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At least 109 records · Page 6

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NASA NTRS1998

Comparison of MPEG digital video with super VHS tape for diagnostic echocardiographic readings

BACKGROUND: Digital recording of echocardiographic studies is on the clinical horizon. However, full digital capture of complete echocardiographic studies in traditional video format is impractical, given current storage capacity and network bandwidth. To overcome these constraints, we evaluated the diagnostic image quality of digital video by using MPEG (Motion Picture Experts Group) compression. METHODS AND RESULTS: Fifty-eight complete, consecutive studies were recorded simultaneously with the use of MPEG-1 and sVHS videotape. Each matched MPEG and sVHS study pair was reviewed by two from a total of six readers, and findings were recorded with the use of a detailed, computerized reporting tool. Intrareader and interreader discrepancies were characterized as major or minor and analyzed in total and for specific subgroups of findings (left and right ventricular parameters, valvular insufficiency, and left ventricular regional wall motion). Intrareader discrepancies were reviewed by a consensus panel for agreement with either MPEG or sVHS findings. There was an exact concordance between MPEG and sVHS readings in 83% of findings. The majority of discrepancies were minor, with major discrepancies in only 2.7% of findings. There was no difference in the rate of consensus panel agreement with MPEG or sVHS for instances of intrareader discrepancy, either in total or for any subgroup of findings. Interreader discrepancy rates were nearly identical for both MPEG and sVHS. CONCLUSIONS: MPEG-1 digital video is equivalent to sVHS videotape for diagnostic echocardiography. MPEG increases the range of practical options for digital echocardiography and offers, for the first time, the advantages of digital recording in a familiar video format.

Non-NASA Center↗

NASA NTRS2007

Algorithms for Lunar Flash Video Search, Measurement, and Archiving

Lunar meteoroid impact flashes provide a method to estimate the flux of the large meteoroid flux and thus their hazard to spacecraft. Although meteoroid impacts on the Moon have been detected using video methods for over a decade, the difficulty of manually searching hours of video for the rare, extremely brief impact flashes has discouraged the technique's systematic implementation. A prototype has been developed for the purpose of automatically searching Lunar video records for impact flashes, eliminating false detections, editing the returned possible flashes, and archiving and documenting the results. The theory and organization of the program is discussed with emphasis on the filtering out of several classes of false detections and retaining the brief portions of the raw video necessary for in depth analysis of the flashes detected. Several utilities for measurement, analysis, and location of the flashes on the moon included in the program are demonstrated. Application of the program to a year's worth of Lunar observations is discussed along with examples of impact flashes as well as several classes of false impact flashes.

Swift, Wesley↗

NASA NTRS2009

The Next Generation Advanced Video Guidance Sensor: Flight Heritage and Current Development

The Next Generation Advanced Video Guidance Sensor (NGAVGS) is the latest in a line of sensors that have flown four times in the last 10 years. The NGAVGS has been under development for the last two years as a long-range proximity operations and docking sensor for use in an Automated Rendezvous and Docking (AR&D) system. The first autonomous rendezvous and docking in the history of the U.S. Space Program was successfully accomplished by Orbital Express, using the Advanced Video Guidance Sensor (AVGS) as the primary docking sensor. That flight proved that the United States now has a mature and flight proven sensor technology for supporting Crew Exploration Vehicles (CEV) and Commercial Orbital Transport Systems (COTS) Automated Rendezvous and Docking (AR&D). NASA video sensors have worked well in the past: the AVGS used on the Demonstration of Autonomous Rendezvous Technology (DART) mission operated successfully in "spot mode" out to 2 km, and the first generation rendezvous and docking sensor, the Video Guidance Sensor (VGS), was developed and successfully flown on Space Shuttle flights in 1997 and 1998. This paper presents the flight heritage and results of the sensor technology, some hardware trades for the current sensor, and discusses the needs of future vehicles that may rendezvous and dock with the International Space Station (ISS) and other Constellation vehicles. It also discusses approaches for upgrading AVGS to address parts obsolescence, and concepts for minimizing the sensor footprint, weight, and power requirements. In addition, the testing of the various NGAVGS development units will be discussed along with the use of the NGAVGS as a proximity operations and docking sensor.

Howard, Richard T.↗

NASA NTRS2007

Advanced High-Definition Video Cameras

A product line of high-definition color video cameras, now under development, offers a superior combination of desirable characteristics, including high frame rates, high resolutions, low power consumption, and compactness. Several of the cameras feature a 3,840 2,160-pixel format with progressive scanning at 30 frames per second. The power consumption of one of these cameras is about 25 W. The size of the camera, excluding the lens assembly, is 2 by 5 by 7 in. (about 5.1 by 12.7 by 17.8 cm). The aforementioned desirable characteristics are attained at relatively low cost, largely by utilizing digital processing in advanced field-programmable gate arrays (FPGAs) to perform all of the many functions (for example, color balance and contrast adjustments) of a professional color video camera. The processing is programmed in VHDL so that application-specific integrated circuits (ASICs) can be fabricated directly from the program. ["VHDL" signifies VHSIC Hardware Description Language C, a computing language used by the United States Department of Defense for describing, designing, and simulating very-high-speed integrated circuits (VHSICs).] The image-sensor and FPGA clock frequencies in these cameras have generally been much higher than those used in video cameras designed and manufactured elsewhere. Frequently, the outputs of these cameras are converted to other video-camera formats by use of pre- and post-filters.

Glenn, William↗

NASA NTRS2006

Very High-Speed Digital Video Capability for In-Flight Use

digital video camera system has been qualified for use in flight on the NASA supersonic F-15B Research Testbed aircraft. This system is capable of very-high-speed color digital imaging at flight speeds up to Mach 2. The components of this system have been ruggedized and shock-mounted in the aircraft to survive the severe pressure, temperature, and vibration of the flight environment. The system includes two synchronized camera subsystems installed in fuselage-mounted camera pods (see Figure 1). Each camera subsystem comprises a camera controller/recorder unit and a camera head. The two camera subsystems are synchronized by use of an MHub(TradeMark) synchronization unit. Each camera subsystem is capable of recording at a rate up to 10,000 pictures per second (pps). A state-of-the-art complementary metal oxide/semiconductor (CMOS) sensor in the camera head has a maximum resolution of 1,280 1,024 pixels at 1,000 pps. Exposure times of the electronic shutter of the camera range from 1/200,000 of a second to full open. The recorded images are captured in a dynamic random-access memory (DRAM) and can be downloaded directly to a personal computer or saved on a compact flash memory card. In addition to the high-rate recording of images, the system can display images in real time at 30 pps. Inter Range Instrumentation Group (IRIG) time code can be inserted into the individual camera controllers or into the M-Hub unit. The video data could also be used to obtain quantitative, three-dimensional trajectory information. The first use of this system was in support of the Space Shuttle Return to Flight effort. Data were needed to help in understanding how thermally insulating foam is shed from a space shuttle external fuel tank during launch. The cameras captured images of simulated external tank debris ejected from a fixture mounted under the centerline of the F-15B aircraft. Digital video was obtained at subsonic and supersonic flight conditions, including speeds up to Mach 2 and altitudes up to 50,000 ft (15.24 km). The digital video was used to determine the structural survivability of the debris in a real flight environment and quantify the aerodynamic trajectories of the debris.

Corda, Stephen↗

NASA NTRS2010

Digital Video Over Space Systems and Networks

This slide presentation reviews the use of digital video with space systems and networks. The earliest use of video was the use of film precluding live viewing, which gave way to live television from space. This has given way to digital video using internet protocol for transmission. This has provided for many improvements with new challenges. Some of these ehallenges are reviewed. The change to digital video transmitted over space systems can provide incredible imagery, however the process must be viewed as an entire system, rather than piece-meal.

Grubbs, Rodney↗

NASA NTRS2011

A Miniaturized Video System for Monitoring Drosophila Behavior

Long-term spaceflight may induce a variety of harmful effects in astronauts, resulting in altered motor and cognitive behavior. The stresses experienced by humans in space - most significantly weightlessness (microgravity) and cosmic radiation - are difficult to accurately simulate on Earth. In fact, prolonged and concomitant exposure to microgravity and cosmic radiation can only be studied in space. Behavioral studies in space have focused on model organisms, including Drosophila melanogaster. Drosophila is often used due to its short life span and generational cycle, small size, and ease of maintenance. Additionally, the well-characterized genetics of Drosophila behavior on Earth can be applied to the analysis of results from spaceflights, provided that the behavior in space is accurately recorded. In 2001, the BioExplorer project introduced a low-cost option for researchers: the small satellite. While this approach enabled multiple inexpensive launches of biological experiments, it also imposed stringent restrictions on the monitoring systems in terms of size, mass, data bandwidth, and power consumption. Suggested parameters for size are on the order of 100 mm3 and 1 kg mass for the entire payload. For Drosophila behavioral studies, these engineering requirements are not met by commercially available systems. One system that does meet many requirements for behavioral studies in space is the actimeter. Actimeters use infrared light gates to track the number of times a fly crosses a boundary within a small container (3x3x40 mm). Unfortunately, the apparatus needed to monitor several flies at once would be larger than the capacity of the small satellite. A system is presented, which expands on the actimeter approach to achieve a highly compact, low-power, ultra-low bandwidth solution for simultaneous monitoring of the behavior of multiple flies in space. This also provides a simple, inexpensive alternative to the current systems for monitoring Drosophila populations in terrestrial experiments, and could be especially useful in field experiments in remote locations. Two practical limitations of the system should be noted: first, only walking flies can be observed - not flying - and second, although it enables population studies, tracking individual flies within the population is not currently possible. The system used video recording and an analog circuit to extract the average light changes as a function of time. Flies were held in a 5-cm diameter Petri dish and illuminated from below by a uniform light source. A miniature, monochrome CMOS (complementary metal-oxide semiconductor) video camera imaged the flies. This camera had automatic gain control, and this did not affect system performance. The camera was positioned 5-7 cm above the Petri dish such that the imaging area was 2.25 sq cm. With this basic setup, still images and continuous video of 15 flies at one time were obtained. To reduce the required data bandwidth by several orders of magnitude, a band-pass filter (0.3-10 Hz) circuit compressed the video signal and extracted changes in image luminance over time. The raw activity signal output of this circuit was recorded on a computer and digitally processed to extract the fly movement "events" from the waveform. These events corresponded to flies entering and leaving the image and were used for extracting activity parameters such as inter-event duration. The efficacy of the system in quantifying locomotor activity was evaluated by varying environmental temperature, then measuring the activity level of the flies.

Bhattacharya, Sharmila↗

NASA NTRS2012

Method and system for efficient video compression with low-complexity encoder

Disclosed are a method and system for video compression, wherein the video encoder has low computational complexity and high compression efficiency. The disclosed system comprises a video encoder and a video decoder, wherein the method for encoding includes the steps of converting a source frame into a space-frequency representation; estimating conditional statistics of at least one vector of space-frequency coefficients; estimating encoding rates based on the said conditional statistics; and applying Slepian-Wolf codes with the said computed encoding rates. The preferred method for decoding includes the steps of; generating a side-information vector of frequency coefficients based on previously decoded source data, encoder statistics, and previous reconstructions of the source frequency vector; and performing Slepian-Wolf decoding of at least one source frequency vector based on the generated side-information, the Slepian-Wolf code bits and the encoder statistics.

Chen, Jun↗

NASA NTRS2013

'How To' Clean Room Video

One of the projects that I am completing this summer is a Launch Services Program intern 'How to' set up a clean room informational video. The purpose of this video is to go along with a clean room kit that can be checked out by employees at the Kennedy Space Center and to be taken to classrooms to help educate students and intrigue them about NASA. The video will include 'how to' set up and operate a clean room at NASA. This is a group project so we will be acting as a team and contributing our own input and ideas. We will include various activities for children in classrooms to complete, while learning and having fun. Activities that we will explain and film include: helping children understand the proper way to wear a bunny suit, a brief background on cleanrooms, and the importance of maintaining the cleanliness of a space craft. This project will be shown to LSP management and co-workers; we will be presenting the video once it is completed.

McCarty, Kaley Corinne↗

NASA NTRS2007

Algorithms for Lunar Flash Video Search, Measurement, and Archiving

Lunar meteoroid impact flashes provide a method to estimate the flux of the large meteoroid flux and thus their hazard to spacecraft. Although meteoroid impacts on the Moon have been detected using video methods for over a decade, the difficulty of manually searching hours of video for the rare, extremely brief impact flashes has discouraged the technique's systematic implementation. A prototype has been developed for the purpose of automatically searching lunar video records for impact flashes, eliminating false detections, editing the returned possible flashes, Z and archiving and documenting the results. The theory and organization of the program is discussed with emphasis on the filtering out of several classes of false detections and retaining the brief portions of the raw video necessary for in depth analysis of the flashes detected. Several utilities for measurement, analysis, and location of the flashes on the moon included in the program are demonstrated. Application of the program to a year's worth of lunar observations is discussed along with examples of impact flashes as well as several classes of false impact flashes.

Swift, Wesley↗

NASA NTRS2018

Two and Three Dimensional near Infrared Subcutaneous Structure Imager Using Real Time Nonlinear Video Processing

An imager is provided for viewing subcutaneous structures. In an embodiment of the invention, the imager includes a camera configured to generate a video frame, and an adaptive nonlinear processor. The adaptive nonlinear processor is configured to adjust a signal of the video frame below a first threshold to a maximum dark level and to adjust the signal of the video frame above a second threshold to a maximum light level. The imager further includes a display, configured to display the processed video frame.

Krasowski, Michael J↗

NASA NTRS1993

Image and Video Compression with VLSI Neural Networks

An advanced motion-compensated predictive video compression system based on artificial neural networks has been developed to effectively eliminate the temporal and spatial redundancy of video image sequences and thus reduce the bandwidth and storage required for the transmission and recording of the video signal. The VLSI neuroprocessor for high-speed high-ratio image compression based upon a self-organization network and the conventional algorithm for vector quantization are compared. The proposed method is quite efficient and can achieve near-optimal results.

compression artificial neural networks VLSI↗

NASA NTRSDate not supplied

ArcjetCV: a new machine learning application for extracting time-resolved recession measurements from arc jet test videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

NASA NTRSDate not supplied

Enabling Space Biological Knowledge Discovery Through Image and Video Data Sharing

Increased biomedical risks and challenges associated with deep space missions and experiments (cis-Lunar, Mars transit/surface) require new knowledge discovery and development of novel ecosystems. Supporting distant and long-duration missions and experiments requires biological data (from yeast, microbes, fruit flies, C. elegans, plants, crops, rodents, humans) be findable, accessible, interoperable, reusable (FAIR), and maximally open-access. As data-intensive, bioinformatic, meta-analytical, and computer-assisted approaches continue to be a centerpiece of modern research, the NASA Biological and Physical Sciences division is expanding its Open Science capabilities beyond NASA GeneLab. The NASA Ames Life Sciences Data Archive (ALSDA) is a repository which is responsible for collecting and access to space biological imagery and video, alongside tabular and environmental data. In this presentation, we will discuss strategies dealing with archiving, curating, and accessibility of images from very distinct imaging modalities (e.g., micro-computed tomography, magnetic resonance imaging, photographic images of plants, fluorescence microscopy, behavioral videos, etc.). There are two main challenges: 1. Open-source data storage and 2. Metadata related to the imagery-video. Both have been solved by leveraging two existing open-source systems. For data storage, ALSDA is utilizing components through the Open Microscopy Environment (OME), which can read most imaging proprietary formats and display on a web interface complex multidimensional images (Z stack, multi-channel, temporal, spectral). Most technical metadata from imaging modalities are captured seamlessly. For metadata capturing experimental details, ALSDA (like GeneLab) uses the ISA-Tab specification which relies on the ISA data model to order and classify metadata. The ISA data model uses a tree structure with three files to capture the metadata: The top layer is the Investigations file, the second layer is the Study file(s), and the last layer is the Assay file(s). We believe such an approach may be useful for other types of image research data from other investigators in the AGU community.

imaging↗

NASA NTRSDate not supplied

To the Moon! Space Launch System Modal Testing with Video and Motion Magnification

MIT Lincoln Laboratory and NASA Marshall Space Flight Center have been collaborating on using video camera measurements and motion magnification for modal testing of large aerospace components for several years. This presentation will discuss results from the Space Launch System Integrated Modal Test (IMT) and Dynamic Rollout/Rollback Test (DRRT) in support of the Artemis I mission. During the IMT, the data collection focused on operational mode shapes. In addition, the cameras were repositioned mid-test to better understand the physics of a low-frequency torsion mode. The non-contact nature of video data capture allowed for the quick redeployment of the cameras while not causing any delay in test schedule, whereas traditional instrumentation would have required a pause in testing to attach the sensors to the test article. The motion magnification analysis was able to find the low-frequency operational mode shapes and help the test team better understand the physics of the torsion mode. Building upon the success of the IMT motion magnification work, a camera system was used during the DRRT to find operational mode shapes, if the physics of the low-frequency torsion mode remained with different boundary conditions, and relative deflection of the vehicle and ML tower during the roll. In this paper we will present operational mode shape results, discuss the physics of the torsion mode, and review experimental setup idiosyncrasies to help the community in designing video camera measurement systems.

optical↗

NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

machine learning↗

NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

NASA NTRSDate not supplied

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

machine learning↗