To solve the problem of overlapping community detection in complex networks, many types of methods have been proposed, and Symmetric Binary Nonnegative Matrix Factorization (SBNMF) based overlapping community detection method is one of the most representative methods. However, SBNMF performs poorly when dealing with complex networks with sparse links within communities. In view of this, an Improved SBNMF (ISBNMF) based overlapping community detection method was proposed. Firstly, the factor matrix obtained by the symmetric nonnegative matrix factorization was used to construct a new network with dense links within communities. Then, the SBNMF model based on Frobenius norm was used to factorize the adjacency matrix of the new network. Finally, a binary matrix that can explicitly indicate the community membership of nodes was obtained by means of grid search method or gradient descent method. Extensive experiments were conducted on synthetic and real network datasets. The results show that ISBNMF performs better than SBNMF and other representative methods.

Clustering analysis of a large number of short texts generated by the Internet is of great application value. Because the characteristics of short texts such as sparse features and difficulty of extracting features, the traditional text clustering algorithm faces many challenges in short text clustering. To solve the problem, a short text clustering algorithm based on Weighted Kernel Nonnegative Matrix Factorization (WKNMF) was proposed by using Nonnegative Matrix Factorization (NMF) model. To make full use of hidden semantic features in short texts for clustering, sparse feature space was mapped to high-dimensional implicit vectors by using kernel method. In addition, kernel trick was used to simplify the complex operation of high-dimensional data, and the weight vectors of short texts were dynamically adjusted through iterative optimization updating rules, so that the importance of different short texts to clustering can be distinguished. Experiments were conducted on real Micro-blog data sets and WKNMF algorithm was compared with K-means, Latent Dirichlet Allocation (LDA), Nonnegative Matrix Factorization (NMF) and Self-Organization Map (SOM). The experimental results show that the proposed WKNMF algorithm has a better clustering performance than the contrast algorithms, its accuracy and Normalized Mutual Information (NMI) reach 66.38% and 66.91% respectively.

The transition process of existing link prediction indexes based on random walk exists strong randomness in the unweighted network and does not consider the effect of the similarity of the network structure between different neighbor nodes on transition probability. In order to solve the problems, a new link prediction algorithm based on network representation learning and random walk was proposed. Firstly, the latent structure features of network node were learnt by DeepWalk which is a network representation learning algorithm based on deep learning, and the network nodes were encoded into low-dimensional vector space. Secondly, the similarity between neighbor nodes in vector space was incorporated into the transition process of Random Walk with Restart (RWR) and Local Random Walk (LRW) respectively and the transition probability of each random walk was redefined. Finally, a large number of experiments on five real datasets were carried out. The experimental results show that the AUC (Area Under the receiver operating characteristic Curve) values of the proposed algorithms are improved up to 3.34% compared with eight representative link prediction benchmarks based on network structure.

Most of the exiting hillshading algorithms are implemented based on single-core single-thread programming model, which makes them have lower computational efficiency. To solve this problem, an improved algorithm for parallelizing the existing hillshading algorithms based on multi-core programming model was proposed. Firstly, the original Digital Elevation Model (DEM) data were divided into several data blocks by grid segmentation. Secondly, these data blocks were shaded in parallel using the class Parallel under the .Net environment to generate shaded image of each block. Finally, the shaded images were spliced into a complete hillshading image. The experimental results show that the calculation efficiency of the improved parallelized algorithm is obviously higher than that of the existing shading algorithms based on single-core single-thread programming, and there is an approximate linear growth relation between the number of the involved cores and the shading efficiency. Additionally, it is also found that the three dimensional and realistic effect of the hillshading image is extremely relevant to the parameter setting of the light source.

Concerning the difficulty of its similarity to the expression and the effects of noise in medical image retrieval, a diffusion-based approach on a tensor product graph was proposed to improve the texton-based pairwise similarity metric by context information of other database objects. Firstly, medical image features were described and extracted by texton-based statistical method, and then the pairwise similarities were obtained with weights determined by the similarities between textons. A global similarity metric was achieved by utilizing the tensor product graph to propagate the similarity information along the intrinsic structure of the data manifold. Experimental results of ImageCLEFmed 2009 database show that, the proposed algorithm improves the performance by an average class accuracy of 32% and 19% compared with the Gabor-based retrieval algorithm and the Scale-Invariant Feature Transform (SIFT)-based retrieval algorithm respectively, which can be applied to medical image retrieval.

With the increase of application of satellite networks in emergency communication, and continuous growth of satellite terminal service types, the traffic may experience an instant augmentation showing a significant burst, and the data flow on the terminal also presents self-similarity. A method was propsed to generate satellite terminal self-similar traffic flow by using a superposition ON/OFF model with heavy-tailed distribution of time interval. And the effect of input of self-similar traffic flow on the packet loss rate, delay, and delay jitter was discussed, as well as the requirements on effective bandwidth. The relationship between packet loss rate at network terminal, delay, delay jitter and system cache was obtained by simulation, based on which, a method was put forward to reduce delay and decrease packet loss rate, providing theoretical support for efficient information transmission under condition of limited bandwidth and system cache.

Aiming at solving the misplaced and mismatch problems when 3D hair attaches different head models in the reuse process, a 3D hair reusable method based on scalp layer feature points was proposed. Firstly, according to the data storage structure of the model file, the scalp layer was isolated from the hair model and the feature points were extracted. Secondly, combined with the 2D face image detection method, feature points of the range of hair root on head cortex were extracted. Then shift and zoom coefficients were calculated by the above mentioned feature points. Finally, the fitting process of scalp layer and head model was handled individually. Eventually the 3D hair was adapted to the target head model,which could keep hair styling information without loss. The effect of a close fit between the scalp layer and the head model was achieved. The experimental results show that the proposed method can effectively improve the reusability of 3D hair model, and it is not influenced by the restrictions of the hair model personality part and the distribution area.

To remove the stripe noise from the line scan images, a transform domain destriping method which combined Fourier transform and wavelet decomposition was proposed. Firstly, the image was decomposed using multi-resolution wavelet decomposition to separate the subband which contained the stripe noise from other subbands. Then the subband that contained stripe noise was transformed into Fourier coefficients. The Fourier coefficients were processed by a band-stop filter to remove the stripe noise. The live collected cotton foreign fiber images with stripe noise were used in the simulation experiment. The experimental results indicate that the proposed approach which combined Fourier transform with wavelet decomposition can effectively remove the stripe noise from the image while preserving the characteristics of the original image. It gets better destriping effect than just using Fourier transform or wavelet decomposition separately.

In order to avoid lots of holes among mature immune detectors and deal with the problem of boundary invasion in intrusion detection, analyzing the relationship between number of detectors and detection performance, a detector distribution optimization algorithm with Monte Carlo estimation was proposed: evaluating the coverage of detectors by the Monte Carlo method, and updating the detector set by the offspring to improve detectors' distribution. The experimental tests demonstrate that the algorithm can not only decrease the holes but also achieve a more precise coverage of the nonself space with fewer detectors, and increase the detector's detection performance.

Since there are deficiencies in tracking moving object based on either color feature or gradient feature under complex background, a new algorithm CG_CamShift was proposed with the combination of the two features. This algorithm made full use of the color histogram description of the overall goal and the gradient orientation histogram description of the structural information, and predicted the position of the moving object in combination with the Kalman filtering. It resolved the problem of losing object caused by illumination and shading under complicated background. The experimental results show that the algorithm enhances the tracking accuracy while guaranteeing the real-time performance. In addition, it has stronger robustness.

Based on the statistical characteristics of satellite link error rate,this paper proposed a new link directly monitoring technology using variable length sequence. Using the analysis of bit error rate segmentation strategy and statistical principles of statistical confidence,the monitoring method determined the sequence of the link selection criteria through the training sequence length, the error simulation accuracy and reliability of monitoring and statistical analysis.Experimental results show that link directly monitoring technology using variable length sequence is effective to improve the range of link monitoring and reduce the computational complexity under a certain premise of the channel resources. It has certain advantages in the satellite to link monitoring.

Based on Euclidean distance, the classic Fuzzy C-Means (FCM) clustering algorithm has the limitation of equal partition trend for data sets. And the clustering accuracy is lower when the distribution of data points is not spherical. To solve these problems, a distance correction factor based on dot density was introduced. Then a distance matrix with this factor was built for measuring the differences between data points. Finally, the new matrix was applied to modify the classic FCM algorithm. Two sets of experiments using artificial data and UCI data were operated, and the results show that the proposed algorithm is suitable for non-spherical data sets and outperforms the classic FCM algorithm in clustering accuracy.