scholarly journals Influence of current pulse shape on directly modulated system performance in metro area optical networks

2011 ◽  
Vol 284 (5) ◽  
pp. 1346-1353 ◽  
Author(s):  
Carmina del Rio Campos ◽  
Paloma R. Horche ◽  
Alfredo Martin-Minguez
Keyword(s):  
Optical Networks ◽  
Current Pulse ◽  
System Performance ◽  
Pulse Shape ◽  
Metro Area

System Performance Prediction With the Gaussian Noise Model in 100G PDM-QPSK Coherent Optical Networks

2013 ◽  
Vol 31 (21) ◽  
pp. 3352-3360 ◽  
Author(s):  
Andrew J. Stark ◽  
Yu-Ting Hsueh ◽  
Thomas F. Detwiler ◽  
Mark M. Filer ◽  
Sorin Tibuleac ◽  
...  
Keyword(s):  
Optical Networks ◽  
Performance Prediction ◽  
Gaussian Noise ◽  
System Performance ◽  
Noise Model

scholarly journals Research on restoration method of nuclear pulse current signal of semiconductor detector based on artificial neural networks

2021 ◽  
Vol 2132 (1) ◽  
pp. 012023
Author(s):  
Zhang Qin ◽  
ZhangJian Qin ◽  
JingLong Zhang ◽  
XinTe Qi
Keyword(s):  
Current Pulse ◽  
Pulse Shape ◽  
Semiconductor Detector ◽  
Pulse Current ◽  
Shape Recognition ◽  
Pulse Amplitude ◽  
Absolute Error ◽  
Signal Pulse ◽  
Current Signal ◽  
Restoration Method

Abstract The charge pulse generated by semiconductor detector caused by nuclear event carries nuclide and nuclear reaction information, but the amplified charge pulse amplitude is obviously weak and the noise is so large. Aiming at the difficulty of obtaining the charge signal pulse generated by the detector, a method for recovering the nuclear pulse current signal of semiconductor detector is proposed. Pulse recovery is divided into two parts: pulse shape recovery and pulse amplitude recovery. Point at the pulse shape, a shape recognition network of nuclear pulse current signal based on deep learning is proposed. For pulse amplitude,it can be obtained by Mexican straw hat wavelet forming algorithm. This algorithm can eliminate the baseline fluctuation caused by pulse stacking. The proposed shape recognition network of nuclear pulse current signal is composed of classifier and regressor. The classifier is used to judge whether the data contains a complete rising edge. The data containing the complete rising edge is sent to the regressor for prediction, so as to obtain the parameters related to the current pulse shape. The precision, recall and F-Measure of the classifier in classifying the test set are 98.88%, 98.05% and 98.33%, respectively. The average absolute error of the regressor in predicting the parameters related to the current pulse shape is about 9 ns. The experimental results show that the proposed method can recover the shape and amplitude of the current signal.

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