Aiming at the problem of huge losses caused by untimely and inactive emergency rescue information sharing in emergencies， a three-party game model of emergency rescue information sharing involving high-risk enterprises， rescue teams and government regulatory departments was established. Firstly， the payoff matrix and replicated dynamic equations were constructed based on the revenue. Then， stability analysis was performed for four different scenarios respectively. Finally， the evolution processes and results of the system under different scenarios were simulated through computer to obtain the optimal strategies for information sharing. Experimental results show that under the low benefit scenario， if the extra rewards and punishments are high， the willingness of emergency rescue teams to actively share rises to 0.2 and then gradually decreases until they reject information sharing completely； if the extra cost is high， the willingness of high-risk enterprises to actively share rises to about 0.2 and then rapidly decreases to 0. Meanwhile， the behavioral strategies of participants are most sensitive to the changes of positive benefit， then to the changes in extra rewards and punishments and extra costs. The above results can provide guidance for the selection of information sharing strategies in emergency response.

The current public datasets of Pedestrian Attribute Recognition （PAR） have the characteristics of complicated attribute annotations and various collection scenarios， leading to the large variations of the pedestrian attributes in different datasets， so that it is hard to directly utilize the existing labeled information in the public datasets for PAR in practice. To address this issue， a multi-source and multi-label PAR method based on domain adaptation was proposed. Firstly， to transfer the styles of the different datasets into a unified one， the features of the samples were aligned by the domain adaption method. Then， a multi-attribute one-hot coding and weighting algorithm was proposed to align the labels with the common attribute in multiple datasets. Finally， the multi-label semi-supervised loss function was combined to perform joint training across datasets to improve the attribute recognition accuracy. The proposed feature alignment and label alignment algorithms were able to effectively solve the heterogeneity problem of attributes in multiple PAR datasets. Experimental results after aligning three pedestrian attribute datasets PETA， RAPv1 and RAPv2 with PA-100K dataset show that the proposed method improves the average accuracy by 1.22 percentage points， 1.62 percentage points and 1.53 percentage points respectively， compared to the method StrongBaseline， demonstrating that this method has a strong advantage in cross dataset PAR.

To solve the problems of the poor reduction performance of Filter Bank MultiCarrier (FBMC) signals' Peak-to-Average Power Ratio (PAPR) and the high Side Information Error Rate (SIER) of the existing Selected Mapping (SLM) method to reduce PAPR signals, an SLM method with embedded Side Information (SI) was presented to reduce PAPR. At the transmitter, a group of phase rotation vectors with embedded SI were designed, and the candidate data blocks were generated by multiplying the phase rotation vectors with the transmitting data blocks. By using the outputs of Inverse Discrete Fourier Transform (IDFT) of the real and imaginary components of the candidate data blocks, the candidate FBMC signals based on cyclic time shift were designed and the candidate signal with the lowest PAPR was selected and transmitted. At the receiver, by using the difference between the phase rotations of the SI subcarrier data, a low-complexity SI detector unrelated to modulation order of transmitted symbols was proposed. Simulation results show that the proposed method can effectively reduce the PAPR of FBMC signals at the transmitter and obtain good SI detection and Bit Error Rate (BER) performances at the receiver.

Aiming at the tracking problem of multiple extended targets under the circumstances with unknown measurement noise covariance, an extension of standard Extended Target Probability Hypothesis Density (ET-PHD) filter and the way to realize its analysis were proposed by using ET-PHD filter and Variational Bayesian (VB) approximation theory. Firstly, on the basis of the target state equations and measurement equations of the standard ET-PHD filter, the augmented state variables of target state and measurement noise covariance as well as the joint transition function of the above variables were defined. Then, the prediction and update equations of the extended ET-PHD filter were established based on the standard ET-PHD filter. And finally, under the condition of linear Gaussian assumptions, the joint posterior intensity function was expressed as the Gaussian and Inverse-Gamma (IG) mixture distribution, and the analysis of the extended ET-PHD filter was realized. Simulation results demonstrate that the proposed algorithm can obtain reliable tracking results, and can effectively track multiple extended targets in the circumstances with unknown measurement noise covariance.

To solve the anomalous smoothing problems caused by the missed detection or target disappearance in the particle Probability Hypothesis Density (PHD) smoother, an improved method based on the modified target survival probability was proposed. Firstly, the prediction and update formulas of forward filtering were modified to obtain the target intensity function of filtering and estimate the number of survival targets in filtering process. On this basis, using the estimated value changes of forward filtering of survival number to judge whether targets disappearance or missed detection occurring, and the survival probability used in backward smoothing calculation was defined. Then, the iterative calculating formula for backward smoothing was improved with the obtained survival probability, and the particle weights were obtained on this basis. The simulation results show that the proposed method can solve the anomalous smoothing problems in PHD smoother effectively, its time averaged Optimal SubPattern Assignment (OSPA) distance error is decreased from 7.75 m to 1.05 m compared with standard algorithm, which indicates that the tracking performance of the proposed method is improved significantly.

In order to solve the problem of human aggressive behavior recognition, an aggressive behavior recognition method based on human joint points was proposed. Firstly, OpenPose was used to obtain the human joint point data of a single frame image, and nearest neighbor frame feature weighting method and piecewise polynomial regression were used to realize the completion of missing values caused by body self-occlusion and environmental factors. Then, the dynamic "safe distance" threshold was defined for each human body. If the true distance between the two people was less than the threshold, the behavior feature vector was constructed, including the human barycenter displacement between frames, the angular velocity of human joint rotation and the minimum attack distance during interaction. Finally, the improved LightGBM (Light Gradient Boosting Machine) algorithm, namely w-LightGBM (weight LightGBM), was used to realize the classification and recognition of aggressive behaviors. The public dataset UT-interaction was used to verify the proposed method, and the accuracy reached 95.45%. The results show that this method can effectively identify the aggressive behaviors from various angles.

An SYN packet might be dropped when initiating a Transmission Control Protocol (TCP) connection in data centers, causing the tasks missing the expected deadline; accordingly, without changing the existing devices, applications and the TCP itself, a viable mechanism based on Weighted Random Early Detection (WRED) was proposed to avoid the drop of SYN packets. Three related key problems were solved by the proposed method:how to mark and recognize the SYN packets; how to reserve space for the SYN packets in switches; how much space is required for reserving. Compared with the original TCP, the TCP connection establishment time was greatly reduced after optimizing. The simulation results show that the connection initialization optimization method can solve the problem of tasks missing the expected deadline.

Currently, various image inpainting algorithms based on Curvature-Driven Diffusion (CDD) model only make use of the reference information of four neighborhood pixels. Therefore, they cannot keep shape edges and their inpainting precisions high enough. To conquer these difficulties, the image inpainting algorithm based on double-cross CDD was presented, in which the reference information for damaged pixel was extended from four into eight neighborhood pixels. Firstly, one inpainting value for damaged pixel could obtain from the reference information of four neighborhood pixels using the original CDD algorithm. Secondly, another new inpainting value was computed with the newly introduced four neighborhood pixels. Finally, the final inpainting value was a weighted mean of the above-mentioned two inpainting computational value. The proposed method, original CDD algorithm and its improved editions were implemented and compared in the experiments. The experimental results show that the proposed algorithm can effectively improve the inpainting precision and keep shape edges without increasing time complexity.

To alleviate the computational bottleneck of spectral clustering, in this paper a general ensemble algorithm, called indirect spectral clustering, was developed. The algorithm first grouped a given large dataset into many overclusters and then regarded each obtained overcluster as a basic object. And then the standard spectral clustering ran at this object level. By convention, when applying this new idea to large text datasets, the cosine distance would be the appropriate manner in measuring the similarities between overclusters. The empirical studies on 20-Newgroups dataset show that the proposed algorithm has a 14.72% higher accuracy on average than the K-Means algorithm and has a 0.88% lower accuracy than the normalizedcut spectral clustering. However, the proposed algorithm saves 16.8 times computation time compared to the normalizedcut spectral clustering. In conclusion, with the increase of data size, the computation time of the normalizedcut spectral clustering might become unacceptable; however, the proposed algorithm might efficiently give the nearoptimal solutions.