Effects of underwater optical turbulence on light carrying orbital angular momentum and its classification using machine learning

2021 ◽  
Vol 68 (19) ◽  
pp. 1041-1053
Author(s):  
Svetlana Avramov-Zamurovic ◽  
Charles Nelson ◽  
Joel M. Esposito
Keyword(s):  
Machine Learning ◽  
Angular Momentum ◽  
Orbital Angular Momentum ◽  
Optical Turbulence

Hybrid machine learning detection for orbital angular momentum over turbulent MISO wireless channel

2020 ◽  
Vol 14 (22) ◽  
pp. 4116-4126
Author(s):  
Alaa ElHelaly ◽  
Mai Kafafy ◽  
Ahmed H. Mehanna ◽  
Mohamed M. Khairy
Keyword(s):  
Machine Learning ◽  
Angular Momentum ◽  
Orbital Angular Momentum ◽  
Wireless Channel ◽  
Hybrid Machine

An angular velocity sensor using machine learning and optical orbital angular momentum

2020 ◽  
Author(s):  
Elizabeth F. Strong ◽  
Alexander Q. Anderson ◽  
Brendan M. Heffernan ◽  
Michael P. Brenner ◽  
Juliet T. Gopinath ◽  
...  
Keyword(s):  
Machine Learning ◽  
Angular Momentum ◽  
Angular Velocity ◽  
Orbital Angular Momentum ◽  
Velocity Sensor

Experimental study of machine-learning-based orbital angular momentum shift keying decoders in optical underwater channels

2019 ◽  
Vol 452 ◽  
pp. 116-123 ◽  
Author(s):  
Xiaozhou Cui ◽  
Xiaoli Yin ◽  
Huan Chang ◽  
Huanyu Liao ◽  
Xiaozheng Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Angular Momentum ◽  
Orbital Angular Momentum ◽  
Shift Keying ◽  
Underwater Channels

scholarly journals Machine learning-aided classification of beams carrying orbital angular momentum propagated in highly turbid water

2020 ◽  
Vol 37 (10) ◽  
pp. 1662 ◽  
Author(s):  
Svetlana Avramov-Zamurovic ◽  
Abbie T. Watnik ◽  
James R. Lindle ◽  
K. Peter Judd ◽  
Joel M. Esposito
Keyword(s):  
Machine Learning ◽  
Angular Momentum ◽  
Orbital Angular Momentum ◽  
Turbid Water

scholarly journals Recognition of orbital-angular-momentum modes with different topological charges and their unknown superpositions via machine learning

2021 ◽  
Vol 104 (5) ◽  
Author(s):  
Ling-Feng Zhang ◽  
Ya-Yi Lin ◽  
Zhen-Yue She ◽  
Zhi-Hao Huang ◽  
Jia-Zhen Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Angular Momentum ◽  
Orbital Angular Momentum ◽  
Topological Charges

Analysis of an adaptive orbital angular momentum shift keying decoder based on machine learning under oceanic turbulence channels

2018 ◽  
Vol 429 ◽  
pp. 138-143 ◽  
Author(s):  
Xiao-zhou Cui ◽  
Xiao-li Yin ◽  
Huan Chang ◽  
Yi-lin Guo ◽  
Zi-jian Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Angular Momentum ◽  
Orbital Angular Momentum ◽  
Oceanic Turbulence ◽  
Shift Keying

Identifying orbital angular momentum modes in turbulence with high accuracy via machine learning

2019 ◽  
Vol 21 (7) ◽  
pp. 075703 ◽  
Author(s):  
RiDong Sun ◽  
Lixin Guo ◽  
Mingjian Cheng ◽  
Jiangting Li ◽  
Xu Yan
Keyword(s):  
Machine Learning ◽  
Angular Momentum ◽  
Orbital Angular Momentum ◽  
High Accuracy

scholarly journals Machine-learning recognition of light orbital-angular-momentum superpositions

2021 ◽  
Vol 103 (6) ◽  
Author(s):  
B. Pinheiro da Silva ◽  
B. A. D. Marques ◽  
R. B. Rodrigues ◽  
P. H. Souto Ribeiro ◽  
A. Z. Khoury
Keyword(s):  
Machine Learning ◽  
Angular Momentum ◽  
Orbital Angular Momentum

scholarly journals Orbital angular momentum acoustic modes demultiplexing by machine learning methods

2019 ◽  
pp. 300-307
Author(s):  
D A Stankevich
Keyword(s):  
Machine Learning ◽  
Angular Momentum ◽  
Orbital Angular Momentum ◽  
Inner Product ◽  
Underwater Acoustic Communication ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Signal Parameters ◽  
Product Method ◽  
Stationary Signals

Orbital angular momentum (OAM) multiplexing is a promising method for MIMO multiplexing strategy. OAM multiplexing has previously been demonstrated for underwater acoustic communication, where data transmission was carried out within a single acoustic beam. Inner-product method is most often used for OAM demultiplexing, but it is sensitive to changes of signal parameters. For example, parameters changes can be associated with wave propagation through heterogeneous medium. I propose and demonstrate an approach using of machine learning methods to increase demultiplexing accuracy to 96% for non-stationary signals. In article presents experimental and numerical investigation results of proposed method.

Machine learning-aided classification of beams carrying orbital angular momentum propagated in highly turbid water: publisher’s note

2020 ◽  
Vol 38 (1) ◽  
pp. 148
Author(s):  
Svetlana Avramov-Zamurovic ◽  
Abbie T. Watnik ◽  
James R. Lindle ◽  
K. Peter Judd ◽  
Joel M. Esposito
Keyword(s):  
Machine Learning ◽  
Angular Momentum ◽  
Orbital Angular Momentum ◽  
Turbid Water
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