| Detection of olive oil adulteration with waste cooking oil via Raman spectroscopy combined with iPLS and SiPLS Y Li, T Fang, S Zhu, F Huang, Z Chen, Y Wang Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 189, 37-43, 2018 | 131 | 2018 |
| The challenges of modern computing and new opportunities for optics C Li, X Zhang, J Li, T Fang, X Dong PhotoniX 2, 1-31, 2021 | 102 | 2021 |
| Pre-diabetes diagnosis based on ATR-FTIR spectroscopy combined with CART and XGBoots X Yang, T Fang, Y Li, L Guo, F Li, F Huang, L Li Optik 180, 189-198, 2019 | 38 | 2019 |
| Classification accuracy improvement of the optical diffractive deep neural network by employing a knowledge distillation and stochastic gradient descent β-Lasso joint training … T Fang, J Li, X Zhang, X Dong Optics Express 29 (26), 44264-44274, 2021 | 9 | 2021 |
| Determination of proteins in human serum by near-infrared spectroscopy with partial least square analysis H Guo, F Huang, Y Li, T Fang, S Zhu, Z Chen Analytical Letters 49 (18), 2964-2976, 2016 | 9 | 2016 |
| Visible-near infrared spectroscopy modeling on the contents of serum bilirubin based on iPLS and SiPLS HX Guo, SQ Zhu, FT LI YP, FR HUANG, SF ZHENG, ZQ CHEN Journal of Optoelectronics laser 27 (10), 1136, 2016 | 5 | 2016 |
| Rapid diagnosis of type II diabetes using fourier transform mid-infrared attenuated total reflection spectroscopy combined with support vector machine T Fang, Y Li, F Li, F Huang Analytical Letters 51 (9), 1400-1416, 2018 | 4 | 2018 |
| Efficient training for the hybrid optical diffractive deep neural network T Fang, J Li, T Wu, M Cheng, X Dong AI and Optical Data Sciences III 12019, 185-190, 2022 | | 2022 |
| Optical Training Framework for Optical Diffractive Deep Neural Network via Direct Feedback Alignment T Fang, J Li, B Zhang, T Wu, X Dong Frontiers in Optics, JW7A. 27, 2021 | | 2021 |
