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Can I read Deep Learning–based Vortex Decomposition and Switching Based on Fiber Vector Eigenmodes on EtoBox?
Deep Learning–based Vortex Decomposition and Switching Based on Fiber Vector Eigenmodes by Mengdie Hou; Mengjun Xu; Jiangtao Xu; Jiafeng Lu; Yi An; Liangjin Huang; Xianglong Zeng; Fufei Pang; Jun Li; Lilin Yi is a Engineering article available to read on EtoBox.
What is Deep Learning–based Vortex Decomposition and Switching Based on Fiber Vector Eigenmodes about?
Structured optical fields, such as cylindrical vector (CV) and orbital angular momentum (OAM) modes, have attracted considerable attention due to their polarization singularities and helical phase wavefront structure. However, one of the most critical challenges is still the intelligent generation or precise control of these modes. Here, we demonstrate the first simulation and experimental realization of decomposing the CV and OAM modes by reconstructing the multi-view images of projected intensity distribution. Assisted by the deep learning-based stochastic parallel gradient descent (SPGD) algorithm, the modal coefficients and optical field distributions can be retrieved in 1.32 s within an average error of 0.416 % showing high efficiency and accuracy. Especially, the interference pattern and quarter-wave plate are exploited to confirm the phase and distinguish elliptical or circular polarization direction, respectively. The generated donut modes are experimen-
Who reads Deep Learning–based Vortex Decomposition and Switching Based on Fiber Vector Eigenmodes?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Mengdie Hou; Mengjun Xu; Jiangtao Xu; Jiafeng Lu; Yi An; Liangjin Huang; Xianglong Zeng; Fufei Pang; Jun Li; Lilin Yi
- Publisher
- Walter de Gruyter GmbH
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)