Dr. Esther Ososanya, a Professor in Electrical and Computer Engineering at UDC, specializes in embedded systems design and VLSI ASIC applications, with research focused on innovative solutions for unmanned aerial vehicles, robotics, and energy systems, while actively contributing to COVID-19 predictive modeling and vaccine hesitancy studies. Her work, supported by grants from the NSF and DoD, positions her as a leader in advancing smart grid systems, robotics, and autonomous vehicle technology.
Experience
Education
Doctor of Philosophy in Electrical Engineering: Microprocessors Systems, Bradford University, West Yorkshire, U.K. Master of Science in Electrical Engineering: Integrated Circuits Design, Southampton University, Southampton, U.K. Bachelor of Science in Physics, University of Aston, Birmingham, U.K.
Roles
Professor, Electrical and Computer Engineering, UDC, 2001-Present Associate Professor, Electrical and Computer Engineering, Tennessee Technological University, 1993-2001 Visiting Professor, Michigan Technological University, 1988-93 System Design Engineer, Dextralog Scantex, 1988 Post Doctoral Research Fellow, Electrical Engineering, University of Birmingham, 1985-87
Courses Taught
Microcontrollers in ME Computer Organization, Lecture and Lab Senior Project I Senior Project II Advanced Digital Integrated Circuits Design Mechatronics Special Topics in ME Advanced Embedded Systems Design Advanced Digital Integrated Circuits Design
Expertise
IEEE (Institute of Electrical and Electronics Engineers) ASEE (American Society for Engineering Education) DCSPE (District of Columbia Society of Professional Engineers)
Research Focus / Works in Progress
New applications for VLSI ASIC design; using innovative co-design for emerging hetero-integrated microsystems ICs; embedded systems in unmanned aerial vehicles, electric vehicles, robotics, and energy systems
Impact
Selected Publications
Irungu, J., Oladunni, T., Grizzle, A. C., Denis, M., Savadkoohi, M., & Ososanya, E. (2023). ML-ECG-COVID: A machine learning-electrocardiogram signal processing technique for COVID-19 predictive modeling.IEEE Access, 11, 135994–136014. https://doi.org/10.1109/ACCESS.2023.3335384
Irungu, J., Oladunni, T., Grizzle, A. C., Denis, M., Savadkoohi, M., & Ososanya, E. (2023). A CNN transfer learning – electrocardiogram (ECG) signal approach to predict COVID-19. Proceedings of 15th International Conference on Computer and Automation Engineering, 367-371. https://doi.org/10.1109/ICCAE56788.2023.10111114
Qorib, M., Oladunni, T., Denis, M., Ososanya, E., & Cotae, P. (2023). COVID-19 vaccine hesitancy: Text mining, sentiment analysis and machine learning on COVID-19 vaccination Twitter dataset.Expert Systems with Applications, 212, 118715. https://doi.org/10.1016/j.eswa.2022.118715
Qorib, M., Oladunni, T., Denis, M., Ososanya, E., & Cotae, P. (2023). Deep neural network and NCRLexicon classifications of sentiments with emotional reactions from COVID-19 vaccination tweets. International Journal of Environmental Research and Public Health.
Dang, H., Ososanya, E., & Zhang, N. (2022). Comparison of electrical characteristics of Schottky junctions based on CdS nanowires and thin film. Nanotechnology. 33.https://doi.org/10.1088/1361-6528/ac51eb.
Selected Presentations
Rouamba, S., Fonzan, K., Wright, P., Ososanya, E., & Shetty, D. (2024). Unmanned surface vehicle for bahtymetric mapping of shallow water basins. IEEE, Washington, D.C.
Selected Grants
National Science Foundation. (2020). VAPOC: Visualization, analysis, and prediction of COVID-19.
Department of Defense. (2019). Acquisition of advanced robotics and autonomous vehicle echnology (ARAVT) for research in smart grid systems, teaching, and K-12 outreach and the University of the District of Columbia.
