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Chen Liu
Researcher at Shenzhen University
Publications - 168
Citations - 1766
Chen Liu is an academic researcher from Shenzhen University. The author has contributed to research in topics: Energy consumption & Hardware acceleration. The author has an hindex of 15, co-authored 145 publications receiving 957 citations. Previous affiliations of Chen Liu include Guangxi Normal University & Chinese Academy of Sciences.
Papers
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Journal ArticleDOI
Potassium-doped PC71BM for hydrogen storage: Photoelectron spectroscopy and first-principles studies
De-Qu Lin,Long-Xi Wang,Cheng-Hui Song,Ying-Ying Du,Guang-Hua Chen,Chen Liu,Jiaou Wang,Rui Wu,Haijie Qian,Kurash Ibrahim,Hong-Nian Li +10 more
TL;DR: In this article, the fullerene C70 derivative PC71BM is considered as a potential onboard hydrogen storage material and metal-decoration is needed to increase the adsorption energy of H2.
Proceedings ArticleDOI
Case Study on a Software Communications Architecture Component for Hardware Acceleration
TL;DR: This paper makes a comparison of the hardware accelerated amplifier implementation employing a floating-point unit (FPU) engine over pure software implementation with no FPU support and obtained a speedup of up to 2x faster while minimizing the energy consumption.
Proceedings ArticleDOI
Multi-core Approach towards Efficient Biometric Cryptosystems
Charles McGuffey,Chen Liu +1 more
TL;DR: This study focuses on the Cambridge biometric cryptosystem, a system for performing user authentication based on a user's iris data, and converted the implementation of this algorithm from a single-core system to a system that can run on multiple cores.
Book ChapterDOI
Medium Access Control Protocols for Satellite Communications
TL;DR: This chapter surveys the medium access control (MAC) protocols for satellite networks and gives a comprehensive comparison of these protocols.
Journal ArticleDOI
Locate Where You Are by Block Joint Learning Network
Ganchao Liu,Chen Liu,Yuan Yuan +2 more
TL;DR: To overcome the challenges of inconsistency style changes in image matching, the saliency feature based on the attention mechanism and the traditional edge feature operator are introduced in joint feature learning.