Working memory complex network analysis methods mostly use channels as nodes to analyze from the perspective of space, while rarely analyze channel networks from the perspective of time. Focused on the high time resolution characteristics of ElectroEncephaloGraph (EEG) and the difficulty of time series segmentation, a method of constructing and analyzing network from the time perspective was proposed. Firstly, the microstate was used to divide EEG signal of each channel into different sub-segments as nodes of the network. Secondly, the effective features in the sub-segments were extracted and selected as the sub-segment effective features, and the correlation between sub-segment feature vectors was calculated to construct channel time sequence complex network. Finally, the attributes and similarity analysis of the constructed network were analyzed and verified on the schizophrenic EEG data. The experimental results show that the analysis of schizophrenia data by the proposed method can make full use of the time characteristics of EEG signals to understand the characteristics of time series channel network constructed in working memory of patients with schizophrenia from a time perspective, and explain the significant differences between patients and normals.

It is difficult to get correct segmentation results of the images with unknown intensity and distribution of noise, and the existing models are poor in robustness to complex noise environment. Thus, a noise adaptive algorithm for image segmentation was proposed based on local intensity difference. Firstly, Local Correntropy-based K-means (LCK) model and Region-based model via Local Similarity Factor (RLSF) model were analyzed to reduce the sensitivity to noise pixels. Secondly, a correction function based on local intensity statistical information was introduced to reduce the interference of samples to be away from local mean to segmentation results. Finally, the active contour energy function and iterative equation integrated with the correction function were deduced. Experimental results performed on synthetic, and real-world noisy images show that the proposed model is more robust with higher precision, recall and F-score in comparison with Local Binary Fitting (LBF) model, LCK model and RLSF model, and it can achieve good performance on the images with intensity inhomogeneity and noise.

Focusing on the issues of low efficiency, being easy to be influenced by subjective factors and deviation of the evaluation results caused by the traditional edge quality evaluation which relies on experts, a new mapping evaluation operator called Geographical Mapping Operator (GMO) was proposed. Fuzzy comprehensive evaluation was applied to the edge quality evaluation, and the comment rate and evaluation index were determined according to the national standard, as well as the fuzzy weight vectors of evaluation factors were determined through the entropy weight method. Besides, the new operator was proved by theory. When applying the GMO to the actual data, the unqualified quality of earth data accounts for 65% before edging, but the perfect quality of earth data accounts for 55% after edging, which indicates the effectiveness of GMO.

Images usually have multiple connected regions of the same color. For the problem that Pulse Coupled Neural Networks (PCNN) cannot abstract these areas separately, a PCNN model with improved traversal process was proposed. By introduceing the depth-first search traversal algorithm, multi-unconnected regions were activated on different layers, so as to achieve a separation. Finally, the new model was improved again for the effect of image noise. The activated scope in each layer was used to detect noisy pixels, and then the mean-shift algorithm was introduced to eliminate the noisy pixels. The separation effect of multi-regions with the same color in the image and the ability to eliminate noise has been verified by experiment.