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NASA NTRS2011

ARC+(Registered Trademark) and ARC PC Welding Simulators: Teach Welders with Virtual Interactive 3D Technologies

123 Certification Inc., a Montreal based company, has developed an innovative hands-on welding simulator solution to help build the welding workforce in the most simple way. The solution lies in virtual reality technology, which has been fully tested since the early 90's. President and founder of 123 Certification Inc., Mr. Claude Choquet Ing. Msc. IWE. acts as a bridge between the welding and the programming world. Working in these fields for more than 20 years. he has filed 12 patents world-wide for a gesture control platform with leading edge hardware related to simulation. In the summer of 2006. Mr Choquet was proud to be invited to the annual IIW International Weld ing Congress in Quebec City to launch the ARC+ welding simulator. A 100% virtual reality system and web based training center was developed to simulate multi process. multi-materiaL multi-position and multi pass welding. The simulator is intended to train welding students and apprentices in schools or industries. The welding simulator is composed of a real welding e[eetrode holder (SMAW-GTAW) and gun (GMAW-FCAW). a head mounted display (HMD), a 6 degrees of freedom tracking system for interaction between the user's hands and head. as well as external audio speakers. Both guns and HMD are interacting online and simultaneously. The welding simulation is based on the law of physics and empirical results from detailed analysis of a series of welding tests based on industrial applications tested over the last 20 years. The simulation runs in real-time, using a local logic network to determine the quality and shape of the created weld. These results are based on the orientation distance. and speed of the welding torch and depth of penetration. The welding process and resulting weld bc.1d are displayed in a virtual environment with screenplay interactive training modules. For review. weld quality and recorded process values can be displayed and diagnosed after welding. To help in the le.tming process, a learning curve for each student and each Virtual Welding Class'" can be plotted, for an instructor's review or a required third party evaluation.

Choquet, Claude↗

NASA NTRS1967

The analysis of Gemini 7 EEG data by three different computer oriented methods

The problem in assessment of electroencephalograms has been the subject element in visual evaluations of such records as well as the need for extensive training (2 or 3 years) before suitable candidate is capable to perform this task. There have been variety of computer type analyses of EEG during the past ten years or more, and we are still searching for a reliable, inexpensive, online data processing procedure for this task. We are aware our Soviet colleagues are routinely using computer assessment of EEGs, using relatively inexpensive hardware. In our report we endeavor to show how four different assessment procedures (visual assessment, zero crossing technique, smoothing and peak counting technique, and Weibull Statistic) may be utilized, and indicate the advantages and disadvantages peculiar to each. We wish to point out that in the Gemini 7 EEG analog records supplied to the Henry Ford Hospital, Channel II (mid-line central to mid-line occipital electrodes) could not be assessed reliably after approximately 26 hours of flight. Channel I (left central to left occipital electrodes) provided the more assessable EEG up to approximately 54 hours of flight. These channels were compared during the first 26 hours of flight, and no significant difference was found by our various assessment procedures. We, therefore, have limited this report to the findings on the analyses of Channel I during its approximate 54 hours of flight.

Proctor, L. D.↗

NASA NTRS2010

Frequency Stabilization of DFB Laser Diodes at 1572 nm for Spaceborne Lidar Measurements of CO2

We report a fiber-based, pulsed laser seeder system that rapidly switches among 6 wavelengths across atmospheric carbon dioxide (CO2) absorption line near 1572.3 nm for measurements of global CO2 mixing ratios to 1-ppmv precision. One master DFB laser diode has been frequency-locked to the CO2 line center using a frequency modulation technique, suppressing its peak-to-peak frequency drifts to 0.3 MHz at 0.8 sec averaging time over 72 hours. Four online DFB laser diodes have been offset-locked to the master laser using phase locked loops, with virtually the same sub-MHz absolute accuracy. The 6 lasers were externally modulated and then combined to produce the measurement pulse train.

Numata, Kenji↗

NASA NTRS2025

An Efficient GPU-Accelerated Multi-Source Global Fit Pipeline for LISA Data Analysis

The large-scale analysis task of deciphering gravitational wave signals in the LISA data stream will be difficult, requiring a large amount of computational resources and extensive development of computational methods. Its high dimensionality, multiple model types, and complicated noise profile require a global fit to all parameters and input models simultaneously. In this work, we detail our global fit algorithm, called “Erebor,” designed to accomplish this challenging task. It is capable of analysing current state-of-the-art datasets and then growing into the future as more pieces of the pipeline are completed and added. We describe our pipeline strategy, the algorithmic setup, and the results from our analysis of the LDC2A Sangria dataset, which contains Massive Black Hole Binaries, compact Galactic Binaries, and a parameterized noise spectrum whose parameters are unknown to the user. The Erebor algorithm includes three unique and very useful contributions: GPU acceleration for enhanced computational efficiency; ensemble MCMC sampling with multiple MCMC walkers per temperature for better mixing and parallelized sample creation; and special online updates to reversible-jump (or trans-dimensional) sampling distributions to ensure sampler mixing and accurate initial estimates for detectable sources in the data. We recover posterior distributions for all 15 (6) of the injected MBHBs in the LDC2A training (hidden) dataset. We catalog ∼12000 Galactic Binaries (∼8000 as high confidence detections) for both the training and hidden datasets. All of the sources and their posterior distributions are provided in publicly available catalogs.

LISA global fit↗

NASA NTRS2025

Efficient GPU-Accelerated MultiSource Global Fit Pipeline for LISA Data Analysis

The large-scale analysis task of deciphering gravitational-wave signals in the LISA data stream will be difficult, requiring a large amount of computational resources and extensive development of computational methods. Its high dimensionality, multiple model types, and complicated noise profile require a global fit to all parameters and input models simultaneously. In this work, we detail our global fit algorithm, called “Erebor,” designed to accomplish this challenging task. It is capable of analyzing current state-of-the-art datasets and then growing into the future as more pieces of the pipeline are completed and added. We describe our pipeline strategy, the algorithmic setup, and the results from our analysis of the LDC2A Sangria dataset, which contains massive black hole binaries, compact galactic binaries, and a parametrized noise spectrum whose parameters are unknown to the user. The Erebor algorithm includes three unique and very useful contributions: GPU acceleration for enhanced computational efficiency; ensemble Markov Chain Monte Carlo (MCMC) sampling with multiple MCMC walkers per temperature for better mixing and parallelized sample creation; and special online updates to reversible-jump (or transdimensional) sampling distributions to ensure sampler mixing and accurate initial estimates for detectable sources in the data.We recover posterior distributions for all 15 (6) of the injected massive black hole binaries (MBHB) in the LDC2A training (hidden) dataset. We catalog ∼12000 galactic binaries (∼8000 as high confidence detections) for both the training and hidden datasets. All of the sources and their posterior distributions are provided in publicly available catalogs.

LISA↗

NASA NTRS2012

Shuttle Case Study Collection Website Development

As a continuation from summer 2012, the Shuttle Case Study Collection has been developed using lessons learned documented by NASA engineers, analysts, and contractors. Decades of information related to processing and launching the Space Shuttle is gathered into a single database to provide educators with an alternative means to teach real-world engineering processes. The goal is to provide additional engineering materials that enhance critical thinking, decision making, and problem solving skills. During this second phase of the project, the Shuttle Case Study Collection website was developed. Extensive HTML coding to link downloadable documents, videos, and images was required, as was training to learn NASA's Content Management System (CMS) for website design. As the final stage of the collection development, the website is designed to allow for distribution of information to the public as well as for case study report submissions from other educators online.

Ransom, Khadijah S.↗

NASA NTRS2017

Growing Beyond Earth; Students Exploring Plant Varieties for Future Space Exploration

Future space exploration and long duration space flight will pose an array of challenges to the health and wellbeing of astronauts. Since 2015, Fairchild Tropical Botanic Garden (FTBG), in partnership with NASA's Veggie team, has been testing edible crops for space flight potential through a series of citizen science experiments. FTBG's interest in classroom-based science projects, along with NASA's successful operation of the Veggie system aboard the International Space Station (ISS), led to a NASA-FTBG partnership that gave rise to the Growing Beyond Earth STEM Initiative (GBE). Established in 2015, GBE now involves 131 middle and high school classrooms in South Florida, all conducting simultaneous plant science experiments. The results of those experiments (both numeric and visual) are directly shared with the space food production researchers at KSC. Through this session, we will explore the successful classroom implementation and integration into the curriculum, how the data is being used and the impact of the project on participating researchers, teachers, and students. Participating schools were supplied with specialized LED-lit growth chambers, mimicking the Veggie system on ISS, for growing edible plants under similar physical and environmental constraints. Research protocols were provided by KSC scientists, while edible plant varieties were selected mainly by the botanists at FTBG. In a jointly-led professional development workshop, participating teachers were trained to conduct GBE experiments in their classrooms. Teachers were instructed to not only teach basic botany concepts, but to also demonstrate practical applications of math, physics and chemistry. As experiments were underway, students shared data on plant germination, growth, and health in an online spreadsheet. Results from the students research show a promising selection of new plant candidates for possible further testing. Over a two year period, more than 5000 South Florida students, ages 11 to 18, participated in GBE. Evaluation of the program shows an increased knowledge of and interest in science and science careers among students. The program has also boosted the demand for summer high school internships at FTBG, further developing expertise in plant research and science related to space exploration. Supported by a grant from NASA (NNX16AM32G) to Fairchild Tropical Botanic Garden.

Veggie↗

NASA NTRSDate not supplied

Maximizing Spaceflight Biological Data with Omics Analytics: The NASA GeneLab Database

NASA’s GeneLab includes an open-access repository of some 250+ omics datasets generated by biological experiments relevant to spaceflight including simulated cosmic radiation and microgravity. In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics background, GeneLab has become a knowledgebase platform converting raw genetic and proteomic signatures found in flight samples into biological and physiological meanings. A large community of more than 100 scientists has rallied behind GeneLab and organized into four Analysis Working Groups (AWGs: Animal, Plant, Microbe, and Multi-Omics). Together, the AWGs have gained scientific recognition worldwide by establishing a consortium in charge of adopting new complex standards for data analysis workflows and omics sample processing in a rapidly evolving field. We will demonstrate the usage of the repository with smart search capability, an online controlled-access toolshed "Galaxy" to process user data with vetted standard workflows, a workspace for data sharing and a data submission portal with ontology control for better metadata curation. The GeneLab visualization portal will also be demonstrated, showing how anyone without formal training in bioinformatics can now browse the space biology omics data to discover new biology and potential solutions to improve life in space.

Sylvain Vincent Costes↗

NASA NTRSDate not supplied

GeneLab: The NASA System Biology Platform for Space Omics Repository, Analysis and Visualization

GeneLab↗

NASA NTRS2013

Semi-Supervised Eigenbasis Novelty Detection

Recent discoveries in high-time-resolution radio astronomy data have focused attention on a new class of events. Fast transients are rare pulses of radio frequency energy lasting from microseconds to seconds that might be produced by a variety of exotic astrophysical phenomena. For example, X-ray bursts, neutron stars, and active galactic nuclei are all possible sources of short-duration, transient radio signals. It is difficult to anticipate where such signals might appear, and they are most commonly discovered through analysis of high-time- resolution data that had been collected for other purposes. Transients are often faint and difficult to detect, so improved detection algorithms can directly benefit the science yield of all such commensal monitoring. A new detection algorithm learns a low-dimensional linear manifold for describing the normal data. High reconstruction error indicates a novel signal that does not match the patterns of normal data. One unsupervised portion of the manifold model adapts its representation in response to recent data. A second supervised portion of the model is made of a basis trained in advance using labeled examples of RFI; this prevents false positives due to these events. For a linear model, an orthonormalization operation is used to combine these bases prior to the anomaly detection decision. Another novel aspect of the approach lies in combining basis vectors learned in an unsupervised, online fashion from the data stream with supervised basis vectors learned in advance from known examples of false alarms. Adaptive, data-driven detection is achieved that is also informed by existing domain knowledge about signals that may be statistically anomalous, but are not interesting and should therefore be ignored. The method was evaluated using data from the Parkes Multibeam Survey. This data set was originally collected to search for pulsars, which are astronomical sources that emit radio pulses at regular periods. However, several non-pulsar anomalies have recently been discovered in this dataset, making it a compelling test case. By explicitly filtering known false alarm patterns, the approach yields significantly better performance than current transient detection methods.

Wagstaff, Kiri L.↗

NASA NTRSDate not supplied

Building a Real-Time Flood Prediction Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the local government of Howard County, Maryland, to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a statistical model capable of hindcasting the two severe flash flood events that devastated Ellicott City and transitioned to a ‘Long Short-Term Memory’ based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using Nash-Sutcliffe Efficiency. The final product, the Sequentially Trained Real-time EstimAted Model (STREAM) predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

NASA DEVELOP↗

NASA NTRS2021

Modernizing Mechatronics Course With Quantum Engineering

Mechatronics is the synergistic application of mechanics, electronics, control engineering, and computer science in the development of electromechanical products and systems, through integrated design. This paper proposes to extend the mechatronics course beyond traditional engineering topics, and to modernize the mechatronics instructions with complementary quantum engineering topics. With the recent rapid advances in quantum technologies such as quantum communications, sensing, computers, and algorithms, it is imperative to train the next generation of engineers and prepare them for their future careers in the ever-changing industry in such areas. Furthermore, due to such progress and advances in the fields associated with quantum mechanics, the integration of quantum technologies with classical mechanical systems will be inevitable both in terms of educational and technological standpoints in future. To address the educational needs of the future engineers in such areas of significant importance, quantum entanglement and quantum cryptography experiments, as two fundamental topics in quantum mechanics, are brought into the mechatronics course in an initiative that is reported in this paper. The integrated quantum and mechatronics topics also provides opportunities for open discussions on exploring the interface of quantum technologies and classical engineering systems, which can potentially push the engineering boundaries beyond classical possibilities by accessing the quantum advantages. An innovative online remote demonstration of such quantum experiments are developed and presented to the students. This course has been offered to undergraduate students once with successful results. The students were able to remotely access the experiments, perform the experiments and collect data. The successful result of such quantum experiments is also reflected in a course survey, presented in this paper, even though the quantum mechanics topics offered in this course are unfamiliar to engineering students and hence more challenging. The paper reports, and aims to promote, the integration of selected quantum technology topics with the mechatronics course for training engineering students in this rapidly growing area.

Ghazinejad, Maziar↗

NASA NTRSDate not supplied

Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the Howard County government in Maryland to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a prediction model capable of hindcasting the two severe flash flood events that devastated Ellicott City, and transitioned to an Long Short-Term Memory (LSTM) based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using the Nash-Sutcliffe Efficiency (NSE). The final product, called the Sequentially Trained Real-time EstimAted Model (STREAM), predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh (HRRR) model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate the OEM’s emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

Ryan Hammock↗

NASA NTRS2018

NASA's Solar System Exploration Research Virtual Institute: Building Collaboration Through International Partnerships

The NASA Solar System Exploration Research Virtual Institute (SSERVI) is a virtual institute focused on research at the intersection of science and exploration, training the next generation of lunar scientists, and community development. As part of the SSERVI mission, we act as a hub for opportunities that engage the larger scientific and exploration communities in order to form new interdisciplinary, research-focused collaborations. This talk will describe the international partnership program, research efforts and how we are engaging the international science and exploration communities through workshops, conferences, online seminars and classes, student exchange programs and internships.

SSERVI International Partnerships↗

NASA NTRS2017

NASA's Solar System Exploration Research Virtual Institute: Building Collaboration Through International Partnerships

The NASA Solar System Exploration Research Virtual Institute (SSERVI) is a virtual institute focused on re-search at the intersection of science and exploration, training the next generation of lunar scientists, and community development. As part of the SSERVI mission, we act as a hub for opportunities that engage the larger scientific and exploration communities in order to form new interdisciplinary, research-focused collaborations. This talk will describe the international partner re-search efforts and how we are engaging the international science and exploration communities through workshops, conferences, online seminars and classes, student exchange programs and internships.

Virtual Institute↗

NASA NTRSDate not supplied

Biomass Harmonization and SAR Analysis with the Multi-mission Algorithm and Analysis Platform (MAAP)

The Multi‐mission Algorithm and Analysis Platform (MAAP) is a collaborative effort between NASA and the European Space Agency (ESA) to support above ground biomass (AGB) research in an open science framework. MAAP brings together relevant data, algorithms, and computing capabilities in a common cloud environment to address the challenges of sharing and processing data from field, airborne and satellite measurements. MAAP was publicly released in October 2021, providing computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of data. MAAP has allowed scientists from both North America and Europe to collaborate on the generation and analysis/visualization of data derived from multiple, discipline-adjacent missions in an open, collaborative environment that has reached beyond traditional scientific investigation. MAAP has been used to support multiple scientific activities. To date, existing LiDAR data from multiple platforms has been calibrated with field measurements and combined for more comprehensive and accurate estimates of above ground biomass AGB; these LiDAR platforms include airborne (e.g. LVIS), the International Space Station (NASA’s Global Ecosystem Dynamics Investigation (GEDI), and satellites (e.g. ICESat-2). The current challenge is to effectively and seamlessly combine the aforementioned LiDAR-based data with new data sources such as P-band RADAR from ESA’s upcoming BIOMASS mission, existing ESA Sentinel-1 C-band SAR, and the 30 PB/yr of high cadence global coverage L-band SAR data from the upcoming NASA-ISRO SAR (NISAR) mission. Recent analysis using MAAP merged ICESat-2 and optical data (Harmonized Landsat Sentinel) produced the most comprehensively precise estimate of boreal-wide AGB to date. Another effort using MAAP is the production and open distribution of global comparisons of AGB map estimates, including from ICESat-2 and GEDI, to bolster stakeholder uptake for policy applications. These map estimates will feed into the Intergovernmental Panel on Climate Change (IPCC) database, likely aiding the next Global Carbon Stocktake of the UNFCCC. Furthermore, the biomass retrieval intercomparison exercise BRIX-2 could benefit from the MAAP providing standardized test cases (based on airborne campaign and spaceborne data) allowing the community to develop and apply retrieval algorithms based on these test cases, while forthcoming SAR data training curricula could also use the MAAP as a teaching and learning platform. The MAAP is meeting the challenges inherent in international, open science collaboration and large scale computing with a platform that is entirely open source and cloud native, using open standards for data access, manipulation, protocols, and formats. The MAAP data system consists of a dedicated data store whose data is indexed in an online catalog conforming to established metadata, application programmatic interfaces (APIs), and service interface standards, using an implementation of the open sourced NASA Common Metadata Repository. Federation of user identities allows users from either NASA or ESA to access and consume services from the other using a unified metadata catalog for the data utilized across the ESA and NASA MAAP platforms. Similarly, we are exploring how to increase interoperability to achieve a common approach to packaging, orchestrating and executing algorithms, with interoperable access to data for subsetting, fast browse, and cloud-optimized access, all using interoperable standards such as those from the Open Geospatial Consortium (OGC). Designed for interoperability, ESA and NASA utilize a common architecture for the software platform. It provides a cloud-based algorithm development environment (ADE) that enables scientists to develop algorithms collaboratively with access to the MAAP data catalog as well as other data archives. MAAP provides an Eclipse Che-based ADE supporting both Python and R languages, popular in this biomass community. Algorithms developed and containerized within the ADE can be deployed to run to thousands of computational nodes in the MAAP’s data processing system (DPS), dramatically speeding up processing and giving scientists a rapid, iterative turnaround of results. NASA’s implementation of the DPS is based on the Hybrid Science Data System (HySDS) framework, used by NASA flight projects to produce Earth science standard products.

cloud computing↗

NASA NTRS2015

NASA's Solar System Exploration Research Virtual Institute: Science and Technology for Lunar Exploration

The NASA Solar System Exploration Research Virtual Institute (SSERVI) is a virtual institute focused on research at the intersection of science and exploration, training the next generation of lunar scientists, and development and support of the international community. As part of its mission, SSERVI acts as a hub for opportunities that engage the larger scientific and exploration communities in order to form new interdisciplinary, research-focused collaborations. The nine domestic SSERVI teams that comprise the U.S. complement of the Institute engage with the international science and exploration communities through workshops, conferences, online seminars and classes, student exchange programs and internships. SSERVI represents a close collaboration between science, technology and exploration enabling a deeper, integrated understanding of the Moon and other airless bodies as human exploration moves beyond low Earth orbit. SSERVI centers on the scientific aspects of exploration as they pertain to the Moon, Near Earth Asteroids (NEAs) and the moons of Mars, with additional aspects of related technology development, including a major focus on human exploration-enabling efforts such as resolving Strategic Knowledge Gaps (SKGs). The Institute focuses on interdisciplinary, exploration-related science focused on airless bodies targeted as potential human destinations. Areas of study represent the broad spectrum of lunar, NEA, and Martian moon sciences encompassing investigations of the surface, interior, exosphere, and near-space environments as well as science uniquely enabled from these bodies. This research profile integrates investigations of plasma physics, geology/geochemistry, technology integration, solar system origins/evolution, regolith geotechnical properties, analogues, volatiles, ISRU and exploration potential of the target bodies. New opportunities for both domestic and international partnerships are continually generated through these research and community development efforts, and SSERVI can further serve as a model for joint international scientific efforts through its creation of bridges across disciplines and between countries. Since the inception of the NASA Lunar Science Institute (SSERVIs predecessor), it has and will continue to contribute in many ways toward the advancement of lunar science and the eventual human exploration of the Moon.

virtual↗

NASA NTRS2018

NASA SSERVI: Merging Science and Human Exploration

The NASA Solar System Exploration Research Virtual Institute (SSERVI) is a virtual institute focused on research at the intersection of science and exploration, training the next generation of lunar scientists, and community development. As part of the SSERVI mission, we act as a hub for opportunities that engage the larger scientific and exploration communities in order to form new interdisciplinary, research-focused collaborations. This talk will describe the research efforts of the thirteen domestic teams that constitute the U.S. complement of the Institute and how we will engage the community through workshops, conferences, online seminars and classes, student exchange programs and internships.

SSERVI↗