Accurate segmentation of the polyp region in the colonoscopic images can assist doctors in diagnosing intestinal diseases. However， the structure information of polyp region is missing in the down sampling process， and the existing methods have the problems of over segmentation and under segmentation.Aiming at the problems above， a Fusing Filter enhancement and Reverse attention segmentation Network （FFRNet） was proposed. Firstly， Filter Enhancement Module （FEM） was added to the skip-connection to enhance the structure information of local lesion region in the down-sampling features. Secondly， the global features were obtained by aggregating the shallow features. Finally， Multiscale reverse Attention Fusion Mechanism （MAFM） was adopted in the up-sampling process， by combining the global features and up-sampling features to generate the reverse attention weight， the polyp region information was mined in the features layer by layer， and the relationship between the target region and the boundary was established by the guidance network to improve the integrity of the model on polyp region segmentation. On Kvasir and CVC-ClinicDB datasets， compared with Uncertainty Augmented Context Attention Network （UACANet）， FFRNet has Dice Similarity Coefficient （DSC） increased by 0.22% and 0.54% respectively. Experimental results show that FFRNet can effectively improve the accuracy of polyp image segmentation and has good generalization ability.

As a key technology of land use/land cover detection, change detection aims to detect the changed part and its type in the remote sensing data of the same region in different periods. In view of the problems in traditional change detection methods, such as heavy manual labor and poor detection results, a large number of change detection methods based on remote sensing images have been proposed. In order to further understand the change detection technology based on remote sensing images and further study on the change detection methods, a comprehensive review of change detection was carried out by sorting, analyzing and comparing a large number of researches on change detection. Firstly, the development process of change detection was described. Then, the research progress of change detection was summarized in detail from three aspects:data selection and preprocessing, change detection technology, post-processing and precision evaluation, where the change detection technology was mainly summarized from analysis unit and comparison method respectively. Finally, the summary of the problems in each stage of change detection was performed and the future development directions were proposed.

With the continuous improvement of information technology, the amount of image-text data with orientation on various social platforms is growing rapidly, and the sentiment analysis with image and text fusion is widely concerned. The single sentiment analysis method can no longer meet the demand of multi-modal data. Aiming at the technical problems of image and text sentiment feature extraction and fusion, firstly, the widely used image and text emotional analysis datasets were listed, and the extraction methods of text features and image features were introduced. Then, the current fusion modes of image features and text features were focused on and the problems existing in the process of image-text sentiment analysis were briefly described. Finally, the research directions of sentiment analysis in the future were summarized and prospected for. In order to have a deeper understanding of image-text fusion technology, literature research method was adopted to review the study of image-text sentiment analysis, which is helpful to compare the differences between different fusion methods and find more valuable research schemes.

As one of intelligent video surveillance technologies, person Re-identification (Re-id) has great research significance for maintaining social order and stability, and it aims to retrieve the specific person in different camera views. For traditional hand-crafted feature methods are difficult to address the complex camera environment problem in person Re-id task, a large number of deep learning-based person Re-id methods were proposed, so as to promote the development of person Re-id technology greatly. In order to deeply understand the person Re-id technology based on deep learning, a large number of related literature were collated and analyzed. First, a comprehensive introduction was given from three aspects: image, video and cross-modality. The image-based person Re-id technology was divided into two categories: supervised and unsupervised, and the two categories were generalized respectively. Then, some related datasets were listed, and the performance of some algorithms in recent years on image and video datasets were compared and analyzed. At last, the development difficulties of person Re-id technology were summarized, and the possible future research directions of this technology were discussed.

The data in internet social media has the characteristics of fast transmission, high user participation and complete coverage compared with traditional media under the background of the rise of various platforms on the internet.There are various topics that people pay attention to and publish comments in, and there may exist deeper and more fine-grained sub-topics in the related information of one topic. A survey of sub-topic detection based on internet social media, as a newly emerging and developing research field, was proposed. The method of obtaining topic and sub-topic information through social media and participating in the discussion is changing people’s lives in an all-round way. However, the technologies in this field are not mature at present, and the researches are still in the initial stage in China. Firstly, the development background and basic concept of the sub-topic detection in internet social media were described. Secondly, the sub-topic detection technologies were divided into seven categories, each of which was introduced, compared and summarized. Thirdly, the methods of sub-topic detection were divided into online and offline methods, and the two methods were compared, then the general technologies and the frequently used technologies of the two methods were listed. Finally, the current shortages and future development trends of this field were summarized.

Because of the complexity of human language, text sentiment classification algorithms mostly have the problem of excessively huge vocabulary due to redundancy. Deep Belief Network (DBN) can solve this problem by learning useful information in the input corpus and its hidden layers. However, DBN is a time-consuming and computationally expensive algorithm for large applications. Aiming at this problem, a semi-supervised sentiment classification algorithm called text sentiment classification algorithm based on Feature Selection and Deep Belief Network (FSDBN) was proposed. Firstly, the feature selection methods including Document Frequency (DF), Information Gain (IG), CHI-square statistics (CHI) and Mutual Information (MI) were used to filter out some irrelevant features to reduce the complexity of vocabulary. Then, the results of feature selection were input into DBN to make the learning phase of DBN more efficient. The proposed algorithm was applied to Chinese and Uygur language. The experimental results on hotel review dataset show that the accuracy of FSDBN is 1.6% higher than that of DBN and the training time of FSDBN halves that of DBN.

In order to reduce the harm caused by bursty events in social media, a multi-feature based microblog bursty events detection algorithm was proposed. The algorithm combines text emotion filtering and user influence calculation methods. Firstly, the microblog text with negative emotion was obtained through noise filtering and emotion filtering. Then the proposed user influence calculation method was combined with the burst word extraction algorithm to extract the characteristics of burst words. Finally, a cohesive hierarchical clustering algorithm was introduced to cluster bursty word sets, and extract bursty events from them. In the experimental test, the accuracy is 66.84%, which proves that the proposed method can effectively detect bursty events.

For the routing problem that nodes in mobile wireless sensor network, based on random moving model, a low energy consumption routing strategy named DTVC (Data Transmission based on Virtual Currency) was proposed. When two nodes met each other, the buyer and the seller determined the price of data message and selected relay node according to node attributes and data message attributes. To improve the network performance, the number of the data message's replicas was controlled according to node type and data messages in the queue were sorted according to each message's delay tolerance. The nodes in the network were divided into source nodes and relay nodes for each data message and only the source node could copy it. The smaller the delay tolerance was, the greater the priority was. In order to reduce the energy consumption in the network, the data message in the storage queue that had been transmitted successfully was deleted according to the message broadcast by the sink node. The simulation results on Matlab showed that the data delivery rate of DTVC was increased by at least 2.5%, and the average number of replicas was reduced by at least 25% than those of FAD (the message Fault tolerance-based Adaptive data Delivery scheme), FLDEAR (Fuzzy-Logic based Distance and Energy Aware Routing protocol) and a routing algorithm based on energy consumption optional evolution mechanism.

Energy efficient routing is a challenging problem in resource constrained Mobile Wireless Sensor Network (MWSN). Focused on the issue that the energy consumption of the inter-community routing in the mobile sensor network is too fast, an Energy-balanced Routing Algorithm for Inter-community (ERAI) was proposed. In ERAI, a new routing metric FC (Forwarding Capacity) based on the residual energy of nodes and the probability of encounter was designed. Then, this metric FC and the directional information of encountered nodes were used for selection of a relay node to forward the messages. The experimental data show that the death time of the first node of ERAI was later than that of Epidemic and PROPHET by 12.6%-15.6% and 4.5%-8.3% respectively, and the residual energy mean square deviation of ERAI was less than that of Epidemic and PROPHET. The experimental results show that the ERAI can balance the energy consumption of each node to a certain extent, and thus prolongs the network lifetime.

Responding to the problem of the unreasonable distribution of cluster head nodes and "hot spots" caused by uneven load energy in Wireless Sensor Network (WSN), an Unequal partition Area Uneven Clustering routing protocol (UAUC) was proposed. The network was divided according to unequal partition area, and the appropriate cluster head nodes in each area were selected on the basis of the energy factor, the distance factor and the intensity factor. Meanwhile, a load balancing path tree was built between cluster head nodes to solve the problem of "hot spots" in data transmission. In the comparison experiments with LEACH (Low Energy Adaptive Clustering Hierarchy) protocol, DEBUC (Distributed Energy-Balanced Unequal Clustering routing) protocol and HRPNC (Hierarchical Routing Protocol based on Non-uniform Clustering) protocol, UAUC achieved more reasonable distribution of cluster head nodes. The network cycle of UAUC was increased than that of LEACH, DEBUC and HRPNC by 88%, 12% and 17.5% respectively. The average residual energy of UAUC was higher than LEACH, DEBUC and HRPNC. And the variance of node residual energy of UAUC was less than LEACH, DEBUC and HRPNC. What is more, the aggregate of data packet of UAUC was higher than that of LEACH, DEBUC and HRPNC by 400%, 87.5% and 17.5% respectively. The experimental results show that UAUC can effectively improve the energy efficiency and the aggregate of data packet, balance energy consumption and prolong the network lifetime.