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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

A three-point velocity estimation method for two-dimensional coarse-grained imaging data

Time delay and velocity estimation methods have been widely studied subjects in the context of signal processing, with applications in many different fields of physics. The velocity of waves or coherent fluctuation structures is commonly estimated as the distance between two measurement points divided by the time lag that maximizes the cross correlation function between the measured signals, but this is demonstrated to result in erroneous estimates for two spatial dimensions. We present an improved method to accurately estimate both components of the velocity vector, relying on three non-aligned measurement points. We introduce a stochastic process describing the fluctuations as a superposition of uncorrelated pulses moving in two dimensions. Using this model, we show that the three-point velocity estimation method, using time delays calculated through cross correlations, yields the exact velocity components when all pulses have the same velocity. The two- and three-point methods are tested on synthetic data generated from realizations of such processes for which the underlying velocity components are known. The results reveal the superiority of the three-point technique. Finally, we demonstrate the applicability of the velocity estimation on gas puff imaging data of strongly intermittent plasma fluctuations due to the radial motion of coherent, blob-like structures at the boundary of the Alcator C-Mod tokamak.

Materials Science

Surface Trap Dynamics and Gamma‐Ray Detection Enhancement in Passivated FAPbBr 3 Perovskite Crystals

Perovskite‐based direct radiation detectors offer a compelling platform for room‐temperature gamma spectroscopy due to their high sensitivity, tunable optoelectronic properties, and compatibility with solution processing. While prior studies on formamidinium lead tribromide (FAPbBr 3 ) have shown that surface passivation mitigates deep trap state impact on the charge collection efficiency (CCE), the influence of surface‐based shallow traps on time‐resolved detector response remains largely unexplored. In this article, a digital signal processing (DSP) method is applied to analyze waveform rising edge dynamics in FAPbBr 3 single‐crystal detectors, identifying prompt and delayed charge collection components. Using the second derivative of the collected charge signal, the onset of shallow trap reemission is isolated, occurring 1.1 µs after carrier generation. Surface passivation suppressed deep trap states, shifting carrier occupancy toward shallower traps and extending the temporal window of delayed charge collection. This delayed contribution correlates with measurable changes in the temporal and amplitude distributions of detector events. By leveraging time‐domain filtering informed by these dynamics, a selective enhancement in signal quality and gamma peak‐to‐Compton ratio (PCR) is achieved, while preserving over 90% of photoelectric events. An improvement in the PCR to 1.80, from the original 1.09, is enabled by focusing on high collection efficiency events corresponding to reemitted carriers in the 1.1 to 3 µs range after the generation event. These results demonstrate a pathway for leveraging shallow trap dynamics to enhance spectral fidelity in time‐resolved perovskite detectors.

36 MATERIALS SCIENCE

Using time reversal with long duration broadband noise signals to achieve high amplitude and a desired spectrum at a target location

Time Reversal (TR) is a signal processing technique that can be used to focus acoustic waves to a specific location in space, with most applications aiming to create an impulsive focus. Here, this study instead aims to focus long-duration noise signals using TR. This paper seeks to generate higher amplitude noise at a desired location over an existing method of broadcasting equalized noise. Additionally, this paper explores various characteristics associated with focusing long duration noise using TR. The dependence of the focal amplitude on the duration of the focused signal is explored as well as the implications of using multiple sources when focusing noise. The focal amplitude decreases with longer duration and then levels off when the duration exceeds a few seconds. Coherent addition of focused noise is observed if all loudspeakers have coherent noise signals convolved with their reversed impulse responses. Lastly, focusing noise with a desired spectrum is explored.

42 ENGINEERING

Topological and Dynamical Representations for Radio Frequency Signal Classification

Radio Frequency (RF) signals are found throughout our world, carrying over-the-air information for both digital and analog uses with applications ranging from WiFi to the radio. One area of focus in RF signal analysis is determining the modulation schemes employed in these signals which is crucial in many RF signal processing domains from secure communication to spectrum monitoring. This work investigates the accuracy and noise robustness of novel Topological Data Analysis (TDA) and dynamic representation based approaches paired with a small convolution neural network for RF signal modulation classification with a comparison to state-of-the-art deep neural network approaches. We show that using TDA tools, like Vietoris-Rips and lower star filtrations, and the Takens' embedding in conjunction with a standard shallow neural network we can capture the intrinsic dynamical, geometric, and topological features of the underlying signal's manifold, offering informative representations of the RF signals. Our approach is effective in handling the modulation classification task and is notably noise robust, outperforming the commonly used deep neural network approaches in mode classification. Moreover, our fusion of dynamical and topological information is able to attain similar performance to deep neural network architectures with significantly smaller training datasets.

Myers, Audun D.

Machine learning for reducing noise in RF control signals at industrial accelerators

Industrial particle accelerators typically operate in dirtier environments than research accelerators, leading to increased noise in RF and electronic systems. Furthermore, given that industrial accelerators are mass produced, less attention is given to optimizing the performance of individual systems. As a result, industrial accelerators tend to underperform their own hardware capabilities. Improving signal processing for these machines will improve cost and time margins for deployment, helping to meet the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging. Our work focuses on using machine learning techniques to reduce noise in RF signals used for pulse-to-pulse feedback in industrial accelerators. Here we review our algorithms and observed results for simulated RF systems, and discuss next steps with the ultimate goal of deployment on industrial systems.

43 PARTICLE ACCELERATORS

Grid Edge Waveform Analytics Framework for Event Detection and Classification

This paper provides a grid edge waveform analytics framework for power system event detection and classification in the local as well as in the wide area. This framework overviews data excellence for event detection and classification. The data excellence describes the data acquisition process and requirements, data processing, data quality, and data integrity. Power system event detection in the local area based on different features such as energy-based, cyclostationary approach, template matching, and wavelet transform are also discussed. Furthermore, local area event detection and classification using approaches such as statistical, signal processing, artificial intelligence, and hybrid are also discussed. Moreover, an overview of wide-area event detection and classification along with several other aspects such as wide-area events, wide-area event detection approaches, event location and system performance, event pattern recognition, inter-area oscillation, and wide-area frequency response under variable deployment of inverter-based resources are also provided. The proposed framework is the first step toward the goal of developing appropriate tools and methodologies to detect and classify local as well as wide-area events using waveform analytics. The appropriate event detection and classification framework development is especially important now as more and more grid edge devices with communication capabilities are being deployed in the modern power grid than ever before.

Bhusal, Narayan

Multioutput Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data

Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Demonstrating the Potential of Adaptive LMS Filtering on FPGA-Based Qubit Control Platforms for Improved Qubit Readout in 2D and 3D Quantum Processing Units

Advancements in quantum computing underscore the critical need for sophisticated qubit readout techniques to accurately discern quantum states. This abstract presents our research intended for optimizing readout pulse fidelity for 2D and 3D Quantum Processing Units (QPUs), the latter coupled with Superconducting Radio Frequency (SRF) cavities. Focusing specifically on the application of the Least Mean Squares (LMS) adaptive filtering algorithm, we explore its integration into the FPGA-based control systems to enhance the accuracy and efficiency of qubit state detection by improving Signal-to-Noise Ratio (SNR). Implementing the LMS algorithm on the Zynq UltraScale+ RFSoC Gen 3 devices (RFSoC 4x2 FPGA and ZCU216 FPGA) using the Quantum Instrumentation Control Kit (QICK) open-source platform, we aim to dynamically test and adjust the filtering parameters in real-time to characterize and adapt to the noise profile presented in quantum computing readout signals. Our preliminary results demonstrate the LMS filter's capability to maintain high readout accuracy while efficiently managing FPGA resources. These findings are expected to contribute to developing more reliable and scalable quantum computing architectures, highlighting the pivotal role of adaptive signal processing in quantum technology advancements.

Johnson, Hans

Demonstrating the Potential of Adaptive LMS Filtering on FPGA-Based Qubit Control Platforms for Improved Qubit Readout in 2D and 3D Quantum Processing Units

Advancements in quantum computing underscore the critical need for sophisticated qubit readout techniques to accurately discern quantum states. This abstract presents our research intended for optimizing readout pulse fidelity for 2D and 3D Quantum Processing Units (QPUs), the latter coupled with Superconducting Radio Frequency (SRF) cavities. Focusing specifically on the application of the Least Mean Squares (LMS) adaptive filtering algorithm, we explore its integration into the FPGA-based control systems to enhance the accuracy and efficiency of qubit state detection by improving Signal-to-Noise Ratio (SNR). Implementing the LMS algorithm on the Zynq UltraScale+ RFSoC Gen 3 devices (RFSoC 4x2 FPGA and ZCU216 FPGA) using the Quantum Instrumentation Control Kit (QICK) open-source platform, we aim to dynamically test and adjust the filtering parameters in real-time to characterize and adapt to the noise profile presented in quantum computing readout signals. Our preliminary results demonstrate the LMS filter's capability to maintain high readout accuracy while efficiently managing FPGA resources. These findings are expected to contribute to developing more reliable and scalable quantum computing architectures, highlighting the pivotal role of adaptive signal processing in quantum technology advancements.

Johnson, Hans

Search for New Physics via EFT in collisions containing a top quark and a boosted Z or Higgs boson

We present a generator-level sensitivity study of physics beyond the Standard Model within the framework of Effective Field Theory (EFT). The targeted signal processes are single top quark production in association with a Lorentz-boosted Higgs or Z boson (tHq and tZq) in proton-proton collisions at a center-of-mass energy of 13.6 TeV. Event selection requires one electron or muon, a high-transverse-momentum, large-radius (AK8) jet reconstructing hadronic H → b̄b or Z → b̄b decays, at least two distinct, non-overlapping small-radius (AK4) jets with one originating from a b quark, and a missing transverse energy greater than 30 GeV. Signal and background samples are generated via Monte Carlo simulation using MadGraph with MadJax to perform EFT matrix-element calculations. Deviations from the Standard Model are parametrized through dimension-six EFT operators that probe modified top quark interactions. This generator-level study identifies kinematic observables with enhanced sensitivity to EFT operators and motivates future reconstruction-level analyses of tHq and tZq channels.

Meshramkar, Daniel [Baylor U., Waco]

Search for New Physics via EFT in collisions containing a top quark and a boosted Z or Higgs boson

We present a generator-level sensitivity study of physics beyond the Standard Model within the framework of Effective Field Theory (EFT). The targeted signal processes are single top quark production in association with a Lorentz-boosted Higgs or Z boson (tHq and tZq) in proton-proton collisions at a center-of-mass energy of 13.6 TeV. Event selection requires one electron or muon, a high-transverse-momentum, large-radius (AK8) jet reconstructing hadronic H → b̄b or Z → b̄b decays, at least two distinct, non-overlapping small-radius (AK4) jets with one originating from a b quark, and a missing transverse energy greater than 30 GeV. Signal and background samples are generated via Monte Carlo simulation using MadGraph with MadJax to perform EFT matrix-element calculations. Deviations from the Standard Model are parametrized through dimension-six EFT operators that probe modified top quark interactions. This generator-level study identifies kinematic observables with enhanced sensitivity to EFT operators and motivates future reconstruction-level analyses of tHq and tZq channels.

Meshramkar, Daniel [Baylor U., Waco]

xesn: Echo state networks powered by Xarray and Dask

Xesn is a Python package that allows scientists to easily design Echo State Networks (ESNs) for forecasting problems. ESNs are a Recurrent Neural Network architecture introduced by Jaeger (2001) that are part of a class of techniques termed Reservoir Computing. One defining characteristic of these techniques is that all internal weights are determined by a handful of global, scalar parameters, thereby avoiding problems during backpropagation and reducing training time significantly. Because this architecture is conceptually simple, many scientists implement ESNs from scratch, leading to questions about computational performance. Xesn offers a straightforward, standard implementation of ESNs that operates efficiently on CPU and GPU hardware. The package leverages optimization tools to automate the parameter selection process, so that scientists can reduce the time finding a good architecture and focus on using ESNs for their domain application. Importantly, the package flexibly handles forecasting tasks for out-of-core, multi-dimensional datasets, eliminating the need to write parallel programming code. Xesn was initially developed to handle the problem of forecasting weather dynamics, and so it integrates naturally with Python packages that have become familiar to weather and climate scientists such as Xarray (Hoyer & Hamman, 2017). However, the software is ultimately general enough to be utilized in other domains where ESNs have been useful, such as in signal processing (Jaeger & Haas, 2004).

97 MATHEMATICS AND COMPUTING

Investigating the Relationship Between Bolide Entry Angle and Apparent Direction of Infrasound Signal Arrivals

Infrasound sensing offers critical capabilities for detecting and geolocating bolide events globally. However, the observed back azimuths, directions from which infrasound signals arrive at stations, often differ from the theoretical expectations based on the bolide’s peak brightness location. For objects with shallow entry angles, which traverse longer atmospheric paths, acoustic energy may be emitted from multiple points along the trajectory, leading to substantial variability in back azimuth residuals. This study investigates how the entry angle of energetic bolides affects the back azimuth deviations, independent of extrinsic factors such as atmospheric propagation, station noise, and signal processing methodologies. A theoretical framework, the Bolide Infrasound Back-Azimuth EXplorer Model (BIBEX-M), was developed to compute predicted back azimuths solely from geometric considerations. The model quantifies how these residuals vary as a function of source-to-receiver distance, revealing that bolides entering at shallow angles, e.g., 10°, can produce average residuals of 20°, with deviations reaching up to 46° at distances below 1000 km, and remaining significant even at 5000 km (up to 8°). In contrast, bolides with steeper entry angles, e.g., > 60°, show smaller deviations, typically under 5° at 1000 km and diminishing to less than ~1° beyond 5000 km. These findings attest to the need for careful interpretation when evaluating signal detections and estimating bolide locations. This work is not only pertinent to bolides but also to other high-energy, extended-duration atmospheric phenomena such as space debris and reentry events, where similar geometric considerations can influence infrasound arrival directions.

Acoustics

Rational Modulation of Plant Root Development Using Engineered Cytokinin Regulators

Achieving precise control over quantitative developmental phenotypes is a key objective in plant biology. Recent advances in synthetic biology have enabled tools to reprogram entire developmental pathways; however, the complexity of designing synthetic genetic programs and the inherent interactions between various signaling processes remains a critical challenge. Here, we leverage Type-B response regulators to modulate the expression of genes involved in cytokinin-dependent growth and development processes. We rationally engineered these regulators to modulate their transcriptional activity (i.e., repression or activation) and potency while reducing their sensitivity to cytokinin. By localizing the expression of these engineered transcription factors using tissue-specific promoters, we can predictably tune cytokinin-regulated traits. As a proof of principle, we deployed this synthetic system in Arabidopsis thaliana to either decrease or increase the number of lateral roots. The simplicity and modularity of our approach makes it an ideal system for controlling other developmental phenotypes of agronomic interest in plants.

Cell signaling

Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations

Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however, these methods aren't designed to handle the low signal-to-noise ratios inherent within non-vision signal processing tasks. While they are powerful, they are currently not the method of choice in the inherently noisy and dynamic critical infrastructure domain, such as smart-grid sensing, anomaly detection, and non-intrusive load monitoring. Currently, these models can be brittle, which makes them susceptible to noisy input. This also means they have sub-optimal stability of explanation outputs. Experts and technicians using these models to make decisions in real world scenarios need assurance that a model is performing as it is supposed to. The classification or prediction outputs it generates should be sound and grounded, not likely to change in the presence of shifting noise landscapes. In this work, we explore the idea of Neural Stochastic Differential Equations (NSDE's) to improve the robustness of models trained to classify time series data and the effect of NSDE's on the explainability of outputs. We then test the effectiveness of these approaches by applying them to a non-intrusive load monitoring (NILM) dataset that consists of simulated harmonic signals injected into a real building.

Brogan, Joel

A machine learning framework for accurate and robust analysis of radiation detector pulses

The microscopic properties of atomic nuclei are used to study various scientific questions. They are essential for understanding the fundamental forces of nature and the chemical evolution of the universe. Detecting decay radiation from radioactive nuclei makes it possible to probe these fundamental nuclear properties. Detector waveform traces may contain additional information about the radiation. Generally, advanced signal processing techniques are needed to extract this additional information, often involving fitting the waveform with model response functions using non-linear least-squares optimization with second-order gradient methods. While this is a powerful technique, it is also computationally expensive, leading to slow processing time, which scales with the volume of data. To address this problem, we have developed a machine learning (ML) approach that infers the characteristics of traces from a model detector response function. In particular, we are interested in classifying whether a single recorded trace consists of one or two pulse constituents and estimating the pulse parameters. Furthermore, our proposed ML method can precisely extract the pulses’ parameters, such as energy and timing information, and accurately classify the pulse multiplicity of a trace. Unlike non-learning-based approaches, our ML approach uses neural networks that are significantly faster at inference, as they do not require any optimization during this stage.

Curve fitting

Window Cooling Studies and Disk Vibration Testing on a Subset of Mo-100 Disks

Production of metastable Technetium-99 (Tc-99m), a radioactive tracer that emits gamma rays, is vital to the medical imaging community. Tc-99m is extracted from the decay of Molybdenum-99 (Mo-99) which has a half-life of about 2-3 days. The work presented in this report is part of the NNSA’s mission to produce Mo-99 commercially, within the US, without the use of highly enriched uranium (HEU) in support of nonproliferation and global security. Los Alamos National Laboratory (LANL) is working with NorthStar Medical Radioisotopes (NMR) on their efforts to produce Mo-99 through the irradiation of Mo-100 targets using an electron beam. The NMR target comprises a stack of approximately 60-70 Mo-100 disks with diameter 24 mm and thickness 0.74 mm held in stainless steel laminations, each separated using 0.25 mm thick stainless-steel spacers. The symmetric target stack is housed in an Inconel vessel with two Inconel windows on either side. Two electron accelerators are used to produce 40 MeV, 3.16 µA electron beams each that penetrate the Inconel windows and irradiate the Mo-100 disks. Approximately 90% of the total 250 kW beam power is deposited in the NMR target during the irradiation process, with a smaller percentage adding up to 2.2 kW of heat deposited on the Inconel window. During irradiation, pressurized helium gas flows through thin gaps between the disks cooling the beam window, target disks, disk laminations and spacers. Both NMR and LANL have found during cold testing of the target system (no heat deposition) that the Mo 100 disks undergo significant mass loss and disk breakage due to vibrations induced by the flowing helium gas. The mass loss is not only undesirable due to monetary loss from reduced final quantities of Mo-99, but also due to the hazards associated with radioactive material trapped in the cooling lines and particle filters. The effect of flow rate and target geometry on the flow induced vibrations need to be quantified, and recommendations provided to minimize this mass loss. LANL has previously also tested NMR’s Inconel beam window by heating the window, while flowing pressurized helium, using the average heat deposited on the window. However, the NMR beam is pulsed with a duty cycle of 12.5%, which introduces oscillation in temperature around the nominal 600 °C steady state value with each pulse. Available fatigue curves for Inconel are few, established for room temperature, and they are based on mechanical strain cycles not thermally induced strain as in the NMR target. The effect of pulsed beam heating on the Inconel window therefore needs to be quantified. This report details the experiments conducted to assess the factors that lead to mass loss in the NMR target disks as well as to understand the effect of a pulsed beam on NMR’s Inconel window. This work describes LANL’s experimental characterization of the flow induced vibrations and disk mass loss in a reduced scale set-up containing 5 to 10 Mo-100 disks. We use high speed imaging, displacement measurements and microphone measurements combined with signal processing to estimate the vibration frequency of each disk. The effect of disk thickness, target fit and duration of testing on the mass loss is described. We find that in the current configuration of NMR targets, the vibrations and mass loss on the first disk are minimized, while those in the adjacent disks are highest. The microphone and high-speed image data show that increased flow rates and increased duration of testing increases vibration frequency and mass loss. The mass loss is due to both disk rotation and back and forth motion. There are visible wear marks on the disks with the highest mass loss. We also note that the current NMR window gap reduces flow induced vibrations compared to the previous smaller gaps. Improved target holders significantly reduce disk mass loss to almost negligible quantities. This work finds that the larger window to first disk gap and improved target holder geometry should allow NMR to successfully conduct irradiations with minimal mass loss. The window tests were conducted to understand the effect of a pulsed beam on both the window longevity and to estimate the window temperature and displacement during pulsing. The experiments presented here were performed at significantly low power, due to the limitations of the induction heating system. The window temperature rose to approximately 73 °C with a significantly reduced power of 45 W without beam pulsing. With a 5 Hz pulse rate, 12.5% duty cycle, the window temperature remained constant at 26 °C. These experiments will be repeated with improved coil geometry and reported in upcoming journal papers.

42 ENGINEERING

Material‐Driven Neuronal Oscillators and Filters via Active Reactance in CC‐NDR and VC‐NDR Electro‐Thermal Memristors

The continued scaling of artificial intelligence and telecommunications hardware is increasingly constrained by the power, bandwidth, and area limitations of transistor-based circuits. Neuromorphic processor units, analog oscillators, and active inductors and capacitors rely on complex multi-transistor architectures restricting material choices and incurring energy and footprint overhead. Here, we show that active reactance in electro-thermal memristors provides an intrinsic, material driven route to neuronal oscillator dynamics and signal processing. Using a physics-based compact modeling framework, we bridge negative differential resistance (NDR) and bias-tunable reactance, which underlies spiking dynamics in electro-thermal memristors. Memristors with negative temperature coefficients of resistance (TCR) manifest current-controlled (CC-) NDR and act as active inductors, thus generating spiking above a critical circuit capacitance; whereas memristors with positive TCR manifest voltage-controlled (VC-) NDR and active capacitance, leading to spiking above a critical inductance. By creating a compact model for La 0.7 Ca 0.3 MnO 3 as a representative VC-NDR material and comparing it with LaCoO 3 manifesting CC-NDR, we explain the physical origins of their distinct current-voltage characteristics, reactive phase shifts and consequent spiking behaviors. Finally, we demonstrate tunable filtering enabled by the active reactance of electro-thermal memristors, establishing them as a compact hardware platform for neuronal oscillator functionality and integrated filtering beyond conventional CMOS.

active reactance