Aiming to the problems of low embedding capacity and poor visual quality of the extracted secret images in existing generative data hiding algorithms， a generative data hiding algorithm based on multi-scale attention was proposed. First， a generator with dual encode-single decode based on multi-scale attention was designed. The features of the cover image and secret image were extracted independently at the encoding end in two branches， and fused at the decoding end by a multi-scale attention module. Skip connections were used to provide different scales of detail features， thereby ensuring high-quality of the stego-image. Second， self-attention module was introduced into the extractor of the U-Net structure to weaken the deep features of the cover image and enhance the deep features of the secret image. The skip connections were used to compensate for the detail features of the secret image， so as to improve the accuracy of the extracted secret data. At the same time， the adversarial training of the multi-scale discriminator and generator could effectively improve the visual quality of the stego-image. Experimental results show that the proposed algorithm can achieve an average Peak Signal-to-Noise Ratio （PSNR） and Structure Similarity Index Measure （SSIM） of 40.93 dB and 0.988 3 for the generated stego-images， and an average PSNR and SSIM of 30.47 dB and 0.954 3 for the extracted secret images under the embedding capacity of 24 bpp.

To ensure that extracted features contain rich information， current deep learning-based image registration algorithms usually employ deep convolutional neural networks， which have high computational complexity and low discrimination of similar feature points. To address the above issues， a Self-supervised Image Registration Algorithm based on Multi-Feature Fusion （SIRA-MFF） was proposed. First， shallow convolutional neural networks were used to extract image features and reduce the computational complexity. Moreover， the problem of single feature information in shallow networks was remedied by adding feature point direction descriptors to the feature extraction layer. Second， an embedding and interaction layer was added after the feature extraction layer to enlarge the receptive field of feature points， by which local and global information of feature points was fused to improve the discrimination of similar feature points. Finally， the feature matching layer was optimized to obtain the best matching scheme. A cross-entropy based loss function was also designed for model training. The SIRA-MFF achieved the Average Matching Accuracy （AMA） of 95.18% and 93.26% on the two test sets generated from the ILSVRC2012 dataset， which was better than comparison algorithms. In the IMC-PT-SparseGM-50 test set， the SIRA-MFF achieved the AMA of 89.69%， which was also better than comparison algorithms； and compared to ResMtch algorithm， SIRA-MFF decreased the operation time of a single image by 49.45%. Experimental results show that SIRA-MFF has higher accurate and stronger robust.

Applying the compact constraint calculation method of S-box based on Mixed Integer Linear Programming （MILP） model can solve the low efficiency of differential path search of SPONGENT in differential cryptanalysis. To find the best description of S box， a compactness verification algorithm was proposed to verify the inequality constraints in S-box from the perspective of the necessity of the existence of constraints. Firstly， the MILP model was introduced to analyze the inequality constraints of SPONGENT S-box， and the constraint composed of 23 inequalities was obtained. Then， an index for evaluating the existence necessity of constraint inequality was proposed， and a compactness verification algorithm for verifying the compactness of group of constraint inequalities was proposed based on this index. Finally， the compactness of the obtained SPONGENT S-box constraint was verified by using the proposed algorithm. Calculation analysis show that the 23 inequalities have a unique impossible difference mode that can be excluded， that is， each inequality has the necessity of existence. Furthermore， for the same case， the number of inequalities was reduced by 20% compared to that screened by using the greedy algorithm principle. Therefore， the obtained inequality constraint of S-box in SPONGENT is compact， and the proposed compactness verification algorithm outperforms the greedy algorithm.

To address the degradation problem of traditional depth estimation models caused by image quality degradation in haze environment， a model based on Conditional Generative Adversarial Network （CGAN） was proposed to estimate the depth of single haze image by fusing dual attention mechanism. Firstly， for the network structure of the generator of the model， the DenseUnet structure fused with dual attention mechanism was proposed. The dense blocks were used as basic blocks in the encoding and decoding processes of U-net. Dense and jump connections were used to enhance information flow， as well as extract the underlying structural features and high-level depth information of the direct transmission rate map. Then， the global dependencies of spatial features and channel features were adaptively adjusted by the dual attention module. At the same time， a new structure-preserving loss function was proposed by combining the least absolute value function， perceptual loss， gradient loss， and adversarial loss. Finally， using the direct transmission rate map of the haze image as a condition of CGAN， the depth map of the haze image was estimated through the adversarial learning of the generator and the discriminator. Training and testing were performed on the indoor dataset NYU Depth v2 and the outdoor dataset DIODE. Experimental results show that the proposed model has a finer geometric structure and richer local details. Compared with the fully convolutional residual network， on NYU Depth v2， the proposed model has the Logarithmic Mean Error （LME） and Root Mean Square Error （RMSE） error reduced by 7% and 10%， respectively. Compared with the deep ordinal regression network， on DIODE， the proposed model has the accuracy with threshold less than 1.25 increased by 7.6%. It can be seen that the proposed model improves the estimation accuracy and generalization ability of depth estimation under the interference of haze.

Aiming at the lack of consideration of the psychological behaviors of decision makers in software quality evaluation methods， a TOmada de Decisao Interativa e Multicritevio （TODIM） software quality evaluation method based on interval 2-tuple linguistic information was proposed. Firstly， interval 2-tuple linguistic information was used to characterize the evaluation information of experts for software quality. Secondly， the subjective and objective weights of software quality attributes were calculated by subjective weighting method and Technique for Order Preference by Similarity to Ideal Solution （TOPSIS） respectively. On this basis， the comprehensive weights of software quality attributes were obtained by combined weighting method. Thirdly， in order to better describe the psychological behaviors of experts in the process of software quality evaluation， TODIM was introduced into software quality evaluation. Finally， the method was used to evaluate the software quality of assistant dispatcher terminal in high-speed railway dispatching system. The result shows that the third assistant dispatcher terminal software provided by the railway software supplier has the highest dominance value and its quality is the best. The results of comparing this method with the regret theory and Preference Ranking Organization METHod for Enrichment Evaluations （PROMETHEE-II） show that the three methods are consistent in the selection of the best quality software， but the overall rankings of the three methods are somewhat different， indicating that the constructed method has strong superiority in describing the interaction between multiple criteria and the psychological behaviors of decision makers.

Aiming at the problems of current aviation card readers, include poor portability, slow speed and tags' little capacity, a design method of large capacity Radio Frequency Identification (RFID) system based on STM32 was proposed. Using STM32 microprocessor as a core and adopting CR95HF radio chip, a new handled RFID card reader which worked in High Frequency (HF) and supported ISO 15693, ISO 18092 protocols was designed. The design of power, antenna and optimization of software speed, error rate was discussed in detail. A new large compiled capacity passive tag was also designed whose capacity is up to 32KB to form a large capacity RFID system with card reader. The experimental results show that, compared with the traditional card reader, the reading and writing speed of the card reader increases by 2.2 times, error rate reduces by 91.7% and tag capacity increases 255 times. It provides a better choice for fast, accurate and high data requirements of aviation logistics.

The existing trust metrics based on the cloud model lack of the multi-granularity and timeliness consideration. For this reason, a trust metric algorithm based on weighted multi-attribute cloud was proposed. First of all, multi-attribute trust cloud on trust metric was used to refine the grain size, and time decay function was introduced in the entity trust computing; second, multi-attribute comprehensive and multi-path merge was used to get entity ultimate trust cloud. Finally, the trust level of the entity was obtained by comparison with basis trust cloud using cloud similarity comparison algorithm. The simulation results under grid computing environment show that when the node interaction reached 100 times, the interaction success rate of weighted multi-attribute cloud metric was 80%, significantly higher than 65% of the traditional method. The simulation results show that the cloud using the weighted multi-attribute trust cloud metric measurement method can improve the accuracy of trust metric.

In order to study the influence of multipath, shadows and the weather variations on meteorological satellite channel, the Suzuki and extension Suzuki channel models were studied according to the analyses on the different weather conditions. Then the two-state Markov model was introduced into satellite channel model, which could describe the transformation between two kinds of channel state models caused by the changes of weather. Finally, the meteorological satellite channel simulator was designed and simulated based on the the shaping filter method. The results show that the proposed meteorological satellite channel simulator can be used in the description of actual meteorological satellite channel propagation characteristics.

With regard to poor accuracy of multi-pose face detection, a hyper-sphere Support Vector Machine (SVM) was used to detect human faces. A model was proposed in this paper, which was composed by thirteen SVMs. These SVMs were divided into three levels, the first level had one SVM, the second level had three SVMs, and the third level had nine SVMs. Each SVM was a hyper-sphere support vector machine, which was exploited to detect multi-pose faces from various angles. The 3-tier model was applied to fast reduce detection area. On one hand, it accelerated the speed of detection; on the other hand it was favorable to make a careful detection in a small local area. In addition, the k-Nearest Neighbor (kNN) algorithm was improved in this paper. The improved kNN algorithm was applied to deal with the detection of hyper-sphere overlap samples. The experimental results show that the proposed algorithm can promote about 5% in the face detection accuracy than the traditional SVM-based face detection algorithm, but also ensure the speed of face detection.

For designing the logistics network of the multi-level and multi-commodity flow, according to network status, the logistics network was divided into static network and dynamic network. This paper analyzed the infrastructure construction of static network and logistics activities of dynamic network. It constructed the operating cost function and construction cost function which can describe the different stages of network. Considering the problem of environmental pollution caused by the operating process, the management cost function was also constructed. Based on the above functions, general logistics network design and re-design optimization models were proposed, whose constraints were supply capacity and goal was minimizing the total cost. The two optimization models were converted to variational inequalities. By the method of modified projection, the paper calculated and verified the model, and obtained the facilities construction program and logistics organization program under the optimal costs.

To handle the problem of low consistency between the objective and subjective evaluations of image fusion, considering the features of Human Visual System (HVS), a new metric to evaluate the quality of the fusion image based on the Visual Attention Mechanism (VAM) and the regional structural similarity was proposed. This quality metric utilized the global salience got by VAM and the local salient information to estimate how well the salient information contained within the sources was presented by the composite image. Since human eyes are more sensitive to region, by giving higher weight to those regions with high saliency value in the source images, the new metric evaluated the quality of the fused image by computing the weighted regional structural similarity of the fused image and source images in all regions. The correlation analysis between objective measure and subjective evaluation was performed and the results demonstrate that the new metric is more consistent with human subjective evaluation, compared with the traditional objective measurements and the widely used EFQI.

The multi-side covering algorithm is designed guided by the idea of divide-and-conquer to the mass high-dimensional data. According to the sum of the absolute value of the component deviation, subsets of attributes were selected to construct respective covering domains for different parts of training samples, thus reducing the complexity of learning. But the selection of initial attribute set should be acquired by experience or experiments. In order to reduce the subjectivity with the selection of initial attribute set and the complexity with the regulation of attribute set, the relief feature selection approach was used to ensure the optimal feature subset that can be appropriate for different data sets, build a hierarchical overlay network, and experiment on the actual data set. The experimental results show that this algorithm is provided with higher precision and efficiency. Therefore, the algorithm can effectively achieve the classification of the complex issues.

Concerning the issue of extracting features more efficiently from a sequence of gait frames and real-time recognition, an effective human recognition method based on Mean Gait Energy Image (MGEI) was described, which utilized Kernel Principal Component Analysis (KPCA). A pre-processing technique was used to segment the moving silhouette from the walking figure. The algorithm obtained the gait quasi-periodicity through analyzing the width information of the lower limbs' gait contour edge, and the MGEI was calculated from gait period. KPCA extracted principal component with nonlinear method and described the relationship among three or more pixels of the identified images. In this paper, KPCA could make use of the high correlation between different MGEIs for feature extraction by selecting the proper kernel function, and Euclidean distance weighted by variance reciprocal was designed as the classifier. The experimental results show that the proposed approach has better recognition performance and the computation time is greatly reduced.

Aiming at the deficiencies of RBTPN in modeling real-time scheduling of distributed embedded system, we put forward a new extended time Petri Net model on its base. The model introduces transition rate factor on transitions that need processor resources, and gets working rate function of transitions that have the same priority, so that the model combine preemptive scheduling based on fixed priority and round-robin scheduling on single processor in the scheduling modeling of distributed embedded system Then we gave construction method of the models reachable graph so as to get the characteristics of scheduling sequences.

As far as the issue on Web user access pattern is concerned, adopting Maximal Frequent Path (MFP) can mine more universal patterns. A new user access pattern tree named WUAP-tree was devised. Furthermore, a new algorithm named WUAP-mine was proposed for mining user access patterns, which was based on E-OEM model for page topological structure and users' browse path. The algorithm utilized WUAP-tree that could neither generate candidate sets nor use recursive ways. It could mine frequent Web users access patterns by scanning transaction database and output-depth-first traversing WUAPtree only once. The algorithm is very easy to query Web user access patterns from WUAPtree directly. At last, theoretical analysis and experimental results prove its effectiveness and efficiency.