Fully homomorphic encryption (FHE) allows arbitrary computation on encrypted data and has great potential in privacy-preserving cloud computing and securely outsource computational tasks. However, the excessive computation complexity is the key limitation that restricting the practical application of FHE. In this paper we proposed a FPGA-based high parallelism architecture to accelerate the FHE schemes based on the ring learning with errors (RLWE) problem, specifically, we presented a fast implementation of leveled fully homomorphic encryption scheme BGV. In order to reduce the computation latency and improve the performance, we applied both circuit-level and block-level pipeline strategies to improve clock frequency, and as a result, enhance the processing speed of polynomial multipliers and homomorphic evaluation functions. At the same time, multiple polynomial multipliers and modular reduction units were deployed in parallel to further improve the hardware performance. Finally, we implemented and tested our architecture on a Virtex UltraScale FPGA platform. Runing at 150MHz, our implementation achieved 4.60x~9.49x speedup with respect to the optimized software implementation on Intel i7 processor running at 3.1GHz for homomorphic encryption and decryption, and the throughput was increased by 1.03x~4.64x compared to the hardware implementation of BGV. While compared to the hardware implementation of FV, the throughput of our accelerator also achieved 5.05x and 167.3x speedup for homomorphic addition and homomorphic multiplication operation respectively.

In this paper, we investigate the problem of key radar signal sorting and recognition in electronic intelligence (ELINT). Our major contribution is the development of a combined approach based on clustering and PRI transform algorithm, to solve the problem that the traditional methods based on Pulse Description Words (PDW) were not exclusively targeted at tiny particular signals and, less time-efficient. We achieve this in three steps: firstly, PDW presorting is carried out by the DBSCAN clustering algorithm, then, PRI estimates of each cluster are obtained by the PRI transformation algorithm, finally, by judging the matching between various PRI estimates and key targets, it is determined whether the current signal contains key target signals or not. Simulation results show that the proposed method should improve the time efficiency of key signal recognition and deal with the complex signal environment with noise interference and overlapping signals.

In the past, most of the entity prediction methods based on embedding lacked the training of local core relationships, resulting in a deficiency in the end-to-end training. Aiming at this problem, we propose an end-to-end knowledge graph embedding representation method. It involves local graph convolution and global cross learning in this paper, which is called the TransC graph convolutional network (TransC-GCN). Firstly, multiple local semantic spaces are divided according to the largest neighbor. Secondly, a translation model is used to map the local entities and relationships into a cross vector, which serves as the input of GCN. Thirdly, through training and learning of local semantic relations, the best entities and strongest relations are found. The optimal entity relation combination ranking is obtained by evaluating the posterior loss function based on the mutual information entropy. Experiments show that this paper can obtain local entity feature information more accurately through the convolution operation of the lightweight convolutional neural network. Also, the maximum pooling operation helps to grasp the strong signal on the local feature, thereby avoiding the globally redundant feature. Compared with the mainstream triad prediction baseline model, the proposed algorithm can effectively reduce the computational complexity while achieving strong robustness. It also increases the inference accuracy of entities and relations by 8.1% and 4.4%, respectively. In short, this new method can not only effectively extract the local nodes and relationship features of the knowledge graph but also satisfy the requirements of multilayer penetration and relationship derivation of a knowledge graph.

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