Chromatic dispersion estimation from input and output intensity waveforms in a fiber optic link with non-zero chirp Mach-Zehnder modulator

2013 ◽  
Author(s):  
Zbigniew T. Lach
Keyword(s):  
Fiber Optic ◽  
Chromatic Dispersion ◽  
Output Intensity ◽  
Input And Output ◽  
Mach Zehnder Modulator

Generation of Frequency 12-Tupling Millimeter-Wave Using one DPMZM to Overcome Chromatic Dispersion

2015 ◽  
Vol 713-715 ◽  
pp. 1184-1187
Author(s):  
Dong Liang ◽  
Qing Gui Tan ◽  
Wei Jiang ◽  
Xiao Jun Li ◽  
Zhong Bo Zhu
Keyword(s):  
Millimeter Wave ◽  
Chromatic Dispersion ◽  
Wave Generation ◽  
Time Shift ◽  
Power Penalty ◽  
Eye Diagram ◽  
Baseband Signal ◽  
Mach Zehnder Modulator ◽  
Theoretical Analyses

In this paper, a approach for frequency 12-tupling millimeter-wave generation using one DPMZM (Dual-Parallel Mach-Zehnder Modulator) to overcome chromatic dispersion is demonstrated. The millimeter-wave with the baseband signal carried only by minus 6-th order sideband is generated by properly adjusting a series of parameters. As the signal transmittes along the fiber, there is no time shift caused by chromatic dispersion. Theoretical analyses and simulated results show that when the optical mm-wave carrying 2.5Gbps baseband signal transmits over 55 Km, the eye diagram still keeps open and clear. The power penalty is about 0.7 dB after transmits over 40 Km.

Application of Improved BP Neural Networks Based on LM Algorithm in Characteristic Curve Fitting of Fiber-Optic Micro-Bend Sensor

2014 ◽  
Vol 889-890 ◽  
pp. 825-828 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang
Keyword(s):  
Fiber Optic ◽  
Characteristic Curve ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Testing System ◽  
Input And Output ◽  
Key Factor ◽  
Simulation Results ◽  
Lm Algorithm ◽  
Bend Sensor

As a key factor in a testing system, sensor nonlinearity has always been the study focus in the field of engineering and techniques. In order to accurately reflect the practical characteristics of a fiber-optic micro-bend sensor, Levenberg-Marguardt (LM) algorithm is used to optimize the correction of the weight values of standard back propagation neural network (BPNN). The learning process of improved BPNN based on LM algorithm (LM-BPNN) is also illustrated mathematically, and LM-BPNN is applied in fitting the input and output characteristic curve of a fiber-optic micro-bend sensor. The simulation results show that LM-BPNN is superior both in its convergence rate and fitting precision over standard BPNN.

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