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The next time you’re surfing the Web on your iPhone while watching your smart TV and downloading a book to your Kindle, take a moment to consider that you’re simultaneously using a variety of embedded systems — computers fully contained within a device for the purpose of controlling that device. Each of those gadgets — and almost any consumer electronics device — is similarly built, incorporating microcontrollers or microprocessors, RAM and storage, much like your personal computer. Because of the advancement of technology, these embedded systems have grown very complex, with multiple processing cores and heterogeneous hardware and software components, built by different manufacturers but expected to work together flawlessly. Embedded systems designers have the daunting task of building systems that work together in a small footprint while addressing uncertainties like voltage and temperature variations.

“Think of it as a software suite ... but specifically developed for use by embedded system designers.”


Dr. Cristinel Ababei

Dr. Cristinel Ababei understands these related challenges better than most. Before becoming an assistant professor in the Opus College of Engineering’s Department of Electrical and Computer Engineering, he designed systems in the private sector. Recently, Ababei received a $250,000 grant from the National Science Foundation to develop a new design method to address the uncertainties and increased complexities in system design.

Assistant Professor, Electrical and Computer Engineering

Smart tools for designing smarter devices

“Basically, I’m building a software tool for designers to develop systems that are more robust against uncertainties,” he says. “Think of it as a software suite, similar to how you might think of Microsoft Office as a suite of programs, but specifically developed for use by embedded system designers.” According to Ababei, one of the biggest design challenges is optimizing against unknown variables, a challenge his new system overcomes by introducing enhanced evolutionary algorithms that directly address design uncertainties at a system level. “The main goal is to improve reliability and performance of future complex embedded systems,” he says. Ababei’s future-based outlook is also a driving reason behind why he made this new method open source, meaning it is free for designers to use and improve upon. “I want other people to build on it, to compare results,” he says. “This tool should evolve as system design evolves so designers can continue to keep pace with technology.” JESSE LEE

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Discover Magazine 2016  

Discover Magazine 2016