Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “interactive visualization”

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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

The NASA-GES-DISC Satellite Data/Products Access, Distribution, Services and Dissemination to Users

The NASA/GES/DISC/DAAC is a virtual data portal that provides convenient access to Atmospheric, Oceanic and Land datasets and value added products from various current NASA missions and instruments as well as heritage datasets from AIRS/AMSU/HSB, AVHRR, CZCS, LIMS, MODIS, MSU, OCTS, SeaWiFS, SORCE, SSI, TOMS, TOVS, UARS and TRMM. The GES-DISC-DAAC also provided a variety of services that allow users to analyze and visualize gridded data interactively online without having to download any data.

Vicente, Gilberto A.↗
NASA NTRS2010

Stereo Imaging Tactical Helper

The Stereo Imaging Tactical Helper (SITH) program displays left and right images in stereo using the display technology made available by the JADIS framework. An overlay of the surface described by the disparity map (generated from the left and right images) allows the map to be compared to the actual images. In addition, an interactive cursor, whose visual depth is controlled by the disparity map, is used to ensure the correlated surface matches the real surface. This enhances the ability of operations personnel to provide quality control for correlation results, as well as to greatly assist developers working on correlation improvements. While its primary purpose is as a quality control tool for inspecting correlation results, SITH is also straightforward for use as a basic stereo image viewer

Toole, Nicholas T.↗
NASA NTRS2013

Stereoscopic Height and Wind Retrievals for Aerosol Plumes with the MISR INteractive eXplorer (MINX)

The Multi-angle Imaging SpectroRadiometer (MISR) instrument aboard the Terra satellite acquires imagery at 275-m resolution at nine angles ranging from 0deg (nadir) to 70deg off-nadir. This multi-angle capability facilitates the stereoscopic retrieval of heights and motion vectors for clouds and aerosol plumes. MISR's operational stereo product uses this capability to retrieve cloud heights and winds for every satellite orbit, yielding global coverage every nine days. The MISR INteractive eXplorer (MINX) visualization and analysis tool complements the operational stereo product by providing users the ability to retrieve heights and winds locally for detailed studies of smoke, dust and volcanic ash plumes, as well as clouds, at higher spatial resolution and with greater precision than is possible with the operational product or with other space-based, passive, remote sensing instruments. This ability to investigate plume geometry and dynamics is becoming increasingly important as climate and air quality studies require greater knowledge about the injection of aerosols and the location of clouds within the atmosphere. MINX incorporates features that allow users to customize their stereo retrievals for optimum results under varying aerosol and underlying surface conditions. This paper discusses the stereo retrieval algorithms and retrieval options in MINX, and provides appropriate examples to explain how the program can be used to achieve the best results.

MISR↗
NASA NTRS2014

The Host Galaxies of X-Ray Selected Active Galactic Nuclei to z - 2.5: Structure, Star-Formation and Their Relationships from CANDELS and Herschel/Pacs

We study the relationship between the structure and star-formation rate (SFR) of X-ray selected low and moderate luminosity active galactic nuclei (AGNs) in the two Chandra Deep Fields, using Hubble Space Telescope imaging from the Cosmic Assembly Near Infrared Extragalactic Legacy Survey (CANDELS) and deep far-infrared maps from the PEP+GOODS-Herschel survey. We derive detailed distributions of structural parameters and FIR luminosities from carefully constructed control samples of galaxies, which we then compare to those of the AGNs. At z is approximately 1, AGNs show slightly diskier light profiles than massive inactive (non-AGN) galaxies, as well as modestly higher levels of gross galaxy disturbance (as measured by visual signatures of interactions and clumpy structure). In contrast, at z ∼ 2, AGNs show similar levels of galaxy disturbance as inactive galaxies, but display a red central light enhancement, which may arise due to a more pronounced bulge in AGN hosts or due to extinguished nuclear light. We undertake a number of tests of both these alternatives, but our results do not strongly favour one interpretation over the other. The mean SFR and its distribution among AGNs and inactive galaxies are similar at z greater than 1.5. At z less than 1, however, clear and significant enhancements are seen in the SFRs of AGNs with bulge-dominated light profiles. These trends suggest an evolution in the relation between nuclear activity and host properties with redshift towards a minor role for mergers and interactions at z greater than 15

host↗
NASA NTRS2018

Discovery of Activities via Statistical Clustering of Fixation Patterns

Human behavior often consists of a series of distinct activities, each characterized by a unique pattern of interaction with the visual environment. This is true even in a restricted domain, such as a piloting an aircraft, where activities with distinct visual signatures might be things like communicating, navigating, and monitoring. We propose a novel analysis method for gaze-tracking data, to perform blind discovery of these hypothetical activities. The method is in some respects similar to recurrence analysis, but here we compare not individual fixations, but groups of fixations aggregated over a fixed time interval. The duration of this interval is a parameter that we will refer to as delta. We assume that the environment has been divided into a set of N different areas-of-interest (AOIs). For a given interval of time of duration delta, we compute the proportion of time spent fixating each AOI, resulting in an N-dimensional vector. These proportions can be converted to integer counts by multiplying by delta divided by the average fixation duration (another parameter that we fix at 280 milliseconds). We compare different intervals by computing the chi-square statistic. The p-value associated with the statistic is the likelihood of observing the data under the hypothesis that the data in the two intervals were generated by a single process with a single set of probabilities governing the fixation of each AOI. The method has been applied to approximately 100 hours of eye movement data collected from pilots in a high-fidelity B747 flight simulator, and the results have been compared to synthetic data in which the each activity is represented as first-order Markov process with random probabilities assigned to the AOIs. Randomly-generated synthetic activities can require thousands of fixations to be discriminated with statistical significance, while the human data can be clustered using averaging windows of some 10's of seconds, suggesting that the actual activities are much more narrowly focused than random Markov models.

activity analysis↗
NASA NTRS2019

Discovery of Activities via Statistical Clustering of Fixation Patterns

Human behavior often consists of a series of distinct activities, each characterized by a unique pattern of interaction with the visual environment. This is true even in a restricted domain, such as a pilot flying an airplane; in this case, activities with distinct visual signatures might be things like communicating, navigating, monitoring, etc. We propose a novel analysis method for gaze-tracking data, to perform blind discovery of these hypothetical activities. We compare, not individual fixations, but groups of fixations aggregated over a fixed time interval (Tau). We assume that the environment has been divided into a finite set of discrete areas-of-interest (AOIs). For a given time interval, we compute the proportion of time spent fixating each AOI, resulting in an N-dimensional vector, where N is the number of AOIs. These proportions can be converted to integer counts by multiplying by Tau divided by the average fixation duration, a parameter that we fix at 283 milliseconds. We compare different intervals by computing the chi-squared statistic. The p-value associated with the statistic is the likelihood of observing the data under the hypothesis that the data in the two intervals were generated by a single process with a single set of probabilities governing the fixation of each AOI. We cluster the intervals, first by merging adjacent intervals that are sufficiently similar, optionally shifting the boundary between non-merged intervals to maximize the difference. Then we compare and cluster non-adjacent intervals. The method is evaluated using synthetic data generated by a hand-crafted set of activities. While the method generally finds more activities than put into the simulation, we have obtained agreement as high as 80 percent between the inferred activity labels and ground truth.

Eye Movements↗
NASA NTRSDate not supplied

Science Workflows using Kamodo

Kamodo is a powerful python software package based on data functionalization. Once a given data set is functionalized, a large variety of capabilities are easily accessible in Kamodo, including unit conversions, custom analysis via function composition, interactive publication quality visualizations, and LaTeX encoding. The entirety of capabilities available in Kamodo are easily applied to both simulated and observed data across the multiple domains of Heliophysics and even in other disciplines. This work includes a variety of science workflows using Kamodo in combination with other resources, including with other python software packages, that expand the utility of Kamodo even further. These workflows include model-data comparisons, ensemble modeling examples, satellite mission planning examples, and other applications, all of which are freely available on CCMC’s Kamodo Github page for the community to adapt to their own uses (https://github.com/nasa/Kamodo). We invite the community to use these workflows and to contribute their own to share.

software↗
NASA NTRSDate not supplied

Science Workflows using Kamodo

Kamodo is a powerful python software package based on data functionalization. Once a given data set is functionalized, a large variety of capabilities are easily accessible in Kamodo, including unit conversions, custom analysis via function composition, interactive publication quality visualizations, and LaTeX encoding. The entirety of capabilities available in Kamodo are easily applied to both simulated and observed data across the multiple domains of Heliophysics and even in other disciplines. This work includes a variety of science workflows using Kamodo in combination with other resources, including with other python software packages, that expand the utility of Kamodo even further. These workflows include model-data comparisons, ensemble modeling examples, satellite mission planning examples, and other applications, all of which are freely available on CCMC’s Kamodo Github page for the community to adapt to their own uses (https://github.com/nasa/Kamodo). We invite the community to use these workflows and to contribute their own to share.

python↗
NASA NTRS2024

Creating Camera Controls for First-Person Camera in VulkanSceneGraph

The way users interact with a virtual, three-dimensional (3D) scene is heavily influenced by the way the camera used to view the scene is controlled. A first-person camera is a common form of camera control for computer programs. Its role is to create an immersive viewer experience, which allows users to traverse a scene as they might in the real world. Allowing for the implementation of the first-person camera makes for a more holistic, well-rounded way to interact with the visualization program. Accomplishing this involves mathematical calculations that define how the camera should be moved for the computer system. These movements equate to the rotation, translation, and scale of the changes. It should be noted that a computer does not inherently process what movement directions (left, right, up, or down) mean. The mathematical equations used define these principles in a way the computer can process. Further aspects to consider are detecting when the camera has moved and how far. This is most frequently accomplished through user |input through external devices. These devices, for the purpose of this report, include mouse input and keyboard input. Additionally, the in-development program this report is referencing works with the VulkanSceneGraph (VSG) library to create the scene and build the base of the camera controls. Although VSG is a powerful library with many capabilities, additional Application Programming Interfaces (APIs) might be needed during development to produce the desired results, as is the case in this program. Through combining proper mathematical calculations, utilizing additional APIs, and implementing the existing VSG library capabilities, implementing first-person camera controls is possible in a 3D scene.

Kristie O'Brien↗
NASA NTRSDate not supplied

Exploring our Changing Planet through NASA’s Earth Information Center and Novel Methods for Earth Science Communication

In June 2023, NASA unveiled the Earth Information Center (EIC), an interagency initiative which invites the global community to explore how our planet is changing through interactive installations, captivating visualizations, and novel storytelling. Existing in both physical and virtual space, the EIC serves as a gateway to actionable information collected through an expanding fleet of Earth observing satellites and sensors. The EIC features data driven visualizations, near real-time information, immersive experiences, and curated stories that highlight the applications of publicly available data to address environmental challenges across nine thematic areas: agriculture, biodiversity, disasters, greenhouse gases, air quality, sea level rise, sustainable energy, water resources, and wildfires. Designed by an interdisciplinary team, EIC exhibits are developed to reach a wide base of end-users across multiple learning modalities. During the first year of operation, the inaugural location of the EIC at NASA Headquarters in Washington, DC, welcomed an estimated 24,000 visitors, hosted over 145 tours for domestic and international organizations, supported NASA’s Earth Day 2024 programming, and led six STEM programs for diverse student communities. With lessons learned from the first year of being open to the public and a variety of novel science communication tools in development, the EIC is expanding the mission’s reach by collaborating with museums and visitor centers. Through these collaborations, we aim to inform a broader demographic about the unprecedented changes observed in Earth’s climate and inspire communities to learn more about their one and only home, planet Earth.

Nicole Ramberg-Pihl↗
NASA NTRS1982

The display of molecular models with the Ames Interactive Modeling System (AIMS)

A visualization of molecular models can lead to a clearer understanding of the models. Sophisticated graphics devices supported by minicomputers make it possible for the chemist to interact with the display of a very large model, altering its structure. In addition to user interaction, the need arises also for other ways of displaying information. These include the production of viewgraphs, film presentation, as well as publication quality prints of various models. To satisfy these needs, the display capability of the Ames Interactive Modeling System (AIMS) has been enhanced to provide a wide range of graphics and plotting capabilities. Attention is given to an overview of the AIMS system, graphics hardware used by the AIMS display subsystem, a comparison of graphics hardware, the representation of molecular models, graphics software used by the AIMS display subsystem, the display of a model obtained from data stored in molecule data base, a graphics feature for obtaining single frame permanent copy displays, and a feature for producing multiple frame displays.

Egan, J. T.↗
NASA NTRS1994

Data Visualization and Animation Lab (DVAL) overview

The general capabilities of the Langley Research Center Data Visualization and Animation Laboratory is described. These capabilities include digital image processing, 3-D interactive computer graphics, data visualization and analysis, video-rate acquisition and processing of video images, photo-realistic modeling and animation, video report generation, and color hardcopies. A specialized video image processing system is also discussed.

Stacy, Kathy↗
NASA NTRS1992

Radar Analysis and Visualization Environment (RAVEN): Software for polarimetric radar analysis

Imaging radar data provides information about the geometric and dielectric properties of the Earth's surface. The Jet Propulsion Laboratory (JPL) polarimetric Airborne Synthetic Aperture Radar (AIRSAR) is currently obtaining imaging radar data for use in geologic, vegetation, snow and ice, and ocean studies. In the near future, the Shuttle Imaging Radar C (SIR-C/X-SAR) and the Earth Observing System Synthetic Aperture Radar (EOS SAR) will also collect polarimetric radar data. A need exists for a user-friendly, interactive software package for analysis of these polarimetric radar data sets. Previous software packages, such as JPL's Multiview, while providing some analysis capabilities for these data, did not allow interactive viewing and were tied to specific image display hardware with operating system dependencies. A prototype software system, the 'Radar Analysis and Visualization Environment' (RAVEN) under development at the Center for the Study of Earth from Space (CSES) at the University of Colorado, is designed to deal with data from the JPL AIRSAR instrument and other proposed polarimetric radar instruments. RAVEN is being developed using the Interactive Data Language (IDL). It takes advantage of high speed disk access and fast processors running under the UNIX operating system in an X-windows environment to allow for rapid, interactive visualization of AIRSAR data in both image and graphical ways. It provides a user-friendly interface through the use of menus, sliders, buttons, and display windows.

Kierein-Young, K. S.↗
DOE OSTI2023

State of the Art of Molecular Visualization in Immersive Virtual Environments

Abstract Visualization plays a crucial role in molecular and structural biology. It has been successfully applied to a variety of tasks, including structural analysis and interactive drug design. While some of the challenges in this area can be overcome with more advanced visualization and interaction techniques, others are challenging primarily due to the limitations of the hardware devices used to interact with the visualized content. Consequently, visualization researchers are increasingly trying to take advantage of new technologies to facilitate the work of domain scientists. Some typical problems associated with classic 2D interfaces, such as regular desktop computers, are a lack of natural spatial understanding and interaction, and a limited field of view. These problems could be solved by immersive virtual environments and corresponding hardware, such as virtual reality head‐mounted displays. Thus, researchers are investigating the potential of immersive virtual environments in the field of molecular visualization. There is already a body of work ranging from educational approaches to protein visualization to applications for collaborative drug design. This review focuses on molecular visualization in immersive virtual environments as a whole, aiming to cover this area comprehensively. We divide the existing papers into different groups based on their application areas, and types of tasks performed. Furthermore, we also include a list of available software tools. We conclude the report with a discussion of potential future research on molecular visualization in immersive environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Open Chemistry, JupyterLab, REST, and quantum chemistry

Quantum chemistry must evolve if it wants to fully leverage the benefits of the internet age, where the worldwide web offers a vast tapestry of tools that enable users to communicate and interact with complex data at the speed and convenience of a button press. The Open Chemistry project has developed an open-source framework that offers an end-to-end solution for producing, sharing, and visualizing quantum chemical data interactively on the web using an array of modern tools and approaches. These tools build on some of the best open-source community projects such as Jupyter for interactive online notebooks, coupled with 3D accelerated visualization, state-of-the-art computational chemistry codes including NWChem and Psi4, and emerging machine learning and data mining tools such as ChemML and ANI. They offer flexible formats to import and export data, along with approaches to compare computational and experimental data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

DataHub - Science data management in support of interactive exploratory analysis

DataHub addresses four areas of significant need: scientific visualization and analysis; science data management; interactions in a distributed, heterogeneous environment; and knowledge-based assistance for these functions. The fundamental innovation embedded within the DataHub is the integration of three technologies, viz. knowledge-based expert systems, science visualization, and science data management. This integration is based on a concept called the DataHub. With the DataHub concept, science investigators are able to apply a more complete solution to all nodes of a distributed system. Both computational nodes and interactive nodes are able to effectively and efficiently use the data services (access, retrieval, update, etc.) in a distributed, interdisciplinary information system in a uniform and standard way. This allows the science investigators to concentrate on their scientific endeavors, rather than to involve themselves in the intricate technical details of the systems and tools required to accomplish their work. Thus, science investigators need not be programmers. The emphasis is on the definition and prototyping of system elements with sufficient detail to enable data analysis and interpretation leading to information. The DataHub includes all the required end-to-end components and interfaces to demonstrate the complete concept.

Handley, Thomas H., Jr.↗
DOE OSTI2020

RCSB Protein Data Bank 1D tools and services

Abstract Motivation Interoperability between polymer sequences and structural data is essential for providing a complete picture of protein and gene features and helping to understand biomolecular function. Results Herein, we present two resources designed to improve interoperability between the RCSB Protein Data Bank, the NCBI and the UniProtKB data resources and visualize integrated data therefrom. The underlying tools provide a flexible means of mapping between the different coordinate spaces and an interactive tool allows convenient visualization of the 1-dimensional data over the web. Availabilityand implementation https://1d-coordinates.rcsb.org and https://rcsb.github.io/rcsb-saguaro. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗