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In this paper, we consider two kinds of special matrices, which are called Ppoeplitz matrix and Ppankel matrix. The idea of matrix transformation is used to compute the determinants and inverses of the Ppoeplitz matrix and the Ppankel matrix. We develop efficient formulas for inverting the Ppoeplitz matrix and the Ppankel matrix. Specifically, for inverting the Ppoeplitz matrix and the Ppankel matrix using our proposed formulas, which only 11 entries need to be calculated when n is even, only 12 entries need to be calculated when n is odd. An example is given to illustrate the formulas.

Finger vein recognition is a promising biometric technology that has received significant research attention. However, most of the existing works often relied on a single feature, which failed to fully exploit the discriminative information in finger vein images, and therefore led to a limited recognition performance. To overcome this limitation, this paper proposes an encoding coefficient similarity-based multifeature sparse representation method for finger vein recognition. The proposed method not only uses multiple features to extract comprehensive information from finger vein images, but also obtains more discriminative information through constraints in the objective function. The sparsity constraint retains the key information of each feature, and the similarity constraint explores the shared information among the features. Furthermore, the proposed method is capable of fusing all kinds of features, not limited to specific ones. The optimization problem of the proposed method is efficiently solved using the alternating direction multiplier method algorithm. Experimental results on two public finger vein databases HKPU-FV and SDU-FV show that the proposed method achieves good recognition performance. [ABSTRACT FROM AUTHOR]

Summary: In cloud datacenter network, deadline‐aware disaster evacuation transfers the endangered data out of disaster zone using limited residual network resources. Previous work has not jointly considered the selection of safe datacenter and reasonable allocation of bandwidth proportion in time‐varying postdisaster network environment. Therefore, they cannot make full use of network transmission capability. Based on our earlier work, we propose a new time‐varying disaster evacuation strategy with flexible traffic scheduling. We aim to maximize disaster evacuation capability in the disaster spread scenario. We construct a new disaster‐aware time‐expanded network model to divide time slots according to progressive disaster spread, and optimize the utilization of evacuation capability in the current disaster stage. In each time slot, we carry out two‐step optimization including safe datacenter selection and proportional bandwidth allocation. Especially, we select store‐and‐forward node to ensure the safety of evacuated data in the next time slot, and use marked evacuation routing search based on transmission requirement to improve the utilization of evacuation capability. Through extensive simulations we demonstrate that our strategy achieves better performance with higher evacuation transmission efficiency in the disaster spread scenario. [ABSTRACT FROM AUTHOR]