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All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Automated Discovery and Modeling of Sequential Patterns Preceding Events of Interest

The integration of emerging data manipulation technologies has enabled a paradigm shift in practitioners' abilities to understand and anticipate events of interest in complex systems. Example events of interest include outbreaks of socio-political violence in nation-states. Rather than relying on human-centric modeling efforts that are limited by the availability of SMEs, automated data processing technologies has enabled the development of innovative automated complex system modeling and predictive analysis technologies. We introduce one such emerging modeling technology - the sequential pattern methodology. We have applied the sequential pattern methodology to automatically identify patterns of observed behavior that precede outbreaks of socio-political violence such as riots, rebellions and coups in nation-states. The sequential pattern methodology is a groundbreaking approach to automated complex system model discovery because it generates easily interpretable patterns based on direct observations of sampled factor data for a deeper understanding of societal behaviors that is tolerant of observation noise and missing data. The discovered patterns are simple to interpret and mimic human's identifications of observed trends in temporal data. Discovered patterns also provide an automated forecasting ability: we discuss an example of using discovered patterns coupled with a rich data environment to forecast various types of socio-political violence in nation-states.

Rohloff, Kurt↗
NASA NTRS2021

Exploring Sentinel-1 and Sentinel-2 diversity for Flood inundation mapping using deep learning

Identification of flood water extent from satellite images has historically relied on either synthetic aperture radar (SAR) or multi-spectral (MS) imagery. MS sensors are limited to cloud free conditions, whereas SAR imagery is plagued by noise-like speckle. Prior studies that use combinations of MS and SAR data to overcome individual limitations of these sensors have not fully examined sensitivity of flood mapping performance to different combinations of SAR and MS derived spectral indices or band transformations in color space. This study explores the use of diverse bands of Sentinel 2 (S2) through well-established water indices and Sentinel 1 (S1) derived SAR imagery along with their combinations to assess their capability for generating accurate flood inundation maps. The robustness in performance of S-1 and S-2 band combinations was evaluated using 446 hand labeled flood inundation images spanning across 11 flood events from Sen1Floods11 dataset which are highly diverse in terms of land cover as well as location. A modified K-fold cross validation approach is used to evaluate the performance of 32 combinations of S1 and S2 bands using a fully connected deep convolutional neural network known as U-Net. Our results indicated that usage of elevation information has improved the capability of S1 imagery to produce more accurate flood inundation maps. Compared to a median F1 score of 0.62 when using only S1 bands, the combined use of S1 and elevation information led to an improved median F1 score of 0.73. Water extraction indices based on S2 bands have a statistically significant superior performance in comparison to S1. Among all the band combinations, HSV (Hue, Saturation, Value) transformation of S2 bands provides a median F1 score of 0.9, outperforming the commonly used water spectral indices owing to HSV’s transformation’s superior contrast distinguishing abilities. Additionally, U-Net algorithm was able to learn the relationship between raw S2 based water extraction indices and their corresponding raw S2 bands, but not of HSV owing to relatively complex computation involved in the latter. Results of the paper establishes important benchmarks for the extension of S1 and S2 data-based flood inundation mapping efforts over large spatial extents.

Goutam Konapala↗
NASA NTRS1992

Development of a residual acceleration data reduction and dissemination plan

A major obstacle in evaluating the residual acceleration environment in an orbiting space laboratory is the amount of data collected during a given mission: gigabytes of data will be available as SAMS units begin to fly regularly. Investigators taking advantage of the reduced gravity conditions of space should not be overwhelmed by the accelerometer data which describe these conditions. We are therefore developing a data reduction and analysis plan that will allow principal investigators of low-g experiments to create experiment-specific residual acceleration data bases for post-flight analysis. The basic aspects of the plan can also be used to characterize the acceleration environment of earth orbiting laboratories. Our development of the reduction plan is based on the following program of research: the identification of experiment sensitivities by order of magnitude estimates and numerical modelling; evaluation of various signal processing techniques appropriate for the reduction, supplementation, and dissemination of residual acceleration data; and testing and implementation of the plan on existing acceleration data bases. The orientation of the residual acceleration vector with respect to some set of coordinate axes is important for experiments with known directional sensitivity. Orientation information can be obtained from the evaluation of direction cosines. Fourier analysis is commonly used to transform time history data into the frequency domain. Common spectral representations are the amplitude spectrum which gives the average of the components of the time series at each frequency and the power spectral density which indicates the power or energy present in the series per unit frequency interval. The data reduction and analysis scheme developed involves a two tiered structure to: (1) identify experiment characteristics and mission events that can be used to limit the amount of accelerator data an investigator should be interested in; and (2) process the data in a way that will be meaningful to the experiment objectives. A general outline of the plan is given.

Rogers, Melissa J. B.↗
NASA NTRS1998

Deterministic Reconfigurable Control Design for the X-33 Vehicle

In the event of a control surface failure, the purpose of a reconfigurable control system is to redistribute the control effort among the remaining working surfaces such that satisfactory stability and performance are retained. Four reconfigurable control design methods were investigated for the X-33 vehicle: Redistributed Pseudo-Inverse, General Constrained Optimization, Automated Failure Dependent Gain Schedule, and an Off-line Nonlinear General Constrained Optimization. The Off-line Nonlinear General Constrained Optimization approach was chosen for implementation on the X-33. Two example failures are shown, a right outboard elevon jam at 25 deg. at a Mach 3 entry condition, and a left rudder jam at 30 degrees. Note however, that reconfigurable control laws have been designed for the entire flight envelope. Comparisons between responses with the nominal controller and reconfigurable controllers show the benefits of reconfiguration. Single jam aerosurface failures were considered, and failure detection and identification is considered accomplished in the actuator controller. The X-33 flight control system will incorporate reconfigurable flight control in the baseline system.

Wagner, Elaine A.↗
NASA NTRSDate not supplied

Tool CREOL: Using Earth Observations to Monitor Ecosystem Health for the Preservation of Coastal Louisiana

Submerged aquatic vegetation (SAV) in Louisiana’s Breton National Wildlife Refuge (BNWR) has been steadily declining due to anthropogenic and climate induced changes, contributing to the degradation of the barrier island system. In the Chandeleur Islands region, events such as Hurricane Katrina and the Deepwater Horizon Oil Spill have reduced the barrier islands’ ability to control storm surge and have decreased seagrass extent. Seagrasses promote aquatic biodiversity, absorb excess nutrients, and reduce the rate of shoreline erosion by trapping suspended sediment. The tool for Coastal Remote Ecological Observations in Louisiana (Tool CREOL), a novel GUI, was developed using Google Earth Engine to assess historical changes to and present conditions of the Chandeleur Islands’ land cover, water quality, and seagrass extent. Developed in collaboration with the Louisiana Coastal Protection and Restoration Authority and the Louisiana Department of Natural Resources, the tool uses Earth observations acquired from NASA’s Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI to analyze land cover, turbidity, chlorophyll-a, and seagrass extent from 1984 to today. Tool CREOL enables continuous monitoring of the Chandeleur Islands and will aid in the identification of ideal areas for island restoration and seagrass revegetation.

Taryn Waite↗
DOE OSTI2025

A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes

Due to their persistent widespread severe winds, derechos pose significant threats to human safety and property, with impacts comparable to many tornadoes and hurricanes. Yet, automated detection of derechos remains challenging due to the absence of spatiotemporally continuous observations and the complex criteria employed to define the phenomenon. This study presents an objective derecho detection approach capable of automatically identifying derechos through both observations and model results. The approach is grounded in a physically based definition of derechos and integrates three algorithms: (1) the Python Flexible Object Tracker (PyFLEXTRKR) algorithm to track mesoscale convective systems (MCSs), (2) a semantic segmentation convolutional neural network to identify bow echoes, and (3) a comprehensive classification algorithm to detect derechos within MCS life cycles and distinguish derecho-producing from non-derecho-producing MCSs. Using this approach, we developed a novel high-resolution (4 km and hourly) observational dataset of derechos and accompanying derecho-producing MCSs over the United States east of the Rocky Mountains from 2004 to 2021. The dataset consists of two subsets based on different gust speed data sources and is analyzed to document the climatology of derechos in the United States. On average, 12–15 derechos are identified per year, aligning with previous estimations (∼6–21 events annually). The spatial distribution and seasonal variation patterns are consistent with prior studies, showing peak occurrences in the Great Plains and the Midwest during the warm season. Additionally, during the study period, derechos account for approximately 3.1 % of measured damaging gusts (≥25.93 m s−1) over the eastern United States. The dataset is publicly available at https://doi.org/10.5281/zenodo.14835362 (Li et al., 2025).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Review of the Theoretical and Experimental Status of Dark Matter Identification with Cosmic-Ray Antideuterons

Recent years have seen increased theoretical and experimental effort towards the first-ever detection of cosmic-ray antideuterons, in particular as an indirect signature of dark matter annihilation or decay. In contrast to indirect dark matter searches using positrons, antiprotons, or gamma-rays, which suffer from relatively high and uncertain astrophysical backgrounds, searches with antideuterons benefit from very suppressed conventional backgrounds, offering a potential breakthrough in unexplored phase space for dark matter. This article is based on the first dedicated cosmic-ray antideuteron workshop, which was held at UCLA in June 2014. It reviews broad classes of dark matter candidates that result in detectable cosmic-ray antideuteron fluxes, as well as the status and prospects of current experimental searches. The coalescence model of antideuteron production and the influence of antideuteron measurements at particle colliders are discussed. This is followed by a review of the modeling of antideuteron propagation through the magnetic fields, plasma currents, and molecular material of our Galaxy, the solar system, the Earth's geomagnetic field, and the atmosphere. Finally, the three ongoing or planned experiments that are sensitive to cosmic-ray antideuterons, BESS, AMS-02, and GAPS, are detailed. As cosmic-ray antideuteron detection is a rare event search, multiple experiments with orthogonal techniques and backgrounds are essential. Therefore, the combination of AMS-02 and GAPS antideuteron searches is highly desirable. Many theoretical and experimental groups have contributed to these studies over the last decade, this review aims to provide the first coherent discussion of the relevant dark matter theories that antideuterons probe, the challenges to predictions and interpretations of antideuteron signals, and the experimental efforts toward cosmic antideuteron detection.

Antideuteron Cosmic ray Dark matter↗
DOE OSTI2024

EGS Collab Experiment 2: Microseismic Monitoring

This dataset contains continuous seismic waveform data recorded during stimulation and thermal circulation tests for the Enhanced Geothermal Systems (EGS) Collab Experiment #2, conducted from February to September 2022 at the Sanford Underground Research Facility in Lead, South Dakota. This experiment aimed to study and validate models of geothermal systems by injecting high-pressure fluids into rock formations 1200-1500 meters below the surface, inducing microseismic events. The seismic monitoring system included 16 three-component accelerometers and a 24-channel hydrophone array, installed in boreholes surrounding the test area. Data were recorded at high sampling rates using a continuous waveform recording system to monitor seismic activity in real time. The dataset contains the raw data stored in binary format, with files named based on timestamps, and includes calibration certificates for some sensors to facilitate corrections to real units. Users are strongly advised to consult the accompanying detailed report, which outlines the experimental setup, sensor specifications, installation procedures, and data processing methods. The report also describes important nuances, such as the hardware filters on hydrophones, sensor calibration details, and the naming conventions for the recorded data. Proper use of this dataset may require familiarity with seismic data analysis tools, such as the Obspy Python package, and an understanding of the SEED naming conventions used for channel identification.

15 GEOTHERMAL ENERGY↗
NASA NTRS1981

Effects of lateral resolution on the identification of volcanotectonic provinces on earth and Venus

In an attempt to learn what volcanotectonic features can still be discerned in continental and oceanic areas of the earth when topographic data are degraded to simulate the data sampled by the Pioneer-Venus altimeter, two digital topographic data sets (the 30-second continental U.S. altitude data and the 30 x 30 nautical mile bathymetry data for the North Pacific) were degraded and displayed in the same way as the altimeter data from Venus. The Appalachians were reduced to a gentle swell, with a wavelength of 300 km, and a height of 500 m. The Cordillera was seen as a broad swell, 2500 km wide, and about 2 km high. The east Pacific rise, east Pacific fractures, seamount chains, the Hawaiian swell, and most trenches were discernible in the degraded Pacific data; whereas rises, transforms, seamount chains, and trenches were not seen in the Venus data, even after corrections were made for the higher surface temperature and the absence of oceans on Venus. It was concluded that a plate tectonic regime, similar to earth's does not currently appear to exist on Venus. As shown by the Cordillera data, the Pioneer-Venus information is not of sufficiently high quality to discern whether the highlands of Venus preserve evidence for orogenic events related to plate tectonics.

Arvidson, R. E.↗
NASA NTRS2014

Application of Machine Learning Algorithms to the Study of Noise Artifacts in Gravitational-Wave Data

The sensitivity of searches for astrophysical transients in data from the Laser Interferometer Gravitationalwave Observatory (LIGO) is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high-enough rate such that accidental coincidence across multiple detectors is non-negligible. Furthermore, non-Gaussian noise artifacts typically dominate over the background contributed from stationary noise. These "glitches" can easily be confused for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational-waves. We apply Machine Learning Algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. Terrestrial noise sources may manifest characteristic disturbances in these auxiliary channels, inducing non-trivial correlations with glitches in the gravitational-wave data. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; high dimensionality is an area where MLAs are particularly well-suited. We demonstrate the feasibility and applicability of three very different MLAs: Artificial Neural Networks, Support Vector Machines, and Random Forests. These classifiers identify and remove a substantial fraction of the glitches present in two very different data sets: four weeks of LIGO's fourth science run and one week of LIGO's sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth science run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar limiting performance, suggesting that most of the useful information currently contained in the auxiliary channel parameters we extract is already being used. Future performance gains are thus likely to involve additional sources of information, rather than improvements in the MLAs themselves.

gravitational-wave data↗
NASA NTRS2014

Thermodynamics in the Suppressed Phase of the Madden-Julian Oscillation Using a Multiplatform Strategy

The Madden-Julian Oscillation (MJO) represents a prominent mode of intraseasonal tropical variability. It is manifest by coherent large-scale changes in atmospheric circulation, convection, and thermodynamic processes. Preconditioning of the environment prior to the active phase of the MJO has been noted, but the balance of theorized mechanisms to accomplish this process remains unresolved. Further, there is a lack of consensus on the means by which primary initiation of an MJO event occurs. Observational and modeling efforts have recently been undertaken to advance our understanding of the physical underpinnings governing MJO development. However these intensive studies are often limited in space and/or time and are potentially subject to model deficiencies. Satellite observations, especially those providing vertical resolution of temperature and moisture, provide an opportunity to expand our knowledge of processes critical to MJO initiation and preconditioning. This work will provide an analysis of suppressed phase thermodynamics with an emphasis on the use of a complementary suite of satellite observations including AIRS/AMSU-A profiles, CERES radiative fluxes, and cloud properties observed by MODIS. Emphasis of this work will regard the distribution of cloud regimes, their radiative-convective effects, and their relationship to moist static energy during the recharge and suppressed stages of MJO initiation and eastward propagation. The analyses will make use of cloud regimes from MODIS observations to provide a compositing technique that enables the identification of systematic connections between different cloud regimes and the larger scale environment. Within these cloud regimes, the relationship between the associated cloud-radiative effects observed by CERES, vertically-resolved and vertically-integrated thermodynamics using AIRS/AMSU-A observations, and atmospheric boundary layer fluxes will be demonstrated.

Roberts, J. Brent↗
DOE OSTI2020

Characterization and identification of HPC applications at leadership computing facility

High Performance Computing (HPC) is an important method for scientific discovery via large-scale simulation, data analysis, or artificial intelligence. Leadership-class supercomputers are expensive, but essential to run large HPC applications. The Petascale era of supercomputers began in 2008, with the first machines achieving performance in excess of one petaflops, and with the advent of new supercomputers in 2021 (e.g., Aurora, Frontier), the Exascale era will soon begin. However, the high theoretical computing capability (i.e., peak FLOPS) of a machine is not the only meaningful target when designing a supercomputer, as the resources demand of applications varies. A deep understanding of the characterization of applications that run on a leadership supercomputer is one of the most important ways for planning its design, development and operation. In order to improve our understanding of HPC applications, user demands and resource usage characteristics, we perform correlative analysis of various logs for different subsystems of a leadership supercomputer. This analysis reveals surprising, sometimes counter-intuitive patterns, which, in some cases, conflicts with existing assumptions, and have important implications for future system designs as well as supercomputer operations. For example, our analysis shows that while the applications spend significant time on MPI, most applications spend very little time on file I/O. Combined analysis of hardware event logs and task failure logs show that the probability of a hardware FATAL event causing task failure is low. Combined analysis of control system logs and file I/O logs reveals that pure POSIX I/O is used more widely than higher level parallel I/O. Based on holistic insights of the application gained through combined and co-analysis of multiple logs from different perspectives and general intuition, we engineer features to "fingerprint" HPC applications. We use t-SNE (a machine learning technique for dimensionality reduction) to validate the explainability of our features and finally train machine learning models to identify HPC applications or group those with similar characteristic. To the best of our knowledge, this is the first work that combines logs on file I/O, computing, and inter-node communication for insightful analysis of HPC applications in production.

Liu, Zhengchun↗
DOE OSTI2022

Identifying Critical Infrastructure in Imagery Data Using Explainable Convolutional Neural Networks

To date, no method utilizing satellite imagery exists for detailing the locations and functions of critical infrastructure across the United States, making response to natural disasters and other events challenging due to complex infrastructural interdependencies. This paper presents a repeatable, transferable, and explainable method for critical infrastructure analysis and implementation of a robust model for critical infrastructure detection in satellite imagery. This model consists of a DenseNet-161 convolutional neural network, pretrained with the ImageNet database. The model was provided additional training with a custom dataset, containing nine infrastructure classes. The resultant analysis achieved an overall accuracy of 90%, with the highest accuracy for airports (97%), hydroelectric dams (96%), solar farms (94%), substations (91%), potable water tanks (93%), and hospitals (93%). Critical infrastructure types with relatively low accuracy are likely influenced by data commonality between similar infrastructure components for petroleum terminals (86%), water treatment plants (78%), and natural gas generation (78%). Local interpretable model-agnostic explanations (LIME) was integrated into the overall modeling pipeline to establish trust for users in critical infrastructure applications. The results demonstrate the effectiveness of a convolutional neural network approach for critical infrastructure identification, with higher than 90% accuracy in identifying six of the critical infrastructure facility types.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

Construction of Polarimetric Radar-Based Reference Rain Maps for the Iowa Flood Studies Campaign

The Global Precipitation Measurement (GPM) Mission Iowa Flood Studies (IFloodS) campaign was conducted in central and northeastern Iowa during the months of April-June, 2013. Specific science objectives for IFloodS included quantification of uncertainties in satellite and ground-based estimates of precipitation, 4-D characterization of precipitation physical processes and associated parameters (e.g., size distributions, water contents, types, structure etc.), assessment of the impact of precipitation estimation uncertainty and physical processes on hydrologic predictive skill, and refinement of field observations and data analysis approaches as they pertain to future GPM integrated hydrologic validation and related field studies. In addition to field campaign archival of raw and processed satellite data (including precipitation products), key ground-based platforms such as the NASA NPOL S-band and D3R Ka/Ku-band dual-polarimetric radars, University of Iowa X-band dual-polarimetric radars, a large network of paired rain gauge platforms, and a large network of 2D Video and Parsivel disdrometers were deployed. In something of a canonical approach, the radar (NPOL in particular), gauge and disdrometer observational assets were deployed to create a consistent high-quality distributed (time and space sampling) radar-based ground "reference" rainfall dataset, with known uncertainties, that could be used for assessing the satellite-based precipitation products at a range of space/time scales. Subsequently, the impact of uncertainties in the satellite products could be evaluated relative to the ground-benchmark in coupled weather, land-surface and distributed hydrologic modeling frameworks as related to flood prediction. Relative to establishing the ground-based "benchmark", numerous avenues were pursued in the making and verification of IFloodS "reference" dual-polarimetric radar-based rain maps, and this study documents the process and results as they pertain specifically to efforts using the NPOL radar dataset. The initial portions of the "process" involved dual-polarimetric quality control procedures which employed standard phase and correlation-based approaches to removal of clutter and non-meteorological echo. Calculation of a scale-adaptive KDP was accomplished using the method of Wang and Chandrasekar (2009; J. Atmos. Oceanic Tech.). A dual-polarimetric blockage algorithm based on Lang et al. (2009; J. Atmos. Oceanic Tech.) was then implemented to correct radar reflectivity and differential reflectivity at low elevation angles. Next, hydrometeor identification algorithms were run to identify liquid and ice hydrometeors. After the quality control and data preparation steps were completed several different dual-polarimetric rain estimation algorithms were employed to estimate rainfall rates using rainfall scans collected approximately every two to three minutes throughout the campaign. These algorithms included a polarimetrically-tuned Z-R algorithm that adjusts for drop oscillations (via Bringi et al., 2004, J. Atmos. Oceanic Tech.), and several different hybrid polarimetric variable approaches, including one that made use of parameters tuned to IFloodS 2D Video Disdrometer measurements. Finally, a hybrid scan algorithm was designed to merge the rain rate estimates from multiple low level elevation angle scans (where blockages could not be appropriately corrected) in order to create individual low-level rain maps. Individual rain maps at each time step were subsequently accumulated over multiple time scales for comparison to gauge network data. The comparison results and overall error character depended strongly on rain event type, polarimetric estimator applied, and range from the radar. We will present the outcome of these comparisons and their impact on constructing composited "reference" rainfall maps at select time and space scales.

Radar↗
NASA NTRS2019

Radio Follow-up of a Candidate γ-Ray Transient in the Sky Localization Area of GW170608

After the identification of a candidate γ-ray transient in the error region of the binary black hole (BBH) merger GW150914 by the Fermi satellite, the question of whether BBH mergers can be associated with electromagnetic counterparts remains highly debated. Here, we present radio follow-up observations of GW170608, a BBH merger that occurred during the second observing run (O2) of the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO). Our radio follow-up focused on a specific field contained in the GW170608 sky localization area, where a candidate high-energy transient was detected by the Fermi Large Area Telescope (LAT). We make use of data collected at 1.4 GHz with the Karl G. Jansky Very Large Array (VLA), as well as with the VLA Lowband Ionosphere and Transient Experiment (VLITE). Our analysis is sensitive to potential radio afterglows with luminosity densities L(1.4GHz) ≳ 6 x 10^(28) erg/sHz. In the most optimistic theoretical models, ≈20% of BBH events occurring in massive hosts could be associated with outflows as radio luminous as this. Although we find no evidence for the presence of a radio counterpart associated with the LAT candidate in the GW170608 error region, our analysis demonstrates the feasibility of future radio follow-up observations of well-localized BBHs. Comparing our radio upper limits with theoretical expectations for the radio afterglows potentially associated with jets launched in BBH mergers, we find that for jets of energy ≈10^(49) erg seen on-axis, only jet angles θ(jet)≳40° are compatible with the observations.

Kyle Artkop↗
NASA NTRS2003

Multiplexing Technology for Acoustic Emission Monitoring of Aerospace Vehicles

The initiation and propagation of damage mechanisms such as cracks and delaminations generate acoustic waves, which propagate through a structure. These waves can be detected and analyzed to provide the location and severity of damage as part of a structural health monitoring (SHM) system. This methodology of damage detection is commonly known as acoustic emission (AE) monitoring, and is widely used on a variety of applications on civil structures. AE has been widely considered for SHM of aerospace vehicles. Numerous successful ground and flight test demonstrations have been performed, which show the viability of the technology for damage monitoring in aerospace structures. However, one significant current limitation for application of AE techniques on aerospace vehicles is the large size, mass, and power requirements for the necessary monitoring instrumentation. To address this issue, a prototype multiplexing approach has been developed and demonstrated in this study, which reduces the amount of AE monitoring instrumentation required. Typical time division multiplexing techniques that are commonly used to monitor strain, pressure and temperature sensors are not applicable to AE monitoring because of the asynchronous and widely varying rates of AE signal occurrence. Thus, an event based multiplexing technique was developed. In the initial prototype circuit, inputs from eight sensors in a linear array were multiplexed into two data acquisition channels. The multiplexer rapidly switches, in less than one microsecond, allowing the signals from two sensors to be acquired by a digitizer. The two acquired signals are from the sensors on either side of the trigger sensor. This enables the capture of the first arrival of the waves, which cannot be accomplished with the signal from the trigger sensor. The propagation delay to the slightly more distant neighboring sensors makes this possible. The arrival time from this first arrival provides a more accurate source location determination. The multiplexer also identifies which channels are acquired by encoding TTL logic pulses onto the latter portion of the signals. This prototype system was demonstrated using pencil lead break (Hsu-Neilsen) sources on an aluminum plate. It performed as designed providing rapid low noise trigger based switching with encoded channel identification. this multiplexing approach is not limited to linear arrays, but can be easily extended to monitor sensors in planar ot three dimensional arrays. A 32 channel multiplexing system is under development that will allow arbitrary sensor placement. Another benefit of this multiplexing system is the reduction in the expense of data acquisition hardware. In addition, the reduced weight and power requirements are of extreme importance for proposed AE systems on aerospace vehicles.

Prosser, William↗
NASA NTRS2007

Surveillance and Control of Malaria Transmission in Thailand using Remotely Sensed Meteorological and Environmental Parameters

These slides address the use of remote sensing in a public health application. Specifically, this discussion focuses on the of remote sensing to detect larval habitats to predict current and future endemicity and identify key factors that sustain or promote transmission of malaria in a targeted geographic area (Thailand). In the Malaria Modeling and Surveillance Project, which is part of the NASA Applied Sciences Public Health Applications Program, we have been developing techniques to enhance public health's decision capability for malaria risk assessments and controls. The main objectives are: 1) identification of the potential breeding sites for major vector species; 2) implementation of a risk algorithm to predict the occurrence of malaria and its transmission intensity; 3) implementation of a dynamic transmission model to identify the key factors that sustain or intensify malaria transmission. The potential benefits are: 1) increased warning time for public health organizations to respond to malaria outbreaks; 2) optimized utilization of pesticide and chemoprophylaxis; 3) reduced likelihood of pesticide and drug resistance; and 4) reduced damage to environment. !> Environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. The NASA Earth science data sets that have been used for malaria surveillance and risk assessment include AVHRR Pathfinder, TRMM, MODIS, NSIPP, and SIESIP. Textural-contextual classifications are used to identify small larval habitats. Neural network methods are used to model malaria cases as a function of the remotely sensed parameters. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records. Discrete event simulations are used for modeling the detailed interactions among the vector life cycle, sporogonic cycle and human infection cycle, under the explicit influences of selected extrinsic and intrinsic factors. The output of the model includes the individual infection status and the quantities normally observed in field studies, such as mosquito biting rates, sporozoite infection rates, gametocyte prevalence and incidence. Results are in good agreement with mosquito vector and human malaria data acquired by Coleman et al. over 4.5 years in Kong Mong Tha, a remote village in western Thailand. Application of our models is not restricted to the Greater Mekong Subregion. Our models have been applied to malaria in Indonesia, Korea, and other regions in the world with similar success.

Kiang, Richard K.↗
NASA NTRS2011

Dual-Polarimetric Radar-Based Tornado Debris Paths Associated with EF-4 and EF-5 Tornadoes over Northern Alabama During the Historic Outbreak of 27 April 2011

An historic tornado and severe weather outbreak devastated much of the southeastern United States between 25 and 28 April 2011. On 27 April 2011, northern Alabama was particularly hard hit by a large number of tornadoes, including several that reached EF-4 and EF-5 on the Enhanced Fujita damage scale. In northern Alabama alone, there were approximately 100 fatalities and hundreds of more people who were injured or lost their homes during the havoc caused by these violent tornadic storms. Two long-track and violent (EF-4 and EF-5) tornadoes occurred within range of the University of Alabama in Huntsville (UAHuntsville) Advanced Radar for Meteorological and Operational Research (ARMOR, C-band dual-polarimetric). A unique capability of dual-polarimetric radar is the near-real time identification of lofted debris associated with ongoing tornadoes on the ground. The focus of this paper is to analyze the dual-polarimetric radar-inferred tornado debris signatures and identify the associated debris paths of the long-track EF-4 and EF-5 tornadoes near ARMOR. The relative locations of the debris and damage paths for each tornado will be ascertained by careful comparison of the ARMOR analysis with NASA MODIS (Moderate Resolution Imaging Spectroradiometer) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) satellite imagery of the tornado damage scenes and the National Weather Service tornado damage surveys. With the ongoing upgrade of the WSR-88D (Weather Surveillance Radar - 1988 Doppler) operational network to dual-polarimetry and a similar process having already taken place or ongoing for many private sector radars, dual-polarimetric radar signatures of tornado debris promise the potential to assist in the situational awareness of government and private sector forecasters and emergency managers during tornadic events. As such, a companion abstract (Schultz et al.) also submitted to this conference explores "The use of dual-polarimetric tornadic debris signatures in an operational setting."

Carey, Lawrence D.↗