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Fei Richard Yu
Researcher at Carleton University
Publications - 49
Citations - 3023
Fei Richard Yu is an academic researcher from Carleton University. The author has contributed to research in topics: Wireless network & Edge computing. The author has an hindex of 19, co-authored 47 publications receiving 1909 citations. Previous affiliations of Fei Richard Yu include Xidian University & University of British Columbia.
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Big Data Analytics in Intelligent Transportation Systems: A Survey
TL;DR: Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced.
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A Distributed Consensus-Based Cooperative Spectrum-Sensing Scheme in Cognitive Radios
TL;DR: A fully distributed and scalable cooperative spectrum-sensing scheme based on recent advances in consensus algorithms that not only has proven sensitivity in detecting the primary user's presence but also has robustness in choosing a desirable decision threshold.
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Communication systems for grid integration of renewable energy resources
TL;DR: The communication systems used in a real renewable energy project, Bear Mountain Wind Farm in British Columbia, Canada are presented and some research challenges and possible solutions about the communication systems for grid integration of renewable energy resources are outlined.
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Prediction-Based Topology Control and Routing in Cognitive Radio Mobile Ad Hoc Networks
TL;DR: This paper proposes a distributed Prediction-based Cognitive Topology Control scheme to provision cognition capability to routing in CR-MANETs and constructs an efficient and reliable topology, which is aimed at mitigating re-routing frequency and improving end-to-end network performance such as throughput and delay.
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Big Data Analytics in Mobile Cellular Networks
TL;DR: A unified data model based on the random matrix theory and machine learning is introduced and an architectural framework for applying the big data analytics in the mobile cellular networks is presented.