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Displaying 121-132 of 286 Results

G

GUMP: General Urban Area Microclimate Predictions Tool

  • PI Kevin Adkins

    CO-I Nickolas Macchiarella

    CO-I National Aeronautics and Space Administration NASA

  • Hyperlocal weather predictions are often necessary in order to determine whether a particular sUAS route will be safe to fly. The General Urban area Microclimate Predictions tool (GUMP) seeks to provide such predictions through the use of machine learning (ML) models and computational fluid dynamics (CFD) simulations. The computed wind flow field is converted into an intuitive risk map for sUAS operators through the use of appropriate thresholds on wind velocities.

H

Hazard Detection and Avoidance for Lunar Landing

  • PI Troy Henderson

  • This project develops and demonstrates algorithms for detecting and avoiding areas of large rocks and high slopes for a lunar lander
Hidden Dangers on the Flight Deck: A Stakeholder Analysis of the Issues Surrounding Commercial Pilot Mental Health

  • PI Mark Miller

    CO-I Katie Coleman Assad

  • A comprehensive stakeholder analysis of commercial pilot mental health in the US.
High Spectral Resolution Observations of Lunar Exospheric Emissions

  • PI Edwin Mierkiewicz

  • We are employing high-resolution Fabry-Perot spectroscopy of neutral sodium and potassium emission to investigate the morphology and dynamics of the lunar sodium exosphere. Likely atmosphere source mechanisms are thermal desorption, photo-desorption, ion sputtering, and meteoric impact ablation.

Human Factors Awareness Training for FAA Aviation Safety Specialists Within Aircraft Certification and FAA Flight Standards

  • PI Scott Martin

  • In this project, which is funded by the FAA, Embry‑Riddle and Kent University will develop training for individuals within the FAA’s Aviation Safety Flight Standards Service who have expertise and job responsibilities related to the evaluation of aircraft systems design, maintenance, operations, procedures and pilot performance.
Humanistic STEM: Blending Humanities and STEM to Increase Undergraduate Student Engagement, Knowledge, and Skills

  • PI Debra Bourdeau

  • NSF IUSE #2120807

I

ICARUS Drone Net

  • PI Samuel Siewert

    CO-I Iacopo Gentilini

    CO-I Mehran Andalibi

    CO-I Stephen Bruder

  • Drone Net is a conceptual architecture to integrate passive sensor nodes in a local sensor network along with traditional active sensing methods for small Unmanned Aerial Systems traffic management. The goal of the proposed research architecture is to evaluate use of multiple passive sensor nodes integrating Electro-Optical/Infrared and acoustic arrays networked around a UAS Traffic Management operating region (Class G uncontrolled airspace). The Drone Net approach will be compared to and/or used in addition to RADAR and Automatic Dependent Surveillance-Broadcast tracking and identification.  We hypothesize that this approach can better manage non-compliant small UAS along with compliant UAS and general aviation in sensitive airspace, urban locations, and geo-fenced regions. 

Ice Cloud Parameterizations and Aircraft Icing

  • PI Dorothea Ivanova

  • Ice and mixed phase clouds have an important impact on aviation, but they are often poorly represented in the models.
Identifying Cost-Effective Security Barrier Technologies for K-12 Schools: An Interdisciplinary Evaluation

  • PI Thomas Foley

    CO-I Reginald Parker

    CO-I Michele Gazica

    CO-I Brooke Shannon

    CO-I Erin Bowen

    CO-I Muna Slewa

    CO-I Michael Brady

    CO-I Richard Rodriguez

    CO-I Perry Feder

  • This study proposes to test and collect data on the effectiveness of commonly used physical security systems in delaying intruders. The purpose of this study is to support the design of better physical security systems for schools. The study will also gather data on parent and teacher perceptions of the quality of security in schools.
If at first you do not succeed: the student benefits of multiple trials on summative assessments.

  • PI Emily Faulconer

    CO-I John Griffith

    CO-I Hayden Frank

  • This paper explores the combination of multiple attempts on assessments with immediate feedback through the LMS. The data reveals that students who do not earn an A are likely to use additional attempts and they tend to do better on future attempts.
Implementing Active Learning Techniques in an Undergraduate Aviation Meteorology Course

  • PI Daniel Halperin

    PI Joseph Keebler

    CO-I Robert Eicher

    CO-I Thomas Guinn

    CO-I Kim Chambers

  • ​Student feedback from end-of-course evaluations repeatedly indicated a desire to change the format of the course by de-emphasizing the PowerPoint-based lectures. The goal of the present study was to determine whether including a set of new active-learning techniques in an Aviation Weather course would result in better student understanding (as measured by exam scores) and make the course more engaging (as measured by end-of-course evaluations). During 2018-19, three instructors implemented five different active-learning techniques into their classes (i.e., the experimental group), while two instructors continued to use the unrevised course materials (i.e., the control group). The new active-learning techniques, described below, included daily quizzes, polling questions, flipped classroom sessions, in-class activities, and assertion-evidence-based lectures. All sections used the same assignments and exams, allowing for direct assessment of the effectiveness of the active-learning techniques. Analyses of Variance (ANOVA) tables were used to determine the statistical significance of the differences in exam scores. Indirect assessments in the form of end-of-course evaluations were also examined. 
Improved Image Processing for Orbit Estimation

  • PI Troy Henderson

  • This project seeks to improve orbit estimation methods using advanced image processing techniques applied to images from ground and space-based telescopes.