The target size of the Unmanned Aerial Vehicle （UAV） is small， and the characteristics among multiple UAVs are not obvious. At the same time， the interference of birds and flying insects brings a huge challenge to the accurate detection and stable tracking of the UAV targets. Aiming at the problem of poor detection performance and unstable tracking of small target UAVs by using traditional target detection algorithms， a real-time tracking algorithm for multiple UAVs based on improved PaddlePaddle-YOLO （PP-YOLO） and Simple Online and Realtime Tracking with a Deep association metric （Deep-SORT） was proposed. Firstly， the squeeze-excitation module was integrated into PP-YOLO detection algorithm to achieve feature extraction and detection of UAV targets. Secondly， the Mish activation function was introduced into ResNet50-vd structure to solve the problem of vanishing gradient in the back propagation process and further improve the detection precision. Thirdly， Deep-SORT algorithm was used to track UAV targets in real time， and the backbone network that extracts appearance features was replaced with ResNet50， thereby improving the original network’s weak perceptual ability of small appearances. Finally， the loss function Margin Loss was introduced， which not only improved the class separability， but also strengthened the tightness within the class and the difference between classes. Experimental results show that the detection mean Average Precision （mAP） of the proposed algorithm is increased by 2.27 percentage points compared to that of the original PP-YOLO algorithm， and the tracking accuracy of the proposed algorithm is increased by 4.5 percentage points compared to that of the original Deep-SORT algorithm. The proposed algorithm has a tracking accuracy of 91.6%， can track multiple UAV targets within 600 m in real time， and effectively solves the problem of "frame loss" in the tracking process.

Concerning that the existing infectious disease spread model do not consider the influence and mechanism of specific special network structure and resource factors on controlling infectious disease outbreak， a discrete dynamic propagation model was established by combining the two-layer star coupling network with the Susceptible-Infected-Susceptible （SIS） model of infectious disease. In this model， the structural characteristics and the concept of average degree of the star network were used to derive the discrete equations of the proportion of infected population in every layer with resources and various parameters. Theory analysis and simulation experimental results indicate that， the multi-layer star coupling infectious disease spread network has resource thresholds. When the node is a leaf node， the network has two resource thresholds. Increasing the number of resources to control the spread of infectious diseases is only effective between the two resource thresholds. At this time， the proportion of population infected with infectious diseases decreases with the increase of resources invested. When the node is a central node， the resource threshold in the network reduces from two to one with the increase of proportion of infected population in other layers. Additionally， the control effect of the coupling strength of the inter-layer central node and the inter-layer leaf node on the epidemic varies with the location of the nodes.

The existing public key encryption schemes with auxiliary inputs are impractical since they are only of Indistinguishability under Chosen Plaintext Attack (IND-CPA) security. This paper constructed a novel public-key encryption scheme resilient to auxiliary input leakage, which was based on CS '98 encryption scheme and Goldreich-Levin theorem over large field GF(q). The proposed scheme was based on Indistinguishability under Adaptive Chosen Ciphertext Attack (IND-CCA2) security, allowing an attacker to query decryption oracle with auxiliary input leakage when it tried to attack the challenge ciphertext. Compared with the BHHO (Boneh, Halevi, Hamburg, Ostrovsky) encryption scheme, the proposed scheme realizes the more strict IND-CCA2 security in spite of the encryption's and decryption's overhead being nearly doubled.

Based on Complementary Ensemble Empirical Mode Decomposition (CEEMD)-fuzzy entropy and Echo State Network (ESN) with Leaky integrator neurons (LiESN), a kind of combined forecast method was proposed for improving the precision of short-term power load forecasting. Firstly, in order to reduce the calculation scale of partial analysis for power load series and improve the accuracy of load forecasting, the power load time series was decomposed into a series of power load subsequences with obvious differences in complex degree by using CEEMD-fuzzy entropy, according to the characteristics of each subsequence, and then the corresponding LiESN forecasting submodels were built, the ultimate forecasting results could be obtained by the superposition of the forecasting model. The CEEMD-LiESN method was applied to the instance of short term electricity load forecasting of the New England region. The experimental results show that the proposed combination forecasting method has a high prediction precision.

In order to meet the high requirements of real-time collision detection in increasingly complex virtual environment, a fast collision detection algorithm based on image space was proposed. It made efficiently use of the Graphics Processing Unit (GPU). Based on the hierarchical binary tree and the collision detection between Oriented Bounding Boxes (OBB), the algorithm could quickly eliminate disjoint bumps of the virtual scene. With the potential collision set, the efficiency of the algorithm has a significantly improvement on the basis of RECODE algorithm. The experimental results show that the algorithm achieves good results, and has a higher efficiency, especially in a highly complex virtual environment.

In order to eliminate the influence of Non-Line-Of-Sight (NLOS) transmission error in wireless sensor network node localization, and solve the problem of possible convergence in the Newton iterative algorithm, a Newton iterative localization algorithm based on the weighted residual was proposed. First, residual weighting algorithm was used for positioning to get the unknown node's preliminary position, then the node position was used as an initial value to iterate and calculate in Newton iterative localization algorithm, finally the precise position of the unknown node was obtained. The simulation results show that this algorithm can effectively restrain the effect of NLOS propagation error, improve the precision of sensor network node localization, and has stable performance.

Active queue management is a focus of current research. Random Early Detection (RED) is one kind of classical queue management algorithms. Linear RED is simple and easy to calculate; however, when average queue size is near to the minimum and maximum threshold, the loss rate is unreasonable. After verifying the nonlinear character between average queue size and packet loss rate, an improved RED algorithm named JRED was presented. The simulation on NS2 shows that the average throughput is improved, and the packet loss rate is decreased. With the JRED algorithm, the stableness and reliability of network are enhanced.