To solve the problems that the traditional multi-threshold segmentation methods have the computational complexity increased with the increase of the number of thresholds, and have very low efficiency of multi-threshold segmentation for a given image, a multi-threshold segmentation method based on Symbiotic Organisms Search (SOS) algorithm combined with Kapur entropy threshold was proposed. Firstly, the Elite Opposition-Based Learning (EOBL) was added into the symbiotic stage of SOS algorithm, so as to solve the problem that the traditional SOS algorithms tend to fall into local optimum when dealing with complex optimization problems. Then, the Levy flight mechanism was introduced to expand the search range of SOS algorithm and enhance the randomness of the algorithm's search trajectory. Finally, the obtained Modified Symbiotic Organisms Search (MSOS) algorithm was applied to find the optimal threshold values for forest fire images. Experimental results show that compared with other optimization algorithms such as Particle Swarm Optimization (PSO) algorithm,Harmony Search Algorithm (HSA) and Bat Algorithm (BA), the MSOS algorithm has the superiority in segmenting images, so it is practical and valuable in practical engineering problems.

Aiming at the disadvantages of traditional Support Vector Machine (SVM) in the wrapper feature selection:low classification accuracy, redundant feature subset selection and poor computational efficiency, the meta-heuristic optimization algorithm was used to simultaneously optimize SVM and feature selection. In order to improve the classification effect of SVM and the ability of feature subset selection, firstly, the Spotted Hyena Optimizer (SHO) algorithm was improved by using the adaptive Differential Evolution (DE) algorithm, chaotic initialization and tournament selection strategy, so as to enhance its local search ability as well as improve its optimization efficiency and solution accuracy; secondly, the improved algorithm was applied to the simultaneous optimization of feature selection and SVM parameter adjustment; finally, a feature selection simulation experiment was carried out on the UCI datasets, and the classification accuracy, the number of selected features, the fitness value and the running time were used to comprehensively evaluate the optimization performance of the proposed algorithm. Experimental results show that the simultaneous optimization mechanism of the improved algorithm can reduce the number of selected features with high classification accuracy, and compared to the traditional algorithms, this algorithm is more suitable for solving the problem of wrapper feature selection, which has good application value.

Aiming at the problems that a wide variety of Chinese medicinal materials have small samples, and it is difficult to classify the vessels of them, an improved convolutional neural network method was proposed based on multi-channel color space and attention mechanism model. Firstly, the multi-channel color space was used to merge the RGB color space with other color spaces into 6 channels as the network input, so that the network was able to learn the characteristic information such as brightness, hue and saturation to make up for the insufficient samples. Secondly, the attention mechanism model was added to the network, in which the two pooling layers were connected tightly by the channel attention model, and the multi-scale cavity convolutions were combined by the spatial attention model, so that the network focused on the key feature information in the small samples. Aiming at 8 774 vessel images of 34 samples collected from Chinese medicinal materials, the experimental results show that by using the multi-channel color space and attention mechanism model method, compared with the original ResNet network, the accuracy is increased by 1.8 percentage points and 3.1 percentage points respectively, and the combination of the two methods increases accuracy by 4.1 percentage points. It can be seen that the proposed method greatly improves the accuracy of small-sample classification.

Functional hyper-networks are widely used in brain disease diagnosis and classification studies. However, the existing research on hyper-network construction lacks the ability to interpret the grouping effect or only considers the information of group level information of brain regions, the hyper-network constructed in this way may lose some useful connections or contain some false information. Therefore, considering the group structure problem of brain regions, the sparse group Lasso (Least absolute shrinkage and selection operator) (sgLasso) method was introduced to further improve the construction of hyper-network. Firstly, the hyper-network was constructed by using the sgLasso method. Then, two groups of attribute indicators specific to the hyper-network were introduced for feature extraction and feature selection. The indictors are the clustering coefficient based on single node and the clustering coefficient based on a pair of nodes. Finally, the two groups of features with significant difference obtained after feature selection were subjected to multi-kernel learning for feature fusion and classification. The experimental results show that the proposed method achieves 87.88% classification accuracy by using the multi-feature fusion, which indicates that in order to improve the construction of hyper-network of brain function, the group information should be considered, but the whole group information cannot be forced to be used, and the group structure can be appropriately expanded.

As many geometric modelings essentially are the problems of the surface blending with constrained conditions, a nonlinear homotopy mapping method was presented to compute the surface equation of three-dimensional modeling on the base of linear homotopy method. In the method, the interpolation polynomial was computed firstly by using the position of cross-over section or biological slices as the interpolation points. Then, this interpolation polynomial was regarded as the nonlinear continuous homotopy mapping function and substituted into the polynomials of primary surfaces and auxiliary surfaces respectively to get blending surface equation. Thus, two univariable equations were obtained when the interpolated variable in interpolation polynomial was used as the variable but the others in the equations of primary surfaces and auxiliary surfaces were used as parameters. Furtherly, Sylvester resultant was used to eliminate the interpolated variable in these two equations to achieve the modeling surface which satisfied the constraints. The proposed method can realize surface modeling with control points and geometric modeling with constraints, and it is more practical because it can redefine and change the the intermediate position and shape.

To solve the problem of discontinuity when blending two surfaces with coplanar perpendicular axis, this paper discussed how to improve the equations about the blending surface so as to obtain the smooth and continuous blending surface. At first, this paper analyzed the reason of the uncontinuousness in the blending surface and pointed out that the items in one variable were removed when other variables equaled to some specified values. In this case, the blending equation was independent to this variable in these values and this indicated that the belending surface was disconnected. Then, a method which guarantees the blending surface countinuous was presented on the basis of above discussion. Besides this, this paper discussed how to smoothen it once the continuous blending surface was computed out. As for the G0 blending surface, regarding the polynomial of auxiliary surface as a factor, this factor was mulitiplied to a function f′ with degree one and the result was added to the primary surface fi. The smoothness of blending surface can be implemented by changing the coefficients in f. For the Gn blending surface, a compensated polynomial with degree at most 2 was added to the proposed primary blending equation directly when computing blending surface. This method smoothens the blending surface but does not increase the degree of G0 blending surface.

An improved method for Multi-Scale Retinex with Color Restoration (MSRCR) algorithm was proposed, to remove the fog at the far prospect and solve gray hypothesis problem. First, original fog image was inverted. Then, MSRCR algorithm was used on it. The inverted image was to be inverted again and then was linearly superposed with the result which was processed by MSRCR algorithm directly .At the same time , the reflection component which was got during the process of the extraction was linearly superposed with the original luminance, and the mean and variance were calculated to decide the contrast stretching degree adaptively, finally, it was uniformly stretched to the display device.The experimental results show that the proposed algorithm can get a better effect of removing the fog. Evaluation values of the processed image, including standard difference, average brightness, information entropy, and squared gradient, are improved than the original algorithm. It is easy to implement and has important significance for real-time video to remove fog.

As the most widely used autonomous intra-domain routing protocol for large-scale network, the security of Open Shortest Path First (OSPF) is not only about the normal running of autonomous intra-domain, but also closely related to inter-domain even the whole network. Based on asymmetric encryption algorithm, the traditional digital signature solution can realize the security validation of end-to-end; however, it ignores the issue of point-to-point. Additionally, the problem of storage and extra overhead also needs to be solved urgently. On the basis of symmetrical encryption algorithm, a new solution named HS-OSPF was put forward. HS-OSPF extended the original two-level hierarchical structure as well as designed a reasonable, efficient key distribution and management scheme. The result shows that the shortcomings of traditional solution are overcome, key storage and system overhead are reduced and real-time of security communication is improved.

The suitability analysis of matching area plays a significant role in the field of vision-based navigation. There are many feature indexes that can only unilaterally describe the suitability of matching area. An algorithm was proposed to integrate several feature indexes to solve conflicts among different feature indexes and provide a kind of method that can measure the suitable confidence and unsuitable confidence of a feature.And then the confidences were fused by using the Dempster-Shafer (DS) rules. At last the algorithm was verified by simulation experiment.

According to the characteristics of wireless single-hop broadcast networks, a network coding scheme based on sliding windows named NCBSW was proposed. The scheme designed a coding window which slid in a chronological order in the matrix of data packets waiting for retransmission, and the data packets used to encode were chosen from the sliding window. Meanwhile, the scheme ensured the solvability of coded packets. The simulation results show that the proposed scheme has a better performance as compared to the retransmission approach in wireless broadcasting based on network coding (NCWBR) in terms of the number of retransmission, delay, network overhead and energy consumption.

The new generation of scalable P2P systems adopts routing algorithms which support a distributed hash table(DHT) functionality.CAN(Content-Addressable Network) uses DHT to relocate resources on a virtual d-dimensional cartesian coordinate space.CAN is scalable,fault-tolerant and completely self-organizing.The concept of CAN was introduced,the construction and the routing of CAN was described,several improvements for CAN was also discussed.