In the cross-domain sentiment analysis， the labeled samples in the target domain are seriously insufficient， the distributions of features in different domains are very different， and the emotional polarities expressed by features in one domain differ a lot from the emotional polarities in another domain， all of these problems lead to low classification accuracy. To deal with the above problems， an aspect-level cross-domain sentiment analysis method based on capsule network was proposed. Firstly， the feature representations of text were obtained by BERT （Bidirectional Encoder Representation from Transformers） pre-training model. Secondly， for the fine-grained aspect-level sentiment features， Recurrent Neural Network （RNN） was used to fuse the context features and aspect features. Thirdly， capsule network and dynamic routing were used to distinguish overlapping features， and the sentiment classification model was constructed on the basis of capsule network. Finally， a small amount of data in the target domain was used to fine-tune the model to realize cross-domain transfer learning. The optimal F1 score of the proposed method is 95.7% on Chinese dataset and 91.8% on English dataset， which effectively solves the low accuracy problem of insufficient training samples.

In order to speed up the state convergence in the multi-Agent formation control process, a formation control method based on multi-hop network technology was proposed. Firstly, the relative velocity deviation between Agents of Multi-Agent System (MAS) was introduced into the control protocol. Then, the absolute displacement deviation between Agents and the standard displacement was introduced. Finally, the multi-hop network technology was applied in the communication topology, so more information was passed around and each Agent enlarges its "available" neighborhood. A six-Agent formation control example was used to verify the proposed protocol. The simulation results show that the proposed control method can make the system build up the specified formation; and the time required for state convergence is reduced by nearly 10 seconds compared with the control method that does not take into account multi-hop network technology, which verifies that the proposed method is more efficient.

To effectively improve the denoising effect of the original anisotropic diffusion model that used only the 4 neighborhood pixels information and ignored the diagonal neighborhood pixels information of the pixel to be repaired in the image denoising process, a image denoising algorithm using UK-flag shaped anisotropic diffusion model was proposed. This model not only made full use of the reference information of the 4 neighborhood pixels as in original algorithm, but also used another 4 diagonal neighborhood pixels information in the denoising process. Then the model using the 8 direction pixels information for image denoising was presented, and it was proved to be rational. The proposed algorithm, the original algorithm, and an improved similar algorithm were used to remove the noise from 4 images with noise. The experimental results show that the proposed algorithm has an average increase of 1.90dB and 1.43dB in Peak Signal-to-Noise Ratio (PSNR) value respectively, and an average increase of 0.175 and 0.1 in Mean Structure Similitary Index (MSSIM) value respectively, compared with the original algorithm and the improved similar algorithm, which concludes that the proposed algorithm is more suitable for image denoising. algorithm not only made full use of the reference information of the 4 neighborhood pixels as in original algorithm, but also another 4 diagonal neighborhood pixels information was used in the denoising process, and the algorithm was proved to be rationality. The experimental results showed that the proposed algorithm could increase the PSNR (peak signal-to-noise ratio) value 1.69db, and the MSSIM(mean structure similitary index) value 0.14, compared with the other similar algorithms in image denoising, which conclud that this proposed algorithm is more suitable for image denoising.

To overcome the limitations of traditional face recognition methods for single sample, an improved gradient face algorithm named Orthogonal Gradient Binary Pattern (OGBP), which is robust to variations of illumination, face expression and posture, was proposed. Firstly, the features of the image samples were extracted by orthogonal gradient binary pattern. Then the feature vectors of each direction were concatenated into the general feature vector for face recognition. Finally the Principle Component Analysis (PCA) method was used to reduce dimensions and the nearest neighbor classifier was used for face image classification and recognition. Experimental results on YALE and AR face database indicate that the proposed method is simple, effective and better than the original gradient face algorithm, and also has better performance in face description for single sample.

Concerning the low accuracy of face recognition systems, a face recognition algorithm based on Histograms of Nonsubsampled contourlet Oriented Gradient (HNOG) was proposed. Firstly, a face image was decomposed with Non-Subsampled Contourlet Transform (NSCT) and the coefficients were divided into several blocks. Then histograms of oriented gradient were calculated all over the blocks and used as face features. Finally, multi-channel nearest neighbor classifier was used to classify the faces. The experimental results on YALE , ORL and CAS-PEAL-R1 face databases show that the descriptor HNOG is discriminative, the feature dimension is small and the feature is robust to variations of illumination, face expression and position.

The Supply Chain Management (SCM) would not be a private work to one specific enterprise since the open and multi-partners joint mode is born with the development of EPCglobal, the booming architecture of global SCM. Through the Electric Product Code (EPC) tag on objects and the distributed information system, EPCglobal provides the circulating goods with a corresponding information chain, through which users can trace the supply chain back to the origin. Considering the essentials and development trends of SCM, the more directly SCM communicates with the circumstances, the more effective the supervision on SCM is. Therefore, a framework that integrated WSN into EPCglobal was proposed, and the information of EPC tag and environment-aware data was bound seamlessly. The integration scheme could power the EPCglobal's ability to acquire and process the data in producing and storing. Also a unified interface was provided to help users access the combined data simply. To integrate WSN and EPCglobal not only is another try to combine two heterogeneous networks skillfully, but also broaden and deepen the information management of supply chain.

An encryption algorithm which combined the process of MPEG video compressing with video encryption based on Lorenz chaotic system was put forward to deal with the security problem of video information. Three dimensional chaotic sequences of Lorenz system were used to encrypt DC, AC and motion vector coefficients during the compressing of I frame, B frame and P frame. The luminance information of I frame was encrypted among blocks by the chaotic sequence. The algorithm is secure and real-time because the encryption is done during the process of video compressing.

The paper defined fractal art based on the essential theory of fractal-chaos, expressed structural means of fractal figures in line with its track and distribution, and made use of these methods to construct a series of M-J fractal-chaos figures, showing the beautiful fine structure of the fractal set. The paper provided a definitely new view and an application base for applying fractal-chaos theory and technique in the area of digital media.

In order to deal with the problem that current digital rights expression models have less ability to describe dynamic semantics, a new model, DDRM(Dynamic Digical Rights Model), which can describe action state was presented. Based on first-order dynamic logic, a new symbol system of first-order dynamic logic, DrFDL(Digital rights Fist-order Dynamic Logic), was defined to describe digital rights conception DrFDL semantic structure which can reflect dynamic property of action was presented based on DDRM. In addition, a license syntax based on DDRM was provided for rights expression. Then DrFDL logic was used to express the formal semantics of the licenses produced from this syntax and the determinacy with validity of these licenses was explored at last.