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At least 361 records · Page 20

All sources NASA NTRS DOE OSTI

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 NTRS2018

Video Distribution and Storage Unit

Disclosed is a satellite payload device, including a plurality of sensors configured to detect images of a satellite, a video distribution and storage unit configured to collect, compress, store, and transmit the images, and a control computer configured to request a portion of the plurality of sensors from which to receive sensor data that is routed to the video distribution and storage unit.

Kadari, Madhusudhan↗

NASA NTRS2019

ICAROUS Simulation Video for AUVSI XPONENTIAL 2019

Video demonstrate the ability of ICAROUS (Independent Configurable Architecture for Reliable Operations of Autonomous Systems) to perform autonomous traffic avoidance and merging with cooperative aircraft in simulation. The Video will be shown in the NASA booth at AUVSI XPONENTIAL (Chicago, IL 4/29/19-5 /2/19) to aid in demonstrating and explaining the capabilities of the ICAROUS research system.

Malekpour, Mahyar R↗

NASA NTRS2020

TCL4 Corpus Christi Video with Narration

This is a video for use in presenting an overview of NASA's UAS Traffic Management (UTM) Project's Technical Capability Level 4 (TCL4) flight test that was conducted in the summer of 2019 in the urban flight areas of Corpus Christi, TX. The video presents a graphical view of the specific flight areas that were used and touches upon the main objectives that were demonstrated as part of the event. A number of examples with visual representations of Unmanned Aircraft Vehicles based on collected data are presented in various situations that were constructed to target certain concept elements of UTM. Examples of situations presented are: High density operations, operations in close proximity, off-nominal events, and contingency management situations.

UAS Traffic Management↗

NASA NTRS2021

Integrated System for Autonomous and Adaptive Caretaking (ISAAC) Simulated Cargo Logistics Demo Video

This video shows a simulation of autonomous cargo logistics using ISAAC (Integrated System for Autonomous and Adaptive Caretaking). The video shows autonomous planning and execution of a cargo scenario in which the R2 robot moves a cargo bag from a stowed location to a transfer location in the US Lab on the International Space Station. The Astrobee robot then moves the bag from the transfer location to a new temporary stowage location in the Japanese Experiment Module (JEM).

ISAAC↗

NASA NTRS2022

A Comparative Evaluation of Snowflake Particle Shape Estimation Techniques Used By the Precipitation Imaging Package (PIP), Multi-Angle Snowflake Camera (MASC), and Two-Dimensional Video Disdrometer (2DVD)

Measurements of snowflake particle shape are important for studying snow microphysics. While a number of instruments exist that are capable of measuring particle shape, this study focuses on the measurement techniques of three digital video disdrometers: the Precipitation Imaging Package (PIP), the Multi-Angle Snowflake Camera (MASC), and the Two-Dimensional Video Disdrometer (2DVD). To gain a better understanding of the relative strengths and weaknesses of these instruments and to provide a foundation upon which comparisons can be made between studies using data from different instruments, we perform a comparative analysis of the shape measurement algorithms employed by each of the three instruments by applying the algorithms to snowflake images captured by PIP during the ICE-POP 2018 field campaign. Our analysis primarily focuses on the measurement of the aspect ratio of either the particle itself, in the case of PIP and MASC, or of the particle bounding box, in the case of PIP and 2DVD. Both PIP and MASC use shape-fitting algorithms to measure aspect ratio. While our analysis of the MASC aspect ratio suggests that the measurements are reliable, our findings indicate that both the ellipse and rectangle aspect ratios produced by PIP underperformed considerably due to the shortcomings of the PIP shape-fitting techniques. We also demonstrate that reliable measurements of aspect ratio can be retrieved from PIP by reprocessing the raw PIP images using either the MASC ellipse-fitting algorithm or a tensor-based ellipse-fitting algorithm. Because of differences in instrument design, 2DVD produces measurements of particle horizontal and vertical extent rather than length and width. Furthermore, the 2DVD measurements of particle horizontal extent can be contaminated by horizontal particle motion. Our findings indicate that, although the correction technique used to remove the horizontal motion contamination performs remarkably well with snowflakes despite being designed for use with raindrops, the 2DVD measurements of particle horizontal extent are less reliable than those measured by PIP.

Charles N Helms↗

DOE OSTI2021

Two and three-dimensional near infrared subcutaneous structure imager using realtime nonlinear video processing

A method includes placing an imaging device in contact with a sample, irradiating the sample via an illuminator such that radiation scatters off of the structure, and providing the scattered radiation to a detector to generate a digital video signal. A processor then performs various operations including subtracting a sum of a black state value and a user-defined black level clamping value from the digital video signal to create subtracted image data, substituting negative values in the subtracted image data with a reference value to create modified image data, and amplifying the modified image data by a user-defined gain to create contrast enhanced image data.

Krasowski, Michael J.↗

DOE OSTI2022

Utah FORGE GeoThermOPTIMAL Video

This is a project description video by Dr. William W. Fleckenstein related to their "Development of Multi-Stage Fracturing System and Wellbore Tractor to Enable Zonal Isolation During Stimulation and EGS Operations in Horizontal Wellbores" R&D project at Utah FORGE which is linked bellow.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Laser Disdrometer Quantities (LDQUANTS) and Video Disdrometer Quantities (VDISQUANTS) Value-Added Products Report

Disdrometers are useful instruments that measure the distribution of raindrop sizes (drop size distribution: DSD) and the associated rainfall rates/accumulation as those raindrops fall to the ground. These DSDs are a quantity of interest to members of the modeler and observational communities. However, use of disdrometer data for model evaluation, radar monitoring, or other activities requires careful quality control and processing for key DSD properties of interest (e.g., the number concentration of drops) to ensure appropriate physical (scattering, fall speed) assumptions. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Laser Disdrometer Quantities and Video Disdrometer Quantities Value-Added Product (LDQUANTS/VDISQUANTS VAP) uses standard methods from Tokay et al. (e.g., 2013, 2014) to filter drops with unrealistic fall speeds. Further, it estimates several microphysical/geophysical quantities of parameterized DSDs (gamma or exponential assumption type fitting methods) as in previous disdrometer studies and ARM long-term efforts (e.g., Testud et al. 2001, Giangrande et al. 2014, Thompson et al. 2015). Disdrometers are also beneficial for cross-checks with other instrumentation, including rain gauges and radars. To support research interests and related radar monitoring activities, this product calculates radar equivalent quantities, including dual polarization radar quantities (e.g., Z, Differential Reflectivity ZDR, etc.), using T-Matrix scattering and additional wavelength, temperature, and drop shape assumptions (e.g., Thurai et al. 2007). All of these efforts allow disdrometer data sets to become more useful and easily handled by modeling and observational studies, or routine instrumentation checks.

47 OTHER INSTRUMENTATION↗

NASA NTRS1965