Traditional video conference systems depend largely on computational complexity to ensure system security, but with the development of high-performance computers, the existing encryption system will be seriously threatened. To solve this problem, a hybrid encryption scheme for quantum secure video conferencing combined with blockchain is proposed in this study. In the system solution architecture, first, the quantum key distribution network is embedded in the classic network; then, the “classical + quantum” hybrid encryption scheme is designed according to the secret level required for the video conference content. Besides, the real-time monitoring module of the quantum key distribution network is designed to ensure that users can check the running state of the network at any time. Meeting minutes can be shared by combining with blockchain. In order to quickly query meeting minutes, a cache-efficient query method based on B+ tree is proposed. The experimental results show that compared with the traditional video conference system, the quantum secure video conference system sufficiently integrates the technical advantages of the quantum key distribution to resist the security threats such as channel eavesdropping and high-performance computational attacks while ensuring the stable operation of the classic system, thus providing a video conference system with a higher security level. Meanwhile, the query time cost of blockchain with different lengths is tested, and the query efficiency of the proposed method is 3.15-times higher than the original query efficiency of blockchain.

Computational ghost imaging (Computational GI) is a method of acquiring object information by measuring light field intensity, which would be used to achieve imaging in a complicated environment. The main issue to be addressed in computational GI technology is how to achieve rapid and high-quality imaging while assuring the secure transmission of detection data in practical distant imaging applications. In order to address the mentioned issues, this paper proposes a remote secure computational GI method based on quantum communication technology. Using the quantum key distribution (QKD) network, the computational GI system can be reconstructed while solving the problem of information security transmission between the detector and the reconstructed computing device. By exploring the influence of different random measurement matrices on the quality of image reconstruction, it is found that the randomness of the matrix is positively correlated with the imaging quality. The higher the randomness, the higher the imaging quality. Based on this discovery, a new type of quantum cryptography measurement matrix is constructed using quantum cryptography with high randomness. In addition, through further orthogonalization and normalization of the matrix, the matrix has both good randomness and orthogonality, and high-quality imaging results can be obtained at a low sampling rate. The feasibility and effectiveness of the method are verified by random detection, numerical simulation, and simulation imaging experiments. Compared with the traditional computational GI system, the method proposed in this paper has higher transmission security, rapid imaging, and high-quality imaging under this premise, which provides a new idea for the practical development of computational GI technology.

As the medical information systems continue to develop, the sharing of electronic medical records (EMRs) is becoming a vital tool for improving the quality and efficiency of medical services. However, during the process of sharing EMRs, establishing mutual-trust relationships and increasing users’ participation are urgent problems to be solved. Current solutions mainly focus on incentive mechanisms for users’ honest and active participation, but often ignore the potential impact of research institutions’ behavior on users’ trust and participation. To address this, this paper proposes an incentive mechanism based on evolutionary game theory. It combines the unchangeable nature of blockchain and the dynamic adjustment characteristics of evolutionary games to build a secure and trustworthy incentive system. This system considers the potential malicious behaviors of both users and research institutions, encouraging research institutions to protect users’ privacy, reduce users’ concerns, and guide users to actively contribute data. At the same time, it ensures data security and system trust through clear rewards and punishments. Based on this, we have carried out a comprehensive simulation using game theory. The results confirm that our designed incentive mechanism can effectively achieve its expected goals.

Based on the "H" type quadrotor prototype, a 6DOF mathematical model is developed by the Newton-Euler method in order to proceed the research of position and attitude control of complex nonlinear problems. Then, in this context equivalent sliding mode controllers are designed to solve the altitude and attitude control of the quadrotor in addition that PID controllers are designed for the horizontal position control. At last, simulations are carried out under MATLAB/Simulink simulation environment. The simulation results illustrate that the designed controllers effectively realize the control of the quadrotor revealing good tracking ability and anti - interference ability.

In order to satisfy the demand of mobile secure communication for quantum keys, this study proposes a commercial quantum key distribution (QKD) based quantum key injection scheme for mobile termina...

Ghost imaging is a technique for indirectly reconstructing images by utilizing the second-order or higher-order correlation properties of the light field, which exhibits a robust ability to resist interference. On the premise of ensuring the quality of the image, effectively broadening the imaging range can improve the practicality of the technology. In this paper, a dual-path computational ghost imaging method based on convolutional neural networks is proposed. By using the dual-path detection structure, a wider range of target image information can be obtained, and the imaging range can be expanded. In this paper, for the first time, we try to use the two-channel probe as the input of the convolutional neural network and successfully reconstruct the target image. In addition, the network model incorporates a self-attention mechanism, which can dynamically adjust the network focus and further improve the reconstruction efficiency. Simulation results show that the method is effective. The method in this paper can effectively broaden the imaging range and provide a new idea for the practical application of ghost imaging technology.