151-160 of 192 results
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NSF REU Site: Swarms of Unmanned Aircraft Systems in the Age of AI/Machine Learning
PI Houbing Song
CO-I Richard Stansbury
Embry-Riddle Aeronautical University establishes a new Research Experiences for Undergraduates (REU) Site to engage participants in research in drone swarms. The emerging concept of drone swarms, which is defined as the ability of drones to autonomously make decisions based on shared information, creates new opportunities with major societal implications. However, future drone swarm applications and services pose new networking challenges. A resurgence of Artificial Intelligence and machine learning research presents a tremendous opportunity for addressing these networking challenges. There is an overwhelming need to foster a robust workforce with competencies to enable future drone swarm applications and services in the age of AI/machine learning.
The project establishes a new Research Experiences for Undergraduates (REU) Site with a focus on networking research for drone swarms in the age of AI/machine learning at Embry-Riddle Aeronautical University. The goals of the REU Site are: (1) attract undergraduate students to state-of-the-art drone swarm research, especially those from underrepresented groups, and from institutions with limited opportunities; (2) develop the research capacity of participants by guiding them to perform research on drone swarms; (3) grow the participants’ technical skills to enable a wide variety of beneficial applications of drone swarms; (4) promote the participants’ integrated AI/machine learning and drone swarm competencies; and (5) prepare participants with professional skills for careers. The focus of the REU Site is on the design, analysis and evaluation of innovative computing and networking technologies for future drone swarm applications and services. To be specific, research activities will be conducted in three focus areas, notably dynamic network management, network protocol design, and operationalizing AI/machine learning for drone swarms. Each year eight undergraduate students will participate in a ten-week summer REU program to perform networking research for drone swarms under the guidance of research mentors with rich experiences in AI/machine learning and drone swarms. This REU site is expected to foster workforce knowledge and skills about developing new computing and networking technologies for future drone swarm applications and services. This site is supported by the Department of Defense ASSURE program in partnership with the NSF REU program.
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Unmanned Aircraft Systems (UAS) Application to Support Aircraft Rescue and Fire Fighting (ARFF)
PI Brent Terwilliger
CO-I David Ison
CO-I Dennis Vincenzi
CO-I Dahai Liu
This continuing research project features refinement of UAS application methods to support of ARFF responses. Previously, modeling and simulation, in combination with UAS attribute performance models, was implemented to better understand challenges, limitations, and potential benefits of UAS support. However, based on the findings and recommendations of the original inquiry, the research will be expanded to include examination of operator knowledge, skills, and abilities (KSAs), performance rating standards, and appropriate training requirements and delivery approaches.
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A Curriculum Wide Software Development Case Study
PI Massood Towhidnejad
CO-I Thomas Hilburn
This NSF funded research develops case studies of software development for use in software engineering and computing instruction.
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Encouraging Students to Pursue an Engineering Education and Career
PI Massood Towhidnejad
This NSF-sponsored project provides scholarship for engineering students pursuing degrees in computer science, computer engineering, electrical engineering, mechanical engineering and software engineering.
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From Middle School to Industry Vertical Integration to Inspire Interest in Computational Thinking
PI Massood Towhidnejad
CO-I Thomas Hilburn
While students typically do not see immediate advantages of the topics being studies, top down integration exposes students to larger, more complex projects, giving them better appreciation for topics as they realize the “big picture.”
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Developing Artifact Peer Review Assignment Methodologies to Maximize the Value of Peer Review for Students
PI Matthew Verleger
This engineering education research project seeks to develop a proof-of-concept peer review matching algorithm and demonstrate if it is a valuable and viable methodology for conducting peer review. Peer review is a proven method that has positive impact on student learning. The project will test the algorithm on Model Eliciting Activities in the engineering classroom, and investigate how changing peer review can affect student learning.
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Platform for Investigating Concept Networks on the Instrumentality of Knowledge (PICNIK)
PI Matthew Verleger
This engineering education research project seeks to develop a concept network for engineering and a platform for helping students identify how concepts are connected across a curriculum. The goal is to better understand and improve how students value the concepts being taught throughout their education.
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Bayesian Analysis of Stellar Evolution
PI Theodore von Hippel
Bayesian Analysis of Stellar Evolution is an international collaboration studying stellar evolution with an emphasis on stellar ages. We also develop and support a Bayesian software suite that recovers star cluster and stellar parameters from photometry, currently called BASE-9.
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151-160 of 192 results