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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

A prototype to automate the video subsystem routing for the video distribution subsystem of Space Station Freedom

The Video Distribution Subsystem (VDS) for Space Station Freedom provides onboard video communications. The VDS includes three major functions: external video switching; internal video switching; and sync and control generation. The Video Subsystem Routing (VSR) is a part of the VDS Manager Computer Software Configuration Item (VSM/CSCI). The VSM/CSCI is the software which controls and monitors the VDS equipment. VSR activates, terminates, and modifies video services in response to Tier-1 commands to connect video sources to video destinations. VSR selects connection paths based on availability of resources and updates the video routing lookup tables. This project involves investigating the current methodology to automate the Video Subsystem Routing and developing and testing a prototype as 'proof of concept' for designers.

Betz, Jessie M. Bethly↗
NASA NTRS1991

A video event trigger for high frame rate, high resolution video technology

When video replaces film the digitized video data accumulates very rapidly, leading to a difficult and costly data storage problem. One solution exists for cases when the video images represent continuously repetitive 'static scenes' containing negligible activity, occasionally interrupted by short events of interest. Minutes or hours of redundant video frames can be ignored, and not stored, until activity begins. A new, highly parallel digital state machine generates a digital trigger signal at the onset of a video event. High capacity random access memory storage coupled with newly available fuzzy logic devices permits the monitoring of a video image stream for long term or short term changes caused by spatial translation, dilation, appearance, disappearance, or color change in a video object. Pretrigger and post-trigger storage techniques are then adaptable for archiving the digital stream from only the significant video images.

Williams, Glenn L.↗
DOE OSTI2022

3DBFSVBF (3D BatFinder Smart Video BioFilter and Multi-class BatFinder Smart Video BioFilter) [SWR-22-88]

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source 3D computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The machine learning has been trained on 3D data and allows for automated real-time data collection, identification and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. This machine learning model is an added feature to the previous BatFinder Smart Video BioFilter and increases the accuracy of that systems classification by increasing the accuracy of identifying bats (90% accuracy) and insects (69% accuracy) to a 97% accuracy. There are two object classifier machine learning models, Binary and multi-classification. Binary object classifier labeled BatFinder_Smart_Video_BioFilter.h5 distinguishes between biological objects and non-biological objects. The main goal of this object classifier is to ignore the turbine blades while detecting biological object flying withing the rotor swept area of the turbine. Non-biological objects have a probability of 0 and biological objects have a probability of 1. Multi-classifier labeled Multiclass_BatFinder_Smart_Video_BioFilter.h5 distinguishes between bats, birds, insects and non-biological.

Yarbrough, John↗
DOE OSTI2025

Videos, photos, and AI-derived grain size data associated with “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization” under review. This data package includes five data types: 1) raw photos and videos from drone survey and walking smartphone surveys; 2) images derived from raw videos; 3) manual labeling of reference scales; 4) metadata for all images and photo resolution derived from artificial intelligence (AI) models or manual labels, 5) grain size data obtained from AI models for all photos, 6) metadata and grain size data after quality control, 7) summaries of sample efficiency for all data, and 8) computational fluid dynamics (CFD) data used to support hydro-biogeochemical (HBGC) parameter estimation. Such data is used to 1) demonstrate significant improvements in accuracy, efficiency, and quality control for grain size data collection with the help of AI models, 2) study the spatial heterogeneity of grain size and observation reproducibility based on tens of thousands of data points generated by the AI models, and 3) evaluate the impacts of grain size heterogeneity on key HBGC parameters across sediment-to-reach and hourly-to-yearly scales. In particular, the data package contains 116 folders and 179696 files. The files include 41 videos in .mov format, 64047 photos in .jpg format, 13541 video-derived photos in .png format, 12747 segmentation mask data in .tif format, 12747 segmentation data in .json format, 24771 .csv files that with metadata and grain size for each individual photo as well as water depth and velocity data from CFD and observation, 51791 .txt files of raw AI predicted labels, and 11 flight record data in .srt format. The summary for all metadata and grain size statistics information is included in “Scales_V3_NG.csv” and “Statistics_V3_NG.csv”. The summary for data that pass data quality control (QC) level 0-2 is included in “QCStatistics_V3_NG.csv”. The QC level 0 represents photos whose photo resolution is positive, excluding photos that miss reference scale. The QC level 1 means reference scale circularity uncertainty is less than 5% for smartphone images while representing photo resolution is larger than 0.44 mm/pixel for drone images. The QC level 2 means excluding photos whose grain number is less than 100, a minimum number of grains recommended by classic literature. The summary for each video’s name, length, frame rates, survey area, grain number, survey efficiency, etc. can be found in “QCSummary_V3_NG.csv”. The summary for site name, GPS coordinates, and number of images at each site can be found in “SitesSummary_V3_*.csv” files. Overall computational efficiency summary is reported in Table 4 of accompanying manuscript. Additionally, the nitrate concentration data used in this work was downloaded from an existing dataset published on ESS-DIVE (Boat-Dragged Sensor Hanford Reach.csv; Conner A. et al., 2020). We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Port of Benton, and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the data were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate data collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

Video Observations Encompassing the 2002 Leonid Storm: First Results and a Revised Photometric Procedure for Video Meteor Analysis

During the 2001 Leonid storm, Marshall Space Flight Center, with the cooperation of the University of Western Ontario and the United States Air Force, deployed 6 teams of observers equipped with intensified video systems to sites located in North America, the Pacific, and Mongolia. The campaign was extremely successful, with the entire period of enhanced Leonid activity (over 16 hours) captured on video tape in a consistent manner. We present the first results from the analysis of this unique, 2 terabyte data set and discuss the problems involved in reducing large amounts of video meteor data. In particular, the question of how to determine meteor masses though photometric analysis will be re-examined, and new techniques will be proposed that eliminate some of the deficiencies suffered by the techniques currently employed in video meteor analysis.

Cooke, William J.↗
NASA NTRS2002

Video Observations Encompassing the 2002 Leonid Storm: First Results and a Revised Photometric Procedure for Video Meteor Analysis

During the 2001 Leonid storm, Marshall Space Flight Center, with the cooperation of the University of Western Ontario and the United States Air Force, deployed 6 teams of observers equipped with intensified video systems to sites located in North America, the Pacific, and Mongolia. The campaign was extremely successful, with the entire period of enhanced Leonid activity (over 16 hours) captured on video tape in a consistent manner. We present the first results from the analysis of this unique, 2 terabyte data set and discuss the problems involved in reducing large amounts of video meteor data. In particular, the question of how to determine meteor masses though photometric analysis will be re-examined, and new techniques will be proposed that eliminate some of the deficiencies suffered by the techniques currently employed in video meteor analysis.

Cooke, William J.↗
DOE OSTI2022

BFSVBF (BatFinder Smart Video BioFilter) [SWR-22-87] and Multi-class BatFinder Smart Video BioFilter Keras

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The code has gone through five rounds of development with images used to train the models. This advancement allows for automated real-time data collection, identification, and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. We will discuss the two open source and publicly available machine learning models developed within this scope of this work: 1) a binary model with a 97.5% accuracy in identifying the difference between an object and an empty scene, including wind turbine and clouds; and 2) a multiple classification model with the capability of identifying the type of object detected: bats (90% accuracy), birds (83% accuracy), insects (69% accuracy) and non-biological (99% accuracy).

Yarbrough, John↗
NASA NTRS2023

Video That's Out of This World: A Snapshot of Video Distribution from Across the Solar System

While extraterrestrial scientific research advances to the Moon and Mars, so does the need for secure high-quality video links to capture data and share the experience. This paper describes the space to ground architecture needed to satisfy the cultural mantra challenging “Pictures or it didn’t happen!” as NASA travels beyond Low Earth Orbit. The Marshall Space Flight Center’s Delay Tolerant Network Marshall Enterprise (DTNME) protocol is the framework for video distribution beyond the terrestrial Internet, appropriate for communications across the solar system. Utilizing the International Space Station (ISS) as a test facility, NASA engineers and contractors will demonstrate new technologies and capabilities to transmit high-quality video imagery for NASA’s return to the Moon, Mars exploration, and next generation of explorers.

Photography↗
NASA NTRS2023

Video That's Out of This World: A Snapshot of Video Distribution from Across the Solar System

While extraterrestrial scientific research advances to the Moon and Mars, so does the need for secure high-quality video links to capture data and share the experience. This paper describes the space to ground architecture needed to satisfy the cultural mantra challenging “Pictures or it didn’t happen!” as NASA travels beyond Low Earth Orbit. The Marshall Space Flight Center’s Delay Tolerant Network Marshall Enterprise (DTNME) protocol is the framework for video distribution beyond the terrestrial Internet, appropriate for communications across the solar system. Utilizing the International Space Station (ISS) as a test facility, NASA engineers and contractors will demonstrate new technologies and capabilities to transmit high-quality video imagery for NASA’s return to the Moon, Mars exploration, and next generation of explorers.

Photography↗
NASA NTRS2023

Video That's Out of This World: A Snapshot of Video Distribution from Across the Solar System

While extraterrestrial scientific research advances to the Moon and Mars, so does the need for secure high-quality video links to capture data and share the experience. This paper describes the space to ground architecture needed to satisfy the cultural mantra challenging “Pictures or it didn’t happen!” as NASA travels beyond Low Earth Orbit. The Marshall Space Flight Center’s Delay Tolerant Network Marshall Enterprise (DTNME) protocol is the framework for video distribution beyond the terrestrial Internet, appropriate for communications across the solar system. Utilizing the International Space Station (ISS) as a test facility, NASA engineers and contractors will demonstrate new technologies and capabilities to transmit high-quality video imagery for NASA’s return to the Moon, Mars exploration, and next generation of explorers.

Photography↗
NASA NTRS1994

Controlling telerobots with video data and compensating for time-delayed video using Omniview

Remote viewing is critical for teleoperations, but the inherent limitations of standard video reduce the operator's effectiveness. These limitations have been compensated for in many ways, from using the operator's adaptability, to augmenting his capability with feedback from a variety of sensors and simulations. Omniview can overcome some of these limitations and improve the operator's efficiency without adding additional sensors or computational burden. It can minimize the potential collisions with facility equipment, provide peripheral vision, and display multiple images simultaneously from a single input device. The Omniview technology provides electronic pan, tilt, magnify, and rotational orientation within a hemispherical field-of-view without any moving parts. Image sizes, viewing directions, scale, offset, etc., may be adjusted to fit operator needs. This paper discusses the derivation of the image transformation, the design of the electronics, and two applications to telepresence that are under development. These are Video Emulated Tweening (VET), and Manipulator Guidance and Positioning (ManGAP). The VET effort uses Omniview to compensate for time-delayed video in teleoperation of remote vehicles. In ManGAP two Omniview systems are used to provide two sets of orientation vectors to points in the field-of-view (FOV). These vectors then provide absolute position information to both control the position of the telerobot, and to avoid collisions with the work sight equipment.

Kuban, Dan↗
NASA NTRSDate not supplied

Video That's Out of This World: A Snapshot of Video Distribution from Across the Solar System

While extraterrestrial scientific research advances to the Moon and Mars, so does the need for secure high-quality video links to capture data and share the experience. This paper describes the space to ground architecture needed to satisfy the cultural mantra challenging “Pictures or it didn’t happen!” as NASA travels beyond Low Earth Orbit. The Marshall Space Flight Center’s Delay Tolerant Network Marshall Enterprise (DTNME) protocol is the framework for video distribution beyond the terrestrial Internet, appropriate for communications across the solar system. Utilizing the International Space Station (ISS) as a test facility, NASA engineers and contractors will demonstrate new technologies and capabilities to transmit high-quality video imagery for NASA’s return to the Moon, Mars exploration, and next generation of explorers.

Jennifer L. Christopher↗
DOE OSTI2024

Increased accuracy and signal-to-noise ratio through recent improvements in infra-red video bolometer fabrication and calibration

The infra-red video bolometer (IRVB) is a diagnostic equipped with an infra-red camera that measures the total radiated power in thousands of lines of sight within a large field of view. Recently validated in MAST-U [Fderici et al., Rev. Sci. Instrum. 94, 033502 (2023)], it offers a high spatial resolution map of the radiated power in the divertor region, where large gradients are expected. The IRVB’s sensing element comprises a thin layer of high Z absorbing material, typically platinum, usually coated with carbon to reduce reflections [Peterson et al., Rev. Sci. Instrum. 79, 10E301 (2008)].Here, the possibility of using a relatively inert material such as titanium, is explored that can be produced in layers up to 1 μm compared to 2.5 μm for Pt and then coat it with Pt of the desired thickness (0.3 μm per side here) and carbon. This leads to a higher temperature signal (about 3 times) and better spatial resolution (about 4 times), resulting in higher accuracy in the measured power [Peterson et al., Rev. Sci. Instrum. 79, 10E301 (2008)]. This assembly is also expected to improve foil uniformity, as the Pt layer is obtained via deposition rather than mechanical processes [Mukai et al., Rev. Sci. Instrum. 87, 2014 (2016)].Given its multi-material composition, measuring the thermal properties of the foil assembly is vital. Various methods using a calibrated laser as a heat source have been developed, analyzing the temperature profile shape [Sano et al., Plasma and Fusion Res. 7, 2405039 (2012)] and [Mukai et al., Rev. Sci. Instrum. 89, 10E114 (2018)] or fitting the calculated laser power for different intensities and frequencies [Fderici et al., Rev. Sci. Instrum. 94, 033502 (2023)]. Here, a simpler approach is presented, which relies on analyzing the separate components of the foil heat equation for a single laser exposure in a given area. This can then be iterated over the entire foil to capture local deviations.

Magnetic Fusion Energy↗
NASA NTRS1975

Sequential color video to parallel color video converter

The engineering design, development, breadboard fabrication, test, and delivery of a breadboard field sequential color video to parallel color video converter is described. The converter was designed for use onboard a manned space vehicle to eliminate a flickering TV display picture and to reduce the weight and bulk of previous ground conversion systems.

Source record↗
NASA NTRS1992

Modeling of video traffic in packet networks, low rate video compression, and the development of a lossy+lossless image compression algorithm

During this reporting period we have worked on three somewhat different problems. These are modeling of video traffic in packet networks, low rate video compression, and the development of a lossy + lossless image compression algorithm, which might have some application in browsing algorithms. The lossy + lossless scheme is an extension of work previously done under this grant. It provides a simple technique for incorporating browsing capability. The low rate coding scheme is also a simple variation on the standard discrete cosine transform (DCT) coding approach. In spite of its simplicity, the approach provides surprisingly high quality reconstructions. The modeling approach is borrowed from the speech recognition literature, and seems to be promising in that it provides a simple way of obtaining an idea about the second order behavior of a particular coding scheme. Details about these are presented.

Sayood, K.↗
DOE OSTI2023

Longwave infrared (6.6–11.4 µm) dual-comb spectroscopy with 240,000 comb-mode-resolved data points at video rate

Using sub-3-cycle pulses from mode-locked Cr:ZnS lasers at λ ≈ 2.4 µm as a driving source, we performed high-resolution dual-frequency-comb spectroscopy in the longwave infrared (LWIR) range. A duo of highly coherent broadband (6.6–11.4 µm) frequency combs were produced via intrapulse difference frequency generation in zinc germanium phosphide (ZGP) crystals. Fast (up to 0.1 s per spectrum) acquisition of 240,000 comb-mode-resolved data points, spaced by 80 MHz and referenced to a Rb clock, was demonstrated, resulting in metrology grade molecular spectra of N 2 O (nitrous oxide) and CH 3 OH (methane). The key to high-speed massive spectral data acquisition was low intensity and phase noise of the LWIR combs and high (7.5%) downconversion efficiency, resulting in a LWIR power of 300 mW for each comb.

Vasilyev, Sergey (ORCID:0000000225203547)↗
NASA NTRS1993

VENI, video, VICI: The merging of computer and video technologies

The topics covered include the following: High Definition Television (HDTV) milestones; visual information bandwidth; television frequency allocation and bandwidth; horizontal scanning; workstation RGB color domain; NTSC color domain; American HDTV time-table; HDTV image size; digital HDTV hierarchy; task force on digital image architecture; open architecture model; future displays; and the ULTIMATE imaging system.

Horowitz, Jay G.↗