There is a possibility of overlapping access data paths and shared computation among different queries in database systems， and batch processing of queries in workloads is called Multiple-Query-at-a-Time model. Several developed multi-query processing frameworks have been proven effective， but all of them lack a general framework for building complete query processing and optimization methods. On the basis of a query time operator merging optimization framework constructed based on equivalent transformation， a relational operator concurrent computing framework for heterogeneous architectures， called OmegaDB， was proposed. In this framework， by studying the GPU-oriented relational operator flow-batch computing model， and constructing the relational data query pipeline， a flow-batch computing method aggregating multiple-query was implemented on the CPU-GPU heterogeneous architecture. On experiments and prototype implementation， the advantages of OmegaDB were verified through theoretical analysis and experimental results by comparing with Relational Database Management System （RDBMS）， and the potential of OmegaDB in utilizing new hardware was shown. According to the theoretical study of query optimization frameworks of Multiple-Query-at-a-Time models based on the traditional relational algebraic rules， several optimization methods were proposed and future research directions were prospected. Using TPC-H business intelligence computing as a benchmarking program， the results show that OmegaDB achieves up to 24 times end-to-end speedup while consuming lower disk I/O and CPU time than the modern advanced commercial database system SQL SERVER.

As generalization ability of the out-dated traditional data processing technology is weak, and the technology did not take into account the multi-source data security issues, a blockchain oriented online federated incremental learning algorithm was proposed. Ensemble learning and incremental learning were applied to the framework of federated learning, and stacking ensemble algorithm was used to integrate the local models and the model parameters in model training phase were uploaded to the blockchain with fast synchronization. This made the accuracy of the constructed global model only fall by 1%, while the safety in the stage of training and the stage of storage was improved, so that the costs of the data storage and the transmission of model parameters were reduced, and at the same time, the risk of data leakage caused by model gradient updating was reduced. Experimental results show that the accuracy of the model is over 91.5% and the variance of the model is lower than 10 ^-5, and compared with the traditional integrated data training model, the model has the accuracy slightly reduced, but has the security of data and model improved with the accuracy of the model guaranteed.

To improve the performance of gait recognition based on smartphone accelerometer, a recognition method based on Multiple Classifier Fusion (MCF) was proposed. Firstly, as the gait features extracted from the existing methods were relatively simple, the speed variation of the relative gradual acceleration extracted from each single gait cycle and the acceleration variation per unit time were taken as two new types of features (16 in total). Secondly, combing the new features with the frequently-used time domain and frequency domain features to form a new feature set, which could be used to train multiple classifiers with excellent recognition effect and short training time. Finally, Multiple Scale Voting (MSV) was used to fuse the output of the multiple classifiers to obtain the final classification result. To test the performance of the proposed method, the gait data of 32 volunteers were collected. Experimental results show that the recognition rate of new features for a single classifier is increased by 5.95% on average, and the final recognition rate of MSV fusion algorithm is 97.78%.

Aiming at the issue of low efficiency existing in resource scheduling of cloud computing, a new resource scheduling algorithm of cloud computing based on Quality of Service (QoS) was proposed. Firstly, the quality function and convergence factor were used in Ant Colony Optimization (ACO) algorithm to ensure the efficiency of pheromone updating and the feedback factor was set to improve the selection of probability. Secondly, the local search efficiency of Shuffled Frog Leading Algorithm (SFLA) was improved by setting crossover factor and mutation factor in the SFLA. Finally, the local search and global search of the SFLA were introduced for updating in each iteration of ACO algorithm, which improved the efficiency of algorithm. The simulation experimental results of cloud computing show that, compared with the basic ACO algorithm, SFLA, Improved Particle Swarm Optimization (IPSO) algorithm and Improved Artificial Bee Colony algorithm (IABC), the proposed algorithm has advantages in four indexes of QoS:the least completion time, the lowest cost of consumption, the highest satisfaction and the lowest abnormal value. The proposed algorithm can be effectively used in resource scheduling of cloud computing.

Aiming at the problem that maps may be not consistent caused by accumulation of errors in visual Simultaneous Location and Mapping (SLAM), a Visual SLAM (V-SLAM) system based on improved closed-loop detection algorithm was proposed. To reduce the cumulative error caused by long operation of mobile robots, an improved closed-loop detection algorithm was introduced. By improving the similarity score function, the perceived ambiguity was reduced and finally the closed-loop recognition rate was improved. At the same time, to reduce the computational complexity, the environment image and depth information were directly obtained by Kinect, and feature extraction and matching was carried out by using small and robust ORB (Oriented FAST and Rotated BRIEF) features. RANdom SAmple Consensus (RANSAC) algorithm was used to delete mismatching pairs to obtain more accurate matching pairs, and then the camera poses were calculated by PnP. More stable and accurate initial estimation poses are critical to back-end processing, which were attained by g2o to carry on unstructured iterative optimization for camera poses. Finally, in the back-end Bundle Adjustment (BA) was used as the core of the map optimization method to optimize poses and road signs. The experimental results show that the system can meet the real-time requirements, and can obtain more accurate pose estimation.

Aiming at the poor positioning accuracy of traditional wireless indoor positioning technology, an indoor positioning system based on Ultra Wide Band (UWB) technology was designed and implemented. Firstly, to solve the problem of autonomous localization and navigation, the system architecture of real-time interaction between the location server and the mobile terminal APP was proposed. Secondly, a radio message was added to the Two-Way Ranging (TWR) algorithm to reduce the ranging error caused by clock drift, so as to improve the performance of the algorithm. Finally, the hyperboloid equation set achieved by Time Difference Of Arrival (TDOA) positioning algorithm was linearized and then solved by Jacobi iteration method, which avoids the cases that standard TDOA localization algorithm is difficult to directly solve. The test results show that the system based on this method can work stably and control the range of error within 30 cm in the corridor room scene. Compared with the positioning systems based on WiFi and Bluetooth, the positioning accuracy of this system is improved by about 10 times, which can meet the requirement of precise mobile positioning in complex indoor environment.

In order to realize the rapid analysis of massive real-time data, a Distributed Real-time Data Analysis System (DRDAS) was designed, which resolved the collection, storage and real-time analysis for mass concurrent data. And according to the operation principle of Spark Streaming, a dynamic sampling K-means parallel algorithm was proposed, which could quickly and efficiently detect all kinds of DDoS (Distributed Denial of Service) attacks. The experimental results show that the DRDAS has good scalability, fault tolerance and real-time processing ability, and along with new K-means parallel algorithm, the DRDAS can real-time detect various DDoS attacks, and shorten the detecting time of attacks.

A density clustering method based on eXtended Classifier Systems (XCS) was proposed, which could be used to cluster the two-dimensional data sets with arbitrary shapes and noises. The proposed method was called Density XCS Clustering (DXCSc), which mainly included the following three processes:1) Based on the learning classification system, regular population of input data was generated and compressed. 2) The generated rules were regarded as two-dimensional data points, and then the two-dimensional data points were clustered based on idea of density clustering. 3) The regular population after density clustering was properly aggregated to generate the final regular population. In the first process, the learning classifier system framework was used to generate and compact the regular population. In the second process, the rule cluster centers were characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities. In the third process, the relevant clusters were properly merged using the graph segmentation method. In the experiments, the proposed DXCSc was compared with K-means, Affinity Propagation (AP) and Voting-XCSc on a number of challenging data sets. The experimental results show that the proposed approach outperforms K-means and Voting-XCSc in precision.

Current analytical method based on short-text can not describe the evolution process of burst-event in a simple and accurate manner. In order to solve the problem, a new method was proposed to express the evolution process of burst-event based on short-text data sets. Firstly, a method of measuring event status was proposed to describe the state of event at each time for analyzing the development process of the event. Secondly, according to the structured information of short-text, the value of event status was set from two aspects: text information and user information. Thirdly, with the consideration of the impact factor of text information, the weight of text information was calculated by constructing related formulas. Fourthly, with the consideration of the impact factor of user information, a modified PageRank algorithm was proposed, and users were divided into different layers to calculate the weight of user information by constructing related formulas. Finally, the weight of text information and the weight of user information were combined to calculate the value of event status. The experimental results show that considering user information in turn, the modified PageRank algorithm, and the idea of dividing the users into different layers all can correct 1~2 points of description and improve the accuracy of expressing the evolution process of event.

Because of the literature search system failing to comprehend users' real-time demands, a method to find users' real-time demands for literature search systems was proposed. Firstly, this method analyzed the users' personalized search behaviors such as browsing and downloading. Secondly, it established users' real-time Requirement Documents (RD) based on the relations between users' search behaviors and users' requirements. And then it extracted keyword network from requirement documents. Finally, it gained users' demand graphs which were formed by core nodes extracted from keyword network by means of random walk. The experimental results show that the method by extracting demand graphs increases the F-measure by 2.5%, in the comparison of the K-medoids algorithm on average, under the condition that users' demands are emulated in the experiment. And it also increases the F-measure by 5.3%, in the comparison with the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm on average, under the condition that users really searches for papers. So, when the method is used in literature search systems where users' requirements are stable, it will be able to gain users' demands to enhance users' search experiences.

An improved Canny operator was proposed to ensure the accuracy of the Light Emitting Diode (LED) wafer edge detection applied in the vision system of automatic LED die bonder. In the traditional Non-Maxima Suppression (NMS) process, inaccuracy would be caused by selecting two pixels near gradient direction as contrastive points. To solve this problem, bilinear interpolation based on the center pixel and three pixels around was made along gradient direction to implement new NMS process. In addition, the gray histogram of LED wafer was characterized by typical three peaks, and then the high threshold and the low threshold could be got by Otsu dual threshold method with new evaluation function to replace traditional artificial adjustment. The experimental results indicate that greater wafer outline and poles edges can be got by the improved Canny algorithm which is appropriate for LED wafer edge detection.

In order to make up for the limitation of research scenario of call admission control in heterogeneous wireless networks and reduce blindness of access network selection, the scenario was extended from integrated system with two networks to integrated system with multiple networks, and a joint call admission control algorithm based on reputation model was proposed. The reputation model was applied in the network selection and feedback mechanism. On the user side, the terminals chose the access network according to the networks' reputation; on the network side, the networks made decisions by adaptive bandwidth management and buffer queuing policy to enhance the probability of successful acceptation. Simulation results show that by using the proposed algorithm, new call blocking probability and handoff call dropping probability can be reduced effectively.

In this paper, the main idea was to improve the foreground detection method based on statistical model. On one hand, historical maximum probability of which feature vector belongs to background was recorded in the background model, which could improve the matched vectors updating speed and make it blended into the background quickly. On the other hand, a method using spatial feature match was proposed to reduce the shadow effect in the foreground detection. The experimental results show that, compared with the MoG method and Lis statistical model method, the method proposed in this paper has obvious improvement in shadow remove and obscured background restoration of big target object.

The suitability analysis of matching area plays a significant role in the field of vision-based navigation. There are many feature indexes that can only unilaterally describe the suitability of matching area. An algorithm was proposed to integrate several feature indexes to solve conflicts among different feature indexes and provide a kind of method that can measure the suitable confidence and unsuitable confidence of a feature.And then the confidences were fused by using the Dempster-Shafer (DS) rules. At last the algorithm was verified by simulation experiment.

Due to the multi-hierarchy, multi-attribute process of product design, the key technology of knowledge reuse in product design based on axiomatic design was studied. With the axiomatic design framework, the design process was decomposed, and the Z map of the requirement domain, functional domain, structure domain, process domain of the product design was put forward. Then, a case-based knowledge reuse method with overall consideration of inter-domanin and intra-domain design information was presented, which adopted the similarity-based retrieving in knowledge reuse algorithm, and got the best reuse objects. Finally, a case was provided to validate and prove the efficiency and feasibility of the method.

In this paper, the authors studied the continuous berth allocation problem for the container terminals with assumption of quay crane being dynamically allocated and establish a dynamic berth allocation scheme for minimizing vessel's time in port.Based on brother-son method, an algorithm was developed to solve the berth allocation problem with presetting the position of the vessels and quay crane being dynamically allocated in the right position without crossing. In comparison to the existing method，the method can reduce the vessel's time with the same example, which show that the proposed model and algorithm are more efficient.

According to the characteristics of wireless sensor network link with video transmission instability and poor reconstruction quality, this paper proposed an reliable transmission of routing algorithm (EDLOR) which is adaptive to Multiple Description Coding (MDC). Firstly, it took fully consideration about the video coding rate, delay-constrained, network packet loss and other factors. Secondly, it aimed at how to optimize multiple description of the Peak Signal-to-Noise Ratio(PSNR). As a result, the overall video distortion minimization was gained. Thirdly, these MDCs would be assigned to designed path for transmission according the computed results. It is shown from experimental results that EDLOR can improve the overall video quality through promoting the average PSNR and lowering packet loss rate.

To reduce the influence of cumulative odometer localization error, an autonomous localization approach for mobile robot based on odometer and Pan-Tilt-Zoom (PTZ) vision was presented. Interrupt S-shaped searching strategy was put forward, as well as a vision localization approach based on finite automaton. The position credibility models of odometer and PTZ vision were established according to their positioning error sources. A localization framework combining odometer and PTZ vision based on real-time position credibility was designed. An application experiment of vision localization and localization using odometer and PTZ vision was carried out. The validity and practicability of the method are confirmed.

The hyperspectral remote sensing image is rich in spectrum information, so it has advantages in object recognition. One-Class Support Vector Machine (OCSVM) not only holds the advantages of support vector machines but also only needs the train samples of the recognized objects. The algorithm proposed in this paper selected mathematical model, designed kernel function, adjusted parameter adaptively, and added the theory of OCSVM into the object recognition algorithm for hyperspectral image which improved the precision of recognition and reduced the demand of train samples. Lastly, the experiments were conducted on two hyperspectral images, and the results prove the validity of the proposed method.

In the super-peer network, the super-peer topology structure and its mechanism of dynamic maintenance and search routing are important factors affecting network performance and search efficiency. In this paper, a new structure named k-Perfect Difference Graph (PDG) was proposed by analyzing the characteristics and the deficiencies of PDG, new Super-peer Network based on k-PDG (KPDGN) was constructed, and then the mechanism of dynamic maintenance and search routing was presented in KPDGN. The analysis and simulation results show that compared with current supper-peer topology, KPDGN has good performance with constant degree and fixed adjacent nodes, which reduces the bandwidth consumption during searching and the cost of topology construction and maintenance.

A kind of Boolean operational method based on a three-dimensional grid model was proposed. Firstly, through collision detection algorithm based on hierarchical bounding box tree of Oriented Bounding Box (ORB), the intersecting triangles could be got. Through the intersection test of the triangles, the intersecting lines could be obtained and the intersecting lines topology relations with the triangles could be established. Secondly, a regional division for the intersecting triangles was made through processing the three types of intersecting lines, so as to get a series of polygons, and carry out Delaunay triangulations for polygon to get the result area. Lastly, relation adjacency list was constructed based on solid containing relations, the polygons internal relation and external relation with other entities were judged, and the triangles were located according to the mesh model topology relations. Simultaneously, according to such Boolean operations as the intersection, union, and differences, according to the grid model topology relations were judged, the position of the triangles were judged and then the final results could be obtained. Experimental results show that this algorithm can achieve better results. Experimental results show that the lithology of intersecting parts is consistent with the entities and can verify the correctness and feasibility of the algorithm.

This paper proposed a stable and precise algorithm for 3D mesh model. Firstly, the algorithm, based on the original topology of mesh model, realized quick location of intersectional area of mesh curve by combining the intersection test of the layers of nodes bounding box. Then the algorithm utilized the improved triangular intersection algorithm to calculate the discrete intersectional segments and re-triangulates every intersectional triangle. Through building the topology of intersectional segments and triangles, the algorithm could quickly trace and pick up the discrete segments, and classify and combine the local area, and realize the precise Boolean operation. The algorithm could effectively deal with all kinds of instances, and could be implemented in programs easily. And the experimental results prove that the algorithm accords with requirements of the project.

This paper focused on the scheduling problem for Quasi-Bus Rapid Transit with multi-line based on the maximum of the social benefit and enterprise profit. The multi-object math-emtical model of scheduling was established, of which hybrid genetic tabu algorithm was designed for B-asing according to the characteristics of the problem. The model has been validated through surveyed data of the public bus station in Long Gang district of Shenzhen city. The result and analysis show that this algorithm is more efficient than genetic algorithm and tabu search on solving this problem.

 Automatic interpretation of synthetic aperture radar (SAR) images was one of the most important application fields in image processing. Focusing on the medium resolution SAR images and combining with the previous algorithms, a novel technique to detecting ship targets from coastal regions based on fuzzy theory was proposed. In this new method, the input image was first processed with improved fuzzy enhancement algorithm so as to alter the characteristics of the gray-level distribution; then, with the threshold method, the sea and land regions could be separated; following that, the maximum entropic algorithm was employed to further process the image and regions of interest which contained candidate ship targets could be extracted; Then ROIs were segmented and the features of ship targets were extracted. Finally the ship targets could be detected based on fuzzy reasoning technique.