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.

To solve the signal demodulation problem of asymmetric Paired Carry Multiple Access (PCMA) composed of the same frequency of main station and small station signals, a framework to realize this kind of signal demodulation was constructed. Parameter estimation is an indispensable part in the realization of two-way signal separation and demodulation for asymmetric PCMA communication systems. For the estimation accuracy of amplitude parameters, a searching amplitude estimation algorithm based on fourth-power method was proposed. Firstly, the demodulation model for asymmetric PCMA systems was established and the basic assumptions were made. Then the phase errors under different assumptions were compared with each other and the influence of phase error on the amplitude estimation algorithm was analyzed. Finally, a new amplitude estimation algorithm was proposed. Experimental results show that, under same Signal-to-Noise Ratio (SNR), the demodulation performance of the small station signal under normal phase error is inferior to its demodulation performance under mean value condition. When the order of magnitude of the Bit Error Rate (BER) is 10 ^-4, the demodulation performance of small station signal is improved by 1 dB with the improved algorithm, proving that the improved algorithm is better than fourth-power method.

The knowledge in the closed-loop lifecycle management system is independent and can't be shared. In order to solve the problems, aiming at the characteristics of the closed-loop lifecycle, a new knowledge integration and semantic annotation method was proposed. Firstly, the connotation of knowledge integration and semantic annotation in closed-loop lifecycle management system was expatiated briefly. Secondly, a multi-dimensional and multi-level knowledge integration framework was constructed by using ontology technology for low temperature plasma equipment. Then, on the above basis, a computing method of extracting and matching the document semantic vector and ontology semantic vector was designed. The knowledge document semantic annotation of one sub-system in low temperature plasma equipment was completed. Finally, the test experiments were designed and verified. The experimental results show that, by using knowledge document data set in the closed-loop lifecycle management system to complete the semantic annotation, the average accuracy rate of the proposed method is 84%, and its average recall rate is 79%. The proposed knowledge integration and semantic annotation method can realize the sharing and reuse of the knowledge document in the closed-loop lifecycle management system.

Concerning the problem of excessive interaction flow, single point failure and low detection efficiency in existing Distributed Intrusion Detection System (DIDS), a new distributed intrusion detection model based on artificial immune theory was proposed. The new distributed intrusion detection model presented a central detector configuration and method of use and combined misuse detection and anomaly detection. The simulation model was designed based on OMNeT+〖KG-*3〗+ network simulation platform and experiments were run. According to the simulation results, the model overcomes excessive interaction flow problem of the fully distributed system, solves the problem of single point failure and improves the detection efficiency effectively. The simulation results verify the validity and effectiveness of the improved model.

Concerning the low training speed of mammography multi-classification, the Hypersphere Multi-Class Support Vector Data Description (HSMC-SVDD) algorithm was proposed. The Hypersphere One-Class SVDD (HSOC-SVDD) was extended to a HSMC-SVDD as a kind of immediate multi-classification. Through extracting gray-level co-occurrence matrix features of mammography, then Kernel Principle Component Analysis (KPCA) was used to reduce dimension, finally HSMC-SVDD was used for classification. As each category trained only one HSOC-SVDD, its training speed was higher than that of the present multi-class classifiers. The experimental results show that compared with the combined classifier, in which the average train time is 40.2 seconds, proposed by Wei (WEI L Y, YANG Y Y, NISHIKAWA R M,et al.A study on several machine-learning methods for classification of malignant and benign clustered micro-calcifications. IEEE Transactions on Medical Imaging, 2005, 24(3): 371-380), the training time of HSMC-SVDD classifier is 21.369 seconds, the accuracy is up to 76.6929% and it is suitable for solving classification problems of many categories.

In order to realize aircraft optimal target aiming in the situation of combat duel, a control method based on Gauss Pseudospectral Method (GPM) was proposed. Taking agility and multi-constraint into consideration, the dynamic equation of the aircraft was modeled, the two-stage target aiming condition expression was deduced, and the optimal index was designed. Afterwards, the aircraft optimal aiming control was described as the multi-stage optimal control problem with constraint and unknown final time. The GPM was used to equally convert the continuous optimal boundary value problem to a discrete Nonlinear Programming (NLP) problem and the initial solution was preprocessed through Genetic Algorithm (GA), then, the Sequential Quadratic Programming (SQP) algorithm was applied to solve it. The simulation results show that it can realize target aiming effectively and satisfy weapon launch condition.

The existing trust metrics based on the cloud model lack of the multi-granularity and timeliness consideration. For this reason, a trust metric algorithm based on weighted multi-attribute cloud was proposed. First of all, multi-attribute trust cloud on trust metric was used to refine the grain size, and time decay function was introduced in the entity trust computing; second, multi-attribute comprehensive and multi-path merge was used to get entity ultimate trust cloud. Finally, the trust level of the entity was obtained by comparison with basis trust cloud using cloud similarity comparison algorithm. The simulation results under grid computing environment show that when the node interaction reached 100 times, the interaction success rate of weighted multi-attribute cloud metric was 80%, significantly higher than 65% of the traditional method. The simulation results show that the cloud using the weighted multi-attribute trust cloud metric measurement method can improve the accuracy of trust metric.

Incomplete utilization of polarimetric information is one of the important factors that impact the result of polarimetric Synthetic Aperture Radar (SAR) image classification. In order to achieve the comprehensive utilization of polarimetric information, quotient space granularity composition theory, combined with multiple classifiers to construct different quotient space, was applied in classification of polarimetric SAR. Firstly, using different polarization decomposition method to get different characteristics, and based on these characteristics, setting different Support Vector Machine (SVM) classifiers to classify the image. Secondly, integrating these quotient spaces based on granularity composition theory to get more fine-grained result in order to achieve the upgrading of the classification accuracy. Finally, an experiment for AIRSAR image was given. The result shows the misclassification of targets is inhibited significantly and the classification accuracy of each class is improved.

To meet the physiological perception of human eyes, a color image enhancement algorithm based on improved Intersecting Cortical Model (ICM) was proposed. The internal activities and dynamic threshold were improved to nonlinear attenuation, which satisfied the nonlinear perception of human eyes. And the decay factor was replaced by the step factor, while maintaining some of the significant features of the original model. It applied the Threshold Versus Intensity (TVI) function of the human visual system on the intensity component of the input image to adjust the dynamic range compression. At the same time, it also adjusted the saturation component of the input image by nonlinearity. Compared to the original ICM, this algorithm reduced the complexity and improved the adaptability. The experimental results confirm that the method can obtain clear and bright results.

Based on the analysis of the data fusion algorithms, a data fusion processing system based on the cluster was designed according to the algorithms feature and the mapping methods were given, which achieve real-time processing of large numbers of targets,and the conclusion of test in our laboratory was given.

SIP(Session Initiation Protocol) plays an important role in 3G environment, and JAIN SIP implemented SIP by Java language. After the introduction of SIP standard, we analyzed the architecture and implementation mechanism of JAIN SIP, then described its development process using code segments and interactive graphs. Developing SIP-based applications with JAIN SIP will improve the development efficiency and the applications will gain high reliability and portability.

The Transmission Control Protocol (TCP) was designed for reliable fixed network, it treat package loss or delayed as network congestion. But in Mobile Ad Hoc networks, other factors such as channel error, network partition and route changes also could invoke package loss, at the moment slow-start and fast recovery property of TCP will decreased the transmission performance. In this paper, we firstly discussed several main factors which infect TCP performance in Ad Hoc networks, and then presented an overall view on some representative improved schemes of TCP in Ad Hoc networks. Finally, we had an analysis and compare on these schemes.