Detection of common wound infection bacteria based on FAIMS technology

Shenyi QIAN; Daiyi LI; Tong SUN; Bin YU

doi:10.1007/s11704-019-8218-x

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Front. Comput. Sci. ›› 2019, Vol. 13 ›› Issue (4) : 907-909. DOI: 10.1007/s11704-019-8218-x

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Detection of common wound infection bacteria based on FAIMS technology

Shenyi QIAN¹ ,
Daiyi LI¹ ,
Tong SUN¹^,² ,
Bin YU²

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Shenyi QIAN, Daiyi LI, Tong SUN, Bin YU. Detection of common wound infection bacteria based on FAIMS technology. Front. Comput. Sci., 2019, 13(4): 907‒909 https://doi.org/10.1007/s11704-019-8218-x

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[1]	Sheybani R, Anita S. Highly sensitive label-free dual sensor array for rapid detection of wound bacteria. Biosensors & Bioelectronics, 2017, 92: 425–433 CrossRef Google scholar

[2]	Wang Y, Wang H, Li J, Gao H. Efficient graph similarity join for information integration on graphs. Frontiers of Computer Science, 2016, 10(2): 317–329 CrossRef Google scholar

[3]	Riaz F, Hassan A, Rehman S, Qamar U. Texture classification using rotation- and scale-invariant gabor texture features. IEEE Signal Processing Letters, 2013, 20(6): 607–610 CrossRef Google scholar

[4]	Tan X Y, Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing, 2010, 19(6): 1635–1650 CrossRef Google scholar

[5]	Cheng S M, Wang J, Ma Y H, Wang Y W. Discrimination of different types damage of tomato seedling by electronic nose. Chinese Journal of Sensors & Actuators, 2012, 25(9): 1184–1188