Aiming at the problems of low accuracy and poor real-time performance of Noctiluca scintillans red tide extraction in the field of satellite remote sensing， a Noctiluca scintillans red tide extraction method from Unmanned Aerial Vehicle （UAV） images based on deep learning was proposed. Firstly， the high-resolution RGB （Red-Green-Blue） videos collected by UAV were used as the monitoring data， the backbone network was modified to VGG-16 （Visual Geometry Group-16） and the spatial dropout strategy was introduced on the basis of the original UNet++ network to enhance the feature extraction ability and prevent the overfitting respectively. Then， the VGG-16 network pre-trained by using ImageNet dataset was applied to perform transfer learning to increase the network convergence speed. Finally， in order to evaluate the performance of the proposed method， experiments were conducted on the self-built red tide dataset Redtide-DB. The Overall Accuracy （OA）， F1 score， and Kappa of the Noctiluca scintillans red tide extraction of the proposed method are up to 94.63%， 0.955 2， 0.949 6 respectively， which are better than those of three traditional machine learning methods — K-Nearest Neighbors （KNN）， Support Vector Machine （SVM） and Random Forest （RF） as well as three typical semantic segmentation networks （PSPNet （Pyramid Scene Parsing Network）， SegNet and U-Net）. Meanwhile， the red tide images of different shooting equipment and shooting environments were used to test the generalization ability of the proposed method， and the corresponding OA， F1 score and Kappa are 97.41%， 0.965 9 and 0.938 2， respectively， proving that the proposed method has a certain generalization ability. Experimental results show that the proposed method can realize the automatic accurate Noctiluca scintillans red tide extraction in complex environments， and provides a reference for Noctiluca scintillans red tide monitoring and research work.

Aiming at the problem of data sparseness in recommendation algorithms, social data are usually introduced as auxiliary information for social recommendation. The traditional social recommendation algorithms ignore users' interest transfer, which makes the model unable to describe the dynamic characteristics of user interests, and the algorithms also ignore the dynamic characteristics of social influences, which causes the model to treat long before social behaviors and recent social behaviors equally. Aiming at these two problems, a social recommendation model named SLSRec with dynamic integration of social information was proposed. First, self-attention mechanism was used to construct a sequence model of user interaction items to implement the dynamic description of user interests. Then, an attention mechanism with forgetting with time was designed to model the short-term social interests, and an attention mechanism with collaborative characteristics was designed to model long-term social interests. Finally, the long-term and short-term social interests and the user's short-term interests were combined to obtain the user's final interests and generate the next recommendation. Normalized Discounted Cumulative Gain (NDCG) and Hit Rate (HR) indicators were used to compare and verify the proposed model, the sequence recommendation models (Self-Attention Sequence Recommendation (SASRec) model) and the social recommendation model (neural influence Diffusion Network for social recommendation (DiffNet) model) on the sparse dataset brightkite and the dense dataset Last.FM. Experimental results show that compared with DiffNet model, SLSRec model has the HR index increased by 8.5% on the sparse dataset; compared with SASRec model, SLSRec model has the NDCG index increased by 2.1% on the dense dataset, indicating that considering the dynamic characteristics of social information makes the recommendation results more accurate.

In the era of big data, research in topic evolution is mostly based on the classical probability topic model, the premise of word bag hypothesis leads to the lack of semantic in topic and the retrospective process in analyzing evolution. An online incremental feature ontology based topic evolution algorithm was proposed to tackle these problems. First of all, feature ontology was built based on word co-occurrence and general WordNet ontology base, with which the topic in text stream was modeled. Secondly, a text stream topic matrix construction algorithm was put forward to realize online incremental topic evolution analysis. Finally, a text topic ontology evolution diagram construction algorithm was put forward based on the text steam topic matrix, and topic similarity was computed using sub-graph similarity calculation, thus the evolution of topics in text stream was obtained with time scale. Experiments on scientific literature showed that the proposed algorithm reduced time complexity to O(nK+N), which outperformed classical probability topic evolution model, and performed no worse than sliding-window based Latent Dirichlet Allocation (LDA). With ontology introduced, as well as the semantic relations, the proposed algorithm can demonstrate the semantic feature of topics in graphics, based on which the topic evolution diagram is built incrementally, thus has more advantages in semantic explanatory and topic visualization.

Long coherent integration duration is needed for weak Global Positioning System (GPS) signal acquisition. However, it is limited in 10ms by the navigation data bit flipping, which is far from enough. To further improve the acquisition sensitivity, a bit-flipping prediction algorithm was proposed. Firstly, the 5ms signal with possible involve bit-flipping, which was detected through the comparison of the coherent integration results of the several blocks of data, was canceled. Then coherent integration was applied to its sub-block signal in rest 15ms and the results did differential coherent integration to overcome its bit-flip and reduced the square loss of non-coherence. At the same time, the summation operation was done ahead of coherent integration to depress its computational complexity. The theoretical research and simulation results show that the acquisition sensitivity and acquisition efficiency have been improved, and the algorithm even can capture the weak signal with Signal-to-Noise Ratio (SNR) less than -50dB.

To further reduce the energy consumption of Wireless Sensor Network (WSN) strong k-barrier coverage for crossing behavior detection, the minimum energy consumption of strong k-barrier coverage was proved to be NP-hard firstly, and then a heuristic algorithm named HARPN which could adjust the sensing power of nodes was proposed. In HARPN, four rules of computing node's sensing radius were put forward according to the distance between wireless nodes in barriers and the state of the preorder nodes, and then sensing power of nodes was determined based on the size of node's sensing radius. On the premise that sensing barriers must be connected, the energy consumption of overall barriers should be reduced as much as possible. The theoretical analysis and simulations show that the adaptability and stability of HARPN are stronger than the others, and its average energy consumption is about 62% of Heuristic-2's under the same network conditions of barrier fluctuation, which means the network lifetime is prolonged.

A free search algorithm was proposed for the discrete optimization problem. However，solutions simply got from free search algorithm often have crossover phenomenon. Then, an algorithm free search algorithm combined with cross elimination was put forward, which not only greatly improved the convergence rate of the search process but also enhanced the quality of the results. The experimental results using Traveling Saleman Problem (TSP) standard data show that the performance of the proposed algorithm increases by about 1.6% than that of the genetic algorithm.