Ice borehole video of basal domain of ice stream C in the 2000-2001 field season
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ice sheets in-situ↗
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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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Web Audio/Video Streaming Tool
In order to promote NASA-wide educational outreach program to educate and inform the public of space exploration, NASA, at Kennedy Space Center, is seeking efficient ways to add more contents to the web by streaming audio/video files. This project proposes a high level overview of a framework for the creation, management, and scheduling of audio/video assets over the web. To support short-term goals, the prototype of a web-based tool is designed and demonstrated to automate the process of streaming audio/video files. The tool provides web-enabled users interfaces to manage video assets, create publishable schedules of video assets for streaming, and schedule the streaming events. These operations are performed on user-defined and system-derived metadata of audio/video assets stored in a relational database while the assets reside on separate repository. The prototype tool is designed using ColdFusion 5.0.
Guruvadoo, Eranna K.↗
Live Aircraft Encounter Visualization at FutureFlight Central
Researchers at the National Aeronautics and Space Administration (NASA) have developed an aircraft data streaming capability that can be used to visualize live aircraft in near real-time. During a joint Federal Aviation Administration (FAA)/NASA Airborne Collision Avoidance System flight series, test sorties between unmanned aircraft and manned intruder aircraft were shown in real-time at NASA Ames' FutureFlight Central tower facility as a virtual representation of the encounter. This capability leveraged existing live surveillance, video, and audio data streams distributed through a Live, Virtual, Constructive test environment, then depicted the encounter from the point of view of any aircraft in the system showing the proximity of the other aircraft. For the demonstration, position report data were sent to the ground from on-board sensors on the unmanned aircraft. The point of view can be change dynamically, allowing encounters from all angles to be observed. Visualizing the encounters in real-time provides a safe and effective method for observation of live flight testing and a strong alternative to travel to the remote test range.
Video Streaming↗
Glacial facies observed in video images of basal ice of Ice Stream C of West Antarctica
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ice sheets climate change in-situ exploration↗
Fiber optic voice/data network
An asynchronous, high-speed, fiber optic local area network originally developed for tactical environments with additional benefits for other environments such as spacecraft, and the like. The network supports ordinary data packet traffic simultaneously with synchronous T1 voice traffic over a common token ring channel; however, the techniques and apparatus of this invention can be applied to any deterministic class of packet data networks, including multitier backbones, that must transport stream data (e.g., video, SAR, sensors) as well as data. A voice interface module parses, buffers, and resynchronizes the voice data to the packet network employing elastic buffers on both the sending and receiving ends. Voice call setup and switching functions are performed external to the network with ordinary PABX equipment. Clock information is passed across network boundaries in a token passing ring by preceeding the token with an idle period of non-transmission which allows the token to be used to re-establish a clock synchronized to the data. Provision is made to monitor and compensate the elastic receiving buffers so as to prevent them from overflowing or going empty.
Bergman, Larry A.↗
Passive IFF: Autonomous Nonintrusive Rapid Identification of Friendly Assets
A proposed optoelectronic instrument would identify targets rapidly, without need to radiate an interrogating signal, apply identifying marks to the targets, or equip the targets with transponders. The instrument was conceived as an identification, friend or foe (IFF) system in a battlefield setting, where it would be part of a targeting system for weapons, by providing rapid identification for aimed weapons to help in deciding whether and when to trigger them. The instrument could also be adapted to law-enforcement and industrial applications in which it is necessary to rapidly identify objects in view. The instrument would comprise mainly an optical correlator and a neural processor (see figure). The inherent parallel-processing speed and capability of the optical correlator would be exploited to obtain rapid identification of a set of probable targets within a scene of interest and to define regions within the scene for the neural processor to analyze. The neural processor would then concentrate on each region selected by the optical correlator in an effort to identify the target. Depending on whether or not a target was recognized by comparison of its image data with data in an internal database on which the neural processor was trained, the processor would generate an identifying signal (typically, friend or foe ). The time taken for this identification process would be less than the time needed by a human or robotic gunner to acquire a view of, and aim at, a target. An optical correlator that has been under development for several years and that has been demonstrated to be capable of tracking a cruise missile might be considered a prototype of the optical correlator in the proposed IFF instrument. This optical correlator features a 512-by-512-pixel input image frame and operates at an input frame rate of 60 Hz. It includes a spatial light modulator (SLM) for video-to-optical image conversion, a pair of precise lenses to effect Fourier transforms, a filter SLM for digital-to-optical correlation-filter data conversion, and a charge-coupled device (CCD) for detection of correlation peaks. In operation, the input scene grabbed by a video sensor is streamed into the input SLM. Precomputed correlation-filter data files representative of known targets are then downloaded and sequenced into the filter SLM at a rate of 1,000 Hz. When there occurs a match between the input target data and one of the known-target data files, the CCD detects a correlation peak at the location of the target. Distortion- invariant correlation filters from a bank of such filters are then sequenced through the optical correlator for each input frame. The net result is the rapid preliminary recognition of one or a few targets.
Moynihan, Philip↗
Optical Communications Link to Airborne Transceiver
An optical link from Earth to an aircraft demonstrates the ability to establish a link from a ground platform to a transceiver moving overhead. An airplane has a challenging disturbance environment including airframe vibrations and occasional abrupt changes in attitude during flight. These disturbances make it difficult to maintain pointing lock in an optical transceiver in an airplane. Acquisition can also be challenging. In the case of the aircraft link, the ground station initially has no precise knowledge of the aircraft s location. An airborne pointing system has been designed, built, and demonstrated using direct-drive brushless DC motors for passive isolation of pointing disturbances and for high-bandwidth control feedback. The airborne transceiver uses a GPS-INS system to determine the aircraft s position and attitude, and to then illuminate the ground station initially for acquisition. The ground transceiver participates in link-pointing acquisition by first using a wide-field camera to detect initial illumination from the airborne beacon, and to perform coarse pointing. It then transfers control to a high-precision pointing detector. Using this scheme, live video was successfully streamed from the ground to the aircraft at 270 Mb/s while simultaneously downlinking a 50 kb/s data stream from the aircraft to the ground.
Regehr, Martin W.↗
Perceptual-components architecture for digital video
A perceptual-components architecture for digital video partitions the image stream into signal components in a manner analogous to that used in the human visual system. These components consist of achromatic and opponent color channels, divided into static and motion channels, further divided into bands of particular spatial frequency and orientation. Bits are allocated to an individual band in accord with visual sensitivity to that band and in accord with the properties of visual masking. This architecture is argued to have desirable features such as efficiency, error tolerance, scalability, device independence, and extensibility.
Watson, Andrew B.↗
1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks
Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D CNN architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.
Machine learning↗
1d-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks
Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D CNN architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.
Machine learning↗
1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks
Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.
Machine learning↗
1D-Convolutional Neural Network Architecture for Generalized Time-series Segmentation
Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization will be described as well as the results of application to three separate data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation. In all three test cases the 1D-CNN performs better than tailored integral/derivative/thresholding algorithms across a range of signal-to-noise levels.
CNN↗
1D-Convolutional Neural Network Architecture for Generalized Time-series Segmentation
Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization will be described as well as the results of application to three separate data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation. In all three test cases the 1D-CNN performs better than tailored integral/derivative/thresholding algorithms across a range of signal-to-noise levels.
CNN↗
Recommendations for Secure Transport: Material Conveyance Physical Protection Technology
Nuclear material is at higher risk of theft and sabotage during transport than during any other phase of the nuclear fuel lifecycle. Nuclear materials are transported in the public domain where adversaries have an upper hand by taking advantage of the time and location of the theft or sabotage attempt. As such, even modest threat profiles for transport of nuclear and radioactive material can require substantial detection and delay measures to support timely response. Conveyance tracking augmented with technology that improves on-the-scene situational awareness at a remote monitoring center has been adopted as a de-facto standard approach for transportation security. At present, the extended tracking and situational awareness capabilities needed for a nuclear material shipment, as is provided by the purpose designed, Transportation – Security, Tracking and Reporting (T-STAR) System, do not exist in a single commercial off-the-shelf (COTS) solution. Typically, the COTS systems that excel in one area are deficient in other areas, presenting challenges to designing well-rounded, robust systems. Still, COTS solutions can offer the basic set of tracking and situational awareness capabilities by indicating last update time, current location, and route taken. By incorporating vehicle and driver performance measures via the vehicle’s controller area network (CAN bus) and video alongside other data streams allows telemetry and other shipment information to be assessed in novel ways.This paper describes the operation, capabilities and features of various conveyance protection systems and approaches, assesses emerging technologies and how they could be used through a unified interface, and enumerates additional ways to provide early detection using vehicle, onboard technologies, effective delay technologies and approaches and simple equipment to improve protection during transport.
Shannon, Michael↗
Ground-based PIV and numerical flow visualization results from the surface tension driven convection experiment
The Surface Tension Driven Convection Experiment (STDCE) is a Space Transportation System flight experiment to study both transient and steady thermocapillary fluid flows aboard the United States Microgravity Laboratory-1 (USML-1) Spacelab mission planned for June, 1992. One of the components of data collected during the experiment is a video record of the flow field. This qualitative data is then quantified using an all electric, two dimensional Particle Image Velocimetry (PIV) technique called Particle Displacement Tracking (PDT), which uses a simple space domain particle tracking algorithm. Results using the ground based STDCE hardware, with a radiant flux heating mode, and the PDT system are compared to numerical solutions obtained by solving the axisymmetric Navier Stokes equations with a deformable free surface. The PDT technique is successful in producing a velocity vector field and corresponding stream function from the raw video data which satisfactorily represents the physical flow. A numerical program is used to compute the velocity field and corresponding stream function under identical conditions. Both the PDT system and numerical results were compared to a streak photograph, used as a benchmark, with good correlation.
Pline, Alexander D.↗
Ground-based PIV and numerical flow visualization results from the Surface Tension Driven Convection Experiment
The Surface Tension Driven Convection Experiment (STDCE) is a Space Transportation System flight experiment to study both transient and steady thermocapillary fluid flows aboard the United States Microgravity Laboratory-1 (USML-1) Spacelab mission planned for June, 1992. One of the components of data collected during the experiment is a video record of the flow field. This qualitative data is then quantified using an all electric, two dimensional Particle Image Velocimetry (PIV) technique called Particle Displacement Tracking (PDT), which uses a simple space domain particle tracking algorithm. Results using the ground based STDCE hardware, with a radiant flux heating mode, and the PDT system are compared to numerical solutions obtained by solving the axisymmetric Navier Stokes equations with a deformable free surface. The PDT technique is successful in producing a velocity vector field and corresponding stream function from the raw video data which satisfactorily represents the physical flow. A numerical program is used to compute the velocity field and corresponding stream function under identical conditions. Both the PDT system and numerical results were compared to a streak photograph, used as a benchmark, with good correlation.
Pline, Alexander D.↗
Portable Airborne Laser System Measures Forest-Canopy Height
(PALS) is a combination of laser ranging, video imaging, positioning, and data-processing subsystems designed for measuring the heights of forest canopies along linear transects from tens to thousands of kilometers long. Unlike prior laser ranging systems designed to serve the same purpose, the PALS is not restricted to use aboard a single aircraft of a specific type: the PALS fits into two large suitcases that can be carried to any convenient location, and the PALS can be installed in almost any local aircraft for hire, thereby making it possible to sample remote forests at relatively low cost. The initial cost and the cost of repairing the PALS are also lower because the PALS hardware consists mostly of commercial off-the-shelf (COTS) units that can easily be replaced in the field. The COTS units include a laser ranging transceiver, a charge-coupled-device camera that images the laser-illuminated targets, a differential Global Positioning System (dGPS) receiver capable of operation within the Wide Area Augmentation System, a video titler, a video cassette recorder (VCR), and a laptop computer equipped with two serial ports. The VCR and computer are powered by batteries; the other units are powered at 12 VDC from the 28-VDC aircraft power system via a low-pass filter and a voltage converter. The dGPS receiver feeds location and time data, at an update rate of 0.5 Hz, to the video titler and the computer. The laser ranging transceiver, operating at a sampling rate of 2 kHz, feeds its serial range and amplitude data stream to the computer. The analog video signal from the CCD camera is fed into the video titler wherein the signal is annotated with position and time information. The titler then forwards the annotated signal to the VCR for recording on 8-mm tapes. The dGPS and laser range and amplitude serial data streams are processed by software that displays the laser trace and the dGPS information as they are fed into the computer, subsamples the laser range and amplitude data, interleaves the subsampled data with the dGPS information, and records the resulting interleaved data stream.
Nelson, Ross↗
Crew Activity Analyzer
The crew activity analyzer (CAA) is a system of electronic hardware and software for automatically identifying patterns of group activity among crew members working together in an office, cockpit, workshop, laboratory, or other enclosed space. The CAA synchronously records multiple streams of data from digital video cameras, wireless microphones, and position sensors, then plays back and processes the data to identify activity patterns specified by human analysts. The processing greatly reduces the amount of time that the analysts must spend in examining large amounts of data, enabling the analysts to concentrate on subsets of data that represent activities of interest. The CAA has potential for use in a variety of governmental and commercial applications, including planning for crews for future long space flights, designing facilities wherein humans must work in proximity for long times, improving crew training and measuring crew performance in military settings, human-factors and safety assessment, development of team procedures, and behavioral and ethnographic research. The data-acquisition hardware of the CAA (see figure) includes two video cameras: an overhead one aimed upward at a paraboloidal mirror on the ceiling and one mounted on a wall aimed in a downward slant toward the crew area. As many as four wireless microphones can be worn by crew members. The audio signals received from the microphones are digitized, then compressed in preparation for storage. Approximate locations of as many as four crew members are measured by use of a Cricket indoor location system. [The Cricket indoor location system includes ultrasonic/radio beacon and listener units. A Cricket beacon (in this case, worn by a crew member) simultaneously transmits a pulse of ultrasound and a radio signal that contains identifying information. Each Cricket listener unit measures the difference between the times of reception of the ultrasound and radio signals from an identified beacon. Assuming essentially instantaneous propagation of the radio signal, the distance between that beacon and the listener unit is estimated from this time difference and the speed of sound in air.] In this system, six Cricket listener units are mounted in various positions on the ceiling, and as many as four Cricket beacons are attached to crew members. The three-dimensional position of each Cricket beacon can be estimated from the time-difference readings of that beacon from at least three Cricket listener units
Murray, James↗