ABSTRACT
In order to tackle the security and privacy problems in optical networks, a novel mesh-based optical security network exploiting double masking (DM) scheme for multipoint confidential communication is proposed and studied theoretically. For each node in the network, a pair of mutually asynchronous vertical-cavity surface-emitting lasers (VCSELs) are required as transceivers, and the delay fiber (DF) is used to set different time delays as network node markers. In this security network, the encryption of the message is implemented on the transmitter of the source node by using the DM scheme, and the encrypted message is transmitted to the receiver of the destination node through the optical network for decryption. Each network node can output its individual chaotic signals separately with different time delay markers. By regulating different internal parameter mismatches, the synchronization characteristics of transceivers in a security network are numerically analyzed by using the cross correlation coefficient. Simulation results show that the chaos synchronization between transceivers enjoys fantastic robustness to mismatched parameters. Meanwhile, the tolerance of the DM scheme to the inherent parameter mismatch is excellent, so it is suitable for constructing secure networks in optical networks. Besides, based on the high quality synchronization with a correlation coefficient of 0.983, the communication performances of the longest path channel are investigated for a given metropolitan area network scale. Two pieces of 10 Gb/s messages can be effectively concealed in the chaos and decoded gratifyingly behind 100 km transmission, and the system has reliable security to resist illegal attacks. Finally, the network performance simulation is conducted for diverse configurations of the mesh-based optical networks. All the results confirmed the chaotic encryption scheme provides a novel way for any two legitimate nodes to establish security keys in optical networks.
ABSTRACT
The essence of stock market forecasting is to reveal the intrinsic operation rules of stock market, however it is a terribly arduous challenge for investors. The application of nanophotonic technology in the intelligence field provides a new approach for stock market forecasting with its unique advantages. In this work, a novel nanophotonic reservoir computing (RC) system based on silicon optomechanical oscillators (OMO) with photonic crystal (PhC) cavities for stock market forecasting is implemented. The long-term closing prices of four representative stock indexes are accurately forecast with small prediction errors, and the forecasting results with distinct characteristics are exhibited in the mature stock market and emerging stock market separately. Our work offers solutions and suggestions for surmounting the concept drift problem in stock market environment. The comprehensive influence of RC parameters on forecasting performance are displayed via the mapping diagrams, while some intriguing results indicate that the mature stock markets are more sensitive to the variation of RC parameters than the emerging stock markets. Furthermore, the direction trend forecasting results illustrate that our system has certain direction forecasting ability. Additionally, the stock forecasting problem with short listing time and few data in the stock market is solved through transfer learning (TL) in stock sector. The generalization ability (GA) of our nanophotonic reservoir computing system is also verified via four stocks in the same region and industry. Therefore, our work contributes to a novel RC model for stock market forecasting in the nanophotonic field, and provides a new prototype system for more applications in the intelligent information processing field.
