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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Distributed seams for gigapixel panoramas

Gigapixel panoramas are an increasingly popular digital image application. They are often created as a mosaic of many smaller images. The mosaic acquisition can take many hours causing the individual images to differ in exposure and lighting conditions. A blending operation is often necessary to give the appearance of a seamless image. The blending quality depends on the magnitude of discontinuity along the image boundaries. Often, new boundaries, or seams, are first computed that minimize this transition. Current techniques based on multi-labeling Graph Cuts are too slow and memory intensive for gigapixel sized panoramas. In this paper, we present a parallel, out-of-core seam computing technique that is fast, has small memory footprint, and is capable of running efficiently on different types of parallel systems. Its maximum memory usage is configurable, in the form of a cache, which can improve performance by reducing redundant disk I/O and computations. It shows near-perfect scaling on symmetric multiprocessing systems and good scaling on clusters and distributed shared memory systems. Lastly, our technique improves the time required to compute seams for gigapixel imagery from many hours (or even days) to just a few minutes, while still producing boundaries with energy that is on-par with Graph Cuts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A Scalable Online Monitoring System Based on Elasticsearch for Distributed Data Acquisition in Cms

The part of the CMS Data Acquisition (DAQ) system responsible for data readout and event building is a complex network of interdependent distributed applications. To ensure successful data taking, these programs have to be constantly monitored in order to facilitate the timeliness of necessary corrections in case of any deviation from specified behaviour. A large number of diverse monitoring data samples are periodically collected from multiple sources across the network. Monitoring data are kept in memory for online operations and optionally stored on disk for post-mortem analysis. We present a generic, reusable solution based on an open source NoSQL database, Elasticsearch, which is fully compatible and non-intrusive with respect to the existing system. The motivation is to benefit from an off the-shelf software to facilitate the development, maintenance and support efforts. Elasticsearch provides failover and data redundancy capabilities as well as a programming language independent JSON-over-HTTP interface. The possibility of horizontal scaling matches the requirements of a DAQ monitoring system. The data load from all sources is balanced by redistribution over an Elasticsearch cluster that can be hosted on a computer cloud. In order to achieve the necessary robustness and to validate the scalability of the approach the above monitoring solution currently runs in parallel with an existing in-house developed DAQ monitoring system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1995

Laser-Induced Incandescence Calibration via Gravimetric Sampling

Various beam imaging and/or sheet forming optics delivered light at 1064 nm from a pulsed Nd:YAG laser for use either as a beam of 3 mm radius or as a laser sheet. Imaging measurements were performed with a grated intensified array camera equipped with an ultraviolet f4.5 lens and a 40 mm extension tube. Point measurements were performed using an ultraviolet 250 mm focal length lens to collect and focus the laser induced incandescence (LII) signal into a 1 meter long quartz optical fiber which directed the LII signal to a 1/4 meter monochromator. An aperture preceding the lens restricted the signal collection region to 1 cm along the laser beam at the center of the gravimetric chimney. Signals from the PMT were processed by a boxcar integrator whereas the images were captured digitally using a frame-grabber with 16 MByte of on-board memory. Both 'point' and planar measurements were made with detector gates of 250 ns to minimize possible morphology bias in collection of the LII signal. Additionally, the imaging measurements were performed with broadband spectral collection of the LII signal to maximize the signal and again minimize any potential effects of morphology dependent heating and/or cooling rates. Digital delay generators controlled the firing of he laser, detector gates and data acquisition. Neutral density filters were used for both sets of measurements to maintain signal levels within linear dynamic ranges of the detectors, the range being determined prior to experiments.

VanderWal, R. L.↗
DOE OSTI2012

Active Flash: Out-of-core Data Analytics on Flash Storage

Next generation science will increasingly come to rely on the ability to perform efficient, on-the-fly analytics of data generated by high-performance computing (HPC) simulations, modeling complex physical phenomena. Scientific computing workflows are stymied by the traditional chaining of simulation and data analysis, creating multiple rounds of redundant reads and writes to the storage system, which grows in cost with the ever-increasing gap between compute and storage speeds in HPC clusters. Recent HPC acquisitions have introduced compute node-local flash storage as a means to alleviate this I/O bottleneck. We propose a novel approach, Active Flash, to expedite data analysis pipelines by migrating to the location of the data, the flash device itself. We argue that Active Flash has the potential to enable true out-of-core data analytics by freeing up both the compute core and the associated main memory. By performing analysis locally, dependence on limited bandwidth to a central storage system is reduced, while allowing this analysis to proceed in parallel with the main application. In addition, offloading work from the host to the more power-efficient controller reduces peak system power usage, which is already in the megawatt range and poses a major barrier to HPC system scalability. We propose an architecture for Active Flash, explore energy and performance trade-offs in moving computation from host to storage, demonstrate the ability of appropriate embedded controllers to perform data analysis and reduction tasks at speeds sufficient for this application, and present a simulation study of Active Flash scheduling policies. These results show the viability of the Active Flash model, and its capability to potentially have a transformative impact on scientific data analysis.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks

Abstract The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal–noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-Gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9%, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Rule-based mechanisms of learning for intelligent adaptive flight control

How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

Handelman, David A.↗
DOE OSTI2021

Drought legacies mediated by trait trade‐offs in soil microbiomes

Abstract Soil microbiomes play a key role in driving biogeochemical cycles of the Earth system. As drought frequency and intensity increase due to climate change, soil microbes and the processes they control will be impacted. Even after a drought ends, microbiomes and other systems take time to recover and may display a memory of previous climate conditions. Still, the mechanisms involved in these legacy effects remain unclear, making it difficult to predict climate and biogeochemical rates in the future. Here, we used a trait‐based microbiome model (DEMENTpy) to implement trade‐off‐mediated mechanisms that may lead to drought legacy effects on litter decomposition. Trade‐offs were assumed to follow the Y‐A‐S framework that defines three primary life‐history strategies of microorganisms: high growth Yield, resource Acquisition, and Stress tolerance. We represented cellular trade‐offs between osmolytes required for drought tolerance and investment in enzymes involved in litter decomposition. Simulations were run under varying levels of drought severity and dispersal. With high levels of dispersal, no legacy effects were predicted by DEMENTpy following drought. With limited dispersal, severe drought resulted in a persistent legacy of altered community‐level traits and reduced litter decomposition. Moderate drought resulted in a transient legacy that disappeared after two years, consistent with recent empirical observations in Southern California ecosystems. These results imply that greater movement along the trade‐off between enzyme investment and osmolyte production resulted in stronger legacy effects. More generally, factors that shift the position of a microbiome in YAS space may alter the legacy outcome following drought. Our trait‐based modeling study motivates additional empirical measurements to quantify YAS traits and trade‐offs that are needed to make accurate predictions of soil microbiome resilience and functioning. Also, our study illustrates an emerging approach for representing trait trade‐offs in microbiomes and vegetation that dictate ecosystem responses to drought and other environmental perturbations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

Contour Detector and Data Acquisition System for the Left Ventricular Outline

A real-time contour detector and data acquisition system is described for an angiographic apparatus having a video scanner for converting an X-ray image of a structure characterized by a change in brightness level compared with its surrounding into video format and displaying the X-ray image in recurring video fields. The real-time contour detector and data acqusition system includes track and hold circuits; a reference level analog computer circuit; an analog compartor; a digital processor; a field memory; and a computer interface.

Reiber, J. H. C.↗
NASA NTRS1989

The KAPTUR environment: An operations concept

This report presents a high-level specification and operations concept for KAPTUR, a development environment based on knowledge acquisition for preservation of tradeoffs and underlying rationales. KAPTUR is intended to do what its name implies: to capture knowledge that is required or generated during the development process, but that is often lost because it is contextual, i.e., it does not appear directly in the end-products of development. Such knowledge includes issues that were raised during development, alternatives that were considered, and the reasons for choosing one alternative over others. Contextual information is usually only maintained as a memory in a developer's mind. As time passes, the memories become more vague and individuals become unavailable, and eventually the knowledge is lost. KAPTUR seeks to mitigate this process of attrition by recording and organizing contextual knowledge as it is generated.

Source record↗
NASA NTRS1994

Sample Acquisition and Instrument Deployment (SAID)

This report details the interim progress for contract NASW-4818, Sample Acquisition and Instrument Deployment (SAID), a robotic system for deploying science instruments and acquiring samples for analysis. The system is a conventional four degree of freedom manipulator 2 meters in length. A baseline design has been achieved through analysis and trade studies. The design considers environmental operating conditions on the surface of Mars, as well as volume constraints on proposed Mars landers. Control issues have also been studied, and simulations of joint and tip movements have been performed. A passively braked shape memory actuator with the ability to measure load has been developed. The wrist also contains a mechanism which locks the lid output to the bucket so that objects can be grasped and released for instrument deployment. The wrist actuator has been tested for operational power and mechanical functionality at Mars environmental conditions. The torque which the actuator can produce has been measured. Also, testing in Mars analogous soils has been performed.

Boyd, Robert C.↗
NASA NTRS1989

Data acquisition for a real time fault monitoring and diagnosis knowledge-based system for space power system

The design and implementation of the real-time data acquisition and processing system employed in the AMPERES project is described, including effective data structures for efficient storage and flexible manipulation of the data by the knowledge-based system (KBS), the interprocess communication mechanism required between the data acquisition system and the KBS, and the appropriate data acquisition protocols for collecting data from the sensors. Sensor data are categorized as critical or noncritical data on the basis of the inherent frequencies of the signals and the diagnostic requirements reflected in their values. The critical data set contains 30 analog values and 42 digital values and is collected every 10 ms. The noncritical data set contains 240 analog values and is collected every second. The collected critical and noncritical data are stored in separate circular buffers. Buffers are created in shared memory to enable other processes, i.e., the fault monitoring and diagnosis process and the user interface process, to freely access the data sets.

Wilhite, Larry D.↗
NASA NTRS2006

Real Time Phase Noise Meter Based on a Digital Signal Processor

A digital signal-processing meter for phase noise measurement on sinusoidal signals is dealt with. It enlists a special hardware architecture, made up of a core digital signal processor connected to a data acquisition board, and takes advantage of a quadrature demodulation-based measurement scheme, already proposed by the authors. Thanks to an efficient measurement process and an optimized implementation of its fundamental stages, the proposed meter succeeds in exploiting all hardware resources in such an effective way as to gain high performance and real-time operation. For input frequencies up to some hundreds of kilohertz, the meter is capable both of updating phase noise power spectrum while seamlessly capturing the analyzed signal into its memory, and granting as good frequency resolution as few units of hertz.

phase noise↗
DOE OSTI2024

Detection of Isotopes in Urban Source Search Gamma Spectra Using Hopfield Neural Network

Source search campaigns involve the measurement of consecutive background gamma-ray spectra obtained with a mobile detector-spectrometer, such as tellurium-doped sodium iodide NaI(Tl) scintillator detector-spectrometer. Counts are measured with NaI(Tl) detector in short acquisition intervals, such as one second. Detection of isotopes in search data is challenging because of the low signal-to-noise ratio (S/N) of the spectral lines and highly varying background due to naturally occurring radioactive material (NORM). In this work, we investigate the performance of the detection of orphan Cs-137, Am-241, and I-131 isotopes in a gamma ray data set obtained in a search survey. Isotopic detection is performed with a Hopfield Neural Network (HNN), which implements an auto-associative memory by matching incoming measurements to the closest one of its memory-stored signature patterns. HNN is trained on examples of spectra of calibrated isotopic sources with well-resolved spectral lines. During testing, HNN processes spectra with partially complete or distorted isotopic lines. The performance of the HNN detection algorithm is benchmarked with Precision, Recall, and F1 scores. Test results demonstrate the potential of HNN to detect the isotope of interest with high accuracy (F1 > 90%) for all isotopes in this study.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2016

DeepSAT: A Deep Learning Approach to Tree-Cover Delineation in 1-m NAIP Imagery for the Continental United States

High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with ~60 million pixels) and has a total size of ~65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to ~8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the state of California spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles has produced true positive rates of around 88 percent for fragmented forests and 74 percent for urban tree cover areas, with false positive rates lower than 2 percent for both landscapes.

Imagery↗
DOE OSTI2013

Morris County Improvement Authority, Morris County, New Jersey Renewable Energy Initiative

The Morris County Improvement Authority (Authority), a public body corporate and politic of the State of New Jersey and created and controlled by the County, at the direction of the County and through the Program guaranteed by the County, financed 3.2 MW of solar projects (Solar Projects) at fifteen (15) sites for seven (7) local government units (Local Units) in and including the County. The Program uses a Power Purchase Agreement (PPA) structure, where the Solar Developer constructs, operates and maintains all of the Solar Projects, for the benefit of the Local Units and the Authority, for the maximum State law allowable PPA period of fifteen (15) years. Although all fifteen (15) sites were funded by the Authority, only the Mennen Arena site was considered for the purposes of the required local match funding for this grant. Specifically at the Mennen Arena site, the Authority financed 1.6 MW of solar panels. On October 18, 2013, the DOE Grant was drawn down following completion of the necessary application documents and final execution of an agreement memorializing the contemplated transaction by the Local Units, the County, The Authority and the solar developer. The proceeds of the DOE Grant were then applied to reduce the PPA price to all Local Units across the program and increase the savings from approximately 1/3 to almost half off the existing and forecasted utility pricing over the fifteen (15) year term, without adversely affecting all of the other benefits. With the application of the rate buy down, the price of electricity purchased under the PPA dropped from 10.9 to 7.7 cents/kWh. This made acquisition of renewable energy much more affordable for the Local Units, and it enhanced the success of the program, which will encourage other counties and local units to develop similar programs.

14 SOLAR ENERGY↗
NASA NTRS1985

Method and apparatus for I-V data acquisition from solar cells

A method and apparatus for logging current-voltage (I-V) characteristic d of a solar cell module (10) in two modes using a portable instrument. One mode controls the load current through a circuit (36) in 256 equal intervals while voltage is measured from open circuit to at least halfway into the knee of the curve and the other mode controls the load voltage through a circuit (34) in 256 equal intervals from the lowest voltage measurement possible (short circuit) to at least halfway into the knee of the curve, under control of a microcomputer (12). All measurements are packed by discarding each measurement that is within 0.5% of the value predicted from two previous measurements, except every ninth (9th) measurement which is retained. The remaining data is further packed into a memory block of a detachable storage medium (14) by recording the data points in sequence following a header containing data common to all points, with each point having the value of the controlled parameter recorded as the number of increments from the previous point recorded followed by the measured value. The detachable storage medium is preferably a solid state device for reliability, and is transferable to a playback terminal which unpacks the data for analysis and display.

Cole, Steven W.↗
DOE OSTI2023

TomocuPy – efficient GPU-based tomographic reconstruction with asynchronous data processing

Fast 3D data analysis and steering of a tomographic experiment by changing environmental conditions or acquisition parameters require fast, close to real-time, 3D reconstruction of large data volumes. Here a performance-optimized TomocuPy package is presented as a GPU alternative to the commonly used central processing unit (CPU) based TomoPy package for tomographic reconstruction. TomocuPy utilizes modern hardware capabilities to organize a 3D asynchronous reconstruction involving parallel read/write operations with storage drives, CPU–GPU data transfers, and GPU computations. In the asynchronous reconstruction, all the operations are timely overlapped to almost fully hide all data management time. Since most cameras work with less than 16-bit digital output, the memory usage and processing speed are furthermore optimized by using 16-bit floating-point arithmetic. As a result, 3D reconstruction with TomocuPy became 20–30 times faster than its multi-threaded CPU equivalent. Full reconstruction (including read/write operations and methods initialization) of a 2048 3 tomographic volume takes less than 7 s on a single Nvidia Tesla A100 and PCIe 4.0 NVMe SSD, and scales almost linearly increasing the data size. To simplify operation at synchrotron beamlines, TomocuPy provides an easy-to-use command-line interface. Efficacy of the package was demonstrated during a tomographic experiment on gas-hydrate formation in porous samples, where a steering option was implemented as a lens-changing mechanism for zooming to regions of interest.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1992

Digital tracking loops for a programmable digital modem

In this paper, an analysis and hardware emulation of the tracking loops for a very flexible programmable digital modem (PDM) will be presented. The modem is capable of being programmed for 2, 4, 8, 16-PSK, 16-QAM, MSK, and Offset-QPSK modulation schemes over a range of data rates from 2.34 to 300 Mbps with programmable spectral occupancy from 1.2 to 1.8 times the symbol rate; these operational parameters are executable in burst or continuous mode. All of the critical processing in both the modulator and demodulator is done at baseband with very high-speed digital hardware and memory. Quadrature analog front-ends are used for translation between baseband and the IF center frequency. The modulator is based on a table lookup approach, where precomputed samples are stored in memory and clocked out according to the incoming data pattern. The sample values are predistorted to counteract the effects of the other filtering functions in the link as well as any transmission impairments. The demodulator architecture was adapted from a joint estimator-detector (JED) mathematical analysis. Its structure is applicable to most signalling formats that can be represented in a two-dimensional space. The JED realization uses interdependent, mutually aiding tracking loops with post-detection data feedback. To expedite and provide for more reliable synchronization, initial estimates for these loops are computed in a parallel acquisition processor. The cornerstone of the demodulator realization is the pre-averager received data filter which allows operation over a broad range of data rates without any hardware changes and greatly simplifies the implementation complexity. The emulation results confirmed tracking loop operation over the entire range of operational parameters listed above, as well as the capability of achieving and maintaining synchronization at BER's in excess of 10(exp -1). The emulation results also showed very close agreement with the tracking loop analysis, and validated the resolution apportionment of the various hardware elements in the tracking loops.

Poklemba, John J.↗