L
Lei Guo
Researcher at Chongqing University of Posts and Telecommunications
Publications - 1837
Citations - 37002
Lei Guo is an academic researcher from Chongqing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Control theory. The author has an hindex of 75, co-authored 1589 publications receiving 27943 citations. Previous affiliations of Lei Guo include Chinese Ministry of Education & AT&T.
Papers
More filters
Journal ArticleDOI
Biodegradation of polycyclic aromatic hydrocarbons (PAHs) by bacterial mixture
TL;DR: In this article, the degradation of polycyclic aromatic hydrocarbons (PAHs) was investigated using nine natural PAHs-degrading bacteria obtained from polluted areas.
Proceedings ArticleDOI
Dynamic modeling of a bicycle robot with front-wheel drive based on Kane's method
TL;DR: In this article, a front-wheel drive bicycle robot with two road wheels was analyzed under the pure rolling presupposition of the two wheels and the kinematics analysis revealed the nonholonomic constraints in the system.
Proceedings ArticleDOI
Joint analysis of gyral folding and fiber shape patterns
TL;DR: A novel computational framework is proposed to characterize 3-hinge gyral folding patterns and jointly analyze the correlation between folding and DTI-derived fiber orientation patterns in these regions and the results demonstrated their close relationships.
Journal ArticleDOI
A new shared-risk link groups (SRLG)-disjoint path provisioning with shared protection in WDM optical networks
TL;DR: This paper addresses the protection problem in WDM optical networks and presents a New Shared-risk link groups (SRLG)-Disjoint Path Provisioning (NSDPP) approach with shared protection to tolerate the single-risk failure.
Journal ArticleDOI
A compressive sensing-based approach to end-to-end network traffic reconstruction utilising partial measured origin-destination flows
Laisen Nie,Dingde Jiang,Lei Guo +2 more
TL;DR: This paper investigates the problem of network traffic estimation and proposes a novel compressive sensing‐based approach using partial measured origin‐destination (OD) flows and puts forward an optimal greedy adaptive dictionary learning algorithm in order to make the traffic matrix sparse.