Wenjie Xiong

TL;DR: In this paper, diffractive deep neural networks (DNNs) have been used for all-optical signal processing of VBs by configuring the phase and amplitude distribution of diffractive screens.

TL;DR: In this article, a convolutional neural network (CNN)-based atmospheric turbulence compensation method was proposed for optical orbital angular momentum (OAM) multiplexing communication, which can automatically extract the characteristic parameters from the distorted intensity distribution of VBs.

TL;DR: It is anticipated that the CNN methods might provide an effective way for identifying OAM modes with high-accuracy and -speed, which may have great potentials in OAM communication, quantum information processing, and astronomical application, etc.

TL;DR: In this paper, a multilayer feed-forward neural network (FNN) model was proposed to identify orbital angular momentum (OAM) modes with high-speed and accuracy.

TL;DR: In this article, a few-layer optical diffractive neural networks (ODNNs) are proposed to perform optical logical operations with the orbital angular momentum (OAM) mode and spatial position of multiple OAM modes.