Students and faculty in the Department of Electrical, Computer, Software, and Systems Engineering are some of the more prolific researchers in the Embry-Riddle family. The department's research expenditures are nearly one-half those of the entire College of Engineering, with support from federal agencies including NSF, FAA, and NOAA as well as industry partners. The department is heavily involved in projects managed by ERAU's NEAR Lab and by the COE's Eagle Flight Research Center.

Strategic department research directions include three areas critical for the future of aerospace. These are:

  • Detect and avoid technologies for unmanned aircraft systems;
  • Assured systems for aerospace, including cybersecurity and development assurance;
  • Modeling and simulation for aviation and aerospace.

Detect and avoid technologies enable unmanned aircraft systems to "see and be seen" by other aircraft and by air traffic controllers on the ground. Of particular challenge is detect and avoid of uncooperative aircraft, those aircraft that aren't equipped to announce their position either automatically or in response to interrogations from the ground.

Assured systems are those that are robust in the face of cybersecurity challenges, with assured development being system design approaches that yield assured systems without high overhead.

Modeling and simulation for aviation involves everything from the logistics of getting passengers onto aircraft to planning how to get all air traffic around predicted bad weather without upsetting arrival times and locations.

11-20 of 23 results

  • 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

  • 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

  • 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

  • A Biologically Inspired Architecture Screening Tool to Improve Electric Grid Transient Response Design

    PI Bryan Watson

    The objective of this research is to develop and validate a new approach to design-for-transient resilience that provides additional insights, is less expensive, and can be used early in the design process.



    Electrical distribution needs to protect society by providing reliable power, even under changing conditions. The current approach to design electrical distribution grids often focuses on steady state design requirements or response to a subset of potential faults. Even small and gradual changes in loading, however, can cause voltage transients and lead to major blackouts due to voltage collapse. As electric demand increases and infrastructure operates near its design limits, these events are likely to become more common. While designers can examine slowly changing load transients, this occurs after creating a model of the proposed grid, which can be costly. Thus, this research examines the following gap: A cost-effective approach is needed early in the electrical distribution design process to screen candidate architectures for their expected response to slowly changing operating conditions. 

    There is an opportunity to examine unexpected voltage collapse through the lens of ecosystem critical transitions. Critical transitions occur when an ecosystem shifts suddenly from one stable configuration (e.g. forest) to another (e.g. grassland) due to slowly changing environmental conditions (e.g. annual rainfall). The mathematical framework established to evaluate and classify critical transitions has been well studied but has not been used to design electrical distribution. The central hypothesis examined in this proposal is If we screen initial electrical distribution architectures with graph theory (Ecological Network Analysis), then the resulting designs will have improved critical transition performance over non-screened architectures. Critical transition performance has two aspects: 

    1.superior ability to absorb additional loading before voltage collapse (i.e. margin to critical transition), and 

    2. transition to desirable, stable secondary configurations following voltage collapse, rather than cascading throughout the system and causing a complete blackout (i.e. type of Bifurcation).

    The objective of this research is to develop and validate a new approach to design-for-transient resilience that provides additional insights, is less expensive, and can be used early in the design process.

    Categories: Faculty-Staff

  • Creating Connections: Bed bugs to UAV Swarms

    PI Bryan Watson

    ​The overarching goal of our research is to advance our understanding of bed bug behavior and use this understanding to improve performance of aerospace swarms.

    Modern aerospace systems need a new approach for swarm consensus that is distributed, operates with local knowledge, and uses simple agents. The overarching goal of our research is to advance our understanding of bed bug behavior and use this understanding to improve performance of aerospace swarms. The first step is to understand individual bed bug response to stimuli (CO2, heat, light) and individual neural characteristics, before considering group dynamics. The objective of this research was to establish a collaboration between biologists and engineers at ERAU to design and implement a test-platform to enable new data collection for bed bug movement. This collaboration begins by examining individual bed bug response to CO2 concentration. Our central hypothesis is that if we record bed bug response to CO­2 exposure, then we will be able to improve our understanding of collective decision making because the bed bugs coordinate their response to environmental conditions. The research involved five undergraduate students from three campuses.

    Categories: Faculty-Staff

  • Learning from Zombie Ants to Increase UAV Swarm Resilience to Faulted Agents

    PI Bryan Watson

    This proposal examines the issue of faulted-agent mitigation through the lens of Biologically Inspired Design.

    Modern aerospace systems often approach problems by connecting many smaller agents, rather than using a single, more expensive platform. For example, it is often advantageous to have a fleet of lower-cost UAVs searching an area than a single, highly capable platform (airship). These sophisticated networks, however, are vulnerable to cascading faults.  For example, errors in data from a single UAV could lead the entire search party away from their intended target. Although recognized as a vulnerability for multi-agent systems, current fault-mitigation methods have significant limitations. Centralized monitoring methods are too computationally expensive and do not work well at large scale, while solutions that rely on agents reporting their own failures may not work in situations where the units are under attack or experiencing certain types of faults (e.g. communication failures). Additionally, current approaches often have strict assumptions that may not apply in real-world systems. As a result, large-scale aerospace systems are at risk of individual agent failures that can spread throughout the entire network, causing problems with system operation, and putting personnel in danger. This proposal examines the issue of faulted-agent mitigation through the lens of Biologically Inspired Design. The objective of this research is to investigate and evaluate a new biologically inspired approach to increase multi-agent system resilience. The Ophiocordyceps camponoti-rufipedis (OCR) or Zombie Ant Fungus provides an example of fault resilience in nature. The fungus infects the ant's nervous system and alters their behavior, ultimately leading to death. However, ant colonies have developed a unique foraging and organizational structure that contains the spread of the fungus. The central hypothesis is that an examination of colony response to OCR will allow derivation of information sharing protocols to increase multi-agent system resilience to fault propagation.

    Categories: Faculty-Staff

  • Intelligent signal processing for secure mobile wireless communications with spectrum and energy efficiency

    PI Thomas Yang

    In modern wireless communications, scenarios often arise in which the receiver is required to perform detection of multi-user transmissions on the same channel or suppress co-channel interferers. In these scenarios, signal separation techniques based on statistical properties can be highly effective.

    In modern wireless communications, scenarios often arise in which the receiver is required to perform detection of multi-user transmissions on the same channel or suppress co-channel interferers. In these scenarios, signal separation techniques based on statistical properties can be highly effective. However, for wireless systems operating in highly dynamic environments (such as mobile and vehicular communications), the rapidly time-varying channel condition remains a major challenge for block-based signal processing, in which the estimation of statistical properties is performed through averaging over a block of data samples. When the channel parameters change with time, long blocks mean substantial variation of mixing matrices within each block, which inevitably degrades the source separation performance. On the other hand, short blocks render the estimation of signals’ statistical properties inaccurate and biased, thus resulting in poor estimation performance.

    We addresses the above-mentioned challenge via the adoption of signal separation algorithms specifically designed for dynamic channel conditions, and artificial data injection applied to short processing data blocks in wireless receivers. Through theoretical and simulation studies, we concluded that the data injection method has great potential in improving signal detection accuracy and/or processing speed for multi-user detection in wireless receivers under dynamic channel conditions. The physical layer security of these mobile communication systems is also being addressed. The research is supported by Air Force Research Laboratory’s Information Directorate (AFRL/RI).

    Categories: Faculty-Staff

11-20 of 23 results