Mode decomposition refers to a set of techniques aimed to recover modal content in
multimode optical fibers. In this Letter, we examine the appropriateness of the similarity …

Modal decomposition (MD) of fiber modes based on direct far-field measurement combining
the convolutional neural network (CNN) with a stochastic parallel gradient descent (SPGD) …

In the area of optical fiber mode decomposition (MD), data-driven deep-learning (DL)
algorithms have achieved remarkable strides with unlimited theoretically synthetic data, but …

Complete spatial characterization of few-mode fiber lasers is important in photonics. In this
letter, based on fast image processing, a noise-resistant complete spatial characterization …

The issue of mode decomposition (MD) in optical fibers is regarded as a regression problem
has been widely studied and applied, however, in contrast, the identification of different …

Mode decomposition based on neural networks (NN-MD) has the advantages of high
accuracy, fast decomposition speed, and elimination of the need for iteration. However …

A fast dynamic mode analyzer (FDMA), based on the matrix operation mode decomposition
technique, holds significant potential for characterizing the comprehensive spatial features …

Four-channel off-axis holography is proposed to simultaneously understand the polarization
states and the mode coefficients of linearly polarized (LP) modes in few-mode fiber. Far-field …

Mode decomposition is a powerful tool for analyzing the modal content of optical multimode
radiation. There are several basic principles on which this tool can be implemented …

A novel mode decomposition method for multimode fiber (MMF) is proposed by using a
hybrid network, which combined deep-learning convolutional neural network (DL-CNN) with …