F
Fan Li
Researcher at Beijing Institute of Technology
Publications - 176
Citations - 4590
Fan Li is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Computer science & Static routing. The author has an hindex of 26, co-authored 143 publications receiving 3305 citations. Previous affiliations of Fan Li include University of North Carolina at Charlotte & Xidian University.
Papers
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Journal ArticleDOI
Distributed optical fiber sensing in coherent Φ-OTDR with a broadband chirped-pulse conversion algorithm.
TL;DR: In this article , a broadband chirped-pulse conversion algorithm (BCPCA) was proposed to convert a finite-step scanning probe pulse into an equivalent broadband probe pulse by convolving a chirp factor on the received signal in coherent phase-sensitive optical time domain reflectometry.
Book ChapterDOI
FRSM: A Novel Fault-Tolerant Approach for Redundant-Path-Enabled Service Migration in Mobile Edge Computing
Jiale Zhao,Mengxuan Dai,Yunni Xia,Yong Ma,Meibin He,Kai-Hsiang Peng,Jianqi Li,Fan Li,Xiao Fu +8 more
TL;DR: In this paper , the authors proposed a fault-tolerant method for redundant path service migration in mobile edge computing (MEC), which combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths.
Journal ArticleDOI
Signal Activity Detection for Fiber Optic Distributed Acoustic Sensing with Adaptive-Calculated Threshold
TL;DR: A novel signal activity detection method with the adaptive-calculated threshold is proposed to solve the problem of inability to calculate the threshold accurately and efficiently without affecting the measured signals.
Proceedings ArticleDOI
Field trial of abyss-class DAS system in South China Sea
Dimin Deng,Tuanwei Xu,Hanyu Zhang,Chun Long Yu,Lilong Ma,Kai Cao,Yinghao Jiang,Fan Li,Shiguo Wu +8 more
TL;DR: In this paper , a field trial with an abyss-class DAS system was carried out at 1423 meters depth in the South China Sea, and more than 600GB data was collected for 21 days.
Journal ArticleDOI
Adaptive and Efficient Qubit Allocation Using Reinforcement Learning in Quantum Networks
TL;DR: In this paper , the authors formulate the qubit competition problem as the Cooperative-Qubit-Allocation Problem (CQAP) by taking into account both the waiting time and the fidelity of end-to-end entanglement with the given transmission link set, and adopt a reinforcement learning (RL) algorithm to self-adaptively and cooperatively allocate qubits among quantum repeaters.