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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Physics-based signal processing algorithms for micromachined cantilever arrays

A method of using physics-based signal processing algorithms for micromachined cantilever arrays. The methods utilize deflection of a micromachined cantilever that represents the chemical, biological, or physical element being detected. One embodiment of the method comprises the steps of modeling the deflection of the micromachined cantilever producing a deflection model, sensing the deflection of the micromachined cantilever and producing a signal representing the deflection, and comparing the signal representing the deflection with the deflection model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

LADSS Signal Processing: Lecture 3 [Slides]

Signals were recorded and synchronized in LPBF experiments. Pyrometry and Acoustic sensors were compared and combined. Intelligent feature combination leads to better predictions. Predictions are accurate over entire lasing pattern.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Online Event Detection in Synchrophasor Data with Graph Signal Processing

Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency.

Event detection↗
DOE OSTI2020

Online Event Detection in Synchrophasor Data with Graph Signal Processing

Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Acoustic emission signal processing technique to characterize reactor in-pile phenomena

Existing and developing advanced sensor technologies and instrumentation will allow non-intrusive in-pile measurement of temperature, extension, and fission gases when coupled with advanced signal processing algorithms. The transmitted measured sensor signals from inside to the outside of containment structure are corrupted by noise and are attenuated, thereby reducing the signal strength and the signal-to-noise ratio. Identification and extraction of actual signal (representative of an in-pile phenomenon) is a challenging and complicated process. In the paper, empirical mode decomposition technique is utilized to reconstruct actual sensor signal by partially combining intrinsic mode functions. Reconstructed signal will correspond to phenomena and/or failure modes occurring inside the reactor. In addition, it allows accurate non-intrusive monitoring and trending of in-pile phenomena.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Multivariable quantum signal processing (M-QSP): prophecies of the two-headed oracle

Recent work shows that quantum signal processing (QSP) and its multi-qubit lifted version, quantum singular value transformation (QSVT), unify and improve the presentation of most quantum algorithms. QSP/QSVT characterize the ability, by alternating ansätze, to obliviously transform the singular values of subsystems of unitary matrices by polynomial functions; these algorithms are numerically stable and analytically well-understood. That said, QSP/QSVT require consistent access to a single oracle, saying nothing about computing joint properties of two or more oracles; these can be far cheaper to determine given an ability to pit oracles against one another coherently. This work introduces a corresponding theory of QSP over multiple variables: M-QSP. Surprisingly, despite the non-existence of the fundamental theorem of algebra for multivariable polynomials, there exist necessary and sufficient conditions under which a desired stable multivariable polynomial transformation is possible. Moreover, the classical subroutines used by QSP protocols survive in the multivariable setting for non-obvious reasons, and remain numerically stable and efficient. Up to a well-defined conjecture, we give proof that the family of achievable multivariable transforms is as loosely constrained as could be expected. The unique ability of M-QSP to obliviously approximate joint functions of multiple variables coherently leads to novel speedups incommensurate with those of other quantum algorithms, and provides a bridge from quantum algorithms to algebraic geometry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Digital Signal Processing of Radar Pulse Echoes

Modern high-performance radar systems are employing ever-more Digital Signal Processing (DSP), replacing ever-more formerly analog components. Precisely predicting the performance of digital filters and correlators requires an awareness of some of the finer points and characteristics of digital filters. We examine a representative radar receiver DSP chain that is processing a Linear Frequency Modulated (LFM) chirp.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Large-scale real-time signal processing in physics experiments: the ALICE TPC FPGA pipeline

For LHC Run 3, the ALICE Time Projection Chamber was upgraded to operate in continuous readout mode. Interaction rates of up to 50 kHz in Pb-Pb collisions require real-time processing of more than 3 TB s -1 of raw detector data. This requirement is met by a custom FPGA-based processing pipeline that performs the complete front-end data treatment fully in-stream, including common-mode correction, pedestal subtraction, ion-tail filtering, zero suppression, and dense data packing. A central element of the design is a highly parallel common-mode correction algorithm operating directly on the streaming data. It robustly identifies signal-free readout channels on a time-bin basis and applies pad-dependent scaling to compensate for local variations in capacitive coupling in the GEM readout. In combination with pedestal subtraction and ion-tail filtering, this enables accurate baseline restoration under extreme high-occupancy conditions, preventing signal loss while efficiently suppressing noise prior to zero suppression. The pipeline operates continuously at the full detector bandwidth and reduces the raw input rate of approximately 3 TB s -1 to about 900 GBps for Pb-Pb collisions at the target interaction rate. Overall, it represents a large-scale FPGA-based real-time signal-processing implementation for high-energy physics detector readout.

Digital signal processing (DSP)↗
DOE OSTI2025

In-pixel integration of signal processing and AI/ML based data filtering for particle tracking detectors

We present the first physical realization of in-pixel signal processing with integrated AI-based data filtering for particle tracking detectors. Building on prior work that demonstrated a physics-motivated edge-AI algorithm suitable for ASIC implementation, this work marks a significant milestone toward intelligent silicon trackers. Our prototype readout chip performs real-time data reduction at the sensor level while meeting stringent requirements on power, area, and latency. The chip is taped-out in 28nm TSMC CMOS bulk process, which has been shown to have sufficient radiation hardness for particle experiments. This development represents a key step toward enabling fully on-detector edge AI, with broad implications for data throughput and discovery potential in high-rate, high-radiation environments such as the High-Luminosity LHC.

Parpillon, Benjamin [Fermilab; Illinois U., Chicag↗
DOE OSTI2018

Performance of Advanced Signal Processing and Pattern Recognition Algorithms Using Raw Data from Ultrasonic Guided Waves and Fiber Optics Transducers

The Light Water Reactor Sustainability Program was initiated to evaluate technologies that could be used to perform online monitoring of piping and other secondary system structural components in commercial nuclear power plants. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real-time measurements, rather than at a pre-determined inspection interval. This transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for most nuclear power generation costs. This report describes the current state of research related to ultrasonic guided wave testing and its application to detecting defects in commercial nuclear power plants. The report analyzes the applicability of the guided wave technology to secondary piping systems, as well as studying the potential for expanding the range of guided wave technology to include bent piping and other piping components. The ultrasonic guided waves can inspect long stretches of straight piping; however, more complex geometries such as elbows, welds, and tees are causing spurious reflections and coherent noise, which significantly decreases the sensitivity of the technique. To deal with these limitations, high-definition fiber optic sensors are applied to complex piping geometries, and advanced machine learning algorithms are used to detect deviations from healthy states. This report also analyzes the performance of advanced signal processing and machine learning-based pattern recognition algorithms in detecting defects in secondary structures. It is demonstrated on guided wave data collected at nuclear power plants that the independent component analysis can separate different coherent noise components and segregate them from useful signals. It also demonstrates that advanced machine learning techniques, such as neural networks, support vector machines, and deep learning networks, can detect minor defects present in inspected structures. Recommendation about the applicability of advanced machine learning techniques to online piping monitoring are also given.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Wavelet-Based Genomic Signal Processing for Centromere Identification and Hypothesis Generation

Various ‘omics data types have been generated for Populus trichocarpa , each providing a layer of information which can be represented as a density signal across a chromosome. We make use of genome sequence data, variants data across a population as well as methylation data across 10 different tissues, combined with wavelet-based signal processing to perform a comprehensive analysis of the signature of the centromere in these different data signals, and successfully identify putative centromeric regions in P. trichocarpa from these signals. Furthermore, using SNP (single nucleotide polymorphism) correlations across a natural population of P. trichocarpa , we find evidence for the co-evolution of the centromeric histone CENH3 with the sequence of the newly identified centromeric regions, and identify a new CENH3 candidate in P. trichocarpa .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Efficient Signal Processing in BOTDA: Utilizing PCA and PCA-Based Neural Networks for Temperature Monitoring

This work presents a comparative analysis of the various signal processing techniques used in the Brillouin gain spectrum (BGS) peak estimation. Traditional fitting methods such as Lorentzian curve fitting (LCF) are slow and less effective in noisy data. PCA-based methods were tested on the experimental data: A Euclidian distance-based approach, and a probabilistic deep neural network (PDNN) based approach, both using 5 principal components to represent a single BGS. Both methods significantly reduce computational time with respect to LCF, whereas PDNN offers uncertainty insights along with the parameter value. Measuring a range of temperatures, analyzing accuracy, and speed, it can be concluded that PCA trained PDNN outperforms other methods, and appears to be helpful in scenario where large datasets are generated.

Brillouin optical time domain analysis↗
DOE OSTI2014

Aperiodic signals processing via parameter-tuning stochastic resonance in a photorefractive ring cavity

Based on solving numerically the generalized nonlinear Langevin equation describing the nonlinear dynamics of stochastic resonance by Fourth-order Runge-Kutta method, an aperiodic stochastic resonance based on an optical bistable system is numerically investigated. The numerical results show that a parameter-tuning stochastic resonance system can be realized by choosing the appropriate optical bistable parameters, which performs well in reconstructing aperiodic signals from a very high level of noise background. The influences of optical bistable parameters on the stochastic resonance effect are numerically analyzed via cross-correlation, and a maximum cross-correlation gain of 8 is obtained by optimizing optical bistable parameters. This provides a prospective method for reconstructing noise-hidden weak signals in all-optical signal processing systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Enhanced Signal Processing of Distributed Brillouin Fiber Sensors using a Decoupled Radial Basis Function Network

A novel decoupled radial basis function network (D-RBFN) is proposed to accelerate signal processing and address the big data challenges associated with ultra-long distance Brillouin optical time-domain analysis (BOTDA) systems. The proposed frame- work is demonstrated on a dataset measured over a 100 km distance using a bi-directional Raman assisted BOTDA system.

Venketeswaran, Abhishek↗
DOE OSTI2019

Digital signal processing in coupled photonic crystal waveguides and its application to an all-optical AND logic gate

The realization of all-optical AND logic gates for pulsed signal operation based on the photonic bandgap transmission phenomenon is proposed. We are using realistic planar air-hole type coupled photonic crystal waveguides (C-PCWs) with Kerr-type nonlinear background medium. The novelty of our analysis is that the proposed AND logic gate operates with the temporal solitons, which maintain a stable envelope propagating in the nonlinear C-PCWs, enabling true ultrafast full-optical digital signal processing in the time-domain. The bandgap transmission takes place when the operating frequency is chosen at the very edge of the dispersion curve of one of the supermodes in the C-PCWs. In this regard, our original fast and accurate method is used to efficiently calculate the supermodes of the C-PCW system. The underlying semi-analytical full-wave modal analysis is based on the evaluation of the lattice sums for complex wavenumbers using the transition-matrix method in combination with the generalized reflection-matrix approach. As a proof of concept successful pulse operation of the all-optical AND logic gate is demonstrated in the framework of extensive full-wave finite-difference time-domain electromagnetics analysis.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Software-Defined Ultrasonic Communication System Based on Time-reversal Signal Processing

Ultrasonic communication through solid channels suffers from channel dispersion, attenuation, and multipath effect. For example, in elbow shape pipes or complex structures, the elastic waves are reflected and reverberated in multipath resulting in a major dispersion of the signals. In solid channels such as flat bar, multipath propagation is the main reason for bit-wavelet spreading and intersymbol interference (ISI). The conventional modulation method like OOK (on-off-keying) or PSK (phase-shift-keying) fails to provide a useful Signal-to-Noise Ratio (SNR). Time-reversal (TR) signal processing method is a promising technique that can compress the spreading of the bit-wavelet and strengthen the peak signal energy on the receiver. In this study, Finite Element Analysis (FEA) using ABAQUS software tools is used to characterize the bit-wavelet spreading effect caused by dispersive communication channels. Also, this study presents the impact of TR ultrasonic communication as a function of excitation pulse width, channel length, and carrier frequency through a flat-bar channel. A Software-Defined System-on-Chip (SD-SoC) platform is examined as a viable solution for the low-complexity and energy-efficient ultrasonic communication system. The proposed TR based SD-SoC ultrasonic communication system can make full use of signal dispersion for peak performance by minimizing the ISI within a dispersive and reverberant metal channel.

FEM simulation↗
DOE OSTI2015

Burst design and signal processing for the speed of sound measurement of fluids with the pulse-echo technique

The pulse-echo technique determines the propagation time of acoustic wave bursts in a fluid over a known propagation distance. It is limited by the signal quality of the received echoes of the acoustic wave bursts, which degrades with decreasing density of the fluid due to acoustic impedance and attenuation effects. Signal sampling is significantly improved in this work by burst design and signal processing such that a wider range of thermodynamic states can be investigated. Applying a Fourier transformation based digital filter on acoustic wave signals increases their signal-to-noise ratio and enhances their time and amplitude resolutions, improving the overall measurement accuracy. In addition, burst design leads to technical advantages for determining the propagation time due to the associated conditioning of the echo. It is shown that the according operation procedure enlarges the measuring range of the pulse-echo technique for supercritical argon and nitrogen at 300 K down to 5 MPa, where it was limited to around 20 MPa before.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗