(Peer-Reviewed) Deep learning enables temperature-robust spectrometer with high resolution
Jiaan Gan, Mengyan Shen, Xin Xiao, Jinpeng Nong 农金鹏, Fu Feng 冯甫
Nanophononics Research Center, Shenzhen Key Laboratory of Micro-Scale Optical Information Technology, Shenzhen University, Shenzhen, 518060, China
中国 深圳 深圳大学 深圳市微尺度光信息技术重点实验室 纳米光子学研究中心
Abstract
Traditional multi-mode fiber spectrometers rely on algorithms to reconstruct the transmission matrix of the fiber, facing the challenge that the same wavelength can lead to many totally de-correlated speckle patterns as the transfer matrix changes rapidly with environment fluctuations (typically temperature fluctuation).
In this manuscript, we theoretically propose a multi-mode-fiber (MMF) based, artificial intelligence assisted spectrometer which is ultra-robust to temperature fluctuation. It has been demonstrated that the proposed spectrometer can reach a resolution of 0.1 pm and automatically reject the noise introduced by temperature fluctuation. The system is ultra-robust and with ultra-high spectral resolution which is beneficial for real life applications.
Flicker minimization in power-saving displays enabled by measurement of difference in flexoelectric coefficients and displacement-current in positive dielectric anisotropy liquid crystals
Junho Jung, HaYoung Jung, GyuRi Choi, HanByeol Park, Sun-Mi Park, Ki-Sun Kwon, Heui-Seok Jin, Dong-Jin Lee, Hoon Jeong, JeongKi Park, Byeong Koo Kim, Seung Hee Lee, MinSu Kim
Opto-Electronic Advances
2025-09-25
Dual-frequency angular-multiplexed fringe projection profilometry with deep learning: breaking hardware limits for ultra-high-speed 3D imaging
Wenwu Chen, Yifan Liu, Shijie Feng, Wei Yin, Jiaming Qian, Yixuan Li, Hang Zhang, Maciej Trusiak, Malgorzata Kujawinska, Qian Chen, Chao Zuo
Opto-Electronic Advances
2025-09-25
