Search NASA⌕ Search

SEARCH · Search NASA

Results for “vision screening”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Machine vision based teleoperation aid

When teleoperating a robot using video from a remote camera, it is difficult for the operator to gauge depth and orientation from a single view. In addition, there are situations where a camera mounted for viewing by the teleoperator during a teleoperation task may not be able to see the tool tip, or the viewing angle may not be intuitive (requiring extensive training to reduce the risk of incorrect or dangerous moves by the teleoperator). A machine vision based teleoperator aid is presented which uses the operator's camera view to compute an object's pose (position and orientation), and then overlays onto the operator's screen information on the object's current and desired positions. The operator can choose to display orientation and translation information as graphics and/or text. This aid provides easily assimilated depth and relative orientation information to the teleoperator. The camera may be mounted at any known orientation relative to the tool tip. A preliminary experiment with human operators was conducted and showed that task accuracies were significantly greater with than without this aid.

Hoff, William A.↗
NASA NTRS1996

Field Geology/Processes

The field geology/process group examined the basic operations of a terrestrial field geologist and the manner in which these operations could be transferred to a planetary lander. Four basic requirements for robotic field geology were determined: geologic content; surface vision; mobility; and manipulation. Geologic content requires a combination of orbital and descent imaging. Surface vision requirements include range, resolution, stereo, and multispectral imaging. The minimum mobility for useful field geology depends on the scale of orbital imagery. Manipulation requirements include exposing unweathered surfaces, screening samples, and bringing samples in contact with analytical instruments. To support these requirements, several advanced capabilities for future development are recommended. Capabilities include near-infrared reflectance spectroscopy, hyper-spectral imaging, multispectral microscopy, artificial intelligence in support of imaging, x ray diffraction, x ray fluorescence, and rock chipping.

Allen, Carlton↗
DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

Visual specification of robot motion

The authors describe the use of stereo pairs of images to specify robot motion. The experimental setup includes a SUN workstation, a PUMA 560 robot, and an Imaging 151 vision system. An X-window environment displays stereo images of the work scene. Image processing is performed to extract linear edge segments from the images and the results are displayed on screen. Using a pointing device, the user selects a group of edges from the object relevant to the task. The 3D structure of this group of features is found by stereo triangulation and they can be displayed in 3D from any point of view. A viewpoint orthogonal to the plane defined by these 3D edges is used to specify the robot position relative to object position. The actual robot will then be moved to the specified position.

Shiu, Y. C.↗
NASA NTRS1989

Liquid-Crystal Optical Correlator

Optical correlator uses commercially-available liquid-crystal television (LCTV) screen as spatial light modulator. Correlations with this device done at video frame rates, making such operations as bar-code recognition possible at reasonable cost. With further development, such correlator useful in automation, robotic vision, and optical image processing.

Liu, Hua-Kuang↗
NASA NTRS2002

Visions of our Planet's Atmosphere, Land and Oceans: NASA/NOAA Electronic Theater 2002

The NASA/NOAA Electronic Theater presents Earth science observations and visualizations in a historical perspective. Fly in from outer space to the Olympic Medals Plaza, the new Gateway Center, and the University of Utah Stadium Site of the Olympic Opening and Closing Ceremonies in Salt Lake City. Fly in and through the Park City, and Snow Basin sites of the 2002 Winter Olympic Alpine Venues using 1 m IKONOS "Spy Satellite" data. See the four seasons of the Wasatch Front as observed by Landsat 7 at 15m resolution and watch the trees turn color in the Fall, snow come and go in the mountains and the reservoirs freeze and melt. Go back to the early weather satellite images from the 1960s and see them contrasted with the latest US and international global satellite weather movies Including hurricanes & "tornadoes". See the latest visualizations of spectacular images from NASA/NOAA remote sensing missions like Terra, GOES, TRMM, SeaWiFS, Landsat 7 including new 1 - min GOES rapid scan image sequences of Nov 9th 2001 Midwest tornadic thunderstorms and have them explained. See how High-Definition Television (HDTV) is revolutionizing the way we communicate science. (In cooperation with the American Museum of Natural History in NYC) See dust storms in Africa and smoke plumes from fires in Mexico. See visualizations featured on the covers Of Newsweek, TIME, National Geographic, Popular Science & on National & International Network TV. New computer software. tools allow us to roam & zoom through massive global images e.g. Landsat tours of the US, and Africa, showing desert and mountain geology as well as seasonal changes in vegetation. See animations of the polar ice packs and the motion of gigantic Antarctic Icebergs from SeaWinds data. Spectacular new visualizations of the global atmosphere & oceans are shown. See vertexes and currents in the global oceans that bring up the nutrients to feed tin) algae and draw the fish, whales and fisherman. See the how the ocean blooms in response to these currents and El Nino/La Nina climate changes. See the city lights, fishing fleets, gas flares and biomass burning of the Earth at night observed by the "night-vision" DMSP military satellite. The demonstration is interactively driven by a SGI Octane Graphics Supercomputer with two CPUs, 4 Gigabytes of RAM and 0.5 Terabyte of disk using two projectors across a super sized panoramic 48 foot screen. In addition new HDTV technology will be demonstrated from a portable computer server.

Hasler, A. F.↗
NASA NTRS2001

Visions of our Planet's Atmosphere, Land and Oceans: NASA/NOAA Electronic Theater 2002

The NASA/NOAA Electronic Theater presents Earth science observations and visualizations in a historical perspective. Fly in from outer space to the Olympic Medals Plaza, the new Gateway Center, and the University of Utah Stadium Site of the Olympic Opening and Closing Ceremonies in Salt Lake City. Fly in and through the Park City, and Snow Basin sites of the 2002 Winter Olympic Alpine Venues using 1 m IKONOS "Spy Satellite" data. See the four seasons of the Wasatch Front as observed by Landsat 7 at 15m resolution and watch the trees turn color in the Fall, snow come and go in the mountains and the reservoirs freeze and melt. Go back to the early weather satellite images from the 1960s and see them contrasted with the latest US and international global satellite weather movies including hurricanes & "tornadoes". See the latest visualizations of spectacular images from NASA/NOAA remote sensing missions like Terra, GOES, TRMM, SeaWiFS, Landsat 7 including new 1 - min GOES rapid scan image sequences of Nov 9th 2001 Midwest tornadic thunderstorms and have them explained. See how High-Definition Television (HDTV) is revolutionizing the way we communicate science. (In cooperation with the American Museum of Natural History in NYC) See dust storms in Africa and smoke plumes from fires in Mexico. See visualizations featured on the covers of Newsweek, TIME, National Geographic, Popular Science & on National & International Network TV. New computer software tools allow us to roam & zoom through massive global images e.g. Landsat tours of the US, and Africa, showing desert and mountain geology as well as seasonal changes in vegetation. See animations of the polar ice packs and the motion of gigantic Antarctic Icebergs from SeaWinds data. Spectacular new visualizations of the global atmosphere & oceans are shown. See vortexes and currents in the global oceans that bring up the nutrients to feed tiny algae and draw the fish, whales and fisherman. See the how the ocean blooms in response to these currents and El Nino/La Nina climate changes. See the city lights, fishing fleets, gas flares and bio-mass burning of the Earth at night observed by the "night-vision" DMSP military satellite. The demonstration is interactively driven by a SGI Octane Graphics Supercomputer with two CPUs, 4 Gigabytes of RAM and 0.5 Terabyte of disk using two projectors across a super sized panoramic 48 foot screen. In addition new HDTV technology will be demonstrated from a portable computer server.

Hasler, A. F.↗
DOE OSTI2025

Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting

Abstract Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of small molecules with the specific genetic makeup of the patient. Anti-cancer DRP is a particularly challenging task requiring costly experiments as underlying pathogenic mechanisms are broad and associated with multiple genomic pathways. The scientific community has exerted significant efforts to generate public drug screening datasets, giving a path to various machine learning models that attempt to reason over complex data space of small compounds and biological characteristics of tumors. However, the data depth is still lacking compared to application domains like computer vision or natural language processing domains, limiting current learning capabilities. To combat this issue and improves the generalizability of the DRP models, we are exploring strategies that explicitly address the imbalance in the DRP datasets. We reframe the problem as a multi-objective optimization across multiple drugs to maximize deep learning model performance. We implement this approach by constructing Multi-Objective Optimization Regularized by Loss Entropy loss function and plugging it into a Deep Learning model. We demonstrate the utility of proposed drug discovery methods and make suggestions for further potential application of the work to achieve desirable outcomes in the healthcare field.

Biochemistry & Molecular Biology↗
NASA NTRS2000

Evaluation of Helmet Mounted Display Alerting Symbology

Proposed helicopter helmet mounted displays will be used to alert the pilot to a variety of conditions, from threats to equipment problems. The present research was performed under the NASA Safe All-weather Flight Operations Research (SAFOR) program supported by a joint Army/NASA research agreement. The purpose of the research was to examine ways to optimize the alerting effectiveness of helmet display symbology. The research used two approaches to increasing the effectiveness of alerts. One was to increase the ability of the alert to attract attention by using the entire display surface. The other was to include information about the required response in the alert itself. The investigation was conducted using the NASA Ames Research Center's six-degree-of-freedom vertical motion simulator (VMS) with a rotorcraft cockpit. Helmet display symbology was based on the AH-64's pilot night vision system (PNVS), cruise mode symbology. A standardized mission was developed, that consisted of 11 legs. The mission included four tasks, which allowed variation in the frequency of alerts. The general trend in the data points to a small benefit from both the full-screen alert and the partial information alert.

DeMaio, Joe↗
NASA NTRSDate not supplied

ICME for NASA Aerospace Applications: Batteries for Electric Aviation

NASA’s approach to computational materials modeling is detailed in the NASA Vision 2040 Roadmap for Multiscale Modeling and Simulation of Materials and Systems. This report is in the spirit of national initiatives such as the Material Genome Initiative (MGI), Integrated Computational Materials Engineering (ICME), and others. We utilize a combination of fundamental modeling, computational high-throughput screening, and data science methods, e.g., machine learning, are used to find innovative solutions to NASA or national technology challenges. Applications of interest are wide ranging from advanced alloys to batteries to coatings, among others. In this talk, we present three examples for recent work related to NASA applications. First, doping advanced sulfur battery cathodes with selenium boosts electrical conductivity important for electric aircraft applications. First principles calculations will be discussed that result in compositional design maps for these materials. Second, development of icephobic coatings is important to mitigate safety hazards associated with icing for aircraft. Molecular dynamics simulations are reported for ice-surface interfaces to understand adhesion mechanisms and help screen optimal ice-phobic coatings. Third, shape memory alloys have numerous applications as actuators, superelastic materials, etc. for aerospace. We report machine learning models that predict martensitic transition temperatures across a broad swath of compositional space.

John Lawson↗
DOE OSTI2024

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗
NASA NTRS1994

A Third Arm for the Surgeon

In laparoscopic surgery, tiny incisions are made in the patient's body and a laparoscope (an optical tube with a camera at the end) is inserted. The camera's image is projected onto two video screens, whose views guide the surgeon through the procedure. AESOP, a medical robot developed by Computer Motion, Inc. with NASA assistance, eliminates the need for a human assistant to operate the camera. The surgeon uses a foot pedal control to move the device, allowing him to use both hands during the surgery. Miscommunication is avoided; AESOP's movement is smooth and steady, and the memory vision is invaluable. Operations can be completed more quickly, and the patient spends less time under anesthesia. AESOP has been approved by the FDA.

Source record↗
NASA NTRS2018

Pilot Study on the Investigation of Tear Fluid Biomarkers as an Indicator of Ocular, Neurological, and Immunological Health in Astronauts

The purpose of this pilot study is to investigate the collection, preparation, and analysis of tear biomarkers as a means of assessing ocular, neurological, and immunological health. At present, no published data exists on the cytokine profiles of tears from astronauts exposed to long periods of microgravity and space irradiations. In addition, no published data exist on cytokine (biomarker) profiles of tears that have been collected from irradiated non-human biological systems (primates and other animal models). A goal for the proposed pilot study is to discover novel tear biomarkers which can help inform researchers, clinicians, epidemiologist and healthcare providers about the health status of a living biological system, as well as informing them when a disease state is triggered. This would be done via analysis of the onset of expression of pro-inflammatory cytokines, leading up to the full progression of a disease (i.e. cancer, loss of vision, radiation-induced oxidative stress, cardiovascular disorders, fibrosis in major organs, bone loss). Another goal of this pilot study is to investigate the state of disease against proposed medical countermeasures, in order to determine whether the countermeasures are efficacious in preventing or mitigating these injuries. An example of an up and coming tear biomarker technology, Ascendant Dx, a clinical stage diagnostic company, is developing a screening test to detect breast cancer using proteins from tears. The team utilized Liquid Chromatography -Mass Spectrometry with Mass analysis (LC MS/MS) as a discovery platform followed by validation with ELISA to come up with a panel of protein biomarkers that can differentiate breast cancer samples from control ("cancer free") samples with results far surpassing the results of imaging techniques in use today. Continued research into additional proteins is underway to increase the sensitivity and specificity of the test and development efforts are on the way to transfer the test onto a fast, accurate and inexpensive point of care platform. In conclusion, the expected results from this proposed pilot study are to: a) establish an SOP for retrieving/storing/transporting tear fluid samples from multicentre sites b) establish a normal range for relevant biomarkers in tears; and c) establish a database (biobank) of tears of space naïve versus veteran astronauts, to establish a personal baseline for long-term ocular health monitoring

Morton, Stephen↗
NASA NTRS2015

Ocular Outcomes Comparison Between 14- and 70-day Head-down Tilt Bed Rest

INTRODUCTION: Ophthalmological changes, including optic disc edema with optic nerve sheath distension, posterior globe flattening with hyperopic shift, choroidal folds and cotton wool spots have been detected in some astronauts involved in long-duration spaceflights. (sup 1) It is hypothesized that elevated intracranial pressure resulting from microgravity-induced cephalad fluid shifts may be responsible for most of these findings. Head-down tilt bed rest (HTDBR) is a ground-based microgravity analog which also produces cephalad fluid shifts. It is conceivable that prolonged HDTBR exposure may induce ocular changes similar to those experienced in microgravity. Therefore, the purpose of the present study was to compare structural and functional ocular outcomes between 14- and 70-day HDTBR in healthy human subjects. It is hypothesized that 70-d HDTBR induced ocular changes of greater magnitude as compared to 14-d HDTBR. METHODS: Two HDTBR studies were conducted at the NASA Flight Analogs Research Unit, located at The University of Texas Medical Branch at Galveston, TX. Identical NASA standard screening procedures and BR conditions (e.g., strict sleep-wake cycle, standardized diet, continuous video monitoring) were implemented in both studies. Participants spent 14 and/or 70 consecutive days in a 6deg HDT position and did not engage in exercise. Subjects received weekly ocular examinations before, during, and after HDTBR. Ocular testing included: distance and near best-corrected visual acuity (BCVA), cycloplegic refraction, intraocular pressure (IOP) measurement, color vision, red dot test, modified Amsler grid test, confrontational visual field, color fundus photography and Spectral-domain OCT scans of the macula and the optic disc. Pre/post HDTBR differences between the two studies will be evaluated for BCVA, spherical equivalent, IOP, retinal nerve fiber layer (RNFL) thickness and macular OCT parameters. RESULTS: 16 (12 males and 4 females) and 6 (5 males and 1 female) subjects participated in the 14 and 70-day HDTBR studies, respectively. One subject participated in both studies. The demographic and ophthalmological characteristics of the 14-day HDTBR study have been presented elsewhere.2 In the 70- day study, subjects age averaged 39.5 +/- 7.8 years; pre- and post-HDTBR Goldmann IOP were, on average, 15.4 and 14.6 mmHg in the right eye, and 15.3 and 14.4 mmHg in the left eye, respectively; preand post-HDTBR Spectralis OCT (Heidelberg Engineering, GmbH, Heidelberg, Germany) average RNFL thickness were 103.8 and 106 μm in the right eye, and 103 and 104.3 μm in the left eye, respectively. In both studies, color vision, red dot test, modified Amsler grid test and confrontational visual field were within normal limits at all time points; no changes were detected on stereoscopic color optic disc photography. For the above outcomes, preliminary results of the pre/post HDTBR differences between the two studies will be presented at the 2015 Human Research Program Investigators' Workshop. CONCLUSIONS: Although 14-day HDTBR did not seem to induce clinically relevant ocular changes, (sup 2) a systematic evaluation and comparison with the 70-day HDTBR study results will elucidate whether the magnitude of ocular structural and functional changes is affected by the time spent in the recumbent position. Further research will also address the effects of integrated resistance and aerobic training during HDTBR on the ocular outcomes. REFERENCES: 1. Mader TH et al. Optic disc edema, globe flattening, choroidal folds, and hyperopic shifts observed in astronauts after long-duration space flight. Ophthalmology. 2011;118(10):2058-69. 2. Taibbi G et al. Ocular outcomes evaluation in a 14-day head-down bed rest study. Aviat Space Environ Med. 2014;85(10):983-92.

Cromwell, R.L.↗
NASA NTRS2008

Software Simulates Sight: Flat Panel Mura Detection

In the increasingly sophisticated world of high-definition flat screen monitors and television screens, image clarity and the elimination of distortion are paramount concerns. As the devices that reproduce images become more and more sophisticated, so do the technologies that verify their accuracy. By simulating the manner in which a human eye perceives and interprets a visual stimulus, NASA scientists have found ways to automatically and accurately test new monitors and displays. The Spatial Standard Observer (SSO) software metric, developed by Dr. Andrew B. Watson at Ames Research Center, measures visibility and defects in screens, displays, and interfaces. In the design of such a software tool, a central challenge is determining which aspects of visual function to include while accuracy and generality are important, relative simplicity of the software module is also a key virtue. Based on data collected in ModelFest, a large cooperative multi-lab project hosted by the Optical Society of America, the SSO simulates a simplified model of human spatial vision, operating on a pair of images that are viewed at a specific viewing distance with pixels having a known relation to luminance. The SSO measures the visibility of foveal spatial patterns, or the discriminability of two patterns, by incorporating only a few essential components of vision. These components include local contrast transformation, a contrast sensitivity function, local masking, and local pooling. By this construction, the SSO provides output in units of "just noticeable differences" (JND) a unit of measure based on the assumed smallest difference of sensory input detectable by a human being. Herein is the truly amazing ability of the SSO, while conventional methods can manipulate images, the SSO models human perception. This set of equations actually defines a mathematical way of working with an image that accurately reflects the way in which the human eye and mind behold a stimulus. The SSO is intended for a wide variety of applications, such as evaluating vision from unmanned aerial vehicles, measuring visibility of damage to aircraft and to the space shuttles, predicting outcomes of corrective laser eye surgery, inspecting displays during the manufacturing process, estimating the quality of compressed digital video, evaluating legibility of text, and predicting discriminability of icons or symbols in a graphical user interface.

Source record↗
NASA NTRSDate not supplied

Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

BACKGROUND The medical capabilities necessary for long-duration exploration missions (LDEMs) will differ tremendously from those currently available to crew medical officers (CMOs) on the International Space Station (ISS). Ground support will be more challenging due to distance-related communication delays and data transmission, and resource utilization must be optimized given limited ability for resupply. Clinical decision support systems (CDSSs) can help mitigate these limitations. The recent launch of generative artificial intelligence (AI) tools based upon large language models (LLM) support the creation of a smart assistant for onboard triage, diagnosis, and guided treatment of medical conditions during these missions. The Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) tool can help predict which clinical problems and outcomes are likely to occur for a design reference mission (DRM) and assist Medical Operations and systems engineering teams in creating a medical system that may optimally mitigate the predicted risks. The purpose of this study was to identify AI tools currently available or in development for the assistive diagnosis and care of medical conditions predicted for an extended duration Lunar mission. METHODS The 119 medical conditions currently built into the IMPACT suite were categorized into systems, and these diagnoses were used as keywords for our literature search. Using PubMed and Google Scholar, we performed a literature survey of AI tools applicable to these conditions. Article inclusion criteria included publication between the years 2017-2023, as the sentinel paper discussing the “selective attention” driving ChatGPT and other generative transformer models was published in June 2017. Where applicable, we reviewed only the top 1000 research articles (based on relevance) for each of the keywords/phrases. AI tools whose training sets were exclusive to a pediatric patient population were excluded. We also excluded any medical diagnostic tools (such as CT, MRI, mass spectrometry) or procedures (such as endoscopy, surgery) that are unlikely to be available during LDEMs due to mass and volume constraints, CMO knowledge, skills, and abilities, and/or inherent procedural risks. RESULTS Our survey highlighted several AI-driven tools for the triage, diagnosis, and management of those medical conditions highlighted by IMPACT. Selected publications for each medical condition were then screened for inclusion within ten systems-based categories including: general diagnostic tools (25), tools to diagnose and manage respiratory (40), dermatologic (34), neurologic (28), auditory and vestibular (30), ophthalmic (34), musculoskeletal (104), infection-associated (92), and gynecologic (19) conditions, as well as tools that could be deployed in the setting of trauma and emergency (34). CONCLUSIONS Numerous AI-driven tools were highlighted within this literature survey, ranging from chatbot assistants that triage knee pain to vision transformer models for diagnosis of ophthalmic conditions using ocular surface images captured with a mobile phone. Remaining challenges include optimizing connectivity and integration of existing and developing systems into the vehicles or habitats. Notably, findings from this survey could help guide the initial design of an all-encompassing, onboard medical AI assistant for use during future LDEMs.

R A Lacinski↗
NASA NTRS2007

Solutions Network Formulation Report. Reducing Light Pollution in U.S. Coastal Regions Using the High Sensitivity Cameras on the SAC-C and Aquarius/SAC-D Satellites

Light pollution has significant adverse biological effects on humans, animals, and plants and has resulted in the loss of our ability to view the stars and planets of the universe. Over half of the U.S. population resides in coastal regions where it is no longer possible to see the stars and planets in the night sky. Forty percent of the entire U.S. population is never exposed to conditions dark enough for their eyes to convert to night vision capabilities. In coastal regions, urban lights shine far out to sea where they are augmented by the output from fishing boat, cruise ship and oil platform floodlights. The proposed candidate solution suggests using HSCs (high sensitivity cameras) onboard the SAC-C and Aquarius/SAC-D satellites to quantitatively evaluate light pollution at high spatial resolution. New products modeled after pre-existing, radiance-calibrated, global nighttime lights products would be integrated into a modified Garstang model where elevation, mountain screening, Rayleigh scattering, Mie scattering by aerosols, and atmospheric extinction along light paths and curvature of the Earth would be taken into account. Because the spatial resolution of the HSCs on SAC-C and the future Aquarius/SAC-D missions is greater than that provided by the DMSP (Defense Meteorological Satellite Program) OLS (Operational Linescan System) or VIIRS (Visible/Infrared Imager/Radiometer Suite), it may be possible to obtain more precise light intensity data for analytical DSSs and the subsequent reduction in coastal light pollution.

Andrews, Jane C.↗
NASA NTRS1989

An error-resistant linguistic protocol for air traffic control

The research results described here are intended to enhance the effectiveness of the DATALINK interface that is scheduled by the Federal Aviation Administration (FAA) to be deployed during the 1990's to improve the safety of various aspects of aviation. While voice has a natural appeal as the preferred means of communication both among humans themselves and between humans and machines as the form of communication that people find most convenient, the complexity and flexibility of natural language are problematic, because of the confusions and misunderstandings that can arise as a result of ambiguity, unclear reference, intonation peculiarities, implicit inference, and presupposition. The DATALINK interface will avoid many of these problems by replacing voice with vision and speech with written instructions. This report describes results achieved to date on an on-going research effort to refine the protocol of the DATALINK system so as to avoid many of the linguistic problems that still remain in the visual mode. In particular, a working prototype DATALINK simulator system has been developed consisting of an unambiguous, context-free grammar and parser, based on the current air-traffic-control language and incorporated into a visual display involving simulated touch-screen buttons and three levels of menu screens. The system is written in the C programming language and runs on the Macintosh II computer. After reviewing work already done on the project, new tasks for further development are described.

Cushing, Steven↗