Nonlinear compensation using optical phase conjugation (OPC) have been considered a promising technique to increase the reach of high-speed fiber-optic transmission systems. OPC-based nonlinear compensation employs an optical phase conjugation located at a middle of the fiber link to generate a complexed conjugated signal with respect the signal in the first half of the link for propagation in the second half. OPC technique assumes a symmetry for signal propagating in the first and second half to obtain a perfect nonlinear and chromatic dispersion. However, as most of practical OPC schemes are realized by nonlinear effects such as four-wave mixing or a combination of second-harmonic generation and difference frequency generation, the frequency shift induced by OPC affects the signal symmetrical requirement for nonlinear compensation because the chromatic dispersion is different for the first and second half transmissions. In this paper, we investigate the impact of frequency shift on the nonlinear compensation using OPC for high symbol rate, high level modulation format signals. This will be important to understand the tolerance of the OPC techniques against such a practical condition for actual system implementations.
The paper attemps to forecast the future trend of Vietnam index (VN-index) by using long-short term memory (LSTM) networks. In particular, an LSTM-based neural network is employed to study the temporal dependence in time-series data of past and present VN index values. Empirical forecasting results show that LSTM-based stock trend prediction offers an accuracy of about 60% which outperforms moving-average-based prediction.
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