In order to solve the problems of Discriminative Correlation Filter(DCF) tracking algorithm such as model drift, rough scale and tracking failure when the tracking object suffers from rotation or non-rigid deformation, an accurate object tracking algorithm based on Distance Weighting Overlap Prediction and Ellipse Fitting Optimization(DWOP-EFO) was proposed. Firstly, the overlap and center-distance between bounding-boxes were both used as the basis for the evaluation of dynamic anchor boxes, which can narrow the spatial distance between the prediction result and the object region,easing the model drift problem. Secondly,in order to further improve the tracking accuracy,a lightweight object segmentation network was applied to segment the object from background, and the ellipse fitting algorithm was applied to optimize the segmentation contour result and output stable rotated bounding box, achieving accurate estimation of the object scale. Finally, a scale-confidence optimization strategy was used to realize gating output of the scale result with high confidence. The proposed algorithm can alleviate the problem of model drift, enhance the robustness of the tracker, and improve the accuracy of the tracker. Experiments were conducted on two widely used evaluation datasets Visual Object Tracking challenge(VOT2018) and Object Tracking Benchmark(OTB100). Experimental results demonstrate that the proposed algorithm improves Expected-Average-Overlap(EAO) index by 2.2 percentage points compared with Accurate Tracking by Overlap Maximization(ATOM) and by 1.9 percentage points compared with Learning Discriminative Model Prediction for tracking(DiMP). Meanwhile, on evaluation dataset OTB100, the proposed algorithm outperforms ATOM by 1.3 percentage on success rate index and shows significant performance especially on attribute of non-rigid deformation. the proposed algorithm runs over 25 frame/s averagely on evaluation datasets which realizes real-time tracking.

In the existing shipping monitoring image recognition model named Convolutional 3D (C3D), the intermediate representation learning ability is limited, the extraction of effective features is easily disturbed by noise, and the relationship between global features and local features is ignored in feature extraction. In order to solve these problems, a new shipping monitoring image recognition model based on attention mechanism network was proposed. The model was based on the Convolutional Neural Network (CNN) framework. Firstly, the shallow features of the image were extracted by the feature extractor. Then, the attention information was generated and the local discriminant features were extracted based on the different response strengths of the CNN to the active features of different regions. Finally, the multi-branch CNN structure was used to fuse the local discriminant features and the global texture features of the image, thus the interaction between the local discriminant features and the global texture features of the image was utilized to improve the learning ability of CNN to the intermediate representations. Experimental results show that, the recognition accuracy of the proposed model is 91.8% on the shipping image dataset, which is improved by 7.2 percentage points and 0.6 percentage points compared with the current C3D model and Discriminant Filter within a Convolutional Neural Network (DFL-CNN) model respectively. It can be seen that the proposed model can accurately judge the state of the ship, and can be effectively applied to the shipping monitoring project.

Focused on the problems of Single Image Super-Resolution (SISR) reconstruction methods, such as the loss of high frequency information during the process of image reconstruction, the introduction of noise during the process of upsampling and the difficulty of determining the interdependence relationships between the channels of the feature map, a deep progressive back-projection attention network was proposed. Firstly, a progressive upsampling method was used to gradually scale the Low Resolution (LR) image to a given magnification in order to alleviate problems such as high-frequency information loss caused by upsampling. Then, at each stage of progressive upsampling, iterative back-projection idea was merged to learn mapping relationship between High Resolution (HR) and LR feature maps and reduce the introduced noise in the upsampling process. Finally, the attention mechanism was used to dynamically allocate attention resources to the feature maps generated at different stages of the progressive back-projection network, so that the interdependence relationships between the feature maps were learned by the network model. Experimental results show that the proposed method can increase the Peak Signal-to-Noise Ratio (PSNR) by up to 3.16 dB and the structural similarity by up to 0.218 4.

Concerning the difficult acquisition of training data in HyperSpectral Image(HSI),a new semi-supervised classification framework for HSI was adopted,in which both limited labeled data and abundant unlabeled data were used to train deep neural networks. At the same time,the unbalanced distribution of hyperspectral samples leads to huge differences in the classification difficulty of different samples,and the original cross-entropy loss function is unable to describe this distribution feature,so the classification effect is not ideal. To address this problem,a multi-classification objective function based on focal loss was proposed in the semi-supervised classification framework. Finally,considering the influence of spatial information of HSI on classification,combined with Markov Random Field(MRF),the sample space features were used to further improve the classification effect. The proposed method was compared with various classical methods on two commonly used HSI datasets. Experimental results show that the proposed method can obtain classification results superior to other comparison methods.

The traditional point-to-point saturation attack is not ideal choice facing high-density and multi-azimuth swarming intelligence targets. The maximum killing effect with weapon number less than target number can be achieved by selecting the appropriate types of weapons and the location of aiming points to realize the fire coverage. Considering the operational requirements of security targets, damage threshold and preference assignment, the Constrained Multi-objective Weapon-Target Assignment (CMWTA) mathematical model was established at first. Then, the calculation method of the constraint violation value was designed, and the individual coding, detection and repair as well as constraint domination were fused to deal with multiple constraints. Finally, the convergence metric for multi-objective weapon-target assignment model was designed, and the approaches were verified by the frameworks of Multi-Objective Evolutionary Algorithm (MOEA). In the comparison of three MOEA frameworks, the capacity of the Pareto sets of SPEA2 (Strength Pareto Evolutionary Algorithm Ⅱ) is mainly distributed in [21,25], that of NSGA-Ⅱ (Non-dominated Sorting Genetic Algorithm Ⅱ) is mainly distributed in [16,20], and that of MOEA/D (Multi-Objective Evolutionary Algorithm based on Decomposition) is less than 16. In the verification of the repair algorithm, the algorithm makes the convergence metrics of three MOEA frameworks increased by 20 %, and the proportion of infeasible non-dominated solutions in Pareto solution set of 0%. The experimental results show that SPEA2 outperforms NSGA-Ⅱ and MOEA/D on distribution and convergence metric in solving CMWTA model, and the proposed repair algorithm improves the efficiency of solving feasible non-dominated solutions.

In order to solve the problem of Fully-Convolutional Siamese visual tracking network (SiamFC) that the tracking target drifts when the similar semantic information interferers occur, resulting in tracking failure, a Multi-level Feature Enhanced Siamese network (MFESiam) was designed to improve the robustness of the tracker by enhancing the representation capabilities of the high-level and shallow-level features respectively. Firstly, a lightweight and effective feature fusion strategy was adopted for shallow-level features. A data enhancement technology was utilized to simulate some changes in complex scenes, such as occlusion, similarity interference and fast motion, to enhance the texture characteristics of shallow features. Secondly, for high-level features, a Pixel-aware global Contextual Attention Module (PCAM) was proposed to improve the localization ability to capture long-range dependence. Finally, many experiments were conducted on three challenging tracking benchmarks:OTB2015, GOT-10K and 2018 Visual-Object-Tracking (VOT2018). Experimental results show that the proposed algorithm has the success rate index on OTB2015 and GOT-10K better than the benchmark SiamFC by 6.3 percentage points and 4.1 percentage points respectively and runs at 45 frames per second to achieve the real-time tracking. The expected average overlap index of the proposed algorithm surpasses the champion in the VOT2018 real-time challenge, that is the high-performance Siamese with Region Proposal Network (SiamRPN), which verifies the effectiveness of the proposed algorithm.

Concerning the problem that microservice composition platform is short of analysis and verification of Quality of Service (QoS) indicators, a formal verification method based on model checking was proposed to analyze and evaluate the factors that affect the microservice composite platform performance. First, the service resource configuration process of microservice composition was divided into three phases:service request, configuration and service execution. These three phases were implemented by three modules:service request queue, resource configurator of service requests and virtual machine for providing service resources. After that, the implementation processes of the three modules were modeled as Labelled Markov Reward Models (LMRM), and the global model of microservice composition process was obtained by using a synchronization concept similar to the process algebra. Then, the logic formula of continuous random reward logic was used to describe the expected QoS indicators. Last, the formal model and logic formula were regarded as the input of model detection tool PRISM to obtain the verification results. Experimental results prove that LMRM can realize the QoS verification and analysis as well as the construction of microservice composite platform.

For the current channel attention mechanism used for super-resolution reconstruction, there are problems that the attention prediction destroys the direct corresponding relationship between each channel and its weight and the mechanism only considers the first-order or second-order channel attention without comprehensive consideration of the advantage complementation. Therefore, a mixed-order channel attention network for image super-resolution reconstruction was proposed. First of all, by using the local cross-channel interaction strategy, increase and reduction in channel dimension used by the first-order and second-order channel attention models were changed into a fast one-dimensional convolution with kernel k, which not only makes the channel attention prediction more direct and accurate but makes the resulting model simpler than before. Besides, the improved first and second-order channel attention models above were adopted to comprehensively take the advantages of channel attentions of different orders, thus improving network discrimination. Experimental results on the benchmark datasets show that compared with the existing super-resolution algorithms, the proposed method has the best recovered texture details and high frequency information of the reconstructed images and the Perceptual Indictor (PI) on Set5 and BSD100 datasets are increased by 0.3 and 0.1 on average respectively. It shows that this network is more accurate in predicting channel attention and comprehensively uses channel attentions of different orders, so as to improve the performance.

Focusing on the issue that it is difficult to segment a specific object in a complex video scene, a video object segmentation method based on Dual Pyramid Network (DPN) was proposed. Firstly, the one-way transmission of modulating network was used to make the segmentation model adapt to the appearance of a specific object, which means, a modulator was learned based on visual and spatial information of target object to modulate the intermediate layers of segmentation network to make the network adapt to the appearance changes of specific object. Secondly, global context information was aggregated in the last layer of segmentation network by different-region-based context aggregation method. Finally, a left-to-right architecture with lateral connections was developed for building high-level semantic feature maps at all scales. The proposed video object segmentation method is a network which is able to be trained end-to-end. Extensive experimental results show that the proposed method achieves results which can be competitive to the results of the state-of-the-art methods using online fine-tuning on DAVIS2016 dataset, and outperforms other methods on DAVIS2017 dataset.

In order to solve the problem that Fully-Convolutional Siamese network (SiamFC) tracking algorithm is prone to model drift and results in tracking failure when the tracking target suffers from dramatic appearance changes, a new Dual Attention Siamese network (DASiam) was proposed to adapt the network model without online updating. Firstly, a modified Visual Geometry Group (VGG) network which was more expressive and suitable for the target tracking task was used as the backbone network. Then, a novel dual attention mechanism was added to the middle layer of the network to dynamically extract features. This mechanism was consisted of a channel attention mechanism and a spatial attention mechanism. The channel dimension and the spatial dimension of the feature maps were transformed to obtain the double attention feature maps. Finally, the feature representation of the model was further improved by fusing the feature maps of the two attention mechanisms. The experiments were conducted on three challenging tracking benchmarks:OTB2013, OTB100 and 2017 Visual-Object-Tracking challenge (VOT2017) real-time challenges. The experimental results show that, running at the speed of 40 frame/s, the proposed algorithm has higher success rates on OTB2013 and OTB100 than the baseline SiamFC by the margin of 3.5 percentage points and 3 percentage points respectively, and surpass the 2017 champion SiamFC in the VOT2017 real-time challenge, verifying the effectiveness of the proposed algorithm.

In order to solve the problem of tracking failure of the Sum of template and pixel-wise learners (Staple) tracking algorithm for in-plane rotation and partial occlusion, a simple and effective Channel Stability-weighted Staple (CSStaple) tracking algorithm was proposed.Firstly, a standard correlation filter classifier was employed to detect the response value of each channel. Then, the stability weight of each channel was calculated and multiplied to the weight of each layer to obtain correlation filtering response. Finally, by integrating the response of the color complementary learner, the final response result was obtained, and the location of the maximum value in the response was the tracking result. The proposed algorithm was compared with several state-of-the-art tracking algorithms including Channel and Spatial Reliability Discriminative Correlation Filter (CSR-DCF) tracking, Hedged Deep Tracking (HDT), Kernelized Correlation Filter (KCF) Tracking and Staple. The experimental results show that, the proposed algorithm performs best in the success rate, it is 2.5 percentage points higher and 0.9 percentage points higher than Staple on OTB50 and OTB100 respectively, which proves the effectiveness of the proposed algorithm for target in-plane rotation and partial occlusion.

For multiple scale factors of face super-resolution, a face super-resolution method based on very deep convolutional neural network was proposed; and through experiments, it was found that the increase of network depth can effectively improve the accuracy of face reconstruction. Firstly, a network that consists of 20 convolution layers were designed to learn an end-to-end mapping between the low-resolution images and the high-resolution images, and many small filters were cascaded to extract more textural information. Secondly, a residual-learning method was introduced to solve the problem of detail information loss caused by increasing depth. In addition, the low-resolution face images with multiple scale factors were merged to one training set to enable the network to achieve the face super resolution with multiple scale factors. The results on the CASPEAL test dataset show that the proposed method based on this very deep convolutional neural network has 2.7 dB increasement in Peak Signal-to-Noise Ratio (PSNR), and 2% increasement in structural similarity compared to the Bicubic based face reconstruction method. Compared with the SRCNN method, there is also a greater improvement. as well as a greater improvement in accuracy and visual improvement. It means that deeper network structures can achieve better results in reconstruction.

Due to random movement of the segmented target, rapid change of background, arbitrary variation and shape deformation of object appearance, in this paper, a new unsupervised video segmentation algorithm based on multiple spatial-temporal feature representations was presented. By combination of salient features and other features obtained from pixels and superpixels, a coarse-to-fine-grained robust feature representation was designed to represent each frame in a video sequence. Firstly, a set of superpixels was generated to represent foreground and background in order to improve computational efficiency and get segmentation results by graph-cut algorithm. Then, the optical flow method was used to propagate information between adjacent frames, and the appearance of each superpixel was updated by its non-local sptatial-temporal features generated by nearest neighbor searching method with efficient K-Dimensional tree (K-D tree) algorithm, so as to improve robustness of segmentation. After that, for segmentation results generated in superpixel-level, a new Gaussian mixture model based on pixels was constructed to achieve pixel-level refinement. Finally, the significant feature of image was introduced, as well as segmentation results generated by graph-cut and Gaussian mixture model, to obtain more accurate segmentation results by voting scheme. The experimental results show that the proposed algorithm is a robust and effective segmentation algorithm, which is superior to most unsupervised video segmentation algorithms and some semi-supervised video segmentation algorithms.

In order to solve the problems of having nonlinear observation equations and being susceptible to initial value of filtering in bearings-only target tracking, a range-parameterized hybrid coordinates Square Root Cubature Kalman Filter (SRCKF) algorithm was proposed. Firstly,it applied the SRCKF to hybrid coordinates,obtained better tracking effect than the SRCKF under Cartesian coordinates. And then it combined the range parameterization strategy with the SRCKF under hybrid coordinates, and eliminated the impact of unobservable range. The simulation results show that the proposed algorithm can significantly improve the accuracy and robustness although the computational complexity increases slightly.

To reduce the effect caused by Wireless Sensor Network (WSN) node signal power attenuation and node interference on transmission efficiency, an interference-aware routing based on random signal power fading model was proposed for WSN. First, according to probability theory, two probabilistic interference models for successfully transmitting data under different distribution of interfering nodes were put forward; interference, node routing convergence and residual energy issues were used as a measure to establish a interference-aware route. Then, interference, route convergence and residual energy were regarded as assessment weights to determine the best next-hop node. NS2 simulation data shows that compared with interference-aware routing algorithms based on differentiated services and coding, the proposed algorithm has better performance in packet delivery success rate, energy consumption and average delay time.

The current method of image classification which uses the Speed Up Robust Feature (SURF) is low in efficiency and accuracy. To overcome these shortages, this paper proposed an approach for image classification which uses the statistical features of the SURF set. This approach took all dimensions and scale information of the SURF as independent random variables, and split the data with the sign of Laplace response. Firstly, the SURF vector set of the image was got. Then the feature vector was constructed with the first absolute order central moments and weighted first absolute order central moments of each dimision. Finally, the Support Vector Machine (SVM) accomplished the image classification process with this vector. The experimental results show that the precision of this approach is better than that of the methods of SURF histogram and 3-channel-Gabor texture features by increases of 17.6% and 5.4% respectively. By combining this approach with the HSV histogram, a high-level feature fusion method was got, and good classification performance was obtained. Compared with the fused method of the SURF histogram and HSV histogram, the fused method of 3-channel-Gabor texture features and HSV histogram, and the multiple-instance-learning method based on the model of Bag of Visual Word (BoVW), the fused method of this approach and HSV histogram has better precision with the increases of 5.2%, 6.8% and 3.2% respectively.

Considering the problem that the role of the user cannot be changed dynamically over time in access control model of cloud computing, a new access control model was proposed based on trust of users' behaviors for cloud computing. The trust level was determined according to the trust value synthesized from direct trust and recommendation trust, the roles were activated and granted permission to access resources, then services provided the requested resources, so as to achieve the purposes of access control. Besides, the basic elements and implementation process were proposed. The experimental results demonstrate that the proposed model can improve the objectivity of the trust evaluation of users' behaviors, and it can resist all illegal users access to cloud computing and enhance reliability and security of the data in cloud computing.

There are two inefficient steps in GEP-Cluster algorithm: one is screening and aggregation of clustering centers and the other is the calculation of distance between data objects and clustering centers. To solve the inefficiency, an auto-clustering algorithm based on Compute Unified Device Architecture (CUDA) and Gene Expression Programming (GEP), named as CGEP-Cluster, was proposed. Specifically, the screening, and aggregation of clustering center step was improved by Gene Read & Compute Machine (GRCM) method, and CUDA was used to parallel the calculation of distance between data objects and clustering centers. The experimental results show that compared with GEP-Cluster algorithm, CGEP-Cluster algorithm can speed up by almost eight times when the scale of data objects is large. CGEP-Cluster can be used to implement automatic clustering with the clustering number unknown and large data object scale.

The method of transplanting open source uClinux to S698 microprocessor, which was made in China and had its own intellectual property, was studied. After the introduction of S698 architecture, the development environment was built, bootstrap, romfs, OS kernel transplant and construction were analyzed, and the solution to these problems was brought forward. The transplant was implemented on S698 Application Development Board.

The definition of proximate rule was proposed based on the characteristic of text classification rule extraction. Based on the CHI values, the features of text set were selected firstly and feature significance information was provided to the further feature selection. Then rough set was used to select further the attributes on the discrete decision table. Finally precise rules or proximate rules were extracted using rough set theory. The method combined CHI value feature selection and rough set theory fully so as to avoid both feature reduction on a large scale decision table and the discretization of the decision table. The method improved the effectiveness and the practicability of extracting text rule greatly. Experiment results demonstrate the effectiveness of the method.

A skew angle detection approach using Hough transform was proposed for OMR images. The proposed method doesn’t need to identify exact position of locating marks and can bear high noise. In order to avoid heavy computing of Hough transform, a low-resolution image was created by sampling OMR image. Also, a fast iteration algorithm based on run-length center for written marks segmentation was presented. Experiment results show that the algorithm can achieve skew correction and segmentation of OMR image efficiently and accurately.