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At least 829 records · Page 46

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Scattering phase shifts from a quantum computer

We calculate two-body scattering phase shifts on a quantum computer using a leading order short-range effective field theory Hamiltonian. The algorithm combines the variational quantum eigensolver and the quantum subspace expansion. As an example, we consider scattering in the deuteron 3S1 partial wave. We calculate scattering phase shifts with a quantum simulator and on real hardware. Here, we also study how noise impacts these calculations and discuss noise mitigation required to extend our work to larger quantum processing units. With current hardware, up to five superconducting qubits can produce acceptable results, and larger calculations will require a significant noise reduction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1993

Photorefractive Semiconductors and Applications

Photorefractive semiconductors are attractive for information processing, becuase of fast material response, compatibility with semiconductor lasers, and availability of cross polarization diffraction for enhancing signal-to-noise ration. This paper presents recent experimental results on information processing using photorefractive GaAs, InP and CdTe, including image processing with semiconductor lasers.

Photorefractive Semiconductors↗

NASA NTRS1991

A superior edge preserving filter with a systematic analysis

A new, adaptive, edge preserving filter for use in image processing is presented. It had superior performance when compared to other filters. Termed the contiguous K-average, it aggregates pixels by examining all pixels contiguous to an existing cluster and adding the pixel closest to the mean of the existing cluster. The process is iterated until K pixels were accumulated. Rather than simply compare the visual results of processing with this operator to other filters, some approaches were developed which allow quantitative evaluation of how well and filter performs. Particular attention is given to the standard deviation of noise within a feature and the stability of imagery under iterative processing. Demonstrations illustrate the performance of several filters to discriminate against noise and retain edges, the effect of filtering as a preprocessing step, and the utility of the contiguous K-average filter when used with remote sensing data.

Holladay, Kenneth W.↗

NASA NTRS2012

Investigation of Twin Jet Aeroacoustic Properties in the Presence of a Hybrid Wing Body Shield

In preparation for upcoming wind tunnel acoustic experiments of a Hybrid Wing Body (HWB) vehicle with two jet engine simulator units, a series of twin jet aeroacoustic investigations were conducted leading to increased understanding and risk mitigation. A previously existing twin jet nozzle system and a fabricated HWB aft deck fuselage are combined for a 1.9% model scale study of jet nozzle spacing and jet cant angle effects, elevon deflection into the jet plume, and acoustic shielding by the fuselage body. Linear and phased array microphone measurements are made, and data processing includes the use of DAMAS (Deconvolution Approach for the Mapping of Acoustic Sources). Closely-spaced twin jets with a 5 inward cant angle exhibit reduced noise levels compared to their parallel flow counterparts at similar and larger nozzle spacings. A 40 elevon deflection into the twin jet plume, which is required for HWB ground rotation, can significantly increase upstream noise levels (more than 5 dB OASPL) with only minimal increases in the downstream direction. Lastly, DAMAS processing can successfully measure the noise source distribution of multiple shielded jet sources.

Doty, Michael J.↗

NASA NTRS1984

The spatial structure of terrain - A process signal in satellite digital images

Pattern recognition procedures applied to Landsat imagery carry an implicit assumption that the digital data are independently distributed. That assumption is incorrect over virtually any terrain. Deviations from independence occur because slopes follow a systematic pattern of variation arising from the slope-forming processes. That pattern can be identified using the stochastic process methodology of Box and Jenkins.Angles of adjacent slopes are autocorrelated and the bidirectional reflectance function transfers these systematic slope changes to the sensor. Imagery becomes autocorrelated through this transfer. Autocorrelation in the imagery can be removed through direct calculation from a digital elevation model or by use of stochastic process methodology. The latter has the advantage that the residuals are white noise; and it is applicable in any area, even where a D.E.M. is unavailable. The stochastic process signal can be used to study terrain processes.

Craig, R. G.↗

NASA NTRS1994

Microsecond-scale electric field pulses in cloud lightning discharges

From wideband electric field records acquired using a 12-bit digitizing system with a 500-ns sampling interval, microsecond-scale pulses in different stages of cloud flashes in Florida and New Mexico are analyzed. Pulse occurrence statistics and waveshape characteristics are presented. The larger pulses tend to occur early in the flash, confirming the results of Bils et al. (1988) and in contrast with the three-stage representation of cloud-discharge electric fields suggested by Kitagawa and Brook (1960). Possible explanations for the discrepancy are discussed. The tendency for the larger pulses to occur early in the cloud flash suggests that they are related to the initial in-cloud channel formation processes and contradicts the common view found in the atmospheric radio-noise literature that the main sources of VLF/LF electromagnetic radiation in cloud flashes are the K processes which occur in the final, or J type, part of the cloud discharge.

Villanueva, Y.↗

DOE OSTI2021

GLEAM: Galaxy Line Emission & Absorption Modeling

We present Galaxy Line Emission & Absorption Modeling (gleam), a Python tool for fitting Gaussian models to emission and absorption lines in large samples of 1D extragalactic spectra. gleam is tailored to work well in batch mode without much human interaction. With gleam, users can uniformly process a variety of spectra, including galaxies and active galactic nuclei, in a wide range of instrument setups and signal-to-noise regimes. gleam also takes advantage of multiprocessing capabilities to process spectra in parallel. With the goal of enabling reproducible workflows for its users, gleam employs a small number of input files, including a central, user-friendly configuration in which fitting constraints can be defined for groups of spectra and overrides can be specified for edge cases. For each spectrum, gleam produces a table containing measurements and error bars for the detected spectral lines and continuum and upper limits for nondetections. For visual inspection and publishing, gleam can also produce plots of the data with fitted lines overlaid. In the present paper, we describe gleam’s main features, the necessary inputs, expected outputs, and some example applications, including thorough tests on a large sample of optical/infrared multi-object spectroscopic observations and integral field spectroscopic data. gleam is developed as an open-source project hosted at https://github.com/multiwavelength/gleam and welcomes community contributions.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Coexistent quantum channel characterization using quantum process tomography with spectrally resolved detection

The coexistence of classical and quantum signals over the same optical fiber is critical for quantum networks operating within the existing communications infrastructure. Here, we characterize the quantum channel that results from distributing approximate single-photon polarization-encoded qubits simultaneously with classical light of varying intensities through a 25 km fiber-optic channel. We use spectrally resolved quantum process tomography with a newly developed Bayesian reconstruction method to estimate the quantum channel from experimental data, both with and without classical noise. Furthermore, we show that the coexistent fiber-based quantum channel has high process fidelity with an ideal depolarizing channel if the noise is dominated by Raman scattering. These results aid future development of quantum repeater designs and quantum error-correcting codes which benefit from realistic channel error models.

Chapman, Joseph↗

NASA NTRS1988

Controlling basins of attraction in a neural network-based telemetry monitor

The size of the basins of attraction around fixed points in recurrent neural nets (NNs) can be modified by a training process. Controlling these attractive regions by presenting training data with various amount of noise added to the prototype signal vectors is discussed. Application of this technique to signal processing results in a classification system whose sensitivity can be controlled. This new technique is applied to the classification of temporal sequences in telemetry data.

Bell, Benjamin↗

DOE OSTI2021

Moving from scientific research to consenting guidance for MRE environmental risk

Potential environmental effects from tidal and wave devices are of concern to regulators, advisors, and other stakeholders in many nations. Monitoring results from early deployments and the first commercial arrays, coupled with targeted research studies, are providing a growing base of knowledge of how components of tidal turbines and wave energy converters might interact with marine animals and habitats. Efforts are underway to organize and direct these findings towards facilitating consenting that allays concerns and allows the marine renewable energy (MRE) industry to move forward. The OES-Environmental international initiative has developed scientific evidence bases for several key interactions from MRE devices, organized around stressors (portions of MRE systems that may cause injury or stress to the marine ecosystem), and receptors (the animals, habitats, and ecosystem processes that may be affected). This paper summarizes the evidence bases for four stressors (underwater noise, electromagnetic fields, habitat change, and changes in oceanographic systems) and presents the process of moving from the scientific knowledge into guidance documents to support the regulatory process. The guidance documents will serve as a broad guide that can be used internationally to look at stressor-receptor interactions of interest within a regulatory context. The evidence bases and guidance documents aim to assist MRE developers, regulators, and advisors with project scoping, consenting, and licensing processes.

Copping, Andrea E.↗

NASA NTRS1985

Equivalent-noise model for contrast detection and discrimination

Models for the detection and the discrimination of low-contrast signals by human observers typically assume that the observer is limited by the filtering action of the visual system and by the noisy character of its processing. For some models both the filtering and the noise can be represented by a noise in the stimulus domain, the input equivalent noise of the model. The present study derives some formulas for computing this noise, describes the calculation of a sample, and discusses some implications of this approach.

Ahumada, A. J., Jr.↗

NASA NTRS1992

On the use of stochastic process-based methods for the analysis of hyperspectral data

Further development in remote sensing technology requires refinement of information system design aspects, i.e., the ability to specify precisely the data to collect and the means to extract increasing amounts of information from the increasingly rich and complex data stream created. One of the principal directions of advance is that data from much larger numbers of spectral bands can be collected, but with significantly increased signal-to-noise ratio. The theory of stochastic or random processes may be applied to the modeling of second-order variations. A multispectral data set with a large number of spectral bands is analyzed using standard pattern recognition techniques. The data were classified using first a single spectral feature, then two, and continuing on with greater and greater numbers of features. Three different classification schemes are used: a standard maximum likelihood Gaussian scheme; the same approach with the mean values of all classes adjusted to be the same; and the use of a minimum distance to means scheme such that mean differences are used.

Landgrebe, David A.↗

NASA NTRS2007

Switched Band-Pass Filters for Adaptive Transceivers

Switched band-pass filters are key components of proposed adaptive, software- defined radio transceivers that would be parts of envisioned digital-data-communication networks that would enable real-time acquisition and monitoring of data from geographically distributed sensors. Examples of sensors to be connected to such networks include security cameras, radio-frequency identification units, and geolocation units based on the Global Positioning System. Through suitable software configuration and without changing hardware, these transceivers could be made to operate according to any of a number of complex wireless-communication standards that could be characterized by diverse modulation schemes, bandwidths, and data-handling protocols. The adaptive transceivers would include field-programmable gate arrays (FPGAs) and digital signal-processing hardware. In the receiving path of a transceiver, the incoming signal would be amplified by a low-noise amplifier (LNA). The output spectrum of the LNA would be processed by a band-pass filter operating in the frequency range between 900 MHz and 2.4 GHz. Then a down-converter would translate the signal to a lower frequency range to facilitate analog-to-digital conversion, which would be followed by baseband processing by one or more FPGAs. In the transmitting path, a digital stream would first be converted to an analog signal, which would then be up-converted to a selected frequency band before being applied to a transmitting power amplifier. The aforementioned band-pass filter in the receiving path would be a combination of resonant inductor-and-capacitor filters and switched band-pass filters. The overall combination would implement a switch function designed mathematically to exhibit desired frequency responses and to switch the signal in each frequency band to an analog-to-digital converter appropriate for that band to produce a digital intermediate-frequency signal for digital signal processing.

Wang, Ray↗

DOE OSTI2023

Quantum Noise Mitigation: Introducing the Robust Quantum Circuit Scheduler for Enhanced Fidelity and Throughput

Undoubtedly, quantum computing offers valuable acceleration for solving intricate problems. One of the primary hurdles lies in executing large-scale quantum applications on backend machines. Qubit noise, among other factors, dramatically influences the execution process. Implementing effective scheduling techniques for quantum circuits is crucial for practical quantum computing and preventing excessive waiting times. The quantum realm is distinct from classical computing in terms of optimization, performance, utilization, and waiting periods. Consequently, the parameters and components of quantum circuit scheduling diverge from those of classical computing. This paper presents Quantum Noise Mitigation: Introducing the Robust Quantum Circuit Scheduler for Enhanced Fidelity and Throughput, a straightforward yet effective scheduling framework and policy that enhances noise resilience, throughput, and the fidelity of quantum circuits. Drawing inspiration from classical methods, our scheduling approach incorporates additional constraints tailored for quantum logic. The outcome demonstrates a substantial improvement in fidelity and resource management, which is vital for real-world quantum applications.

Baheri, Betis↗

DOE OSTI2021

Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their wide range of monitoring applications across many industries, including aerospace, defense, security, civil engineering, and energy. FOS technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities and multiparametric monitoring with resilience to harsh environmental conditions. The major limitations posed by FOS are 1) cross‐sensitivity, 2) enormous volume and large data generation, 3) low data processing speed, 4) degradation of signal‐to‐noise ratio over the fiber length, and 5) overall cost of sensor and interrogator systems. These challenges can be overcome by building advanced data analytics engines enabled by recent breakthroughs in machine learning (ML) and artificial intelligence (AI). This article presents a comprehensive review of recent studies that integrate ML and AI algorithms with FOS technologies. This review also highlights several FOS technology development directions that promise a significant impact on widespread use for several industrial applications, with an emphasis on energy systems monitoring. A perspective on future directions for further research development is also provided.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Structure of the Ecuadorian Upper Plate From a Joint Seismic‐Gravity Inversion

The Ecuadorian portion of the South American subduction zone presents an interesting case study in the structure and complex evolution of an upper plate. There are outstanding questions about its tectonic history, composition, and magmatic processes. While previous studies have employed ambient noise tomography to image the Ecuadorian upper plate, surface wave inversions alone often lack sensitivity at relevant shallow depths. This limitation can be overcome with an independent, complementary data set, such as gravity. We have jointly inverted Rayleigh wave phase velocities and Bouguer gravity anomalies to provide a more detailed seismic velocity model of the Ecuadorian upper plate. Our joint inversion has yielded several key improvements from previous models. First, we observe much shallower slow velocities beneath major basins (the Manabí, Progreso, and Gulf of Guayaquil), better aligning with expected basin structure. Second, we identify a high-velocity block beneath the entire forearc, corresponding to the Piñon Terrane, with velocities suggesting the presence of ultramafic material. Third, we highlight a new narrow swath of slow velocities beneath the Ecuadorian Andes, which closely follows the active volcanoes along the Eastern Cordillera. The extent of these slow velocities coincides with the termination of active arc volcanism and the predicted location of the subducted Carnegie Ridge. The predicted compositions for the mid to lower crust in the region preclude a purely compositional explanation for these velocities, suggesting that some level of partial melt is necessary.

Birkey, Andrew [Univ. of Delaware, Newark, DE (Uni↗

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↗

DOE OSTI2024

Cross-correlation image analysis for real-time single particle tracking

Accurately measuring the translations of objects between images is essential in many fields, including biology, medicine, chemistry, and physics. One important application is tracking one or more particles by measuring their apparent displacements in a series of images. Popular methods, such as the center of mass, often require idealized scenarios to reach the shot noise limit of particle tracking and, therefore, are not generally applicable to multiple image types. More general methods, such as maximum likelihood estimation, reliably approach the shot noise limit, but are too computationally intense for use in real-time applications. These limitations are significant, as real-time, shot-noise-limited particle tracking is of paramount importance for feedback control systems. To fill this gap, we introduce a new cross-correlation-based algorithm that approaches shot-noise-limited displacement detection and a graphics processing unit-based implementation for real-time image analysis of a single particle.

Instruments & Instrumentation↗