In this study, secure communication in cognitive radio networks is investigated. A cooperative jammer is utilised to transmit artificial noise for interfering with the eavesdropper. The authors' objective is to maximise the available secrecy rate of the secondary user (SU) under the interference power constraint at the primary user and the global transmit power constraint at the transmitters. In that case, beamforming vectors of the SU transmitter and the cooperative jammer, and power allocation between them need to be optimised. However, the optimisation problem is non-convex and hard to solve. In order to tackle it, two suboptimal algorithms are proposed: complete orthogonal projection and partial orthogonal projection. In these algorithms, the original optimisation problems are first decoupled into two sub-problems, which deal with the transmit beamforming vectors and power allocation, respectively. Then, the optimal solution is obtained by iteration alternately. Finally, simulation results verify the effectiveness of the proposed algorithms.
Traditional deep convolutional networks have shown that both RGB and depth are complementary for video action recognition. However, it is difficult to enhance the action recognition accuracy because of the limitation of the single convolutional networks to extract the underlying relationship and complementary features between these two kinds of modalities. The authors proposed a novel two stream convolutional networks for multi‐modality action recognition by joint optimisation learning to extract global features from RGB and depth sequences. Specifically, a non‐local multi‐modality compensation block is introduced to learn the semantic fusion features for the recognition performance. Experimental results on two multi‐modality human action datasets, including NTU RGB+D 120 and PKU‐MMD dataset, verify the effectiveness of our proposed recognition framework and demonstrate that the proposed non‐local multi‐modality compensation block can learn complementary features and enhance the recognition accuracy.
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