In order to solve the problem of most current research work only considering the factors affecting pedestrian interaction， based on State-Refinement Long Short-Term Memory （SR-LSTM） and attention mechanism， a Social-Interaction Generative Adversarial Network （SIGAN） for pedestrian trajectory prediction was proposed， namely SRA-SIGAN， where GAN was utilized to learn movement patterns of target pedestrians. Firstly， SR-LSTM was used as a location encoder to extract the information of motion intention. Secondly， the influence of pedestrians in the same scene was reasonably assigned by setting the velocity attention mechanism， thereby handling the pedestrian interaction better. Finally， the predicted future trajectory was generated by the decoder. Experimental results on several public datasets show that the performance of SRA-SIGAN model is good on the whole. Specifically on the Zara1 dataset， compared with SR-LSTM model，the Average Displacement Error （ADE）and Final Displacement Error （FDE）of SRA-SIGAN were reduced by 20.0% and 10.5%，respectively；compared with the SIGAN model，the ADE and FDE of SRA-SIGAN were decreased by 31.7% and 24.4%，respectively.

Aiming at difficult multi-target identification recognition and low localization accuracy in mobile cellular networks, a multi-objective automatic identification and localization method was presented based on cellular network structure to improve the detection efficiency of target number and the localization accuracy of each target. Firstly, multi-target existence was detected through the analysis of the result variance of multiple positioning in the monitoring area. Secondly, cluster analysis on locating points was conducted by k-means unsupervised learning in this study. As it is difficult to find an optimal cluster number for k-means algorithm, a k-value fission algorithm based on beam resolution was proposed to determine the k value, and then the cluster centers were determined. Finally, to enhance the signal-to-noise ratio of received signals, the beam directions were determined according to cluster centers. Then, each target was respectively positioned by Time Difference Of Arrival (TDOA) algorithm by the different beam direction signals received by the linear constrained narrow-band beam former. The simulation results show that, compared to other TDOA and Probability Hypothesis Density (PHD) filter algorithms in recent references, the presented multi-objective automatic identification and localization method for solving multi-target localization problems can improve the signal-to-noise ratio of the received signals by about 10 dB, the Cramer-Mero lower bound of the delay estimation error can be reduced by 67%, and the relative accuracy of the positioning accuracy can be increased more than 10 percentage points. Meanwhile, the proposed algorithm is simple and effective, is relatively independent in each positioning, has a linear time complexity, and is relatively stable.

Concerning the quantization error when the local features were quantified by the visual vocabulary in traditional Bag-of-Visual-Word (BoVW) model, an image retrieval model based on visual vocabulary with weighted feature space information was proposed. Considered the clustering method which was used to generate the visual codebook, the statistic information of the feature space was analyzed during the clustering process. Through the comparison of different weighting methods by experiments, the best weighting method, mean weighted average, was found to weight the visual words to improve the descriptive ability of the codebook. The experiment on ImageNet dataset shows that, compared to homologous visual codebook, non-homologous visual codebook has less impact on dividing the visual space, and the effects of the weighted feature space based visual codebook on big dataset are better.

Constant improvement of botnet structure has caused great threat to network security, so it is very important to study the inherent characteristics of botnet structure to defense this kind of attack. This paper simulated the unstructured P2P botnet from the perspective of complex network, then proposed metrics and applied the theory of complex centrality to analyze the robustness of the unstructured P2P botnet when it encountered nodes failure. The experimental results demonstrate that the unstructured P2P botnet displays high robustness when it encounters random nodes failure, but its robustness drops quickly when it encounters central nodes failure.