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All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Role of the middle ear muscle apparatus in mechanisms of speech signal discrimination

A method of impedance reflexometry was used to examine 101 students with hearing impairment in order to clarify the interrelation between speech discrimination and the state of the middle ear muscles. Ability to discriminate speech signals depends to some extent on the functional state of intraaural muscles. Speech discrimination was greatly impaired in the absence of stapedial muscle acoustic reflex, in the presence of low thresholds of stimulation and in very small values of reflex amplitude increase. Discrimination was not impeded in positive AR, high values of relative thresholds and normal increase of reflex amplitude in response to speech signals with augmenting intensity.

Moroz, B. S.↗
NASA NTRS1981

Mass discrimination during weightlessness

An experiment concerned with the ability of astronauts to discriminate between the mass of objects when both the objects and the astronauts are in weightless states is described. The main object of the experiment is to compare the threshold for weight-discrimination on Earth with that for mass-discrimination in orbit. Tests will be conducted premission and postmission and early and late during the mission while the crew is experiencing weightlessness. A comparison of early and late tests inflight and postflight will reveal the rate of adaptation to zero-gravity and 1-g. The mass discrimination box holds 24 balls which the astronaut will compare to one another in a random routine.

Ross, H.↗
NASA NTRS1985

Color measurement and discrimination

Theories of color measurement attempt to provide a quantative means for predicting whether two lights will be discriminable to an average observer. All color measurement theories can be characterized as follows: suppose lights a and b evoke responses from three color channels characterized as vectors, v(a) and v(b); the vector difference v(a) - v(b) corresponds to a set of channel responses that would be generated by some real light, call it *. According to theory a and b will be discriminable when * is detectable. A detailed development and test of the classic color measurement approach are reported. In the absence of a luminance component in the test stimuli, a and b, the theory holds well. In the presence of a luminance component, the theory is clearly false. When a luminance component is present discrimination judgements depend largely on whether the lights being discriminated fall in separate, categorical regions of color space. The results suggest that sensory estimation of surface color uses different methods, and the choice of method depends upon properties of the image. When there is significant luminance variation a categorical method is used, while in the absence of significant luminance variation judgments are continuous and consistant with the measurement approach.

Wandell, B. A.↗
NASA NTRS1997

Spatial layout affects speed discrimination

We address a surprising result in a previous study of speed discrimination with multiple moving gratings: discrimination thresholds decreased when the number of stimuli was increased, but remained unchanged when the area of a single stimulus was increased [Verghese & Stone (1995). Vision Research, 35, 2811-2823]. In this study, we manipulated the spatial- and phase relationship between multiple grating patches to determine their effect on speed discrimination thresholds. In a fusion experiment, we merged multiple stimulus patches, in stages, into a single patch. Thresholds increased as the patches were brought closer and their phase relationship was adjusted to be consistent with a single patch. Thresholds increased further still as these patches were fused into a single patch. In a fission experiment, we divided a single large patch into multiple patches by superimposing a cross with luminance equal to that of the background. Thresholds decreased as the large patch was divided into quadrants and decreased further as the quadrants were maximally separated. However, when the cross luminance was darker than the background, it was perceived as an occluder and thresholds, on average, were unchanged from that for the single large patch. A control experiment shows that the observed trend in discrimination thresholds is not due to the differences in perceived speed of the stimuli. These results suggest that the parsing of the visual image into entities affects the combination of speed information across space, and that each discrete entity effectively provides a single independent estimate of speed.

NASA Discipline Neuroscience↗
NASA NTRS2013

Discriminating Phytoplankton Functional Types (PFTs) in the Coastal Ocean Using the Inversion Algorithm Phydotax and Airborne Imaging Spectrometer Data

There is a need in the Biological Oceanography community to discriminate among phytoplankton groups within the bulk chlorophyll pool to understand energy flow through ecosystems, to track the fate of carbon in the ocean, and to detect and monitor-for harmful algal blooms (HABs). The ocean color community has responded to this demand with the development of phytoplankton functional type (PFT) discrimination algorithms. These PFT algorithms fall into one of three categories depending on the science application: size-based, biogeochemical function, and taxonomy. The new PFT algorithm Phytoplankton Detection with Optics (PHYDOTax) is an inversion algorithm that discriminates taxon-specific biomass to differentiate among six taxa found in the California Current System: diatoms, dinoflagellates, haptophytes, chlorophytes, cryptophytes, and cyanophytes. PHYDOTax was developed and validated in Monterey Bay, CA for the high resolution imaging spectrometer, Spectroscopic Aerial Mapping System with On-board Navigation (SAMSON - 3.5 nm resolution). PHYDOTax exploits the high spectral resolution of an imaging spectrometer and the improved spatial resolution that airborne data provides for coastal areas. The objective of this study was to apply PHYDOTax to a relatively lower resolution imaging spectrometer to test the algorithm's sensitivity to atmospheric correction, to evaluate capability with other sensors, and to determine if down-sampling spectral resolution would degrade its ability to discriminate among phytoplankton taxa. This study is a part of the larger Hyperspectral Infrared Imager (HyspIRI) airborne simulation campaign which is collecting Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery aboard NASA's ER-2 aircraft during three seasons in each of two years over terrestrial and marine targets in California. Our aquatic component seeks to develop and test algorithms to retrieve water quality properties (e.g. HABs and river plumes) in both marine and in-land water bodies. Results presented are from the 10 April 2013 overflight of the Monterey Bay region and focus primarily on the first objective - sensitivity to atmospheric correction. On-going and future work will continue to evaluate if PHYDOTax can be applied to historical (SeaWiFS and MERIS), existing (MODIS, VIIRS, and HICO), and future (PACE, GEO-CAPE, and HyspIRI) satellite sensors. Demonstration of cross-platform continuity may aid in calibration and validation efforts of these sensors.

Biological Oceanography↗
NASA NTRS2019

Application of High-Dimensional Fuzzy K-Means Cluster Analysis to CALIOP/CALIPSO Version 4.1 Cloud-Aerosol Discrimination

This study applies fuzzy k-means (FKM) cluster analyses to a subset of the parameters reported in the CALIPSO lidar level 2 data products in order to classify the layers detected as either clouds or aerosols. The results obtained are used to assess the reliability of the cloud–aerosol discrimination (CAD) scores reported in the version 4.1 release of the CALIPSO data products. FKM is an unsupervised learning algorithm, whereas the CALIPSO operational CAD algorithm (COCA) takes a highly supervised approach. Despite these substantial computational and architectural differences, our statistical analyses show that the FKM classifications agree with the COCA classifications for more than 94 % of the cases in the troposphere. This high degree of similarity is achieved because the lidar-measured signatures of the majority of the clouds and the aerosols are naturally distinct, and hence objective methods can independently and effectively separate the two classes in most cases. Classification differences most often occur in complex scenes (e.g., evaporating water cloud filaments embedded in dense aerosol) or when observing diffuse features that occur only intermittently (e.g., volcanic ash in the tropical tropopause layer). The two methods examined in this study establish overall classification correctness boundaries due to their differing algorithm uncertainties. In addition to comparing the outputs from the two algorithms, analysis of sampling, data training, performance measurements, fuzzy linear discriminants, defuzzification, error propagation, and key parameters in feature type discrimination with the FKM method are further discussed in order to better understand the utility and limits of the application of clustering algorithms to space lidar measurements. In general, we find that both FKM and COCA classification uncertainties are only minimally affected by noise in the CALIPSO measurements, though both algorithms can be challenged by especially complex scenes containing mixtures of discrete layer types. Our analysis results show that attenuated backscatter and color ratio are the driving factors that separate water clouds from aerosols; backscatter intensity, depolarization, and mid-layer altitude are most useful in discriminating between aerosols and ice clouds; and the joint distribution of backscatter intensity and depolarization ratio is critically important for distinguishing ice clouds from water clouds.

Zeng, Shan↗
DOE OSTI2026

Pushing the Limits of Pulse Shape Discrimination in a Large Liquid Xenon Detector

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 prompt fraction discriminator is optimized and achieves ER leakage as low as $15\%$. For specific background processes such as $^{124}$Xe double electron capture, the leakage is reduced further to about $5\%$. 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; KIPAC, Menlo Park]↗
DOE OSTI2021

A propagation-based fault detection and discrimination method and the optimization of sensor deployment

Industrial processes can be affected by faults having a serious impact on operation when not promptly detected and diagnosed. Here in this paper, a propagation-based fault detection and discrimination(PFDD) method is proposed to develop a strategy for fault diagnosis while in the design phase of a system. The PFDD method constructs the system model using the Integrated System Fault Analysis(ISFA) technique. Based on the system model, the propagation of hardware and software faults are simulated qualitatively. Given the results of the simulation, the process by which a fault propagates can be characterized using the qualitative features of system variables including the deviation of the system variables from their expected values, the variation of the system variables over time, and the order in which each variable is influenced during the propagation of the fault. The strategy by which a fault can be detected and discriminated is defined using those features. The PFDD method supports the detection and discrimination of faults in both steady states and transient states. Based on the PFDD method, the optimization of sensor deployment in a system is discussed. A brute force algorithm is developed to examine the system’s capability at diagnosing faults and the cost of sensor deployment for all possible configurations of sensors. The optimal sensor deployment strategy can be derived accordingly. However, the brute force method is only applicable to small-scale systems due to its high computational cost. A genetic algorithm is used to optimize sensor deployment in large-scale systems. The PFDD and sensor deployment optimization methods are applied to the Experimental Breeder Reactor II (EBR-II) for verification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Pulse-shape discrimination in water-based scintillators

This work describes a class of liquid scintillators that contain mostly water (>50 wt% of the entire composition) and can discriminate between interactions induced by neutrons and gamma rays. Here, by balancing the interface interactions between the components of the formulation, these scintillators form emulsions that can be thermodynamically stable. This approach, which considers a quantity known as the hydrophilic–lipophilic difference, requires consideration of the salinity and temperature as well as characterization of the surfactants and oil phase. Emulsions comprised of water and various oils were characterized first. Then, the effect of scintillating dyes in the oil phase was considered, followed by the construction of partial phase diagrams of the emulsions. For transparent oil-in-water emulsions with a single phase, the scintillation light yield and metrics related to pulse-shape discrimination were measured. The best performing scintillators contained 33 wt% of a scintillating oil phase and exhibited a light yield that was as high as 18% of the light yield of a commercially available liquid scintillator that does not contain water (EJ-309). These water-based liquid scintillators exhibited a figure of merit of neutron/gamma ray discrimination as high as 1.79 at about 1500 keV ee .

36 MATERIALS SCIENCE↗
DOE OSTI2023

Automated integration gate selection for Gaussian mixture model pulse shape discrimination

Pulse shapes differ between neutron and gamma particles when measured with detector devices employing pulse shape discriminating (PSD) scintillators. Digitized waveforms can be used in detection systems to perform pulse shape discrimination for this application. Prior Gaussian Mixture Model (GMM) methods require access to the pulse full-waveform. Reducing the waveform to a smaller set of combined samples reduces computational cost while affecting PSD performance. In this work, we develop a method for selecting the best performing combination of integration gates, or contiguous summed segments of the digitized pulse for PSD. The method uses a discrimination score based on the GMM PSD approach. Furthermore, the final selection is performed using Bayesian Optimization. PSD detection results are compared with varying numbers of selected gates on time-of-flight (TOF) data. This method can be used to fully automate the selection of gates in an unsupervised (without ground truth labels) setting.

42 ENGINEERING↗
DOE OSTI2021

A position and pulse shape discriminant p -terphenyl detector module

We present the development of a neutron detector array module made with para-terphenyl, a bright, fast, n/γ discriminating crystalline organic scintillator. The module is comprised of 2 cm × 2 cm × 2 cm p-terphenyl crystals that have been optically coupled together to create a pseudo-bar module. While only relying on two photo detectors, the module is capable of distinguishing interactions between up to eight crystals. Furthermore, the module retains the p-terphenyl’s pulse shape discrimination (PSD) capability. Together this makes the pseudo-bar module a promising position-sensitive neutron detector. Here we present characteristics of the pseudo-bar module - its timing resolution as well as its pulse shape and position discrimination capabilities, and briefly discuss future plans for utilizing an array of pseudo-bar modules in a useful neutron detector system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

The effects of low- Z shielding on uranium isotope discrimination using the time-emission profiles of long-lived delayed neutrons

Characteristic radiation signals can often be obscured or distorted by relatively small amounts of shielding, as is the case for highly-enriched uranium (HEU). Methods that can detect and identify special nuclear material (SNM) even in the presence of shielding are therefore of high value in many nuclear security and nonproliferation applications. It has been previously shown that the buildup and decay time profiles of long-lived delayed neutron emission from induced fission are unique and can be used to successfully discriminate between uranium isotopes and infer enrichment without the need for a calibration standard. Because the long-lived delayed neutron precursors exhibit decay times on the order of tens of seconds, their time profiles are not sensitive to the delays associated with scattering or diffusion in shielding materials, which occur on much shorter time scales. The use of capture-based neutron detectors can also mitigate the effects of shielding, because unlike recoil-based detectors, they do not have a low-energy detection threshold, so that neutrons that lose their energy in the shielding are still detectable. In this work, the effects of low-Z shielding on the discrimination of uranium isotopes using delayed neutron time profile measurements are investigated for the first time. Furthermore, the delayed neutron buildup and decay time profiles of polyethylene-shielded HEU and depleted uranium objects are measured using two different capture-gated detector designs. The recorded profiles show good agreement with the shapes predicted from six-group delayed neutron models even with the shielding in place, and the enrichment of the HEU and DU objects was estimated accurately to within 5%. While this method shows potential for discriminating and characterizing extreme cases of enrichment when a moderate amount of shielding is present, simulation results suggest that significant limitations exist for intermediate enrichment levels due to distortions to the delayed neutron time profile caused by neutron reflection in the shielding.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Quantum discriminator for binary classification

Abstract Quantum computers have the unique ability to operate relatively quickly in high-dimensional spaces—this is sought to give them a competitive advantage over classical computers. In this work, we propose a novel quantum machine learning model called the Quantum Discriminator, which leverages the ability of quantum computers to operate in the high-dimensional spaces. The quantum discriminator is trained using a quantum-classical hybrid algorithm in $$\mathcal {O}(N\log N)$$ O ( N log N ) time, and inferencing is performed on a universal quantum computer in $$\mathcal {O}(N)$$ O ( N ) time. The quantum discriminator takes as input the binary features extracted from a given datum along with a prediction qubit, and outputs the predicted label. We analyze its performance on the Iris and Bars and Stripes data sets, and show that it can attain 99% accuracy in simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Multivariate discrimination in quantum target detection

In this work, we describe a simple multivariate technique of likelihood ratios for improved discrimination of signal and background in multi-dimensional quantum target detection. Furthermore, the technique combines two independent variables, time difference and summed energy, of a photon pair from the spontaneous parametric down-conversion source into an optimal discriminant. The discriminant performance was studied in experimental data and in Monte-Carlo modelling with clear improvement shown compared to previous techniques. As novel detectors become available, we expect this type of multivariate analysis to become increasingly important in multi-dimensional quantum optics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

ML–Enabled FPGA Framework for Fast Quantum State Discrimination in Mid-Circuit Measurement Regimes

Accurate and low-latency quantum state discrimination is essential for protocols involving mid-circuit measurement (MCM) and conditional feed-forward. In superconducting quantum systems, conventional readout pipelines transfer measurement data to host processors for post-processing, introducing millisecond-scale delays that far exceed qubit coherence times. To overcome this bottleneck, we present an in-situ machine learning (ML) inference engine implemented on an FPGA for real-time quantum state discrimination. Our design performs inference directly on digitized readout signals with 40 ns latency, supports both qubit and qutrit readout, and enables conditional operations without host-side intervention. This capability is critical for MCM and for feedback-driven protocols such as quantum error correction. We validate the system on superconducting transmon hardware, demonstrating robust discrimination fidelity across multiple qubit and qutrit channels. We further demonstrate conditional qutrit logic driven by FPGA-resident classification, highlighting the potential of low-latency ML-on-FPGA control for NISQ applications and scalable fault-tolerant quantum computing.

Vora, Neel [Lawrence Berkeley National Laboratory ↗
DOE OSTI2021

Simultaneous imaging of two-phase velocities in particle-laden flows by two-color optical phase discrimination

In this Letter, we present a particle image/tracking velocimetry (PIV/PTV) technique for simultaneous velocity measurement of both fluid and particle phases, adopting newly developed optical phase discrimination methods and novel optical particles. Spherical acrylic (PMMA) particles of diameter ∼ <#comment/> O ( 100 µ <#comment/> m ) were used as the particle phase, while fine B A M : E u 2 + phosphors of diameter ∼ <#comment/> O ( 1 µ <#comment/> m ) were used as the fluid tracer. Under Nd:YAG 355 nm laser excitation, both the laser-induced fluorescence (LIF) from PMMA and laser-induced phosphorescence (LIP) from B A M : E u 2 + provided sufficiently strong signals for PIV imaging with two non-intensified cameras and were clearly separable for phase discrimination using spectral filters and temporal profiles. The advantages of the PIV/PTV method include the relatively low cost of B A M : E u 2 + phosphors, high sphericity and narrow size distribution of PMMA particles with LIF emission, and direct optical discrimination eliminating artifacts, while requiring much less computational capacity for PIV/PTV processing of complex particle-laden flows.

Bi, Xiaopeng (ORCID:0000000190847325)↗
DOE OSTI2025

Differential Seismic Phase Detection Probability as a Potential Discriminant of Explosions and Earthquakes

Deep learning models trained to estimate the probability of seismic P and S phases are rapidly expanding the scale of local event detections. Here, we evaluate the potential for deep learning model output phase detection probabilities to contribute to event‐type classification, particularly discrimination of single‐fired borehole explosions and earthquakes at local distances (<300 km). Motivated by the empirical success of P/S amplitude ratios, we consider the difference between P and S pick probability output from previously developed phase detection models, P prob −S prob ⁠, as a discriminant. Test data include M L ∼1–4 earthquakes and explosions observed by common seismographs in ten geologically diverse localities. Depending on the picking model and training data, binary classification using P prob −S prob with at least three stations can achieve approximately equivalent classification accuracy as P/S amplitude ratios without requiring any customization. Joint classification with P/S and P prob −S prob improves accuracy for most quality control scenarios. Pick probabilities are an efficient attribute to consider in explosion discrimination because they can be automated byproducts of event detection. They avoid the binary choice of picking or not picking weakly visible S waves common to explosions.

Duan, Chenglong [Rice Univ., Houston, TX (United S↗
NASA NTRS1970

Wide-range pulse-height discriminator

Improved pulse amplitude discriminator has discriminator level in millivolt range, permits simple adjustment over wide range, and is stable within one percent at temperatures between minus 20 degrees and plus 60 degrees C. The discriminator responds to narrow pulses /500 nsec/, draws little power /milliwatts/, and requires simple circuitry.

Cancro, C. A.↗