Michigan Engineering | University of Michigan With the help of two NSF awards totaling $1.7m, Prof. Hessam Mahdavifar is tackling new problems to improve the reliability of communication systems for 5G and beyond.
Channel Coding for Next Generation 5G and Beyond
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Commission co-chairs: Climate change solutions need broad commitment
An update on the work done by U-M’s Commission on Carbon Neutrality, co-chaired by Prof. Stephen Forrest.
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Can organic solar cells last – even into the next millennium? These might.
Finally, proof that organic photovoltaics can be as reliable as inorganic, with real-life desert testing
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Remote attack on temperature sensors threatens safety in incubators and industry
The researchers demonstrated that an adversary could remotely manipulate the temperature sensor measurements without tampering with the targeted system or triggering automatic temperature alarms.
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AI-powered Whatsapp Bot fights fake news in India
CE undergrad Amulya Parmar designed a machine learning algorithm to curb fake news as part of the Tavtech Fellowship program.
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The “Magic Ratio” that could power tomorrow’s solar cells
A Q&A with Rachel Goldman
The post The “Magic Ratio” that could power tomorrow’s solar cells appeared first on Michigan Engineering News.
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Electrify goes to Detroit
Electrify hosted its first Detroit Tech Camp at the Michigan Engineering Zone this summer to give Detroit-area students greater access to engage with science, technology, engineering, and mathematics.
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New attack on autonomous vehicle sensors creates fake obstacles
Up to this point, no attacks had been discovered targeting a car’s LiDAR system—but a major new finding from researchers at the University of Michigan has demonstrated what that might look like.
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Rewriting the rules for supercomputers
Machine learning will teach the world’s fastest machines to work smarter, not harder.
The post Rewriting the rules for supercomputers appeared first on Engineering Research News.
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Taking machine-learning models in health care from concept to bedside
The authors provide an overview of common challenges to implementing ML in a health-care setting, and describe the necessity of breaking down the silos in ML.