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Ying He
Researcher at University of Technology, Sydney
Publications - 45
Citations - 549
Ying He is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Cellular network & Distributed antenna system. The author has an hindex of 10, co-authored 43 publications receiving 297 citations. Previous affiliations of Ying He include Chinese Academy of Sciences & Intel.
Papers
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Journal ArticleDOI
Comprehensive Energy Consumption Model for Unmanned Aerial Vehicles, Based on Empirical Studies of Battery Performance
TL;DR: A consistent and complete energy consumption model for UAVs is presented based on empirical studies of battery usage for various UAV activities and the impact of different flight scenarios and conditions on UAV energy consumption is considered.
Journal ArticleDOI
MESNet: A Convolutional Neural Network for Spotting Multi-Scale Micro-Expression Intervals in Long Videos
TL;DR: Zhang et al. as mentioned in this paper proposed a network based convolutional neural network (CNN) for spotting multi-scale spontaneous micro-expression intervals in long videos, which is composed of three modules.
Proceedings ArticleDOI
Empirical Power Consumption Model for UAVs
TL;DR: This paper presents a consistent and complete power consumption model for UAVs based on empirical studies of battery usage for various UAV activities, and can be readily used for energy efficient UAV mission planning.
Patent
Uplink interference management in shared spectrum networks
TL;DR: In this paper, a network control system configured to manage radio communication devices for a Spectrum Access System (SAS) shared spectrum wireless network is disclosed, which includes a receiver to receive an estimate of a proximity to a Priority Access License (PAL) radio communication device for a plurality of user terminals.
Proceedings ArticleDOI
Spotting Macro-and Micro-expression Intervals in Long Video Sequences
TL;DR: The baseline results for the Third Facial Micro-Expression Grand Challenge (MEGC 2020) as discussed by the authors showed that both macro-and micro-expression intervals in CAS(ME)2 and SAMM Long Videos are spotted by employing the method of Main Directional Maximal Difference Analysis (MDMD).