51-60 of 75 results

  • NSF REU Site: Cybersecurity Research of Unmanned Aerial Vehicles

    PI Laxima Niure Kandel

    CO-I M. Ilhan Akbas

    ​This funding institutes a Research Experience for Undergraduates (REU) Site at Embry-Riddle Aeronautical University (ERAU). Each year, over the summer, ten highly motivated undergraduates will conduct an intense 10-week Unmanned Aerial Vehicles (UAV) cybersecurity research program complemented by professional development activities that prepare them for future cybersecurity careers and graduate schools.

    This funding institutes a Research Experience for Undergraduates (REU) Site at Embry-Riddle Aeronautical University (ERAU). Each year, over the summer, ten highly motivated undergraduates will conduct an intense 10-week Unmanned Aerial Vehicles (UAV) cybersecurity research program complemented by professional development activities that prepare them for future cybersecurity careers and graduate schools. Students will research existing UAV cyber threats and mitigation strategies and explore new techniques and algorithms to safeguard UAV systems. The REU program will focus on providing unparalleled opportunities for undergraduate students, especially those from underrepresented and minority groups and from institutions with limited resources, by engaging them in real-world cybersecurity research of UAVs. Through small-group, high-quality mentoring practices, the REU training will not only aid in enhancing the safety and security of UAVs in personal and commercial applications but will also build research confidence among REU participants.

    The overall objective of this project is to immerse undergraduate students in research-intensive training in the cybersecurity field and encourage them to think creatively and independently through hands-on project activities. REU participants will be engaged in faculty-led projects such as UAV cyber-attacks, UAV cyber defense mechanisms, privacy protection methods for UAV communications, and Physical Layer-based cybersecurity. They will participate in activities that range from literature reviews, technical seminars, and workshops to the preparation, presentation, and dissemination of research findings. The three major goals of the REU Site are: (1) to expose undergraduate students to a variety of cybersecurity projects that are bound to build the interest, skills, and knowledge necessary to pursue cybersecurity careers; (2) to increase the number of underrepresented undergraduates in cybersecurity and STEM fields through diversity recruitment emphasis, and (3) to provide undergraduate students with strong professional skills for their future careers and graduate schools. The REU Site will leverage ERAUs? state-of-the-art facilities, research labs, and faculty expertise to promote interest in cybersecurity and develop research skills of the undergraduate students which, in turn, will contribute towards cybersecurity education, training, and workforce development.

    Categories: Faculty-Staff

  • CyberCorps Scholarship for Service: High-skilled Workforce Development for the Aviation and Aerospace Cybersecurity Domains

    PI Omar Ochoa

    CO-I Keith Garfield

    CO-I Laxima Niure Kandel

    CO-I Krishna Sampigethaya

    This project promotes workforce development in this vital sector by building on undergraduate and graduate cybersecurity programs at Embry-Riddle Aeronautical University (ERAU), where both ERAU campuses (Daytona Beach, FL and Prescott, AZ) have a history of collaborative education and research activities within the aviation and aerospace cybersecurity domain. 



    Aviation and aerospace cybersecurity is of critical importance to the Nation. As a key component of the overall U.S. transportation infrastructure, it protects people and contributes to American prosperity and leadership. This project promotes workforce development in this vital sector by building on undergraduate and graduate cybersecurity programs at Embry-Riddle Aeronautical University (ERAU), where both ERAU campuses (Daytona Beach, FL and Prescott, AZ) have a history of collaborative education and research activities within the aviation and aerospace cybersecurity domain. Known locally as "Cyber Eagles," the project will advance the collaboration ecosystem across education programs and research centers to prepare students for productive cybersecurity careers and leadership roles in federal and state agencies. The program will recruit diverse scholars and create a supportive environment through effective mentorship, a well-developed curriculum, student involvement activities, and research experiences. These project components will help establish a pathway that enables students to participate in an environment where they can excel and enter a rewarding career in government aviation and aerospace administration agencies.

    The project aims to develop a high-skilled workforce to cover the Nation’s needs in the area of aviation and aerospace cybersecurity, focusing on the safety-criticality aspects of airborne systems and the protection of associated hardware and software assets. The project will fund 20 scholarships to students over a five-year period. Student scholars will benefit from the strong ties that ERAU has with Federal and state aviation and transportation administration agencies and the aviation and aerospace industry. Scholars will have the opportunity to meet and learn from top cybersecurity engineers and managers from government and industry through aviation and aerospace-themed projects, events, and symposia hosted by ERAU. Furthermore, the project will take advantage of on-site expertise at ERAU in all computation and communication services related to flight operations, including airborne hardware and software, avionics equipment, and network and communication data links among aircraft, ground stations, radar systems, and satellite systems. This expertise places the scholarship students in a unique position to contribute to cybersecurity protection during the design, development, and operation stages of systems specific for the aviation and aerospace domain.

    This project is supported by the CyberCorps® Scholarship for Service (SFS) program, which funds proposals establishing or continuing scholarship programs in cybersecurity and aligns with the U.S. National Cyber Strategy to develop a superior cybersecurity workforce. Following graduation, scholarship recipients are required to work in cybersecurity for a Federal, state, local, or tribal Government organization for the same duration as their scholarship support.

    Categories: Faculty-Staff

  • Expanding the Nation’s STEM Talent Pool by Accelerating Graduate Degree Completion in Computer, Software, and Cybersecurity Engineering

    PI Omar Ochoa

    CO-I Massood Towhidnejad

    CO-I Debarati Basu

    ​The project will increase student persistence in STEM fields by linking scholarships with a newly created effective ecosystem that combines evidence-based practices such as faculty mentoring, academic advising, participation in the learning community, professional development activities, guidance in acquiring internships and research opportunities.

    This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by fostering student success and supporting the retention and graduation of domestic, high-achieving, low-income students with demonstrated financial need at the Embry-Riddle Aeronautical University, a non-profit private institution. Over its six-year duration, this project will fund scholarships to 25 undergraduate students to pursue four-year bachelor’s degrees in Computer Science, Software Engineering, or Computer Engineering. Subsequently the scholars will pursue a one-year accelerated master’s degree in one of the following areas: Software Engineering, Electrical, and Computer Engineering, or Cybersecurity Engineering. First-year students will receive up to five years of scholarship support. The project will increase student persistence in STEM fields by linking scholarships with a newly created effective ecosystem that combines evidence-based practices such as faculty mentoring, academic advising, participation in the learning community, professional development activities, guidance in acquiring internships and research opportunities. With the help of mentors, the scholars will create individual development plans outlining their career goals and steps toward achieving those goals. The project will also include the evaluation of the impact of the ecosystem on supporting the academic success of scholars and the identification of best practices and lessons learned. This project will significantly contribute towards creating a model that actively engages students from groups underrepresented in STEM fields of study, broadens participation in STEM, and infuses 25 talented and diverse engineers with advanced degrees in engineering into the American workforce.

    The overall goal of this project is to increase undergraduate and graduate STEM degree completion of domestic, low-income, high-achieving undergraduates with demonstrated financial need in STEM field. Three specific aims guide the project. First is to deliver financial support to domestic, low-income, high-achieving students who will pursue an undergraduate and accelerated master’s degree in engineering. Second is to leverage evidence-based practices to foster student success, increase retention and degree attainment. Third, and finally, is to evaluate the impact of the newly created ecosystem in supporting the academic success of scholars in engineering, and disseminate best practices and lessons learned. Little is known about the factors that affect the academic success of domestic, low-income, high-achieving undergraduate students in engineering fields at a private institution, and how factors such as gender, ethnic background and discipline impact their success, which is the focus of this project. Two research questions will be investigated in this project: (a) Does the academic success of scholars improve across the years by being part of this project? (b) What were the factors effecting the academic success of the scholars, and what are the accomplishments, best practices, and lessons learned from implementing the ecosystem for the scholars? This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students.

    Categories: Faculty-Staff

  • Machine Learning Engineering: Infusing Software Engineering through the Semantic Web

    PI Omar Ochoa

    ​The Semantic Web provides a wealth of high-quality, structured, and contextual data, which can be used to train machine learning models.

    The Semantic Web provides a wealth of high-quality, structured, and contextual data, which can be used to train machine learning models. This can lead to the creation of models, i.e., the engineering of Machine Learning, that adhere to non-functional requirements, which include considerations such as safety, security, and reliability, which are key elements of Software Engineering. These requirements do not concern a system's functionality, but rather its quality attributes. By incorporating these concepts into the engineering of machine learning models, one can strive to create models that are secure, reliable, and exhibit the desired quality attributes. Furthermore, Verification and Validation, or V&V, is integral to successful software engineering, by ensuring that a system is implemented correctly and meets specified requirements. In engineering Machine Learning, it's equally important to define processes and methods to thoroughly test and validate models to ensure they're performing as expected and providing accurate results. Together, the fusion of Software Engineering principles into Machine Learning Engineering, aided by the Semantic Web's capabilities, can bolster trustworthiness in machine learning systems. This trustworthiness ensures that the systems can be relied upon to behave as expected. In essence, by combining these fields, one can develop machine learning systems that are reliable, secure, interpretable, and trustworthy, upholding the core principles of Software Engineering. Our research group focuses on the most recent developments in these areas, i.e., Knowledge Graphs and Large Language Models, to accomplish these goals.

    Categories: Faculty-Staff

  • Investigate Detect and Avoid Track Classification and Filtering

    PI Richard Prazenica

    CO-I Troy Henderson

    CO-I Morad Nazari

    CO-I Tyler Spence

    This research will identify key sources of uncertainty in representative detect and avoid architectures and assess the downstream risks and effects of spurious information on downstream system performance

    In this project, which is funded by the FAA ASSURE program, the research team consisting of The Ohio State University, Embry‑Riddle Aeronautical University, Mississippi State University, University of North Dakota and Cal Analytics will work together to:

    • Identify the key sources of misleading surveillance information produced by airborne and ground-based detect and avoid (DAA) systems. Develop risk modeling and analysis tools to assess the system-wide effects of false or misleading information on alerting and separation, as well as impacts on pilots in command (PIC) and air traffic operators.
    • Provide guidance and recommendations for track classification and filter performance and safety requirements to standards bodies, including Radio Technical Commission for Aeronautics (RTCA) and American Society for Testing and Materials (ASTM) DAA working groups, and inform Federal Aviation Administration (FAA) rulemaking on DAA operations.

    Current guidance provided by the Federal Aviation Administration has made beyond visual line of sight (BVLOS) missions an executive priority. Key to the success of these missions is the development of DAA systems capable of providing accurate pilot in the loop, or autonomous deconfliction guidance. Current standards for DAA services provided by RTCA and ASTM do not address the requirements for system performance with respect to generation of false or misleading information to the PIC or autonomous response services of the unmanned aircraft system. This research will identify key sources of uncertainty in representative DAA architectures and assess the downstream risks and effects of spurious information on downstream system performance. Additionally, recommendations will be developed for track classification accuracy requirements that provide sufficient safety margins for enabling DAA services in support of BVLOS missions.

    Categories: Faculty-Staff

  • Researching How You Teach Holistic Modeling (RHYTHM)

    PI Kelsey Rodgers

    CO-I Matthew Verleger

    CO-I Lisa Davids

    "Models are a critical part of the analysis and design of engineered systems. The purpose of multiple types of models (physical, mathematical, computational, and financial) is to provide a simplified representation of reality that mimics the features of the engineered system, and that predicts the behavior of the system. This project, a collaboration between Embry-Riddle Aeronautical University, San Jose State University, and the University of Louisville, aims to improve engineering students' modeling competence. The project plans to achieve this goal by transforming first-year engineering courses to teach modeling as an engineering tool. The project will change existing course materials, pedagogy, and assessment methods across the three institutions. Each institution will implement its own specific strategy to teach mathematical, physical, computational, and financial modeling, thus providing three different approaches. By comparing student's modeling abilities across the institutions and approaches, the project aims to identify the most impactful approaches for teaching multiple modeling in introductory undergraduate engineering courses.

    The project is guided by a "holistic modeling perspective" theoretical framework, that builds on the successful "Models and Modeling Perspective" and "Computational Adaptive Expertise" frameworks. The objectives of the project are to: (1) implement, test, and refine holistic modeling environments for institutions that have flexibility in changing curriculum and for instructors that have different degrees of interest in changing their course(s); (2) implement, test, and refine methods to assess students' modeling abilities; and (3) evaluate and present the results of modeling abilities attained by students at three different universities. A unified language and discussion around modeling will be adopted in all revised courses. An assessment tool to measure students' modeling competence will be developed and implemented at each university. This work builds upon existing research in the development of more easily adaptable and adoptable modeling pedagogies and modeling languages. The following broad research question guides the research: How do students' definitional knowledge, ability to apply, and ability to create models change based on different degrees of modeling integration in the classroom?

    This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria."



    Categories: Faculty-Staff

  • CAREER: Additively Manufactured 3D Reconfigurable Antennas

    PI Eduardo Rojas

    ​The focus of this CARRER development project is on an emerging antenna fabrication technique that combines additive manufacturing (AM) and pulsed laser machining that has the potentials to fundamentally alter the existing state of the art. 

    Antennas are key components of ubiquitous wireless communication, radar, and navigation systems that affect widespread societal needs, such as aerospace systems, healthcare, and space exploration. Most of the antennas used in a variety of applications including cellular phones to unmanned aircraft systems (UAS) are based on flat planar structures or wire geometries that are developed using traditional manufacturing technique. This approach does not allow designers the opportunity to fully leverage the geometry, space, and materials available to design better performing antennas. The focus of this CARRER development project is on an emerging antenna fabrication technique that combines additive manufacturing (AM) and pulsed laser machining that has the potentials to fundamentally alter the existing state of the art. The proposed research will allow engineers to implement smaller, efficient, lighter, and reconfigurable antenna embodiments in three-dimensions (3D) for future applications with increasing complexity. The research proposed in this project is fully integrated with an education and outreach plan. The educational plan will impact the next generation of professionals by exposing high school students to hands-on activities and videos to explain basic antenna engineering concepts. The videos will be made by accomplished engineers in the engineering field to have a strong role-model-based motivational component to stimulate them to pursue STEM careers. An advanced cellular phone-based teaching tool that allows engineering undergraduate students to visualize complex 3D concepts in electromagnetics and antenna engineering is also proposed.

    The overall goal of this project is to pursue the discovery of the next generation of antennas with reconfigurable performance while conserving size, weight and cost. Research initiatives include: (a) the investigation of novel additive manufacturing processes for the fabrication of conformal 3D multiple curved antennas based on laser-enhanced direct print AM (LE-DPAM) with femtosecond laser machining and 5-axis kinematics, (b) the study of bio-inspired 3D superior antenna geometries that are not possible to manufacture using traditional methods but are conceivable using LE-DPAM, (c) the development of design methods based on a novel 3D to 2D conformal mapping technique, (d) the study of embedded material- and IC-based reconfigurability mechanisms including the use of electrically tunable inks that can be deposited on conformal surfaces, as well as IC-based switches for reconfiguration of antenna feeds and loads, and (e) the investigation of the structure-property relationships of commercially available and custom-formulated inks that provide excellent electromagnetic performance while addressing the needs for aviation and space environments.

    Categories: Faculty-Staff

  • FAA ASSURE Center of Excellence for Unmanned Aircraft Systems

    PI Richard Stansbury

    ERAU has completed or is conducting research tasks addressing the impact of maintenance induced failures on UAS safety; the function allocation of systems operations between automated systems, remote pilots, and support crew, surveillance criticality for detect, and avoid systems; impact of UAM air traffic on air traffic controllers; data analysis to determine the impact of UAS on the NAS, UAS flight data recorder requirements, etc.



    ASSURE or the Alliance of System Safety for UAS through Research Excellence is a multi-university center designated by the Federal Aviation Administration (FAA) as its Center of Excellence for Unmanned Aircraft Systems established in 2015. As a core and founding member of ASSURE, ERAU sponsorship to conduct research enabling the integration of unmanned aircraft systems (UAS), advanced air mobility (AAM), and urban air mobility (UAM) in the National Airspace System (NAS). New funding opportunities come available 1-3 times per year.

    ERAU has completed or is conducting research tasks addressing the impact of maintenance induced failures on UAS safety; the function allocation of systems operations between automated systems, remote pilots, and support crew, surveillance criticality for detect, and avoid systems; impact of UAM air traffic on air traffic controllers; data analysis to determine the impact of UAS on the NAS, UAS flight data recorder requirements, etc.

    Categories: Faculty-Staff

  • ASSURE A55

    PI Richard Stansbury

    PI Christopher Herbster

    The aviation industry uses flight data recorders (FDR) and cockpit voice recorders (CVR) to investigate accidents and incidents. FDRs record sensor data to provide information about an aircraft’s technical status, while CVRs record sounds from the cockpit to draw conclusions through crew communications and environmental sounds.

    The aviation industry uses flight data recorders (FDR) and cockpit voice recorders (CVR) to investigate accidents and incidents. FDRs record sensor data to provide information about an aircraft’s technical status, while CVRs record sounds from the cockpit to draw conclusions through crew communications and environmental sounds. The American National Standards Institute (ANSI) Unmanned Aircraft Systems Standardization Collaborative (UASSC) standardization roadmap v2.0 indicates that there are significant gaps regarding these flight recorders for UAS. Therefore, the purpose of this project is to close these gaps and define appropriate requirements for FDR and CVR for UAS in the national airspace.

    The project is divided into subtasks. The first major step is the literature review of current data recorder standards, technologies, and their requirements for UAS and UAM aircraft. The requirements of various government organizations and institutions are analyzed in this step. The next step is to examine the requirements found. Within this task, it is investigated how applicable the existing requirements are to various categories of UAS. If there are problems adapting these requirements, the corresponding standards will be adjusted. The research will especially focus on test procedures for crash survival, methods for data recording, and the minimum data required.

    Categories: Faculty-Staff

  • 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

51-60 of 75 results