scholarly journals An Effective Way for Simulating Oceanic Turbulence Channel on the Beam Carrying Orbital Angular Momentum

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Sunxiang Pan ◽  
Le Wang ◽  
Wennai Wang ◽  
Shengmei Zhao
Keyword(s):  
Angular Momentum ◽  
Power Spectrum ◽  
Phase Structure ◽  
Phase Screen ◽  
Inversion Method ◽  
Propagation Characteristics ◽  
Oceanic Turbulence ◽  
Frequency Components ◽  
Screen Model ◽  
Spectrum Inversion

Abstract In this paper, we present an effective way for simulating oceanic turbulence channel on the beam carrying orbital angular momentum (OAM). The influence caused by oceanic turbulence channel on the phase and intensity of the propagation beam is equivalent to that the beam passing through several individual phase screens generated by power spectrum inversion method at regular intervals. A modified subharmonic compensation method is then further balance the phase screen for the losses of lower frequency components in the power spectrum inversion method. The feasibility is verified by the theoretical phase structure function and the propagation characteristics of an OAM beam in underwater environment. The results show that the phase structure function and the propagation characteristics of the OAM beam evaluated by the phase screen model all coincide with those theoretical results at high spatial frequency. Simultaneously, the low frequency components could be effectively compensated by the modified subharmonic method. With the increase of the subharmonic order and sample level, the performance evaluated by the phase screen model are closer to the theoretical ones. It has provided an effective way for simulating oceanic turbulence channel for the underwater optical communications.

Validity of beam propagation characteristics through oceanic turbulence simulated by phase screen method

2020 ◽  
Vol 49 (7) ◽  
pp. 20190452
Author(s):  
牛超君 Chaojun Niu ◽  
王晓斌 Xiaobin Wang ◽  
卢芳 Fang Lu ◽  
韩香娥 Xiang’e Han
Keyword(s):  
Beam Propagation ◽  
Phase Screen ◽  
Propagation Characteristics ◽  
Oceanic Turbulence ◽  
Screen Method

Validity of beam propagation characteristics through oceanic turbulence simulated by phase screen method

2020 ◽  
Vol 49 (7) ◽  
pp. 20190452
Author(s):  
牛超君 Chaojun Niu ◽  
王晓斌 Xiaobin Wang ◽  
卢芳 Fang Lu ◽  
韩香娥 Xiang’e Han
Keyword(s):  
Beam Propagation ◽  
Phase Screen ◽  
Propagation Characteristics ◽  
Oceanic Turbulence ◽  
Screen Method

Experimental Techniques for Controlling Chaos in Lasers

1998 ◽  
Vol 08 (09) ◽  
pp. 1759-1768 ◽  
Author(s):  
R. Meucci ◽  
A. Labate ◽  
M. Ciofini
Keyword(s):  
Power Spectrum ◽  
Chaotic Dynamics ◽  
Feedback Loop ◽  
Control Parameter ◽  
Experimental Techniques ◽  
Controlling Chaos ◽  
Sinusoidal Modulation ◽  
Control Schemes ◽  
Frequency Components

This paper presents two control schemes for the chaotic dynamics of CO 2 laser with feedback which can be applied after the recognition of a leading frequency of the motion in the power spectrum. The first one is realized by means of a selective feedback loop which rejects all the frequency components except that of the leading cycle to be stabilized. The second one consists in a resonant sinusoidal modulation of the control parameter.

Experimental Investigation of Underwater Propagation Characteristics of Orbital Angular Momentum

2018 ◽  
Vol 38 (6) ◽  
pp. 0606004
Author(s):  
潘孙翔 Pan Sunxiang ◽  
赵生妹 Zhao Shengmei ◽  
王乐 Wang Le ◽  
姚浩 Yao Hao ◽  
李威 Li Wei
Keyword(s):  
Experimental Investigation ◽  
Angular Momentum ◽  
Orbital Angular Momentum ◽  
Propagation Characteristics

Bayesian inversion of CSEM and magnetotelluric data

Geophysics ◽  
2012 ◽  
Vol 77 (1) ◽  
pp. E33-E42 ◽  
Author(s):  
Arild Buland ◽  
Odd Kolbjørnsen
Keyword(s):  
Prior Knowledge ◽  
Inversion Method ◽  
Earth Model ◽  
Magnetotelluric Data ◽  
Inversion Result ◽  
Electromagnetic Data ◽  
Magnetic Components ◽  
Resistivity Model ◽  
Frequency Components ◽  
Inversion Procedure

We have developed a Bayesian methodology for inversion of controlled source electromagnetic (CSEM) data and magnetotelluric (MT) data. The inversion method provided optimal solutions and also the associated uncertainty for any sets of electric and magnetic components and frequencies from CSEM and MT data. The method is based on a 1D forward modeling method for the electromagnetic (EM) response for a plane-layered anisotropic earth model. The inversion method was also designed to invert common midpoint (CMP)-sorted data along a 2D earth profile assuming locally horizontal models in each CMP position. The inversion procedure simulates from the posterior distribution using a Markov chain Monte Carlo (McMC) approach based on the Metropolis-Hastings algorithm. The method that we use integrates available geologic prior knowledge with the information in the electromagnetic data such that the prior model stabilizes and constrains the inversion according to the described knowledge. The synthetic examples demonstrated that inclusion of more data generally improves the inversion results. Compared to inversion of the inline electric component only, inclusion of broadside and magnetic components and an extended set of frequency components moderately decreased the uncertainty of the inversion. The results were strongly dependent on the prior knowledge imposed by the prior distribution. The prior knowledge about the background resistivity model surrounding the target was highly important for a successful and reliable inversion result.

scholarly journals Analysing 21cm signal with artificial neural network

2017 ◽  
Vol 12 (S333) ◽  
pp. 39-42
Author(s):  
Hayato Shimabukuro ◽  
Benoit Semelin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Spectrum ◽  
Machine Learning Techniques ◽  
Inversion Method ◽  
Observation Data ◽  
The Neural Network ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractThe 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.

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