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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. Here, this paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241 Am, 60 Co, 137 Cs, 192 Ir, 239 Pu, and 235 U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Why don't we share data and code? Perceived barriers and benefits to public archiving practices

The biological sciences community is increasingly recognizing the value of open, reproducible and transparent research practices for science and society at large. Despite this recognition, many researchers fail to share their data and code publicly. This pattern may arise from knowledge barriers about how to archive data and code, concerns about its reuse, and misaligned career incentives. Here, we define, categorize and discuss barriers to data and code sharing that are relevant to many research fields. We explore how real and perceived barriers might be overcome or reframed in the light of the benefits relative to costs. By elucidating these barriers and the contexts in which they arise, we can take steps to mitigate them and align our actions with the goals of open science, both as individual scientists and as a scientific community.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1990

Automatic ground control point recognition with parallel associative memory

The basic principle of the associative memory is to match the unknown input pattern against a stored training set, and responding with the 'closest match' and the corresponding label. Generally, an associative memory system requires two preparatory steps: selecting attributes of the pattern class, and training the system by associating patterns with labels. Experimental results gained from using Parallel Associative Memory are presented. The primary concern is an automatic search for ground control points in aerial photographs. Synthetic patterns are tested followed by real data. The results are encouraging as a relatively high level of correct matches is reached.

Al-Tahir, Raid↗
NASA NTRS2004

Contribution to the Study of Regional Magnetization of Satellite Magnetic Measurement: Magsat, Orsted, and Champ (with Gravity Field)

A discussion of and introduction to satellite-altitude geopotential field studies and their interpretation with emphasis on results from metalliferous regions will be given. The magnetic and gravimetric measurements from satellite altitudes show heterogeneity in deeper parts of the lithosphere. These patterns of magnetic anomalies do not only reveal the largest iron ore deposits such as Kiruna, Sweden, Kursk, Russia, and Banugi, Central African Republic but also linear features indicating structural discontinuities. Changes of magnetic amplitude of these patterns are caused by intersecting transverse fractures localizing magmatism and concentration of metals. In addition satellite altitude data are related to variations in crustal thickness and heat flow. Deep-rooted structural discontinuities, defined by combination of geological and geophysical criteria, with spacing of several hundred kilometers, reveal a quite uniform pattern in the deeper parts of the lithosphere. As these structures provide favorable pathways for the ascent of heat, magmas and ore-forming fluids, their recognition is of crucial importance and can be used in the compilation of a new type of mineral prognosis map. An example from Europe includes a pattern of east west trending structural discontinuities or belts and their junction with the NW-trending Tornqvist-Teisseyre Line. The Upper Silesian-Cracovian Zn-Pb district occurs along one of the latitudinal belts. Leslaw Teper of the University of Silesia has been invited to show the fractures in crystalline basement beneath the sediments hosting the Zn-Pb ores.

Taylor, Patrick T.↗
NASA NTRS2004

Fast Query-Optimized Kernel-Machine Classification

A recently developed algorithm performs kernel-machine classification via incremental approximate nearest support vectors. The algorithm implements support-vector machines (SVMs) at speeds 10 to 100 times those attainable by use of conventional SVM algorithms. The algorithm offers potential benefits for classification of images, recognition of speech, recognition of handwriting, and diverse other applications in which there are requirements to discern patterns in large sets of data. SVMs constitute a subset of kernel machines (KMs), which have become popular as models for machine learning and, more specifically, for automated classification of input data on the basis of labeled training data. While similar in many ways to k-nearest-neighbors (k-NN) models and artificial neural networks (ANNs), SVMs tend to be more accurate. Using representations that scale only linearly in the numbers of training examples, while exploring nonlinear (kernelized) feature spaces that are exponentially larger than the original input dimensionality, KMs elegantly and practically overcome the classic curse of dimensionality. However, the price that one must pay for the power of KMs is that query-time complexity scales linearly with the number of training examples, making KMs often orders of magnitude more computationally expensive than are ANNs, decision trees, and other popular machine learning alternatives. The present algorithm treats an SVM classifier as a special form of a k-NN. The algorithm is based partly on an empirical observation that one can often achieve the same classification as that of an exact KM by using only small fraction of the nearest support vectors (SVs) of a query. The exact KM output is a weighted sum over the kernel values between the query and the SVs. In this algorithm, the KM output is approximated with a k-NN classifier, the output of which is a weighted sum only over the kernel values involving k selected SVs. Before query time, there are gathered statistics about how misleading the output of the k-NN model can be, relative to the outputs of the exact KM for a representative set of examples, for each possible k from 1 to the total number of SVs. From these statistics, there are derived upper and lower thresholds for each step k. These thresholds identify output levels for which the particular variant of the k-NN model already leans so strongly positively or negatively that a reversal in sign is unlikely, given the weaker SV neighbors still remaining. At query time, the partial output of each query is incrementally updated, stopping as soon as it exceeds the predetermined statistical thresholds of the current step. For an easy query, stopping can occur as early as step k = 1. For more difficult queries, stopping might not occur until nearly all SVs are touched. A key empirical observation is that this approach can tolerate very approximate nearest-neighbor orderings. In experiments, SVs and queries were projected to a subspace comprising the top few principal- component dimensions and neighbor orderings were computed in that subspace. This approach ensured that the overhead of the nearest-neighbor computations was insignificant, relative to that of the exact KM computation.

Mazzoni, Dominic↗
DOE OSTI2024

Anti-distortion bioinspired camera with an inhomogeneous photo-pixel array

The bioinspired camera, comprising a single lens and a curved image sensor—a photodiode array on a curved surface—, was born of flexible electronics. Its economical build lends itself well to space-constrained machine vision applications. The curved sensor, much akin to the retina, helps image focusing, but the curvature also creates a problem of image distortion, which can undermine machine vision tasks such as object recognition. Here we report an anti-distortion single-lens camera, where 4096 silicon photodiodes arrayed on a curved surface in a nonuniform pattern assimilated to the distorting optics are the key to anti-distortion engineering. That is, the photo-pixel distribution pattern itself is warped in the same manner as images are warped, which correctively reverses distortion. Acquired images feature no appreciable distortion across a 120° horizontal view, as confirmed by their neural-network recognition accuracies. This distortion correction via photo-pixel array reconfiguration is a form of in-sensor computing.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2023

EVA Task and 3D Pose Recognition from Video

Extravehicular Activity (EVA) has been known to involve potential risks of biomechanical stresses and injuries to crewmembers. Gathering of EVA motion patterns is necessary for risk analysis and mitigation. However, many existing techniques, such as motion capture systems, are not only cost-prohibitive but are impractical for retrospective analysis of past missions. In this work, a software tool was developed, which can estimate the 3D poses of a spacesuit from photographs or videos, without using special sensors or equipment. The tool is based on the state-of-the-art artificial intelligence and machine learning (AI/ML) system, which was trained by studying and capturing motion patterns of past and current spacesuit test data. The AI/ML tool was further enhanced using synthetically generated data, in which the suit postures, backgrounds, camera angles and illumination conditions were parametrically adjusted and rendered for training. The tool, incorporated the methodologies of Convolutional Neural Network (CNN), was trained, and tested in the cloud computing environment. The trained model was then applied on new imagery and video to extract estimated joint positions and suit outlines. The joint positions were further processed to capture activity (“digging”), pose labels (“bending”), and other useful downstream information. The model performance on new imagery and video was successfully assessed for accuracy and reliability. This AI/ML based posture recognition tool thus allows for the quantification of injury risk and task performance characterization for both current and past missions and training, which can immensely help to improve EVA task and suit design.

Kyung Han Kim↗
NASA NTRS1976

Multimode and multistate ladder oscillator and frequency recognition device

A ladder oscillator composed of capacitive and inductive impedances connected together to form a ladder network which has a chosen number N oscillation modes at N different frequencies. Each oscillation mode is characterized by a unique standing wave voltage pattern along the nodes of the ladder oscillator, with the mode in which the ladder oscillator is oscillating being determinable from the amplitudes or phase of the oscillations at the nodes. A logic circuit may be connected to the nodes of the oscillator to compare the phases of selected nodes and thereby determine which mode the oscillator is oscillating in. A ladder oscillator composed of passive capacitive and inductive impedances can be utilized as a frequency recognition device, since the passive ladder oscillator will display the characteristic standing wave patterns if an input signal impressed upon the ladder oscillator is close to one of the mode frequencies of the oscillator. A CL ladder oscillator having series capacitive impedances and shunt inductive impedances can exhibit sustained and autonomous oscillations if active nonlinear devices are connected in parallel with the shunt inductive impedances. The active CL ladder oscillator can be synchronized to input frequencies impressed upon the oscillator, and will continue to oscillate after the input signal has been removed at a mode frequency which is, in general, nearest to the input signal frequency. Autonomous oscillations may also be obtained as desired from the active CL ladder oscillator at the mode frequencies.

Aumann, Herbert M.↗
NASA NTRS1999

Target Recognition Using Neural Networks for Model Deformation Measurements

Optical measurements provide a non-invasive method for measuring deformation of wind tunnel models. Model deformation systems use targets mounted or painted on the surface of the model to identify known positions, and photogrammetric methods are used to calculate 3-D positions of the targets on the model from digital 2-D images. Under ideal conditions, the reflective targets are placed against a dark background and provide high-contrast images, aiding in target recognition. However, glints of light reflecting from the model surface, or reduced contrast caused by light source or model smoothness constraints, can compromise accurate target determination using current algorithmic methods. This paper describes a technique using a neural network and image processing technologies which increases the reliability of target recognition systems. Unlike algorithmic methods, the neural network can be trained to identify the characteristic patterns that distinguish targets from other objects of similar size and appearance and can adapt to changes in lighting and environmental conditions.

Ross, Richard W.↗
NASA NTRS2004

Deep Structure of the Earth and Concentration of Metals in the Lithosphere: A Geodynamic Approach

A discussion of and introduction to satellite-altitude geopotential fields studies and their interpretation with emphasis on results from metalliferous regions will be given. The magnetic and gravimetric measurements from satellite altitudes show heterogeneity in deeper parts of the lithosphere. These patterns of magnetic anomalies do not only reveal the largest iron ore deposits such as Kiruna, Sweden and Kursk, Russia, but also linear features indicating structural discontinuities. Changes of magnetic amplitude of these patterns are caused by intersecting transverse fractures localizing magmatism and concentration of metals. The role of trans-regional mantle-rooted structural discontinuities in the concentration of metals will be discussed and a new type of mineral prognosis map will be presented. Deep-rooted structural discontinuities, defined by combination of geological and geophysical criteria, with spacing of several hundred kilometers, reveal a quite uniform pattern in the deeper parts of the lithosphere. As these structures provide favorable pathways for the ascent of heat, magmas and ore-forming fluids, their recognition is of crucial importance and can be used in the compilation of a new type of mineral prognosis map. Examples are shown from the United States, Canada, China, Burma, South America, Europe and Australia. The European example includes a pattern of east west trending structural discontinuities or belts and their junction with the NW-trending Tornqvist-Teisseyre Line. The Upper Silesian-Cracovian Zn-Pb district occurs along one of the latitudinal belts. Leslaw Teper of the University of Silesia has been invited to show the fractures in crystalline basement beneath the sediments hosting the Zn-Pb ores.

Taylor, Patrick T.↗
NASA NTRS2003

Array biosensor for detection of toxins

The array biosensor is capable of detecting multiple targets rapidly and simultaneously on the surface of a single waveguide. Sandwich and competitive fluoroimmunoassays have been developed to detect high and low molecular weight toxins, respectively, in complex samples. Recognition molecules (usually antibodies) were first immobilized in specific locations on the waveguide and the resultant patterned array was used to interrogate up to 12 different samples for the presence of multiple different analytes. Upon binding of a fluorescent analyte or fluorescent immunocomplex, the pattern of fluorescent spots was detected using a CCD camera. Automated image analysis was used to determine a mean fluorescence value for each assay spot and to subtract the local background signal. The location of the spot and its mean fluorescence value were used to determine the toxin identity and concentration. Toxins were measured in clinical fluids, environmental samples and foods, with minimal sample preparation. Results are shown for rapid analyses of staphylococcal enterotoxin B, ricin, cholera toxin, botulinum toxoids, trinitrotoluene, and the mycotoxin fumonisin. Toxins were detected at levels as low as 0.5 ng mL(-1).

Review↗
DOE OSTI2021

Variation in Gene Expression between Two Sorghum bicolor Lines Differing in Innate Immunity Response

Microbe associated molecular pattern (MAMPs) triggered immunity (MTI) is a key component of the plant innate immunity response to microbial recognition. However, most of our current knowledge of MTI comes from model plants (i.e., Arabidopsis thaliana) with comparatively less work done using crop plants. In this work, we studied the MAMP triggered oxidative burst (ROS) and the transcriptional response in two Sorghum bicolor genotypes, BTx623 and SC155-14E. SC155-14E is a line that shows high anthracnose resistance and the line BTx623 is susceptible to anthracnose. Our results revealed a clear variation in gene expression and ROS in response to either flagellin (flg22) or chitin elicitation between the two lines. While the transcriptional response to each MAMP and in each line was unique there was a considerable degree of overlap, and we were able to define a core set of genes associated with the sorghum MAMP transcriptional response. The GO term and KEGG pathway enrichment analysis discovered more immunity and pathogen resistance related DEGs in MAMP treated SC155-14E samples than in BTx623 with the same treatment. The results provide a baseline for future studies to investigate innate immunity pathways in sorghum, including efforts to enhance disease resistance.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

Radar sensitivity and antenna scan pattern study for a satellite-based Radar Wind Sounder (RAWS)

Modeling global atmospheric circulations and forecasting the weather would improve greatly if worldwide information on winds aloft were available. Recognition of this led to the inclusion of the LAser Wind Sounder (LAWS) system to measure Doppler shifts from aerosols in the planned for Earth Observation System (EOS). However, gaps will exist in LAWS coverage where heavy clouds are present. The RAdar Wind Sensor (RAWS) is an instrument that could fill these gaps by measuring Doppler shifts from clouds and rain. Previous studies conducted at the University of Kansas show RAWS as a feasible instrument. This thesis pertains to the signal-to-noise ratio (SNR) sensitivity, transmit waveform, and limitations to the antenna scan pattern of the RAWS system. A dop-size distribution model is selected and applied to the radar range equation for the sensitivity analysis. Six frequencies are used in computing the SNR for several cloud types to determine the optimal transmit frequency. the results show the use of two frequencies, one higher (94 GHz) to obtain sensitivity for thinner cloud, and a lower frequency (24 GHz) to obtain sensitivity for thinner cloud, and a lower frequency (24 GHz) for better penetration in rain, provide ample SNR. The waveform design supports covariance estimation processing. This estimator eliminates the Doppler ambiguities compounded by the selection of such high transmit frequencies, while providing an estimate of the mean frequency. the unambiguous range and velocity computation shows them to be within acceptable limits. The design goal for the RAWS system is to limit the wind-speed error to less than 1 ms(exp -1). Due to linear dependence between vectors for a three-vector scan pattern, a reasonable wind-speed error is unattainable. Only the two-vector scan pattern falls within the wind-error limits for azimuth angles between 16 deg to 70 deg. However, this scan only allows two components of the wind to be determined. As a result, a technique is then shown, based on the Z-R-V relationships, that permit the vertical component (i.e., rain) to be computed. Thus the horizontal wind components may be obtained form the covariance estimator and the vertical component from the reflectivity factor. Finally, a new candidate system is introduced which summarizes the parameters taken from previous RAWS studies, or those modified in this thesis.

Stuart, Michael A.↗
DOE OSTI2023

Early Alarm: Robust Event Analysis for Power Systems using 1-D Fully Convolutional Network

This work presents a novel deep learning model for early, accurate, and robust detection, recognition, and temporal localization of multi-type events in large-scale power systems. The proposed method develops a unified 1-D fully convolutional network (FCN) model that takes time series of raw frequency signals measured from a power system as input, extracts distinguishing features, and predicts at every temporal point in the time series if an event is happening and what the type of the event is. Compared to existing methods, the proposed model eliminates the necessity for hand-crafted feature extraction or complicated data pre-processing, can flexibly handle input signals of arbitrary length, and precisely infer the event occurrence time. Most importantly, the model is intentionally trained with incomplete patterns, such that it is more robust to partial features of an event which is common in real-world online recognition, resulting in early alarm for power system failures. Extensive experimental results demonstrate that the proposed method achieves superior performance to the state-of-the-art, and also shows strong robustness to noise and system oscillations.

Li, Chengcheng↗
NASA NTRS1996

Cloning and characterization of the murine homolog of the sno proto-oncogene reveals a novel splice variant

The cellular function(s) of the SNO protein remain undefined. To gain a better understanding of possible developmental roles of this cellular proto-oncogene, we have cloned two murine sno cDNAs and have investigated their expression patterns in embryonic and postnatal tissues. A single major transcript of 7.5 kb is detected in multiple tissues by Northern blot. However, reverse transcriptase polymerase chain reaction (RT-PCR) and RNAse protection assays revealed a novel splice variant in every tissue examined. Two isoforms, termed sno N and sno-dE3 (dE3, deletion within exon 3), were identified. The sno-dE3 isoform employs a novel 5' splice site located within the coding region of the third exon and deletes potential kinase recognition motifs. Transcripts of both sno isoforms accumulate ubiquitously but are most abundant in the developing central nervous system. The in situ hybridization patterns of sno expression during murine development suggest potential roles in tissues with a high degree of cellular proliferation. Expression in terminally differentiated tissues such as muscle and neurons indicates that SNO may have multiple functional activities.

Non-NASA Center↗
NASA NTRS2001

Complex expression patterns of lymphocyte-specific genes during the development of cartilaginous fish implicate unique lymphoid tissues in generating an immune repertoire

Cartilaginous fish express canonical B and T cell recognition genes, but their lymphoid organs and lymphocyte development have been poorly defined. Here, the expression of Ig, TCR, recombination-activating gene (Rag)-1 and terminal deoxynucleosidase (TdT) genes has been used to identify roles of various lymphoid tissues throughout development in the cartilaginous fish, Raja eglanteria (clearnose skate). In embryogenesis, Ig and TCR genes are sharply up-regulated at 8 weeks of development. At this stage TCR and TdT expression is limited to the thymus; later, TCR gene expression appears in peripheral sites in hatchlings and adults, suggesting that the thymus is a source of T cells as in mammals. B cell gene expression indicates more complex roles for the spleen and two special organs of cartilaginous fish-the Leydig and epigonal (gonad-associated) organs. In the adult, the Leydig organ is the site of the highest IgM and IgX expression. However, the spleen is the first site of IgM expression, while IgX is expressed first in gonad, liver, Leydig and even thymus. Distinctive spatiotemporal patterns of Ig light chain gene expression also are seen. A subset of Ig genes is pre-rearranged in the germline of the cartilaginous fish, making expression possible without rearrangement. To assess whether this allows differential developmental regulation, IgM and IgX heavy chain cDNA sequences from specific tissues and developmental stages have been compared with known germline-joined genomic sequences. Both non-productively rearranged genes and germline-joined genes are transcribed in the embryo and hatchling, but not in the adult.

Non-NASA Center↗
NASA NTRS1989

Evolution and extinction in the marine realm: some constraints imposed by phytoplankton

The organic and mineralized remains of planktonic algae provide a rich record of microplankton evolution extending over nearly half of the preserved geological record. In general, Phanerozoic patterns of phytoplankton radiation and extinction parallel those documented for skeletonized marine invertebrates, both augmenting and constraining thought about evolution in the oceans. Rapidly increasing knowledge of Proterozoic plankton is making possible the recognition of additional episodes of diversification and extinction that antedate the Ediacaran radiation of macroscopic animals. In contrast to earlier phytoplankton history, the late Mesozoic and Cainozoic record is documented in sufficient detail to constrain theories of mass extinction in more than a general way. Broad patterns of diversity change in planktonic algae show similarities across the Cretaceous-Tertiary and Eocene-Oligocene boundaries, but detailed comparisons of origination and extinction rates in calcareous nannoplankton, as well as other algae and skeletonized protozoans, suggest that the two episodes were quite distinct. Common causation appears unlikely, casting doubt on monolithic theories of mass extinction, whether periodic or not. Studies of mass extinction highlight a broader class of insights that paleontologists can contribute to evolutionary biology: the evaluation of evolutionary change in the context of evolving Earth-surface environments.

NASA Discipline Number 52-30↗
DOE OSTI2024

FiberFlex: Real-time FPGA-based Intelligent and Distributed Fiber Sensor System for Pedestrian Recognition

In recent years, security monitoring of public places and critical infrastructure has heavily relied on the widespread use of cameras, raising concerns about personal privacy violations. To balance the need for effective security monitoring with the protection of personal privacy, we explore the potential of optical fiber sensors for this application. This article proposes FiberFlex, an intelligent and distributed fiber sensor system. Ultizing Field Programmable Gate Arrays (FPGA) high-level synthesis (HLS) acceleration, FiberFlex offers real-time pedestrian detection by co-designing the entire pipeline of optical signal acquisition, processing, and recognition networks based on the principles of optical fiber sensing. As a promising alternative to traditional camera-based monitoring systems, FiberFlex achieves pedestrian detection by analyzing the vibration patterns caused by pedestrian footsteps, enabling security monitoring while preserving individual privacy. FiberFlex comprises three modules: First , fiber-optic sensing system: A fiber-optic distributed acoustic sensing (DAS) system is built and used to measure the ground vibration waves generated by people walking. Second , algorithms: We first collect the training data by measuring the ground vibration waves, label the data, and use the data to train the neural network models to perform pedestrian recognition. Third , hardware accelerators: We use HLS tools to design hardware modules on FPGA for data collection and pre-processing and integrate them with the downstream neural network accelerators to perform in-line real-time pedestrian detection. The final detection results are sent back from FPGA to the host CPU. We implement our system FiberFlex with the in-house built DAS system and AMD/Xilinx Kintex7 FPGA KC705 board and verify the whole system using the real-world collected data. We conduct recognition tests on five test subjects of varying ages, heights, and weights in a fixed sensing area. Each subject experienced 20 real-time recognition tests using their daily walking habits, and the subjects were given adequate rest between tests. After 100 tests on five test subjects, the overall real-time recognition accuracy exceeded \(88.0\%\) . The whole system uses 55 W of power, 33 W in the optical DAS system and 22 W in the FPGA. Relying on its end-to-end interdisciplinary design, FiberFlex seamlessly combines fiber-optic sensors with FPGA accelerators to enable low-power real-time security monitoring without compromising privacy, making it a valuable addition to the existing security monitoring network. According to FiberFlex, more valuable research can be conducted in the future, such as fall monitoring for the elderly, migration of identification networks between different application scenarios, and improvement of anti-interference performance in more complex environments. In future perception networks, where the “eyes” are not feasible, let’s use fiber optic touch instead.

Distributed↗