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Shoulder joint allows full range of natural motion: wearer senses little or no resisting force or torque. Developed for space suit, joint offers advantages in protective garments for underwater work, firefighting, or cleanup of hazardous materials.
The driving precision landing requirement for the Autonomous Landing and Hazard Avoidance Technology project is to autonomously land within 100m of a predetermined location on the lunar surface. Traditional lunar landing approaches based on inertial sensing do not have the navigational precision to meet this requirement. The purpose of Terrain Relative Navigation (TRN) is to augment inertial navigation by providing position or bearing measurements relative to known surface landmarks. From these measurements, the navigational precision can be reduced to a level that meets the 100m requirement. There are three different TRN functions: global position estimation, local position estimation and velocity estimation. These functions can be achieved with active range sensing or passive imaging. This paper gives a survey of many TRN approaches and then presents some high fidelity simulation results for contour matching and area correlation approaches to TRN using active sensors. Since TRN requires an a-priori reference map, the paper concludes by describing past and future lunar imaging and digital elevation map data sets available for this purpose.
Natural and anthropogenic hazards are frequently responsible for disaster events, leading to damaged physical infrastructure, which can result in loss of electrical power for affected locations. Remotely-sensed, nighttime satellite imagery from the Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) can monitor power outages in disaster-affected areas through the identification of missing city lights. When combined with locally-relevant geospatial information, these observations can be used to estimate power outages, defined as geographic locations requiring manual intervention to restore power. In this study, we produced a power outage product based on Suomi-NPP VIIRS DNB observations to estimate power outages following Hurricane Sandy in 2012. This product, combined with known power outage data and ambient population estimates, was then used to predict power outages in a layered, feedforward neural network model. We believe this is the first attempt to synergistically combine such data sources to quantitatively estimate power outages. The VIIRS DNB power outage product was able to identify initial loss of light following Hurricane Sandy, as well as the gradual restoration of electrical power. The neural network model predicted power outages with reasonable spatial accuracy, achieving Pearson coefficients (r) between 0.48 and 0.58 across all folds. Our results show promise for producing a continental United States (CONUS)- or global-scale power outage monitoring network using satellite imagery and locally-relevant geospatial data.
The driving precision landing requirement for the Autonomous Landing and Hazard Avoidance Technology project is to autonomously land within 100m of a predetermined location on the lunar surface. Traditional lunar landing approaches based on inertial sensing do not have the navigational precision to meet this requirement. The purpose of Terrain Relative Navigation (TRN) is to augment inertial navigation by providing position or bearing measurements relative to known surface landmarks. From these measurements, the navigational precision can be reduced to a level that meets the 100m requirement. There are three different TRN functions: global position estimation, local position estimation and velocity estimation. These functions can be achieved with active range sensing or passive imaging. This paper gives a survey of many TRN approaches and then presents some high fidelity simulation results for contour matching and area correlation approaches to TRN using active sensors. Since TRN requires an a-priori reference map, the paper concludes by describing past and future lunar imaging and digital elevation map data sets available for this purpose.
Element One, Inc. is reporting on the results of its DOE SBIR Phase I project for the development and testing of low-cost thin film hydrogen sensors and their subsequent integration into wireless sensing systems and networks. This work is based on Element One’s chemo-chromic sensing technology which indicates hydrogen presence (leaks) colorimetrically and electronically with no power required. The lack of required power makes them particularly suitable for passive Radio Frequency Identification Device (RFID) technology which can be deployed affordably and abundantly to detect leaks in hazardous environments. Element One built upon its experience with chemochromic materials that change color in the presence of hydrogen. Both thin films and pigments have been used, but thin films have the ability to change conductivity by more than three orders of magnitude making them ideal for resistive sensors. Prototype sensors were fabricated, tested and characterized. For the wireless component, Element One partnered with Esensor, Inc. to construct and test a wireless sensing network. The network was tested and validated at the National Renewable Energy Laboratory (NREL) in February and March. The tests involved exposing the wireless system to hydrogen gas concentrations of 1% and 5%. There were no significant problems encountered, and the system is ready for commercial development. The system demonstrated the viability of wireless hydrogen sensing networks for hydrogen leak detection and other applications.
We have developed the P-band Signals of Opportunity (SoOp) sensor based on the Unmanned Aircraft System (UAS) to remotely sense Snow Water Equivalent (SWE) and Root Zone Soil Moisture (RZSM). The P-band UAS SoOp sensor for Hydrology (UASHydro) would operate on the S2 aircraft developed by Black Swift Technologies for sensing of SWE and RZSM with a spatial resolution of about 10m. Root-zone soil moisture and snow water storage in land are critical parameters of the water cycle. The long-term goal of our development would be to use small UAS to perform regional high resolution observation of two key hydrological measurements to improve the estimation of terrestrial water storage for water management, crop production and forecasts of natural hazard. The UASHydro concept utilizes passive receivers to detect the reflection of strong existing P-band radio signals at the 360-380 MHz band from geostationary Mobile Use Objective System (MUOS) communication satellites launched by the US Navy. The SWE remote sensing measurement principle using the P-band SoOp is based on the propagation delay (or phase change) of radio signals through the snowpack. The time delay of the reflected signal due to the snowpack with respect to snow-free conditions is directly proportional to the snowpack SWE, while the soil moisture can be retrieved from the reflectivity at the P-band frequencies for MUOS. We have been conducting ground-based campaigns to test the instrumentation and data processing methods at the Fraser Experimental Forest in Colorado since February 2016. The field campaign data has provided support to the measurement concept. To install the SoOp technologies on the UAS, a lightweight antenna has been built and interfaces with the S2 built by Black Swift Technologies have been completed. A set of flights have been planned starting April 2018 through the end of 2018 in Colorado.
Topics covered include: NASA Applied Sciences Program; USGS Land Remote Sensing: Overview; QuickBird System Status and Product Overview; ORBIMAGE Overview; IKONOS 2004 Calibration and Validation Status; OrbView-3 Spatial Characterization; On-Orbit Modulation Transfer Function (MTF) Measurement of QuickBird; Spatial Resolution Characterization for QuickBird Image Products 2003-2004 Season; Image Quality Evaluation of QuickBird Super Resolution and Revisit of IKONOS: Civil and Commercial Application Project (CCAP); On-Orbit System MTF Measurement; QuickBird Post Launch Geopositional Characterization Update; OrbView-3 Geometric Calibration and Geopositional Accuracy; Geopositional Statistical Methods; QuickBird and OrbView-3 Geopositional Accuracy Assessment; Initial On-Orbit Spatial Resolution Characterization of OrbView-3 Panchromatic Images; Laboratory Measurement of Bidirectional Reflectance of Radiometric Tarps; Stennis Space Center Verification and Validation Capabilities; Joint Agency Commercial Imagery Evaluation (JACIE) Team; Adjacency Effects in High Resolution Imagery; Effect of Pulse Width vs. GSD on MTF Estimation; Camera and Sensor Calibration at the USGS; QuickBird Geometric Verification; Comparison of MODTRAN to Heritage-based Results in Vicarious Calibration at University of Arizona; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Estimating Sub-Pixel Proportions of Sagebrush with a Regression Tree; How Do YOU Use the National Land Cover Dataset?; The National Map Hazards Data Distribution System; Recording a Troubled World; What Does This-Have to Do with This?; When Can a Picture Save a Thousand Homes?; InSAR Studies of Alaska Volcanoes; Earth Observing-1 (EO-1) Data Products; Improving Access to the USGS Aerial Film Collections: High Resolution Scanners; Improving Access to the USGS Aerial Film Collections: Phoenix Digitizing System Product Distribution; System and Product Characterization: Issues Approach; Innovative Approaches to Analysis of Lidar Data for the National Map; Changes in Imperviousness near Military Installations; Geopositional Accuracy Evaluations of QuickBird and OrbView-3: Civil and Commercial Applications Project (CCAP); Geometric Accuracy Assessment: OrbView ORTHO Products; QuickBird Radiometric Calibration Update; OrbView-3 Radiometric Calibration; QuickBird Radiometric Characterization; NASA Radiometric Characterization; Establishing and Verifying the Traceability of Remote-Sensing Measurements to International Standards; QuickBird Applications; Airport Mapping and Perpetual Monitoring Using IKONOS; OrbView-3 Relative Accuracy Results and Impacts on Exploitation and Accuracy Improvement; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Applying High-Resolution Satellite Imagery and Remotely Sensed Data to Local Government Applications: Sioux Falls, South Dakota; Automatic Co-Registration of QuickBird Data for Change Detection Applications; Developing Coastal Surface Roughness Maps Using ASTER and QuickBird Data Sources; Automated, Near-Real Time Cloud and Cloud Shadow Detection in High Resolution VNIR Imagery; Science Applications of High Resolution Imagery at the USGS EROS Data Center; Draft Plan for Characterizing Commercial Data Products in Support of Earth Science Research; Atmospheric Correction Prototype Algorithm for High Spatial Resolution Multispectral Earth Observing Imaging Systems; Determining Regional Arctic Tundra Carbon Exchange: A Bottom-Up Approach; Using IKONOS Imagery to Assess Impervious Surface Area, Riparian Buffers and Stream Health in the Mid-Atlantic Region; Commercial Remote Sensing Space Policy Civil Implementation Update; USGS Commercial Remote Sensing Data Contracts (CRSDC); and Commercial Remote Sensing Space Policy (CRSSP): Civil Near-Term Requirements Collection Update.
Dry cask storage is one of two storage methods approved by the U.S. Nuclear Regulatory Commission for spent fuel after removal from reactor cores. Dry casks consist of a stainless steel canister enclosed in a concrete overpack to contain the hazardous radioactive spent fuel rods and provide radiation shielding. Monitoring spent fuel storage casks is desired to ensure the safe containment of the enclosed spent fuel, but is very difficult due to the related harsh temperature and radiation environment. The sensors and associated electronics to monitor temperature, pressure, and/or radiation need to survive high temperatures and radiation doses for extended time periods. For this reason, there is a severe need for radiation-hardened electrical systems that survive well beyond the existing capabilities of commercially available radiation-rated electronic components, which have primarily been developed for space applications. Junction-gate field-effect transistor (JFET) devices are inherently radiation hardened [exceeding 100 Mrad (Si)]. Additionally, when JFETs are used as building blocks for sensing and communication electronics (i.e., oscillators, amplifiers, filters, and mixers), inherently radiation-hardened circuits can be achieved. To this end, JFET-based radiation-hardened electronics interfacing with cask-embedded sensors capable of driving modulated sensor signals through a stainless steel barrier were designed and tested at a dose rate of approximately 500 krad/h (Si) to beyond a 200-Mrad (Si) total ionizing dose. After 200 Mrad (Si), the sensor and communication circuit signals were correctly decoded at the receiver despite oscillator drift. The results from this experiment demonstrate the potential for creating more complex radiation-hardened JFET-based electrical systems for nuclear environments.
The Mt. Washington Icing Sensors Project (MWISP) was an intensive multiagency field test of remote-sensing technologies and methods for in-flight icing detection. This field test was the first comprehensive examination of multiple remote-sensing technologies, and it included ground and airborne in situ measurements for comparison. The data gathered will allow the assessment of icing remote-sensing technologies leading toward the development of ground-based and airborne systems. These systems will provide flight crews with the information required for avoiding or exiting hazardous icing conditions.
This software implements a motion-planning module for a maritime autonomous surface vehicle (ASV). The module trails a given target while also avoiding static and dynamic surface hazards. When surface hazards are other moving boats, the motion planner must apply International Regulations for Avoiding Collisions at Sea (COLREGS). A key subset of these rules has been implemented in the software. In case contact with the target is lost, the software can receive and follow a "reacquisition route," provided by a complementary system, until the target is reacquired. The programmatic intention is that the trailed target is a submarine, although any mobile naval platform could serve as the target. The algorithmic approach to combining motion with a (possibly moving) goal location, while avoiding local hazards, may be applicable to robotic rovers, automated landing systems, and autonomous airships. The software operates in JPL s CARACaS (Control Architecture for Robotic Agent Command and Sensing) software architecture and relies on other modules for environmental perception data and information on the predicted detectability of the target, as well as the low-level interface to the boat controls.
Amongst the essential steps to be taken towards developing and deploying safe systems with embedded learning-enabled components (LECs) i.e., software components that use ma- chine learning (ML)—are to analyze and understand the con- tribution of the constituent LECs to safety, and to assure that those contributions have been appropriately managed. This paper addresses both steps by, first, introducing the notion of hazard contribution modes (HCMs) a categorization of the ways in which the ML elements of LECs can contribute to hazardous system states; and, second, describing how argumentation patterns can capture the reasoning that can be used to assure HCM mitigation. Our framework is generic in the sense that the categories of HCMs developed i) can admit different learning schemes, i.e., supervised, unsupervised, and reinforcement learning, and ii) are not dependent on the type of system in which the LECs are embedded, i.e., both cyber and cyber-physical systems. One of the goals of this work is to serve a starting point for systematizing L analysis towards eventually automating it in a tool.
Amongst the essential steps to be taken towards developing and deploying safe systems with embedded learning-enabled components (LECs)—i.e., software components that use machine learning (ML)—are to analyze and understand the contribution of the constituent LECs to safety, and to assure that those contributions have been appropriately managed. This paper addresses both steps by, first, introducing the otion of hazard contribution modes (HCMs)—a categorization of the ways in which the ML elements of LECs can contribute to hazardous system states; and, second, describing how argumentation patterns can capture the reasoning that can be used to assure HCM mitigation. Our framework is generic in the sense that the categories of HCMs developed i) can admit different learning schemes, i.e., supervised, unsupervised, and reinforcement learning, and ii) are not dependent on the type of system in which the LECs are embedded, i.e., both cyber and cyber-physical systems. One of the goals of this work is to serve a starting point for systematizing LEC safety analysis towards eventually automating it in a tool.
In a proposed method of sensing small quantities of molecules of interest, surface enhanced Raman scattering (SERS) spectroscopy would be further enhanced by means of intermolecular or supramolecular charge transfer. There is a very large potential market for sensors based on this method for rapid detection of chemical and biological hazards. In SERS, the Raman signals (vibrational spectra) of target molecules become enhanced by factors of the order of 108 when those molecules are in the vicinities of nanostructured substrate surfaces that have been engineered to have plasmon resonances that enhance local electric fields. SERS, as reported in several prior NASA Tech Briefs articles and elsewhere, has remained a research tool and has not yet been developed into a practical technique for sensing of target molecules: this is because the short range (5 to 20 nm) of the field enhancement necessitates engineering of receptor molecules to attract target molecules to the nanostructured substrate surfaces and to enable reliable identification of the target molecules in the presence of interferants. Intermolecular charge-transfer complexes have been used in fluorescence-, photoluminescence-, and electrochemistry-based techniques for sensing target molecules, but, until now, have not been considered for use in SERS-based sensing. The basic idea of the proposed method is to engineer receptor molecules that would be attached to nanostructured SERS substrates and that would interact with the target molecules to form receptor-target supramolecular charge-transfer complexes wherein the charge transfer could be photoexcited.
"Safe" temperature gage for explosive liquids is based on optical transmission. Semiconductor crystal inserted between input and output optical fibers is temperature-sensing element in a new approach to measuring temperature of cryogens. Since no electrical components are immersed in liquid, new sensor minimizes danger of electrically ignited explosions in hazardous cryogens such as oxygen and hydrogen. Gage also useful for handling noncryogenic liquids in aircraft, automobiles, boats and water tanks.
Procedures followed in developing a test case geographic information system derived primarily from remotely sensed data for the North Cache Soil Conservation District (SCD) in northern Utah are outlined. The North Cache SCD faces serious problems regarding water allocation, flood and geologic hazards, urban encroachment into prime farmland, soil erosion, and wildlife habitat. Four fundamental data planes were initially entered into the geo-referenced data base: (1) land use/land cover information for the agricultural and built-up areas of the valley obtained from various forms of aerial photography; (2) vegetation/land cover in mountains classified digitally from LANDSAT; (3) geomorphic terrain units derived from aerial photography and soil maps; and (4) digital terrain maps obtained from DMA digital data. The land use/vegetation/land cover information from manual photographic and LANDSAT interpretation were joined digitally into a single data plane with an integrated legend, and segmented into quadrangle units. These were merged with the digitized geomorphic units and the digital terrain data using a Prime 400 minicomputer. All data planes were geo-referenced to a UTM coordinate grid.
Procedures followed in developing a test case geographic information system derived primarily from remotely sensed data for the North Cache Soil Conservation District (SCD) in northern Utah are outlined. The North Cache SCD faces serious problems regarding water allocation, flood and geologic hazards, urban encroachment into prime farmland, soil erosion, and wildlife habitat. Four fundamental data planes were initially entered into the geo-referenced data base: (1) land use/land cover information for the agricultural and built-up areas of the valley obtained from various forms of aerial photography; (2) vegetation/land cover in mountains classified digitally from Landsat; (3) geomorphic terrain units derived from aerial photography and soil maps; and (4) digital terrain maps obtained from DMA digital data. The land use/vegetation/land cover information from manual photographic and Landsat interpretation were joined digitally into a single data plane with an integrated legend, and segmented into quadrangle units. These were merged with the digitized geomorphic units and the digital terrain data using a Prime 400 minicomputer. All data planes were geo-referenced to a UTM coordinate grid.
Robots make and break connections between umbilical plates and mating connectors on rockets about to be launched. Sensing and control systems include vision, force, and torque subsystems. Enhances safety by making it possible to couple and uncouple umbilical plates quickly, without exposing human technicians to hazards of leaking fuels and oxidizers. Significantly reduces time spent to manually connect umbilicals. Robots based on similar principles used in refueling of National AeroSpace Plane (NASP) and satellites and orbital transfer vehicles in space.
We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.