Privacy protection for 3D point cloud classification based on an optical chaotic encryption scheme.

In allusion to the privacy and security problems in 3D point cloud classification, a novel privacy protection method for 3D point cloud classification based on optical chaotic encryption scheme is proposed and implemented in this paper for the first time. The mutually coupled spin-polarized vertical-cavity surface-emitting lasers (MC-SPVCSELs) subject to double optical feedback (DOF) are studied to generate optical chaos for permutation and diffusion encryption process of 3D point cloud. The nonlinear dynamics and complexity results demonstrate that the MC-SPVCSELs with DOF have high chaotic complexity and can provide tremendously large key space. All the test-sets of ModelNet40 dataset containing 40 object categories are encrypted and decrypted by the proposed scheme, and then the classification results of 40 object categories for original, encrypted, and decrypted 3D point cloud are entirely enumerated through the PointNet++. Intriguingly, the class accuracies of the encrypted point cloud are nearly all equal to 0.0000% except for the plant class with 100.0000%, indicating the encrypted point cloud cannot be classified and identified. The decryption class accuracies are very close to the original class accuracies. Therefore, the classification results verify that the proposed privacy protection scheme is practically feasible and remarkably effective. Additionally, the encryption and decryption results show that the encrypted point cloud images are ambiguous and unrecognizable, while the decrypted point cloud images are identical to original images. Moreover, this paper improves the security analysis via analyzing 3D point cloud geometric features. Eventually, various security analysis results validate that the proposed privacy protection scheme has high security level and good privacy protection effect for 3D point cloud classification.

Nanophotonic reservoir computing for COVID-19 pandemic forecasting.

The coronavirus disease 2019 (COVID-19) has spread worldwide in unprecedented speed, and diverse negative impacts have seriously endangered human society. Accurately forecasting the number of COVID-19 cases can help governments and public health organizations develop the right prevention strategies in advance to contain outbreaks. In this work, a long-term 6-month COVID-19 pandemic forecast in second half of 2021 and a short-term 30-day daily ahead COVID-19 forecast in December 2021 are successfully implemented via a novel nanophotonic reservoir computing based on silicon optomechanical oscillators with photonic crystal cavities, benefitting from its simpler learning algorithm, abundant nonlinear characteristics, and some unique advantages such as CMOS compatibility, fabrication cost, and monolithic integration. In essence, the nonlinear time series related to COVID-19 are mapped to the high-dimensional nonlinear space by the optical nonlinear properties of nanophotonic reservoir computing. The testing-dataset forecast results of new cases, new deaths, cumulative cases, and cumulative deaths for six countries demonstrate that the forecasted blue curves are awfully close to the real red curves with exceedingly small forecast errors. Moreover, the forecast results commendably reflect the variations of the actual case data, revealing the different epidemic transmission laws in developed and developing countries. More importantly, the daily ahead forecast results during December 2021 of four kinds of cases for six countries illustrate that the daily forecasted values are highly coincident with the real values, while the relevant forecast errors are tiny enough to verify the good forecasting competence of COVID-19 pandemic dominated by Omicron strain. Therefore, the implemented nanophotonic reservoir computing can provide some foreknowledge on prevention strategy and healthcare management for COVID-19 pandemic.

Forecasting stock market with nanophotonic reservoir computing system based on silicon optomechanical oscillators.

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.

Security mesh-based optical network exploiting the double masking scheme.

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.