This past summer as part of MUSE (the Mentored Undergraduate Student Experience), Dr. Ambrose Adegbege, Assistant Professor in the Electrical & Computer Engineering department, took on 4 research students within the department: Terrence Skibik, Jeffrey Sabo, Daniel Walsh, and Richard Bustamante. Dr. Adegbege’s research focuses on control optimization and neuromorphic chip and circuit design.
“The main essence of this team’s research revolves around the concept of solving complex mathematical programming problems using low-power and high-dense circuits that emulate the neural architecture and computational dexterity of the human brain. Such problems have wide applications in machine learning, in artificial intelligence, in power electronics and in high-speed optimal control. The goal is to quickly solve these problems where speed is more important than precision. The computational circuit will be realized in the analog domain using both a Field Programmable Analog array (FPAA) and a CMOS-based integrated circuit, and in the digital domain using a Field Programmable Gate Array (FPGA) and a Programmable Logic Controller (PLC).”
Each of his students focused on different areas of these research fields which resulted in the creation of posters to showcase the work they had done over the summer. They showed off these posters at the Celebration of Student Achievement at TCNJ. Terrence Skibik also managed to submit papers to conferences as a result of his research. The breakdown of their specializations are as follows:
| Ultra-Fast VLSI Analog Circuit for Real-time Model Predictive Control
Terrence Skibik |
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| FPGA implementation of Primal-Dual Algorithm for High-Speed Model Predictive Control
Jeffrey Sabo |
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| Embedded Implementation of Fast Model Predictive Control using Programmable Logic Controllers
Richard Bustamante |
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| FPAA Emulation Circuit for Fast Model Predictive Control
Daniel Walsh |
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Dr. Adegbege has had continued success working with students during MUSE in past summers. Last summer he worked with students Haley Blanchard, Mun Kim, and Sean Fernandez. Here’s to hoping the success continues! To learn more about MUSE and how to apply, check out this link.