For the problem of gait stability control for continuous linear walking of a biped robot， a Soft Actor-Critic （SAC） gait control algorithm based on maximum entropy Deep Reinforcement Learning （DRL） was proposed. Firstly， without accurate robot dynamic model built in advance， all parameters were derived from joint angles without additional sensors. Secondly， the cosine similarity method was used to classify experience samples and optimize the experience replay mechanism. Finally， reward functions were designed based on knowledge and experience to enable the biped robot continuously adjust its attitude during the linear walking training process， and the reward functions ensured the robustness of straight walking. The proposed method was compared with other DRL methods such as PPO （Proximal Policy Optimization） and TRPO （Trust Region Policy Optimization） in Roboschool simulation environment. The results show that the proposed method not only achieves fast and stable linear walking of the biped robot， but also has better algorithmic robustness.

In edge computing， computing resources are deployed at edge computing nodes closer to end users， and selecting the appropriate edge computing node deployment location from the candidate locations can enhance the node capacity and user Quality of Service （QoS） of edge computing services. However， there is less research on how to place edge computing nodes to reduce the cost of edge computing. In addition， there is no edge computing node deployment algorithm that can maximize the robustness of edge services while minimizing the deployment cost of edge computing nodes under the constraints of QoS factors such as the delay of edge services. To address the above issues， firstly， the edge computing node placement problem was transformed into a minimum dominating set problem with constraints by building a model about computing nodes， user transmission delay， and robustness. Then， the concept of overlapping domination was proposed， so that the network robustness was measured on the basis of overlapping domination， and an edge computing node placement algorithm based on overlapping domination was designed， namely CHAIN （edge server plaCement algoritHm based on overlAp domINation）. Simulation results show that CHAIN can reduce the system latency by 50.54% and 50.13% compared to the coverage oriented approximate algorithm and base station oriented random algorithm， respectively.

How to allocate spectra to users efficiently and improve the revenue of providers are popular research topics recently. To address the problem of low revenue of providers in spectrum combinatorial auctions， Random Walk for Spectrum Combinatorial Auctions （RWSCA） mechanism was designed to maximize the revenue of spectrum providers by combining the characteristics of asymmetric distribution of user valuations. First， the idea of virtual valuation was introduced， the random walk algorithm was used to search for a set of optimal parameters in the parameter space， and the valuations of buyers were linearly mapped according to the parameters. Then， VCG （Vickrey-Clarke-Groves） mechanism based on virtual valuation was run to determine the users who won the auction and calculate the corresponding payments. Theoretical analysis proves that the proposed mechanism is incentive compatible and individually rational. In spectrum combinatorial auction simulation experiments， the RWSCA mechanism increases the provider’s revenue by at least 16.84%.

There are many Dynamic Multiobjective Optimization Problems （DMOPs） in real life. For such problems， when the environment changes， Dynamic Multi-Objective Evolutionary Algorithm （DMOEA） is required to track the Pareto Front （PF） or Pareto Set （PS） quickly and accurately under the new environment. Aiming at the problem of poor performance of the existing algorithms on population prediction， a dynamic multi-objective optimization algorithm based on Weight Vector Clustering Prediction （WVCP） was proposed. Firstly， the uniform weight vectors were generated in the target space， and the individuals in the population were clustered. According to the clustering results， the distribution of the population was analyzed. Secondly， a time series was established for the center points of clustered individuals. For the same weight vector， the corresponding coping strategies were adopted to supplement individuals according to different clustering situations. If there were cluster centers at all adjacent moments， the difference model was used to predict individuals in the new environment. If there was no cluster center at a certain moment， the centroid of the cluster centers of adjacent weight vectors was used as the cluster center at that moment， and then the difference model was used to predict individuals. In this way， the problem of poor population distribution was solved effectively， and the accuracy of prediction was improved at the same time. Finally， the introduction of individual supplement strategy was beneficial to make full use of historical information. In order to verify the performance of the proposed algorithm， simulation comparison of this algorithm and four representative algorithms was carried out. Experimental results show that the proposed algorithm can solve DMOPs well.

Most of the Multi-objective Optimization Problems （MOP） in real life are Dynamic Multi-objective Optimization Problems （DMOP）， and the objective function， constraint conditions and decision variables of such problems may change with time， which requires the algorithm to quickly adapt to the new environment after the environment changes， and guarantee the diversity of Pareto solution sets while converging to the new Pareto frontier quickly. To solve the problem， an Adaptive Prediction Dynamic Multi-objective Optimization Algorithm based on New Evaluation Index （NEI-APDMOA） was proposed. Firstly， a new evaluation index better than crowding was proposed in the process of population non-dominated sorting， and the convergence speed and population diversity were balanced in different stages， so as to make the convergence process of population more reasonable. Secondly， a factor that can judge the strength of environmental changes was proposed， thereby providing valuable information for the prediction stage and guiding the population to better adapt to environmental changes. Finally， three more reasonable prediction strategies were matched according to environmental change factor， so that the population was able to respond to environmental changes quickly. NEI-APDMOA， DNSGA-Ⅱ-A （Dynamic Non-dominated Sorting Genetic Algorithm-Ⅱ-A）， DNSGA-Ⅱ-B （Dynamic Non-dominated Sorting Genetic Algorithm-Ⅱ-B） and PPS （Population Prediction Strategy） algorithms were compared on nine standard dynamic test functions. Experimental results show that NEI-APDMOA achieves the best average Inverted Generational Distance （IGD） value， average SPacing （SP） value and average Generational Distance （GD） value on nine， four and eight test functions respectively， and can respond to environmental changes faster.

The detection precision is low when the diatom training sample size is small， so a Multi-scale Multi-head Self-attention （MMS） and Online Hard Example Mining （OHEM） based few-shot diatom detection model， namely MMSOFDD was proposed based on the few-shot object detection model Two-stage Fine-tuning Approach （TFA）. Firstly， a Transformer-based feature extraction network Bottleneck Transformer Network-101 （BoTNet-101） was constructed by combining ResNet-101 with a multi-head self-attention mechanism to make full use of the local and global information of diatom images. Then， multi-head self-attention was improved to MMS， which eliminated the limitation of processing single object scale of the original multi-head self-attention. Finally， OHEM was introduced to the model predictor， and the diatoms were identified and localized. Ablation and comparison experiments between the proposed model and other few-shot object detection models were conducted on a self-constructed diatom dataset. Experiment results show that the mean Average Precision （mAP） of MMSOFDD is 69.60%， which is improved by 5.89 percentage points compared with 63.71% of TFA； and compared with 61.60% and 60.90% the few-shot object detection models Meta R-CNN and Few-Shot In Wild （FSIW）， the proposed model has the mAP improved by 8.00 percentage points and 8.70 percentage points respectively. Moreover， MMSOFDD can effectively improve the detection precision of the detection model for diatoms with small size of diatom training samples.

In order to solve the problem that static attribute reduction cannot update attribute reduction efficiently when the number of attributes in the set-valued decision information system changes continuously， an incremental attribute reduction method with knowledge granularity as heuristic information was proposed. Firstly， the related concepts of the set-valued decision information system were introduced， then the definition of knowledge granularity was introduced， and its matrix representation method was extended to this system. Secondly， the update mechanism of incremental reduction was analyzed， and an incremental attribute reduction method was designed on the basis of knowledge granularity. Finally， three different datasets were selected for the experiments. When the number of attributes of the three datasets increased from 20% to 100%， the reduction time of the traditional non-incremental method was 54.84 s， 108.01 s， and 565.93 s respectively， and the reduction time of the incremental method was 7.57 s， 4.85 s， and 50.39 s respectively. Experimental results demonstrate that the proposed incremental method is more faster than the non-incremental method under the condition that the accuracy of attribute reduction is not affected.

To solve various issues faced by Electronic Medical Record （EMR） sharing， such as centralized data provider， passive patient data management， low interoperability efficiency and malicious dissemination， a blockchain-based EMR secure sharing method was proposed. Firstly， a more secure and efficient Universal Designated Verifier Signature Proof （UDVSP） scheme based on the commercial cryptography SM2 digital signature algorithm was proposed. Then， a smart contract with functionalities of uploading， verification， retrieval and revocation was designed， and a blockchain-based EMR secure sharing system was constructed. Finally， the feasibilities of UDVSP scheme and sharing system were demonstrated through security analysis and performance analysis. The security analysis shows that the proposed UDVSP is probably secure. The performance analysis shows that compared with existing UDVSP/UDVS schemes， the proposed UDVSP scheme saves the computation cost at least 87.42% and communication overhead at least 93.75%. The prototype of blockchain smart contract further demonstrates the security and efficiency of the sharing system.

Federated Learning （FL） is a novel privacy?preserving learning paradigm that can keep users' data locally. With the progress of the research on FL， the shortcomings of FL， such as single point of failure and lack of credibility， are gradually gaining attention. In recent years， the blockchain technology originated from Bitcoin has achieved rapid development， which pioneers the construction of decentralized trust and provides a new possibility for the development of FL. The existing research works on blockchain?based FL were reviewed， the frameworks for blockchain?based FL were compared and analyzed. Then， key points of FL solved by the combination of blockchain and FL were discussed. Finally， the application prospects of blockchain?based FL were presented in various fields， such as Internet of Things （IoT）， Industrial Internet of Things （IIoT）， Internet of Vehicles （IoV） and medical services.

In the few-shot object detection task based on transfer learning， due to the lack of attention mechanism to focus on the object to be detected in the image， the ability of the existing models to suppress the surrounding background area of the object is not strong， and in the process of transfer learning， it is usually necessary to fine-tune the meta-features to achieve cross-domain sharing， which will cause meta-feature shift， and lead to the decline of the model’s ability to detect large-sample images. To solve the above problems， an improved meta-feature transfer model Up-YOLOv3 based on the attention mechanism and the meta-feature secondary reweighting mechanism was proposed. Firstly， the Convolution Block Attention Module （CBAM）-based attention mechanism was introduced in the original meta-feature transfer model Base-YOLOv2， so that the feature extraction network was able to focus on the object area in the image and pay attention to the detailed features of the image object class， thereby improving the model’s detection performance for few-shot image objects. Then， the Squeeze and Excitation-Secondary Meta-Feature Reweighting （SE-SMFR） module was introduced to reweight the meta-features of the large-sample image for the second time in order to obtain the secondary reweighted meta-features， so that the model was not only able to improve the performance of few-shot object detection， but also able to reduce the weight shift of the meta-feature information of the large-sample image. Experimental results on PASCAL VOC2007/2012 dataset show that， compared with Base-YOLOv2， Up-YOLOv3 has the detection mean Average Precision （mAP） for few-shot object images increased by 2.3 to 9.1 percentage points； compared with the original meta-feature transfer model based on YOLOv3 Base-YOLOv3， mAP for large-sample object images increased by 1.8 to 2.4 percentage points. It can be seen that the improved model has good generalization ability and robustness for both large-sample images and few-shot images of different classes.

In view of the problems of ant colony algorithm in global path planning under static environment， such as being unable to find the shortest path， slow convergence speed， great blindness of path search and many inflection points， an improved ant colony algorithm was proposed. Taking the grid map as the running environment of the robot， the initial pheromones were distributed unevenly， so that the path search tended to be near the line between the starting point and the target point； the information of the current node， the next node and the target point was added into the heuristic function， and the dynamic adjustment factor was introduced at the same time， so as to achieve the purpose of strong guidance of the heuristic function in the early stage and strengthening the guidance of pheromone in the later stage； the pseudo-random transfer strategy was introduced to reduce the blindness of path selection and speed up finding the shortest path； the volatilization coefficient was adjusted dynamically to make the volatilization coefficient larger in the early stage and smaller in the later stage， avoiding premature convergence of the algorithm； based on the optimal solution， B-spline curve smoothing strategy was introduced to further optimize the optimal solution， resulting in shorter and smoother path. The sensitivity analysis of the main parameters of the improved algorithm was conducted， the feasibility and effectiveness of each improved step of the algorithm were tested， the simulations compared with the traditional ant colony algorithm and other improved ant colony algorithms under 20×20 and 50×50 environments were given， and the experimental results verified the feasibility， effectiveness and superiority of the improved algorithm.

The reasonable exploration of the infeasible region in constrained multi-objective evolutionary algorithms for solving optimization problems with large infeasible domains not only helps the population to converge quickly to the optimal solution in the feasible region， but also reduces the impact of unpromising infeasible region on the performance of the algorithm. Based on this， a Constrained Multi-Objective Evolutionary Algorithm based on Space Shrinking Technique （CMOEA-SST） was proposed. Firstly， an adaptive elite retention strategy was proposed to improve the initial population in the Pull phase of Push and Pull Search for solving constrained multi-objective optimization problems （PPS）， so as to increase the diversity and feasibility of the initial population in the Pull phase. Then， the space shrinking technique was used to gradually reduce the search space during the evolution process， which reduced the impact of unpromising infeasible regions on the algorithm performance. Therefore， the algorithm was able to improve the convergence accuracy while taking account of both convergence and diversity. In order to verify the performance of the proposed algorithm， it was simulated and compared with four representative algorithms including C-MOEA/D （adaptive Constraint handling approach embedded MOEA/D）， ToP （handling constrained multi-objective optimization problems with constraints in both the decision and objective spaces）， C-TAEA （Two-Archive Evolutionary Algorithm for Constrained multi-objective optimization） and PPS on the test problems of LIRCMOP series. Experimental results show that CMOEA-SST has better convergence and diversity when dealing with constrained optimization problems with large infeasible regions.

Concerning the problem of inaccurate identification of taxi shift behaviors， an accurate identification method of taxi shift behaviors based on trajectory data mining was proposed. Firstly， after analyzing the characteristics of taxi parking state data， a method for detecting taxi parking points in non-operating state was proposed. Secondly， by clustering the parking points， the potential taxi shift locations were obtained. Finally， based on the judgment indices of taxi shift event and the kernel density estimation of the taxi shift time， the locations and times of the taxi shift were identified effectively. Taking the trajectory data of 4 416 taxis in Fuzhou as the experimental samples， a total of 5 639 taxi shift locations were identified. These taxi shift locations are in the main working areas of citizens， transportation hubs， business districts and scenic spots. And the identified taxi shift time is mainly from 4：00 to 6：00 in the morning and from 16：00 to 18：00 in the evening， which is consistent with the travel patterns of Fuzhou citizens. Experimental results show that， the proposed method can effectively detect the time-space distribution of taxi shift， and provide reasonable suggestions for the planning and management of urban traffic resources. The proposed method can also help the people to take a taxi more conveniently， improve the operating efficiency of taxis， and provide references for the site selection optimization of urban gas stations， charging stations and other car related facilities.

The current mainstream neural networks cannot satisfy the full expression of sentences and the full information interaction between sentences at the same time when processing answer selection tasks. In order to solve the problems， an answer selection model based on Dynamic Attention and Multi-Perspective Matching （DAMPM） was proposed. Firstly， the pre-trained Embeddings from Language Models （ELMo） was introduced to obtain the word vectors containing simple semantic information. Secondly， the filtering mechanism was used in the attention layer to remove the noise in the sentences effectively， so that the sentence representation of question and answer sentences was obtained in a better way. Thirdly， the multiple matching strategies were introduced in the matching layer at the same time to complete the information interaction between sentence vectors. Then， the sentence vectors output from the matching layer were spliced by the Bidirectional Long Short-Term Memory （BiLSTM） network. Finally， the similarity of splicing vectors was calculated by a classifier， and the semantic correlation between question and answer sentences was acquired. The experimental results on the Text REtrieval Conference Question Answering （TRECQA） dataset show that， compared with the Dynamic-Clip Attention Network （DCAN） method， which is one of the comparison aggregation framework based baseline models， the proposed DAMPM improves the Mean Average Precision （MAP） and Mean Reciprocal Rank （MRR） both by 1.6 percentage points. The experimental results on the Wiki Question Answering （WikiQA） dataset show that， the two performance indices of DAMPM is 0.7 percentage points and 0.8 percentage points higher than those of DCAN respectively. The proposed DAMPM has better performance than the methods in the baseline models in general.

When using Immersed Boundary-Lattice Boltzmann Method （IB-LBM） to solve the flow field， in order to obtain more accurate results， a larger and denser flow field grid is often required， which results in a long time of simulation process. In order to improve the efficiency of the simulation， according to the characteristics of IB-LBM local calculation， combined with three different task scheduling methods in OpenMP， a parallel optimization method of IB-LBM was proposed. In the parallel optimization， three task scheduling modes were mixed to solve the load imbalance problem caused by single task scheduling. The structural decomposition was performed on IB-LBM， and the optimal scheduling mode of each structure part was tested. Based on the experimental results， the optimal scheduling combination mode was selected. At the same time， it could be concluded that the optimal combination is different under different thread counts. The optimization results were verified by speedup， and it could be concluded that when the number of threads is small， the speedup approaches the ideal state； when the number of threads is large， although the additional time consumption of developing and destroying threads affects the optimization of performance， the parallel performance of the model is still greatly improved. The flow field simulation results show that the accuracy of IB-LBM simulation of fluid-solid coupling problems is not affected after parallel optimization.

According to the characteristics of traditional multivariate linear regression method for long processing time and limited memory, a parallel multivariate linear regression forecasting model was designed based on MapReduce for the time-series sample data. The model was composed of three MapReduce processes which were used to solve the eigenvector and standard orthogonal vector of cross product matrix composed by historical data, to forecast the future parameter of the eigenvalues and eigenvectors matrix, and to estimate the regression parameters in the next moment respectively. Experiments were designed and implemented to the validity effectiveness of the proposed parallel multivariate linear regression forecasting model. The experimental results show multivariate linear regression prediction model based on MapReduce has good speedup and scaleup, and suits for analysis and forecasting of large data.

To carry out lens distortion correction of unknown camera parameters, a method calibrating distortion center first and distortion coefficient second was proposed. First, with target imaged at two different focal lengths, distortion center could be positioned using relative position relation of the same target points in two pictures; then according to the invariance of line perspective projection, distortion coefficient could be searched by variable step optimization method. The simulation experiment showed that the average error of distortion center was (0.2243,0.1636) pixel, and the distortion coefficient error was 0.28% when target point number was 25, the noise level was 0.2 pixel. Real image experiment shows that distortion center and distortion coefficient obtained by the proposed method can correct the image well. The method is easy to implement, the calibration of camera's internal and external parameters and the knowledge of line grid's world coordinates can be neglected.

A new corner detection algorithm based on the autocorrelation matrix of Multi-channel Odd Gabor grAdient (MOGA) was proposed to suppress the decrease of corner positioning accuracy caused by the smoothed edge. The input image was transformed by 8-channel odd Gabor filter, and then autocorrelation matrices were constructed for each pixel by Gabor gradient correlation of the pixel and its surrounding pixels. If the sum of the normalized eigenvalues of the pixel was local maxima, the pixel was labeled as a corner. Compared with the classical algorithms, such as Harris and Curvature Scale Space (CSS), the proposed algorithm increased the average rate of correct detection by 17.74%, and decreased the average rate of positioning error by 18.15%. The experimental results show that the proposed algorithm has very good detection performance, and gets higher corner detection rate and better corner positioning accuracy.

In order to solve the problem of lack of co-rated users caused by data sparseness and similarity calculation method, the authors, by making use of the advantage of cloud model transformation between qualitative concept and quantitative numerical value, proposed an improved personal recommendation algorithm based on cloud model and users clustering. The users’ preference on the evaluation of item attribute was transformed to preference on digital characteristics represented by integrated cloud model. By using the improved clustering algorithm, the authors clustered the rating data and the standardized original user attribute information, and at the same time, by taking into account the changes of the users’ interests, recommended the neighbor users’ union generated by similarity based on integrated cloud model of items attributes evaluation between users, clustering of users for item rating, and clustering of user attributes these three methods. The theoretical analysis and experimental results show that the proposed improved algorithm can not only solve the problem of lack of co-rated users caused by data sparseness, but also obtain satisfactory mean absolute error and root-mean-square error even when the users are new. Theoretical analysis and experimental results show that the proposed improved algorithm can not only solve the problem of lack of co-rated users caused by sparseness data, but also obtain satisfactory mean absolute error and root-mean-square error even when the users are new.

Concerning the problem of modulation identification in the Rayleigh channel, a new algorithm based on cumulants was proposed. The method was efficient and could easily classify seven kinds of signals of BPSK (Binary Phase Shift Keying), QPSK (Quadrature Phase Shift Keying), 4ASK (4-ary Amplitude Shift Keying), 16QAM (16-ary Quadrature Amplitude Modulation), 32QAM (32-ary Quadrature Amplitude Modulation), 64QAM (64-ary Quadrature Amplitude Modulation) and OFDM (Orthogonal Frequency Division Multiplexing) by using the decision tree classifier and the feature parameters that were extracted from combination of four-order cumulant and six-order cumulant. Through theoretical derivation and analysis, the algorithm is insensitive to Rayleigh fading and AWGN (Additive White Gaussian Noise). The computer simulation results show that the successful rates are over 90% when SNR (Signal-to-Noise Ratio) is higher than 4dB in Rayleigh channel, which demonstrates the feasibility and effectiveness of the proposed algorithm.

The image quality assessment is to give a reasonable assessment for the quality of image processing algorithm, and No-Reference (NR) quality evaluation method is applied in a lot of situations of being unable to get the original reference image. The result of structure analysis of the infrared image shows that the uncertainty of the image is fuzzy, but not random. Therefore, the concept of fuzzy entropy was introduced into the quality assessment of infrared image. A method of no-reference quality assessment for blurred infrared image was proposed, comparisons and analysis on performance of the algorithm were given from the following aspects: efficiency, consistency and accuracy. The simulation results show that this method has the characteristics of low computation complexity, fast operation speed and consistence of subjective and objective evaluations. And the general performance is better than the assessment based on Mean Squared Error (MSE) and Peak Singal-to-Noise Ratio (PSNR).

With reference to the properties of multiscale and shift invariance of nonsubsampled Contourlet transform, and concerning the characteristics of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images, a medical images fusion method was proposed．The proposed method fused the low frequency subband and high frequency subband of these medical images separately by the regional feature strategy. The paper introduced the judgment criteria of images fusion and expatiated on the principle and implementation of Nonsubsampled Contourlet Transform (NSCT). And this gave the subjective judgment and numeric measurement of the fusion images based on visual effect and information indexes. To evaluate the performance of the proposed algorithm, the authors compared the results with those of the algorithms, such as wavelet transform and Contourlet transform. The CT and MRI images simulation results of mandibular system indicate that the proposed method outperforms the others in terms of both visual quality and objective evaluation criteria, while it can integrate and maintain much more effective and detailed information as well.

Concerning the faults of local extremum in image registration based on mutual information, a new medical image registration method based on Powell and improved genetic algorithm was proposed in this paper. It put forward an improved method regarding the shortcomings of the standard genetic algorithm, such as slow convergence and prematurity that will result in artifacts, and generated the iteration individual by Logistic chaos map. This method utilized the multi-resolution analysis strategy and searched for the optimal of the objective function by this hybrid optimized algorithm in the lowest resolution image level. Then it continued the optimization course and accomplished the image registration by this optimal data with the Powell algorithm. The experimental results indicate that this algorithm can effectively improve the image registration velocity and avoid local extremum of the operator while getting better performance of image precision in contrast to the Powell algorithm and unimproved genetic algorithm.

The evolutionary mode of Differential Evolution (DE) was analyzed, and modified differentiation operator and selection operator with self-accelerated characteristic were proposed. Then the Self-Accelerated and Variable Neighbourhood searching of Differential Evolution (SAVNDE) algorithm was advanced using these new operators and variable neighbourhood search which improved the local search ability of algorithm. On the basis of the three evolution models, the simulation results on five classical benchmark functions show that SAVNDE has the same convergence rate of DE, and can achieve more optimization results in shorter time.

In order to speed up forming Directed Diffusion (DD) gradient field and improve the connectivity of logic networks, a Directed Diffusion Gradient Field based on Double Gradient (DDGF-DG) for Vehicular Sensor Networks (VSN) was proposed. With the help of the estimated gradient value of roadside-node, the large scale vehicular sensor network was divided into a certain number of sub-reigns taking roadside-nodes as the local cores, the local directed diffusion gradient field around every roadside-node was set up in distributed way, and at last, the global directed diffusion gradient field composed of the local directed diffusion gradient fields was formed with the help of double gradient value, so the autonomous management in every sub-reign was realized. Theoretical analysis and simulation results show that DDGF-DG and its dynamic adjustment would reduce the time consumption and improve the real-time connectivity.

Some community structure identification algorithms in complex network research achieve high quality for identifying community correctly but low efficiency, but some algorithms were on the contrary. It is a challenge for community structure identifying algorithms to balance efficiency and correctness for category meanwhile. In this paper, a new algorithm named CoreScan was proposed. Different from existing algorithm, CoreScan found some special node named subcenter in network and using a preprocessing phase to divide nodes into several communities according to subcenter, then cluster nodes by evaluating the D modularity of community. The definition of subcenter was given and some certifications also were shown to guarantee the correction of CoreScan algorithm. At last, the algorithm was tested on two artificial networks and two real-world graphs. The experimental results show that the algorithm achieves the goal of linear efficiency, and the correction of identifying community is no less than existing algorithms. This algorithm is suitable to large-scale network structure investigation.

Concerning the fuzzy and unclear details after color image denoising, the correlation properties of the adjacent pixels as well as the correlation among the color channels were analyzed. First, one-step singular correlation detection algorithm was used to detect the noises of each layer of the pre-treatment color image, and then the most relevant vector median was used to fill value of the noises. Finally, the color image filtering processing was realized. The experimental results show that this method not only accurately detects the salt-pepper noises, but also well restores and protects the original information such as the edge details. The color image filtering accuracy and performance criterion such as Peak-Signal-to Noise Ratio (PSNR) are further improved.

In order to reduce the influence of positioning accuracy casued by the issue of localization algorithm itself and the Received Signal Strength Indicator (RSSI), through introducing the correction mechanism in the previous localization algorithm, such as Gauss-Markov mobility model, and combining the technology of mobile anchor with fixed anchor, a new method was proposed based on weighted correction model. The simulation results show that the precision of node localization is effectively improved, meanwhile, compared with the Gauss-Markov mobility model, the positioning accuracy of the proposed method increases by 36.2%.

The original inverse compositional Active Appearance Model (AAM) only does one fitting process. When the initial position is far away from the destination, the model often falls into local minimum and becomes hard to converge into the correct position. Against this problem, a dual fitting method using Multi-Resolution AAM (MR-AAM) was proposed. Firstly, the first time fitting was to locate the initial position of the face in the low-resolution AAM, and the second time fitting was to use inverse compositional algorithm in the high-resolution AAM. This method can find the exact initial position and achieve better result of facial feature point localization. The experimental results show that the proposed method performs better than traditional method in the fitting accuracy along with the real-time case.

Standard Particle Swarm Optimization (PSO) algorithm falls into local optima easily and has low convergence accuracy when it is used to address the problem of complex functions optimization. In order to overcome the shortcomings, an improved PSO algorithm was proposed. The proposed algorithm integrated the attractionrepulsion mechanism in the field of biology into PSO algorithm and took full advantage of the mutual influence between particles to modify velocity updating formula, and thus maintained population diversity and enhanced the ability of particle to escape from the local optima. The experimental results demonstrate that the proposed algorithm outperforms two existing variants of the PSO algorithm in terms of convergence accuracy while improving the velocity of convergence in the later evolution phase and avoiding premature convergence problem effectively.