Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “discrimination”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Alteration mapping at Goldfield, Nevada, by cluster and discriminant analysis of Landsat digital data

The ability of Landsat multispectral digital data to differentiate among 62 combinations of rock and alteration types at the Goldfield mining district of Western Nevada was investigated by using statistical techniques of cluster and discriminant analysis. Multivariate discriminant analysis was not effective in classifying each of the 62 groups, with classification results essentially the same whether data of four channels alone or combined with six ratios of channels were used. Bivariate plots of group means revealed a cluster of three groups including mill tailings, basalt and all other rock and alteration types. Automatic hierarchical clustering based on the fourth dimensional Mahalanobis distance between group means of 30 groups having five or more samples was performed using Johnson's HICLUS program. The results of the cluster analysis revealed hierarchies of mill tailings vs. natural materials, basalt vs. non-basalt, highly reflectant rocks vs. other rocks and exclusively unaltered rocks vs. predominantly altered rocks. The hierarchies were used to determine the order in which sets of multiple discriminant analyses were to be performed and the resulting discriminant functions were used to produce a map of geology and alteration which has an overall accuracy of 70 percent for discriminating exclusively altered rocks from predominantly altered rocks.

Ballew, G.↗
NASA NTRS1981

Rock type discrimination techniques using Landsat and Seasat image data

Results of a sedimentary rock type discrimination project using Seasat radar and Landsat multispectral image data of the San Rafael Swell, in eastern Utah, are presented, which has the goal of determining the potential contribution of radar image data to Landsat image data for rock type discrimination, particularly when the images are coregistered. The procedure employs several images processing techniques using the Landsat and Seasat data independently, and then both data sets are coregistered. The images are evaluated according to the ease with which contacts can be located and rock units (not just stratigraphically adjacent ones) separated. Results show that of the Landsat images evaluated, the image using a supervised classification scheme is the best for sedimentary rock type discrimination. Of less value, in decreasing order, are color ratio composites, principal components, and the standard color composite. In addition, for rock type discrimination, the black and white Seasat image is less useful than any of the Landsat color images by itself. However, it is found that the incorporation of the surface textural measures made from the Seasat image provides a considerable and worthwhile improvement in rock type discrimination.

Blom, R.↗
NASA NTRS1985

Color measurement and discrimination

The present investigation is concerned with new results which show that for test lights with slow temporal modulations, and thus little effect on the luminance system, the vector-difference hypothesis represents an adequate characterization of discrimination data. It is pointed out that for certain experimental conditions color measurements can be successfully extended to include a difference measure which predicts the discriminability of pairs of lights. When discrimination depends principally on opponent-channel responses, discrimination thresholds can be predicted from the detection contour alone. Attention is given to discriminations with a 6-Hz Gabor function, the categorization of stimulus regions, and the nature of the visual mechanisms.

Wandell, B. A.↗
NASA NTRS1985

Application of a computable model of human spatial vision to phase discrimination

A computable model of human spatial vision is used to make predictions for phase-discrimination experiments. This model is being developed to deal with a broad range of problems in vision and was not specifically formulated to deal with phase discrimination. In the model, cross-correlation of the stimuli with an array of sensors produces feature vectors that are operated on by a position-uncertain ideal observer to simulate detection and discrimination experiments. In this report, the stimuli are compound sinusoidal gratings composed of a fundamental and a higher-frequency component added in various phases. Model predictions are compared with three key results from the literature: (1) the effect of the contrast of the fundamental on phase discrimination, (2) threshold phase difference as a function of the fundamental frequency, and (3) the contrast required for phase discrimination as a function of the frequency ratio of the two grating components. In the first two cases, the predictions capture the main features of the data, although quantitative discrepancies remain. In the third case, the model fails, and this failure suggests additional restrictions on the combination of information across sensors.

Nielsen, K. R. K.↗
NASA NTRS1991

Tactile discrimination and representations of texture, shape, and softness

We present here some of the salient results on the tactual discriminabilities of human subjects obtained through psychophysical experiments, and the associated peripheral neural codes obtained through electrophysiological recordings from monkey single nerve fibers. Humans can detect the presence of a 2 micron high single dot on a smooth glass plate stroked on the skin, based on the responses of Meissner type rapidly adapting fibers (RAs). They can also detect a 0.06 micron high grating on the plate, owing to the response of Pacinian corpuscle fibers. Among all the possible representations of the shapes of objects, the surface curvature distribution seems to be the most relevant for tactile sensing. Slowly adapting fibers respond to both the change and rate of change of curvature of the skin surface at the most sensitive spot in their receptive fields, whereas RAs respond only to the rate of change of curvature. Human discriminability of compliance of objects depends on whether the object has a deformable or rigid surface. When the surface is deformable, the spatial pressure distribution within the contact region is dependent on object compliance, and hence information from cutaneous mechanoreceptors is sufficient for discrimination of subtle differences in compliance. When the surface is rigid, kinesthetic information is necessary for discrimination, and the discriminability is much poorer than that for objects with deformable surfaces.

Srinivasan, M. A.↗
NASA NTRS2000

Discriminating Type 1a and 1b PSCs in Satellite Data

We explore the use of satellite observations in discriminating types of PSCs and their ramifications. Polar Stratospheric Clouds (PSCs), which form in the winter polar vortex, have been identified as effecting ozone loss. One major result from the recent SOLVE mission is in-situ evidence of the existence of very large particles that contain nitric acid. These particles are consistent with Type la PSCs. The significance of this finding is that these large particles will have appreciable sedimentation velocities, taking nitric acid out of the stratospheric regions, causing denitrification. Since nitric acid typically mitigates ozone loss, denitrification leads to increased ozone loss. Type lb PSCs are smaller and do not sediment to any appreciable degree. Satellite measurements are made continuously throughout the winter, and offer more global coverage than in situ measurements. Thus, it would very useful to be able to discriminate PSC types from satellite measurements. Our long-term goals are to better understand the formation mechanisms and effects of PSCs. Discriminating PSC type using satellite data will give us a very important tool in this effort. A multi-wavelength analysis of POAM aerosol extinction during SOLVE has revealed differences in the radiative characteristics of PSC events. We explore the use of POAM observations to discriminate between Type la and lb Pscs. A trajectory model is used to simulate PSC la and lb particles. Calculated radiative properties act as a guide for discriminating the satellite occultation measurements. Aircraft based PSC observations are-used as confirmation of these observations.

Strawa, Anthony W.↗
NASA NTRS1994

Image Discrimination Models Predict Object Detection in Natural Backgrounds

Object detection involves looking for one of a large set of object sub-images in a large set of background images. Image discrimination models only predict the probability that an observer will detect a difference between two images. In a recent study based on only six different images, we found that discrimination models can predict the relative detectability of objects in those images, suggesting that these simpler models may be useful in some object detection applications. Here we replicate this result using a new, larger set of images. Fifteen images of a vehicle in an other-wise natural setting were altered to remove the vehicle and mixed with the original image in a proportion chosen to make the target neither perfectly recognizable nor unrecognizable. The target was also rotated about a vertical axis through its center and mixed with the background. Sixteen observers rated these 30 target images and the 15 background-only images for the presence of a vehicle. The likelihoods of the observer responses were computed from a Thurstone scaling model with the assumption that the detectabilities are proportional to the predictions of an image discrimination model. Three image discrimination models were used: a cortex transform model, a single channel model with a contrast sensitivity function filter, and the Root-Mean-Square (RMS) difference of the digital target and background-only images. As in the previous study, the cortex transform model performed best; the RMS difference predictor was second best; and last, but still a reasonable predictor, was the single channel model. Image discrimination models can predict the relative detectabilities of objects in natural backgrounds.

Ahumada, Albert J., Jr.↗
DOE OSTI2020

Characterization and pulse-shape discrimination of a multi-stacked structure of ZnS: Ag/PMMA for fast-neutron detection in high-flux environments

The Micro-Layered Fast Neutron Detector (MLFD) is a proton-recoil scintillator that incorporates the basic concept of a Hornyak button detector with an improved design using a layered structure of ZnS:Ag and PMMA (polymethyl methacrylate). The MLFD was predicted to have low sensitivity to gamma rays, hence pulse shape discrimination was performed to determine the gamma-ray contribution. Presented in this paper are the pulse-shape discrimination characteristics of the MLFD. Additionally, the charge integration technique was employed to discriminate between the neutrons and gamma rays using a fast digitzer. The MLFD was found to exhibit excellent neutron/gamma-ray discrimination, with the highest Figure-of-Merit (FoM) being 4.56. With PSD, the neutron signal is distinct from gamma-ray, Ĉerenkov and electronic noise signals. This clear separation was achieved due to the large differences in decay times of the neutron and gamma-ray-induced pulses in the ZnS:Ag phosphor. The MLFD can be employed in high gamma-ray exposure fields, up to at least 1287.7 mR/hr, without appreciable degradation in PSD performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗
NASA NTRS1999

Visual discrimination in the pigeon (Columba livia): effects of selective lesions of the nucleus rotundus

The nucleus rotundus is a large thalamic nucleus in birds and plays a critical role in many visual discrimination tasks. In order to test the hypothesis that there are functionally distinct subdivisions in the nucleus rotundus, effects of selective lesions of the nucleus were studied in pigeons. The birds were trained to discriminate between different types of stationary objects and between different directions of moving objects. Multiple regression analyses revealed that lesions in the anterior, but not posterior, division caused deficits in discrimination of small stationary stimuli. Lesions in neither the anterior nor posterior divisions predicted effects in discrimination of moving stimuli. These results are consistent with a prediction led from the hypothesis that the nucleus is composed of functional subdivisions.

NASA Discipline Neuroscience↗
DOE OSTI2020

Discrimination of electronic recoils from nuclear recoils in two-phase xenon time projection chambers

We present a comprehensive analysis of electronic recoil vs. nuclear recoil discrimination in liquid/gas xenon time projection chambers, using calibration data from the 2013 and 2014-16 runs of the Large Underground Xenon (LUX) experiment. We observe strong charge-to-light discrimination enhancement with increased event energy. For events with S1 = 120 detected photons, i.e. equivalent to a nuclear recoil energy of ~100 keV, we observe an electronic recoil background acceptance of <10 –5 at a nuclear recoil signal acceptance of 50%. We also observe modest electric field dependence of the discrimination power, which peaks at a field of around 300 V/cm over the range of fields explored in this study (50-500 V/cm). In the WIMP search region of S1 = 1-80 phd, the minimum electronic recoil leakage we observe is (7.3 ± 0.6) 10 –4 , which is obtained for a drift field of 240-290 V/cm. Pulse shape discrimination is utilized to improve our results, and we find that, at low energies and low fields, there is an additional reduction in background leakage by a factor of up to 3. We develop an empirical model for recombination fluctuations which, when used alongside the Noble Element Scintillation Technique (NEST) simulation package, correctly reproduces the skewness of the electronic recoil data. We use this updated simulation to study the width of the electronic recoil band, finding that its dominant contribution comes from electron-ion recombination fluctuations, followed in magnitude of contribution by fluctuations in the S1 signal, fluctuations in the S2 signal, and fluctuations in the total number of quanta produced for a given energy deposition.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Pushing the limits of pulse shape discrimination in a large liquid xenon detector

Abstract The LUX-ZEPLIN (LZ) experiment is a direct-detection dark matter experiment, optimized to search for weakly interacting massive particles (WIMPs) through WIMP-nucleon interactions. The main challenge in dark matter detection is differentiating between WIMP signals and background events. In LZ, the ratio of ionization to scintillation signals (charge-to-light) is the primary method for rejecting electronic recoil (ER) background. Pulse shape discrimination (PSD) offers a method for additional ER backgrounds rejection in liquid xenon detectors. In this paper, the discrimination power of PSD with the LZ experiment is discussed. To precisely characterize the scintillation pulse shape, an analysis framework is developed to reconstruct the detection time of individual photons. Using LZ calibration data, the photon-timing tail fraction discriminator is optimized and achieves ER leakage as low as $$15\%$$ 15 % . For specific background processes such as $$^{124}$$ 124 Xe double electron capture, the leakage is reduced further to about $$5\%$$ 5 % . PSD is combined with charge-to-light to form two-factor discrimination (TFD). The optimized TFD performance is compared with the performance of the charge-to-light method, with the corresponding false positive rate reduced by up to a factor of two for large scintillation pulses. Finally, PSD and TFD are applied to data from LZ’s WS2024 run and their performance is summarized.

Akerib, D. S. [SLAC National Accelerator Laborator↗
DOE OSTI2020

Discrimination of Small Earthquakes and Buried Single-Fired Chemical Explosions at Local Distances (<150 km) in the Western United States from Comparison of Local Magnitude ( M L ) and Coda Duration Magnitude ( M C )

Seismologists distinguish underground nuclear explosions from more commonly occurring earthquakes using moment tensor inversion, high-frequency P/S amplitude ratios, m b :M s comparisons, and P–pP differential travel times. These methods are generally successful for large seismic events (M >3-4) well recorded at regional-to-teleseismic distances (>150 km); however, it is unclear whether they can be modified to work for small events (M <3) well recorded only at local distances (<150 km). Here, we evaluate a recently proposed, local-distance seismic source discriminant—the difference between local magnitude (M L ) and coda duration magnitude (M C )—using seismograms of earthquakes and buried, single-fired chemical explosions recorded in three regions of the western United States. The quantity M L –M C was previously found to be sensitive to source depth, effectively discriminating mine blasts, induced earthquakes, and very shallow tectonic earthquakes from deeper crustal earthquakes. In this study, we report the first evaluation of M L –M C as a depth discriminant using data from buried, single-fired explosions that, unlike the seismic sources studied earlier, are good analogs for underground nuclear explosions. We find that even when using generic, uncalibrated methods of assigning magnitudes, M L –M C separates single-fired explosions and earthquakes. The area under the receiver operating characteristic curve is 0.92 for 19 explosions and 14 earthquakes in Washington, 0.90 for 22 explosions and 90 earthquakes in Wyoming, and 0.99 for three explosions and 149 earthquakes in Nevada. Finally, M L :M C comparisons have the potential to enhance discrimination based on high-frequency P/S amplitudes ratios—which perform less well at local than regional distances—because the two metrics have complementary sensitivities.

58 GEOSCIENCES↗
DOE OSTI2021

Explosion Discrimination Using Seismic Gradiometry and Spectral Filtering of Data

Here, we present a new method to discriminate between earthquakes and buried explosions using observed seismic data. The method is different from previous seismic discrimination algorithms in two main ways. First, we use seismic spatial gradients, as well as the wave attributes estimated from them (referred to as gradiometric attributes), rather than the conventional three-component seismograms recorded on a distributed array. The primary advantage of this is that a gradiometer is only a fraction of a wavelength in aperture compared with a conventional seismic array or network. Second, we use the gradiometric attributes as input data into a machine learning algorithm. The resulting discrimination algorithm uses the norms of truncated principal components obtained from the gradiometric data to distinguish the two classes of seismic events. Using high-fidelity synthetic data, we show that the data and gradiometric attributes recorded by a single seismic gradiometer performs as well as a conventional distributed array at the event type discrimination task.

58 GEOSCIENCES↗
DOE OSTI2025

Regional Source-Type Discrimination Using Nonlinear Alignment Algorithms

The discrimination problem in seismology aims to accurately classify different underground source types based on local, regional, and/or teleseismic observations of ground motion. Typical discriminant approaches are rooted in fundamental, physics-based differences in radiation pattern or wave excitation, which can be frequency-dependent and may not make use of the full waveform. In this article, we explore whether phase and amplitude distances derived from dynamic time warping (DTW) and elastic shape analysis (ESA) can inform event discrimination. We demonstrate the ability to distinguish underground point sources using synthetic waveforms calculated for a 1D Earth model and various source mechanisms. We then apply the method to recorded data from events in the Korean Peninsula, which includes declared nuclear explosions, a collapse event, and naturally occurring earthquakes. Phase and amplitude distances derived from DTW and ESA are then used to classify the event types via dendrogram and k-nearest-neighbor clustering analyses. Using information from the full waveform, we show how different underground sources can be distinguished at regional distances. We highlight the potential of these nonlinear alignment algorithms for discrimination and comment on ways we can extend the framework presented here.

58 GEOSCIENCES↗
DOE OSTI2025

Assessing Dynamic Time Warping Techniques for Discriminating Seismic Sources at Local and Regional Distances

Effective monitoring of seismic explosions and hazard assessment relies heavily on the accurate discrimination of underground seismic sources. This study investigates the application of novel nonlinear alignment techniques, specifically Dynamic Time Warping (DTW), for event-type discrimination at regional and local distances. Building on prior research that used DTW and Elastic Shape Analysis (ESA) in discrimination at regional distances, we evaluate the performance of recently developed variants of DTW, including a method that employs Pearson cross-correlation as a measure of warping distance and a time distortion coefficient that quantifies the type and degree of time distortion between signals. By analyzing observational datasets that include different source types, we assess the performance of these approaches for realistic monitoring scenarios. Specifically, we consider a dataset recorded at regional distances in the Korean Peninsula and a local-distance subset from the Unconstrained Utah Event Bulletin catalog to evaluate DTW-based discrimination across multiple distance scales. Additionally, we introduce the maximum cross-correlations of warped waveforms as a similarity metric for event classification. Through hierarchical cluster analysis and dendrogram interpretation, we present our findings, highlighting the strengths and limitations of these techniques in seismic event classification.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS1976

DHARMA - Discriminant hyperplane abstracting residuals minimization algorithm for separating clusters with fuzzy boundaries

Learning of discriminant hyperplanes in imperfectly supervised or unsupervised training sample sets with unreliably labeled samples along the fuzzy joint boundaries between sample clusters is discussed, with the discriminant hyperplane designed to be a least-squares fit to the unreliably labeled data points. (Samples along the fuzzy boundary jump back and forth from one cluster to the other in recursive cluster stabilization and are considered unreliably labeled.) Minimization of the distances of these unreliably labeled samples from the hyperplanes does not sacrifice the ability to discriminate between classes represented by reliably labeled subsets of samples. An equivalent unconstrained linear inequality problem is formulated and algorithms for its solution are indicated. Landsat earth sensing data were used in confirming the validity and computational feasibility of the approach, which should be useful in deriving discriminant hyperplanes separating clusters with fuzzy boundaries, given supervised training sample sets with unreliably labeled boundary samples.

Dasarathy, B. V.↗
NASA NTRS1977

Feature discrimination and detection probability in synthetic aperture radar imaging system

Images obtained using synthetic aperture radar (SAR) systems can only represent the intensities of resolution cells in the scene of interest probabilistically since radar receiver noise and Rayleigh scattering of the transmitted radiation are always present. Consequently, when features to be identified differ only by their contribution to the mean power of the radar return, discrimination can be treated by detection theory. In this paper, we develop a 'sufficient statistic' for discriminating between competing features and compare it with some suboptimal methods frequently used. Discrimination is measured by probability of detection error and depends on number of samples or 'looks', signal-to-noise ratio (SNR), and ratio of mean power returns from the competing features. Our results show discrimination and image quality rapidly saturate with SNR (very small improvement for SNR not less than 10 dB) but continue to improve with increasing number of looks.

Lipes, R. G.↗