipelines to enable processing of image data at the rate at which they are generated. The filter can be considered to be divided into the following parts (see figure): a) An image pixel pipeline with a 9 9- pixel window generator, b) An array of processing elements; c) An adder tree; d) A smoothing-and-delaying unit; and e) A subtraction unit. After each 9 9 window is created, the affected pixel data are fed to the processing elements. Each processing element is fed the pixel value for its position in the window as well as the pixel value for the central pixel of the window. The absolute difference between these two pixel values is calculated and used as an address in a lookup table. Each processing element has a lookup table, unique for its position in the window, containing the weight coefficients for the Gaussian function for that position. The pixel value is multiplied by the weight, and the outputs of the processing element are the weight and pixel-value weight product. The products and weights are fed to the adder tree. The sum of the products and the sum of the weights are fed to the divider, which computes the sum of products the sum of weights. The output of the divider is denoted the bilateral smoothed image. The smoothing function is a simple weighted average computed over a 3 3 subwindow centered in the 9 9 window. After smoothing, the image is delayed by an additional amount of time needed to match the processing time for computing the bilateral smoothed image. The bilateral smoothed image is then subtracted from the 3 3 smoothed image to produce the final output. The prototype filter as implemented in a commercially available FPGA processes one pixel per clock cycle. Operation at a clock speed of 66 MHz has been demonstrated, and results of a static timing analysis have been interpreted as suggesting that the clock speed could be increased to as much as 100 MHz.

Huertas, Andres↗
DOE OSTI2021

A time-resolved imaging system for the diagnosis of x-ray self-emission in high energy density physics experiments

A diagnostic capable of recording spatially and temporally resolved x-ray self-emission data was developed to characterize experiments on the MAGPIE pulsed-power generator. The diagnostic used two separate imaging systems: a pinhole imaging system with two-dimensional spatial resolution and a slit imaging system with one-dimensional spatial resolution. The two-dimensional imaging system imaged light onto the image plate. The one-dimensional imaging system imaged light onto the same piece of image plate and a linear array of silicon photodiodes. This design allowed the cross-comparison of different images, allowing a picture of the spatial and temporal distribution of x-ray self-emission to be established. The design was tested in a series of pulsed-power-driven magnetic-reconnection experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRSDate not supplied

Improving Sim-to-Real Transfer in Vision-Based Robot Navigation Via Instance-Level GAN-Based Data Augmentation

Achieving robust vision-based robotic tasks requires large amounts of data, which are often difficult to obtain in real-world scenarios. Simulators and synthetic data offer a cost-effective alternative, but the visual gap between simulation and reality hinders the performance of models when deployed in real-world environments. In this paper, we present a data augmentation pipeline that integrates a foundation model (Segment Anything Model) with an unsupervised image-to-image translation model (CycleGAN) for instance-level domain transfer from simulation to reality. This pipeline enables the generation of realistic labeled data from synthetic images for training supervised machine learning models in vision-based navigation tasks. We evaluate our approach on real-world data for ego-vehicle pose estimation, a critical autonomous navigation task involving the prediction of cross-track position and heading angle relative to road center line markings. The results of our tests show that our GAN-based data augmentation pipeline significantly outperforms models trained solely on simulation data or on data processed with standard image augmentation methods for sim-to-real transfer, enhancing model robustness and generalizability in real-world scenarios. Our method provides a scalable and flexible data augmentation tool for leveraging large synthetic datasets to enhance vision-based robotic navigation tasks.

artificial intelligence↗