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At least 865 records · Page 48

A microcomputer based frequency-domain processor for laser Doppler anemometry

A prototype multi-channel laser Doppler anemometry (LDA) processor was assembled using a wideband transient recorder and a microcomputer with an array processor for fast Fourier transform (FFT) computations. The prototype instrument was used to acquire, process, and record signals from a three-component wind tunnel LDA system subject to various conditions of noise and flow turbulence. The recorded data was used to evaluate the effectiveness of burst acceptance criteria, processing algorithms, and selection of processing parameters such as record length. The recorded signals were also used to obtain comparative estimates of signal-to-noise ratio between time-domain and frequency-domain signal detection schemes. These comparisons show that the FFT processing scheme allows accurate processing of signals for which the signal-to-noise ratio is 10 to 15 dB less than is practical using counter processors.

Horne, W. Clifton↗

Life in a changing world: TCH gene regulation of expression and responses to environmental signals

The Arabidopsis TCH genes were discovered as a consequence of their marked upregulation of expression in response to seemingly innocuous stimuli such as touch. Further analyses have indicated that these genes are upregulated by a variety of diverse stimuli. Understanding the mechanism(s) and factors that control TCH gene regulation will shed light on the signaling pathways that enable plants to respond to changing environmental conditions. The TCH proteins include calmodulin, calmodulin-related proteins and a xyloglucan endotransglycosylase. Expression analyses and localization of protein accumulation indicate that the potential sites of TCH protein function include expanding cells and tissues under mechanical strain. We hypothesize that the TCH proteins may collaborate in cell wall biogenesis.

NASA Discipline Plant Biology↗

Fundamental limits to the generation of highly displaced bright squeezed light using linear optics and parametric amplifiers

High-quality squeezed light is an important resource for a variety of applications. Multiple methods for generating squeezed light are known, having been demonstrated theoretically and experimentally. However, the effectiveness of these methods—in particular, the inherent limitations to the signals that can be produced—has received little consideration. Here we present a comparative theoretical analysis for generating a highly displaced squeezed light from a linear optical method—a beamsplitter mixing a squeezed vacuum and a strong coherent state—and well-studied parametric amplification methods including an optical parametric oscillator, an optical parametric amplifier, and a dissipative optomechanical squeezer seeded with coherent states. We show that the quality of highly displaced squeezed states that can be generated using these methods is limited on a fundamental level by the physical mechanism utilized; across all methods there are significant trade-offs between displacement, squeezing, and overall uncertainty. We explore the nature and extent of these trade-offs specific to each mechanism and identify the optimal operation modes for each. Finally, we identify the conditions for minimum-uncertainty squeezing in arbitrary parametric amplifying systems and show that displacing the output signal will in general violate these conditions, adding noise and degrading squeezing. Published by the American Physical Society 2025

Young, Steve M. (ORCID:0000000173839366)↗

Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections

The traffic volume of each movement at signalized intersections can provide valuable information on real-time traffic conditions that enable traffic control systems to dynamically respond to the fluctuated traffic demands. Real-time movement-based traffic volume prediction is challenging due to various nonlinear spatial relationships at different locations/approaches and the complicated underlying temporal dependencies. In this study, a novel deep intersection spatial-temporal network (DISTN) is developed for real-time movement-based traffic volume prediction at signalized intersections, which considers both spatial and temporal features by the convolutional neural network (CNN) and long short-term memory (LSTM), respectively. In addition, the within-day, daily, and weekly periodic trends of traffic volume are also considered in the proposed model. This is the first time that a deep-learning method has been applied for movement-based traffic volume prediction at signalized intersections. In the numerical experiment, the proposed model is evaluated using real-world data and simulation data to demonstrate its effectiveness. The impacts of various structures of traffic networks on the proposed model are also discussed. Results show that the proposed model outperforms some of the state-of-the-art volume prediction methods currently in the literature.

99 GENERAL AND MISCELLANEOUS↗

Microstructural and Defect Characterization in Ceramic Composites Using an Ultrasonic Guided Wave Scan System

In this study, an ultrasonic guided wave scan system was used to characterize various microstructural and flaw conditions in two types of ceramic matrix composites, SiC/SiC and C/SiC. Rather than attempting to isolate specific lamb wave modes to use for characterization (as is desired for many types of guided wave inspection problems), the guided wave scan system utilizes the total (multi-mode) ultrasonic response in its inspection analysis. Several time and frequency-domain parameters are calculated from the ultrasonic guided wave signal at each scan location to form images. Microstructural and defect conditions examined include delamination, density variation, cracking, and pre/ post-infiltration. Results are compared with thermographic imaging methods. Although the guided wave technique is commonly used so scanning can be eliminated, applying the technique in the scanning mode allows a more precise characterization of defect conditions.

Roth, D. J.↗

Filter failure detection for systems with large space structure dynamics

A failure detection filter is applied to the detection of actuator and sensor failures on a free-free beam. Computer simulation tests are used to verify the filter design and study the effect of unmodeled modes on filter performance. In actuator tests, the failure signal to spillover noise ratio was found to be greatest when the filter bandwidth was 5 rad/sec beyond the input frequency. Observation spillover, however, was found to vary widely in tests run under similar conditions (same input frequency and filter poles) but with different detector gains. In sensor tests, the maximum signal-to-noise ratio for varying filter bandwidth depended upon the initial conditions placed on the unmodeled modes; the performance was good even for initial amplitudes on the first unmodeled mode 7.5% of that on the last modeled mode.

Carignan, C. R.↗

Robustness of the smartpixels classifier for different simulated sensor geometries and non-ideal detector conditions

Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity and faster readout, possibly enabling the use of the pixel detector in high-rate online event selection, such as the ATLAS or CMS first-level trigger systems. This data reduction can be accomplished with a neural network (NN) in the readout chip bonded with the sensor that recognizes and rejects tracks with low transverse momentum (p T ) based on the geometrical shape of the charge deposition (“cluster”). To design viable detectors for deployment, the dependence of the NN as a function of the sensor geometry, external magnetic field, irradiation, and noise must be understood. In this paper, we present first studies of the efficiency and data reduction for planar pixel sensors exploring these parameters. For the CMS HL-LHC sensor geometry, we obtain a signal efficiency of (91.9 ± 0.7)% and a data reduction of (29.7 ± 1.0)%. A smaller sensor pitch in the bending direction improves the p T discrimination, but a larger pitch can be partially compensated with detector thickness. Any accumulated radiation damage also changes the cluster shape, reducing the signal efficiency compared to the baseline by approximately 30–60% in absolute terms, but nearly all of the performance can be recovered through retraining of the network and updating the weights. Finally, the impact of noise was investigated, and retraining the network on noise-injected datasets was found to maintain performance within 6% of the baseline network trained and evaluated on noiseless data. •ASIC-compatible track-momentum classifier is robust in realistic detector conditions.•About 90% signal efficiency and 30% data reduction per layer for CMS HL-LHC geometry.•Single-layer signal efficiency increases for smaller pixel pitch or thicker sensors.•Performance with noise or after radiation damage mostly recovered by retraining.

Shekar, Danush [Illinois U., Chicago] (ORCID:00000↗

Plant Roots: The Hidden Half: Calcium and Gravitropism - Chapter 16

Environmental signals such as light and gravity control many aspects of plant growth and development. In higher plants, the directional growth of an organ in response to stimuli such as gravity and light is considered a tropic movement. Such movement could be either positive or negative with respect to a specific stimulus. In general, stems show a positive response to light and negative response to gravity. In contrast, most roots show a positive response to gravity and a negative response to light. Investigations on plant tropism date back a century when Darwin studied the phototropic responses of maize seedlings (Darwin). Although the precise mechanism of signal perception and transduction in roots is not understood, Darwin recognized over 100 years ago that the root cap is the probable site of signal perception. He discovered that the removal of the root cap eliminates the ability of roots to respond to gravity. Other investigators have since confirmed Darwin's observation (Konings; Evans et al.). In recent years, especially with the advent of the U.S. Space Program, there has been a renewed interest in understanding how plants respond to extracellular signals such as gravity (Halstead and Dutcher). Studies on the mechanisms involved in perception and transduction of gravity signal by roots would ultimately help us to better understand gravitropism and also to grow plants under microgravity conditions as in space. In this chapter, we restrict ourselves to the role of calcium in transduction of the gravity signal. In doing so, emphasis is given to the role of calcium-modulated proteins and their role in signal transduction in gravitropism. Detailed reviews on various other aspects of gravitropism (Scott, Torrey, Wilkins, Fim and Digby, Feldman, Pickard, Moore and Evans, Halstead and Dutcher, Poovaiah et al.) and on the role of calcium as a messenger in signal transduction in general have been published (Helper and Wayne, Poovaiah and Reddy, Roberts and Hartnon, Bowler and Chua, Gilroy and Trewavas). Plant roots have been widely used to study the transduction of gravity and light signals (Poovaiah et al., Roux and Serlin). Most roots show positive gravitropic response in either dark or light. However, roots of some varieties of plants (e.g., Zea mays L., cv Merit, and Zea rwvs L., cv Golden Cross Bantam 70) show positive gravitropic response only in light (Feldman, Miyazaki et al.). Investigations from various laboratories indicate that calcium acts as a messenger in transducing gravity and light signals in plant roots(Pickard, Evans et al., Pooviah et al.).

Poovaiah, B. W.↗

A non-intrusive framework using acoustic signals and deep learning for boiling diagnostics in visual-limited environments

Accurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved predictive accuracy under nominal conditions and demonstrated robustness and generalization under acoustic noise for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results demonstrate the feasibility of hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF.

42 ENGINEERING↗

Using feature importance as an exploratory data analysis tool on Earth system models

Abstract. Machine learning (ML) models are commonly used to generate predictions, but these models can also support the discovery of new science. Generating accurate predictions necessitates that a model captures the structure of the underlying data. If the structure is properly extracted, ML could be a useful exploratory and evidential tool. In this paper, we present a case study that demonstrates the use of ML for exploratory data analysis (EDA) in the climate space. We apply the ML explainability method of spatiotemporal zeroed feature importance (stZFI) to understand how climate-variable associations evolve over space and time. Our analyses focus on data from ensembles of Earth system models (ESMs) which provide data on different climate states and conditions. We elect to work with ESM ensembles since they allow us to compare feature importance across alternative scenarios not available with observed data. The ensembles also account for natural variability so that we can distinguish between signal and noise due to natural climate variability when computing feature importance. The use of perturbed initial condition ensembles introduces variability mimicking the natural variability in the atmosphere; thus the signals emerging using feature importance (FI) can be evaluated against the natural variability in the climate system. For our analyses, we consider the 1991 volcanic eruption of Mount Pinatubo, which was a large stratospheric aerosol injection. We explore the climate pathway associated with the eruption from aerosols to radiation to temperature at both the near-surface and stratospheric levels. In addition to applying the method to data generated from two different ESMs, we apply stZFI to reanalysis data to compare the associations identified by stZFI. We show how stZFI tracks the importance of aerosol optical depth over time on forecasting temperatures. This case study illustrates usefulness of an ML tool (stZFI) for EDA on a well-studied climate exemplar.

Ries, Daniel (ORCID:0000000250294647)↗

Coherent Lidar Design and Performance Verification

This final report summarizes the investigative results from the 3 complete years of funding and corresponding publications are listed. The first year saw the verification of beam alignment for coherent Doppler lidar in space by using the surface return. The second year saw the analysis and computerized simulation of using heterodyne efficiency as an absolute measure of performance of coherent Doppler lidar. A new method was proposed to determine the estimation error for Doppler lidar wind measurements without the need for an independent wind measurement. Coherent Doppler lidar signal covariance, including wind shear and turbulence, was derived and calculated for typical atmospheric conditions. The effects of wind turbulence defined by Kolmogorov spatial statistics were investigated theoretically and with simulations. The third year saw the performance of coherent Doppler lidar in the weak signal regime determined by computer simulations using the best velocity estimators. Improved algorithms for extracting the performance of velocity estimators with wind turbulence included were also produced.

Frehlich, Rod↗

Generalizability analysis of tool condition monitoring ensemble machine learning models

Tool condition monitoring (TCM) is an essential research area for the optimization and automation of metal machining processes, and could help manufacturers reduce costs, production time, machine downtime, energy use, and part scrappage. However, TCM systems developed in prior studies have struggled to reach the high level of generalizability which is necessary for industrial applications. This study addresses TCM system generalizability to new machining conditions, how variations in machining and environmental conditions may be used to improve model generalizability, and ensemble machine learning techniques for TCM. Further, milling tool life experiments were conducted using various machining conditions, and the processes' sound, spindle power, and axial load signals were collected. Different machine learning models were evaluated for the prediction of tool wear levels, including four individual models and five ensemble models. Changes in cutting speed were found to display a large effect on model performance, while the chip load showed some effect, and the feed rate had little effect. A simulated noise data augmentation technique for model improvement is applied within TCM for the first time, and resulted in increased model generalizability and reduced overfitting. Across several performance metrics the extremely randomized trees ensemble machine learning model generally performed the best for this application, achieving a leave-one-group-out cross validation accuracy score of 92.4 %, a 10-fold cross validation score of 98.9 %, and an averaged accuracy across 11 generalizability tests of 87.3 %.

42 ENGINEERING↗

Strain-Gauge Measurement of Weight of Fluid in a Tank

A method of determining the amount of fluid in a tank is based on measurement of strains induced in tank supports by the weight of the fluid. Unlike most prior methods, this method is nonintrusive: there is no need to insert instrumentation in the tank and, hence, no need to run wires, cables, or tubes through the tank wall. Also unlike most prior methods, this method is applicable even if the fluid in the tank is at supercritical pressure and temperature, because it does not depend on the presence of a liquid/gas interface (as in liquid-level-measuring methods). The strain gauges used in this method are of two types: foil and fiber-optic. Four foil gauges and one or more fiber-optic gauges are mounted on each of the tank-supporting legs. An additional fiber-optic gauge is mounted on an object, made of the same material as that of the tank-supporting legs, that is not subjected to any mechanical load. The reading obtained by the additional fiber-optic gauge is used to compensate for apparent strains caused by changes in temperature. The signals from the foil and fiber-optic gauges are conditioned, then digitized for input to a computer. As the tank is filled or emptied, the deformation in each leg increases or decreases, respectively. Measured deformations of all legs are added to obtain a composite deformation indicative of the change in weight of the tank plus fluid. An initial calibration is performed by recording data at two points (usually, empty and full) for which the mass or weight of fluid is known. It is assumed that the deformations are elastic, so that the line passing through the two points can be used as a calibration curve of mass (or weight) of fluid versus deformation. At the time of reporting the information for this article, a set of foil gauges had been tested on the supports of a 500-gallon (1,900-liter) tank. The gauges were found to be capable of measuring the deformations (up to 22 microstrain) that occurred during filling and emptying the tank. The fluid masses calculated from the gauge readings were found to be accurate within 4.5 percent. It has been estimated that once the fiber-optic gauges are put into operation, it should be possible to determine fluid masses with 3 percent or less. It may be possible to increase accuracy further by increasing the signal-to-noise ratio through the use of more deformable tank supporting legs.

Figueroa, Jorge↗

Caution and Warning Alarm Design and Evaluation for NASA CEV Auditory Displays: SHFE Information Presentation Directed Research Project (DRPP) report 12.07

The design of caution-warning signals for NASA s Crew Exploration Vehicle (CEV) and other future spacecraft will be based on both best practices based on current research and evaluation of current alarms. A design approach is presented based upon cross-disciplinary examination of psychoacoustic research, human factors experience, aerospace practices, and acoustical engineering requirements. A listening test with thirteen participants was performed involving ranking and grading of current and newly developed caution-warning stimuli under three conditions: (1) alarm levels adjusted for compliance with ISO 7731, "Danger signals for work places - Auditory Danger Signals", (2) alarm levels adjusted to an overall 15 dBA s/n ratio and (3) simulated codec low-pass filtering. Questionnaire data yielded useful insights regarding cognitive associations with the sounds.

Durand R Begault↗

Evaluation of pulsed RFI effects on digital satellite repeaters

This paper presents an analytical approach for assessing the effect of pulsed RFI on the error probability of a coherent phase-shift keyed signal through a nonlinear satellite repeater. The RFI is assumed to affect the uplink channel and to consist of CW pulses with random power levels and arriving randomly in time with a Poisson distribution. A model to approximate the effect of intermodulation products is introduced and the error probability conditioned on the output of the satellite repeater is computed. The classical moment technique is then used as an efficient method of averaging the conditional error probability over the numerous random parameters associated with the uplink signal.

Huang, T. C.↗

MicroRNA Expression Profiles in Cultured Human Fibroblasts in Space

Microgravity, or an altered gravity environment from the static 1g, has been shown to influence global gene expression patterns and protein levels in living organisms. However, it is unclear how these changes in gene and protein expressions are related to each other or are related to other factors regulating such changes. A different class of RNA, the small non-coding microRNA (miRNA), can have a broad effect on gene expression networks by mainly inhibiting the translation process. Previously, we investigated changes in the expression of miRNA and related genes under simulated microgravity conditions on the ground using the NASA invented bioreactor. In comparison to static 1 g, simulated microgravity altered a number of miRNAs in human lymphoblastoid cells. Pathway analysis with the altered miRNAs and RNA expressions revealed differential involvement of cell communication and catalytic activity, as well as immune response signaling and NGF activation of NF-kB pathways under simulated microgravity condition. The network analysis also identified several projected networks with c- Rel, ETS1 and Ubiquitin C as key factors. In a flight experiment on the International Space Station (ISS), we will investigate the effects of actual spaceflight on miRNA expressions in nondividing human fibroblast cells in mostly G1 phase of the cell cycle. A fibroblast is a type of cell that synthesizes the extracellular matrix and collagen, the structural framework for tissues, and plays a critical role in wound healing and other functions. In addition to miRNA expressions, we will investigate the effects of spaceflight on the cellular response to DNA damages from bleomycin treatment.

Wu, Honglu↗

Spinning the Facts against Genetically Engineered Foods?

A signaling game is developed in order to derive the equilibrium conditions under which special interest groups (SIGs) involved in the controversial debate over genetically engineered (GE) foods have the incentive to truthfully reveal their information or spin facts regarding the health impact of GE foods. Consumers can choose to inspect information provided by SIGs at a cost. The risk of spinning facts is much higher for pro-GE groups, because if it turns out that a certain GE food is unsafe, the penalty will be severe. However, anti-GE groups can still spin facts at low risk even if consumers choose to inspect. This helps explain why some pro-GE groups, particularly the biotech industry, tend to remain silent. Revealing information regarding the safety of GE foods could be counterproductive given pre-existing public skepticism. Consumers may not make “better” decisions with more information provided because more information increases their inspection costs. When it is costlier for consumers to inspect, it is more likely that anti-GE groups will continue to spin facts about the negative health impact of GE foods.

59 BASIC BIOLOGICAL SCIENCES↗

An analog-to-digital conversion system with a logarithmic characteristic

Detailed analysis of an analog-to-digital conversion system consisting of a linear converter and a logarithmic amplifier containing nonlinear elements. It is shown that the small-signal resolution of such a system is much greater than that of linear systems used under the same conditions. A design for a low-power analog-to-digital converter operating at medium speed with a large input signal variation field is outlined.

Bellomo, A.↗