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At least 19 records

Machine-learning-assisted automation of single-crystal neutron diffraction

Neutron scattering is a powerful but expensive technique to study materials and discover new matter. Advanced detector technology has significantly improved the efficiency of neutron experiments, increasing the complexity of neutron data reduction and analysis. Machine learning (ML) brings new directions for neutron diffraction data reduction and experiment operation. Here, this work presents an ML-assisted data reduction and analysis method for precise recognition of Bragg peaks and the corresponding regions of interest; it can then automatically screen and align a measured crystal using the recognized peaks, and subsequently plan and optimize the data collection with user-provided information and uncertainty quantification values of detected peaks. This method shows robust performance in different complex sample environments and enables automated single-crystal neutron diffraction.

47 OTHER INSTRUMENTATION↗

Pattern-Recognition Algorithm for Locking Laser Frequency

A computer program serves as part of a feedback control system that locks the frequency of a laser to one of the spectral peaks of cesium atoms in an optical absorption cell. The system analyzes a saturation absorption spectrum to find a target peak and commands a laser-frequency-control circuit to minimize an error signal representing the difference between the laser frequency and the target peak. The program implements an algorithm consisting of the following steps: Acquire a saturation absorption signal while scanning the laser through the frequency range of interest. Condition the signal by use of convolution filtering. Detect peaks. Match the peaks in the signal to a pattern of known spectral peaks by use of a pattern-recognition algorithm. Add missing peaks. Tune the laser to the desired peak and thereafter lock onto this peak. Finding and locking onto the desired peak is a challenging problem, given that the saturation absorption signal includes noise and other spurious signal components; the problem is further complicated by nonlinearity and shifting of the voltage-to-frequency correspondence. The pattern-recognition algorithm, which is based on Hausdorff distance, is what enables the program to meet these challenges.

Karayan, Vahag↗

Low-level processing for real-time image analysis

A system that detects object outlines in television images in real time is described. A high-speed pipeline processor transforms the raw image into an edge map and a microprocessor, which is integrated into the system, clusters the edges, and represents them as chain codes. Image statistics, useful for higher level tasks such as pattern recognition, are computed by the microprocessor. Peak intensity and peak gradient values are extracted within a programmable window and are used for iris and focus control. The algorithms implemented in hardware and the pipeline processor architecture are described. The strategy for partitioning functions in the pipeline was chosen to make the implementation modular. The microprocessor interface allows flexible and adaptive control of the feature extraction process. The software algorithms for clustering edge segments, creating chain codes, and computing image statistics are also discussed. A strategy for real time image analysis that uses this system is given.

Eskenazi, R.↗

Development of gas chromatographic pattern recognition and classification tools for compliance and forensic analyses of fuels: A review

Gas chromatography (GC) is undoubtedly the analytical technique of choice for analyzing the composition of petroleum-based fuels. Over the past twenty years, as comprehensive two-dimensional gas chromatography (GC×GC) has evolved, fuel analysis has often been highlighted in scientific reports, as their complexity allows for illustration of the impressive peak capacity gains afforded by GC×GC. Indeed, several research groups in recent years have applied GC×GC and chemometrics to demonstrate the potential of these analytical tools to address important compliance (tax evasion, tax credits, physical quality standards) and forensic (arson investigations, oil spills) applications involving fuels. None the less, routine use of GC×GC in forensic laboratories has been limited largely by (1) legal and regulatory guidelines, (2) lack of chemometrics training, and (3) concerns about the reproducibility of GC×GC. The goal of this review is to highlight recent advances in one-dimensional GC (1D-GC) and GC×GC analyses of fuels for compliance and forensic applications, in an effort to assist scientists in overcoming the aforementioned hindrances. An introduction to 1D-GC principles, GC×GC technology (column stationary phases and modulators) and several chemometric techniques will be provided. More specifically, chemometric techniques will be broken down into (1) signal pre-processing, (2) peak decomposition, identification and quantification, and (3) classification and pattern recognition. Examples of compliance and forensic applications will be discussed, with particular emphasis on the demonstrated success of the employed chemometric techniques. Overall, this review will hopefully make 1D-GC and GC×GC coupled with chemometric data analysis tools more accessible to the larger scientific community, and aid in eventual widespread standardization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Structures of HIV-1 Neutralizing Antibody 10E8 Delineate the Mechanistic Basis of Its Multi-Peak Behavior on Size-Exclusion Chromatography

Antibody 10E8 is capable of effectively neutralizing HIV through its recognition of the membrane-proximal external region (MPER), and a suitably optimized version of 10E8 might have utility in HIV therapy and prophylaxis. However, 10E8 displays a three-peak profile on size-exclusion chromatography (SEC), complicating its manufacture. Here we show cis-trans conformational isomerization of the Tyr-Pro-Pro (YPP) motif in the heavy chain 3rd complementarity-determining region (CDR H3) of antibody 10E8 to be the mechanistic basis of its multipeak behavior. We observed 10E8 to undergo slow conformational isomerization and delineate a mechanistic explanation for effective comodifiers that were able to resolve its SEC heterogeneity and to allow an evaluation of the critical quality attribute of aggregation. We determined crystal structures of single and double alanine mutants of a key di-proline motif and of a light chain variant, revealing alternative conformations of the CDR H3. We also replicated both multi-peak and delayed SEC behavior with MPER-antibodies 4E10 and VRC42, by introducing a Tyr-Pro (YP) motif into their CDR H3s. Our results show how a conformationally dynamic CDR H3 can provide the requisite structural plasticity needed for a highly hydrophobic paratope to recognize its membrane-proximal epitope.

60 APPLIED LIFE SCIENCES↗

SSME HPOTP post-test diagnostic system enhancement project

An assessment of engine and component health is routinely made after each test or flight firing of a space shuttle main engine (SSME). Currently, this health assessment is done by teams of engineers who manually review sensor data, performance data, and engine and component operating histories. Based on review of information from these various sources, an evaluation is made as to the health of each component of the SSME and the preparedness of the engine for another test or flight. The objective of this project is to further develop a computer program which automates the analysis of test data from the SSME high-pressure oxidizer turbopump (HPOTP) in order to detect and diagnose anomalies. This program fits into a larger system, the SSME Post-Test Diagnostic System (PTDS), which will eventually be extended to assess the health and status of most SSME components on the basis of test data analysis. The HPOTP module is an expert system, which uses 'rules-of-thumb' obtained from interviews with experts from NASA Marshall Space Flight Center (MSFC) to detect and diagnose anomalies. Analyses of the raw test data are first performed using pattern recognition techniques which result in features such as spikes, shifts, peaks, and drifts being detected and written to a database. The HPOTP module then looks for combination of these features which are indicative of known anomalies, using the rules gathered from the turbomachinery experts. Results of this analysis are then displayed via a graphical user interface which provides ranked lists of anomalies and observations by engine component, along with supporting data plots for each.

Bickmore, Timothy W.↗

EVALUATION OF CONTROLLED END GAS AUTO IGNITION WITH EXHAUST GAS RECIRCULATION IN A STOICHIOMETRIC, SPARK IGNITED, NATURAL GAS ENGINE

Many stationary and heavy-duty on-road natural gas fueled engines today operate under stoichiometric conditions with a three-way catalyst. The disadvantage of stoichiometric natural gas engines compared to lean-burn natural gas and diesel engines is lower efficiency, resulting primarily from lower power density and compression ratio. Exhaust gas recirculation (EGR) coupled with advanced combustion controls can enable operation with higher compression ratio and power density, which yields higher efficiency. This also results in engine operation between the limits of knock and misfire. Operation between these limits has been named controlled end gas auto-ignition (C-EGAI) and can be used to improve the brake efficiency of the engine. Various methods of cylinder pressure-based knock quantification were explored to implement C-EGAI. The indicated quantification methods are used for the implementation of a control scheme for C-EGAI with a relation to the fractional heat release due to auto-ignition. A custom EGR system was built and the effect of EGR on the performance of a stochiometric, spark ignited, natural gas engine is evaluated. C-EGAI is implemented and the optimal parameters are determined for peak performance under EGR and C-EGAI conditions. In this study, knock detection is used for the recognition, magnitude, and location of the auto-ignition events. Cylinder pressure-based knock detection was the primary method for determining the occurrence and location of knock but was also used for implementing the ignition control scheme for controlled end gas auto-ignition. The combustion intensity metric (CIM) enabled parametric ignition timing control which allowed for the creation of a relationship between fractional heat release due to auto-ignition and CIM. Many stationary and heavy-duty on-road natural gas fueled engines today operate under stoichiometric conditions with a three-way catalyst. The disadvantage of stoichiometric natural gas engines compared to lean-burn natural gas and diesel engines is lower efficiency, resulting primarily from lower power density and compression ratio. Exhaust gas recirculation (EGR) coupled with advanced combustion controls can enable operation with higher compression ratio and power density, which yields higher efficiency. This also results in engine operation between the limits of knock and misfire. Operation between these limits has been named controlled end gas auto-ignition (C-EGAI) and can be used to improve the brake efficiency of the engine. Various methods of cylinder pressure-based knock quantification were explored to implement C-EGAI. The indicated quantification methods are used for the implementation of a control scheme for C-EGAI with a relation to the fractional heat release due to auto-ignition. A custom EGR system was built and the effect of EGR on the performance of a stochiometric, spark ignited, natural gas engine is evaluated. C-EGAI is implemented and the optimal parameters are determined for peak performance under EGR and C-EGAI conditions. In this study, knock detection is used for the recognition, magnitude, and location of the auto-ignition events. Cylinder pressure-based knock detection was the primary method for determining the occurrence and location of knock but was also used for implementing the ignition control scheme for controlled end gas auto-ignition. The combustion intensity metric (CIM) enabled parametric ignition timing control which allowed for the creation of a relationship between fractional heat release due to auto-ignition and CIM. Both exhaust gas recirculation and controlled end gas auto-ignition were analyzed with a cooperative fuel research (CFR) engine modified for boosted fuel/air intake. The data was interpreted to provide a proper evaluation of unique analytical methods to quantify the results of C_EGAI and characterize the live auto-ignition events. The control variables for this method of C-EGAI were optimized with EGR conditions to generate the point of peak performance on the CFR engine under stoichiometric, spark ignited, natural gas conditions.

Bayliff, Scott Michael↗

Scalability Analysis of Quantum Models for Stress and Emotion Detection

Stress and emotion detection from high-dimensional physiological signals is a challenging task, particularly when aiming for accurate classification across diverse behavioral states. Quantum machine learning (QML) is promising for modeling such high-dimensional data, but scalability is limited by qubit resources and the exponential cost of classical statevector simulation. This work studies the scalability of quantum support vector machines (QSVMs) for binary stress detection and three-class emotion recognition (Negative/Neutral/Positive) under varying qubit counts and angle-encoding strategies. We also present a comparison study with one-feature-per-qubit (1:1) and two-features-per-qubit (2:1) mappings. Experiments are executed on HPC infrastructure using NVIDIA CUDA-Q to evaluate performance, variance, and class-dependent separability at higher-qubit setups. Results show that larger Hilbert spaces can improve peak accuracy but may increase instability. At the same time, dense 2:1 encoding yields more consistent stress detection performance. For emotion recognition, scaling improves discrimination for classes like Negative and Positive more than Neutral. We find that effective QML scaling is task-dependent and benefits more from encoding design than simply increasing qubit count.

Onim, Md. Saif Hassan [University of Tennessee, Kn↗

X-Ray Fingerprinting Techniques for Recognizing A Hydrological Role in the Formation of Minerals on the Surface of Mars

Previous work has demonstrated the ability of a miniaturized XRD-XRF instrument to perform in-situ analyses without sample preparation or acquisition. Deployment of this instrument on a Martian rover will allow a large number of rapid qualitative analyses, which will maximize the diversity of samples studied and selected for possible return. As a first step in designing a decision tree for recognizing minerals in complex mixtures, d spacings were plotted against intensity for several mineral groups comprising rock and soil types inferred for the surface of Mars (weathered basalt, playa and hydrothermal deposits, clay-rich soils). In all groups, d spacings cluster in a range from about 1-4 angstroms, which can under certain circumstances obscure patterns for individual phases. However, within the silicate family, minerals containing either bound OH- or molecules of H20 (clays, micas, amphiboles, zeolites) are characterized by a shift of peaks to higher d spacings. Large d spacings (greater than about 7 angstroms) thus act as a first-order filter for distinguishing hydrous from anhydrous silicates. The ability to quickly verify the presence of silicates that have interacted with water has important implications for using mineral chemistry and structure to help decipher the hydrologic and atmospheric history of Mars. This represents a beginning for developing more sophisticated methods of pattern recognition. These will combine XRD and XRF analyses with optical data to rapidly7 discern environmentally diagnostic assemblages without the necessity of identifying every peak individual mineral phase.

Metzger, Ellen P.↗

Biomimetic/Optical Sensors for Detecting Bacterial Species

Biomimetic/optical sensors have been proposed as means of real-time detection of bacteria in liquid samples through real-time detection of compounds secreted by the bacteria. Bacterial species of interest would be identified through detection of signaling compounds unique to those species. The best-characterized examples of quorum-signaling compounds are acyl-homoserine lactones and peptides. Each compound, secreted by each bacterium of an affected species, serves as a signal to other bacteria of the same species to engage in a collective behavior when the population density of that species reaches a threshold level analogous to a quorum. A sensor according to the proposal would include a specially formulated biomimetic film, made of a molecularly imprinted polymer (MIP), that would respond optically to the signaling compound of interest. The MIP film would be integrated directly onto an opticalwaveguide- based ring resonator for optical readout. Optically, the sensor would resemble the one described in Chemical Sensors Based on Optical Ring Resonators (NPO-40601), NASA Tech Briefs, Vol. 29, No. 10 (October 2005), page 32. MIPs have been used before as molecular- recognition compounds, though not in the manner of the present proposal. Molecular imprinting is an approach to making molecularly selective cavities in a polymer matrix. These cavities function much as enzyme receptor sites: the chemical functionality and shape of a cavity in the polymer matrix cause the cavity to bind to specific molecules. An MIP matrix is made by polymerizing monomers in the presence of the compound of interest (template molecule). The polymer forms around the template. After the polymer solidifies, the template molecules are removed from the polymer matrix by decomplexing them from their binding sites and then dissolving them, leaving cavities that are matched to the template molecules in size, shape, and chemical functionality. The cavities thus become molecular-recognition sites that bind only to molecules matched to the sites; other molecules are excluded. In a sensor according to the proposal, the MIP would feature molecular recognition sites that would bind the specific signaling molecules selectively according to their size, shape, and chemical functionality (see figure). As the film took up the signaling molecules in the molecular recognition sites, the index of refraction and thickness of the film would change, causing a wavelength shift of the peak of the resonance spectrum. It has been estimated that by measuring this wavelength shift, it should be possible to detect as little as 10 picomoles of a peptide signaling compound.

Homer, Margie↗

Real time analysis of voiced sounds

A power spectrum analysis of the harmonic content of a voiced sound signal is conducted in real time by phase-lock-loop tracking of the fundamental frequency, (f sub 0) of the signal and successive harmonics (h sub 1 through h sub n) of the fundamental frequency. The analysis also includes measuring the quadrature power and phase of each frequency tracked, differentiating the power measurements of the harmonics in adjacent pairs, and analyzing successive differentials to determine peak power points in the power spectrum for display or use in analysis of voiced sound, such as for voice recognition.

Hong, J. P.↗

Lessons learned from the CSIS

A discussion is presented of the Centralized Storm Information System (CSIS), a joint effort by several organizations including NASA and the National Weather Service, which has been developed to aid the forecaster in evaluating weather data. The CSIS was developed to provide a handling, analyzing, intercomparison, and display system for real-time data from all available sources. The CSIS hardware and software, the impact of the CSIS, and the initial experience gained following the implementation of the CSIS are examined. Several general insights learned from this interactive processing system are examined in detail, including the benefits of an evolutionary bottom up rather than top down design approach, the crucial nature of scheduler function to any real time system, the recognition that meteorological interactive terminals requirements are different from image processing terminals requirements, and the knowledge that forecasters generate a large peak load on computer resources.

Mosher, F. R.↗

How Sample Completeness Affects Gamma-Ray Burst Classification

Unsupervised pattern recognition algorithms support the existence of three gamma-ray burst classes; class I (long, large fluence bursts of intermediate spectral hardness), Class II (short, small fluence, hard bursts), and class III (soft bursts of intermediate durations and fluences). The algorithms surprisingly assign larger membership to class III than to either of the other two classes. A known systematic bias has been previously used to explain the existence of class III in terms of class I; this bias allows the fluences and durations of some bursts to be underestimated. We show that this bias primarily affects only the longest bursts and cannot explain the bulk of the class III properties. We resolve the question of class III existence by demonstrating how samples obtained using standard trigger mechanisms fail to preserve the duration characteristics of small peak flux bursts: (Sample incompleteness is thus primarily responsible for the existence of class III.) In order to avoid this incompleteness, we show how a new dual timescale peak flux can be defined in terms of peak flux and fluence. The dual timescale peak flux reserves the duration distribution of faint bursts and correlates either with spectral hardness (and presumably redshift) than either peak flux or fluence. The techniques presented here are generic and have applicability to the studies of other transient events. The results also indicate that pattern recognition algorithms are sensitive to sample completeness; this can influence the study of large astronomical databases such as those found in a Virtual Observatory.

Hakkila, Jon↗

Automated co-adding and energy calibration of large array microcalorimeter data with zero sample knowledge

State-of-the-art microcalorimeter spectrometers now contain large detector arrays with hundreds of individual pixels. Each individual pixel outputs a unique and non-linear response with respect to deposited energy. This work describes a pattern-recognition algorithm to combine these responses into a single energy-calibrated histogram, referred to as co-adding pixels. Photo-peaks from different pixels are matched together based upon how well the match aligns the centroids and heights of neighboring peaks. This usually results in around 100 co-adding calibration points from 30 to 300 keV for a several day acquisition of plutonium items with masses between 0.5 and 10 grams. An additional algorithm energy-calibrates this co-added spectrum using the fluoresced K x-ray emissions from a tantalum absorber and inherent x-ray escape peaks from the tin absorbers. Both algorithms operate without knowledge of the source and are fully automated. This work presents results from the acquisitions of high and low burnup plutonium, 10% enriched uranium, a 153 Gd calibration source, and a 57 Co+ 166m Ho calibration source. In all measurements, resolution defined as the full-width at half-maximum (FWHM) of photo-peaks is preserved between the individual pixel and co-added spectra at around 65 eV for incident photon energies between 60 and 208 keV. The energy calibration algorithm is approximate and yields a calibration curve off by an average of around 200 eV for incident photon energies between 60 and 208 keV.

47 OTHER INSTRUMENTATION↗

How Sample Completeness Affects Gamma-Ray Burst Classification

Unsupervised pattern-recognition algorithms support the existence of three gamma-ray burst classes: class 1 (long, large-fluence bursts of intermediate spectral hardness), class 2 (short, small-fluence, hard bursts), and class 3 (soft bursts of intermediate durations and fluences). The algorithms surprisingly assign larger membership to class 3 than to either of the other two classes. A known systematic bias has been previously used to explain the existence of class 3 in terms of class 1 ; this bias allows the fluences and durations of some bursts to be underestimated, as recently shown by Hakkila et al. We show that this bias primarily affects only the longest bursts and cannot explain the bulk of the class 3 properties. We resolve the question of class 3's existence by demonstrating how samples obtained using standard trigger mechanisms fail to preserve the duration characteristics of small-peak flux bursts. Sample incompleteness is thus primarily responsible for the existence of class 3. In order to avoid this incompleteness, we show how a new, dual-timescale peak flux can be defined in terms of peak flux and fluence. The dual-timescale peak flux preserves the duration distribution of faint bursts and correlates better with spectral hardness (and presumably redshift) than either peak flux or fluence. The techniques presented here are generic and have applicability to the studies of other transient events. The results also indicate that pattern recognition algorithms are sensitive to sample completeness; this can influence the study of large astronomical databases, such as those found in a virtual observatory.

Hakkila, Jon↗

Some problems of Maryland towns as seen by their mayors

Conversations were held with the mayors of six Maryland towns to discuss possible models and needs for technology transfer. An unexpected outcome of the discussions was a considerable insight into local problems as perceived by the mayors. Problems, whether administrative, socio-economic, or technological, are different, from town to town, in degree, not in kind. Recognition of this feature of local priorities is vital to any considerations of external assistance.

Peake, H. J.↗

Optical generation of a circular harmonic filter for rotation and translation invariant optical pattern recognition

A new method to generate a circular harmonic filter with both rotation and translation invariance is described, which is based on a phase-shifted double-exposure (PSDE) technique. An expression for the peak correlation intensity at the origin for the correlation plane as a function of target orientation for the PSDE filter is derived. Experimental results confirming the theoretical predictions are provided.

Cheng, Yeou-Yen↗

Petrography of shock features in the 1953 Manson 2-A drill core

Drilling of Nx core in late 1953 into an anomalous zone of disturbed rocks northwest of Manson, Iowa disclosed presence of extensive breccias including crystalline rocks brought to the surface from depths of 4 km or more. Hole 2-A penetrated breccias dominated by leucocratic igneous and metamorphic lithologies, later interpreted to be part of a general ringed peak complex within a 35 km wide impact structure produced about 65 Ma ago. Proof of this origin was given in 1966 by NMS through recognition of shock metamorphic features in 2-A materials during a cursory examination of samples provided by R.A. Hoppin, University of Iowa. A detailed study of this material now underway has revealed that most breccia clasts in 2-A show abundant and varied evidence of shock damage, including extensive planar deformation features (PDF) in quartz, K-feldspar, plagioclase, and a pyroxene and varying degrees of isotropization and incipient melting in feldspars.

Short, N. M.↗