Wavefront shaping and deep learning in fiber endoscopy
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Optical amplification in hollow core fibers has been achieved using stimulated Brillouin scattering directly in the gaseous medium. More than 50 dB optical gain is observed over 50 m of fiber using 200 mW of pump power. ...
Unlike traditional sensing schemes which rely on discrete point sensors that perform measurements at predetermined positions, distributed optical fiber sensing is a general technique that enables to continuously gather information (typically temperature an ...
In this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the development of neural network architectures. We focus on the importance of v ...
We accurately reconstruct three-dimensional (3-D) refractive index (RI) distributions from highly ill-posed two-dimensional (2-D) measurements using a deep neural network (DNN). Strong distortions are introduced on reconstructions obtained by the Wolf tran ...
We experimentally achieve a 19% capacity gain per Watt of electrical supply power in a 12-span link by eliminating gain flattening filters and optimizing launch powers using deep neural networks in a parallel fiber context. (C) 2020 The Authors ...
Spatiotemporal nonlinear interactions in multimode fibers are of interest for beam shaping and frequency conversion by exploiting the nonlinear interaction of different pump modes from quasi-continuous wave to ultrashort pulses centered around visible to i ...
Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed.The main drawback of these ...
Deep neural networks (DNNs) are employed to recover information after its propagation through a multimode fiber (MMF) in the presence of wavelength drift. The intensity distribution of the speckle patterns generated at the output of an MMF when an input wa ...
Image super-resolution reconstructs a higher-resolution image from the observed low-resolution image. In recent years, machine learning models have been widely employed and deep learning networks have achieved state-of-the-art super-resolution performance. ...
The ability to control a behavioral task or stimulate neural activity based on animal behavior in real-time is an important tool for experimental neuroscientists. Ideally, such tools are noninvasive, low-latency, and provide interfaces to trigger external ...