Abstract
Single fiber scanners (SFSs), with the advantages of compact size, versatility, large field of view, and high resolution, have been applied in many areas. However, image distortions persistently impair the imaging quality of the SFS, although many efforts have been made to address the problem. In this Letter, we propose a simple and complete solution by combining the piezoelectric (PZT) self-induction sensor and machine learning algorithms. The PZT tube was utilized as both the actuator and the fiber position sensor. Additionally, the feedback sensor signal was interrogated by a convolution neural network to eliminate the noise. The experimental results show that the predicted fiber trajectory error was below 0.1%. Moreover, this self-calibration SFS has an excellent robustness to temperature changes (20–50°C). It is believed that the proposed solution has removed the biggest barrier for the SFS and greatly improved its performance and stability in complex environments.
© 2021 Optical Society of America
Full Article | PDF ArticleMore Like This
Jiawei Sun, Bin Zhao, Dong Wang, Zhigang Wang, Jie Zhang, Nektarios Koukourakis, Júergen W. Czarske, and Xuelong Li
Opt. Lett. 49(2) 342-345 (2024)
Tong Wu, Lei Zhang, Jiming Wang, Wenqi Huo, Yuangang Lu, Chongjun He, and Youwen Liu
Opt. Lett. 45(8) 2470-2473 (2020)
Tianliang Wang, Yi Li, Jinchao Tao, Xu Wang, Yanqing Qiu, Bangning Mao, Miaogen Chen, Yanlong Meng, Chunliu Zhao, Juan Kang, Yong Guo, and Changyu Shen
Opt. Lett. 46(22) 5711-5714 (2021)