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Xiaofang Pan
Researcher at Shenzhen University
Publications - 56
Citations - 866
Xiaofang Pan is an academic researcher from Shenzhen University. The author has contributed to research in topics: Computer science & Support vector machine. The author has an hindex of 10, co-authored 42 publications receiving 483 citations. Previous affiliations of Xiaofang Pan include Hong Kong University of Science and Technology.
Papers
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Journal ArticleDOI
Gas Classification Using Deep Convolutional Neural Networks.
TL;DR: Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data and can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).
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Self-gating effect induced large performance improvement of ZnO nanocomb gas sensors.
TL;DR: This study sheds light on the mechanism of performance enhancement with hierarchical nanostructures, but also proposes a rational approach and a simulation platform to design nanostructure based chemical sensors with desirable performance.
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A fast-response/recovery ZnO hierarchical nanostructure based gas sensor with ultra-high room-temperature output response
TL;DR: In this paper, a ZnO hierarchical nanostructure based gas sensor is presented, which features short response/recovery time and ultra-high output response at room temperature (RT).
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Ultra-Low-Power Smart Electronic Nose System Based on Three-Dimensional Tin Oxide Nanotube Arrays.
Jiaqi Chen,Zhuo Chen,Farid Boussaid,Daquan Zhang,Xiaofang Pan,Huijuan Zhao,Amine Bermak,Amine Bermak,Chi-Ying Tsui,Xinran Wang,Zhiyong Fan +10 more
TL;DR: The experimental results demonstrate that the developed E-nose can identify indoor target gases using a simple vector-matching gas recognition algorithm and has achieved state-of-the-art sensitivity for H2 and benzene detection at room temperature with metal oxide sensors.
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Wireless Self-Powered High-Performance Integrated Nanostructured-Gas-Sensor Network for Future Smart Homes.
Zhilong Song,Wenhao Ye,Zhuo Chen,Chen Zhesi,Mutian Li,Wenying Tang,Chen Wang,Zhu'an Wan,Swapnadeep Poddar,Xiaolin Wen,Xiaofang Pan,Yuanjing Lin,Qingfeng Zhou,Zhiyong Fan +13 more
TL;DR: In this paper, a self-powered integrated nanostructured-gas-sensor (SINGOR) system and a wirelessly connected SINGOR network are demonstrated.