Browsing by Subject "visualization"

Now showing 1 - 10 of 11
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    3d terrain visualization and CPU parallelization of particle swarm optimization
    (2018) Wieczorek, Calvin L.; Christopher, Lauren; King, Brian; Lee, John
    Particle Swarm Optimization is a bio-inspired optimization technique used to approximately solve the non-deterministic polynomial (NP) problem of asset allocation in 3D space, frequency, antenna azimuth [1], and elevation orientation [1]. This research uses QT Data Visualization to display the PSO solutions, assets, transmitters in 3D space from the work done in [2]. Elevation and Imagery data was extracted from ARCGIS (a geographic information system (GIS) database) to add overlapping elevation and imagery data to that the 3D visualization displays proper topological data. The 3D environment range was improved and is now dynamic; giving the user appropriate coordinates based from the ARCGIS latitude and longitude ranges. The second part of the research improves the performance of the PSOs runtime, using OpenMP with CPU threading to parallelize the evaluation of the PSO by particle. Lastly, this implementation uses CPU multithreading with 4 threads to improve the performance of the PSO by 42% - 51% in comparison to running the PSO without CPU multithreading. The contributions provided allow for the PSO project to be more realistically simulate its use in the Electronic Warfare (EW) space, adding additional CPU multithreading implementation for further performance improvements.
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    BECA: A Software Tool for Integrated Visualization of Human Brain Data
    (Springer, 2017) Li, Huang; Fang, Shiaofen; Zigon, Bob; Sporns, Olaf; Saykin, Andrew J.; Goñi, Joaquin; Shen, Li; Computer and Information Science, School of Science
    Visualization plays an important role in helping neuroscientist understanding human brain data. Most publicly available software focuses on visualizing a specific brain imaging modality. Here we present an extensible visualization platform, BECA, which employ a plugin architecture to facilitate rapid development and deployment of visualization for human brain data. This paper will introduce the architecture and discuss some important design decisions in implementing the BECA platform and its visualization plugins.
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    Evaluating Gradient Perception in Color-Coded Scalar Fields
    (IEEE, 2019-10) Reda, Khairi; Papka, Michael E.; Human-Centered Computing, School of Informatics and Computing
    Color mapping is a commonly used technique for visualizing scalar fields. While there exists advice for choosing effective colormaps, it is unclear if current guidelines apply equally across task types. We study the perception of gradients and evaluate the effectiveness of three colormaps at depicting gradient magnitudes. In a crowd-sourced experiment, we determine the just-noticeable differences (JNDs) at which participants can reliably compare and judge variations in gradient between two scalar fields. We find that participants exhibited lower JNDs with a diverging (cool-warm) or a spectral (rainbow) scheme, as compared with a monotonic-luminance colormap (viridis). The results support a hypothesis that apparent discontinuities in the color ramp may help viewers discern subtle structural differences in gradient. We discuss these findings and highlight future research directions for colormap evaluation.
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    Exploring diseases based biomedical document clustering and visualization using self-organizing maps
    (IEEE, 2017-10) Shah, Setu; Luo, Xiao; Computer and Information Science, School of Science
    Document clustering is a text mining technique used to provide better document search and browsing in digital libraries or online corpora. In this research, a vector representation of concepts of diseases and similarity measurement between concepts are proposed. They identify the closest concepts of diseases in the context of a corpus. Each document is represented by using the vector space model. A weight scheme is proposed to consider both local content and associations between concepts. Self-Organizing Maps (SOM) are often used as document clustering algorithm. The vector projection and visualization features of SOM enable visualization and analysis of the cluster distribution and relationships on the two dimensional space. The Davies-Bouldin index is used to validate the clusters based on the visualized cluster distributions. The results show that the proposed document clustering framework generates meaningful clusters and can facilitate clustering visualization and information retrieval based on the concepts of diseases.
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    Managing Patient Health Across Diverse Spaces: Using Activity Theory to Model Pervasive Decision Support
    (2012) Faiola, Anthony; Boston-Clay, Crystal; Jones, Josette; Downey, Michael; Newlon, Christine M.
    Clinical decision support (CDS) systems can offer health care providers and patient data that is intelligently filtered and presented in ways to enhance diagnosis and long-term health care management, both within and outside clinical spaces. Challenges to this information management include diagnostic error and inefficiencies from conflicting, incomplete, or suboptimal clinical systems [3] as well as extending care outside the traditional clinical environment. We propose a Clinical Activity Model (CAM) to understand pervasive CDS system design and use across multiple health care spaces as patients move between critical care, recovery, and long-term home care. We discuss CAM in the context of research findings comparing a novel CDS system with traditional modes of data delivery and by describing use of that system as a mobile diagnostic tool to bridge clinical care and home care.
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    Rapid Generation of Parametric Aircraft Structural Models
    (ARC, 2019) Joe, John; Gandhi, Viraj; Dannenhoffer, John F., III; Dalir, Hamid; Mechanical and Energy Engineering, School of Engineering and Technology
    Within the aerospace design, analysis and optimization community, there is an increasing demand for automatic generation of parametric feature tree (build recipe) attributed multidisciplinary models. Currently, this is mainly done by creating separate models for different disciplines such as mid-surface model for aeroelasticity, outer-mold line for aerodynamics and CFD, and built-up element model for structural analysis. Since all of these models are built independently, any changes in design parameters require updates on all the models which is inefficient, time-consuming and prone to deficiencies. Here a browser-based system, called the Engineering Sketch Pad (ESP), is used. It provides the user with the ability to interact with a configuration by building and/or modifying the design parameters and feature tree that define the configuration. ESP is based an open-source constructive solid modeler, named OpenCSM, which is built upon the OpenCASCADE geometry kernel and the EGADS geometry generation system. The use of OpenCSM as part of the AFRL’s CAPS project on Computational Aircraft Prototype Syntheses for automatic commercial and fighter jet models is demonstrated. The rapid generation of parametric aircraft structural models proposed and developed in this work will benefit the aerospace industry with coming up with efficient, fast and robust multidisciplinary design standardization of aircraft structures.
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    Robust High-Level Video Stabilization for Effective AR Telementoring
    (IEEE, 2019-03) Lin, Chengyuan; Rojas-Muñoz, Edgar; Cabrera, Maria Eugenia; Sanchez-Tamayo, Natalia; Andersen, Daniel; Popescu, Voicu; Noguera, Juan Antonio Barragan; Zarzaur, Ben; Murphy, Pat; Anderson, Kathryn; Douglas, Thomas; Griffis, Clare; Wachs, Juan; Medicine, School of Medicine
    This poster presents the design, implementation, and evaluation of a method for robust high-level stabilization of mentees first-person video in augmented reality (AR) telementoring. This video is captured by the front-facing built-in camera of an AR headset and stabilized by rendering from a stationary view a planar proxy of the workspace projectively texture mapped with the video feed. The result is stable, complete, up to date, continuous, distortion free, and rendered from the mentee's default viewpoint. The stabilization method was evaluated in two user studies, in the context of number matching and for cricothyroidotomy training, respectively. Both showed a significant advantage of our method compared with unstabilized visualization.
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    Vision-Based Target Tracking and Autonomous Landing of a Quadrotor on a Ground Vehicle
    (IEEE, 2017-05) Hoang, Tru; Bayasgalan, Enkhmurun; Wang, Ziyin; Tsechpenakis, Gavriil; Panagou, Dimitra; Computer and Information Science, School of Science
    This paper addresses vision-based tracking and landing of a micro-aerial vehicle (MAV) on a ground vehicle (GV). The camera onboard the MAV is mounted so that the optical axis is aligned with the downward-facing axis of the body-fixed frame. A novel supervised learning vision algorithm is proposed as the method to detect the ground vehicle in the image frame. A feedback linearization technique is developed for the MAV to fly over and track the GV so that visibility with the tracked target is maintained with certain guarantees. The efficacy of the visual detection algorithm, and of the tracking and landing controller is demonstrated in simulations and experiments with static and mobile GV.
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    Visualizing Road Appearance Properties in Driving Video
    (IEEE, 2016-07) Wang, Zheyuan; Zheng, Jiang Yu; Kilicarslan, Mehmet; Department of Computer & Information Science, School of Science
    With the increasing videos taken from driving recorders on thousands of cars, it is a challenging task to retrieve these videos and search for important information. The goal of this work is to mine certain critical road properties in a large scale driving video data set for traffic accident analysis, sensing algorithm development, and testing benchmark. Our aim is to condense video data to compact road profiles, which contain visual features of the road environment. By visualizing road edge and lane marks in the feature space with the reduced dimension, we will further explore the road edge models influenced by road and off-road materials, weather, lighting condition, etc.
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    VizCom: A Novel Workflow Model for ICU Clinical Decision-Support
    (2014-04) Faiola, Anthony; Srinivas, Preethi; Karanam, Yamini; Chartash, David; Doebbeling, Bradley N.
    The Intensive Care Unit (ICU) has the highest annual mortality rate (4.4M) of any hospital unit or 25% of all clinical admissions. Studies show a relationship between clinician cognitive load and workflow, and their impact on patient safety and the subsequent occurrence of medical mishaps due to diagnostic error - in spite of advances in health information technology, e.g., bedside and clinical decision support (CDS) systems. The aim of our research is to: 1) investigate the root causes (underlying mechanisms) of ICU error related to the effects of clinical workflow: medical cognition, team communication/collaboration, and the use of diagnostic/CDS systems and 2) construct and validate a novel workflow model that supports improved clinical workflow, with goals to decrease adverse events, increase safety, and reduce intensivist time, effort, and cognitive resources. Lastly, our long-term objective is to apply data from aims one and two to design the next generation of diagnostic visualization-communication (VizCom) system that improves intensive care workflow, communication, and effectiveness in healthcare.