SEARCH · Search NASA

Results for “Advanced Signal Processing”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12

All sources NASA NTRS DOE OSTI

DOE OSTI2019

Novel techniques for constraining neutron-capture rates relevant for r -process heavy-element nucleosynthesis

The rapid-neutron capture process (r process) is identified as the producer of about 50% of elements heavier than iron. This process requires an astrophysical environment with an extremely high neutron flux over a short amount of time ( seconds), creating very neutron-rich nuclei that are subsequently transformed to stable nuclei via beta decay. In 2017, one site for the r process was confiimed: the advanced LIGO and advanced Virgo detectors observed two neutron stars merging, and immediate follow-up measurements of the electromagnetic transients demonstrated an "afterglow" over a broad range of frequencies fully consistent with the expected signal of an r process taking place. Although neutronstar mergers are now known to be r-process element factories, contributions from other sites are still possible, and a comprehensive understanding and description of the r process is still lacking. One key ingredient to large-scale r-process reaction networks is radiative neutron-capture ( n , γ ) rates, for which there exist virtually no data for extremely neutron rich nuclei involved in the r process. Due to the current status of nuclear-reaction theory and our poor understanding of basic nuclear properties such as level densities and average g-decay strengths, theoretically estimated ( n , γ ) rates may vary by orders of magnitude and represent a major source of uncertainty in any nuclear-reaction network calculation of r-process abundances. Here in this review, we discuss new approaches to provide information on neutron-capture cross sections and reaction rates relevant to the r process. In particular, we focus on indirect, experimental techniques to measure radiative neutron-capture rates. While direct measurements are not available at present, but could possibly be realized in the future, the indirect approaches present a first step towards constraining neutron-capture rates of importance to the r process.

(n, γ,) cross sections↗

DOE OSTI2019

Self-Taught Waveform Synthesis and Analysis in the Amplify-and-Forward Relay Channel

Wireless communications plays a pivotal role in multiple complex domains such as tactical networks or space communications. Traditional physical (PHY) layer protocols for digital communications contain chains of signal processing blocks that have been mathematically optimized to transmit information bits efficiently over noisy channels. Unfortunately, the ongoing advancement of hardware and software design, and algorithm development, makes it difficult for some domains to keep up with the constant change in modern communication systems. It has been shown previously that combining deep learning with digital modulation (deepmod) allows a system to learn communications on its own rather than requiring human-invented protocols. This is particularly attractive to space communications where updating PHY layer technologies may be prohibitively complex or expensive. A link using deepmod is able to learn both waveform synthesis (transmit) and analysis (receive) that is self-taught. When deepmod is first initiated it has no knowledge of the channel medium but quickly learns to communicate by synthesizing waveforms that can be successfully decoded at the other end of the link. This is accomplished by a custom deep neural network especially suited for this particular task of learning. In this current work, we show that deepmod learns in both traditional point-to-point channels as well as the more abstract multi-hop amplify-and-forward relay channel. In the experimental results, even though no direct link between transmitter and receiver exists, deepmod-enabled nodes still create latent information bearing waveforms that can be used for communications.

Anderson, Adam L.↗

NASA NTRS1982

Study of sensor spectral responses and data processing algorithms and architectures for onboard feature identification

A computational model of the deterministic and stochastic processes involved in remote sensing is used to study spectral feature identification techniques for real-time onboard processing of data acquired with advanced earth-resources sensors. Preliminary results indicate that: Narrow spectral responses are advantageous; signal normalization improves mean-square distance (MSD) classification accuracy but tends to degrade maximum-likelihood (MLH) classification accuracy; and MSD classification of normalized signals performs better than the computationally more complex MLH classification when imaging conditions change appreciably from those conditions during which reference data were acquired. The results also indicate that autonomous categorization of TM signals into vegetation, bare land, water, snow and clouds can be accomplished with adequate reliability for many applications over a reasonably wide range of imaging conditions. However, further analysis is required to develop computationally efficient boundary approximation algorithms for such categorization.

Huck, F. O.↗

NASA NTRS1989

A Normal Incidence X-ray Telescope (NIXT) sounding rocket payload

Work on the High Resolution X-ray (HRX) Detector Program is described. In the laboratory and flight programs, multiple copies of a general purpose set of electronics which control the camera, signal processing and data acquisition, were constructed. A typical system consists of a phosphor convertor, image intensifier, a fiber optics coupler, a charge coupled device (CCD) readout, and a set of camera, signal processing and memory electronics. An initial rocket detector prototype camera was tested in flight and performed perfectly. An advanced prototype detector system was incorporated on another rocket flight, in which a high resolution heterojunction vidicon tube was used as the readout device for the H(alpha) telescope. The camera electronics for this tube were built in-house and included in the flight electronics. Performance of this detector system was 100 percent satisfactory. The laboratory X-ray system for operation on the ground is also described.

Golub, Leon↗

DOE OSTI2023

AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers

We introduce spatiotemporal-graph models that concurrently process data from the twin advanced LIGO detectors and the advanced Virgo detector. We trained these AI classifiers with 2.4 million IMRPhenomXPHM waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers with component masses m{1,2}∈[3M⊙,50M⊙], and individual spins sz{1,2}∈[−0.9,0.9]; and which include the (ℓ,|m|)={(2,2),(2,1),(3,3),(3,2),(4,4)} modes, and mode mixing effects in the ℓ=3,|m|=2 harmonics. We trained these AI classifiers within 22 hours using distributed training over 96 NVIDIA V100 GPUs in the Summit supercomputer. We then used transfer learning to create AI predictors that estimate the total mass of potential binary black holes identified by all AI classifiers in the ensemble. We used this ensemble, 3 classifiers for signal detection and 2 total mass predictors, to process a year-long test set in which we injected 300,000 signals. This year-long test set was processed within 5.19 minutes using 1024 NVIDIA A100 GPUs in the Polaris supercomputer (for AI inference) and 128 CPU nodes in the ThetaKNL supercomputer (for post-processing of noise triggers), housed at the Argonne Leadership Computing Facility. These studies indicate that our AI ensemble provides state-of-the-art signal detection accuracy, and reports 2 misclassifications for every year of searched data. This is the first AI ensemble designed to search for and find higher order gravitational wave mode signals.

Tian, Minyang↗

NASA NTRS1984

Land Mobile Satellite Service (LMSS) channel simulator: An end-to-end hardware simulation and study of the LMSS communications links

The design and implementation of the Land Mobile Satellite Service (LMSS) channel simulator as a facility for an end to end hardware simulation of the LMSS communications links, primarily with the mobile terminal is described. A number of studies are reported which show the applications of the channel simulator as a facility for validation and assessment of the LMSS design requirements and capabilities by performing quantitative measurements and qualitative audio evaluations for various link design parameters and channel impairments under simulated LMSS operating conditions. As a first application, the LMSS channel simulator was used in the evaluation of a system based on the voice processing and modulation (e.g., NBFM with 30 kHz of channel spacing and a 2 kHz rms frequency deviation for average talkers) selected for the Bell System's Advanced Mobile Phone Service (AMPS). The various details of the hardware design, qualitative audio evaluation techniques, signal to channel impairment measurement techniques, the justifications for criteria of different parameter selection in regards to the voice processing and modulation methods, and the results of a number of parametric studies are further described.

Salmasi, A. B.↗

NASA NTRS1986

On the effectiveness of onboard processing

The effectiveness of onboard processing in communications satellites is discussed, and the interrelationships between the various elements of onboard processing are described. An overview is given of NASA's Advanced Communications Technology Satellite (ACTS), emphasizing its baseboard processor. The onboard processing features of ACTS are described, including frequency reuse, signal regeneration, TDMA/FDMA demand assigned multiple access, forward error correction, routing, and dynamic reconfiguration. The terminals of the ACTS ground segment are described, and comparisons are made between satellite onboard processing and other technologies.

Moy, L. L.↗

NASA NTRS1993

Integrated CFD modeling of gas turbine combustors

3D, curvilinear, multi-domain CFD analysis is becoming a valuable tool in gas turbine combustor design. Used as a supplement to experimental testing. CFD analysis can provide improved understanding of combustor aerodynamics and used to qualitatively assess new combustor designs. This paper discusses recent advancements in CFD combustor methodology, including the timely integration of the design (i.e. CAD) and analysis (i.e. CFD) processes. Allied Signal's F124 combustor was analyzed at maximum power conditions. The assumption of turbulence levels at the nozzle/swirler inlet was shown to be very important in the prediction of combustor exit temperatures. Predicted exit temperatures were compared to experimental rake data, and good overall agreement was seen. Exit radial temperature profiles were well predicted, while the predicted pattern factor was 25 percent higher than the harmonic-averaged experimental pattern factor.

Fuller, E. J.↗

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.↗

DOE OSTI2013

Vascular endothelial growth factor signaling regulates the segregation of artery and vein via ERK activity during vascular development

Highlights: ► VEGF-A signaling regulates the segregation of axial vessels. ► VEGF-A signaling is mediated by PKC and ERK in this process. ► Ectopic activation of ERK is sufficient to rescue defects in vessel segregation. -- Abstract: Segregation of two axial vessels, the dorsal aorta and caudal vein, is one of the earliest patterning events occur during development of vasculature. Despite the importance of this process and recent advances in our understanding on vascular patterning during development, molecular mechanisms that coordinate the segregation of axial vessels remain largely elusive. In this report, we find that vascular endothelial growth factor-A (Vegf-A) signaling regulates the segregation of dorsal aorta and axial vein during development. Inhibition of Vegf-A pathway components including ligand Vegf-A and its cognate receptor Kdrl, caused failure in segregation of axial vessels in zebrafish embryos. Similarly, chemical inhibition of Mitogen-activated protein kinase kinase (Map2k1)/Extracellular-signal-regulated kinases (Erk) and phosphatidylinositol 3-kinases (PI3 K), which are downstream effectors of Vegf-A signaling pathway, led to the fusion of two axial vessels. Moreover, we find that restoring Erk activity by over-expression of constitutively active MEK in embryos with a reduced level of Vegf-A signaling can rescue the defects in axial vessel segregation. Taken together, our data show that segregation of axial vessels requires the function of Vegf-A signaling, and Erk may function as the major downstream effector in this process.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Acoustic Travel-Time Tomography for Wind Energy

New remote sensing technologies for direct observation of the atmospheric flow in the proximity of operating wind turbines are necessary for the advancement of wind turbine design, wind plant control, and the validation of high-fidelity numerical models; fundamental goals of the DOE Wind Energy Technology Office's (WETO's) A2e program. Modern remote sensing technologies for commercial and research applications rely on the backscatter of light or sound waves, which limits spatial and temporal resolution and cannot produce reliable flow estimates near solid bodies or reflective surfaces. Acoustic tomography (AT) is an innovative sensing technology based on fundamental thermodynamic properties of the atmosphere and an inverse algorithm that can reconstruct both velocity and temperature fields within a network of acoustic transducers. AT relies on the direct travel path of acoustic signals between speakers and microphones in the network and can achieve much higher resolutions than conventional backscatter technologies. For wind energy R&D applications, AT represents an extremely economical option over other remote sensing technologies, integrating mature and commonly available acoustic and signal processing hardware. This report is intended as an update on the development of the AT array on the NREL Flatirons Campus (FC) and includes a brief review of the instrumentation, the calibration of equipment, signal processing, and resulting flow field estimates. Designs for future AT systems and applications are detailed in this report, including summaries of new capabilities, engineering and theoretical challenges, and anticipated costs help to outline future directions for AT technology R\&D. The potential for application of AT to utility-scale turbines is also discussed. Investing in the development of the proposed instrument will establish AT as a transformational RS technique that represents a significant advance in measurement instrumentation, enabling new fundamental insights into the structure of the atmospheric boundary layer and supporting applications for wind energy, pollution transport, and resource characterization. AT is also an extremely economical option over other RS instruments, integrating mature and commonly available acoustic and signal processing hardware.

17 WIND ENERGY↗

NASA NTRS1996

Conversion-Integration of MSFC Nonlinear Signal Diagnostic Analysis Algorithms for Realtime Execution of MSFC's MPP Prototype System

NASA's advanced propulsion system Small Scale Magnetic Disturbances/Advanced Technology Development (SSME/ATD) has been undergoing extensive flight certification and developmental testing, which involves large numbers of health monitoring measurements. To enhance engine safety and reliability, detailed analysis and evaluation of the measurement signals are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce the risk of catastrophic system failures and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. During the development of SSME, ASRI participated in the research and development of several advanced non- linear signal diagnostic methods for health monitoring and failure prediction in turbomachinery components. However, due to the intensive computational requirement associated with such advanced analysis tasks, current SSME dynamic data analysis and diagnostic evaluation is performed off-line following flight or ground test with a typical diagnostic turnaround time of one to two days. The objective of MSFC's MPP Prototype System is to eliminate such 'diagnostic lag time' by achieving signal processing and analysis in real-time. Such an on-line diagnostic system can provide sufficient lead time to initiate corrective action and also to enable efficient scheduling of inspection, maintenance and repair activities. The major objective of this project was to convert and implement a number of advanced nonlinear diagnostic DSP algorithms in a format consistent with that required for integration into the Vanderbilt Multigraph Architecture (MGA) Model Based Programming environment. This effort will allow the real-time execution of these algorithms using the MSFC MPP Prototype System. ASRI has completed the software conversion and integration of a sequence of nonlinear signal analysis techniques specified in the SOW for real-time execution on MSFC's MPP Prototype. This report documents and summarizes the results of the contract tasks; provides the complete computer source code; including all FORTRAN/C Utilities; and all other utilities/supporting software libraries that are required for operation.

Jong, Jen-Yi↗

DOE OSTI2026

Scalable quantum computational science: A perspective from block-encodings and polynomial transformations

Significant developments made in quantum hardware and error correction recently have been driving quantum computing toward practical utility. However, gaps remain between abstract quantum algorithmic development and practical applications in computational sciences. In this perspective article, we propose several properties that scalable quantum computational science methods should possess. We further discuss how block-encodings and polynomial transformations can potentially serve as a unified framework with the desired properties. Recent advancements on these topics are presented, including the construction and assembly of block-encodings, and various generalizations of quantum signal processing (QSP) algorithms to perform polynomial transformations. The scalability of QSP methods on parallel and distributed quantum architectures is also highlighted. Promising applications in simulation and observable estimation in chemistry, physics, and optimization problems are presented. We hope this perspective serves as a gentle introduction to state-of-the-art quantum algorithms for the computational science community and inspires future development of scalable quantum computational science methodologies that bridge theory and practice.

Bayesian inference↗

DOE OSTI2016

Detectability prediction for a thermoacoustic sensor in the breazeale nuclear reactor pool

Laboratory experiments have suggested that thermoacoustic engines can be in- corporated within nuclear fuel rods. Such engines would radiate sounds that could be used to measure and acoustically-telemeter information about the op- eration of the nuclear reactor (e.g., coolant temperature or uxes of neutrons or other energetic particles) or the physical condition of the nuclear fuel itself (e.g., changes in temperature, evolved gases) that are encoded as the frequency and/or amplitude of the radiated sound [IEEE Measurement and Instrumen- tation 16(3), 18-25 (2013)]. For such acoustic information to be detectable, it is important to characterize the vibroacoustical environments within reactors. Measurements will be presented of the background noise spectra (with and with- out coolant pumps) and reverberation times within the 70,000 gallon pool that cools and shields the fuel in the 1 MW research reactor on Penn State's campus using two hydrophones, a piezoelectric projector, and an accelerometer. Sev- eral signal-processing techniques will be demonstrated to enhance the measured results. Background vibrational measurement were also taken at the 250 MW Advanced Test Reactor, located at the Idaho National Laboratory, using ac- celerometers mounted outside the reactor's pressure vessel and on plumbing will also be presented. The detectability predictions made in the thesis were validated in September 2015 using a nuclear ssion-heated thermoacoustic sensor that was placed in the core of the Breazeale Nuclear Reactor on Penn State's campus. Some features of the thermoacoustic device used in that experiment will also be revealed. [Work supported by the U.S. Department of Energy.]

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1994

JTEC Panel report on microelectromechanical systems in Japan

This report summarizes recent activities in the development of microelectromechanical systems (MEMS) in Japan. For the purposes of this study, 'MEMS' means batch-fabricated miniature devices that convert physical parameters to or from electrical signals and that depend on mechanical structures or parameters in important ways for their operation. The report covers advanced materials and process technology; sensors and sensing microstructures; microactuators and actuation mechanisms; sensor-circuit integration and system partitioning; advanced packaging, microassembly, and testing technologies; and MEMS design techniques, applications, and infrastructure. The panel found that Japanese industry is emphasizing approaches to MEMS that are similar to those taken by U.S. industry (i.e., approaches based on silicon integrated-circuit technology); these efforts are comparable in their level of development to those in the United States. However, the ten-year, $250 million, MITI-sponsored program in micromachines emphasizes the miniaturization of more traditional (nonlithographic) machining processes, an area in which there is no comparable U.S. effort. Packaging technology and applications for batch-fabricated MEMS devices is considered a major challenge in both countries. The panel concluded that these and other issues will require global leadership and international cooperation to realize the benefits of MEMS in a timely way.

Wise, Kensall D.↗

NASA NTRS1995

Laser Doppler Velocimetry and full-field soot volume fraction

Since its introduction in the mid-sixties, Laser Doppler Velocimetry (LDV) has become one of the most widely used methods for the measurement of flows. Its remote and essentially non-intrusive nature provides an invaluable tool for a variety of difficult measurement situations which would be otherwise inaccessible. The high spatial resolution and rapid temporal response afforded by this technique are well suited to the determination of spatial and temporal details of flow fields, as well as characterization of turbulence. Advances in the understanding of the properties of LDV signals, accompanied by technological advances in coherent laser sources, detectors of high sensitivity and low noise, optical fabrication techniques and high-speed digital signal processing architectures have resulted in systems of increased accuracy and flexibility. As will be shown, recent progress in solid-state lasers and photo-detectors has been beneficial insofar as the compatibility of this method with the unique and severe constraints inherent in microgravity combustion science experiments.

Greenberg, Paul S.↗

DOE OSTI2020

Sensors with Intelligent Measurement Platform and Low-cost Equipment (SIMPLE) – Performance Characterization

Design and performance of a digital signal processing platform for enhancing off-the-shelf voltage and current sensors are presented. The platform was developed for enabling advanced distribution grid applications with existing low-cost sensors. This digital sensor system leverages a variety of off-the-shelf voltage and current sensing technologies, analog-to-digital conversion, and sensor correction algorithms to yield more accurate and reliable digital measurements that support a multitude of applications and use cases. Sample performance and accuracy results are provided, including measurements at power frequency and higher harmonics. The system extends the native performance of the medium voltage sensors used in the system, for example, broadening measurement bandwidth beyond a few kHz, appropriate for measuring switching surges and harmonics on medium voltage distribution systems. The digital sensor system is installed and characterized in the field with a high-bandwidth optical calibration system to determine site-dependent calibration and performance issues.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Advancing Industry 4.0: Multimodal Sensor Fusion for AI-Based Fault Detection in 3D Printing

Additive manufacturing, particularly fused deposition modeling, is transforming modern production by enabling rapid prototyping and complex part fabrication. However, its layer-by-layer process remains vulnerable to faults such as nozzle clogging, filament runout, and layer misalignment, which compromise print quality and reliability. Traditional inspection methods are costly, time-intensive, and often limited to post-process analysis, making them unsuitable for real-time intervention. In this current study, the authors developed a novel, low-cost, and portable faultdetection system that leverages multimodal sensor fusion and artificial intelligence for real-time monitoring in FDM-based 3D printing. The system integrates acoustic, vibration, and thermal sensing into a non-intrusive architecture, capturing complementary data streams that reflect both mechanical and process-related anomalies. Acoustic and thermal sensors operate in a fully contactless manner, while the vibration sensor requires minimal attachment such that it will not interfere with printer hardware, thereby preserving portability and ease of deployment. The multimodal signals are processed into spectrograms and time-frequency features, which are classified using convolutional neural networks for intelligent fault detection. The proposed system advances Industry 4.0 objectives by offering an affordable, scalable, and practical monitoring solution that improves faultdetection accuracy, reduces waste, and supports sustainable, adaptive manufacturing.

42 ENGINEERING↗