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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Transport signatures of phase fluctuations in superconducting qubits

Abstract Josephson junctions supply the nonlinear inductance element in superconducting qubits. In the widely used transmon configuration, where the junction is shunted by a large capacitor, the low charging energy minimizes the sensitivity of the qubit to charge noise while maintaining the necessary anharmonicity to qubit states. We report here low-frequency transport measurements on small standalone junctions and identically fabricated capacitively-shunted junctions that show two distinct features normally attributed to small capacitance junctions near zero bias: reduced switching currents and prominent finite resistance associated with phase diffusion in the current–voltage characteristic. Our transport data reveals the existence of phase fluctuations in transmons arising from intrinsic junction capacitance.

Wisne, M. (ORCID:0009000610684209)↗
NASA NTRS1977

Middle atmosphere ion measurements during January, 1976

Parachute-borne measurements of electrical parameters in the stratosphere and mesosphere are discussed which were made in January 1976 as part of a 'coordinated winter-anomaly program' conducted at Wallops Flight Center, Va. The instruments used included Gerdien condensers, a blunt conductivity probe, and UV lamps for obtaining a measure of atmospheric NO. Measurements of the positive component of electrical conductivity on two 'anomalous' and two 'normal' days are compared, wave-interaction electron densities over the height range from 50 to 90 km are determined for the four days, and the effects of the UV lamps on one 'normal' and one 'anomalous' day are described. The results are shown to support the hypothesis that the enhancements in positive ion conductivity observed on 'anomalous' days are caused by a reduction of the aerosol population that is a 'normal' feature of the stratosphere and mesosphere. A possible scenario for this reduction is considered which involves electric-field transport of aerosol particles out of the upper stratosphere and mesosphere.

Hale, L. C.↗
NASA NTRS1991

Venus ionospheric tail rays - Spatial distributions and interplanetary magnetic field control

The overall properties of Venus ionospheric tail rays (such as density, spatial extent, and distribution) and their relationship to the draped magnetic field configuration behind the planet were investigated using measurements obtained by the Pioneer Venus Orbiter Langmuir probe, a magnetometer, and a plasma-wave detector. The results suggest that tail rays are a normal feature of the steady solar wind interaction with Venus and are not generally associated with a central tail plasma sheet. The statistics of the tail rays occurrence point toward the existence of a distributed terminator ionosphere source, consistent with findings of Brace et al. (1990).

Ong, M.↗
NASA NTRS1994

Observations of hysteresis in solar cycle variations among seven solar activity indicators

We show that smoothed time series of 7 indices of solar activity exhibit significant solar cycle dependent differences in their relative variations during the past 20 years. In some cases these observed hysteresis patterns start to repeat over more than one solar cycle, giving evidence that this is a normal feature of solar variability. Among the indices we study, we find that the hysteresis effects are approximately simple phase shifts, and we quantify these phase shifts in terms of lag times behind the leading index, the International Sunspot Number. Our measured lag times range from less than one month to greater than four months and can be much larger than lag times estimated from short-term variations of these same activity indices during the emergence and decay of major active regions. We argue that hysteresis represents a real delay in the onset and decline of solar activity and is an important clue in the search for physical processes responsible for changing solar emission at various wavelengths.

Bachmann, Kurt T.↗
NASA NTRS1995

Windows Program For Driving The TDU-850 Printer

Program provides WYSIWYG compatibility between video display and printout. PDW is Microsoft Windows printer-driver computer program for use with Raytheon TDU-850 printer. Provides previously unavailable linkage between printer and IBM PC-compatible computers running Microsoft Windows. Enhances capabilities of Raytheon TDU-850 hardcopier by emulating all textual and graphical features normally supported by laser/ink-jet printers and makes printer compatible with any Microsoft Windows application. Also provides capabilities not found in laser/ink-jet printer drivers by providing certain Windows applications with ability to render high quality, true gray-scale photographic hardcopy on TDU-850. Written in C language.

Parrish, Brett T.↗
NASA NTRS1997

Tolerant (parallel) Programming

In order to be truly portable, a program must be tolerant of a wide range of development and execution environments, and a parallel program is just one which must be tolerant of a very wide range. This paper first defines the term "tolerant programming", then describes many layers of tools to accomplish it. The primary focus is on F-Nets, a formal model for expressing computation as a folded partial-ordering of operations, thereby providing an architecture-independent expression of tolerant parallel algorithms. For implementing F-Nets, Cooperative Data Sharing (CDS) is a subroutine package for implementing communication efficiently in a large number of environments (e.g. shared memory and message passing). Software Cabling (SC), a very-high-level graphical programming language for building large F-Nets, possesses many of the features normally expected from today's computer languages (e.g. data abstraction, array operations). Finally, L2(sup 3) is a CASE tool which facilitates the construction, compilation, execution, and debugging of SC programs.

DiNucci, David C.↗
NASA NTRS1998

F-Nets and Software Cabling: Deriving a Formal Model and Language for Portable Parallel Programming

Parallel programming is still being based upon antiquated sequence-based definitions of the terms "algorithm" and "computation", resulting in programs which are architecture dependent and difficult to design and analyze. By focusing on obstacles inherent in existing practice, a more portable model is derived here, which is then formalized into a model called Soviets which utilizes a combination of imperative and functional styles. This formalization suggests more general notions of algorithm and computation, as well as insights into the meaning of structured programming in a parallel setting. To illustrate how these principles can be applied, a very-high-level graphical architecture-independent parallel language, called Software Cabling, is described, with many of the features normally expected from today's computer languages (e.g. data abstraction, data parallelism, and object-based programming constructs).

DiNucci, David C.↗
NASA NTRS2009

Meridional Flow Variations Over Three Solar Cycles - What Happened in Cycle 23?

The meridional flow speed determines the strength of the Sun s polar fields in both surface flux transport models and in flux transport dynamos. The polar fields produced during cycle 23 were half as strong as those produced in the previous two cycles. Helioseismic measurements of the meridional flow over the rising phase of cycle 23 indicated a decrease in flow velocity. This observation was used in flux transport dynamo models to predict a delayed start for cycle 24 and was consistent with weak polar fields and a slower equatorward drift of the active latitudes during cycle 23. On the other hand, the surface flux transport models require a faster meridional flow to produce the weak polar fields. We have begun measurements of the surface meridional flow by tracking the motions of weak (outside active regions) magnetic field elements in magnetograms from SOHO/MDI over cycle 23 and from NSO/Kitt Peak over cycles 21 to 23. We confirm the slowdown of the meridional flow over the rising phase of cycle 23 but find that the flow speed returned to its previous level during the declining phase of cycle 23. Furthermore, this appears to be a normal feature of the meridional flow during sunspot cycles. The flow is fast at minima and slow at maxima. The lack of a significantly different meridional flow during cycle 23 is very problematic for both surface flux transport models and flux transport dynamos.

Hathaway, David H.↗
NASA NTRS2012

Engagement Assessment Using EEG Signals

In this paper, we present methods to analyze and improve an EEG-based engagement assessment approach, consisting of data preprocessing, feature extraction and engagement state classification. During data preprocessing, spikes, baseline drift and saturation caused by recording devices in EEG signals are identified and eliminated, and a wavelet based method is utilized to remove ocular and muscular artifacts in the EEG recordings. In feature extraction, power spectrum densities with 1 Hz bin are calculated as features, and these features are analyzed using the Fisher score and the one way ANOVA method. In the classification step, a committee classifier is trained based on the extracted features to assess engagement status. Finally, experiment results showed that there exist significant differences in the extracted features among different subjects, and we have implemented a feature normalization procedure to mitigate the differences and significantly improved the engagement assessment performance.

Li, Feng↗
NASA NTRS2014

Laser Energy Monitor for Double-Pulsed 2-Micrometer IPDA Lidar Application

Integrated path differential absorption (IPDA) lidar is a remote sensing technique for monitoring different atmospheric species. The technique relies on wavelength differentiation between strong and weak absorbing features normalized to the transmitted energy. 2-micron double-pulsed IPDA lidar is best suited for atmospheric carbon dioxide measurements. In such case, the transmitter produces two successive laser pulses separated by short interval (200 microseconds), with low repetition rate (10Hz). Conventional laser energy monitors, based on thermal detectors, are suitable for low repetition rate single pulse lasers. Due to the short pulse interval in double-pulsed lasers, thermal energy monitors underestimate the total transmitted energy. This leads to measurement biases and errors in double-pulsed IPDA technique. The design and calibration of a 2-micron double-pulse laser energy monitor is presented. The design is based on a high-speed, extended range InGaAs pin quantum detectors suitable for separating the two pulse events. Pulse integration is applied for converting the detected pulse power into energy. Results are compared to a photo-electro-magnetic (PEM) detector for impulse response verification. Calibration included comparing the three detection technologies in single-pulsed mode, then comparing the pin and PEM detectors in double-pulsed mode. Energy monitor linearity will be addressed.

Refaat, Tamer F.↗
NASA NTRS2019

Laser Energy Monitor for Triple-Pulse 2-μm IPDA Lidar Application

Integrated path differential absorption (IPDA) lidar is an active remote sensing technique for monitoring different atmospheric species. The technique relies on wavelength differentiation between strong and weak absorbing features normalized to the transmitted energy. An advanced 2-μm triple-pulse IPDA lidar was developed at NASA Langley Research Center for active sensing of carbon dioxide and water vapor simultaneously. The IPDA transmitter produces three successive laser pulses separated by a short interval (200 μs) with a repetition rate of 50Hz. Measurement of laser pulse energy accurately is a prerequisite for the retrieval of gas mixing ratios from IPDA. The design and calibration of a 2-μm triple-pulse laser energy monitor are presented. Due to the short interval between the three transmitted pulses, conventional thermal energy monitors underestimate the total transmitted energy. The design is based on a high speed, extended range InGaAs pin quantum detector suitable for separating the three pulse events. Pulse integration is applied for converting the detected pulse power into energy. The results obtained from the laser energy monitor were compared to an ultra-fast energy-meter reference for energy scaling and verification. High correlations between the pin energy monitor and the total transmitted energy were obtained. The objective of this development is to reduce measurement biases and errors using the triple-pulse IPDA technique.

Refaat, Tamer F.↗
NASA NTRS2022

MLtool: Universal Supervised Machine Learning Tool to Model Tabulated Data

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine learning↗
NASA NTRS2022

MLtool Python Code

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine Learning↗
DOE OSTI2023

WeakIdent: Weak formulation for identifying differential equation using narrow-fit and trimming

Data-driven identification of differential equations is an interesting but challenging problem, especially when the given data are corrupted by noise. When the governing differential equation is a linear combination of various differential terms, the identification problem can be formulated as solving a linear system, with the feature matrix consisting of linear and nonlinear terms multiplied by a coefficient vector. This product is equal to the time derivative term, and thus generates dynamical behaviors. The goal is to identify the correct terms that form the equation to capture the dynamics of the given data. We propose a general and robust framework to recover differential equations using a weak formulation with two new mechanisms, narrow-fit and trimming, for both ordinary and partial differential equations (ODEs and PDEs). The weak formulation facilitates an efficient and robust way to handle noise, and two new mechanisms, narrow-fit and trimming, improve the coefficient support and value recoveries respectively. For each sparsity level, Subspace Pursuit is utilized to find an initial set of support from the large dictionary. Then, we focus on highly dynamic regions (rows of the feature matrix), and error normalize the feature matrix in the narrow-fit step. The support is further updated via trimming the terms that contribute the least. Finally, the support set of features with the smallest Cross-Validation error is chosen as the result. A comprehensive set of numerical experiments are presented for both systems of ODEs and PDEs with various noise levels. The proposed method gives a robust recovery of the coefficients, and a significant denoising effect which can handle up to 100% noise-to-signal ratio for some equations. We compare the proposed method with several state-of-the-art algorithms for the recovery of differential equations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Cytological and ultrastructural studies on root tissues

The anatomy and fine structure of roots from oat and mung bean seedlings, grown under microgravity conditions for 8 days aboard the Space Shuttle, was examined and compared to that of roots from ground control plants grown under similar conditions. Roots from both sets of oat seedlings exhibited characteristic monocotyledonous tissue organization and normal ultrastructural features, except for cortex cell mitochondria, which exhibited a 'swollen' morphology. Various stages of cell division were observed in the meristematic tissues of oat roots. Ground control and flight-grown mung bean roots also showed normal tissue organization, but root cap cells in the flight-grown roots were collapsed and degraded in appearance, especially at the cap periphery. At the ultrastructural level, these cells exhibited a loss of organelle integrity and a highly-condensed cytoplasm. This latter observation perhaps suggests a differing tissue sensitivity for the two species to growth conditions employed in space flight. The basis for abnormal root cap cell development is not understood, but the loss of these putative gravity-sensing cells holds potential significance for long term plant growth orientation during space flight.

NASA Program Space Biology↗
NASA NTRS2000

Multimodality Instrument for Tissue Characterization

A system with multimodality instrument for tissue identification includes a computer-controlled motor driven heuristic probe with a multisensory tip is discussed. For neurosurgical applications, the instrument is mounted on a stereotactic frame for the probe to penetrate the brain in a precisely controlled fashion. The resistance of the brain tissue being penetrated is continually monitored by a miniaturized strain gauge attached to the probe tip. Other modality sensors may be mounted near the probe tip to provide real-time tissue characterizations and the ability to detect the proximity of blood vessels, thus eliminating errors normally associated with registration of pre-operative scans, tissue swelling, elastic tissue deformation, human judgement, etc., and rendering surgical procedures safer, more accurate, and efficient. A neural network, program adaptively learns the information on resistance and other characteristic features of normal brain tissue during the surgery and provides near real-time modeling. A fuzzy logic interface to the neural network program incorporates expert medical knowledge in the learning process. Identification of abnormal brain tissue is determined by the detection of change and comparison with previously learned models of abnormal brain tissues. The operation of the instrument is controlled through a user friendly graphical interface. Patient data is presented in a 3D stereographics display. Acoustic feedback of selected information may optionally be provided. Upon detection of the close proximity to blood vessels or abnormal brain tissue, the computer-controlled motor immediately stops probe penetration.

Mah, Robert W.↗
NASA NTRS2004

Multimodality instrument for tissue characterization

A system with multimodality instrument for tissue identification includes a computer-controlled motor driven heuristic probe with a multisensory tip. For neurosurgical applications, the instrument is mounted on a stereotactic frame for the probe to penetrate the brain in a precisely controlled fashion. The resistance of the brain tissue being penetrated is continually monitored by a miniaturized strain gauge attached to the probe tip. Other modality sensors may be mounted near the probe tip to provide real-time tissue characterizations and the ability to detect the proximity of blood vessels, thus eliminating errors normally associated with registration of pre-operative scans, tissue swelling, elastic tissue deformation, human judgement, etc., and rendering surgical procedures safer, more accurate, and efficient. A neural network program adaptively learns the information on resistance and other characteristic features of normal brain tissue during the surgery and provides near real-time modeling. A fuzzy logic interface to the neural network program incorporates expert medical knowledge in the learning process. Identification of abnormal brain tissue is determined by the detection of change and comparison with previously learned models of abnormal brain tissues. The operation of the instrument is controlled through a user friendly graphical interface. Patient data is presented in a 3D stereographics display. Acoustic feedback of selected information may optionally be provided. Upon detection of the close proximity to blood vessels or abnormal brain tissue, the computer-controlled motor immediately stops probe penetration. The use of this system will make surgical procedures safer, more accurate, and more efficient. Other applications of this system include the detection, prognosis and treatment of breast cancer, prostate cancer, spinal diseases, and use in general exploratory surgery.

Mah, Robert W.↗
DOE OSTI2024

Frequency-domain computing using nonlinear acoustic-wave device on lithium niobate

Abstract Multiply-accumulation are crucial computing operations in signal processing, numerical simulations, and machine learning. In recent years, optical analog approaches have demonstrated higher computing performance and better power efficiency than their digital counterparts. However, analog computing chips usually need large areas and complex structures for parallel computing, as a single device element only executes one computing operation at a single time. Here, we demonstrate frequency-domain computing using the nonlinear acoustic-wave devices on lithium niobate, featuring a normalized external second-harmonic generation conversion efficiency of ~ 5.7 × 10-4 W-1. The second-order sum-frequency nonlinear process of lithium niobate enables multiplication of inputs encoded in the frequency domain. Compared to the analog schemes, our device features a notably simpler design, and nanofabrication requires only one lift-off. Using a single acoustic-wave device within an area of 0.03 mm2, we can simultaneously conduct over 130,000 multiply-accumulation operations. Our acoustic-wave device shows applications in real and complex vector convolutions and image processing. This demonstration sets the stage for experimental realizations into frequency-domain integrated nonlinear acoustic computing systems, potentially shaping future developments in acoustic neural networks and quantum computing.

chai, mingzhao (ORCID:0009000466226341)↗