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171-180 of 223 results

  • Secret Sharing Over a Gaussian Broadcast Channel: Optimal Coding Scheme Design and Deep Learning Approach at Short Blocklength

    PI Rumia Sultana

    ​We consider a secret sharing model where a dealer shares a secret with several participants through a Gaussian broadcast channel such that predefined subsets of participants can reconstruct the secret and all other subsets of participants cannot learn any information about the secret.

    We consider a secret sharing model where a dealer shares a secret with several participants through a Gaussian broadcast channel such that predefined subsets of participants can reconstruct the secret and all other subsets of participants cannot learn any information about the secret. Our first contribution is to show that, in the asymptotic blocklength regime, it is optimal to consider coding schemes that rely on two coding layers, namely, a reliability layer and a secrecy layer, where the reliability layer is a channel code for a compound channel without any security constraint. Our second contribution is to design such a two-layer coding scheme at short blocklength. Specifically, we design the reliability layer via an autoencoder, and implement the secrecy layer with hash functions. To evaluate the performance of our coding scheme, we evaluate the probability of error and information leakage, which is defined as the mutual information between the secret and the unauthorized sets of users channel outputs. We empirically evaluate this information leakage via a neural network-based mutual information estimator. Our simulation results demonstrate a precise control of the probability of error and leakage thanks to the two-layer coding design.

    Categories: Faculty-Staff

  • 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.



    Our team of researchers from Embry-Riddle Aeronautical University-Worldwide has been actively compiling published performance data associated with commercially-off-the-shelf (COTS) group 1 to 3 fixed-wing and vertical takeoff and landing (VTOL) unmanned aircraft systems (UAS) in an effort to develop statistical models of each category. The captured data, which includes maximum speed, cruise speed, endurance, weights, wind limitations, and costs, is used to calculate capabilities including range (one-way and return), time to objective, station keeping duration, and maneuver requirements. The benefit from assembling such a unified collection of information and the calculation of associated derived capabilities is that these models are anticipated to accurately reflect the capabilities, limitations, and considerations necessary in the assessment of such platforms for various applications and operating environments. These models will be available for combination with simulation or analysis frameworks to better assess end usability of these categories of aircraft for a significant number of applications including, emergency response, disaster relief, precision agriculture, security, tactical, communications, environmental study, infrastructure inspection, cargo delivery, and mapping/surveying.

    Publications:

    Terwilliger, B., Vincenzi, D., Ison, D., & Smith, T. (2015). Assessment of unmanned aircraft platform performance using modeling and simulation (paper no. 15006). In Volume 2015: Proceedings of the 2015 Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC). Arlington, VA: National Training and Simulation Association.

    Terwilliger, B., Vincenzi, D., Ison, D., Herron, R., & Smith, T. (2015). UAS capabilities and performance modeling for application analysis.  In Proceedings of the Association for Unmanned Vehicle Systems International 42nd Annual Symposium. Arlington, VA: Association of Unmanned Vehicle Systems International.

    Ison, D., Terwilliger, B., Vincenzi, D., & Kleinke, S. (2015). Airport bird activity - monitoring and mitigation: The unmanned aerial system (UAS) approach.Presented at the 2015 North American Bird Strike Conference, Montreal, QC.

    Categories: Faculty-Staff

  • sUAS Agricultural Aerial Application Spreading Efficiency

    PI David Thirtyacre

    CO-I Joseph Cerreta

    CO-I Scott Burgess

    The use of UAS in agricultural operations is increasing under the 14CFR Part 137 regulations. However, there is little data on the efficiency of using UAS to spread gradual products. This research seeks to determine efficiency through flight testing.


    One of the most promising uses of aerial applications by a sUAS is in small farms such as cranberry bogs, Christmas tree farms and vineyards, where traditional crewed aerial applicators (like crop dusters) were not practical due to the limited size of the operations (Velusamy et al., 2022). The ERAU-controlled field test for this research aimed to assess the feasibility and cost-effectiveness of using a sUAS spreading system compared to the traditional manual application of weed killer and fertilizer on cranberry bogs that were less than 10 acres.

    The specific research questions were:

    1. What efficiencies are realized by aerial application of granular material on small farms?
    2. What flight planning and operational considerations will maximize the efficiency of aerial application of granular material on small farms?

    Categories: Faculty-Staff

  • UUV-UAS Operational and Training Shared KSAs

    PI David Thirtyacre

    CO-I Joseph Cerreta

    Uncrewed Underwater Vehicles (UAV) and Uncrewed Aircraft Systems (UAS) operate in a similar fashion. However, there are major differences in the environment and control methods. This research investigates the similarities and differences and makes recommendations on effective cross training.

    This study presents a comparative analysis of the operational and human factor considerations involved in piloting mini UUVs and sUASs, highlighting the key similarities and differences in control methods, environmental influences, navigation, emergency procedures and situational awareness. A qualitative experimental field study was conducted between July 2024 and October 2024, involving real-world deployments of both systems in maritime and aerial environments.

    Categories: Faculty-Staff

  • IUSE/PFE: RED Innovation: Using Scrum to Develop an Agile Department

    PI Massood Towhidnejad

    CO-I Omar Ochoa

    CO-I James Pembridge

    Efforts to implement these kinds of changes are often slowed down by department cultures or faculty attitudes about the amount of time and work that would be involved. In this project the Electrical Engineering and Computer Science (EECS) Department at Embry-Riddle Aeronautical University will implement an innovative approach to become a department that responds quickly to student and industry needs.

    The next generation of engineers will need essential technical and professional skills to solve the complex problems facing society. Changes to how departments operate, the curriculum, and teaching practices in engineering programs are required to better prepare students for the profession. Efforts to implement these kinds of changes are often slowed down by department cultures or faculty attitudes about the amount of time and work that would be involved. In this project the Electrical Engineering and Computer Science (EECS) Department at Embry-Riddle Aeronautical University will implement an innovative approach to become a department that responds quickly to student and industry needs. This approach will apply agile development methods typically used in industry to deliver the best products faster. Agile methods involve working on teams in short cycles which allow shared work responsibility, frequent feedback, and adjustments between cycles. The EECS Department will use the Scrum agile method to organize how the department carries out its normal operations. The department will also embed Scrum agile product development into courses across the curriculum. The new approach will allow faculty to achieve quicker changes and implementation of prioritized items for the department. Examples of prioritized items will include incorporating more evidence-based practices in courses such as just-in-time teaching, case-based teaching, active learning, and peer instruction; fostering inclusive learning environments; updating course materials; revising department procedures; and recruiting diverse students and faculty. Consequently, both faculty and students in the department will gain expertise with this agile professional skill. The project will investigate how the changes to department operations enhance faculty and student experiences. The findings would help inform other engineering departments about practices to improve the education of a diverse student population to be well-skilled engineers for the workforce.

    The objectives of this project will be to radically transform the EECS department into an agile department that: 1) develops students into engineers with agile skills desired by industry, and 2) develops an agile faculty culture which models the use of agile practices for students. Faculty will work collectively in Scrum teams to innovate the practices, policies, and culture of the department. Students will use Scrum in individual and team projects throughout the middle two years of the curriculum to progressively build their expertise for the culminating capstone courses in the senior year. The research study will use an explanatory case study design guided by social cognitive theory. Quantitative and qualitative analyses will be performed using data from interviews with faculty and students, feedback from stakeholders, and artifacts from Scrum teams. Research results could lead to transformations in engineering education by offering a model on the novel use of Scrum as an agile organizational practice and its influences on the collective efficacy of faculty. This project is jointly funded by the Division of Undergraduate Education and the Division of Engineering Education and Centers reflecting the alignment of this project with the respective goals of the divisions and their programs.

    Categories: Faculty-Staff

  • Big Data Analytics for Injury Data

    PI Dothang Truong

    This project leverages big data analytics tools for the exploration and transformation of injury data for a major Part 121 carrier with the goal of predictive modeling. This project offers graduate students an opportunity to work with a substantial airline dataset under the supervision of a faculty member. The outcomes have the potential to lead to more extensive future projects in the realm of big data analytics. (This project is under strict NDA).


    Categories: Faculty-Staff

  • 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.



    ​The broader significance and importance of this project is the transformative potential of improving peer review processes, since peer review is used throughout STEM and medical fields. Thus this preliminary investigation can extend outside the realm of improving student learning. This project overlaps with NSF's strategic goals of transforming the frontiers through preparation of an engineering workforce with new capabilities and expertise. Additionally NSF's goal of innovating for society is enabled by supporting the development of innovative learning systems.

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    Categories: Faculty-Staff

  • 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.



    By data mining course materials (i.e., textbooks, course notes, syllabi, video transcripts, websites, etc.), a concept network can be developed for that course. With each additional resource, the network connectedness become more fully representative.  By mapping materials from courses throughout a curriculum, and then overlaying the resulting map on a degree plan of study, students will be able to better identify and value how concepts being taught today are connected and used throughout the rest of their education. For instructors, curricular redesign becomes significantly easier, as they will be able to more fully contextualize how other courses depend on their material.

    Categories: Faculty-Staff

  • 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.

    BASE-9 is useful for analyzing single-age, single-metallicity star clusters, binaries, or single stars, and for simulating such systems. BASE9 uses Markov chain Monte Carlo to estimate the posterior probability distribution for the age, metallicity, distance modulus, and line-of-sight absorption for a cluster, and for the mass, binary mass ratio, and cluster membership probability for every cluster member.

    Categories: Faculty-Staff

  • Diagnosing Kinematic Processes Responsible for Precipitation Distributions in Hurricanes

    PI Joshua Wadler

    This project studies physical processes behind why the spatial distribution of different types of precipitation are related to hurricane intensity change.

    Tropical cyclones, also called hurricanes or typhoons, pose a significant threat to coastal communities through high winds, storm surge, and heavy rainfall. Poor predictions of tropical cyclones can lead to underprepared communities, exacerbating the impacts of these powerful storms. This project aims to understand how different types of precipitation, or rainfall, impact tropical cyclone maximum sustained wind speed. Precipitation is divided into four categories based on how fast the air is rising in clouds. Clouds that have faster rising air are called convection, with the tallest clouds called deep convection and the shallower clouds called moderate convection and shallow convection. The lightest precipitation is called stratiform rain and has the least amount of rising air. The type of precipitation can be identified based on its appearance on radar measurements. This project addresses how each type of precipitation influences the maximum sustained wind speed of the storm through their impact on storm structure. Since different types of precipitation can be identified on radar, this project may offer new insights into forecasting of tropical cyclone maximum sustained wind speed. In addition, this project will support undergraduate student research, an undergraduate mentorship program, a scholarship for a high achieving student, and outreach activities that will help communities susceptible to tropical cyclones understand and prepare for their impacts.

    Categories: Faculty-Staff

171-180 of 223 results