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At least 289 records · Page 16

All sources NASA NTRS DOE OSTI

NASA NTRS2010

NASA Tech Briefs, January 2010

Topics covered include: Cryogenic Flow Sensor; Multi-Sensor Mud Detection; Gas Flow Detection System; Mapping Capacitive Coupling Among Pixels in a Sensor Array; Fiber-Based Laser Transmitter for Oxygen A-Band Spectroscopy and Remote Sensing; Low-Profile, Dual-Wavelength, Dual-Polarized Antenna; Time-Separating Heating and Sensor Functions of Thermistors in Precision Thermal Control Applications; Cellular Reflectarray Antenna; A One-Dimensional Synthetic-Aperture Microwave Radiometer; Electrical Switching of Perovskite Thin-Film Resistors; Two-Dimensional Synthetic-Aperture Radiometer; Ethernet-Enabled Power and Communication Module for Embedded Processors; Electrically Variable Resistive Memory Devices; Improved Attachment in a Hybrid Inflatable Pressure Vessel; Electrostatic Separator for Beneficiation of Lunar Soil; Amorphous Rover; Space-Frame Antenna; Gear-Driven Turnbuckle Actuator; In-Situ Focusing Inside a Thermal Vacuum Chamber; Space-Frame Lunar Lander; Wider-Opening Dewar Flasks for Cryogenic Storage; Silicon Oxycarbide Aerogels for High-Temperature Thermal Insulation; Supercapacitor Electrolyte Solvents with Liquid Range Below -80 C; Designs and Materials for Better Coronagraph Occulting Masks; Fuel-Cell-Powered Vehicle with Hybrid Power Management; Fine-Water-Mist Multiple-Orientation-Discharge Fire Extinguisher; Fuel-Cell Water Separator; Turbulence and the Stabilization Principle; Improved Cloud Condensation Nucleus Spectrometer; Better Modeling of Electrostatic Discharge in an Insulator; Sub-Aperture Interferometers; Terahertz Mapping of Microstructure and Thickness Variations; Multiparallel Three-Dimensional Optical Microscopy; Stabilization of Phase of a Sinusoidal Signal Transmitted Over Optical Fiber; Vacuum-Compatible Wideband White Light and Laser Combiner Source System; Optical Tapers as White-Light WGM Resonators; EPR Imaging at a Few Megahertz Using SQUID Detectors; Reducing Field Distortion in Magnetic Resonance Imaging; Fluorogenic Cell-Based Biosensors for Monitoring Microbes; A Constant-Force Resistive Exercise Unit; GUI to Facilitate Research on Biological Damage from Radiation; On-Demand Urine Analyzer; More-Realistic Digital Modeling of a Human Body; and Advanced Liquid-Cooling Garment Using Highly Thermally Conductive Sheets.

Source record↗

NASA NTRS1986

Thermal sensing of cryogenic wind tunnel model surfaces Evaluation of silicon diodes

Different sensors and installation techniques for surface temperature measurement of cryogenic wind tunnel models were investigated. Silicon diodes were selected for further consideration because of their good inherent accuracy. Their average absolute temperature deviation in comparison tests with standard platinum resistance thermometers was found to be 0.2 K in the range from 125 to 273 K. Subsurface temperature measurement was selected as the installation technique in order to minimize aerodynamic interference. Temperature distortion caused by an embedded silicon diode was studied numerically.

Daryabeigi, K.↗

NASA NTRS1986

Thermal sensing of cryogenic wind tunnel model surfaces - Evaluation of silicon diodes

Daryabeigi, Kamran↗

NASA NTRS2004

Assurance Technology Challenges of Advanced Space Systems

The initiative to explore space and extend a human presence across our solar system to revisit the moon and Mars post enormous technological challenges to the nation's space agency and aerospace industry. Key areas of technology development needs to enable the endeavor include advanced materials, structures and mechanisms; micro/nano sensors and detectors; power generation, storage and management; advanced thermal and cryogenic control; guidance, navigation and control; command and data handling; advanced propulsion; advanced communication; on-board processing; advanced information technology systems; modular and reconfigurable systems; precision formation flying; solar sails; distributed observing systems; space robotics; and etc. Quality assurance concerns such as functional performance, structural integrity, radiation tolerance, health monitoring, diagnosis, maintenance, calibration, and initialization can affect the performance of systems and subsystems. It is thus imperative to employ innovative nondestructive evaluation methodologies to ensure quality and integrity of advanced space systems. Advancements in integrated multi-functional sensor systems, autonomous inspection approaches, distributed embedded sensors, roaming inspectors, and shape adaptive sensors are sought. Concepts in computational models for signal processing and data interpretation to establish quantitative characterization and event determination are also of interest. Prospective evaluation technologies include ultrasonics, laser ultrasonics, optics and fiber optics, shearography, video optics and metrology, thermography, electromagnetics, acoustic emission, x-ray, data management, biomimetics, and nano-scale sensing approaches for structural health monitoring.

Chern, E. James↗

NASA NTRS1980

Radar target for remotely sensing hydrological phenomena

An apparatus for remotely measuring and accessing water status relative to snow and glacial melt, surface runoff, rainfall, evaporation, flow rate, and soil moisture is described. A radar target located at a selected location on the surface of the Earth is designed to collect water and render its cross sectional area variable as a function of the height of the water level within the target. The target is remotely monitored by an orbiting or airborne synthetic aperature radar. The target appears as a bright spot embedded within the radar image. The target brightness is indicative of the height of the water level within the ground located target.

Sivertson, W. E., Jr.↗

NASA NTRS2000

Hyperspectral Imaging Sensors and the Marine Coastal Zone

Hyperspectral imaging sensors greatly expand the potential of remote sensing to assess, map, and monitor marine coastal zones. Each pixel in a hyperspectral image contains an entire spectrum of information. As a result, hyperspectral image data can be processed in two very different ways: by image classification techniques, to produce mapped outputs of features in the image on a regional scale; and by use of spectral analysis of the spectral data embedded within each pixel of the image. The latter is particularly useful in marine coastal zones because of the spectral complexity of suspended as well as benthic features found in these environments. Spectral-based analysis of hyperspectral (AVIRIS) imagery was carried out to investigate a marine coastal zone of South Florida, USA. Florida Bay is a phytoplankton-rich estuary characterized by taxonomically distinct phytoplankton assemblages and extensive seagrass beds. End-member spectra were extracted from AVIRIS image data corresponding to ground-truth sample stations and well-known field sites. Spectral libraries were constructed from the AVIRIS end-member spectra and used to classify images using the Spectral Angle Mapper (SAM) algorithm, a spectral-based approach that compares the spectrum, in each pixel of an image with each spectrum in a spectral library. Using this approach different phytoplankton assemblages containing diatoms, cyanobacteria, and green microalgae, as well as benthic community (seagrasses), were mapped.

Richardson, Laurie L.↗

NASA NTRSDate not supplied

Swarm Mentality: Toward Automatic Swarm State Awareness with Runtime Verification

Cyber-Physical Systems (CPSs) already exhibit impressive performance in all areas of human life, and swarms of CPSs promise to increase their capabilities even further. However, to effectively utilize CPS swarms their complexity of operation has to scale sub-linearly with the number of swarm members. Presenting the swarm to an operator as a single entity almost eliminates the additional per-member overhead entirely. To operate a swarm as one entity, and/or to increase the swarm’s autonomy, the operator and the swarm members need to reason and communicate at the same level of abstraction, i.e. the swarm needs a sense of “self.” Therefore, we require the ability to specify whole swarm properties yet monitor them at the member level. We examine one architecture for achieving this awareness by: 1) Defining a taxonomy for comparing techniques that synthesize this belief-state 2) Propose use of the Runtime Verification formal method to fill this role 3) Present preliminary designs for extending and embedding such a system in the Distributed Spacecraft Autonomy architecture to generate per-member monitors from swarm level specification.

Runtime Verification↗

NASA NTRS2017

MELDI2 Do No Harm Test Series

Mars 2020 will fly the Mars Entry, Descent, and Landing Instrumentation II (MEDLI2) sensor suite consisting of a total of seventeen instrumented thermal sensor plugs, eight pressure transducers, two heat flux sensors, and one radiometer embedded in the thermal protection system (TPS). Of the MEDLI2 instrumentation, eleven instrumented thermal plugs and seven pressure transducers will be installed on the heatshield of the Mars 2020 vehicle while the rest will be installed on the backshell. The goal of the MEDLI2 instrumentation is to directly inform the large performance uncertainties that contribute to the design and validation of a Mars entry system. A better understanding of the entry environment and TPS performance could lead to reduced design margins enabling a greater payload mass-fraction and smaller landing ellipses. To prove that the MEDLI2 system will not degrade the performance of the Mars 2020 TPS, an Aerothermal Do No Harm (DNH) test series was designed and conducted. Like Mars 2020's predecessor, Mars Science Laboratory (MSL), the heatshield material will be Phenolic Impregnated Carbon Ablator (PICA); the Mars 2020 entry conditions are enveloped by the MSL design environments, therefore the development and qualification testing performed during MEDLI is sufficient to show that the similar MEDLI2 heatshield instrumentation will not degrade PICA performance. However, given that MEDLI did not include any backshell instrumentation, the MEDLI2 team was required to design and execute a DNH test series utilizing the backshell TPS material (SLA-561V) with the intended flight sensor suite. To meet the requirements handed down from Mars 2020, the MEDLI2 DNH test series emphasized the interaction between the MEDLI2 sensors and sensing locations with the surrounding backshell TPS and substrucutre. These interactions were characterized by performing environmental testing of four 12" by 12" test panels, which mimicked the construction of the backshell TPS and the integration of the MEDLI2 sensors as seen in Figure 1. The testing included thermal vacuum/ cycling, random vibration, shock, and arc jet testing. The test panels were fabricated by Lockheed Martin, establishing techniques that will be utilized during the Mars 2020 vehicle installation. Each test panel included one thermal sensor plug (two embedded thermocouples), one heat flux sensor, and multiple pressure port holes for evaluation. This presentation will discuss the planning and execution of the MEDLI2 DNH test series. Selected highlights and results of each environmental test will be presented, and lessons learned will be addressed that will feed forward into the planning for the MEDLI2 flight system certification testing.

Heatshield↗

DOE OSTI2024

Self-Supervised Cloud Classification

Abstract Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets. Significance Statement Marine clouds heavily influence Earth’s weather and climate, and improved understanding of marine clouds is required to improve our atmospheric modeling capabilities and physical understanding of the atmosphere. Recently, deep learning has emerged as a powerful research tool that can be used to identify and study specific marine cloud types in the vast number of images collected by Earth-observing satellites. While powerful, these approaches require hand-labeling of training data, which is prohibitively time intensive. This study evaluates a recently developed self-supervised deep learning method that does not require human-labeled training data for processing images of clouds. We show that the trained algorithm performs competitively with algorithms trained on hand-labeled data for image classification tasks. We also discuss potential downstream uses and demonstrate some exciting features of the approach including application to multiple satellite instruments, resilience against changing image brightness, and its learned internal representations of cloud types. The self-supervised technique removes one of the major hurdles for applying deep learning to very large atmospheric datasets.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1999

Digital Earth Initiative: A Joint Interagency Program

The Digital Earth is a virtual representation of our planet that enables a person to explore and interact with the vast amounts of natural and cultural information gathered about the Earth. The Digital Earth comprises data interfaces and standards enabling access to geo-referenced data from remote sensing, cartographic, demographic, medical, and other sources to respond to questions posed by the user. In a recent address at the California Science Center in Los Angeles, Vice President Al Gore articulated a Digital Earth Vision. That vision spoke to developing a multi-resolution, three-dimensional representation of the planet, into which we can roam and zoom into vast quantities of embedded geo-referenced data. The vision was not limited to moving through space but also allowing travel over a time-line, which can be set for days, years, centuries, or even geological epochs. As prototypes become available, it would also be possible to interact with the Digital Earth in multiple places around the country with access to high-speed networks and at a more limited level of access over the Internet. NASA was asked by the Vice President to lead an interagency initiative that would take steps to bring this vision to the public. This talk describes the start-up and plans of the Digital Earth Interagency Working Group in the formulation of its charter, an architecture reference model for Digital Earth, public/private partnerships, cooperative agreement notices, Digital Earth prototypes, and testbeds. Animations employing technologies for virtual roaming and zooming through multi-resolution satellite data set as prototype systems will be presented along with examples of potential user scenarios. Plans for engaging academia and industry in implementing the Digital Earth initiative will be discussed.

Halem, Milton↗

DOE OSTI2020

GEMINI Case Study

Sandia's GEMINI-Scout Mine Rescue Robot is an unmanned ground vehicle designed to enter potentially hazardous environments to explore, assess, and evaluate dangerous situations first responders may face when conducting a rescue mission. GEMINI is approximately four feet long and two feet tall, which enables the robot to maneuver through small locations on rough terrains caused by earthquakes, fires, or radiological incidents. GEMINI uses track propulsion to climb stairs, travel through gravel and sand pits, pivot in place, and traverse 45-degree climbs with few problems. Furthermore, the vehicle's dual tracked-chassis design allows it to operate in hostile, dark, muddy, high-temperature, and explosive debris-strewn environments, while maintaining efficient ground mobility. The mobility and modularity of the vehicle allow for easy integration of sensors to conduct gas and temperature sensing and offers pan/tilt, zoom color, and thermal camera video streaming capabilities. The vehicle is also able to carry a payload of about 50 pounds of batteries and can handle an additional 200 pounds of payload, whether for additional diagnostics, supplies, or clothing for those trapped in an effected area. GEMINI is remotely operated through a wireless connection and an onboard computer running a customized embedded control application, which directly communicates to all onboard components except for the audio and video systems. When line of sight is not possible, operators use a shockresistant fiber optic cable to ensure continuous functionality of the vehicle. This allows for direct local control of the vehicle, which streams collected data back to the operator for enhanced situational awareness. In addition, the vehicle incorporates safety features such as explosion proof housing to ensure safe electronic operations in hazardous gas or flooded environments; a four-channel video link and two-way audio to ensure located survivors can communicate with operators; and an MSHA-approved multi-gas sensor to monitor air quality.

42 ENGINEERING↗

DOE OSTI2024

Experimental Investigation of Low-Frequency Distributed Acoustic Sensor Responses to Two Parallel Propagating Fractures

Low-frequency distributed acoustic sensing (LF-DAS) is a diagnostic tool for hydraulic fracture propagation with far-field monitoring using fiber optic sensors. LF-DAS senses strain rate variation caused by stress field change due to fracture propagation. Fiber optic sensors are installed in the monitoring wells in the vicinity of a fractured well. From the strain responses, fracture propagation can be evaluated. To understand subsurface conditions with multiple propagating fractures, a laboratory-scale hydraulic fracture experiment was performed simulating the LF-DAS response to fracture propagation with embedded distributed optical fiber strain sensors under these conditions. The experiment was performed using a transparent cube of epoxy with two parallel radial initial flaws centered in the cube. Fluid was injected into the sample to generate fractures along the initial flaws. The experiment used distributed high-definition fiber optic strain sensors with tight spatial resolutions. The sensors were embedded at two different locations on opposite sides of the initial flaws, serving as observation/monitoring locations. We also employed finite element modeling to numerically solve the linear elastic equations of equilibrium continuity and stress–strain relationships. The measured strains from the experiment were compared to simulation results from the finite element model. The experimentally derived strain and strain-rate waterfall plots from this study show the responses to both fractures propagating, while the fracture at the lower position took most of the fluid during the experiment. Interestingly, a fracture first began propagating from the upper flaw of the two flaws, but once the lower fracture was initiated, it grew much faster than the upper fracture. Both fibers were intercepted by the lower fracture, further verifying the strain signature as a fracture is approaching and intersecting an offset fiber.

Chemistry↗

NASA NTRS2000

Real Time Intelligent Target Detection and Analysis with Machine Vision

We present an algorithm for detecting a specified set of targets for an Automatic Target Recognition (ATR) application. ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. We address the problem of discriminating between targets and nontarget objects in a scene by evaluating 40x40 image blocks belonging to an image. Each image block is first projected onto a set of templates specifically designed to separate images of targets embedded in a typical background scene from those background images without targets. These filters are found using directed principal component analysis which maximally separates the two groups. The projected images are then clustered into one of n classes based on a minimum distance to a set of n cluster prototypes. These cluster prototypes have previously been identified using a modified clustering algorithm based on prior sensed data. Each projected image pattern is then fed into the associated cluster's trained neural network for classification. A detailed description of our algorithm will be given in this paper. We outline our methodology for designing the templates, describe our modified clustering algorithm, and provide details on the neural network classifiers. Evaluation of the overall algorithm demonstrates that our detection rates approach 96% with a false positive rate of less than 0.03%.

Howard, Ayanna↗

DOE OSTI2024

Super Resolving Unrolled Neural Networks for Remote Sensing

In remote sensing systems, the capabilities of the system are constrained by the complex interactions between size, weight, and power (SWAP) of potential designs. In electro-optical (EO) systems, examples of these critical parameters include the system’s sensitivity and resolution. Those parameters can be increased by ever larger optical apertures and focal planes but at the cost of more SWAP. Multi-image super resolution (MISR) techniques allow resolution to be enhanced via computation rather than more sophisticated optical hardware. These algorithms combine multiple images together into a single, higher resolution image, trading temporal resolution and computation for spatial resolution. Fielded MISR techniques, such as Drizzle, can require several hundred images to create a single super resolved image, implying reduced temporal resolution, increased data acquisition load, and limiting mission applications. Iterative techniques, such as model-based image reconstruction and compressive sensing, have been shown to create super resolved images using fewer images than Drizzle. They do this by posing an optimization problem that balances accuracy between a highly accurate physical model and an image model. In the case of super resolution, the physical model is defined by the relation between low resolution input images and the desired high resolution output image. The image model encodes some assumptions about the super resolved image. These assumptions are meant to suppress reconstruction artifacts that arise due to deterministic physical model error, stochastic measurement noise, and potential undersampling. In practice, the performance of iterative methods are limited by imaging models compatible with optimization. Deep learning-based methods can effectively learn image models of arbitrary complexity, but lack the theoretical explainability and robustness of iterative techniques. Consensus equilibrium (CE) generalizes the iterative techniques beyond optimization, enabling blackbox algorithms such as traditional and neural image denoisers to be used as the image model. CE-based approaches retain much of the explainability and robustness of iterative techniques while allowing the expressiveness of machine learning image models to be used. Additionally, by unrolling iterations of CE with an embedded image denoiser, the image denoiser can be further trained and specialized to the specific application with potentially higher quality reconstructions. Under this project, we demonstrated the feasibility of training an unrolled neural network based upon CE. While we didn’t train one, we showed that the CE process is differentiable and its gradient can be tractably computed. We also explored the usage of a variants of CE akin to generative neural works. Most importantly, we applied the CE framework to a number of problems including non-blind deconvolution, upsampling, single-image super resolution, MISR, event-based sensing, and saturated deconvolution. Our MISR prototype creates high quality reconstructions with an order of magnitude fewer images than previous approaches and, critically, produces these reconstructions fast enough for practical usage.

47 OTHER INSTRUMENTATION↗

NASA NTRS1981

Ionospheric remote sensing of medium-scale gravity waves and tornadic storms

Gravity waves associated with severe storms are investigated on the basis of ionospheric sounding using a ground-based Doppler system. Reverse-group ray tracing computations are used to determine the origin of over 20 gravity waves detected within 800 km of Huntsville, Alabama in association with a group of tornadoes, isolated tornadoes in the presence of a squall line, and isolated tornadoes in the absence of a squall line. Gravity waves associated with tornadoes are found either to be generated by thunderstorms with enhanced convection embedded in a squall line, or in an isolated cloud with enhanced convection. The computed wave sources are observed in all cases to be located near the points where the tornadoes touched down more than an hour after wave excitation. Results show that gravity waves play an important role in troposphere-ionosphere coupling during times of intense convection associated with tornadic storm activity.

Hung, R. J.↗

NASA NTRS2011

NASA Tech Briefs, May 2011

Topics covered include: 1) Method to Estimate the Dissolved Air Content in Hydraulic Fluid; 2) Method for Measuring Collimator-Pointing Sensitivity to Temperature Changes; 3) High-Temperature Thermometer Using Cr-Doped GdAlO3 Broadband Luminescence; 4)Metrology Arrangement for Measuring the Positions of Mirrors of a Submillimeter Telescope; 5) On-Wafer S-Parameter Measurements in the 325-508-GHz Band; 6) Reconfigurable Microwave Phase Delay Element for Frequency Reference and Phase-Shifter Applications; 7) High-Speed Isolation Board for Flight Hardware Testing; 8) High-Throughput, Adaptive FFT Architecture for FPGA-Based Spaceborne Data Processors; 9) 3D Orbit Visualization for Earth-Observing Missions; 10) MaROS: Web Visualization of Mars Orbiting and Landed Assets; 11) RAPID: Collaborative Commanding and Monitoring of Lunar Assets; 12) Image Segmentation, Registration, Compression, and Matching; 13) Image Calibration; 14) Rapid ISS Power Availability Simulator; 15) A Method of Strengthening Composite/Metal Joints; 16) Pre-Finishing of SiC for Optical Applications; 17) Optimization of Indium Bump Morphology for Improved Flip Chip Devices; 18) Measuring Moisture Levels in Graphite Epoxy Composite Sandwich Structures; 19) Marshall Convergent Spray Formulation Improvement for High Temperatures; 20) Real-Time Deposition Monitor for Ultrathin Conductive Films; 21) Optimized Li-Ion Electrolytes Containing Triphenyl Phosphate as a Flame-Retardant Additive; 22) Radiation-Resistant Hybrid Lotus Effect for Achieving Photoelectrocatalytic Self-Cleaning Anticontamination Coatings; 23) Improved, Low-Stress Economical Submerged Pipeline; 24) Optical Fiber Array Assemblies for Space Flight on the Lunar Reconnaissance Orbiter; 25) Local Leak Detection and Health Monitoring of Pressurized Tanks; 26) Dielectric Covered Planar Antennas at Submillimeter Wavelengths for Terahertz Imaging; 27) Automated Cryocooler Monitor and Control System; 28) Broadband Achromatic Phase Shifter for a Nulling Interferometer; 29) Super Dwarf Wheat for Growth in Confined Spaces; 30) Fine Guidance Sensing for Coronagraphic Observatories; 31) Single-Antenna Temperature- and Humidity-Sounding Microwave Receiver; 32) Multi-Wavelength, Multi-Beam, and Polarization-Sensitive Laser Transmitter for Surface Mapping; 33) Optical Communications Link to Airborne Transceiver; 34) Ascent Heating Thermal Analysis on Spacecraft Adaptor Fairings; 35) Entanglement in Self-Supervised Dynamics; 36) Prioritized LT Codes; 37) Fast Image Texture Classification Using Decision Trees; 38) Constraint Embedding Technique for Multibody System Dynamics; 39) Improved Systematic Pointing Error Model for the DSN Antennas; 40) Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks; 41) More-Accurate Model of Flows in Rocket Injectors; 42) In-Orbit Instrument-Pointing Calibration Using the Moon as a Target; 43) Reliability of Ceramic Column Grid Array Interconnect Packages Under Extreme Temperatures; 44) Six Degrees-of-Freedom Ascent Control for Small-Body Touch and Go; and 45) Optical-Path-Difference Linear Mechanism for the Panchromatic Fourier Transform Spectrometer.

Source record↗

DOE OSTI2023

All-silicon quantum light source by embedding an atomic emissive center in a nanophotonic cavity

Silicon is the most scalable optoelectronic material but has suffered from its inability to generate directly and efficiently classical or quantum light on-chip. Scaling and integration are the most fundamental challenges facing quantum science and technology. We report an all-silicon quantum light source based on a single atomic emissive center embedded in a silicon-based nanophotonic cavity. We observe a more than 30-fold enhancement of luminescence, a near-unity atom-cavity coupling efficiency, and an 8-fold acceleration of the emission from the all-silicon quantum emissive center. Our work opens immediate avenues for large-scale integrated cavity quantum electrodynamics and quantum light-matter interfaces with applications in quantum communication and networking, sensing, imaging, and computing.

36 MATERIALS SCIENCE↗

NASA NTRS2012

Climate Change Adaptation Science Activities at NASA Johnson Space Center

The Johnson Space Center (JSC), located in the southeast metropolitan region of Houston, TX is the prime NASA center for human spaceflight operations and astronaut training, but it also houses the unique collection of returned extraterrestrial samples, including lunar samples from the Apollo missions. The Center's location adjacent to Clear Lake and the Clear Creek watershed, an estuary of Galveston Bay, puts it at direct annual risk from hurricanes, but also from a number of other climate-related hazards including drought, floods, sea level rise, heat waves, and high wind events all assigned Threat Levels of 2 or 3 in the most recent NASA Center Disaster/Risk Matrix produced by the Climate Adaptation Science Investigator Working Group. Based on prior CASI workshops at other NASA centers, it is recognized that JSC is highly vulnerable to climate-change related hazards and has a need for adaptation strategies. We will present an overview of prior CASI-related work at JSC, including publication of a climate change and adaptation informational data brochure, and a Resilience and Adaptation to Climate Risks Workshop that was held at JSC in early March 2012. Major outcomes of that workshop that form a basis for work going forward are 1) a realization that JSC is embedded in a regional environmental and social context, and that potential climate change effects and adaptation strategies will not, and should not, be constrained by the Center fence line; 2) a desire to coordinate data collection and adaptation planning activities with interested stakeholders to form a regional climate change adaptation center that could facilitate interaction with CASI; 3) recognition that there is a wide array of basic data (remotely sensed, in situ, GIS/mapping, and historical) available through JSC and other stakeholders, but this data is not yet centrally accessible for planning purposes.

Stefanov, William L.↗