The uneven distribution of cell staining and the diversity of tissue morphologies bring challenges to the automatic segmentation of Hematoxylin-Eosin (HE) stained pathological images. In order to solve the problem, a three-step hierarchical segmentation method of pathological images based on self-supervised learning was proposed to automatically segment the tissues in the pathological images layer-by-layer from coarse to fine. Firstly, feature selection was performed in the RGB color space based on the calculation result of mutual information. Secondly, the image was initially segmented into stable and fuzzy color regions of each tissue structure based on K -means clustering. Thirdly, the stable color regions were taken as training datasets for further segmentation of fuzzy color regions by naive Bayesian classification, and the three complete tissue structures including nucleus, cytoplasm and extracellular space were obtained. Finally, precise boundaries between nuclei were obtained by performing the mixed watershed classification considering both shape and color intensities to the nucleus part, so as to quantitatively calculate the indicators such as the number of nuclei, nucleus proportion, and nucleus-cytoplasm ratio. Experimental results of HE stained meningioma pathological image segmentation show that， the proposed method is highly robust to the difference of staining and cell morphologies, the error of issue area segmentation is within 5%, and the average accuracy of the proposed method in nucleus segmentation accuracy experiment is above 96%, which means that the proposed method can meet the requirements of automatic analysis of clinical images and its quantitative results can provide references for quantitative pathological analysis.

Aiming at the problems of low accuracy and even object lost in moving object tracking under occlusion, deformation, rotation, and illumination changes and poor real-time performance of the traditional tracking algorithm, a target tracking algorithm based on foreground discrimination and Circle Search (CS) was proposed. The image perceptual hashing technique was used to describe and match tracked object, and the tracking process was based on the combination of the above was tracking strategies, which could effectively solve the above problems. Firstly, because the direction of motion uncertain and the inter-frame motion was slow, CS algorithm was used to search the local best matching position (around the tracked object) in the current frame. Then, the foreground discrimination PBAS (Pixel-Based Adaptive Segmenter) algorithm was adopted to search for the global optimal object foreground in the current frame. Finally, the one with higher similarity with the object template was selected as the tracking result, and whether to update the target template was determined according to the matching threshold. The experimental results show that the proposed algorithm is better than the MeanShift algorithm in precision, accuracy, and has a better tracking advantage when the target is not moving fast.

To improve the performance of Mobility Anchor Point (MAP) selection algorithm for Hierarchical Mobile IPv6 (HMIPv6), a novel algorithm supporting load sharing was proposed. The algorithm utilized MAP's preference value to characterize the load on that MAP, introduced sharing threshold to judge whether the MAP called "quasi-MAP" had overloaded, and adjusted the selection policy dynamically by the judgment. It is easy to implement. Simulation results indicate that the proposed algorithm reduces protocol cost greatly and has a good effect on load sharing.

The applications based on parallel Transmission Control Protocol (TCP) are widely used with the increase of network bandwidth. When the nodes open seveval TCP connections for one application, it is unfair for the nodes that use single TCP connection. TCPC, a parallel TCP congestion control scheme based on the strength control, was proposed. In this scheme, the TCP connections share the congestion information and restrict the available connection number to control the aggressiveness of the parallel flows. The experimental results show that TCPC can ensure the fairness while effectively using the bandwidth.

The survey of focused crawling starts with the motivation for this new research and an introduction on basic concepts of focused crawling.The key issues in focused crawling are reviewed,such as webpage analyzing algorithms and the searching strategy on the Web.How to crawl relevant data and information according to different requirements is discussed in detail and three representative architectures of focused crawler systems are analyzed.Some future works for focused crawling research are indicated,including crawling for data analysis and data mining,topic description,finding relevant Web pages,Web data cleaning,and the extension of search space.