To address the uncertainty of cognitive feature selection in brain atlas, a Degree Centrality based Cognitive Feature Selection Method (DC-CFSM) was proposed. First, the Functional Brain Network (FBN) of the subjects in the cognitive experiment tasks was constructed based on the brain atlas, and the Degree Centrality (DC) of each Region Of Interest (ROI) of the FBN was calculated. Next, the difference significances of the subjects' same cortical ROI under different cognitive states during executing cognitive task were statistically compared and ranked. Finally, the Human Brain Cognitive Architecture-Area Under Curve (HBCA-AUC) values were calculated for the ranked regions of interest, and the performances of several cognitive feature selection methods were evaluated. In the experiments on functional Magnetic Resonance Imaging (fMRI) data of mental arithmetic cognitive tasks, the values of HBCA-AUC obtained by DC-CFSM on the Task Positive System (TPS), Task Negative System (TNS), and Task Support System (TSS) of the human brain cognitive architecture were 0.669 2, 0.304 0 and 0.468 5 respectively. Compared with Extremely randomized Trees (Extra Trees), Adaptive Boosting (AdaBoost), random forest, and eXtreme Gradient Boosting (XGB), the recognition rate for TPS of DC-CFSM was increased by 22.17%, 13.90%, 24.32% and 37.19% respectively, while its misrecognition rate for TNS was reduced by 20.46%, 29.70%, 44.96% and 33.39% respectively. DC-CFSM can better reflect the categories and functions of the human brain cognitive system in the selection of cognitive features of brain atlas.

The traditional A ^* algorithm has the disadvantages of long planning time and large search range in unmanned vehicle path planning. After comprehensively analyzing the calculation process of the A ^* algorithm, the A ^* algorithm was improved from four aspects. Firstly, targeted expansion, that is, different quadrants were selected with target for node expansion according to the relative position of the node to be expanded and the target node. Secondly, target visibility judgment, that is, whether there were obstacles between the node to be expanded and the target point was determined, if there were no obstacles, A ^* algorithm jumped out of the exploration process to reduce redundant searches. Thirdly, the heuristic function of the A ^* algorithm was changed, that is, the cost estimation of the n-th generation parent node of the node to be expanded to the target point was increased, thereby reducing the local optimal situation of the cost estimation to the target point. Fourthly, the selection strategy of the expanded nodes was changed, that is, the traditional method of minimizing the heuristic function to select the expanded nodes was changed, and the simulated annealing method was introduced to optimize the selection method of the expanded nodes, so that the search process was performed as close to the target point as possible. Finally, the Matlab simulation experimental results show that, under the simulated map environment, the improved A ^* algorithm has the running time reduced by 67.06%, the number of experienced grids decreased by 73.53%, and the fluctuation range of the optimized path length is ±0.6%.

To solve the problem that Delegated Proof of Stake (DPoS) consensus mechanism has malicious nodes not eliminated in time due to inactive voting and long voting cycle, an improved scheme of DPoS consensus mechanism based on fusing mechanism, credit mechanism and standby witness node was proposed. Firstly, fusing mechanism was introduced to provide the function of negative vote to quicken kicking out evil nodes. Secondly, credit mechanism was introduced to set credit scores and credit grades for nodes, the credit scores and grades of nodes were dynamically adjusted by monitoring the behavior of nodes, therefore the difficulty of obtaining votes for evil nodes was increased. Finally, standby witness node list was added to fill in the vacancy in time after witness right of evil node being cancelled. A test blockchain system based on the improved scheme was built, and the availability and effectiveness of the improved scheme were verified by experiments. The experimental results show that the blockchain based on the improved DPoS consensus mechanism can eliminate the evil nodes in time and is suitable for most scenarios.

At present, the most common way for bitcoin mining is miners joining in a pool. However, there is a phenomenon that the mining pools penetrate each other, which will result in a decrease in the miners' income of the attacked pools, and a reduction in computing power of the attacking pools. Therefore, the overall computing power of the bitcoin system is reduced. Aiming at the problem of mutual attack and non-cooperative mining between mining pools, an Adaptive Zero-Determinant strategy (AZD) was proposed to promote the cooperation of miners. The strategy adopted the idea of comparing expected payoff with cooperation and defection in the next round then choosing a strategy with high payoff. Firstly, miners' payoff in the next round under two situations could be predicted by the combination of Temporal Difference Learning Method (TD(λ)) and Zero-Determinant strategy (ZD). Secondly, by comparing the cooperation payoff with defection payoff in the next round, a more favorable strategy was chosen for miners by Decision Making Process (DMP), so the cooperation probability and defection probability in the next round were changed correspondingly. Finally, through the iterative implementation of AZD strategy, the ming pools in the network would cooperate with each other and mine actively. Simulation results show that compared with adaptive strategy, AZD strategy increases the speed of converging cooperation probability to 1 by 36.54%, compared with ZD strategy, it improves the stability by 50%. This result indicates that AZD strategy can effectively promote the cooperation of miners, improve the convergence rate of cooperation and ensure the stable income of mining pools.

To optimize the balance issue of local area accuracy and efficiency in the progressive mesh generation of the 3D model simplification, a new simplification algorithm for the half-edge collapse progressive mesh based on vector angle change between the local area ring was proposed. Firstly, the normal vector was obtained which restricted by center of gravity measurement distance in the local neighborhood area and consisted of points near the first ring of 3D data points. Secondly, the triangle set was selected as the second ring neighborhood area which intersected with the triangle assembly points of the first ring neighborhood area. Then the value multiplied by the two local normal vectors was made as the edge collapse cost. The smaller the value was, the plainer the region was inclined to be and had the priority of simplification, otherwise it would be retained. Finally, the method of angles judgment of a triangle was adopted as the restriction of half-edge collapse to ensure the regular degree of the triangle in simplification mesh and reduce the error caused by the deformation. The experimental results show that the proposed algorithm can better balance the preserving of local detail features and efficiency in the simplification of progressive mesh of 3D model and can meet the needs of practical applications.

A new realistic rendering method for translucent material jade was put forward by superposition of high light, diffuse and transmission. Firstly, subsurface scattering of translucent material jade was simulated by combination of scattering layer and diffuse profile, then a flexible diffuse profile method was proposed to simulate the diffuse profile characteristic of different types of jade. Secondly, by combining pre-computed local thickness maps and Gaussian linear sum, light transmission effect of transmission layer was realized based on the surface thickness, which was superimposed with specular reflection items based on micro-plane using energy conservation, thus a realistic translucent material representation based on three layers of lighting model was achieved. Experimental results show that the proposed method can achieve photorealistic rendering of different kinds of translucent jade, and ensure the real-time efficiency of 30 frames per second when the triangular patch number reaches 1.6 million.

Concerning the ranging inaccuracy problem based on radio signal phase information under multi-path environments, a two-step ranging approach based on double tags was proposed. Each target was attached with double tags. Through single frequency subcarrier amplitude modulation, firstly, the wrapped phase information of carrier signal was extracted, the distance between reader and tag within half wavelength of carrier signal was calculated and fine ranging estimation value was achieved. Secondly, the unwrapped phase information of subcarrier signal was extracted, and the integral multiple of half wavelength within the distance of reader and tag was calculated. Thirdly, the average multiple was calculated between double tags, the distance of average multiple of half wavelength was used as coarse ranging value. Finally, the final ranging result was estimated by the sum of the fine ranging value and coarse ranging value. Additionally, single reader and double-tag based geometric localization method was introduced to reduce the cost of hardware facilities. The simulation results show that, under multi-path environments, compared with the directly ranging with subcarrier phase, the average ranging error of double tags based two-step ranging approach is reduced by 35%, and the final average localization error is about 0.43 m, and the maximum error is about 1 m. The proposed approach can effectively improve the accuracy of phase based localization technology and also reduce the hardware cost.

Most existing works on memory management focus on the efficiency, which are real-time, but have memory fragmentation problems. To address the problem, an efficient memory management algorithm based on segment tree was proposed. The proposed method built a memory management segment tree by dividing memory space into segments, and allocated and reclaimed memory efficiently and flexibly based on the memory management segment tree to reduce the memory fragmentation. Furthermore, a method was proposed to optimize the space complexity of segment trees. The experimental results show that the proposed method has advantages in terms of efficiency, memory fragmentation, storage space, and so on.

The boundary identification of punctuation marks is an important research field of natural language processing. It is the basis of the application of word segmentation and phrase chunking. In order to solve the problem that the boundary identification of Chinese slight-pause marks which split the coordinate words and phrases in Chinese, the Conditional Random Field (CRF) that used for sequence segmentation and labeling was adopted for slight-pause marks boundary identification. At first, the slight-puase marks boundary recognition task was described in two types, and then the slight-puase marks corpus tagging method and process and feature selection were studied. According to the methods of corpus recommendation and ten-fold cross validation, a series of experiments were carried out in slight-pause marks. The experimental result shows that the proposed method plays an effective role in slight-pause marks boundary identification with selected boundary identification features. And F-measure of boundary identification increased by 10.57% on baseline as well as the F-measure of words divided by slight-pause marks achieves 85.24%.

Concerning the problem that typical time synchronous algorithms used in multi-hop Wireless Sensor Network (WSN) mainly focus on increasing the network synchronization precision, but ignore such issues as the network energy, path hop and error accumulation, a cluster-based and energy-efficient hierarchical time synchronization algorithm was proposed based on a cluster hierarchical network structure. In this algorithm, a cluster node was selected to take a two-way synchronization with the cluster head, and its neighbor nodes achieved bidirectional synchronization indirectly through the passive monitoring mode. In such a way, the number of the packet's transmission was reduced while the digital signature pattern ensured the safety of transmission. In addition, the cycle update coefficient of synchronous group delay was quoted to further reduce the consumption of synchronization packets. The simulation results show that the algorithm reduces the energy consumption and prolongs the network life time, therefore this algorithm has certain practicability.

In order to improve the security and flexibility of digital image information hiding, adopting the theory of sub-affine transformation, a new image encryption algorithm based on three dimensional sub-affine transformation was designed. By analyzing the constraints of three dimensional sub-affine transformation, according to the relation between variables and randomness of variables, a rapid construction method of three dimensional sub-affine transformation was given. Three color components of each pixel in the color space chose different ways to transform for achieving image scrambling, and its periodicity was discussed, for proposing the concept of singularity point and fake period. The experimental analysis shows that the algorithm has good effect of scrambling, larger key space, flexible encryption, and high security. And its diffusion and disturbance are ideal.

Only using estimation of leading edge for target will cause vertical and horizontal ambiguity. Therefore, a new method of Synthetic Aperture Radar (SAR) target aspect estimation based on Radon transform of leading edge was proposed. The new method was introduced to eliminate the ambiguity of horizontal and vertical aspect estimation based on the length of the target region. It is difficult to separate the long leading edge from the short one. By introducing the discrimination rule of the target leading edge, the problem that many traditional algorithms try to settle was solved due to the estimation algorithm of Radon transform. The experimental results on the MSTAR data prove the precision and robustness of the algorithm.

The solutions for porting programs from single-address space operating system to multiple-address space operating system were illustrated and contrasted in the architecture of embedded MIPS. Then a solution named "single device, multiple modules and architecture porting" was introduced to the program porting from Nucleus to Linux. Experiment results show that this solution is executable, low costing and effective.

Low expandability and overweight of telecom backbone in mainstream P2P network model have been hampering the development of P2P system. Though structured overlay network model solves part of problems, it is difficult to be applied into Internet due to its limitations. By analyzing other P2P models, this paper provided a hybrid model based P2P network model, and described some important processes in the model. A manager protocol and new key matching algorithm were also proposed to enhance the management and practicability of P2P network.