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Xiaobo Qu
Researcher at Chalmers University of Technology
Publications - 332
Citations - 9336
Xiaobo Qu is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Computer science & Compressed sensing. The author has an hindex of 42, co-authored 273 publications receiving 6262 citations. Previous affiliations of Xiaobo Qu include Shantou University & National University of Singapore.
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Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy.
TL;DR: In this article, a perspective for DL as an entirely new approach that is likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life sciences is outlined.
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Emergency vehicle lane pre-clearing: From microscopic cooperation to routing decision making
TL;DR: Case studies show that with the proposed algorithm, emergency vehicles are able to drive at a desired speed while minimizing disturbances on normal traffic flows, and a linear relationship between the optimal solution and road density is revealed, which could help to improve EV routing decision makings when high-resolution data is not available.
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Robust recovery of complex exponential signals from random Gaussian projections via low rank Hankel matrix reconstruction.
TL;DR: The theoretical results show that a robust recovery is possible as long as the number of projections exceeds O(Rln2N), and the method provides theoretical guidance on how many samples are required in the state-of-the-art non-uniform sampling in NMR spectroscopy.
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Vandermonde Factorization of Hankel Matrix for Complex Exponential Signal Recovery—Application in Fast NMR Spectroscopy
TL;DR: The Vandermonde structure of the Hankel matrix formed by the exponential signal is exploited and formulate signal recovery asHankel matrix completion with VandermondE factorization (HVaF), which is validated on biological magnetic resonance spectroscopy data.
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A note on hotspot identification for urban expressways
Xiaobo Qu,Qiang Meng +1 more
TL;DR: In this article, a simple ranking and empirical Bayesian methods are proposed to identify the hotspots in a Singapore expressway on the basis of the detailed three-year casualty data in the Historical Crash Damage (HCD) database.