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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Image Detective 2.0: Engaging Citizen Scientists with NASA Astronaut Photography

Image Detective 2.0 engages citizen scientists with NASA astronaut photography of the Earth obtained by crew members on the International Space Station (ISS). Engaged citizen scientists are helping to build a more comprehensive and searchable database by geolocating this imagery and contributing to new imagery collections. Image Detective 2.0 is the newest addition to the suite of citizen scientist projects available through CosmoQuest, an effort led by the Astronomical Society of the Pacific (ASP) and supported through a NASA Science Mission Directorate Cooperative Agreement Notice award. CosmoQuest hosts a number of citizen science projects enabling individuals from around the world to engage in authentic NASA science. Image Detective 2.0, an effort that focuses on imagery acquired by astronauts on the International Space Station, builds on work initiated in 2012 by scientists and education specialists at the NASA Johnson Space Center. Through the many lessons learned, Image Detective 2.0 enhances the original project by offering new and improved options for participation. Existing users, as well as new Image Detective participants joining through the CosmoQuest platform, gain first-hand experience working with astronaut photography and become more engaged with this valuable data being obtained from the International Space Station. Citizens around the world are captivated by astronauts living and working in space. As crew members have a unique vantage point from which to view our Earth, the Crew Earth Observations (CEO) online database, referred to as the Gateway to Astronaut Photography of Earth (https://eol.jsc.nasa.gov/), provides a means for crew members to share their unique views of our home planet from the ISS with the scientific community and the public. Astronaut photography supports multiple uses including scientific investigations, visualizations, education, and outreach. These astronaut images record how the planet is changing over time, from human-made changes like urban growth and agriculture, to natural features and landforms such as tropical cyclones, aurora, coastlines, volcanoes and more. This imagery provides researchers on Earth with data to understand the planet from the perspective of the ISS, and is a useful complement to other remotely sensed datasets collected from robotic satellite platforms.

Higgins, Melissa↗
NASA NTRS2017

Reinventing Image Detective: An Evidence-Based Approach to Citizen Science Online

Usability studies demonstrate that web users are notoriously impatient, spending as little as 15 seconds on a home page. How do you get users to stay long enough to understand a citizen science project? How do you get users to complete complex citizen science tasks online? Image Detective, a citizen science project originally developed by scientists and science engagement specialists at the NASA Johnson Space center to engage the public in the analysis of images taken from space by astronauts to help enhance NASA's online database of astronaut imagery, partnered with the CosmoQuest citizen science platform to modernize, offering new and improved options for participation in Image Detective. The challenge: to create a web interface that builds users' skills and knowledge, creating engagement while learning complex concepts essential to the accurate completion of tasks. The project team turned to usability testing for an objective understanding of how users perceived Image Detective and the steps required to complete required tasks. A group of six users was recruited online for unmoderated and initial testing. The users followed a think-aloud protocol while attempting tasks, and were recorded on video and audio. The usability test examined users' perception of four broad areas: the purpose of and context for Image Detective; the steps required to successfully complete the analysis (differentiating images of Earth's surface from those showing outer space and identifying common surface features); locating the image center point on a map of Earth; and finally, naming geographic locations or natural events seen in the image. Usability test findings demonstrated that the following best practices can increase participation in Image Detective and can be applied to the successful implementation of any citizen science project: (1) Concise explanation of the project, its context, and its purpose; (2) Including a mention of the funding agency (in this case, NASA); (3) A preview of the specific tasks required of participants; (4) A dedicated user interface for the actual citizen science interaction. In addition, testing revealed that users may require additional context when a task is complex, difficult, or unusual (locating a specific image and its center point on a map of Earth). Video evidence will be made available with this presentation.

Romano, Cia↗
NASA NTRS2024

System for Photogrammetric Imaging, Detection, and Ranging (SPIDR)

The System for Photogrammetric Imaging, Detection, and Ranging (SPIDR) project aims to advance the state-of-the-art (SOA) technology in camera tracking. SPIDR will advance this technology in the following ways: 1) perform autonomous tracking of rocket launches, 2) allow modularity for camera payloads to enable various types of imagery capture (standard video, high-speed, infrared, etc.), 3) enable additional imagery assets for increased scope of imagery analysis, and 4) serve as a viable tracking replacement for the existing Kineto Tracking Mount (KTM) system used by the Exploration Ground Systems (EGS) program. The project initially aimed to build an in-house mechanical design and control system, but realigned to a commercial off-the-shelf (COTS) mechanical design. During the FY23 CIF timeline, the SPIDR team made significant advances in building a machine-learning (ML) based object detection model for various rockets and their plumes. The team will continue advancing the model, and begin work on the pan and tilt unit (PTU) control system and tracking algorithm development.

Alden Param↗
NASA NTRS2023

System for Photogrammetry, Imaging, Detection, and Ranging, Software Design

Kennedy Space Center (KSC) uses high level pan/tilt camera systems in order to capture data for image analysis. As of right now, we know that current camera systems need to be man-operated, range greatly in costs, and are supplied by third parties. This project, System for Photogrammetric Imaging, Detection, and Ranging, otherwise known as SPIDR, aims to provide high quality image analysis from data drawn from the pan/tilt camera system to upgrade KSC’s ability to take images, and improve space flight and tracking. What this abstract will do, is explain the software needed to carry out such important tasks.

R.S. Kim↗
NASA NTRS2015

Laser Imaging Detection and Ranging Performance in a High-Fidelity Lunar Terrain Field

The prime objective of this project is to evaluate Laser Imaging Detection and Ranging (LIDAR) systems and compare their performance for hazard avoidance when tested at the NASA Marshall Space Flight Center's (MSFC's) lunar high-fidelity terrain field (see fig. 1). Hazard avoidance is the ability to avoid boulders, holes, or slopes that would jeopardize a safe landing and the deployment of scientific payloads. This capability is critical for any sample return mission intending to land in challenging terrain. Since challenging terrain is frequently where the most scientifically attractive targets are, hazard avoidance will be among the highest priorities for future robotic exploration missions. The maturation of hazard avoidance sensing addressed in this project directly supports the MSFC Tier I priority of sample return.

Chuang, Jason↗
NASA NTRS1987

Spectral differential imaging detection of planets about nearby stars

Direct ground-based optical imaging of planets in orbit about nearby stars may be accomplished by spectral differential imaging using multiple passband acoustooptic filters with a CCD. This technique provides two essential results. First, it provides a means to modulate the stellar flux reflected from a planet while leaving the flux from the star and other sources in the same field of view unmodulated. Second, spectral differential imaging enables the CCD detector to achieve a sufficiently high dynamic range to locate planets near a star in spite of an integrated brightness differential of 5 x 10 to the 8th. Spectral differential imaging at nearby diffraction limited imaging conditions with telescope apodization can reduce the time to conduct a sensitive planetary search to a few hours in some cases. The feasibility of this idea is discussed here and shown to provide, in principle, the discrimination and sensitivity to detect a Jovian-class planet about stars at distances of about 10 parsecs. The detection of brown dwarfs is shown to be feasible as well.

Smith, W. Hayden↗
NASA NTRS1976

Recent progress in photoelectron image detection with CCDs

Intensified CCDs (ICCDs) were built and operated. These ICCDs were electrostatically and magnetically-focussed image tubes which use CCDs as anodes. The ICCDs detected single photoelectrons, and have electron gain very near that which was predicted. The first single-pixel pulse-height distributions were presented, and the leakage current was shown to increase as a function of photoelectron irradiation. The advantages and disadvantages of front- and rear-illuminated designs were discussed in light of differing applications requirements.

Choisser, J. P.↗
DOE OSTI2022

A Review on the State of the Art of Machine Learning and Satellite Imaging: Detecting Scene Changes in Selected Nuclear Fuel Cycle Datasets

The timely detection of clandestine nuclear facilities is one of the greatest challenges faced by the International Atomic Energy Agency’s (IAEA). Idaho National Laboratory is currently applying machine learning (ML) to existing satellite imagery (SI) datasets to find facilities within the nuclear fuel life cycle, with primary focus placed on identifying critical predecessor (i.e., fuel fabrication and fuel enrichment) and successor (i.e., nuclear power plants) facilities. This could provide a satellite image methodology that the IAEA could leverage to discover clandestine facilities. The work presented in this paper describes the evolution of a workflow developed by this team for object detection related to critical infrastructure by expanding that workflow for the purpose of identifying nuclear fuel cycle components and automating dependency assessments. This will be done using two methods housed within a single pipeline. The first method involves implementing a DenseNet161 convolutional neural network to classify the images and explain the results using Local Interpretable Model-Agnostic Explanations (LIME). The second method implements You Only Look Once version 5 (YoloV5), to detect objects within images, provide a probability for the detection, and provide a bounding box that corresponds to the object of interest. The results of this work are anticipated to provide a clear picture of this portion of the nuclear fuel cycle and perform as a stand-alone tool for image assessment that can be expanded to additional fuel cycle components and implemented in international safeguards and national security domains. This capability addresses the IAEA’s need to detect undeclared nuclear materials and activities within a state while encompassing the entire nuclear fuel cycle.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1986

Microchannel plates and image detection at soft X-ray wavelengths

Detector systems based on the high-gain microchannel plate (MCP) electron multiplier have been used extensively for imaging at soft X-ray wavelengths both on the ground and in space. The latest pulse-counting electronic readout systems provide zero readout-noise, spatial resolutions (FWM) of 25 microns or better and can determine the arrival times of detected photons to an accuracy of the order of 100 ns. These systems can br developed to produce detectors with active areas of 100 mm in diameter or greater. The use of CsI photocathodes produces very high detective quantum efficiencies to wavelengths between about 100 and 1A (0.1 to 10 keV) with moderate energy resolution. The operating characteristics of the different types of soft X-ray MCP detector systems are described, and the prospects for future developments are discussed.

Timothy, J. G.↗
NASA NTRS1986

Photocurrent Imaging Detects Solar-Module Defects

Raster-scanned laser beam excites photocurrents in thin-film amorphous silicon devices. Solar-cell laser scanner uses two galvanometer-driven mirrors to scan laser-beam spot over surface of module or cell under test. Position signals from scan controllers used to index storage of photocurrent signal data in scan-converter image memory. Stored image displayed on television monitor.

Kim, Q.↗
NASA NTRS1986

Geostationary microwave imagers detection criteria

Geostationary orbit is investigated as a vantage point from which to sense remotely the surface features of the planet and its atmosphere, with microwave sensors. The geometrical relationships associated with geostationary altitude are developed to produce an efficient search pattern for the detection of emitting media and metal objects. Power transfer equations are derived from the roots of first principles and explain the expected values of the signal-to-clutter ratios for the detection of aircraft, ships, and buoys and for the detection of natural features where they are manifested as cold and warm eddies. The transport of microwave power is described for modeled detection where the direction of power flow is explained by the Zeroth and Second Laws of Thermodynamics. Mathematical expressions are derived that elucidate the detectability of natural emitting media and metal objects. Signal-to-clutter ratio comparisons are drawn among detectable objects that show relative detectability with a thermodynamic sensor and with a short-pulse radar.

Stacey, J. M.↗
DOE OSTI2026

SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images

The rapid advancement of generative models has made the detection of AI-generated images a critical challenge for both research and society. Recent works have shown that most state-of-the-art fake image detection methods overfit to their training data and catastrophically fail when evaluated on curated hard test sets with strong distribution shifts. In this work, we argue that it is more principled to learn a tight decision boundary around the real image distribution and treat the fake category as a sink class. To this end, we propose SimLBR, a simple and efficient framework for fake image detection with Latent Blending Regularization (LBR). Our method significantly improves cross-generator generalization, achieving up to +24.85% accuracy and +69.62% recall on the challenging Chameleon benchmark. SimLBR is also highly efficient, training orders of magnitude faster than existing approaches. Furthermore, we emphasize the need for reliability-oriented evaluation in fake image detection, introducing risk-adjusted metrics and worst-case estimates to better assess model robustness. All the code and models are availabe at: https://github.com/mvrl/SimLBR

Dhakal, Aayush [Washington University, St. Louis]↗
NASA NTRS1973

Single step detection of blurred images in a coherent optical processor.

Two important operations that can be performed by coherent optical processing systems are image detection and image restoration. These operations require complex spatial filters, and if both operations are to be performed, cascaded processing stages employing two separate spatial filters are usually needed. Spatial filters are described that will perform in a single step both operations simultaneously, thus eliminating the need for extra processing stages while providing the advantages of higher efficiency and smaller space requirements. The filters are characterized by their simplicity and are easily fabricated using conventional techniques. Experimental results are presented.

Jablonowski, D. P.↗
NASA NTRS1994

The Astrometric Imaging Telescope: Detection of planetary systems with imaging and astrometry

The Astrometric Imaging Telescope (AIT) is a proposed spaceborne observatory whose primary goal is the detecton and study of extra-solar planetary systems. It contains two instruments that use complementary techniques to address the goal. The first instrument, the Coronagraphic Imager, takes direct images of nearby stars and Jupiter-size planets. It uses a telescope with scattering-compensated optics and a high-efficiency coronagraph to separate reflected planet light from the central star light. Planet detections take hours; confirmations occur in months. With a program duration of about 2 years, about 50 stars are observed. The second instrument, the Astrometric Photometer, shares the same telescope and focal plane. It uses a Ronchi ruling that is translated across the focal plane to simultaneously measure the positions of each target star and about 25 reference stars with sufficient accuracy to detect Uranus-mass planets around hundreds of stars. Enough stars of several spectral types are observed to obtain a statistically significant measurement of the prevalence of planetary systems. This observing program takes about 10 years to complete. The combination of both instruments in a single telescope system results from a number of innovative solutions that are described in this paper.

Pravdo, Steven H.↗