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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Applicability of Loads Estimation Techniques Using Sparse Acceleration Sensor Data to Spacecraft Structural Health Monitoring

The use of structural health monitoring systems on spacecraft structures can play a crucial role in ensuring the safety, reliability, and longevity of the structure by gathering and analyzing onboard sensor data. Of specific importance is monitoring for excessive loading at critical interfaces as any off-nominal structural excitations experienced by spacecraft structures can cause early unpredicted high structural life consumption or damage. The availability and cost of flight-certified sensors along with the size of spacecraft structures and allowable payload mass drives the need for a method to estimate loads using sparsely-located sensors. Numerous approaches such as physics-based, statistical learning, and physics-enhanced statistical learning algorithms have gained popularity among structural prognostics applications. However, developing noise-robust prediction models to assess loads and structural life predictions from a sparse multi-sensor data acquisition system can be a challenging task. This paper discusses the evaluation of physics-based versus machine-learning algorithms for predicting loads and structural life at mission critical locations on the spacecraft structure using a finite element loads analysis with the application of simulated noise and noise reduction techniques. To estimate the loads from accelerations, the physics-based algorithm leverages a loads transformation matrix from a Craig-Bampton reduced finite element model. A System Equivalent Reduction Expansion Process (SEREP) and a pseudo-inverse approach are considered to expand from the onboard sensor degrees of freedom to the Craig-Bampton model degrees of freedom. The machine learning algorithm provides a data driven solution/mapping of the sensor accelerations to the loads at the mission critical locations using a high dimensionality analysis. Although these strategies produce comparable loads prediction without noise, the limitations of these strategies with incorporating simulated noise and noise reduction techniques with low signal to noise ratio signals are evaluated. The study demonstrates the immense potential of statistical learning algorithms for sparse structural prognostic models and enhancing signal denoising techniques. These findings also highlight the need for noise-resilient prognostic models and low-noise data acquisition systems onboard spacecraft structures.

Spacecraft Structural Health Monitoring↗
NASA NTRS1986

Cramer-Rao bounds for signal-to-noise ratio and combiner weight estimation

Cramer-Rao lower bounds on estimator variance are calculated for arbitrary unbiased estimates of signal-to-noise ratio and combiner weight parameters. Estimates are assumed to be based on a discrete set of observables obtained by matched filtering of a biphase modulated signal. The bounds are developed first for a problem model based on one observable per channel symbol period, and then extended to a more general problem in which subperiod observables are also available.

Dolinar, S. J.↗
NASA NTRS2002

Signal-to-Noise Ratio Prediction and Validation for Space Shuttle GPS Flight Experiment

A deterministic method for Space Station Global Positioning System (GPS) Signal-To- Noise Ratio (SNR) predictions is proposed. The complex electromagnetic interactions between GPS antennas and surrounding Space Station structures are taken into account by computational electromagnetic technique. This computer simulator is capable of taking into account multipath effects from dynamically changed solar panels and thermal radiators. A comparison with recent collected Space Station GPS system flight experiment data is presented. The simulation results are in close agreement with flight data.

Hwu, Shian U.↗
NASA NTRS1979

Signal-to-noise ratio for acoustic detection in the deep ocean

A simple method is presented for studying the thermoacoustic wave generated by a heat pulse. The signal-to-noise ratio (S/N) is then calculated for a typical hadronic-electromagnetic cascade in the deep ocean where low frequencies are masked by surface noise. It is found that a maximum useful range of about 16 km is found for typical conditions at 5 km depth. It is shown that in order to obtain useful signals with S/N greater than 100 at distances of 1 to 16 km, the cascade energy must be 10 to the 16th to 10 to the 18th eV. Finally, attention is given to further refinements of the theory of acoustic detection which remain to be investigated.

Bowen, T.↗
NASA NTRS1986

Automated Signal-to-Noise Ratio Measurement

Computer-controlled spectrum analysis gives rapid results for communication systems. Locates carrier signal in intermediate-frequency band and measures both carrier amplitude and amplitude of noise in several channels near carrier frequency. Computer then computes ratio of signal to average noise. Because measurements and calculations are rapid, system used in fading communication channels.

Pineda, J. E.↗
NASA NTRS2004

Development of an Optical-Real Time Polarimetric Imaging System for Aerospace Applications

The design of an optical polarimetric imaging system, aimed to detect cracks or structural defects on the surface of rotating aircraft engine shafts, is presented. The experimental results have been processed by a real time wavelet denouncing system implemented by a Filed Programmable Gate Arrays (FPGA), which has been designed for this purpose. The experimental results clearly indicate that high signal-to-noise ratio signals can be obtained.

Giakos, George C.↗
NASA NTRS1991

Plant gravity sensing

This review of plant gravity sensing examines sensing in organ gravitropism, sensing in single-cell gravitropism, and nongravitropic sensing. Topics related to sensing in organ gravitropism are (1) identification of the gravitropic susceptors, including intracellular asymmetry in equilibrium position and after reorientation, susceptor signal-to-noise ratio, signal integration over threshold stimulation periods, intracellular asymmetry and gravitropic competence, and starch deficiency and gravitropic competence; (2) possible root statocytes and receptors, including identification of presumptive statocytes, cytology, and possible receptors and models of sensing; and (3) negatively gravitropic organs, including identification and distribution of presumptive statocytes and cytology and possible receptors. Topics related to nongravitropic sensing include gravitaxis, reaction wood, gravimorphogenesis, other gravity-influenced organ movements, and cytoplasmic streaming.

Non-NASA Center↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), has typically limited machine learning (ML) in space studies and further study of radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNAseq) data from 6 mouse liver GeneLab datasets (GLDS) with a total of 113 spaceflight and ground-control samples to determine top features relevant to spaceflight including the effect of radiation exposure. Data was normalized within each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. The top MRMR features were used to predict spaceflight vs. ground-control samples using a Random Forest (RF) classifier with 5-fold cross validation (CV). The ML-based gene sets were further compared against differential gene expression results from individual GLDS. CV training using the top 100 MRMR genes show averages of 86% accuracy and 0.95 AUC value on the validation set over 5 folds (Figure 1A). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 811 or 68 DEGs overlapping between at least 2 or 3 studies, respectively (Figure 1B). Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism. Set analysis between the MRMR features and the DEGs showed 60 or 8 genes overlapping with at least 1 or 2 studies, respectively. MRMR feature selection and ensemble ML methods (e.g. RF) improve performance relative to a Naïve Bayes classifier when NGS data sets are analyzed. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise ratio. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from RNASeq analysis. Non-intersecting sets introduce opportunity to explore spaceflight relevant genes and implementing ML methods across existing NGS datasets may overcome sample size limitations. ML coupled with existing analytical methods enhances understanding of disease by revealing common underlying pathways across datasets.

Machine Learning↗
NASA NTRS1991

The behavior of quantization spectra as a function of signal-to-noise ratio

An expression for the spectrum of quantization error in a discrete-time system whose input is a sinusoid plus white Gaussian noise is derived. This quantization spectrum consists of two components: a white-noise floor and spurious harmonics. The dithering effect of the input Gaussian noise in both components of the spectrum is considered. Quantitative results in a discrete Fourier transform (DFT) example show the behavior of spurious harmonics as a function of the signal-to-noise ratio (SNR). These results have strong implications for digital reception and signal analysis systems. At low SNRs, spurious harmonics decay exponentially on a log-log scale, and the resulting spectrum is white. As the SNR increases, the spurious harmonics figure prominently in the output spectrum. A useful expression is given that roughly bounds the magnitude of a spurious harmonic as a function of the SNR.

Flanagan, M. J.↗
NASA NTRS1986

Performance characteristics for an array of two receiving systems with unequal predetection signal-to-noise ratios and enhanced radio frequency carrier margin improvement

Enhanced radio frequency carrier margin improvement for arrayed receiving systems for coherent reception of phase modulated signals with residual carrier provides a significant reduction in carrier loop phase noise and increase in signal to noise ratio in the RF carrier tracking loop with an attendant reduction in telemetry radio loss. A significant increase in Doppler frequency rate capability is also realized relative to operating at a narrower tracking loop bandwidth to obtain the same carrier sensitivity improvement. These performance characteristics are examined for an array of two receiving systems with unequal apertures and statistically independent predetection noise.

Brockman, M. H.↗
NASA NTRS2011

Receiver Signal to Noise Ratios for IPDA Lidars Using Sine-wave and Pulsed Laser Modulation and Direct Detections

Integrated path differential absorption (IPDA) lidar can be used to remotely measure the column density of gases in the path to a scattering target [1]. The total column gas molecular density can be derived from the ratio of the laser echo signal power with the laser wavelength on the gas absorption line (on-line) to that off the line (off-line). 80th coherent detection and direct detection IPDA lidar have been used successfully in the past in horizontal path and airborne remote sensing measurements. However, for space based measurements, the signal propagation losses are often orders of magnitude higher and it is important to use the most efficient laser modulation and detection technique to minimize the average laser power and the electrical power from the spacecraft. This paper gives an analysis the receiver signal to noise ratio (SNR) of several laser modulation and detection techniques versus the average received laser power under similar operation environments. Coherent detection [2] can give the best receiver performance when the local oscillator laser is relatively strong and the heterodyne mixing losses are negligible. Coherent detection has a high signal gain and a very narrow bandwidth for the background light and detector dark noise. However, coherent detection must maintain a high degree of coherence between the local oscillator laser and the received signal in both temporal and spatial modes. This often results in a high system complexity and low overall measurement efficiency. For measurements through atmosphere the coherence diameter of the received signal also limits the useful size of the receiver telescope. Direct detection IPDA lidars are simpler to build and have fewer constraints on the transmitter and receiver components. They can use much larger size 'photon-bucket' type telescopes to reduce the demands on the laser transmitter. Here we consider the two most widely used direct detection IPDA lidar techniques. The first technique uses two CW seeder lasers, one on-line and one offline that are intensity modulated by two different frequency sine-waves signals before being amplified by a common laser amplifier. The receiver uses narrowband amplitude demodulation, or lock-in, Signal processing at the given laser modulation frequencies [3,4]. The laser transmitter operates in a quasi CW mode with the peak power equal to twice the average power. The on-line and off-line lasers can be transmitted at the same time without interference. Another direct detection technique uses a low duty cycle pulsed laser modulation [5,6] with the laser wavelengths alternating between on-line and off-line on successive pulses. The receiver uses time resolved detection and can also provide simultaneous target range measurement. With a lower laser duty cycle it requires a much higher peak laser power for the same average power.

Sun, Xiaoli↗
NASA NTRS2024

Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland Waters

The launch of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the Surface Biology and Geology (SBG) satellite sensors will provide increased spectral resolution compared to existing platforms. These new sensors will require robust calibration and validation datasets, but existing field-based instrumentation is limited in its availability and potential for geographic coverage, particularly for coastal and inland waters, where optical complexity is substantially greater than in the open ocean. The minimum signal-to-noise ratio (SNR) is an important metric for assessing the reliability of derived biogeochemical products and their subsequent use as proxies, such as for biomass, in aquatic systems. The SNR can provide insight into whether legacy sensors can be used for algorithm development as well as calibration and validation activities for next-generation platforms. We extend our previous evaluation of SNR and associated uncertainties for representative coastal and inland targets to include the imaging sensors PRISM and AVIRIS-NG, the airborne-deployed C-AIR radiometers, and the shipboard HydroRad and HyperSAS radiometers, which were not included in the original analysis. Nearly all the assessed hyperspectral sensors fail to meet proposed criteria for SNR or uncertainty in remote sensing reflectance (R rs ) for some part of the spectrum, with the most common failures (>20% uncertainty) below 400 nm, but all the sensors were below the proposed 17.5% uncertainty for derived chlorophyll-a. Instrument suites for both in-water and airborne platforms that are capable of exceeding all the proposed thresholds for SNR and R rs uncertainty are commercially available. Thus, there is a straightforward path to obtaining calibration and validation data for current and next-generation sensors, but the availability of suitable high spectral resolution sensors is limited.

signal-to-noise ratio↗
NASA NTRS2001

Signal-to-noise ratio comparison of encoding methods for hyperpolarized noble gas MRI

Some non-Fourier encoding methods such as wavelet and direct encoding use spatially localized bases. The spatial localization feature of these methods enables optimized encoding for improved spatial and temporal resolution during dynamically adaptive MR imaging. These spatially localized bases, however, have inherently reduced image signal-to-noise ratio compared with Fourier or Hadamad encoding for proton imaging. Hyperpolarized noble gases, on the other hand, have quite different MR properties compared to proton, primarily the nonrenewability of the signal. It could be expected, therefore, that the characteristics of image SNR with respect to encoding method will also be very different from hyperpolarized noble gas MRI compared to proton MRI. In this article, hyperpolarized noble gas image SNRs of different encoding methods are compared theoretically using a matrix description of the encoding process. It is shown that image SNR for hyperpolarized noble gas imaging is maximized for any orthonormal encoding method. Methods are then proposed for designing RF pulses to achieve normalized encoding profiles using Fourier, Hadamard, wavelet, and direct encoding methods for hyperpolarized noble gases. Theoretical results are confirmed with hyperpolarized noble gas MRI experiments. Copyright 2001 Academic Press.

Non-NASA Center↗
NASA NTRS2021

Estimated Ambient Sonic Boom Metric Levels and X-59 Signal-to-Noise Ratios across the USA

NASA is building the X-59 Quiet Supersonic Technology aircraft to produce low noise sonic booms for a series of community noise surveys across the USA. Survey participants will rate their perception of the low-booms from supersonic X-59 flyovers. Several noise metrics are proposed to quantify the noise dose: A-, B-, D-, and E-weighted Sound Exposure Level, Stevens Perceived Level, and Indoor Sonic Boom Annoyance Predictor. Sparse measurements across the survey area will be used to estimate community noise exposure. The level of these low-booms may be comparable to the ambient noise level in some locations, leading to uncertainty in noise exposure estimations. This uncertainty may necessitate increased reliance on sonic boom propagation predictions for exposure estimation. Low-boom signal to ambient noise ratio is one way to quantify uncertainty in measured sonic boom levels. An empirical relationship between A-weighted ambient level and sonic boom metric levels is used in conjunction with the National Park Service’s L50 SPL map to estimate ambient noise levels expressed in terms of sonic boom noise metrics across the USA. These estimates of ambient levels will aid in X-59 community test planning and execution. The signal-to-noise ratio for the undertrack X-59 sonic boom is also estimated, and an example application of these data is presented for comparing potential noise monitor sites prior to a community noise test.

X-59↗
NASA NTRS1994

The ultraviolet emission properties of five low-redshift active galactic nuclei at high signal-to-noise ratio and spectral resolution

We analyze the ultraviolet (UV) emission line and continuum properties of five low-redshift active galactic nuclei (four luminous quasars: PKS 0405-123, H1821 + 643, PG 0953 + 414, and 3C 273, and one bright Seyfert 1 galaxy: Mrk 205). The HST spectra have higher signal-to-noise ratios (typically approximately 60 per resolution element) and spectral resolution (R = 1300) than all previously published UV spectra used to study the emission characteristics of active galactic nuclei. We include in the analysis ground-based optical spectra covering H beta and the narrow (O III) lambda lambda 4959, 5007 doublet. New results are obtained and presented.

Laor, Ari↗
DOE OSTI2026

Machine learning pipeline for denoising low signal-to-noise ratio and out-of-distribution transmission electron microscopy datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

36 MATERIALS SCIENCE↗