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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Block encoding bosons by signal processing

Block Encoding (BE) is a crucial subroutine in many modern quantum algorithms, including those with near-optimal scaling for simulating quantum many-body systems, which often rely on Quantum Signal Processing (QSP). Currently, the primary methods for constructing BEs are the Linear Combination of Unitaries (LCU) and the sparse oracle approach. In this work, we demonstrate that QSP-based techniques, such as Quantum Singular Value Transformation (QSVT) and Quantum Eigenvalue Transformation for Unitary Matrices (QETU), can themselves be efficiently utilized for BE implementation. Specifically, we present several examples of using QSVT and QETU algorithms, along with their combinations, to block encode Hamiltonians for lattice bosons, an essential ingredient in simulations of high-energy physics. We also introduce a straightforward approach to BE based on the exact implementation of Linear Operators Via Exponentiation and LCU (LOVE-LCU). We find that, while using QSVT for BE results in the best asymptotic gate count scaling with the number of qubits per site, LOVE-LCU outperforms all other methods for operators acting on up to qubits, highlighting the importance of concrete circuit constructions over mere comparisons of asymptotic scalings. Using LOVE-LCU to implement the BE, we simulate the time evolution of single-site and two-site systems in the lattice theory using the Generalized QSP algorithm and compare the gate counts to those required for Trotter simulation.

Kane, Christopher F↗
DOE OSTI2023

Digital Signal Processing Using Deep Neural Networks

Currently there is great interest in the utility of deep neural networks (DNNs) for the physical layer of radio frequency (RF) communications. In this manuscript we describe a custom DNN specially designed to solve problems in the RF domain. Our model leverages the mechanisms of feature extraction and attention through the combination of an autoencoder convolutional network with a transformer network, to accomplish several important communications network and digital signals processing (DSP) tasks. We also present a new open dataset and physical data augmentation model that enables training of DNNs that can perform automatic modulation classification, infer, and correct transmission channel effects, and directly demodulate baseband RF signals.

42 ENGINEERING↗
DOE OSTI2022

A graph signal processing‐based multiple model Kalman filter ( GSP‐MMKF ) tool for predictive analytics: An air separation unit process application

Abstract The industrial Air Separations Unit (ASU) is a complicated and tightly operated process. The use of dynamic process analytics is also a key element of safe and economic operation of these processes, with increasing focus on predictive analytics to take preemptive actions. With the availability of real‐time data from hundreds of sensors, the data analysis process should also consider the topology of the data, as seen in sensor networks. In this paper, a novel tool is presented that considers the complex connectivity patterns in the sensor network and uses local adaptive disturbance estimations to predict global network‐scale trends. The paper introduces the emerging field of Graph Signal Processing (GSP) and presents a rigorous derivation of the tool starting from the extraction of the sensor‐network (in a graph theoretical sense) from the data. This network, which is in the form of a matrix, is then used to derive a Kalman‐filter type of state‐space model driven by input disturbances. Multiple disturbance models (e.g., step, ramp, periodic) are included to allow the model to have different kinds of disturbance propagation. Each graph node (representing the sensors used) dynamically adapts to the most recent detected disturbance individually. These estimated disturbances are propagated to the global network using the graph. Modifications to ensure stability are also discussed. The fidelity of the tool is tested on certain downtime events and the paper concludes by discussing the advantages of the method and planned future improvements.

Ghosh, Sambit↗
DOE OSTI2016

Effect of alpha and Gaussian refractive index profiles on the design of highly nonlinear optical fibre for efficient nonlinear optical signal processing

We propose an index profiled, highly nonlinear ultra-flattened dispersion fibre (HN-UFF) with appreciable values of fibre parameters such as dispersion, dispersion slope, effective area, nonlinearity, bending loss and splice loss. The designed fibre has normal zero flattened dispersion over S, C, L, U bands and extends up to 1.9857 μm. The maximum dispersion variation observed for this fibre is as low as 1.61 ps km{sup −1} nm{sup −1} over the 500-nm optical fibre transmission spectrum. This fibre also has two zero dispersion wavelengths at 1.487 and 1.9857 μm and the respective dispersion slopes are 0.02476 and 0.0068 ps nm{sup −2} km{sup −1}. The fibre has a very low ITU-T cutoff wavelength of 1.2613 μm and a virtuous nonlinear coefficient of 9.43 W{sup −1} km{sup −1}. The wide spectrum of zero flattened dispersion and a good nonlinear coefficient make the designed fibre very promising for different nonlinear optical signal processing applications. (fibre optics)

42 ENGINEERING↗
DOE OSTI2022

Quantum signal processing for simulating cold plasma waves

Numerical modeling of radio-frequency waves in plasma with sufficiently high spatial and temporal resolution remains challenging even with modern computers. However, such simulations can be sped up using quantum computers in the future. In this work, we propose how to do such modeling for cold plasma waves, in particular, for an X wave propagating in an inhomogeneous one-dimensional plasma. The wave system is represented in the form of a vector Schrödinger equation with a Hermitian Hamiltonian. Block encoding is used to represent the Hamiltonian through unitary operations that can be implemented on a quantum computer. To perform the modeling, we apply the so-called quantum signal processing algorithm and construct the corresponding circuit. Quantum simulations with this circuit are emulated on a classical computer, and the results show agreement with traditional classical calculations. We also discuss how our quantum circuit scales with the resolution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Neutron Detector Signal Processing to Calculate the Effective Neutron Multiplication Factor of Subcritical Assemblies

This report describes different methodologies to calculate the effective neutron multiplication factor of subcritical assemblies by processing the neutron detector signals using MATLAB scripts. The subcritical assembly can be driven either by a spontaneous fission neutron source (e.g. californium) or by a neutron source generated from the interactions of accelerated particles with target materials. In the latter case, when the particle accelerator operates in a pulsed mode, the signals are typically stored into two files. One file contains the time when neutron reactions occur and the other contains the times when the neutron pulses start. In both files, the time is given by an integer representing the number of time bins since the start of the counting. These signal files are used to construct the neutron count distribution from a single neutron pulse. The built-in functions of MATLAB are used to calculate the effective neutron multiplication factor through the application of the prompt decay fitting or the area method to the neutron count distribution. If the subcritical assembly is driven by a spontaneous fission neutron source, then the effective multiplication factor can be evaluated either using the prompt neutron decay constant obtained from Rossi or Feynman distributions or the Modified Source Multiplication (MSM) method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Applications of rigged Hilbert spaces in quantum mechanics and signal processing

Simultaneous use of discrete and continuous bases in quantum systems is not possible in the context of Hilbert spaces, but only in the more general structure of rigged Hilbert spaces (RHS). In addition, the relevant operators in RHS (but not in Hilbert space) are a realization of elements of a Lie enveloping algebra and support representations of semigroups. We explicitly construct here basis dependent RHS of the line and half-line and relate them to the universal enveloping algebras of the Weyl-Heisenberg algebra and su(1, 1), respectively. The complete sub-structure of both RHS and of the operators acting on them is obtained from their algebraic structures or from the related fractional Fourier transforms. This allows us to describe both quantum and signal processing states and their dynamics. Two relevant improvements are introduced: (i) new kinds of filters related to restrictions to subspaces and/or the elimination of high frequency fluctuations and (ii) an operatorial structure that, starting from fix objects, describes their time evolution.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Automated Reconstruction, Signal Processing and Particle Identification in DUNE

Liquid Argon TPC (LArTPC) technology is increasingly prevalent in large-scale detectors de- signed to observe neutrino scattering events induced by accelerators or by natural sources. LArTPCs consist of a very high fraction of active detector material with spatial resolutions on the order of a few millimeters. Three-dimensional interactions are imaged in multiple two- dimensional views by the process of projection onto planes of wires. The goal of automated reconstruction is to correctly classify each neutrino scattering event by the flavor of the incoming neutrino, to separate charged-current events from neutral-current and other backgrounds, and to measure the energies of the incoming neutrinos. Detection of neutrinos from supernova bursts and also searching for nucleon decay are important uses of automated reconstruction algorithms. Be- cause the amount of spatial detail is high and the amount of scattering is high, this reconstruction presents many challenges that are now being investigated with sophisticated techniques. Signal processing, track and shower identification, particle identification, and event classification by a variety of innovative algorithms are reviewed

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Efficient fully-coherent quantum signal processing algorithms for real-time dynamics simulation

Simulating the unitary dynamics of a quantum system is a fundamental problem of quantum mechanics, in which quantum computers are believed to have significant advantage over their classical counterparts. One prominent such instance is the simulation of electronic dynamics, which plays an essential role in chemical reactions, non-equilibrium dynamics, and material design. These systems are time-dependent, which requires that the corresponding simulation algorithm can be successfully concatenated with itself over different time intervals to reproduce the overall coherent quantum dynamics of the system. In this paper, we quantify such simulation algorithms by the property of being fully-coherent: the algorithm succeeds with arbitrarily high success probability 1 − δ while only requiring a single copy of the initial state. Here we subsequently develop fully-coherent simulation algorithms based on quantum signal processing (QSP), including a novel algorithm that circumvents the use of amplitude amplification while also achieving a query complexity additive in time t, ln(1/δ), and ln(1/ϵ) for error tolerance ϵ: $Θ‖\mathscr{H}‖|t|+ln(1/ϵ)+ln(1/δ)$. Furthermore, we numerically analyze these algorithms by applying them to the simulation of the spin dynamics of the Heisenberg model and the correlated electronic dynamics of an H2 molecule. Since any electronic Hamiltonian can be mapped to a spin Hamiltonian, our algorithm can efficiently simulate time-dependent ab initio electronic dynamics in the circuit model of quantum computation. Accordingly, it is also our hope that the present work serves as a bridge between QSP-based quantum algorithms and chemical dynamics, stimulating a cross-fertilization between these exciting fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Crystal structure↗
DOE OSTI2017

Decoding Signal Processing at the Single-Cell Level

The ability of cells to detect and decode information about their extracellular environment is critical to generating an appropriate response. In multicellular organisms, cells must decode dozens of signals from their neighbors and extracellular matrix to maintain tissue homeostasis while still responding to environmental stressors. How cells detect and process information from their surroundings through a surprisingly limited number of signal transduction pathways is one of the most important question in biology. Despite many decades of research, many of the fundamental principles that underlie cell signal processing remain obscure. However, in this issue of Cell Systems, Gillies et al present compelling evidence that the early response gene circuit can act as a linear signal integrator, thus providing significant insight into how cells handle fluctuating signals and noise in their environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Laser Doppler anemometer signal processing for blood flow velocity measurements

A new method for analysing the signal in a laser Doppler anemometer based on the differential scheme is proposed, which provides the flow velocity measurement in strongly scattering liquids, particularly, blood. A laser Doppler anemometer intended for measuring the absolute blood flow velocity in animal and human near-surface arterioles and venules is developed. The laser Doppler anemometer signal structure is experimentally studied for measuring the flow velocity in optically inhomogeneous media, such as blood and suspensions of scattering particles. The results of measuring the whole and diluted blood flow velocity in channels with a rectangular cross section are presented. (laser applications and other topics in quantum electronics)

42 ENGINEERING↗
DOE OSTI2013

All-optical signal processing at 10 GHz using a photonic crystal molecule

We report on 10 GHz operation of an all-optical gate based on an Indium Phosphide Photonic Crystal Molecule. Wavelength conversion and all-optical mixing of microwave signals are demonstrated using the 2 mW output of a mode locked diode laser. The spectral separation of the optical pump and signal is crucial in suppressing optical cross-talk.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Signal processing to reduce dark noise impact in precision timing

We introduce a technique to mitigate the effects of low frequency noise on precision timing. The example of Dark Count Noise Rate (DCR) in Silicon Photomultipliers (SiPMs) is emphasized. This technique exploits the correlation between time shifts on the leading edge of a signal and the residual slope of the baseline (due to noise) which remains after baseline subtraction. In fast timing applications (such as for Time-of-flight particle ID) the signal arrival time is typically captured on the signal leading edge. The signal risetime is often fixed by the physics of the sensor and input circuit. Then accurate pulse timing can be achieved by correcting a leading edge threshold time (depending on a slope proportional to both the Amplitude and the risetime) to a “constant fraction” time. This compensation for time walk due to amplitude fluctuations breaks down once we introduce interference from low frequency noise on the leading edge. In this paper we demonstrate that an additional measurement of the slope at threshold can be used to correct for this noise jitter.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Bistatic SAR: Signal Processing and Image Formation.

This report describes the significant processing steps that were used to take the raw recorded digitized signals from the bistatic synthetic aperture RADAR (SAR) hardware built for the NCNS Bistatic SAR project to a final bistatic SAR image. In general, the process steps herein are applicable to bistatic SAR signals that include the direct-path signal and the reflected signal. The steps include preprocessing steps, data extraction to for a phase history, and finally, image format. Various plots and values will be shown at most steps to illustrate the processing for a bistatic COSMO SkyMed collection gathered on June 10, 2013 on Kirtland Air Force Base, New Mexico.

Wahl, Daniel E.↗
DOE OSTI2017

Liquid argon TPC signal formation, signal processing and reconstruction techniques

This document describes a reconstruction chain that was developed for the ArgoNeuT and MicroBooNE experiments at Fermilab. These experiments study accelerator neutrino interactions that occur in a Liquid Argon Time Projection Chamber. Reconstructing the properties of particles produced in these interactions benefits from the knowledge of the micro-physics processes that affect the creation and transport of ionization electrons to the readout system. A wire signal deconvolution technique was developed to convert wire signals to a standard form for hit reconstruction, to remove artifacts in the electronics chain and to remove coherent noise. A unique clustering algorithm reconstructs line-like trajectories and vertices in two dimensions which are then matched to create of 3D objects. In conclusion, these techniques and algorithms are available to all experiments that use the LArSoft suite of software.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

In vivo electroretinographic studies of the role of GABA C receptors in retinal signal processing

The retina expresses all three classes of receptors for the inhibitory neurotransmitter GABA (GABAR). Our study investigated roles of GABAR, especially GABA(C)R (GABA(A)-rho), in retinal signaling in vivo by studying effects on the mouse electroretinogram (ERG) of genetic deletion of GABA(C)R versus pharmacological blockade using receptor antagonists. Brief full-field flash ERGs were recorded from anesthetized GABA(C)R(-/-) mice, and WT C57BL/6 (B6) mice, before and after intravitreal injection of GABA(C)R antagonists, TPMPA, 3-APMPA, or the more recently developed 2-AEMP; GABA(A)R antagonist, SR95531; GABA(B)R antagonist, CGP, and agonist, baclofen. Intravitreal injections of TPMPA and SR95531 were also made in Brown Norway rats. The effect of 2-AEMP on GABA-induced current was tested directly in isolated rat rod bipolar cells, and 2-AEMP was found to preferentially block GABA(C)R in those cells. Maximum amplitudes of dark (DA) and light-adapted (LA) ERG b-waves were reduced in GABA(C)R(-/-) mice, compared to B6 mice, by 30-60%; a-waves were unaltered and oscillatory potential amplitudes were increased. In B6 mice, after injection of TPMPA (also in rats), 3-APMPA or 2-AEMP, ERGs became similar to ERGs of GABA(C)R(-/-) mice. Blockade of GABA(A)Rs and GABA(B)Rs, or agonism of GABA(B)Rs did not alter B6 DA b-wave amplitude. Furthermore, the negative scotopic threshold response (nSTR) was slightly less sensitive in GABA(C)R(-/-) than in B6 mice, and unaltered by 2-AEMP. However, amplitudes of nSTR and photopic negative response (PhNR), both of which originate from inner retina, were enhanced by TPMPA and 3-APMPA, each of which has GABA(B) agonist properties, and further increased by baclofen. The finding that genetic deletion of GABA(C)R, the GABA(C)R antagonist 2-AEMP, and other antagonists all reduced ERG b-wave amplitude, supports a role for CABA(C)R in determining the maximum response amplitude of bipolar cells contributing to the b-wave. GABA(C)R antagonists differed in their effects on nSTR and PhNR; antagonists with GABA(B) agonist properties enhanced light-driven responses whereas 2-AEMP did not.

59 BASIC BIOLOGICAL SCIENCES↗