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111-120 of 201 results

  • Pilot-in-the-Loop UAS Mobile Research Test-Bed

    PI Hever Moncayo

    CO-I May Chan

    CO-I Ashwini Agrawal

    CO-I Agustin Giovagnoli

    This project aims to develop and implement a Mobile UAV Ground Control Station (GCS) supporting aviation safety research with pilot-in-the-loop capabilities using unmanned aerial systems platforms, in which flight conditions, such as systems failures, could be simulated in real-time to characterize pilot response, control laws performance, and human-machine and control laws interactions.

    ​A fruitful achievement of this project will provide a platform to validate and assess new concepts and technologies that are beneficial for improving engineering fidelity of early systems integration testing based on pilots feedback and their interaction with on-board flight controls systems.

    Categories: Faculty-Staff

  • Shielded UAS Operations Detect and Avoid

    PI Hever Moncayo

    ​This effort is intended to identify risks and recommend solutions to the FAA that enable shielded UAS operations

    ​This project is funded under the FAA ASSURE program. Certain small UAS (sUAS) Beyond Visual Line of Sight (BVLOS) operations, such as structural inspection, may be in close proximity to structures that are collision hazards for manned aircraft. These types of operations that are in close proximity to manned aviation flight obstacles such that they provide significant protection from conflicts and collisions with manned aircraft are termed “shielded” operations. This effort is intended to identify risks and recommend solutions to the FAA that enable shielded UAS operations. Several topics related to this project include simulation of dynamic systems, simulation environment programming, guidance, control and dynamics, and hardware implementation.

    Categories: Faculty-Staff

  • Vision and Wireless-Based Surveying for Intelligent OSAM Navigation (VISION)

    PI Hever Moncayo

    CO-I Kadriye Merve Dogan

    ​In this project, which is a SpaceWERX Phase I STTR program with Orbital Prime, we are developing algorithms to increase autonomy of OSAM applications. 

    ​In this project, which is a SpaceWERX Phase I STTR program with Orbital Prime, we are developing algorithms to increase autonomy of OSAM applications. This includes the application of machine learning techniques to improve accuracy of position and orientation estimation for proximity operations in space. Machine learning include deep learning combined with vision-based navigation designed and tested in both, virtual simulation environment and actual thrust-based spacecraft system.

    Categories: Faculty-Staff

  • Mitigating GPS and ADS-B Risks for UAS

    PI Hever Moncayo

    ​In this project, the research team is investigating different strategies to mitigate such risks and proposing methodologies to increase safety of UAS operations within the National Airspace.

    This project is funded under the FAA ASSURE program. Unvalidated or unavailable GPS and “ADS-B In” data poses security and safety risks to automated UAS navigation and to Detect and Avoid operations. Erroneous, spoofed, jammed or dropouts of GPS data may result in unmanned aircraft position and navigation being incorrect. This may result in a fly away beyond radio control, flight into infrastructure or flight into controlled airspace. Erroneous, spoofed, jammed or dropouts of “ADSB-In” data may result in automated unmanned aircraft being unable to detect and avoid other aircraft or result in detecting and avoiding illusionary aircraft.

    In this project, the research team is investigating different strategies to mitigate such risks and proposing methodologies to increase safety of UAS operations within the National Airspace. Several topics related to this project include simulation of dynamic systems, artificial intelligence, flight testing of UAS and hardware implementation.

    Categories: Faculty-Staff

  • Deep-Learning-Based Unobtrusive Estimation of Pilot Adverse Interactions and Loss of Energy State Awareness

    PI Hever Moncayo

    ​This project aimed at gaining more insight into the mechanisms of pilot SD and LESA occurrence, capturing their dynamic fingerprint, and developing on-board intelligent schemes capable of predicting and detecting these dangerous phenomena associated to pilot behaviors. 

    Findings: Final report submitted 9/24. Each of the mathematical models showed good capabilities of estimating each of the pilot parameters and represent a promising tool towards the characterization of pilot behavior using learning components. Continuation can be pursued by generalizing or extending the proposed results to other aircraft-pilot dynamics, possibly eVTOLs for AAM.

    Student and Curriculum impact. The simulation and testing tools will be integrated as part of the experiential learning of the course AE623 Guidance, Navigation and Control that will be taught by the PI next Fall 2025. The proposed technique also allowed a master student in Aerospace Engineering to complement and enhance her thesis outcomes.

    Scholarly products: NASA ULI submission, NSF Dynamics Control and Systems

    • Brutch, T. Schill, and H. Moncayo, Machine learning approach to estimation of human-pilot model parameters, in Guidance Navigation and Control Architectures for Autonomous Systems III, AIAA SciTech 2024 Forum, 2024-1200 (AIAA, Orlando, FL, 2024).
    • S. Brutch and H. Moncayo, Performance analysis of machine learning algorithms to humanpilot-model parameter estimation, in IS-30, Human - Automation Interaction, Accepted for presentation in AIAA SciTech 2025 Forum (AIAA, Orlando, FL, 2025).
    • S. Brutch, Rocio. Jado-Puente, and H. Moncayo, A physics-informed deep learning model for estimating human pilot behavior and mitigating adverse interactions, in Guidance Navigation and Control Architectures for Autonomous Systems III, AIAA SciTech 2024 Forum, 2024-1200

    Categories: Faculty-Staff

  • Collaborative Research: Wideband Multi-Beam Antenna Arrays: Low-Complexity Algorithms and Analog-CMOS Implementations

    PI Sirani Mututhanthrige Perera

    PI Arjuna Habarakada Madanayake

    PI Soumyajit Mandal

    Explosion of millimeter-wave (mm-wave) bandwidth opens up applications in 5G wireless systems spanning communications, localization, imaging, and radar. This project addresses challenges in mathematics, engineering, and science in developing efficient wideband beamformers based on sparse factorizations of the matrix called-delay Vandermonde matrices (DVM). The proposed highly integrated approach is attractive for mobile applications including 5G smart devices, the internet of things, mobile robotics, unmanned aerial vehicles, and other emerging applications focused on mm-waves.



    A multi-beam array receiver is deeply difficult to realize in integrated circuit (IC) form due to the underlying complexity of its signal flow graph. Through the proposed work, mathematical methods based on the theories of i) sparse factorization and complexity of the structured complex DVM with the introduction of a super class for the discrete Fourier transform(which is DVM), and ii) approximation transforms are proved to solve this problem.

    The resulting matrices are realized with multi-GHz bandwidths using analog ICs. The novel DVM algorithm solves the longstanding "beam squint" problem, i.e., the fact that the beam direction changes with input frequency, making true wideband operation impossible. Moreover, the proposed multi-beamforming networks in analog IC form will be realized efficiently while addressing precision circuit design, digital calibration, built-in self-test, etc. Besides scientific merits, both minority students and female students will be mentored to pursue careers in the STEM disciplines through the proposed project.


    This project was funded by the National Science Foundation (the division of Electrical, Communications, and Cyber Systems) with award numbers 1711625 and 1711395. 

    Categories: Faculty-Staff

  • A data analytics framework for the application of pedestrian dynamics to public health

    PI Sirish Namilae

    CO-I Mandar Kulkarni



    The central hypothesis of this NIH funded project is that combining location-based service (LBS) data with pedestrian dynamics modeling can uncover movement patterns of people in complex situations with many public health applications. In Aim 1, we will develop an application-agnostic pedestrian dynamics modeling framework that assimilates LBS data. We will compare our approach to methods that do not utilize LBS in order to evaluate accuracy of human movement across multiple scenarios. In Aim 2, we will apply the pedestrian movement and interaction information to a variety of public health domains. These include: viral infection spread at local and global scales, enhancing walkability for active aging, and safe evacuation of the elderly. Finally, in Aim 3, we will translate our pedestrian dynamics modeling framework into public health practice. We will provide our platform to different stakeholders and obtain feedback on user satisfaction to improve the system design.

    Categories: Faculty-Staff

  • Nanoscale Design of Interfacial Kinematics in Composite Manufacturing

    PI Sirish Namilae

    CO-I Marwan Al-Haik

    This NSF-funded research will elucidate the role of interfacial kinematics and energetics in the evolution of inter-ply interfaces in composite structures during manufacturing. The research team will develop a novel experimental method for in-situ characterization of surface and interface deformations during composite processing, utilizing a customized commercial composite autoclave with a digital image correlation system. The surface strain and displacement measurements will be combined with ex-situ X-ray tomography and thermal characterization to map the interfacial thermomechanical response as a function of design and processing parameters. Additionally, the interfacial behavior will be engineered through the rapid and controlled growth of ZnO nanowires on carbon fibers to create a nanoscale interfacial component that increases the fiber bending resistance and creates an interlocking effect at the interfaces to mitigate defects propagation. The experimental research will be complemented by molecular dynamics simulations of the sliding of amorphous polymer interfaces and mesoscale simulation of flow in porous media. This comprehensive approach of in-situ characterization, interface design, and modeling will lead to a fundamental understanding of the ply movement during composite manufacturing and development of methods to reduce the occurrence of processing-induced defects.



    Categories: Faculty-Staff

  • Software Infrastructure For Analysis of Infection Propagation Through Air Travel

    PI Sirish Namilae

    This NSF funded  project seeks to develop a novel software that will provide a variety of pedestrian dynamics models, infection spread models, as well as data so that scientists can analyze the effect of different mechanisms on the spread of directly transmitted diseases in crowded areas. The initial focus of this project is on air travel. However, the software can be extended to a broader scope of applications in movement analysis and epidemiology, such as in theme parks and sports venues. Development of the proposed software will involve several innovations. It will include a novel phylogeography model that links fine-scale human movement data with virus genetic information to more accurately model geographic diffusion of viruses. New models for pedestrian movement will enable modeling of complex human movement patterns. A recommendation system for the choice of pedestrian dynamics models and a domain specific language for the input of policies and human behaviors will enhance usability by researchers in diverse fields. Community building initiatives will catalyze inter-disciplinary research to ensures the long-term sustainability of the project through a critical mass of contributors and users.



    Categories: Faculty-Staff

  • Experimental Testbed for the Validation of Autonomous ISAM/OSAM Systems

    PI Morad Nazari

    CO-I Kadriye Merve Dogan

    CO-I Thomas Lovell

    The ability to validate individual hardware and software components of these technologies on a large scale is still in its early stages. Thus, the goal of this research is to establish an effective experimental testbed for the validation of autonomous in-space servicing and maintenance (ISAM) / on-orbit servicing and maintenance (OSAM) systems.


    A new era of affordable space flight, satellite refueling, on-orbit inspection, orbit transfer and end-of-life servicing has begun as a result of the space industry's continued focus on safe, resilient and adaptable space vehicles. These developments have laid the groundwork for assembly and manufacturing in orbit or space for potential use in active debris removal, reuse and recycling of materials. Advanced navigation and control technologies are required to ensure and lengthen the mission life cycles of these orbital assets, which include launch vehicles, satellites and space stations. Orbit/attitude determination, relative motion, robot manipulator kinematics and spacecraft rendezvous/docking can benefit from new advances in geometric mechanics Udwadia-Kalaba, adaptive control, learning, sensor fusion, computer vision and data communication. These efforts aim to equip future enterprises with the ability to perform in-space servicing and maintenance (ISAM) and on-orbit servicing and maintenance (OSAM) of failed or damaged space assets, as well as in-space manufacturing and platform assembly. However, the ability to validate individual hardware and software components of these technologies on a large scale is still in its early stages. Thus, the goal of this research is to establish an effective experimental testbed for the validation of autonomous ISAM/OSAM systems.

    Categories: Faculty-Staff

111-120 of 201 results