Deep Learning Aided Misalignment-robust Blind Receiver for Underwater Optical Communication

2021 ◽  
pp. 1-1
Author(s):  
Huaiyin Lu ◽  
Wenjun Chen ◽  
Ming Jiang
Keyword(s):  
Deep Learning ◽  
Optical Communication ◽  
Blind Receiver

scholarly journals Dual-Output Mode Analysis of Multimode Laguerre-Gaussian Beams via Deep Learning

Optics ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 87-95
Author(s):  
Xudong Yuan ◽  
Yaguang Xu ◽  
Ruizhi Zhao ◽  
Xuhao Hong ◽  
Ronger Lu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Communication ◽  
Computation Time ◽  
Absolute Error ◽  
Test Sample ◽  
Mode Analysis ◽  
Gaussian Beams ◽  
Analysis Method ◽  
Data Set ◽  
The Mean

The Laguerre-Gaussian (LG) beam demonstrates great potential for optical communication due to its orthogonality between different eigenstates, and has gained increased research interest in recent years. Here, we propose a dual-output mode analysis method based on deep learning that can accurately obtain both the mode weight and phase information of multimode LG beams. We reconstruct the LG beams based on the result predicted by the convolutional neural network. It shows that the correlation coefficient values after reconstruction are above 0.9999, and the mean absolute error (MAE) of the mode weights and phases are about 1.4 × 10-3 and 2.9 × 10-3, respectively. The model still maintains relatively accurate prediction for the associated unknown data set and the noise-disturbed samples. In addition, the computation time of the model for a single test sample takes only 0.975 ms on average. These results show that our method has good abilities of generalization and robustness and allows for nearly real-time modal analysis.

scholarly journals Artificial Intelligence in Optical Communications: From Machine Learning to Deep Learning

2021 ◽  
Vol 2 ◽  
Author(s):  
Danshi Wang ◽  
Min Zhang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Optical Communication ◽  
Optical Communications ◽  
Channel Modeling ◽  
Modeling Method ◽  
Learning Performance ◽  
Sequential Data

Techniques from artificial intelligence have been widely applied in optical communication and networks, evolving from early machine learning (ML) to the recent deep learning (DL). This paper focuses on state-of-the-art DL algorithms and aims to highlight the contributions of DL to optical communications. Considering the characteristics of different DL algorithms and data types, we review multiple DL-enabled solutions to optical communication. First, a convolutional neural network (CNN) is used for image recognition and a recurrent neural network (RNN) is applied for sequential data analysis. A variety of functions can be achieved by the corresponding DL algorithms through processing the different image data and sequential data collected from optical communication. A data-driven channel modeling method is also proposed to replace the conventional block-based modeling method and improve the end-to-end learning performance. Additionally, a generative adversarial network (GAN) is introduced for data augmentation to expand the training dataset from rare experimental data. Finally, deep reinforcement learning (DRL) is applied to perform self-configuration and adaptive allocation for optical networks.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Parasitic element influence on laser driver performances for 1.3 μm fiber optical communication

1993 ◽  
Vol 3 (9) ◽  
pp. 1751-1759 ◽  
Author(s):  
N. Hassaine ◽  
K. Sauv ◽  
A. Konczykowska ◽  
R. Lefevre
Keyword(s):  
Optical Communication ◽  
Parasitic Element ◽  
Laser Driver ◽  
Fiber Optical ◽  
Fiber Optical Communication

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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