Aspect-Based Sentiment Analysis （ABSA） is an important task in natural language processing. The existing methods generally adopt single-type nodes to model semantic associations between aspects and opinion terms， failing to distinguish the contributions of different node types to sentiment representation effectively. Moreover， current models usually construct syntactic trees using dependency parsers for semantic analysis， ignoring fine-grained semantic information of the word itself and implicit sentiment intensity modification relations in the text. To address the above issues， a Heterogeneous enhancement and Multi-Source knowledge fusion Graph Convolutional Network （HMS-GCN） model was proposed. First， a heterogeneous graph with differentiated constraints was constructed to capture semantic and sentiment information in sentences multi-dimensionally by using the relations of multi-type nodes and their corresponding edges in the heterogeneous graph， and importance of different types of nodes and their internal components were focused on by utilizing a dual-channel attention mechanism. Second， sememe knowledge was adopted to enhance the semantic representation of initial textual features， and hierarchical phrase structural information was integrated on the basis of the syntactic adjacency matrix， so as to improve the model’s syntactic dependency comprehension. Finally， the heterogeneous graph network information and enhanced knowledge features were fused for sentiment classification prediction. Experimental results on public datasets （Twitter， Lap14， Rest15， Rest16） show that compared to models such as ISSK-GCN （Integrating Syntax and Sentiment Knowledge Graph Convolutional Network）， the proposed model achieves the best results， validating the effectiveness of the differentiated node modeling and knowledge enhancement strategies.

In order to reduce the high computing complexity of Affine Scale Invariant Feature Transform (ASIFT) algorithm, a robust and rapid matching method for large angle aviation oblique images based on homography transformation was proposed, which was named H-SIFT. Firstly, the homography matrix between the two oblique images was calculated by making full use of the rough Exterior Orientation (EO) elements of the images, then a homography transformation was made to the left image to get its rectified image for eliminating geometric distortion, scale and rotation. Secondly, the matches between the rectified image and the right image were got by using Scale Invariant Feature Transform (SIFT) algorithm. During the matching process, the coarse matches were got by using two matching constraints, Nearest Neighbor Distance Ratio (NNDR) and consistency checking, then the false matches in them were eliminated by using the RANdom SAmple Consensus (RANSAC) algorithm. Finally, as the matching points on the rectified image were got, the corresponding matching points on the left image were calculated by using the homography matrix. The experiments on three pairs of typical oblique images obtained by Si Wei Digital Camera 5 (SWDC-5) demonstrate that the matching points obtained by the proposed algorithm are significantly improved in the computation efficiency, quantity and distribution than ASIFT algorithm, as the proposed algorithm not only takes just about 0.93%, 0.88%, 0.97% time of ASIFT algorithm, but also gets the correct matching points about 2.18, 1.31, 1.70 times of ASIFT algorithm.

In wireless environment, TCP erroneously responds to all losses by invoking congestion control algorithm, which results in degraded end-to-end performance. Concerning this problem, this paper proposed a method of wireless TCP based on monitoring the packet loss rate due to bit error in real-time. The data sender cooperated with the router configured with the function of explicit congestion notification to distinguish the characteristics of packet losses; at the same time, the sender took advantage of the monitor result to adjust the TCP segment size. The simulation results show that the throughput of modified of TCP outperforms TCP_SACK and TCP_Reno by 100% approximately when the bit error rate is 1E-4.

High energy efficiency is one of the most important targets for Ad hoc network design,and topology control is an effective measure at network level to achieve this goal.Two basic topology control mechanisms for energy efficiency were introduced firstly.And then,more attention is paid on the analyzing and summarizing of present research on energy efficient topology control that are based on transmission power adjustment.At last,some promising directions are suggested.Then present research on energy efficient topology control based on transmission power adjustment was analyzed,and some promising were suggested.

 Aimed at image watermark robustness, an evaluative method based on synergetic order parameter and its usage was first advanced. Many kinds of robust experiment of Image carrier were done. After procession, the synergetic original order parameter of patterns in the set were calculated and compared. Through validation by the order parameter evolvement curves, their robust conclusions were gotten. This method is more convenient and applied than the correlative coefficient method, although the two methods have same conclusion.