Biblio

Confidentiality and integrity security are the key challenges in future 5G networks. To encounter these challenges, various signature and key agreement protocols are being implemented in 5G systems to secure high-speed mobile-to-mobile communication. Many security ciphers such as SNOW 3G, Advanced Encryption Standard (AES), and ZUC are used for 5G security. Among these protocols, the AES algorithm has been shown to achieve higher hardware efficiency and throughput in the literature. In this paper, we implement the AES algorithm on Field Programmable Gate Array (FPGA) and real-time performance factors of the AES algorithm were exploited to best fit the needs and requirements of 5G. In addition, several modifications such as partial pipelining and deep pipelining (partial pipelining with sub-module pipelining) are implemented on Virtex 6 FPGA ML60S board to improve the throughput of the proposed design.

With big data and artificial intelligence, we conduct the research of the buyers' identification and involvement, and their investments such as time, experience and consultation in various channels are analyzed and iterated. We establish a set of AI channel governance system with the functions of members' behavior monitoring, transaction clearing and deterrence; Through the system, the horizontal spillover effect of their behavior is controlled. Thus, their unfair perception can be effectively reduced and the channel performance can be improved as well.

Nowadays, video surveillance cameras are widely used in many smart city applications for ensuring road safety. We can use video data from them to solve such tasks as traffic management, driving control, environmental monitoring, etc. Most of these applications are based on object recognition and tracking algorithms. However, the video image quality is not always meet the requirements of such algorithms due to the influence of different external factors. A variety of adverse weather conditions produce noise on the images, which often makes it difficult to detect objects correctly. Lately, deep learning methods show good results in image processing, including denoising tasks. This work is devoted to the study of using these methods for image quality enhancement in difficult weather conditions such as snow, rain, fog. Different deep learning techniques were evaluated in terms of their impact on the quality of object detection/recognition. Finally, the system for automatic image denoising was developed.