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Your search keyword '"Shen, Zhenyu"' showing total 19 results
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19 results on '"Shen, Zhenyu"'

15. Impedance Based MEMS Biosensor for Detection of Foodborne Pathogen

Author

Liu, Jiayu, primary, Jasim, Ibrahem, additional, Dastider, Shibajyoti G., additional, Abdullah, Amjed, additional, Shen, Zhenyu, additional, Dweik, Ferris, additional, Zhao, Lu, additional, Yuksek, Nuh S., additional, Zhang, Shuping, additional, Dweik, Majed, additional, and Almasri, Mahmoud, additional

Published
2018
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17. Ionization and attachment coefficients in C4F7N/N2 gas mixtures for use as a replacement to SF6.

Author

Long, Yunxiang, Guo, Liping, Shen, Zhenyu, Chen, Cheng, Chen, Yiheng, Li, Fang, and Zhou, Wenjun

Subjects
GAS mixtures, IONIZATION (Atomic physics), ELECTRIC fields
Abstract

C4F7N/N2 gas mixtures are considered as new candidate insulation gases to replace SF6. In this study, the Townsend first ionization coefficient α and the attachment coefficient η were measured in C4F7N/N2 gas mixtures using the steady-state Townsend method over a range of electric fields E/N from 200 to 500 Td. The C4F7N concentrations studied were 5.07, 7.00, 13.10 and 19.09%. In addition, the effective ionization coefficients and the critical electric field were also obtained. A comparison of the experimental results with those for SF6 gas indicates that 19.09% C4F7N/80.91% N2 provides better insulation performance with a higher normalized critical electric field of 395 Td. However, when the liquefaction temperature is considered, the C4F7N/N2 gas mixtures with C4F7N content varying from 7.00 to 13.10% may be an appropriate substitute gas for SF6 in electrical industry applications. [ABSTRACT FROM AUTHOR]

Published
2019
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18. We Can Track You if You Take the Metro: Tracking Metro Riders Using Accelerometers on Smartphones.

Author

Hua, Jingyu, Shen, Zhenyu, and Zhong, Sheng

Abstract

Motion sensors, especially accelerometers, on smartphones have been discovered to be a powerful side channel for spying on users’ privacy. In this paper, we reveal a new accelerometer-based side-channel attack which is particularly serious: malware on smartphones can easily exploit the accelerometers to trace metro riders stealthily. We first address the challenge to automatically filter out metro-related data from a mass of miscellaneous accelerometer readings, and then propose a basic attack which leverages an ensemble interval classifier built from supervised learning to infer the riding trajectory of the user. As the supervised learning requires the attacker to collect labeled training data for each station interval, this attack confronts the scalability problem in big cities with a huge metro network. We thus further present an improved attack using semi-supervised learning, which only requires the attacker to collect labeled data for a very small number of distinctive station intervals. We conduct real experiments on a large self-built dataset, which contains more than 120 h of data collected from six metro lines of three major cities. The results show that the inferring accuracy could reach 89% and 94% if the user takes the metro for four and six stations, respectively. We finally discuss possible countermeasures against the proposed attack. [ABSTRACT FROM PUBLISHER]

Published
2017
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