The existing image tamper detection networks based on deep learning often have problems such as low detection accuracy and weak algorithm transferability. To address the above issues， a two-channel progressive feature filtering network was proposed. Two channels were used to extract the two-domain features of the image in parallel， one of which was used to extract the shallow and deep features of the image spatial domain， and the other channel was used to extract the feature distribution of the image noise domain. At the same time， a progressive subtle feature screening mechanism was used to filter redundant features and gradually locate the tampered regions； in order to extract the tamper mask more accurately， a two-channel subtle feature extraction module was proposed， which combined the subtle features of the spatial domain and the noise domain to generate a more accurate tamper mask. During the decoding process， the localization ability of the network to tampered regions was improved by fusing filtered features of different scales and the contextual information of the network. The experimental results show that in terms of detection and localization， compared with the existing advanced tamper detection networks ObjectFormer， Multi-View multi-Scale Supervision Network （MVSS-Net） and Progressive Spatio-Channel Correlation Network （PSCC-Net）， the F1 score of the proposed network is increased by an 10.4， 5.9 and 12.9 percentage points on CASIA V2.0 dataset； when faced with Gaussian low-pass filtering， Gaussian noise， and JPEG compression attacks， compared with Manipulation Tracing Network （ManTra-Net） and Spatial Pyramid Attention Network （SPAN）， the Area Under Curve （AUC） of the proposed network is increased by 10.0 and 5.4 percentage points at least. It is verified that the proposed network can effectively solve the problems of low detection accuracy and poor transferability in the tamper detection algorithm.

Panoramic videos have attracted wide attention due to their unique immersive and interactive experience. The high bandwidth and low delay required for wireless streaming of panoramic videos have brought challenges to existing network streaming systems. Tile-based viewport adaptive streaming can effectively alleviate the streaming pressure brought by panoramic video， and has become the current mainstream scheme and hot research topic. By analyzing the research status and development trend of tile-based viewport adaptive streaming， the two important modules of this streaming scheme， namely viewport prediction and bit rate allocation， were discussed， and the methods in relevant fields were summarized from different perspectives. Firstly， based on the panoramic video streaming framework， the relevant technologies were clarified. Secondly， the user experience quality indicators to evaluate the performance of the streaming system were introduced from the subjective and objective dimensions. Then， the classic research methods were summarized from the aspects of viewport prediction and bit rate allocation. Finally， the future development trend of panoramic video streaming was discussed based on the current research status.

Aiming at the limitations of existing long non-coding RNA （lncRNA） -disease association prediction models in comprehensively utilizing interaction and semantic information of heterogeneous biological networks， an lncRNA-Disease Association prediction model based on Semantic and Global dual Attention mechanism （SGALDA） was proposed. Firstly， an lncRNA-disease-microRNA （miRNA） heterogeneous network was constructed based on similarity and known associations. And a feature extraction module was designed based on message passing types to extract and fuse the neighborhood features of homogeneous and heterogeneous nodes on the network， so as to capture multi-level interactive relations on the heterogeneous network. Secondly， the heterogeneous network was decomposed into multiple semantic sub-networks based on meta-paths. And a Graph Convolutional Network （GCN） was applied on each sub-network to extract semantic features of nodes， so as to capture the high-order interactive relations on the heterogeneous network. Thirdly， a semantic and global dual attention mechanism was used to fuse semantic and neighborhood features of the nodes to obtain more representative node features. Finally， lncRNA-disease associations were reconstructed by using the inner product of lncRNA node features and disease node features. The 5-fold cross-validation results show that the Area Under Receiver Operating Characteristic curve （AUROC） of SGALDA is 0.994 5±0.000 2， and the Area Under Precision-Recall curve （AUPR） of SGALDA is 0.916 7±0.001 1， both of them are the highest among AUROCs sand AUPRs of all the comparison models. It proves SGALDA’s good prediction performance. Case studies on breast cancer and stomach cancer further prove the ability of SGALDA to identify potential lncRNA-disease associations， indicating that SGALDA has the potential to be a reliable lncRNA-disease association prediction model.

Most existing computational models for predicting associations between circular RNA （circRNA） and diseases usually use biological knowledge such as circRNA and disease-related data， and mine the potential association information by combining known circRNA-disease association information pairs. However， these models suffer from inherent problems such as sparsity and too few negative samples of networks composed of the known association， resulting in poor prediction performance. Therefore， inductive matrix completion and self-attention mechanism were introduced for two-stage fusion based on graph auto-encoder to achieve circRNA-disease association prediction， and the model based on the above is GIS-CDA （Graph auto-encoder combining Inductive matrix complementation and Self-attention mechanism for predicting CircRNA-Disease Association）. Firstly， the similarity of circRNA integration and disease integration was calculated， and graph auto-encoder was used to learn the potential features of circRNAs and diseases to obtain low-dimensional representations. Secondly， the learned features were input to inductive matrix complementation to improve the similarity and dependence between nodes. Thirdly， the circRNA feature matrix and disease feature matrix were integrated into circRNA-disease feature matrix to enhance the stability and accuracy of prediction. Finally， a self-attention mechanism was introduced to extract important features in the feature matrix and reduce the dependence on other biological information. The results of five-fold crossover and ten-fold crossover validation show that the Area Under Receiver Operating Characteristic curve （AUROC） values of GIS-CDA are 0.930 3 and 0.939 3 respectively， the former of which is 13.19，35.73，13.28 and 5.01 percentage points higher than those of the prediction models based on computational model of KATZ measures for Human CircRNA-Disease Association （KATZHCDA）， Deep Matrix Factorization for CircRNA-Disease Association （DMFCDA）， RWR （Random Walk with Restart） and Speedup Inductive Matrix Completion for CircRNA-Disease Associations （SIMCCDA）， respectively； the Area Under Precision-Recall curve （AUPR） values of GIS-CDA are 0.227 1 and 0.234 0 respectively， the former of which is 21.72， 22.43， 21.96 and 13.86 percentage points higher than those of the above comparison models respectively. In addition， ablation experiments and case studies on circRNADisease， circ2Disease and circR2Disease datasets， further validate the good performance of GIS-CDA in predicting the potential circRNA-disease association.

Aiming at the problem that the clustering results of K-Means clustering algorithm are affected by the sample distribution because of using the mean to update the cluster centers， a Neural Tangent Kernel K-Means （NTKKM） clustering algorithm was proposed. Firstly， the data of the input space were mapped to the high-dimensional feature space through the Neural Tangent Kernel （NTK）， then the K-Means clustering was performed in the high-dimensional feature space， and the cluster centers were updated by taking into account the distance between clusters and within clusters at the same time. Finally， the clustering results were obtained. On the car and breast-tissue datasets， three evaluation indexes including accuracy， Adjusted Rand Index （ARI） and FM index of NTKKM clustering algorithm and comparison algorithms were counted. Experimental results show that the effect of clustering and the stability of NTKKM clustering algorithm are better than those of K-Means clustering algorithm and Gaussian kernel K?Means clustering algorithm. Compared with the traditional K?Means clustering algorithm， NTKKM clustering algorithm has the accuracy increased by 14.9% and 9.4% respectively， the ARI increased by 9.7% and 18.0% respectively， and the FM index increased by 12.0% and 12.0% respectively， indicating the excellent clustering performance of NTKKM clustering algorithm.

Multi-kernel learning method is an important type of kernel learning method， but most of multi-kernel learning methods have the following problems： most of the basis kernel functions in multi-kernel learning methods are traditional kernel functions with shallow structure， which have weak representation ability when dealing with the problems of large data scale and uneven distribution； the generalization error convergence rates of the existing multi-kernel learning methods are mostly O \left(\mathrm{1}/\sqrt[]{n}\right) ， and the convergence speeds are slow. Therefore， a multi-kernel learning method based on Neural Tangent Kernel （NTK） was proposed. Firstly， the NTK with deep structure was used as the basis kernel function of the multi-kernel learning method， so as to enhance the representation ability of the multi-kernel learning method. Then， a generalization error bound with a convergence rate of O \left(\mathrm{1}/n\right) was proved based on the measure of principal eigenvalue ratio. On this basis， a new multi-kernel learning algorithm was designed in combination with the kernel alignment measure. Finally， experiments were carried out on several datasets. Experimental results show that compared with classification algorithms such as Adaboost and K-Nearest Neighbor （KNN）， the newly proposed multi-kernel learning algorithm has higher accuracy and better representation ability， which also verifies the feasibility and effectiveness of the proposed method.

Concerning that the monitoring data in large-scale smart grid are massive, real-time and dynamic, a new data storage approach supporting large-scale smart grid based on data-centric was proposed, which is a hierarchical extension scheme for storing massive dynamic data. Firstly, the extended Hash coding method could adjust the number of storage nodes dynamically to avoid data loss of sudden or frequent events and increase system availability. Then, the multi-threshold leveling method was used to distribute data to multiple storage nodes, which could avoid hotspot storage problem and achieve load balance. Simulation results show that this method is able to satisfy the need of massive data storage, to obtain better load balance, to lower the total energy consumption and to extend the life cycle of the whole network.

Based on the detailed analysis on the process of link key generation and the method of PIN code attack, an approach which increased the complexity of PIN code by transforming the input of users PIN code was presented and implemented. The test results show that it can defense PIN code attack effectively and insure the security of bluetooth network when users input the simple PIN code.

On the basis of contour extraction of 3D fragments, an algorithm for rapid fragment matching in the 3D fragement reassembly was presented. In the algorithm, the type of feature point was calculated according to its neighbor surface type, and then higher similarity contour segments belonging to different contours were found, thus the normal vector was utilized to verify the matching probability of similar space curves. The similarity between feature segments of different contours was measured according to feature point type, feature point curvature and the hausdorff distance of feature segments. This algorithm is suitable for point-based surface.

Currently, the most recognized framework for scalable video coding is based on Motion Compensation Temporal Filtering (MCTF). Some improvements on deblocking filter and update step for MCTF were proposed. During the update process, the constraint was requested that the pixels of the update block had the uniform prediction motion vector. Before update process, filtering residual image was performed in both edge and sample dot. The highpassed frames were deblocked to bring about less block artifacts into the lowpass frames, and better reference frames for the following motion compensation. In the mean time, the deblock of the highpassed frames could also improve the coding efficiency for the lowactivated video sequences. Experimental results show that the coding efficiency of the lowpass frames and the subject quality of frames are improved.