192 Papers
618 Citations
Wei Sun is an academic researcher from Hefei University of Technology. The author has contributed to research in topics: Computer science & Wireless sensor network. The author has an hindex of 21, co-authored 154 publications. Previous affiliations of Wei Sun include Erasmus University Rotterdam & Zhongkai University of Agriculture and Engineering.
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Papers
KRN4 Controls Quantitative Variation in Maize Kernel Row Number.
Lei Liu,Yanfang Du,Xiaomeng Shen,Manfei Li,Wei Sun,Juan Huang,Zhijie Liu,Yongsheng Tao,Yonglian Zheng,Jianbing Yan,Zuxin Zhang +10 more
TL;DR: In this paper, a 3-Kb intergenic region about 60 Kb downstream from the SBP-box gene Unbranched3 (UB3) was found to be responsible for quantitative variation in kernel row number (KRN) by regulating the level of UB3 expression.
Disaggregating and harmonising soil map units through resampled classification trees
TL;DR: In this article, the authors developed an approach called "Disaggregation and Harmonization of Soil Map Units Through Resampled Classification Trees" (DSMART), which samples the polygons of a legacy soil map and uses classification trees to generate a number of realisations of the potential soil class distribution.
154
GRF-interacting factor1 Regulates Shoot Architecture and Meristem Determinacy in Maize
Dan Zhang,Wei Sun,Renee Singh,Yuanyuan Zheng,Zheng Cao,Manfei Li,China Lunde,Sarah Hake,Zuxin Zhang +8 more
TL;DR: The interactions with these diverse direct and indirect targets help explain the paradoxical phenotypes of maize GIF1 and provide insights into the biological functions of gif1.
AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization Systems
Qiyue Li,Heng Qu,Liu Zhi,Nana Zhou,Wei Sun,Stephan Sigg,Jie Li +6 more
- 01 Jun 2021
TL;DR: In this article, the authors proposed an Amplitude-Feature Deep Convolutional Generative Adversarial Network (AF-DCGAN) model to increase the diversity of the CSI amplitude feature map.
113
An empirical evaluation of factors influencing camera calibration accuracy using three publicly available techniques
Wei Sun,R. Cooperstock +1 more
- 27 Mar 2006
TL;DR: An empirical study investigating the effects of training dataquantity, measurement error, pixel coordinate noise, and the choice of camera model, on camera calibration accuracy, based on three publicly available techniques, developed by Tsai, Heikkilä and Zhang.
109