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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

The effect of visual context on manual localization of remembered targets

This paper examines the contribution of egocentric cues and visual context to manual localization of remembered targets. Subjects pointed in the dark to the remembered position of a target previously viewed without or within a structured visual scene. Without a remembered visual context, subjects pointed to within 2 degrees of the target. The presence of a visual context with cues of straight ahead enhanced pointing performance to the remembered location of central but not off-center targets. Thus, visual context provides strong visual cues of target position and the relationship of body position to target location. Without a visual context, egocentric cues provide sufficient input for accurate pointing to remembered targets.

NASA Center JSC↗
NASA NTRSDate not supplied

NASA GeneLab Multi-study Visualization Portal

NASA GeneLab has helped advance the field of Space Biology by providing a public repository where researchers can store, share, analyze and visualize the results of space flight related omics experiments. The GeneLab data visualization portal allows any user, regardless of bioinformatics knowledge or access to computational resources, to interact with the experimental data, draw their own conclusions, and gain insights about the effects of space on living systems. These tools help democratize scientific research and foster the NASA Open Science initiative. The new multi-study feature of the GeneLab visualization platform allows users to mine study metadata from RNA sequencing (RNA-seq) experiments to identify samples of interest by filtering datasets based on organism, tissue, assay technology type, and/or factor. Once samples are selected from multiple datasets, users can combine and normalize the sample data, then utilize the visualization displays, including Principal Component Analysis (PCA) plots, to assess sample distributions. Finally, users can perform differential gene expression analysis on the combined data and visualize the results through PCA plots, Volcano plots, Pair plots, Heatmap, Ideogram and Gene Set Enrichment Analysis. All user-generated results and visualizations will be available for download. Here, we present a biological study using samples from multiple GeneLab RNA-seq datasets and analyzed using the multi-study visualization platform to demonstrate inter- and intra-study variability, as well as commonly differentially expressed genes between spaceflight and ground control conditions across datasets. This new feature opens a wide range of possibilities and opportunities for further development including combining other assay technology types and integration with batch effect correction techniques and machine learning applications. Overall, this tool allows users to increase the statistical power of individual experiments, validate hypothesis, identify patterns, and opens the door to new and exciting research.

space biology↗
DOE OSTI2017

The Impact of Streaming Data on Sensemaking with Mixed-Initative Visual Analytics

Visual data analysis helps people gain insights into data via interactive visualizations. People generate and test hypotheses and questions about data in context of the domain. This process can generally be referred to as sensemaking. Much of the work on studying sensemaking (and creating visual analytic techniques in support of it) has been focused on static datasets. However, how do the cognitive processes of sensemaking change when data are changing? Further, what implication for design does this create for mixed-initiative visual analytics systems? This paper presents the results of a user study analyzing the impact of streaming data on sensemaking. To perform this study, we developed a mixed-initiative visual analytic prototype, the Streaming Canvas, that affords the analysis of streaming text data. We compare the sensemaking process of people using this tool for a static and streaming dataset. We present the results of this study and discuss the implications on future visual analytic systems that combine machine learning and interactive visualization to help people make sense of streaming data.

visualization↗
DOE OSTI2020

Multiscale and Multivariate Transportation System Visualization for Shopping District Traffic and Regional Traffic

In this paper, we present a suite of visualization techniques for sensor-based transportation system data at different scales to facilitate the exploration of interconnected traffic dynamics at intersections and highways. Additionally, these techniques are designed for analyzing multivariate traffic data from radar-based highway sensors and camera-based intersection sensors recording turn movements and vehicle speed, in the Chattanooga Metropolitan Area, with the capability of (a) revealing multiscale mobility patterns using different levels of data aggregation (e.g., individual sensor for microscale, multiple sensors along a corridor for mesoscale, and a larger number of sensors across the region for macroscale visualization) at different intervals (e.g., 5-min intervals, time of day, full day, and day-of-the-week), and (b) exploring the spatial variation of multiple traffic-related variables (e.g., volumes, speeds, turn movements, and traffic light colors) provided by the sensors. We close with a case study to demonstrate the effectiveness of our multiscale and multivariate visualization techniques. At microscale, we focused on intersection data from a shopping district around Shallowford Road in East Chattanooga. For mesoscale visualization, we studied the Shallowford Road corridor and an adjacent stretch of I-75. At macroscale, we included highway data from the Chattanooga Metropolitan Area. All visualizations were integrated into a web-based situational awareness tool to promote user access and interaction. At a minimum, each visualization provides the option for selecting dates for real-time (depending on sensor availability) and historical data, and additional information on hovering, though most provide more detailed information, including different views of the selected data, or interactive highlights.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Community Fabric: Visualizing communities and structure in dynamic networks

We present Community Fabric, a novel visualization technique for simultaneously visualizing communities and structure within dynamic networks. In dynamic networks, the structure of the network is continuously evolving throughout time and these underlying topological shifts tend to lead to communal changes. Community Fabric helps the viewer more easily interpret and understand the interplay of structural change and community evolution in dynamic graphs. To achieve this, we take a new approach, hybridizing two popular network and community visualizations. Community Fabric combines the likes of the Biofabric static network visualization method with traditional community alluvial flow diagrams to visualize communities in a dynamic network while also displaying the underlying network structure. Our approach improves upon existing state-of-the-art techniques in several key areas. We describe the methodologies of Community Fabric, implement the visualization using modern web-based tools, and apply our approach to three example data sets.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Evaluation of Visualization Using a 50/50 (Contrast Media/Glucose 5% Solution) Technique for Radioembolization as an Alternative to a Standard Sandwich Technique

BackgroundRadioembolization (RE) with {sup 90}yttrium ({sup 90}Y) resin microspheres generally employs a sandwich technique with separate sequential administration of contrast medium (CM), followed by vehicle (e.g., glucose 5% [G5] solution), then {sup 90}Y resin microspheres (in G5), then G5, and then CM again to avoid contact of CM and microspheres under fluoroscopic guidance. This study evaluates the visualization quality and safety of a modified sandwich technique with a 50/50-mixture of CM (Imeron 300) and G5 for administration of {sup 90}Y resin microspheres.Materials and MethodsA retrospective analysis of 81 RE procedures in patients with primary or secondary liver tumors was performed. The quality of angiographic visualization of the hepatic vessels was assessed before the first injection and immediately before the whole dose has been injected. Visualization and flow rate were graded on a 5-point scale: 1 = very good to 5 = not visible/no antegrade flow. Univariate logistic regression models and multiple linear regression models were used to evaluate the prognostic variables associated with visualization and flow scores.ResultsVisualization quality was inversely related to flow rate, the lower the flow rate the better the grade of the visualization. Visualization quality was also inversely related to body-mass-index (BMI). Performing RE with the 50/50-CM/G5 mixture resulted in a mean injection time for 1 GBq of 15 min. No clinically significant adverse events, including radiation-induced liver disease were reported.ConclusionRE with a 50/50-mixture of CM and G5 for administration of {sup 90}Y resin microspheres in a modified sandwich technique is a safe administration alternative and provides good visualization of hepatic vessels, which is inversely dependent on flow rate and BMI. Injection time was reduced compared with our experience with the standard sandwich technique.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Rationally Designed, Short-Acting RPE65 Inhibitors for Visual Cycle-Associated Retinopathies

Abstract The visual cycle is a metabolic pathway essential for visual function. The bisretinoid byproducts of this pathway can induce retinal toxicity, as occurs in Stargardt disease type 1 (STGD1). Emixustat, which inhibits bisretinoid production, is a visual cycle modulator (VCM) that targets RPE65. However, it causes visual impairment due to its unfavorable duration of action. Here, we report ester-containing analogs of emixustat that are susceptible to hydrolytic clearance and function as short-acting VCMs. We show that the esterase-mediated metabolism of these compounds can be tuned while maintaining high-affinity RPE65 targeting. Compounds 6 (EYE-002) and 7 (EYE-003) containing diethyl acetate and valproate esters, respectively, allowed faster recovery of visual cycle function compared to emixustat. These molecules protected against retinal degeneration in mouse models of photic retinopathy and STGD1. These data demonstrate that shorter attenuation of the visual cycle can therapeutically intervene in retinal diseases with fewer visual side effects compared to emixustat.

Pharmacology & Pharmacy↗
DOE OSTI2023

2022 Prentice Award Lecture: Advancing Retinal Imaging and Visual Function in Patient Management and Disease Mechanisms

Patient-based research plays a key role in probing basic visual mechanisms. Less-well recognized is the role of patient-based retinal imaging and visual function studies in elucidating disease mechanisms, which are accelerated by advances in imaging and function techniques and are most powerful when combined with the results from histology and animal models. A patient's visual complaints can be one key to patient management, but human data are also key to understanding disease mechanisms. Unfortunately, pathological changes can be difficult to detect. Before advanced retinal imaging, the measurement of visual function indicated the presence of pathological changes that were undetectable with existing clinical examination. Over the past few decades, advances in retinal imaging have increasingly revealed the unseen. This has led to great strides in the management of many diseases, particularly diabetic retinopathy and macular edema, and age-related macular degeneration. It is likely widely accepted that patient-based research, as in clinical trials, led to such positive outcomes. Both visual function measures and advanced retinal imaging have clearly demonstrated differences among retinal diseases. Contrary to initial thinking, sight-threatening damage in diabetes occurs to the outer retina and not only to the inner retina. This has been clearly indicated in patient results but has only gradually entered the clinical classifications and understanding of disease etiology. There is strikingly different pathophysiology for age-related macular degeneration compared with photoreceptor and retinal pigment epithelial genetic defects, yet research models and even some treatments confuse these. It is important to recognize the role that patient-based research plays in probing basic visual mechanisms and elucidating disease mechanisms, combining these findings with the concepts from histology and animal models. Thus, this article combines sample instrumentation from my laboratory and progress in the fields of retinal imaging and visual function.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Numerical and Visual Representations of Uncertainty Lead to Different Patterns of Decision Making

Although visualizations are a useful tool for helping people to understand information, they can also have unintended effects on human cognition. This is especially true for uncertain information, which is difficult for people to understand. Prior work has found that different methods of visualizing uncertain information can produce different patterns of decision making from users. However, uncertainty can also be represented via text or numerical information, and few studies have systematically compared these types of representations to visualizations of uncertainty. We present two experiments that compared visual representations of risk (icon arrays) to numerical representations (natural frequencies) in a wildfire evacuation task. Like prior studies, we found that different types of visual cues led to different patterns of decision making. In addition, our comparison of visual and numerical representations of risk found that people were more likely to evacuate when they saw visualizations than when they saw numerical representations. These experiments reinforce the idea that design choices are not neutral: seemingly minor differences in how information is represented can have important impacts on human risk perception and decision making.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Comparative Study of the Perceptual Sensitivity of Topological Visualizations to Feature Variations

Color maps are a commonly used visualization technique in which data are mapped to optical properties, e.g., color or opacity. Color maps, however, do not explicitly convey structures (e.g., positions and scale of features) within data. Topology-based visualizations reveal and explicitly communicate structures underlying data. Although our understanding of what types of features are captured by topological visualizations is good, our understanding of people's perception of those features is not. Further, this paper evaluates the sensitivity of topology-based isocontour, Reeb graph, and persistence diagram visualizations compared to a reference color map visualization for synthetically generated scalar fields on 2-manifold triangular meshes embedded in 3D. In particular, we built and ran a human-subject study that evaluated the perception of data features characterized by Gaussian signals and measured how effectively each visualization technique portrays variations of data features arising from the position and amplitude variation of a mixture of Gaussians. For positional feature variations, the results showed that only the Reeb graph visualization had high sensitivity. For amplitude feature variations, persistence diagrams and color maps demonstrated the highest sensitivity, whereas isocontours showed only weak sensitivity. These results take an important step toward understanding which topology-based tools are best for various data and task scenarios and their effectiveness in conveying topological variations as compared to conventional color mapping.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Uncertainty-Informed Volume Visualization using Implicit Neural Representation

The increasing adoption of Deep Neural Networks (DNNs) has led to their application in many challenging scientific visualization tasks. While advanced DNNs offer impressive generalization capabilities, understanding factors such as model prediction quality, robustness, and uncertainty is crucial. These insights can enable domain scientists to make informed decisions about their data. However, DNNs inherently lack ability to estimate prediction uncertainty, necessitating new research to construct robust uncertainty-aware visualization techniques tailored for various visualization tasks. In this work, we propose uncertainty-aware implicit neural representations to model scalar field data sets effectively and comprehensively study the efficacy and benefits of estimated uncertainty information for volume visualization tasks. We evaluate the effectiveness of two principled deep uncertainty estimation techniques: (1) Deep Ensemble and (2) Monte Carlo Dropout (MC-Dropout). These techniques enable uncertainty-informed volume visualization in scalar field data sets. Our extensive exploration across multiple data sets demonstrates that uncertainty-aware models produce informative volume visualization results. Moreover, integrating prediction uncertainty enhances the trustworthiness of our DNN model, making it suitable for robustly analyzing and visualizing real-world scientific volumetric data sets.

Saklani, Shanu↗
DOE OSTI2020

Preserving Contextual Awareness during Selection of Moving Targets in Animated Stream Visualizations

In many types of dynamic interactive visualizations, it is often desired to interact with moving objects. Stopping moving objects can make selection easier, but pausing animated content can disrupt perception and understanding of the visualization. To address such problems, we explore selection techniques that only pause a subset of all moving targets in the visualization. We present various designs for controlling pause regions based on cursor trajectory or cursor position. We then report a dual-task experiment that evaluates how different techniques affect both target selection performance and contextual awareness of the visualization. Our findings indicate that all pause techniques significantly improved selection performance as compared to the baseline method without pause, but the results also show that pausing the entire visualization can interfere with contextual awareness. However, the problem with reduced contextual awareness was not observed with our new techniques that only pause a limited region of the visualization. Thus, our research provides evidence that region-limited pause techniques can retain the advantages of selection in dynamic visualizations without imposing a negative effect on contextual awareness.

Ragan, Eric↗
NASA NTRS1983

Spatial orientation - Visual-vestibular-somatic interaction

The compensation signals from somatosensory and vestibular receptors, which act to compensate for disturbances produced by the displacement of our eyes relative to other parts of our bodies and for visual disturbances such as tilted frames or moving visual fields, are investigated. Disturbances were evoked by tilting the head and by rotating a large visual display, while compensation signals related to gravity were altered by placing the subjects horizontally on a board or seating them vertically. The first experiment studied the effects of visual disturbance on the ability of supine observers to set a line to the longitudinal body axis while the head was tilted toward one shoulder or while the head was straight. Results showed that the effects of the visual disturbance were greater when the head was tilted than when it was straight, which indicates that the effects of visual disturbance were greater for a task that required compensation. The second experiment compared the performance of supine and erect observers. No differences were found between the performance of observers on a task requiring the use of compensation signals under these two conditions, which suggests that the enrichment of compensatory signals did not reduce the effects of visual disturbancs.

Parker, D. E.↗
NASA NTRS1990

Scientific data visualization software - Trends and directions

Scientific data visualization has finally come of age as an important and accepted discipline. While scientists have been using computer graphics to visualize experimental data and computational results for at least 30 years, recent improvements in cost/performance of graphics workstations, more readily available software, and a new-found identity based on the report "Visualization in Scientific Computing" (McCormick, DeFanti, and Brown, 1987) have solidified the discipline. The thesis here is that scientists are forced to work too hard to create these visualizations, but that the evolving set of visualization tools can greatly reduce the requisite effort. The architecture of a new class of software can lead to a more widespread availability of interactive visualization tools, which can make the process of creating a visualization as simple as doing interactive chart and graph layout.

Foley, James D.↗
NASA NTRS1992

Visual cues in low-level flight - Implications for pilotage, training, simulation, and enhanced/synthetic vision systems

This paper reviews some of the sources of visual information that are available in the out-the-window scene and describes how these visual cues are important for routine pilotage and training, as well as the development of simulator visual systems and enhanced or synthetic vision systems for aircraft cockpits. It is shown how these visual cues may change or disappear under environmental or sensor conditions, and how the visual scene can be augmented by advanced displays to capitalize on the pilot's excellent ability to extract visual information from the visual scene.

Foyle, David C.↗
NASA NTRS1992

A novel visual hardware behavioral language

Most hardware behavioral languages just use texts to describe the behavior of the desired hardware design. This is inconvenient for VLSI designers who enjoy using the schematic approach. The proposed visual hardware behavioral language has the ability to graphically express design information using visual parallel models (blocks), visual sequential models (processes) and visual data flow graphs (which consist of primitive operational icons, control icons, and Data and Synchro links). Thus, the proposed visual hardware behavioral language can not only specify hardware concurrent and sequential functionality, but can also visually expose parallelism, sequentiality, and disjointness (mutually exclusive operations) for the hardware designers. That would make the hardware designers capture the design ideas easily and explicitly using this visual hardware behavioral language.

Li, Xueqin↗
NASA NTRS1994

RAVE: Rapid Visualization Environment

Visualization is used in the process of analyzing large, multidimensional data sets. However, the selection and creation of visualizations that are appropriate for the characteristics of a particular data set and the satisfaction of the analyst's goals is difficult. The process consists of three tasks that are performed iteratively: generate, test, and refine. The performance of these tasks requires the utilization of several types of domain knowledge that data analysts do not often have. Existing visualization systems and frameworks do not adequately support the performance of these tasks. In this paper we present the RApid Visualization Environment (RAVE), a knowledge-based system that interfaces with commercial visualization frameworks and assists a data analyst in quickly and easily generating, testing, and refining visualizations. RAVE was used for the visualization of in situ measurement data captured by spacecraft.

Klumpar, D. M.↗
NASA NTRS1992

A Framework for the Design of Effective Graphics for Scientific Visualization

This proposal presents a visualization framework, based on a data model, that supports the production of effective graphics for scientific visualization. Visual representations are effective only if they augment comprehension of the increasing amounts of data being generated by modern computer simulations. These representations are created by taking into account the goals and capabilities of the scientist, the type of data to be displayed, and software and hardware considerations. This framework is embodied in an assistant-based visualization system to guide the scientist in the visualization process. This will improve the quality of the visualizations and decrease the time the scientist is required to spend in generating the visualizations. I intend to prove that such a framework will create a more productive environment for tile analysis and interpretation of large, complex data sets.

Miceli, Kristina D.↗