SATisfiability problem （SAT） is a NP complete problem， which is widely used in artificial intelligence and machine learning. Exact SATisfiability problem （XSAT） is an important subproblem of SAT. Most of the current research on XSAT is mainly at the theoretical level， but few efficient solution algorithms are studied， especially the stochastic local search algorithms with efficient verifiability. To address above problems and analyze some properties of both basic and equivalent coding formulas， a stochastic local search algorithm WalkXSAT was proposed for solving XSAT directly. Firstly， the random local search framework was used for basic search and condition determination. Secondly， the appropriate unsatisfiable scoring value of the text to which the variables belonged was added， and the variables that were not easily and appropriately satisfied were prioritized. Thirdly， the search space was reduced using the heuristic strategy of anti-repeat selection of flipped variables. Finally， multiple sources and multiple formats of examples were used for comparison experiments. Compared with ProbSAT algorithm， the number variable flips and the solving time of WalkXSAT are significantly reduced when directly solving the XSAT. In the example after solving the basic encoding transformation， when the variable size of the example is greater than 100， the ProbSAT algorithm is no longer effective， while WalkXSAT can still solve the XSAT in a short time. Experimental results show that the proposed algorithm WalkXSAT has high accuracy， strong stability， and fast convergence speed.

Current skill teaching methods of manipulator mainly construct a virtual space through three-dimensional reconstruction technology for manipulator to simulate and train. However， due to the different visual angles between human and manipulator， the traditional visual information reconstruction methods have large reconstruction errors， long time， and need harsh experimental environment and many sensors， so that the skills learned by manipulator in virtual space can not be well transferred to the real environment. To solve the above problems， a visual information real-time reconstruction method for manipulator operation was proposed. Firstly， information was extracted from real-time RGB images through Mask-Region Convolutional Neural Network（Mask-RCNN）. Then， the extracted RGB images and other visual information were jointly encoded， and the visual information was mapped to the three-dimensional position information of the manipulator operation space through Residual Neural Network-18 （ResNet-18）. Finally， an outlier adjustment method based on Cluster Center DIStance constrained （CC-DIS） was proposed to reduce the reconstruction error， and the adjusted position information was visualized by Open Graphics Library （OpenGL）. In this way， the three-dimensional real-time reconstruction of the manipulator operation space was completed. Experimental results show that the proposed method has high reconstruction speed and reconstruction accuracy. It only takes 62.92 milliseconds to complete a three-dimensional reconstruction with a reconstruction speed of up to 16 frames per second and a reconstruction relative error of about 5.23%. Therefore， it can be effectively applied to the manipulator skill teaching tasks.

The domestic DCU （Deep Computer Unit） adopts the parallel execution model of Single Instruction Multiple Thread （SIMT）. When the programs are executed， inconsistent control flow is generated in the kernel function， which causes the threads in the warp be executed serially. And that is warp divergence. Aiming at the problem that the performance of the kernel function is severely restricted by warp divergence， a compilation optimization method to reduce the warp divergence time — Partial-Control-Flow-Merging （PCFM） was proposed. Firstly， divergence analysis was performed to find the fusible divergent regions that are isomorphic and contained a large number of same instructions and similar instructions. Then， the fusion profit of the fusible divergent regions was evaluated by counting the percentage of instruction cycles saved after merging. Finally， the alignment sequence was searched， the profitable fusible divergent regions were merged. Some test cases from Graphics Processing Unit （GPU） benchmark suite Rodinia and the classic sorting algorithm were selected to test PCFM on DCU. Experimental results show that PCFM can achieve an average speedup ratio of 1.146 for the test cases. And the speedup of PCFM is increased by 5.72% compared to that of the branch fusion + tail merging method. It can be seen that the proposed method has a better effect on reducing warp divergence.

In view of the problems of vascular pleomorphism on transverse sections and sampling imbalance in the process of detection， an improved Libra Region-Convolutional Neural Network （R-CNN） cerebral arterial stenosis detection algorithm was proposed to detect internal carotid artery and vertebral artery stenosis in Computed Tomography Angiography （CTA） images. Firstly， ResNet50 was used as the backbone network in Libra R-CNN， Deformable Convolutional Network （DCN） was introduced into the 3， 4， 5 stages of backbone network， and the offsets were learnt to extract the morphological features of blood vessels on different transverse sections. Secondly， the feature maps extracted from the backbone network were input into Balanced Feature Pyramid （BFP） with the Non-local Neural Network （Non-local NN） introduced for deeper feature fusion. Finally， the fused feature maps were input to the cascade detector， and the final detection result was optimized by increasing the Intersection-over-Union （IoU） threshold. Experimental results show that compared with Libra R-CNN algorithm， the improved Libra R-CNN detection algorithm increases 4.3， 1.3， 6.9 and 4.0 percentage points respectively in AP， AP₅₀， AP₇₅ and AP_S， respectively_{on the cerebral artery CTA dataset； on the public CT dataset of colon polyps， the improved Libra R-CNN detection algorithm has the AP， AP50， AP75 and APS increased by 6.6， 3.6， 13.0 and 6.4 percentage points， respectively. By adding DCN， Non-local NN and cascade detector to the backbone network of Libra R-CNN algorithm， the features are further fused to learn the semantic information of cerebral artery structure and make the results of narrow area detection more accurate， and the improved algorithm has the ability of generalization in different detection tasks.}

An echo cancellation method on digital repeater of mobile communication system was presented to solve the problem of traditional adaptive filter, which cannot eliminate the sub-path echo in complex multipath channel. Firstly, based on phase space reconstruction theory, the signal that came from the donor antenna and contained echo was reconstructed; hence, the number of sensors was not fewer than the number of sources in blind source separation. Secondly, Independent Component Analysis (ICA) algorithm was used to separate reconstructed signal. Finally, desired signal was determined by the correlation of sent signal and separated signal. In the experiment on the multi-carrier Global System of Mobile communication (GSM) source with complex multipath echo, correlation coefficient of desired signal was up to 0.9593. The echo cancellation method based on blind source separation proves to be an effective way to eliminate the echo in complex multipath channel.

According to the problem in the transitional period of IPv4/ IPv6 and the shortages of the mainly techniques during this period, this paper proposed the IPv6 tunnel technology based on multi-core, designed its model, used the multi-core flow technology, the lock-technique and the multi-core loadshare model to speed up transmitting, protected the critical resources and improved the whole performance. Finally, the IPv6 tunnel technology based on multi-core is proved to have better transition and service performance than that based on singlecore.

An improved SNMP-Based network topology discovery algorithm was put forward. This algorithm could, based on MIB-Ⅱand TCP/IP protocol addressing principles, accelerate the speed of topology discovery and reduce the network loads. This system was developed at CERNET2. The results of experiments were presented on a map. The results show that this system can discover the network topology quickly and exactly.