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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Wireless Augmented Reality Communication System

The system of the present invention is a highly integrated radio communication system with a multimedia co-processor which allows true two-way multimedia (video, audio, data) access as well as real-time biomedical monitoring in a pager-sized portable access unit. The system is integrated in a network structure including one or more general purpose nodes for providing a wireless-to-wired interface. The network architecture allows video, audio and data (including biomedical data) streams to be connected directly to external users and devices. The portable access units may also be mated to various non-personal devices such as cameras or environmental sensors for providing a method for setting up wireless sensor nets from which reported data may be accessed through the portable access unit. The reported data may alternatively be automatically logged at a remote computer for access and viewing through a portable access unit, including the user's own.

Devereaux, Ann↗
NASA NTRS2017

Autonomous Diagnostic Imaging Performed by Untrained Operators using Augmented Reality as a Form of "Just-in-Time" Training

We will address the Human Factors and Performance Team, "Risk of performance errors due to training deficiencies" by improving the JIT training materials for ultrasound and OCT imaging by providing advanced guidance in a detailed, timely, and user-friendly manner. Specifically, we will (1) develop an audio-visual tutorial using AR that guides non-experts through an abdominal trauma ultrasound protocol; (2) develop an audio-visual tutorial using AR to guide an untrained operator through the acquisition of OCT images; (3) evaluate the quality of abdominal ultrasound and OCT images acquired by untrained operators using AR guidance compared to images acquired using traditional JIT techniques (laptop-based training conducted before image acquisition); and (4) compare the time required to complete imaging studies using AR tutorials with images acquired using current JIT practices to identify areas for time efficiency improvements. Two groups of subjects will be recruited to participate in this study. Operator-subjects, without previous experience in ultrasound or OCT, will be asked to perform both procedures using either the JIT training with AR technology or the traditional JIT training via laptop. Images acquired by inexperienced operator-subjects will be scored by experts in that imaging modality for diagnostic and research quality; experts will be blinded to the form of JIT used to acquire the images. Operator-subjects also will be asked to submit feedback to improve the training modules used during the scans to improve future training modules. Scanned-subjects will be a small group individuals from whom all images will be acquired.

Martin, D. S.↗
NASA NTRS2017

Autonomous Diagnostic Imaging Performed by Untrained Operator Using Augmented Reality as a Form of "Just-in-Time" Training

We will address the Human Factors and Performance Team, "Risk of performance errors due to training deficiencies" by improving the JIT training materials for ultrasound and OCT imaging by providing advanced guidance in a detailed, timely, and user-friendly manner. Specifically, we will (1) develop an audio-visual tutorial using AR that guides non-experts through an abdominal trauma ultrasound protocol; (2) develop an audio-visual tutorial using AR to guide an untrained operator through the acquisition of OCT images; (3) evaluate the quality of abdominal ultrasound and OCT images acquired by untrained operators using AR guidance compared to images acquired using traditional JIT techniques (laptop-based training conducted before image acquisition); and (4) compare the time required to complete imaging studies using AR tutorials with images acquired using current JIT practices to identify areas for time efficiency improvements. Two groups of subjects will be recruited to participate in this study. Operator-subjects, without previous experience in ultrasound or OCT, will be asked to perform both procedures using either the JIT training with AR technology or the traditional JIT training via laptop. Images acquired by inexperienced operator-subjects will be scored by experts in that imaging modality for diagnostic and research quality; experts will be blinded to the form of JIT used to acquire the images. Operator-subjects also will be asked to submit feedback to improve the training modules used during the scans to improve future training modules. Scanned-subjects will be a small group individuals from whom all images will be acquired.

Martin, David S.↗
NASA NTRS2017

Engineering a Live UHD Program from the International Space Station

The first-ever live downlink of Ultra-High Definition (UHD) video from the International Space Station (ISS) was the highlight of a “Super Session” at the National Association of Broadcasters (NAB) Show in April 2017. Ultra-High Definition is four times the resolution of “full HD” or “1080P” video. Also referred to as “4K”, the Ultra-High Definition video downlink from the ISS all the way to the Las Vegas Convention Center required considerable planning, pushed the limits of conventional video distribution from a space-craft, and was the first use of High Efficiency Video Coding (HEVC) from a space-craft. The live event at NAB will serve as a pathfinder for more routine downlinks of UHD as well as use of HEVC for conventional HD downlinks to save bandwidth. A similar demonstration was conducted in 2006 with the Discovery Channel to demonstrate the ability to stream HDTV from the ISS. This paper will describe the overall work flow and routing of the UHD video, how audio was synchronized even though the video and audio were received many seconds apart from each other, and how the demonstration paves the way for not only more efficient video distribution from the ISS, but also serves as a pathfinder for more complex video distribution from deep space. The paper will also describe how a “live” event was staged when the UHD video coming from the ISS had a latency of 10+ seconds. In addition, the paper will touch on the unique collaboration between the inherently governmental aspects of the ISS, commercial partners Amazon and Elemental, and the National Association of Broadcasters.

Grubbs, Rodney↗
NASA NTRS2017

Live Ultra-High Definition from the International Space Station

The first ever live downlink of Ultra-High Definition (UHD) video from the International Space Station (ISS) was the highlight of a 'Super Session' at the National Association of Broadcasters (NAB) in April 2017. The Ultra-High Definition video downlink from the ISS all the way to the Las Vegas Convention Center required considerable planning, pushed the limits of conventional video distribution from a space-craft, and was the first use of High Efficiency Video Coding (HEVC) from a space-craft. The live event at NAB will serve as a pathfinder for more routine downlinks of UHD as well as use of HEVC for conventional HD downlinks to save bandwidth. HEVC may also enable live Virtual Reality video downlinks from the ISS. This paper will describe the overall work flow and routing of the UHD video, how audio was synchronized even though the video and audio were received many seconds apart from each other, and how the demonstration paves the way for not only more efficient video distribution from the ISS, but also serves as a pathfinder for more complex video distribution from deep space. The paper will also describe how a 'live' event was staged when the UHD coming from the ISS had a latency of 10+ seconds. Finally, the paper will discuss how NASA is leveraging commercial technologies for use on-orbit vs. creating technology as was required during the Apollo Moon Program and early space age.

Grubbs, Rodney↗
NASA NTRSDate not supplied

The Sun and Space Weather

Audio and video provided as separate files. • CHART 3 audio – “SrFeb022013_1954ut28.21_Ashcraft.mp3” • CHART 9 Sun video – “IA18167_304Whip_best.mpg” • CHART 15 sun animation – “Sun_Animation_Mar23A.mp4” • CHART 33 SDO plus SOHO C2 video – “X2_C2_combo.mov”

Sun↗
NASA NTRSDate not supplied

Bone Conduction Headphone Research and Human-in-the-Loop Test Results

The new space suit being developed for exploration EVAs on the surface of the Moon and in microgravity environments is called xEMU. A government reference model had been developed and had undergone testing. This suit contains new upgrades from the current EMU on ISS, such as integrated speakers and microphones, eliminating the need for astronauts to wear a Communications Carrier Assembly (CCA) on their head during spacewalks. However, upon testing, it was noticed that this feature causes acoustic coupling, that results in communications echo and feedback. A proposed solution to this issue is to have the astronaut wear a bone conduction headset for audio capabilities while on EVA to receive incoming voice communications from Mission Control and other EVA or IVA crew members. This would eliminate the acoustic coupling and echo effect while being more ergonomically sound than the current CCA and leave the ear open to suit ambient sound for situational awareness. A human-in-the-loop evaluation was performed, comparing five commercially available bone conduction headsets, to evaluate comfort, fit, and adjustability for long-duration wear. Five engineering test subjects with different head sizes were utilized to wear the headsets for six hours and provide succinct feedback and score the headsets on a variety of factors, to help determine which of the headsets performed the best and could advance to future bone conduction audio testing. Three of the headsets were well-received among the diverse group of subjects, and at least one of these will advance to further testing to be considered for future use under the xEMU helmet for exploration EVAs. Rating results and evaluation methods for this bone conduction headset evaluation will be presented.

Bridget M Cavanaugh↗
NASA NTRSDate not supplied

Enhancing Air Traffic Control Planning with Automatic Speech Recognition

The decisions made during the Federal Aviation Administration Air Traffic Control System Command Center's planning teleconferences hold significant sway over the National Airspace System. Held every two hours, these teleconferences convene air traffic managers and stakeholders from across the nation to discuss airspace conditions, weather, and constraints, leading to the formulation and adjustment of traffic management initiatives. Given the critical nature of these decisions, the need for accurate and efficient record-keeping is paramount. In recent years, the application of automatic speech recognition has gained popularity across diverse industries, including aviation. While traditional applications focus on transcribing air traffic control communication, this paper explores a unique application of automatic speech recognition by converting the audio from planning teleconferences into text transcriptions. This innovative approach addresses key challenges in the field, presenting potential benefits for quality assurance, real-time participation, and downstream natural language processing tasks. A notable breakthrough in the machine learning community, namely the transformer neural network architecture, forms the backbone of the proposed solution in this paper. The transformer architecture's role in this research represents a paradigm shift in the efficiency of automatic speech recognition models. By reducing the amount of in-domain training data required, this architecture allows for the fine-tuning of such models like Whisper, originally pretrained on vast English speech datasets. The adaptability of the transformer architecture proves invaluable in capturing the nuances of aviation terminology and specific language used in planning teleconferences. Leveraging the Whisper model as a baseline, our research details the fine-tuning and validation using a dataset comprising 20 hours of meticulously transcribed planning teleconferences. Notably, the baseline pretrained Whisper model exhibited a word error rate of 18.77%. Through the fine-tuning process, the model achieved a substantial improvement, demonstrating an impressive performance with a reduced word error rate of 6.82%. This substantial decrease in WER not only highlights the effectiveness of the transformer architecture but also emphasizes the practical advancements achieved through the application of automatic speech recognition in this specific domain. The utilization of automatic speech recognition in planning teleconferences in this work introduces several novelties. Firstly, the creation of text transcriptions offers a valuable tool for quality assurance and facilitates the efficient review of teleconferences. This is an important aspect of the proposed solution, given the time-sensitive and high-stakes nature of decisions made during these meetings. Furthermore, text-searchable transcriptions provide a streamlined approach for locating and validating critical information, potentially saving hours of manual effort in searching through audio recordings. Moreover, our research identifies a key use case for external facilities and stakeholders. In situations where attendance at the planning teleconference is not feasible, having access to text transcriptions in real-time or shortly after the teleconference ends, proves to be a time-saving and informative resource. This feature enhances collaboration and ensures that stakeholders can stay abreast of important discussions and decisions even in their absence. Despite the efficiency gains facilitated by the transformer architecture in automatic speech recognition technology, it is essential to acknowledge the human factors in data creation. Subject matter experts play a crucial role in accurately transcribing planning teleconferences due to the specificity and complexity of the information discussed. The research dataset, consisting of 20 hours of transcribed planning teleconferences, forms the foundation for fine-tuning and validating the Whisper model. The achieved word error rate of 6.82% demonstrates promising advancements, particularly in recognizing essential aviation terminology within the teleconferences. In conclusion, this paper presents a comprehensive exploration of the application of automatic speech recognition in Air Traffic Control System Command Center planning teleconferences, leveraging the transformer architecture for enhanced efficiency. The novel contributions lie in the improved accessibility of decision-making records, real-time participation opportunities for external stakeholders, and the potential for downstream natural language processing advancements. As the aviation industry continues to evolve, the integration of automatic speech recognition technologies holds the promise of revolutionizing decision-making processes and contributing to the overall safety and efficiency of air traffic management.

ATM↗
NASA NTRS2024

Bone Conduction Headphone Research and Testing for xEMU Communications Applications

The new space suit being developed for exploration EVAs on the surface of the Moon and in microgravity environments is referred to as xEMU. A government reference model had been developed and has undergone extensive functional and environmental testing. This suit contains new upgrades from the current EMU on ISS, such as integrated speakers and microphones, eliminating the need for astronauts to wear a Communications Carrier Assembly (CCA) on their heads during spacewalks. However, this design approach results in speaker-to-microphone acoustic coupling and communications echo and feedback. A proposed solution to this issue is to replace the integrated open speakers with an astronaut worn bone conduction headset for audio capabilities while on EVA to receive incoming voice communications from Mission Control and other EVA or IVA crew members. This would eliminate the acoustic coupling and echo effect while being more ergonomically sound than the current CCA and leave the ear open to suit-ambient sound for situational awareness. A human-in-the-loop evaluation was performed, comparing five commercially available bone conduction headsets, to evaluate comfort, fit, and adjustability for long-duration wear. Five engineering test subjects with different head sizes were utilized to wear the headsets for six hours and provide succinct feedback and score the headsets on a variety of factors, to help determine which of the headsets performed the best and could advance to future bone conduction audio testing. Three of the headsets were well-received among the diverse group of subjects, and at least one of these will advance to further testing to be considered for future use under the xEMU helmet for exploration EVAs. Rating results and evaluation methods for this bone conduction headset evaluation will be presented.

Bridget Cavanaugh↗
NASA NTRSDate not supplied

Risk Characterization Research for Artemis II: Human Factors and Behavioral Performance

BACKGROUND Artemis II will be the first time NASA astronauts go beyond low-Earth orbit (LEO) since the Apollo era, and the first astronauts heading into space in the Orion vehicle. As such, it provides a critical opportunity to refine our understanding of the likelihood and consequences associated with the Behavioral Medicine (BMed), Team, Human System Integration Architecture (HSIA), and Sleep Risks, and prepare for future Moon and Mars missions. However, Artemis II research efforts are uniquely shaped by in-mission data collection constraints. There is currently no in-mission crew time available to complete measures. In-mission data will need to be collected unobtrusively from available data streams (e.g., audiovisual, existing records such as schedules, and actigraphy). Accordingly, the overarching goal of our research is to utilize Artemis II data to further define the likelihood and consequences of these risks, and to create an unobtrusive research infrastructure that can be expanded to include future Artemis missions. This goal spans four aims across three research phases: (1) identify and operationally define key performances metrics and constructs across the four aforementioned risks, (2) develop an unobtrusive methodology and coding scheme for in-mission data collection, (3) characterize performance decrements due to Bmed, Team, HSIA, and Sleep Risks, and (4) develop a data infrastructure for future Artemis missions. The following details results of Phase I efforts in which we address Aims 1 and 2 to develop an unobtrusive measurement plan and coding scheme to capture key constructs, contributing factors, and performance decrements across each risk area. METHOD As part of Phase I, we conducted an interdisciplinary literature review and consulted with SMEs to identify unobtrusive methodologies that leverage text, audio, and/or video data as well as conceptualize key performance metrics, contributing factors, and BMed, Team, HSIA, and Sleep risk constructs related to performance decrements. The Phase I effort resulted in a finalized pre- and post-mission protocol for Artemis II, along with a measurement and coding scheme for in-mission Artemis II data. Phase II will involve data collection from the upcoming Artemis II mission. Phase III will include data processing, coding, depiction, analysis, and report writing of the Artemis II data. RESULTS & DISCUSSION To date, we have completed Phase I efforts. Specifically, we identified BMed, Team, HSIA, and Sleep risk constructs related to performance metrics, summarized how these constructs can be measured using audiovisual data collected during the mission, and worked with NASA’s HFBP Element to finalize a data collection protocol that leverages audiovisual input from the Orion spacecraft system. Our protocol includes novel unobtrusive methodologies that adhere to in-mission data streams and subsequent constraints (e.g., limited storage space on GoPro cameras, ambient noise impeding audio files) to best capture in-mission phenomena across each risk area. We will present our results from Phase I efforts, namely best practices for unobtrusive measurement as identified through literature reviews and SME consultation as well as codebook excerpts for use in Artemis II. We will include a description of planned work as we prepare for Phase II and Phase III of this research plan and the Artemis II mission itself. SUMMARY We describe progress on our Human Factors and Behavioral Performance Research for Artemis II study.

behavioral health↗
DOE OSTI2021

Deepfaked online content is highly effective in manipulating people’s attitudes and intentions

In recent times, disinformation has spread rapidly through social media and news sites, biasing our (moral) judgements of other people and groups. “Deepfakes”, a new type of AI-generated media, represent a powerful new tool for spreading disinformation online. Although Deepfaked images, videos, and audio may appear genuine, they are actually hyper-realistic fabrications that enable one to digitally control what another person says or does. Given the recent emergence of this technology, we set out to examine the psychological impact of Deepfaked online content on viewers. Across seven preregistered studies (N = 2558) we exposed participants to either genuine or Deepfaked content, and then measured its impact on their explicit (self-reported) and implicit (unintentional) attitudes as well as behavioral intentions. Results indicated that Deepfaked videos and audio have a strong psychological impact on the viewer, and are just as effective in biasing their attitudes and intentions as genuine content. Many people are unaware that Deepfaking is possible; find it difficult to detect when they are being exposed to it; and most importantly, neither awareness nor detection serves to protect people from its influence. All preregistrations, data and code available at osf.io/f6ajb.

Hughes, Sean↗
DOE OSTI2025

House Advantage or House of Cards? Stacking the Deck for Data Videos Leads to Null Results: Preprint

Videos are becoming a ubiquitous means of sharing information on social media platforms. In response, data videos - short clips combining visualization with dynamic storytelling, audio descriptions, and spatial referencing - have gained popularity for communicating data. These affordances suggest that data videos might communicate data patterns, trends, and concepts more effectively than static visualizations, enhancing comprehension. However, existing research has not systematically tested this claim. To address this gap, we conducted three controlled studies to measure comprehension differences between data videos and static visualizations. Despite leveraging visual cues and audio explanations, no data video led to significantly better comprehension than an analogous static visualization. Our results suggest data videos are not categorically better and that future research should examine the tradeoffs between their engagement benefits and costs.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

Mitigation of Attentional Tunneling in the Flight Deck Using a Spatial Auditory Display

The role of attentional bias to a specific form of information, e.g., visual versus aural instructions, was evaluated to address the issue of attentional fixation (i.e., "cognitive tunneling") in the flight deck. A method of mitigating attentional tunneling using a spatial auditory display was evaluated in the presence and absence of a biasing factor. Data were gathered for participant recognition, accuracy and response times to aural and visual instructions under different conditions of distractor stimuli. Results showed a significant main effect for target acquisition with the use of spatial audio techniques. No significant main effects or interactions were found for target acquisition timing. Target acquisition was not affected by a "gaming score" biasing factor associated with the distractor tasks.

readback errors↗
NASA NTRS2023

Computer Human Interface Challenges in Space Exploration

NASA’s plans to return humans to the Lunar surface require overcoming a variety of challenging technical and operational obstacles. In 2022, NASA formed the Extravehicular Activity (EVA) and Human Surface Mobility (HSM) Program (EHP) at the Johnson Space Center with responsibilities including development of space suits and surface mobility systems for Lunar missions. This program includes a Technology Development and Partnerships office chartered to identify high priority gaps in capabilities for Lunar surface mobility and to coordinate resources to close those gaps. This presentation details the EHP technology roadmap for “Informatics and Decision Support,” a subset of spacecraft avionics focused on effective and autonomous crew interaction with spaceflight systems. The gaps, grouped into displays, audio systems, and information technology infrastructure, are largely driven by the unique interaction requirements for human spacecraft and the severe radiation environments beyond low earth orbit. The roadmap identifies ongoing activities and paths to technology infusion into Lunar spacecraft. NASA is seeking input on the content and ideas for alternative paths to gap closure. Closing these gaps is important to successful human operations on the Lunar surface and vital to NASA’s long-term goal of human missions to Mars.

Spacecraft Displays↗
DOE OSTI2021

Systematic Design and Experimental Demonstration of Transmission-Type Multiplexed Acoustic Metaholograms

Acoustic holograms have promising applications in sound-field reconstruction, particle manipulation, ultrasonic haptics, and therapy. This study reports on the theoretical, numerical, and experimental investigation of multiplexed acoustic holograms at both audio and ultrasonic frequencies via a rationally designed transmission-type acoustic metamaterial. The proposed metahologram is composed of two Fabry–Pérot resonant channels per unit cell, which enables the simultaneous modulation of the transmitted amplitude and phase at two desired frequencies. In contrast to conventional acoustic metamaterial-based holograms, the design strategy proposed here provides a new degree of freedom (frequency) that can actively tailor holograms that are otherwise completely passive and significantly enhances the information encoded in acoustic metamaterials. To demonstrate the multiplexed acoustic metamaterial, the projection of two different high-quality metaholograms is first shown at 14 and 17 kHz, with the patterns of the letters N and S. Then, two-channel ultrasound focusing and annular beams generation for the incident ultrasonic frequencies of 35 and 42.5 kHz are demonstrated. Furthermore, these multiplexed acoustic metaholograms offer a technical advance to tackle the rising challenges in the fields of acoustic metamaterials, architectural acoustics, and medical ultrasound.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Sensor selection and tool wear prediction with data‐driven models for precision machining

Abstract Estimation of tool wear in precision machining is vital in the traditional subtractive machining industry to reduce processing cost, improve manufacturing efficiency and product quality. In this vein, fusion of time and frequency‐domain features of commonly sensed signals can provide an early indication of tool wear and improve its prediction accuracy for prognostics and health management. This paper presents a data‐driven methodology and a complete tool chain for the inference of precision machining tool wear from fused machine measurements, such as cutting force, power, audio and vibration signals, and quantify the usefulness of each measurement. Indicators of tool wear are extracted from time‐domain signal statistics, frequency‐domain analysis, and time‐frequency domain analysis. Correlation coefficients between the extracted features (indicators) and the tool wear are used to select the most informative features. Principal Component Analysis and Partial Least‐Squares are used to reduce the dimensionality of the feature space. Regression models, including linear regression, support vector regression, Decision tree regression, neural network regression and Gaussian process regression, are used to predict the tool wear using data from a Haas milling machine performing spiral boss face milling. The performance of the regression models based on subsets of sensors validates the preliminary estimates about the saliency of the sensors. The experimental results show that the proposed methods can predict the machine tool wear precisely, with readily available sensor measurements. Neural network and Gaussian process regression were able to achieve good estimates of tool wear at different machine operating conditions. The most informative signal in predicting tool wear was shown to be the vibration signal. Time‐frequency domain features were the most informative features among the combination of features of three domains. In addition, using partial least squares components extracted from the original features of signals led to higher prediction accuracy.

Han, Seulki↗