We propose a two-phase diversity scheme to achieve the end-to-end spatial diversity gain for physical-layer network coding (PNC) based two-way relay with a multiple-antenna relay node. A novel binary PNC-specific maximal-ratio-combining like (MRC-L) scheme is proposed to obtain receive diversity in the multiple-access (MA) phase with linear complexity; the Max-Min criterion based transmit antenna selection (TAS) is adopted to obtain transmit diversity in the broadcast (BC) phase. Both the brief diversity analysis and the Monte Carlo (MC) simulation results demonstrate that the proposed scheme achieves full diversity and outperforms other comparable schemes in terms of end-to-end diversity or power advantage.

Scale Invariant Feature Transform (SIFT) algorithm is a very excellent approach for feature detection. It is characterized by data intensive computation. The current studies of accelerating SIFT algorithm are mainly reflected in three aspects: optimizing the parallel parts of the algorithm based on general-purpose multi-core processors, designing the customized multi-core processor dedicated for SIFT, and implementing it based on the FPGA platform. The real-time performance of SIFT has been highly improved. However, the factors such as the input image size, the number of octaves and scale factors in the SIFT algorithm are restricted for some solutions, the flexibility that ensures the high execution performance under variable factors should be improved. This paper proposes a reconfigurable solution to solve this problem. We fully exploit the algorithm and adopt several techniques, such as full parallel execution, block computation and CORDIC transformation, etc., to improve the execution efficiency on a REconfigurable MUltimedia System called REMUS. Experimental results show that the execution performance of the SIFT is improved by 33%, 50% and 8 times comparing with that executed in the multi-core platform, FPGA and ASIC separately. The scheme of dynamic reconfiguration in this work can configure the circuits to meet the computation requirements under different input image size, different number of octaves and scale factors in the process of computing.

A specific physical layer network coding (PNC) scheme is proposed for the two-way relay channel. Unlike the traditional binary PNC that focuses mainly on BPSK modulation, the proposed PNC scheme is tailored for general MPSK modulation. In particular, the product of the two modulated signals is considered as a network-coded symbol. The proposed network coding operation occurs naturally in the inner or outer product of the received signal. A novel PNC-specific detection principle is then developed to estimate the network-coded symbol. Simulations show that the proposed scheme achieves almost optimal performance in terms of end-to-end bit error rate (BER), where the relay node is equipped with multiple antennas.

With the distributed data mining technique having been widely used in a variety of fields, the privacy preserving issue of sensitive data has attracted more and more attention in recent years. Our major concern over privacy preserving in distributed data mining is the accuracy of the data mining results while privacy preserving is ensured. Corresponding to the horizontally partitioned data, this paper presents a new hybrid algorithm for privacy preserving distributed data mining. The main idea of the algorithm is to combine the method of random orthogonal matrix transformation with the proposed secure multi-party protocol of matrix product to achieve zero loss of accuracy in most data mining implementations.

Accurate segmentation of the region in the iris picture has a crucial influence on the reliability of the recognition system. In this letter, we present an end to end deep neural network based on U-Net. It uses dense connection blocks to replace the original convolutional layer, which can effectively improve the reuse rate of the feature layer. The proposed method takes U-net's skip connections to combine the same-scale feature maps from the upsampling phase and the downsampling phase in the upsampling process (merge layer). In the last layer of downsampling, it uses dilated convolution. The dilated convolution balances the iris region localization accuracy and the iris edge pixel prediction accuracy, further improving network performance. The experiments running on the Casia v4 Interval and IITD datasets, show that the proposed method improves segmentation performance.