To address the problems of low accuracy， difficult deployment and high calibration cost of visual manipulator in complex system environments， a robust joint modelling and optimization method for visual manipulators was proposed. Firstly， the subsystem models of the visual manipulator were integrated together， and the sample data such as servo motor rotation angles and manipulator end-effector coordinates were collected randomly in the workspace of the manipulator. Then， an Adaptive Multiple-Elites-guided Composite Differential Evolution algorithm with shift mechanism and Layered Optimization mechanism （AMECoDEs-LO） was proposed. Simultaneous optimization of the joint system parameters was completed by using the method of parameter identification. Principal Component Analysis （PCA） was performed by AMECoDEs-LO on stage data in the population， and with the idea of parameter dimensionality reduction， an implicit guidance for convergence accuracy and speed was realized. Experimental results show that under the cooperation of AMECoDEs-LO and the joint system model， the visual manipulator does not require additional instruments during calibration， achieving fast deployment and a 60% improvement in average accuracy compared to the conventional method. In the cases of broken manipulator linkages， reduced servo motor accuracy and increased camera positioning noise， the system still maintains high accuracy， which verifies the robustness of the proposed method.

Due to complex structure of Three-Dimensional Stacked Integrated Circuit （3D SIC）， it is more difficult to design an efficient test structure for it to reduce test cost than for Two-Dimensional Integrated Circuit （2D IC）. For decreasing cost of 3D SIC testing， a Three-Dimensional Linear Feedback Shift Register （3D-LFSR） test structure was proposed based on Linear Feedback Shift Register （LFSR）， which can effectively adapt to different test phases of 3D SIC. The structure was able to perform tests independently in the pre-stacking tests. After the stacking， the pre-stacking test structure was reused and reconfigured into a test structure suitable for the current circuit to be tested， and the reconfigured test structure was able to further reduce test cost. Based on this structure， the corresponding test data processing method and test flow were designed， and the mixed test mode was adopted to reduce the test time. Experimental results show that compared with the dual-LFSR structure， 3D-LFSR structure has the average power consumption reduced by 40.19%， the average area overhead decreased by 21.31%， and the test data compression rate increased by 5.22 percentage points. And， using the hybrid test mode reduces the average test time by 20.49% compared to using the serial test mode.

Focusing on the characteristics of small objects in images， such as less feature information， low percentage， and easy to be influenced by the environment， a small object detection algorithm based on split mixed attention was proposed， namely SMAM-YOLO. Firstly， by combining Channel Attention （CA） and Spatial Attention （SA）， as well as recombining the connection structures， a Mixed Attention Module （MAM） was proposed to enhance the model’s representation of small object features in spatial dimension. Secondly， according to the different influence of receptive fields with different sizes on the object， a Split Mixed Attention Module （SMAM） was proposed to adaptively adjust the size of the receptive field according to the scale of the input feature map， and the mixed attention was used to enhance the ability to capture small object feature information in different branches. Finally， the core residual module in YOLOv5 was improved by using SMAM， and a feature extraction module CSMAM was proposed on the basis of CSPNet （Cross Stage Partial Network） and SMAM， and the additional computational overhead of CSMAM can be ignored. Experimental results on TinyPerson dataset show that compared with the baseline algorithm YOLOv5s， when the Intersection over Union （IoU） threshold is 0.5， the mean Average Precision （mAP₅₀） of SMAM-YOLO algorithm is improved by 4.15 percentage points， and the detection speed reaches 74 frame/s. In addition， compared with some existing mainstream small object detection models， SMAM-YOLO algorithm improves the mAP₅₀ by 1.46 - 6.84 percentage points on average， and it can meet the requirements of real-time detection.

With regard to the Location-Routing Problem （LRP） of domestic waste transfer stations and incineration stations， by considering the economic objective and the obnoxious effect of waste facilities， a piecewise function of obnoxious effect related to wind direction and distance was designed， a Two-Echelon Multi-Objective LRP （2E-MOLRP） model was formulated， and a non-dominated algorithm combining Whale Optimization Algorithm （WOA） and Simulated Annealing （SA） algorithm was proposed， namely WOA-SA. Firstly， the random method and Clarke and Wright （CW） saving algorithm were used to optimize the initial population. Secondly， a nonlinear dynamic inertia weight coefficient was adopted to adjust the convergence speed of the WOA-SA. Thirdly， the global optimization ability was enhanced by designing the parallel structure of WOA-SA. Finally， the Pareto solution set was obtained by using the non-dominated sorting method. The analysis was carried out on 35 benchmark cases such as Prins and Barreto as well as a simulated case of Tianjin. The results show that the WOA-SA can find the Best Known Solution （BKS） of 20 benchmark cases， and has the mean values of the difference between the solution results and the BKSs of 0.37% and 0.08% on Prins and Barreto cases， which proves the good convergence and stability of the WOA-SA. The proposed model and algorithm were applied to the instance， and provided three schemes with different obnoxious effect values and economic costs for decision makers with different decision preferences. Therefore， the cost of waste recycling and the obnoxious effect of facilities on environment were reduced.

Short text classification is an important research problem of Natural Language Processing （NLP）， and is widely used in news classification， sentiment analysis， comment analysis and other fields. Aiming at the problem of data sparsity in short text classification， by introducing node and edge weight features of corpora， based on Graph ATtention network （GAT）， a new graph attention network named Node-Edge GAT （NE-GAT） by fusing node and edge weight features was proposed. Firstly， a heterogeneous graph was constructed for each corpus， Gravity Model （GM） was used to evaluate the importance of word nodes， and edge weights were obtained through Point Mutual Information （PMI） between nodes. Secondly， a text-level graph was constructed for each sentence， node importance and edge weights were integrated into the update process of nodes. Experimental results show that， the average accuracy of the proposed model on the test sets reaches 75.48%， which is better than those of the models such as Text Graph Convolution Network （Text-GCN）， Text-Level-Graph Neural Network （TL-GNN） and Text classification method for INductive word representations via Graph neural networks （Text-ING）. Compared with original GAT， the proposed model has the average accuracy improved by 2.32 percentage points， which verifies the effectiveness of the proposed model.

By removing irrelevant features from the original dataset and selecting good feature subsets， feature selection can avoid the curse of dimensionality and improve the performance of learning algorithm.In the process of feature selection， only the dynamically change information between the selected features and classes is considered， and interaction relevance between the candidate features and the selected features is ignored by Dynamic Change of Selected Feature with the class （DCSF） algorithm. To solve this problem， a Dynamic Relevance based Feature Selection （DRFS） algorithm was proposed. In the proposed algorithm， conditional mutual information was used to measure the conditional relevance between the selected features and classes， and interaction information was used to measure the synergy brought by the candidate features and the selected features， so as to select relevant features and remove redundant features then obtain good feature subsets. Simulation results show that， compared with existing algorithms， the proposed algorithm can effectively improve classification accuracy of feature selection.

For the consensus problem of time-varying multi-agent systems under time-varying topology connection, an event-triggered impulsive control protocol was proposed. In this protocol, for each agent, the controller would be updated only when the related state error exceeded a threshold, and the control inputs would be carried out only at the event triggering instants, and continuous communication between agents was avoided. This protocol would greatly reduce the cost of communication and control for network consensus. The sufficient conditions for the multi-agent systems with time-varying characteristics to achieve consensus under event-triggered impulsive control were analyzed based on the algebraic graph theory, Lyapunov stability and impulsive differential equation. At the same time, it was proved theoretically that there was no Zeno behavior in the event-triggered time sequences. Finally, the effectiveness of the obtained theoretical conclusion was verified through several numerical simulations.

Aiming at the problems of low accuracy of driving command prediction, bulky model structure and a large amount of information redundancy in existing end-to-end autonomous driving methods, a new end-to-end autonomous driving model based on deep visual attention neural network was proposed. In order to effectively extract features of autonomous driving scenes, a deep visual attention neural network, which is composed of the convolutional neural network, the visual attention layer and the long short-term memory network, was proposed by introducing a visual attention mechanism into the end-to-end autonomous driving model. The proposed model was able to effectively extract spatial and temporal features of driving scene images, focus on important information and reduce information redundancy for realizing the end-to-end autonomous driving that predicts driving commands from sequential images input by front-facing camera. The data from a simulated driving environment were used for training and testing. The root mean square errors of the proposed model for prediction of the steering angle in four scenes including country road, highway, tunnel and mountain road are 0.009 14, 0.009 48, 0.002 89 and 0.010 78 respectively, which are all lower than the results of the method proposed by NVIDIA and the method based on the deep cascaded neural network. Moreover, the proposed model has fewer network layers compared with the networks without the visual attention mechanism.

In order to solve the interference of similar background to object tracker in ground battlefield environment, an object tracking algorithm combining correlation filtering and improved color probability model was proposed. Firstly, based on the traditional color probability model, a color probability model emphasizing foreground was proposed by using the difference between foreground object histogram and background histogram. Then, a spatial penalty matrix was generated according to the correlation filter response confidence and maximum response position. This matrix was used to punish the likelihood probability of background pixel determined by the correlation filter, and the response map of the color probability model was obtained by using the method of integral image. Finally, the response maps obtained by the correlation filter and the color probability model were fused, and the maximum response position of the fusion response map was the central position of the object. The proposed algorithm was compared with 5 state-of-the-art algorithms such as Circulant Structure of tracking-by-detection filters with Kernels (CSK), Kernelized Correlation Filters (KCF), Discriminative Scale Space Tracking (DSST), Scale Adaptive Multiple Feature (SAMF) and Staple in tracking performance. The experimental results on OTB-100 standard dataset show that, the proposed algorithm has the overall accuracy improved by 3.06% to 55.98%, and the success rate improved by 2.24% to 54.97%； and under similar background interference, the proposed algorithm has the accuracy improved by 10.28% to 43.9%, and the success rate improved by 8.3% to 48.29%. The experimental results on 36 battlefield video sequences show that, the proposed algorithm has the overall accuracy improved by 2.2% to 45.98%, and the success rate improved by 3.01% to 58.27%. It can be seen that the proposed algorithm can better deal with the interference of similar background in the ground battlefield environment, and provide more accurate position information for the weapon platform.

Energy efficiency of Heterogeneous cellular Network(HetNet)has attracted wide attention in recent years. However,researches on the energy efficiency of macro base stations using non-Poisson process are not enough. To solve this problem,the energy efficiency of the two-tier HetNet was investigated,in which the deployment of macro base stations was modeled by β-Ginibre Point Process( β-GPP). Firstly,a simple approximation method was used to analyze the Signal to Interference Ratio(SIR)distribution in two-tier HetNet,then the coverage probability,the average achievable throughput and the energy efficiency of the system were derived. Finally,an energy efficiency optimization algorithm was proposed to find the optimal transmitting power of pico base station,maximizing the energy efficiency. The simulation results show that when β=1,the distribution density of macro base station is 2×10 ^-4 m ^-2,and the distribution density of pico base station is 2 times of that of macro base station,the proposed energy efficiency optimization scheme can improve the system energy efficiency by about 20%. The experimental results verify the accuracy of the theoretical analysis and the effectiveness of the proposed energy efficiency optimization algorithm.

To solve the problem that the antenna resources in heterogeneous network are limited which leads to the unrealizable Interference Alignment (IA), a partial IA scheme for maximizing the utilization of antenna resources was proposed based on the characteristics of heterogeneous network. Firstly, a system model based on partial connectivity in heterogeneous network was built and the feasibility conditions for entire system to achieve IA were analyzed. Then, based on the heterogeneity of network (the difference between transmitted power and user stability), the users were assigned to different priorities and were distributed with different antenna resources according to their different priorities. Finally, with the goal of maximizing total rate of system and the utilization of antenna resources, a partial IA scheme was proposed, in which the high-priority users had full alignment and low-priority users had the maximum interference removed. In the Matlab simulation experiment where antenna resources are limited, the proposed scheme can increase total system rate by 10% compared with traditional IA algorithm, and the received rate of the high-priority users is 40% higher than that of the low-priority users. The experimental results show that the proposed algorithm can make full use of the limited antenna resources and achieve the maximum total system rate while satisfying the different requirements of users.

To solve the problem of k nearest neighbor query in obstacle space, a k nearest neighbor Query method based on improved Parallel Ant colony algorithm (PAQ) was proposed. Firstly, ant colonies with different kinds of pheromones were utilized to search k nearest neighbors in parallel. Secondly, a time factor was added as a condition of judging path length to directly show the searching time of ants. Thirdly, the concentration of initial pheromone was redefined to avoid the blind searching of ants. Finally, visible points were introduced to divide the obstacle path into multiple Euclidean paths, meawhile the heuristic function was improved and the visible points were selected by ants to conduct probability transfer making ants search in more proper direction and prevent the algorithm from falling into local optimum early. Compared to WithGrids method, with number of data points less than 300, the running time for line segment obstacle is averagely reduced by about 91.5%, and the running time for polygonal obstacle is averagely reduced by about 78.5%. The experimental results show that the running time of the proposed method has obvious advantage on small-scale data, and the method can process polygonal obstacles.

Density peaks clustering is an efficient density-based clustering algorithm. However, it is sensitive to the global parameter d_c. Furthermore, artificial intervention is needed for decision graph to select clustering centers. To solve these problems, a new density peaks clustering algorithm based on shared near neighbors similarity was proposed. Firstly, the Euclidean distance and shared near neighbors similarity were combined to define the local density of a sample, which avoided the setting of parameter d_c of the original density peaks clustering algorithm. Secondly, the selection process of clustering centers was optimized to select initial clustering centers adaptively. Finally, each sample was assigned to the cluster as its nearest neighbor with higher density samples. The experimental results show that, compared with the original density peaks clustering algorithm on the UCI datasets and the artificial datasets, the average values of accuracy, Normalized Mutual Information (NMI) and F-Measure of the proposed algorithm are respectively increased by about 22.3%, 35.7% and 16.6%. The proposed algorithm can effectively improve the accuracy of clustering and the quality of clustering results.

For the current dehazing algorithm easily results in low brightness of near road area and distant sky area with strong dehazing, and high brightness of middle and distant area with weak dehazing, based on a depth learning dehazing algorithm, a dehazing algorithm combined with image scene depth and road image characteristics of fog and sky roads was proposed. Firstly, based on the principle of dehazing algorithm of deep learning, a convolution neural network was constructed to calculate the scene transmittance. And then the image depth map was estimated based on the transmittance and atmospheric scattering model. Two parameters were constructed, the upper threshold and the lower threshold, to divide the depth map into middle, far, and near areas. Based on the enhancement function constructed by the different parts of the depth map, the enhancement amplitude of image processing was determined. Finally, based on the traditional atmospheric scattering model, the intensified illumination intensity was used to adjust the recovery intensity of different areas to obtain the optimized image. The experimental results show that the proposed algorithm is as good as other representative dehazing algorithms and enhance the middle and distant areas of the road image better. It effectively solves the color distortion and low contrast ratio of the near road surface and distant sky in the foggy road image, improves the visual effect of the reconstructed image, and has better image sharpening effect than dark channel prior algorithm, vision enhancement algorithm for homogeneous and heterogeneous fog, and typical dehazing algorithm based on deep learning.

Aiming at the problem of tremendous escalation of energy consumption caused by the dense deployment of micro base stations in heterogeneous cellular networks, the energy efficiency of two-tier heterogeneous cellular networks was analyzed and a new method for maximizing network energy efficiency by adjusting the micro base station transmit power was proposed. Firstly, the heterogeneous cellular network was modeled by using homogeneous Poisson point process, and the coverage probability of base stations at each tier was derived. Secondly, according to the definition of energy efficiency, the total power consumption and total throughput of network were derived respectively, and the closed-form expression of energy efficiency was given. Finally, the impact of the micro base station transmission power on the energy efficiency of network was analyzed, and a micro base station power optimization algorithm was proposed to maximize energy efficiency. The simulation results show that, the transmission power of micro base station has a significant impact on the energy efficiency of heterogeneous cellular network. Furthermore, the energy efficiency of heterogeneous cellular network can be effectively improved by optimizing the transmission power of micro base stations.

Aiming at the problem of the poor real-time performance of Inertial Navigation System (INS)/Global Positioning System (GPS) integrated navigation system caused by GPS information delay, a processing method which takes advantage of dealing with various asynchronous measurements at an information fusion time in factor graph algorithm was proposed. Before the system received GPS information, the factor nodes of the INS information were added to the factor graph model, and the integrated navigation results were obtained by incremental inference to ensure the real-time performance of the system. After the system received the GPS information, the factor nodes about the GPS information were added to the factor graph model to correct the INS error, thereby ensuring high-precision operation of the system for a long time. The simulation results show that, the navigation state that has just been updated by GPS information can correct the INS error effectively, when the correction effect of real-time navigation state on INS error becomes worse, as the time of GPS information delay becomes longer. The factor graph algorithm avoids the adverse effects of GPS information delay on the real-time performance of INS/GPS integrated navigation system, and ensures the accuracy of the system.

Aiming at the vulnerability to continuous query attacks in the methods based on k-anonymity and difficultly in constructing anonymous region when the number of users is few, a method for trajectory privacy protection based on district partitioning was proposed. A user-group that has the history query points of a particular district was obtained by using a third-party auxiliary server, and the historical query points were downloaded from the users in the user-group by P2P protocol. Then the query result was searched in the historical query information to improve the query efficiency. In addition, a pseudo query point was sent to confuse attackers, and the multiple query points were hidden in the same sub-district by district partitioning to keep the attackers from reconstructing real trajectory of the user to ensure security. The experimental results show that the proposed method can improve the security of user trajectory privacy with the increases of distance and cache time. Compared to the Collaborative Trajectory Privacy Preserving (CTPP) method, when the number of users is 1500, the security is averagely increased about 50% and the query efficiency is averagely improved about 35% (the number of sub-districts is 400).

Due to random initialization of the weights, Deep Belief Network (DBN) easily falls into a local optimum, the Cuckoo Search (CS) algorithm was introduced into the traditional DBN model and a lung cancer image recognition algorithm based on CS-DBN was proposed. Firstly, the global optimization ability of CS was used to optimize initial weights of DBN, and on this basis, the layer-by-layer pre-training of DBN was performed. Secondly, the whole network was fine-tuned by using Back Propagation (BP) algorithm, so that the network weights were optimized. Finally, the CS-DBN was applied to the identification of lung tumor images, and CS-DBN was compared with traditional DBN from the four perspectives of Restricted Boltzmann Machine (RBM) training times, training batch sizes, DBN hidden layers numbers, and hidden layer nodes to verify the feasibility and effectiveness of the algorithm. The experimental results show that the recognition accuracy of CS-DBN is obviously higher than that of traditional DBN. Under the conditions of different RBM training times, training batch sizes, DBN hidden layer numbers, and hidden layer nodes, the increase range of CS-DBN identification accuracy over traditional DBN are 1.13 to 4.33, 2 to 3.34, 1.07 to 3.34 and 1.4 to 3.34 percentage points respectively. CS-DBN can improve the accuracy of lung tumor recognition to a certain extent, thereby improving the performance of computer-aided diagnosis of lung tumors.

To solve the problem of low Energy Efficiency (EE) of relay assisted cellular networks where the Macro Base Station (MBS) was equipped with a single-antenna, the downlink transmission of multi-antenna relay-assisted cellular networks were considered, meanwhile, a strategic sleep scheme was proposed. Firstly, according to whether the number of users serviced by the relay exceeds a given threshold, the relay's working mode was dynamically adjusted. And then the coverage probabilities and mean achievable rates of MBS to user (UE), MBS to Relay Station (RS), RS to UE links were deduced. Finally, the energy efficiency of the system was derived based on the power consumption per unit area and the reachable rate per unit area. The simulation results show that when the density of MBS is 2×10 ^-5m ^-2, the energy efficiency of the multi-antenna network with strategic sleep scheme is about 5.6% higher than that of the cellular network with non-sleeping strategy; the system energy efficiency of MBS equipped with multiple antennas is 30% high than without sleep strategy scheme with single antenna. Experimental results indicate that the multi-antenna relay-assisted cellular network with sleep strategy scheme has higher energy efficiency than the single-antenna relay-assisted cellular network.

Aiming at the problems of over-smoothing, singular structure residual noise, contrast loss and stair effect of common denoising methods, an image denoising model with adaptive non-local data fidelity and bilateral total variation regularization was proposed, which provides an adaptive non-local regularization energy function and the corresponding variation framework. Firstly, the data fidelity term was obtained by non-local means filter with adaptive weighting method. Secondly, the bilateral total variation regularization was introduced in this framework, and a regularization factor was used to balance the data fidelity term and the regularization term. At last, the optimal solutions for different noise statistics were obtained by minimizing the energy function, so as to achieve the purpose of reducing residual noise and correcting excessive smoothing. The theoretical analysis and simulation results on simulated noise images and real noise images show that the proposed image denoising model can deal with different statistical noise in image, and the Peak-Signal-to-Noise Ratio (PSNR) of it can be increased by up to 0.6 dB when compared with the adaptive non-local means filter; when compared with the total variation regularization algorithm, the subjective visual effect of the proposed model was improved obviously and the details of image texture and edges was protected very well when denoising, and the PSNR was increased by up to 10 dB, the Multi-Scale Structural Similarity index (MS-SSIM) was increased by 0.3. Therefore, the proposed denoising model can theoretically better deal with the noise and the high frequency detail information of the image, and has good practical application value in the fields of video and image resolution enhancement.

Concerning low accuracy of age classification of face images under unrestricted conditions, a new method of face age classification under unconstrained conditions based on deep Residual Networks (ResNets) and large dataset pre-training was proposed. Firstly, the deep residual networks were used as the basis convolutional neural network model to deal with the problem of face age classification. Secondly, the deep residual networks were trained on the ImageNet dataset to learn the expression of basic image features. Thirdly, the large-scale face age images IMDB-WIKI was cleaned, and the IMDB-WIKI-8 dataset was established for fine-tuning the deep residual networks, and migration learning from the general object image to face age image was achieved to make the model adapt to the distribution of the age group and improve the network learning capability. Finally, the fine-tuned network model was trained and tested on the unconstrained Adience dataset, and the age classification accuracy was obtained by the cross-validation method. Through the comparison of 34/50/101/152-layer residual networks, it could be seen that the more layers of the network have the higher accuracy of age classification. And the best state-of-the-art age classification result on Adience dataset with the accuracy of 65.01% was achieved by using the 152-layer residual network. The experimental results show that the combination of deeper residual network and large dataset pretraining can effectively improve the accuracy of face age classification.

The high similarity between trajectories in anonymity set may lead to the trajectory privacy leak. In order to solve the problem, a trajectory privacy preserving algorithm based on trajectory shape diversity was proposed. The exiting pre-processing method was improved to reduce the loss of information through trajectory synchronization processing. And by l-diversity, the trajectories with shape diversity were chosen as the members of the anonymity set when greedy clustering. Too high shape similarity between member trajectories of the set was prevented to avoid the attack of trajectory shape similarity. The theoretical analysis and experimental results show that, the proposed algorithm can realize k-anonymity of trajectory and l-diversity concurrently, reduce the running time and trajectory information loss, increase the trajectory data availability and realize better privacy protection. The proposed algorithm can be effectively applied to the privacy-preserving trajectory data publishing.

The coordinate system for smartphones' built-in acceleration sensor is fixed on the equipment itself, the data collected by the smartphone is constantly drifting due to the change of smartphone's posture. Affected by this, even the same movement process, the acceleration is difficult to keep consistent with the previous one. To solve this problem, the acceleration was mapped from smartphone to inertial coordinate system by using space coordinate transformation algorithm, to ensure that the sensor data can accurately reflect actual motion state no matter in what gesture the smartphone is. To verify the effectiveness of this method, a new method for online acquiring and real-time processing smartphone's sensor data was designed. With this method, the feasibilities of direction cosine algorithm and quaternion algorithm were tested in rotation experiments. Then, the performance of quaternion algorithm was further tested in pedometer experiments. The experimental results show that the direction cosine algorithm fails to achieve comprehensive coordinate transformation due to the measurement range limit; while the quaternion algorithm based on rotation vector sensor data can achieve full conversion, and the recognition rate of gait using transformed acceleration is over 95%, which can accurately reflect the actual state of motion.

BWDSP100 is a 32-bit static scalar Digital Signal Processor (DSP) with Very Long Instruction Word (VLIW) and Single Instruction Multiple Data (SIMD) features, which is designed for high-performance computing. Its Instruction Level Parallelism (ILP) is acquired though clustering and special SIMD instructions. However, the existing compiler framework can not provide support for these SIMD instructions. Since BWDSP100 has much SIMD vectorization resources and there are very high requirements in radar digital signal processing for the program performance, an SIMD optimization which surpported the selection of single or double word mode was put forward based on the traditional Open64 compiler according to the characteristics of BWDSP100 structure, and it can significantly improve the performance of some compute-intensive programs which are widely used in DSP field. The experimental results show that this algorithm can achieve speedup of 5.66 on average compared with before optimization.

Conventional approaches for Common Weights (CW) generation in Data Envelopment Analysis (DEA) are either non-linear or scale-relevant. To solve this problem, according to the demand of military training performance evaluation, a new method was proposed to generate CW in DEA. The new method took DEA efficient units as the basis of calculation. Firstly, training data were normalized, and then multi-objective programing was employed for CW generation, which can lead to a fairer and more reasonable ranking of performances. The proposed method is not only linear, but also scale-irrelevant. Lastly, a military application illustrates that the proposed method is scientific and effective.

As the instantaneous Channel State Information (CSI) of source to relay and relay to destination affects the overall Bit Error Rate (BER) of the cooperative communication system, a relay selection scheme which evaluated the two-stage channel coefficients was proposed. Firstly, the channel coefficients of source-relay channel and the channel coefficients of relay-destination channel were compared according to the CSI of each candidate relay, and the worse one was found out. Moreover, a node set containing the approximate optimal relays was obtained by sorting the candidate relays based on their worse channel coefficients. Finally, the relay with the highest summation of the two-stage channel coefficients in the set was selected as the one which participated in the cooperative transmission. The simulation results reveal that the Signal-to-Noise Ratio (SNR) of the proposed relay selection scheme respectively decreases by 0.4 dB and 0.2 dB compared with the best worse channel selection scheme and the delay selection scheme based on the nearest neighbor relation, when the number of candidate relay nodes is 100 and 5, and the BER decreases to 10^-4 and 10^-5. In general, the proposed scheme can expend the information transmission range and improve the reliability in the wireless relay network.

In order to improve the transformation efficiency of tile-pyramid image, a 15-parameter projection transformation method was established by quartic polynomial based on the view model of digital earth. The influencing factors for selecting the size of tile image were discussed theoretically, and an optimization method to determine the size and depth of tile-pyramid was given. To test this algorithm, a basic digital earth environment BDE2 was constructed by adopting JOGL. The analysis and experimental results show that tile-pyramid in 10m pixel accuracy constructed by this algorithm only has 10 layers and less than 5×10-5 average error; meanwhile, the proposed algrithm has low complexity, close stitching, high definition and low distortion, and can effectively avoid stitch cracks and characteristics distortion after the image is transformed.

In the non-overlapping filed of multi-camera system, the single-shot person identification methods cannot well deal with appearance and viewpoint changes. Based on the multiple frames acquired from surveillance cameras, a new technique which combined Hidden Markov Model (HMM) with appearance-based feature was proposed. First, considering the structural constraint of human body, the whole-body appearance of each individual was equally vertically divided into sub-images. Then multi-level threshold method was used to extract Segment Representative Color (SRC) and Segment Standard Variation (SSV) feature. The feature dataset acquired from multiple frames was applied to train continuous density HMM,and the final recognition was realized by these well-trained model. Extensive experiments on two public datasets show that the proposed method achieves high recognition rate, improves robustness against viewpoint changes and low resolution, and it is simple and easy to realize.

In order to enrich and improve the ability of the existing models for reasoning and predicting with 3D cardinal direction relations and enhance the usability of the existing models, and then better meet the demands of real applications for complex 3D spatial data, the inverse reasoning of 3D cardinal direction relations was studied. After deeply studying the theory of n-dimensional block algebra, an algorithm for computing the inverse of the basic 3D cardinal direction relations on the basis of 3D block algebra was devised. Theoretical analysis and the results of the example show that the proposed algorithm is correct and complete. This work can better enhance the power of intelligent analysis and processing for the complex 3D direction relations of the spatial database.

Concerning the problem that it is difficult for the users in cloud computing to obtain the high-quality and personalized cloud services provided by a large number of cloud providers, a trust model based on user types and privacy protection for the personalized cloud services was proposed. Firstly, the users were divided into familiar users, strange users and normal users according to the transaction history. Secondly, a fair and reasonable trust evaluation Agent was introduced to protect users' privacy, which could evaluate the trust relationship between requesters and providers based on the user types. Lastly, in view of the dynamics of trust, a new updating mechanism combined with the transaction time and transaction amount was provided based on Quality of Service (QoS). The simulation results show that the proposed model has higher transaction success rate than AARep and PeerTrust. The transaction success rate can be increased by 10% and 16% in the harsh environment where the malicious user ratio reaches 70%. This method can improve transaction success rate, and has a strong ability to withstand harsh environments.