R
Rui Ma
Researcher at Mitsubishi Electric Research Laboratories
Publications - 138
Citations - 1401
Rui Ma is an academic researcher from Mitsubishi Electric Research Laboratories. The author has contributed to research in topics: Amplifier & Transmitter. The author has an hindex of 16, co-authored 133 publications receiving 974 citations. Previous affiliations of Rui Ma include University of Alabama in Huntsville & University of California, Berkeley.
Papers
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Driving behavior recognition using EEG data from a simulated car-following experiment.
TL;DR: A two-layer learning method for driving behavior recognition using EEG data that shows a significant correlation between EEG patterns and car-following behavior is proposed.
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Continuous-time point-queue models in dynamic network loading
TL;DR: A mathematically rigorous study of the continuous-time dynamic user equilibrium (DUE) problem using the recently introduced mathematical paradigm of differential complementarity systems (DCSs) is undertaken.
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Emerging GaN technologies for power, RF, digital, and quantum computing applications: Recent advances and prospects
Koon Hoo Teo,Yuhao Zhang,Nadim Chowdhury,Shaloo Rakheja,Rui Ma,Qingyun Xie,Eiji Yagyu,Koji Yamanaka,Kexin Li,Tomas Palacios +9 more
TL;DR: In this article, the authors provide a glimpse of future GaN device technologies and advanced modeling approaches that can push the boundaries of these applications in terms of performance and reliability, which is a key missing piece to realize the full GaN platform with integrated digital, power, and RF electronics technologies.
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The morning commute problem with ridesharing and dynamic parking charges
Rui Ma,H.M. Zhang +1 more
TL;DR: The results show that constant parking charges with constant ridesharing payments may not significantly improve system performance over the traditional morning commute with solo-drivers, while dynamic parking charges can achieve better system performance in terms of vehicle-miles-traveled, vehicle-hours- Traveled and total travel costs.
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A link-node reformulation of ridesharing user equilibrium with network design
TL;DR: Results show that carefully selecting the deployment of HOT lanes can improve the overall system travel time and reduce the problem size and facilitate computation.