Aiming at the problem of huge losses caused by untimely and inactive emergency rescue information sharing in emergencies， a three-party game model of emergency rescue information sharing involving high-risk enterprises， rescue teams and government regulatory departments was established. Firstly， the payoff matrix and replicated dynamic equations were constructed based on the revenue. Then， stability analysis was performed for four different scenarios respectively. Finally， the evolution processes and results of the system under different scenarios were simulated through computer to obtain the optimal strategies for information sharing. Experimental results show that under the low benefit scenario， if the extra rewards and punishments are high， the willingness of emergency rescue teams to actively share rises to 0.2 and then gradually decreases until they reject information sharing completely； if the extra cost is high， the willingness of high-risk enterprises to actively share rises to about 0.2 and then rapidly decreases to 0. Meanwhile， the behavioral strategies of participants are most sensitive to the changes of positive benefit， then to the changes in extra rewards and punishments and extra costs. The above results can provide guidance for the selection of information sharing strategies in emergency response.

Focusing on the problem of chess piece positioning error in the chess robot system under uneven illumination distribution, a general chess piece positioning method based on block convex hull detection and image mask was proposed. Firstly, the set of points on the outline of the chessboard were extracted, the coordinates of the four vertices of the chessboard were detected using the block convex hull method. Secondly, the coordinates of the four vertices of the chessboard in the standard chessboard image were defined, and the transformation matrix was calculated by the perspective transformation principle. Thirdly, the type of the chessboard was recognized based on the difference between the small square areas of different chessboards. Finally, the captured chessboard images were successively corrected to the standard chessboard images, and the difference images of two adjacent standard chessboard images were obtained, then the dilation, image mask multiplication and erosion operations were performed on the difference images in order to obtain the effective areas of chess pieces and calculate their center coordinates. Experimental results demonstrate that, the proposed method has the average positioning accuracy of Go and Chinese chess pieces arrived by 95.5% and 99.06% respectively under four kinds of uneven illumination conditions, which are significantly improved in comparison with other chess piece positioning algorithms. At the same time, the proposed method can solve the inaccurate local positioning problem of chess pieces caused by adhesion of chess pieces, chess piece projection and lens distortion.

The large number of duplicate images in the database not only affects the performance of the learner, but also consumes a lot of storage space. For massive image deduplication, a duplicate detection algorithm for massive images was proposed based on pHash (perception Hashing). Firstly, the pHash values of all images were generated. Secondly, the pHash values were divided into several parts with the same length. If the values of one of the pHash parts of the two images were equal to each other, the two images might be duplicate. Finally, the transitivity of image duplicate was discussed, and corresponding algorithms for transitivity case and non-transitivity case were proposed. Experimental results show that the proposed algorithms are effective in processing massive images. When the similarity threshold is 13, detecting the duplicate of nearly 300000 images by the proposed transitive algorithm only takes about two minutes with the accuracy around 53%.

Morphological reconstruction is a fundamental and critical operation in medical image processing, in which dilation operations are repeatedly carried out on the marker image based on the characteristics of mask image, until no change occurs on the pixels of the marker image. Concerning the problem that traditional CPU-based morphological reconstruction system has low computational efficiency, using Graphics Processing Unit (GPU) to quicken the morphological reconstruction was proposed. Firstly, a GPU-friendly data structure:parallel heap cluster was proposed. Then, based on the parallel heap cluster, a GPU-based morphological reconstruction system was designed and implemented. The experimental results show that compared with traditional CPU-based morphological reconstruction system, the proposed GPU-based morphological reconstruction system can achieve speedup ratio over 20 times. The proposed system demonstrates how to efficiently port complex data structure-based software system onto GPU.

To solve the problem of privacy leakage in crowdsourced location data collection, a locally differentially private location data collection method with crowdsourcing was proposed. Firstly, a Voronoi diagram constructed by point-by-point insertion method was used to partition the road network space. Secondly, a random disturbance satisfying local differential privacy was used to disturb the original location data in each Voronoi grid. Thirdly, a designed spatial range query method was applied to noisy datasets to get the unbiased estimation of the actual result. Finally, experiments were carried out on spatial range queries to compare the proposed algorithm with PTDC (Privacy-preserving Trajectory Data Collection) algorithm. The results show that the query error rate is no more than 40%, and less than 20%in the best situation, and the running time is less than 8 seconds, which are better than those of PTDC algorithm while the proposed method has a higher degree of privacy preserving.

Focusing on large-scale vehicle routing problem with heterogeneous fleet, a new neighborhood mapping method, namely density clustering based removal heuristic algorithm, was proposed under the Adaptive Large Neighborhood Search (ALNS) frame work. ALNS includes two phases:destruction and reconstruction, which provides optimized solution by destroying and reconstructing current solution. In the destruction phase, a routine was randomly selected to get clusters by density clustering, and then the stores were removed from the routine according to the clusters. In reconstruction, Greedy or Regret-2 insert algorithm was randomly chosen to place those removed stores into proper routine. Test results on benchmark instances validate the effectiveness of the proposed method. Compared with other existing algorithms, the ALNS density clustering based removal heuristic algorithm has lower rate of error and better quality of solutions; in real situations, the proposed algorithm can provide optimized solution in limited time.

Efficient feature extraction from RGB-D images and accurate 3D spatial structure learning are two key points for improving the performance of RGB-D scene parsing. Recently, Fully Convolutional Neural Network (FCNN) has powerful ability of feature extraction, however, FCNN can not learn 3D spatial structure information sufficiently. In order to solve the problem, a new neural network architecture called Three-dimensional Spatial Structured Encoding Deep Network (3D-SSEDN) was proposed. The graphical model network and spatial structured encoding algorithm were organically combined by the embedded structural learning layer, the 3D spatial distribution of objects could be precisely learned and described. Through the proposed 3D-SSEDN, not only the Hierarchical Visual Feature (HVF) and Hierarchical Depth Feature (HDF) containing hierarchical shape and depth information could be extracted, but also the spatial structure feature containing 3D structural information could be generated. Furthermore, the hybrid feature could be obtained by fusing the above three kinds of features, thus the semantic information of RGB-D images could be accurately expressed. The experimental results on the standard RGB-D datasets of NYUDv2 and SUNRGBD show that, compared with the most previous state-of-the-art scene parsing methods, the proposed 3D-SSEDN can significantly improve the performance of RGB-D scene parsing.

Nowadays Key-Value store system is widely used in various Internet services. However, the existing Key-Value store systems, mostly run in user-mode, can not meet the demands of high-concurrency and low-latency. It is mainly because user-mode usually provides inefficient access interfaces and transaction processing due to mode switch or context switch. To solve these problems, an in-kernel implementation of Key-Value store system, called KStore, was proposed in this paper. It had an in-kernel index and an in-kernel memory allocator, which were used to manage Key-Value data efficiently. To guarantee the low-latency response, KStore provided a remote interface based on in-kernel Socket, and a local interface based on file system. In addition, KStore processed concurrent requests with a novel mechanism based on in-kernel multi-thread. The experimental results show that KStore gains a remarkable advantage over Memcached in the characteristics of real-time and concurrency.

Concerning the deficiency in scalability of the traditional hierarchical clustering algorithm when dealing with large-scale text, a parallel hierarchical clustering algorithm based on the MapReduce programming model was proposed. The vertical data partitioning algorithm based on the statistical characteristic of the components group of text vector was developed for data partitioning in MapReduce. Additionally, the sorting characteristics of the MapReduce were applied to select the merge points, making the algorithm be more efficient and conducive to improve clustering accuracy. The experimental results show that the proposed algorithm is effective and has good scalability.

The current applications of network coding in single-hop wireless network retransmission are based Single Sender Multiple Receiver (SSMR) scenes. Therefore, this paper proposed a retransmission mechanism named NCWRM (Network Coding Wireless Retransmission Mechanism) which can be used in multiple sender multiple receiver networks. Each node in the network can be either a sender or a receiver. The node can broadcast a coded packet which is combined by multiple lost packets in the second retransmission after packet failed in transmission and the first retransmission. Multiple recipients can simultaneously get their lost packets by decoding the coded packet, which can effectively improve the efficiency of retransmission. Theoretical analysis and simulation results show that NCWRM algorithm can significantly improve system saturation throughput, while reducing overhead and packet loss rate.

As one of the most popular routing protocols proposed by Internet Engineering Task Force (IETF), Ad Hoc on Demand Distance Vector Routing (AODV) has been implanted for many applications in Mobile Ad Hoc Network (MANET). In AODV, enormous broadcasting messages are generated during route discovery procedure, which consumes lots of bandwidth and degrades significantly the Quality of Service (QoS) of networks. To solve this problem, a cross-layer mechanism with a routing protocol Enhanced AODV (E-AODV) was proposed. In E-AODV, the Signal to Noise Ratio (SNR) of received signals was considered as the key criterion to select the next hop. Furthermore, Wireless Transmission Control Protocol (WTCP) was implanted as one important way in E-AODV to obtain a better QoS. The simulation results show that the proposed mechanism can reduce the Data Delivery Latency (DDL) up to 56% and improve the Data Delivery Ratio (DDR) up to 24%.

The traditional three-decision-making model relies on the experience of experts to set the threshold, thus impeding the wide application of three-decision-making model in many fields. To minimize the decision-making risk, a computational model of the risk-loss was built, and a new classification algorithm which needs no priori knowledge was given. The algorithm used model conditions to determine the range of parameters value which minimized the risk-loss, then divided the range into several equal grids, got the smallest range of parameters through searching these grids, and the smallest range was the optimal threshold. At last, a three-decision-making classifier was built by using the threshold, and then this classifier and Bayesian classifier were applied to part of UCI data sets. The comparison shows that the performance of three-decision-making classifier is superior, which shows the effectiveness of the algorithm.

Group key agreement is one of the important stages to carry out secure multicast communication. A group controller node switch method was given pointing to the problem of satellite network topology changed dynamically. It could adjust controlling nodes in a dynamic way. Then, both authentication and integrality mechanism were used to attest agreement messages and group keys, a group key generation and renewing method was proposed, which could improve security of agreement messages. The results of simulation and analysis show that this group key agreement protocol leads to high efficiency and security.

In the current research and design of spectrogram based on Field Programmable Gate Array (FPGA), the direct indication of the spectrogram is not able to reflect the detail variation of spectrum. To solve this problem, a method for the display and enhancement of spectrogram based on FPGA was proposed in this paper. With nonlinear transformation, the high-resolution gray image was compressed to low gray-resolution image, so the detail variation of spectrum would be better reflected. Meanwhile，human vision is less sensitive to the difference between gray-scale pixels than that of colors, so with pseudo-color processing of the gray images, the results are displayed through Video Graphics Array (VGA). The experimental results show that more detail variation of spectrum can be obtained by the method.

Five scheduling mechanisms have been proposed to ensure Quality of Service (QoS) in WiMAX network. Three of them are designed for real-time applications. However, fairness has not been considered while assigning resources in these scheduling mechanisms. In order to guarantee the existing services, new service requests will be rejected when lacking network resources. For resolving this problem, a new scheduling mechanism was proposed for Voice over Internet Protocol (VoIP) service, in which a Priority Decision Maker (PDM) was implanted. When new services and the existing services requested resources simultaneously, higher priority would be assigned to new service requests; and resource allocation would be achieved based on different level priorities by a resource distribution center for guaranteeing fairness. Detailed performance analysis was performed. The simulation results show the proposed scheduling mechanism could significantly increase number of VoIP connections and total throughput, about 15% and 11% respectively.

Security routing protocol is a key element to guarantee satellite network security. To solve the problem that most of routing protocols lack security scheme, the Elliptic Curve Pintsov-Vanstone Signature Scheme (ECPVSS) was used to attain confidentiality and authentication of packets, and trust evaluation scheme could exclude internal malicious node from the route path. Then a security routing protocol oriented to High Altitude Platform (HAP)/Low Earth Orbit (LEO) architecture was formed. The analysis results show that the proposed protocol can prevent network from some common routing attacks.

Concerning Chan location algorithm's poor performance in Non-Line-of-Sight (NLOS) environment, a Chan location algorithm based on the Elman neural network was proposed. The Elman neural network was adopted to correct the NLOS errors with its dynamical characteristics and non-linear approach capacity and then the position was calculated by Chan's algorithm. The simulation results indicate that the location algorithm can improve the performance even in serious NLOS environment. The performance of the proposed algorithm outperforms that of Chan's algorithm and Taylor's algorithm.

Based on the analysis of the basic concepts of quantum evolution, a new Mixed Quantum-Inspired Evolutionary Algorithm for solving Traveling Salesman Problem（TSP） has been proposed, in which 3-Optimize local search heuristic was incorporated with quantum evolutionary mechanism, the nearest neighbor heuristic rule was used to initiate parameters, and the ordered crossover operator was introduced to extend the exploratory range of quantum population. Experiments were carried out on some cases from the well-known TSP library (TSPLIB). The results show that the new algorithm is effective and robust, which is able to find the satisfactory resolution with small size population and tiny relative error, even for medium or large scale problems (city number > 500). Key Words Quantum Computing; Evolutionary Algorithm; Traveling Salesman Problem.

The intelligent information service system architecture of learning enterprises was built based on SOA (Services Oriented Architecture). An individual intelligence information service algorithm (IIISA) was put up, and it was proved. The key parts of IIISA were introduced, including producing and dynamic optimizing main individual factors, constructing individual fuzzy neural network, matching the individual information by function.

To improve the effect of traditional eigenface method on face recognition under large illumination variation,  a new face recognition method was proposed. Unlike second -order PCA face recognition, it used independent component analysis on the PCA residual eigenfaces instead of principal component analysis to extract the independent component feature, and integrated the IC feature in PCA residual face space with the IC feature in original face space to be the ultimate feature for recognition. Experiments prove that it is more efficient than some conventional human face recognition methods, such as eigenface based method, ICA based method, and second-order PCA method, under large illumination and pose variations, and also has a good practicability.

A new search angine ranking algorithm named CategoryRank based on categorization was proposed, in order to yield more accurate search results. With this new algorithm, the notion of importance can be captured more accurately with respect to a particular category. For this, the link graph was first analyzed and computed based on the category difference between two web pages of this link to better reflect user behavior in surfing. Secondly, the category information was applied into each web page to distinguish the importance of this page for different kind of users. Finally, the offline model and online model of this algorithm were combined to detail the application in ranking of search engine.