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.

Concerning the application requirements for the fast classification of large-scale remote sensing images, a parallel classification method based on K-means algorithm was proposed. Combined the CPU process-level and thread-level parallelism features, reasonable strategies of data granularity decomposition and task scheduling between processes and threads were implemented. This algorithm can achieve satisfactory parallel acceleration while ensuring classification accuracy. The experimental results using large-volume and multi-scale remote sensing images show that: the proposed parallel algorithm can significantly reduce the classification time, get good speedup with the maximum value of 13.83, and obtain good load-balancing. Thus it can solve the remote sensing image classification problems of the large area.

Methods of parallel computation are used in validating topology of polygons stored in simple feature model. This paper designed and implemented a parallel algorithm of validating topology of polygons stored in simple feature model. The algorithm changed the master-slave strategy based on characteristics of topology validation and generated threads in master processor to implement task parallelism. Running time of computing and writing topology errors was hidden in this way. MPI and PThread were used to achieve the combination of processes and threads. The land use data of 5 cities in Jiangsu, China, was used to check the performance of this algorithm. After testing, this parallel algorithm is able to validate topology of massive polygons stored in simple feature model correctly and efficiently. Compared with master-slave strategy, the speedup of this algorithm increases by 20%.

To improve the accuracy and efficiency of clustering algorithm, this paper proposed an enhanced algorithm based on Fuzzy C-Means (FCM) and Support Vector Machine (SVM). The sets of data were clustered into c kinds by FCM, and then they were classified by SVM in detail. The cascade SVM model based on fully binary decision tree was constructed, so as to enhance clustering. In order to solve the problem of losing balance in making new features, the idea of using division in a set of data to eliminate the bad effect was put forward. Some correlation algorithms were compared on Iris data set. The experimental results show that the algorithm can improve the precision, save the system resources and enhance the efficiency of clustering.

As surgeons do not have effective prediction on the the implant surgery for vocal cord paralysis, resulting in high rate of failure, the finite element method was used for preoperative simulation. Through Computed Tomography (CT) data of larynx, the 3D geometric model of vocal cords and glottis trachea was extracted by Mimics, and then imported into ANSYS-Fluent to simulate the vocal vibration mode and airflow dynamic coupling characteristics before and after implanted surgery. The experimental data and clinical statistics data were compared to prove the feasibility of the finite element analysis techniques for implant surgery simulation of vocal cord paralysis. The experimental result can provide support for the design of surgery program.

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.