Aiming at the problem that collaborative sparse image Compressive Sensing (CS) reconstruction based on fixed transform bases can not adequately exploit the self similarity of images, an improved reconstruction algorithm combining the Total Variation (TV) with adaptive low-rank regularization was proposed in this paper. Firstly, the similar patches were found by using image block matching method and formed into nonlocal similar patch groups. Then, the weighted low-rank approximation for nonlocal similar patch groups was adopted to replace the 3D wavelet transform filtering used in collaborative sparse representation. Finally, the regularization term of combining the gradient sparsity with low-rank prior of nonlocal similarity patch groups was embedded to reconstruction model, which is solved by Alternating Direction Multiplier Method (ADMM) to obtain the reconstructed image. The experimental results show that, in comparison with the Collaborative Sparse Recovery (RCoS) algorithm, the proposed method can increase the Peak Signal-to-Noise Ratio (PSNR) of reconstructed images about 2 dB on average, and significantly improve the quality of reconstructed image with keeping texture details better for nonlocal self-similar structure is precisely described.

Aiming at the capacity P-median problem of continuous domains under the dense demand, the Centroidal Capacity Constrained Power Diagram (CCCPD) theory was proposed to approximately model the continuous P-median problem and accelerate the solving process. The Power diagram was constructed by extended Balzer's method, centroid restriction was imposed to satisfy the requirements of P-median, and capacity constraint was imposed to meet the capacity requirements of certain demand densities. The experimental results show that the proposed algorithm can quickly obtain an approximate feasible solution, having the advantages of better computing efficiency and capacity accuracy compared to Alper Murata's method and Centroidal Capacity Constrained Voronoi Tessellation (CCCVT) respectively. Additionally, the proposed method has excellent adaptability to complex density functions.

The forecasting performance of combined model is typically influenced by the combination weights assignment. A new combination weights assignment approach based on area correlation method was proposed. This study applied grey model, Polynomial Trend Extrapolation Model (PTEM) and Triple Exponential Smoothing Model (TESM) to develop a combination forecasting model to predict logistics freight volumes, in which the coefficients of combination weights were determined by area correlation method. The new method based on area correlation method shows its superiority in determining combination weights, compared with some other combination weight assignment methods such as equal weight method, entropy weight method and reciprocal of mean absolute percentage error weight method. Since area correlation method can comprehensively evaluate both the correlation and fitting error of forecasting model, it is an effective approach to determine the combination weights.

By analyzing the structure of Low-Density Parity-Check (LDPC) code check matrix in LTE-Advanced standard, this paper proposed a low-cost encoder with high input packet throughput on Quasi Cyclic-LDPC (QC-LDPC) code. With exploiting the number of null matrices in the mother parity check matrix, the whole parity check matrix could be partitioned into an array of block matrices, where each block matrix was either a null sub-matrix or a cyclic shift of an identity sub-matrix, and then it encoded serially. The experimental results show that the proposed encoder's coding time is the same as 32% of the ideal time and the resources consumption is the same as 33% of the ideal situation within the analogous methods. This result achieves the balance between coding time and resources consumption, which means the designed encoder meets the LTE-Advanced standard: low cost with high transmission. In addition, by changing the parameters in the ROM which saves the check matrix, the proposed encoder is flexible to implement the encoding of LDPC code with different code length or rate.

It has been proved in many researches that there are some design flaws in IEEE 802.1X standard. In order to eliminate the Denial of Service (DoS) attack, replay attack, session hijack, Man-In-the-Middle (MIM) attack and other security threats, the protocol was analyzed in view of the state machines. It is pointed out that the origin of these problems is the inequality and incompleteness of state machines as well as the lack of integrity protection and source authenticity on messages. However, an improvement proposal called Dual-way Challenge Handshake and Logoff Authentication was proposed, and a formal analysis was done on it with an improved BAN logic. It is proved that the proposal can effectively resist the security threats mentioned above.