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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

NASA Next-Generation Sensing Technologies for Earth & Planetary Science

Dr. Ved Chirayath's seminar will highlight new sensing technologies and airborne platforms he is developing for Earth & Space Science as director of the NASA Silicon Valley Laboratory for Advanced Sensing. His talk will feature recent work on Fluid Lensing, the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions, MiDAR, a next-generation active hyperspectral remote sensing and optical communications instrument, airborne gas sensing of multipollutant combustion sources, and a plasma actuated unmanned aerial vehicle (UAV) that utilized high-voltage dielectric discharge devices to achieve the first plasma controlled flight in history.Fluid Lensing and the NASA FluidCam instrument have been used extensively to provide distortion-free 3D multispectral imagery from UAVs of shallow marine systems around the world. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) as a new method to remotely sense living and nonliving structures in extreme environments as an analog for future Ocean Worlds robotic exploration missions. Finally, Chirayath will present preliminary results from NeMO-Net, a supercomputer-based neural network that uses a citizen science video game for global multimodal coral reef benthic habitat mapping, fusing remote sensing data from Fluid Lensing, MiDAR, NASA's Earth Observing System, and commercial satellites, to better understand the present and past dynamics of shallow marine systems. Together, these maturing technologies present promising new ways in which to explore terrestrial, marine, and aerial systems on Earth, and, ultimately, aid in the search for extraterrestrial life within our solar system and beyond.

Chirayath, Ved↗
NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over 60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗
NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗
NASA NTRSDate not supplied

Unreal Engine Testbed for Computer Vision of Tall Lunar Tower Assembly

The Tall Lunar Tower project at the NASA Langley Research Center is focused on the design, modeling, fabrication, and testing of a supervised autonomously assembly engineering development unit for tall lunar towers. The lunar south pole environment poses many challenges for robotic assembly of the tall tower, particularly to computer vision camera systems due to a high-contrast lighting environment. This paper will present an Unreal Engine 5 video game engine Lunar South Pole Lighting Testbed to simulate realistic lunar lighting conditions for synthetic image generation. The fidelity of the simulation environment is investigated by comparing the accuracy of computer vision models trained using synthetic image data and trained from real image data collected in a lunar analog environment.

Unreal Engine↗
NASA NTRSDate not supplied

Unreal Engine Testbed for Computer Vision of Tall Lunar Tower Assembly

The Tall Lunar Tower project at the NASA Langley Research Center is focused on the design, modeling, fabrication, and testing of a supervised autonomously assembled engineering development unit for tall lunar towers. The lunar south pole environment poses many challenges for robotic assembly of the tall tower, particularly to computer vision camera systems due to a high-contrast lighting environment. This paper will present an Unreal Engine 5 video game engine Lunar South Pole Lighting Testbed to simulate realistic lunar lighting conditions for synthetic image generation. The fidelity of the simulation environment is investigated by comparing the accuracy of computer vision models trained using synthetic image data and trained from real image data collected in a lunar analog environment.

Unreal Engine↗
NASA NTRS2023

Unreal Engine Testbed for Computer Vision of Tall Lunar Tower Assembly

The Tall Lunar Tower project at the NASA Langley Research Center is focused on the design, modeling, fabrication, and testing of a supervised autonomously assembly engineering development unit for tall lunar towers. The lunar south pole environment poses many challenges for robotic assembly of the tall tower, particularly to computer vision camera systems due to a high-contrast lighting environment. This paper will present an Unreal Engine 5 video game engine Lunar South Pole Lighting Testbed to simulate realistic lunar lighting conditions for synthetic image generation. The fidelity of the simulation environment is investigated by comparing the accuracy of computer vision models trained using synthetic image data and trained from real image data collected in a lunar analog environment.

Unreal Engine↗
NASA NTRS2012

Exploring Earth and the Solar System: Educational Outreach Through NASA's Space Place, SciJinks, and Climate Kids Websites

NASA's Space Place team publishes engaging content and creates an effective environment to inspire a young audience to dare mighty things. NASA uses the Space Place, Climate Kids, and SciJinks websites to cultivate interest among elementary-school-aged children in both science and technology. During my summer internship at Jet Propulsion Laboratory I used Adobe Flash and ActionScript 3 to develop content for the Space Place, Climate Kids, and SciJinks sites. In addition, I was involved in the development process for ongoing and new projects during my internship. My involvement allowed me to follow a project from concept to design, implementation, and release. I personally worked on three projects this summer, two of which are currently in deployment. The first is a scrambled letter-tile guessing game titled Solar System Scramble. The second, Butterfrog Mix-Up, is a rotating-tile puzzle game. The third project is a unfinished prototype for a maze game.

video games↗
NASA NTRS1995

Game-like tasks for comparative research: leveling the playing field

Game-like computer tasks offer many benefits for psychological research. In this paper, the usefulness of such tasks to bridge population differences (e.g., age, intelligence, species) is discussed and illustrated. A task called ALVIN was used to assess humans' and monkeys' working memory for sequences of colors with or without tones. Humans repeated longer lists than did the monkeys, and only humans benefited when the visual stimuli were accompanied by auditory cues. However, the monkeys did recall sequences at levels comparable to those reported elsewhere for children. Comparison of similarities and differences between the species is possible because the two groups were tested with exactly the same game-like paradigm.

NASA Discipline Number 06-10↗
NASA NTRS2000

PTS performance by flight- and control-group macaques

A total of 25 young monkeys (Macaca mulatta) were trained with the Psychomotor Test System, a package of software tasks and computer hardware developed for spaceflight research with nonhuman primates. Two flight monkeys and two control monkeys were selected from this pool and performed a psychomotor task before and after the Bion 11 flight or a ground-control period. Monkeys from both groups showed significant disruption in performance after the 14-day flight or simulation (plus one anesthetized day of biopsies and other tests), and this disruption appeared to be magnified for the flight animal.

Flight Experiment↗
NASA NTRS1996

Video-task acquisition in rhesus monkeys (Macaca mulatta) and chimpanzees (Pan troglodytes): a comparative analysis

This study describes video-task acquisition in two nonhuman primate species. The subjects were seven rhesus monkeys (Macaca mulatta) and seven chimpanzees (Pan troglodytes). All subjects were trained to manipulate a joystick which controlled a cursor displayed on a computer monitor. Two criterion levels were used: one based on conceptual knowledge of the task and one based on motor performance. Chimpanzees and rhesus monkeys attained criterion in a comparable number of trials using a conceptually based criterion. However, using a criterion based on motor performance, chimpanzees reached criterion significantly faster than rhesus monkeys. Analysis of error patterns and latency indicated that the rhesus monkeys had a larger asymmetry in response bias and were significantly slower in responding than the chimpanzees. The results are discussed in terms of the relation between object manipulation skills and video-task acquisition.

NASA Discipline Space Human Factors↗
NASA NTRS2014

Software Accelerates Computing Time for Complex Math

Ames Research Center awarded Newark, Delaware-based EM Photonics Inc. SBIR funding to utilize graphic processing unit (GPU) technology- traditionally used for computer video games-to develop high-computing software called CULA. The software gives users the ability to run complex algorithms on personal computers with greater speed. As a result of the NASA collaboration, the number of employees at the company has increased 10 percent.

Source record↗
NASA NTRS2012

Space Operations Learning Center

The Space Operations Learning Center (SOLC) is a tool that provides an online learning environment where students can learn science, technology, engineering, and mathematics (STEM) through a series of training modules. SOLC is also an effective media for NASA to showcase its contributions to the general public. SOLC is a Web-based environment with a learning platform for students to understand STEM through interactive modules in various engineering topics. SOLC is unique in its approach to develop learning materials to teach schoolaged students the basic concepts of space operations. SOLC utilizes the latest Web and software technologies to present this educational content in a fun and engaging way for all grade levels. SOLC uses animations, streaming video, cartoon characters, audio narration, interactive games and more to deliver educational concepts. The Web portal organizes all of these training modules in an easily accessible way for visitors worldwide. SOLC provides multiple training modules on various topics. At the time of this reporting, seven modules have been developed: Space Communication, Flight Dynamics, Information Processing, Mission Operations, Kids Zone 1, Kids Zone 2, and Save The Forest. For the first four modules, each contains three components: Flight Training, Flight License, and Fly It! Kids Zone 1 and 2 include a number of educational videos and games designed specifically for grades K-6. Save The Forest is a space operations mission with four simulations and activities to complete, optimized for new touch screen technology. The Kids Zone 1 module has recently been ported to Facebook to attract wider audience.

Lui, Ben↗
NASA NTRS2001

Innovative Solution to Video Enhancement

Through a licensing agreement, Intergraph Government Solutions adapted a technology originally developed at NASA's Marshall Space Flight Center for enhanced video imaging by developing its Video Analyst(TM) System. Marshall's scientists developed the Video Image Stabilization and Registration (VISAR) technology to help FBI agents analyze video footage of the deadly 1996 Olympic Summer Games bombing in Atlanta, Georgia. VISAR technology enhanced nighttime videotapes made with hand-held camcorders, revealing important details about the explosion. Intergraph's Video Analyst System is a simple, effective, and affordable tool for video enhancement and analysis. The benefits associated with the Video Analyst System include support of full-resolution digital video, frame-by-frame analysis, and the ability to store analog video in digital format. Up to 12 hours of digital video can be stored and maintained for reliable footage analysis. The system also includes state-of-the-art features such as stabilization, image enhancement, and convolution to help improve the visibility of subjects in the video without altering underlying footage. Adaptable to many uses, Intergraph#s Video Analyst System meets the stringent demands of the law enforcement industry in the areas of surveillance, crime scene footage, sting operations, and dash-mounted video cameras.

Source record↗
NASA NTRS1998

Technology Development, Implementation, and Assessment: K-16 Pre-Service, In-Service, and Distance Learning Initiatives

This summer 22 kindergarten through 8th grade teachers attended a 3-week Teacher Enhancement Institute (TEI) at NASA Langley Research Center. TEI is funded by NASA Education Division and is a collaborative effort between NASA Langley's Office of Education and Christopher Newport University. Selected teacher teams were drawn from Langley's 5-state precollege service region, which includes Kentucky, North Carolina, South Carolina, Virginia, and West Virginia. The goal of TEI was for teachers to learn aeronautics and the broad application of science and technology through a problem-based learning (PBL) strategy. PBL is an instructional method using a real world problem, also known as an ill-structured problem, as the context for an in-depth investigation. Most real life problems are ill-structured, as are all the really important social, political and scientific problems. The teachers were immediately immersed in an ill-structured problem to design a communication strategy for the White House Commission on Aviation Safety and Security to educate and disseminate aviation information to the general public. Specifically, the communication strategy was to focus on aeronautics principles, technology and design associated with US general aviation revitalization and aviation safety programs. The presented problem addressed NASA's strategic outcome to widely communicate the content, relevancy and excitement of its missions and discoveries to the general population. Further, the PBL scenario addressed the technological challenges being taken up by NASA to revolutionize air travel and the way in which aircraft are designed, built, and operated. It also addressed getting people and freight safely and efficiently to any location in the world at a reasonable cost. With a "real" need-to-know problem facing them, the teachers set out to gather information and to better understand the problem using inquiry-based and scientific methods. The learning in this aeronautics scenario was driven by the direction taken by participants. With the support of the TEI faculty, the teachers quickly identified NASA Langley researchers that served as consultants to help solve the problem. To achieve their goal, the teacher teams developed lesson plans for elementary and middle school students, wrote a newspaper, published a brochure to educate the general public, constructed games for children of all ages, and produced a video. As a second problem, the TEI participants will design their own aeronautic lesson plan and immerse their 1997-98 school year students in the problem. The problem is for the students "to create a traveling hands-on, minds-on aeronautics museum exhibit created for children by children." As a culminating activity, the Virginia Air and Space Center in Hampton, VA, will set up a special display of the exhibits in the Summer 1998. The TEI faculty will visit each TEI teacher's classroom during the academic school year to observe the implementation of the unit. In addition to the classroom observations, electronic follow-up sessions will be conducted during the school year to support the teachers' efforts in developing their PBL units to integrate technology in math and science instruction. These sessions eill be conducted using the Internet. Teachers will be connected through a chat-line to share ideas, ask questions, and generate solutions.

Petersen, Richard↗
NASA NTRS2011

Leveraging M and S in Soft Skills Training for the DoD

Soft skills, also called "people skills," are typically hard to observe, quantify and measure. These skills have to do with how we relate to each other; communicating, listening, engaging in dialogue, giving feedback, cooperating as a team member, solving problems and resolving conflicts. Most of the soft skills training is scenario based, utilizing written or video-based scenarios. with limited or no branching, as well as quantitative feedback. This paper will outline a game-based approach to configurable, scenario-based, soft skills training. The paper will discuss the application of realistic visual behavior cues (e.g. body language, vocal inflection, facial expressions) and how these can benefit the learner. Using the concept of a "virtual vignette" this paper will discuss a prototype system intended to leach suicide prevention and provide qualitative feedback to the learner. The paper will also explore other soft skills training applications for this technology

Cimino, James D.↗
NASA NTRS2016

Hybrid Reality Lab Capabilities - Video 2

Our Hybrid Reality and Advanced Operations Lab is developing incredibly realistic and immersive systems that could be used to provide training, support engineering analysis, and augment data collection for various human performance metrics at NASA. To get a better understanding of what Hybrid Reality is, let's go through the two most commonly known types of immersive realities: Virtual Reality, and Augmented Reality. Virtual Reality creates immersive scenes that are completely made up of digital information. This technology has been used to train astronauts at NASA, used during teleoperation of remote assets (arms, rovers, robots, etc.) and other activities. One challenge with Virtual Reality is that if you are using it for real time-applications (like landing an airplane) then the information used to create the virtual scenes can be old (i.e. visualized long after physical objects moved in the scene) and not accurate enough to land the airplane safely. This is where Augmented Reality comes in. Augmented Reality takes real-time environment information (from a camera, or see through window, and places digitally created information into the scene so that it matches with the video/glass information). Augmented Reality enhances real environment information collected with a live sensor or viewport (e.g. camera, window, etc.) with the information-rich visualization provided by Virtual Reality. Hybrid Reality takes Augmented Reality even further, by creating a higher level of immersion where interactivity can take place. Hybrid Reality takes Virtual Reality objects and a trackable, physical representation of those objects, places them in the same coordinate system, and allows people to interact with both objects' representations (virtual and physical) simultaneously. After a short period of adjustment, the individuals begin to interact with all the objects in the scene as if they were real-life objects. The ability to physically touch and interact with digitally created objects that have the same shape, size, location to their physical object counterpart in virtual reality environment can be a game changer when it comes to training, planning, engineering analysis, science, entertainment, etc. Our Project is developing such capabilities for various types of environments. The video outlined with this abstract is a representation of an ISS Hybrid Reality experience. In the video you can see various Hybrid Reality elements that provide immersion beyond just standard Virtual Reality or Augmented Reality.

Delgado, Francisco J.↗
NASA NTRSDate not supplied

A Framework for Sense and Follow Convoys for Collective Autonomous Mobility

Convoys are collections of vehicles that share similar velocity profiles. In the sense-and follow convoy construct, a vehicle can follow a member of an existing convoy resulting in convoy chaining. We prove conditions under which this can occur such that the resulting convoy chain is bounded and the velocities of all members converge to the leader velocity through locally distributed control algorithms. To assess the approach, we utilize a six degree-of-freedom rigid body vehicle dynamics model for each agent. A dynamic inversion based control law is used to track the desired trajectory generated by the convoy algorithms. To simulate the system, we utilize Unity, a cross-platform game engine, where 3-dimensional terrain, building, and vehicle data can be visualized. Numerous simulations are presented to illustrate the concept. Link to video file below:

convoys↗