Aiming at the high-dimensional and small sample problems of functional Magnetic Resonance Imaging (fMRI) data, a Semi-Supervised Feature Selection by Spectral Clustering (SS-FSSC) model was proposed. Firstly, the prior brain region template was used to extract the time series signal. Then, the Pearson correlation coefficient and the Order Statistics Correlation Coefficient (OSCC) were selected to describe the functional connection features between the brain regions, and spectral clustering was performed to the features. Finally, the feature importance criterion based on Constraint score was adopted to select feature subsets, and the subsets were input into the Support Vector Machine (SVM) classifier for classification. By 100 times of five-fold cross-validation on the COBRE (Center for Biomedical Research Excellence) schizophrenia public dataset in the experiments, it is found that when the number of retained features is 152, the highest average accuracy of the proposed model to schizophrenia is about 77%, and the highest accuracy of the proposed model to schizophrenia is 95.83%. Experimental result analysis shows that by only retaining 16 functional connection features for classifier training, the model can stably achieve an average accuracy of more than 70%. In addition, in the results obtained by the proposed model, Intracalcarine Cortex has the highest occurrence frequency among the 10 brain regions corresponding to the functional connections, which is consistent to the existing research state about schizophrenia.

In order to solve the problem that Scale Invariant Feature Transform (SIFT) algorithm has low matching accuracy and long time consuming in image matching, an improved image matching algorithm based on grid motion statistical feature, namely RANSAC-GMS, was proposed. Firstly, the image was pre-matched by Oriented FAST and Rotated BRIEF (ORB) algorithm and Grid-based Motion Statistics (GMS) was used to support the estimator to distinguish the correct matching points from the wrong matching points. Then, an improved RANdom SAmple Consensus (RANSAC) algorithm was used to filter the feature points according to the distance similarity between the matching points, and an evaluation function was used to reorganize the filtered new datasets to eliminate the mismatching points. The experiments were carried out on Oxford standard image library and images taken in reality. Experimental results show that the average matching accuracy of the proposed algorithm in image matching is over 91%. Compared with algorithms such as GMS, SIFT and ORB, the near-scene matching accuracy and the far-scene matching accuracy of the proposed algorithm are improved by 16.15 percentage points and 3.56 percentage points respectively. The proposed algorithm can effectively eliminate mismatching points and achieve further improvement of image matching accuracy.

Persistent flow is an important feature of hidden network attack. It does not generate a large amount of traffic and it occurs regularly in a long period, so that it brings a large challenge for traditional detection methods. Network attacks have invisibility, single monitors have heavy load and limited information. Aiming at the above problems, a method to detect network-wide persistent flows was proposed. Firstly, a sketch data structure was designed and was deployed on each monitor. Secondly, when the network flow arrived at a monitor, the summary information was extracted from network data stream and one bit in the sketch data structure was updated. Thirdly, at the end of measurement period, the summary information from other monitors was synthesized by the main monitor. Finally, the approximate estimation of flow persistence was presented. A bit vector was constructed for each flow by some simple computing, flow persistence was estimated by using probability statistical method, and the persistent flows were detected based on revised persistence estimation. The experiments were conducted on real network traffic, and their results show that compared with the algorithm of Tracing Long Duration flows (TLF), the proposed method increases the accuracy by 50% and reduces the false positive rate, false negative rate by 22%, 20% respectively. The results illustrate that the method of detecting network-wide persistent flows can effectively monitor network traffic in high-speed networks.