Depletion effects in few-mode fibers parametric amplification

The effect of depletion in few-mode fiber parametric amplifiers is accounted for by determining the exact solutions of the nonlinear interaction equations. The analytical results are confirmed by full numerical solutions of the governing equations. The approach enables to explore the parameter space in search of amplifier optimization

Modeling the parametric amplification in few mode fibers

By studying the intermodal non-degenerate four wave mixing, we derived analytical formulas for the phase insensitive and phase sensitive amplification gain and nonlinear mode conversion efficiency in few mode fibers

Modeling Linear and Nonlinear Coupling in Few Mode Fibers

Linear and nonlinear coupling in few mode fibers is studie

A review of using few-mode fibers for optical sensing

202012 bcrcVersion of RecordPublishe

Deep Learning-Based Image Denoising Approach for the Identification of Structured Light Modes in Dusty Weather

Structured light is gaining importance in free-space communication. Classifying spatially-structured light modes is challenging in a dusty environment because of the distortion on the propagating beams. This article addresses this challenge by proposing a deep learning convolutional autoencoder algorithm for modes denoising followed by a neural network for modes classification. The input to the classifier was set to be either the denoised image or the latent code of the convolutional autoencoder. This code is a low-dimensional representation of the inputted images. The proposed machine learning (ML) models were trained and tested using laboratory-generated mode data sets from the Laguerre and Hermite Gaussian mode bases. The results show that the two proposed approaches achieve an average classification accuracy exceeding 98%, and both are better than the classification accuracy reported recently (83–91%) in the literature

Automatic and accurate reconstruction of distal humerus contours through B-Spline fitting based on control polygon deformation

© IMechE 2014. The strong advent of computer-assisted technologies experienced by the modern orthopedic surgery prompts for the expansion of computationally efficient techniques to be built on the broad base of computer-aided engineering tools that are readily available. However, one of the common challenges faced during the current developmental phase continues to remain the lack of reliable frameworks to allow a fast and precise conversion of the anatomical information acquired through computer tomography to a format that is acceptable to computer-aided engineering software. To address this, this study proposes an integrated and automatic framework capable to extract and then postprocess the original imaging data to a common planar and closed B-Spline representation. The core of the developed platform relies on the approximation of the discrete computer tomography data by means of an original two-step B-Spline fitting technique based on successive deformations of the control polygon. In addition to its rapidity and robustness, the developed fitting technique was validated to produce accurate representations that do not deviate by more than 0.2-mm with respect to alternate representations of the bone geometry that were obtained through different - contact-based - data acquisition or data processing methods

Enhancing the recovery of a temporal sequence of images using joint deconvolution

Abstract In this work, we address the reconstruction of spatial patterns that are encoded in light fields associated with a series of light pulses emitted by a laser source and imaged using photon-counting cameras, with an intrinsic response significantly longer than the pulse delay. Adopting a Bayesian approach, we propose and demonstrate experimentally a novel joint temporal deconvolution algorithm taking advantage of the fact that single pulses are observed simultaneously by different pixels. Using an intensified CCD camera with a 1000-ps gate, stepped with 10-ps increments, we show the ability to resolve images that are separated by a 10-ps delay, four time better compared to standard deconvolution techniques