Aiming at the few datasets and low detection rate when the traditional target detection algorithm applying to third-party construction target detection and illegally occupied building detection in the aerial images of drone,an aerial image target detection algorithm based on Aerial-YOLOv2 and transfer learning was proposed. Firstly,the trained network combining with data enhancement and transfer learning strategy was used to expand the dataset size,and K-means clustering analysis was used to obtain the number and size of anchor blocks that meet the characteristics of the proposed dataset. Secondly,the adaptive contrast enhancement was used to pre-process the image. Finally,the improved convolution module was proposed to replace the convolution block in YOLOv2,and the feature fusion multi-scale prediction method was combined for target detection. The comparison experiments of different algorithms and training strategies on the aerial images of drone were carried out. Results show that the accuracy and recall rate of the Aerial-YOLOv2 algorithm combined with various training strategies can respectively reach 95% and 91%,and the detection time per image is 14 ms. It can be seen that the algorithm is suitable for the intelligent detection of third-party construction targets and illegally occupied buildings in the aerial images of drone.

As a new type of attack, the CAN (Controller Area Network) bus-off attack can force the node to generate communication errors continuously and disconnect from the CAN bus through the error handling mechanism of the CAN bus communication. Aiming at the security problem of in-vehicle CAN bus communication caused by the bus-off attack, an intrusion detection algorithm for the in-vehicle CAN bus-off attack was proposed. Firstly, the conditions and characteristics of the CAN bus-off attack were summarized. It was pointed out that the synchronous transmission of normal message and malicious message is the difficulty of realizing the bus-off attack. And the front-end message satisfying the condition of synchronous transmission was used to realize the bus-off attack. Secondly, the characteristics of the CAN bus-off attack were extracted. By accumulating the transmission number of error frames and according to the change of message transmission frequency, the detection of the CAN bus-off attack was realized. Finally, the CAN communication node based on STM32F407ZGT6 was used to simulate the Electronic Control Unit (ECU) in the vehicle, and the synchronous transmission of the malicious message and the attacked message was realized. The experiment of CAN bus-off attack and the verification of intrusion detection algorithm were carried out. Experimental results show that the detection rate of the algorithm for high priority malicious messages is more than 95%, so the algorithm can effectively protect the security of the in-vehicle CAN bus communication network.

At present, the most common way for bitcoin mining is miners joining in a pool. However, there is a phenomenon that the mining pools penetrate each other, which will result in a decrease in the miners' income of the attacked pools, and a reduction in computing power of the attacking pools. Therefore, the overall computing power of the bitcoin system is reduced. Aiming at the problem of mutual attack and non-cooperative mining between mining pools, an Adaptive Zero-Determinant strategy (AZD) was proposed to promote the cooperation of miners. The strategy adopted the idea of comparing expected payoff with cooperation and defection in the next round then choosing a strategy with high payoff. Firstly, miners' payoff in the next round under two situations could be predicted by the combination of Temporal Difference Learning Method (TD(λ)) and Zero-Determinant strategy (ZD). Secondly, by comparing the cooperation payoff with defection payoff in the next round, a more favorable strategy was chosen for miners by Decision Making Process (DMP), so the cooperation probability and defection probability in the next round were changed correspondingly. Finally, through the iterative implementation of AZD strategy, the ming pools in the network would cooperate with each other and mine actively. Simulation results show that compared with adaptive strategy, AZD strategy increases the speed of converging cooperation probability to 1 by 36.54%, compared with ZD strategy, it improves the stability by 50%. This result indicates that AZD strategy can effectively promote the cooperation of miners, improve the convergence rate of cooperation and ensure the stable income of mining pools.

To solve the interference issue caused by Device-to-Device (D2D) users and small cellular users underlaying macro cellular user resources in heterogeneous cellular networks, a joint power controlled resource allocation scheme was proposed. Firstly, the optimal transmit power of each D2D user and small cell user underlaying macro-cell user channel resources was derived according to the system interference model by satisfying the user Signal to Interference and Noise Ratio (SINR) and transmit power constraints. Secondly, the user's channel selection was programmed as a two-sided matching problem between the user and the channel, and a stable matching solution was obtained by using the Gale-Shapley algorithm. Finally, the matching solution was taken as the initial condition, and the allocation scheme was further optimized by the exchange search algorithm. The simulation results show that the system total capacity and energy efficiency of the proposed scheme are 93.62% and 92.14% of the optimal solution. Compared with stochastic resource allocation scheme, the allocation scheme without power control and exchange search, and the allocation scheme with power control without exchange search, the system capacity averagely increases 48.29%, 15.97% and 4.8% respectively, and the system energy efficiency averagely increases 62.72%, 44.48% and 4.45% respectively. The proposed scheme can achieve approximately optimal system total capacity and effectively improves frequency utilization and energy efficiency.

Considering the issue of objective weight overwhelming subjective weight when the subjective weight and objective weight is inconsistent in multi-index evaluation problem, based on G1 method and the objective weighting method, an assembled weighting model combined with G1 method improved by composite power function was proposed. Firstly, an index system was built, and the subjective ranking and subjective initial vector were determined by G1 method. Thus, each objective index vector was calculated by objective weighting method. Secondly, without changing the ranking order, the comprehensive weights integrated with both subjective and objective components were obtained by utilizing composite power function. Lastly, comprehensive evaluation was calculated by using standardized values of indices and comprehensive weights. Merchants data crawled from Dianping.com was adopted for the experiments of comprehensive evaluation. The Root-Mean-Square Error (RMSE) of the new model was 3.891, which is lower than the result of 8.818 obtained by the G1-entropy weighting and the result of 4.752 obtained by the standard deviation improved G1. Meanwhile, the coverage rate obtained by the new model was better than the two baseline models as well. On the other hand, the RMSE obtained by changing subjective ranking order is 5.430, which is higher than the result of 1.17 that obtained by changing subjective initial vector. The experimental results demonstrate that the evaluation values obtained by the new model highly match with the counterparts given by the Dianping.com, and the model can significantly weaken the effect of initial subjective values, which reflects the fundamental status of the subjective ranking.

Concerning the output error model, order identification and parameter estimation were integrated and extended, and the determinant ratio method based on the auxiliary model was put forward. The system order and parameter estimation were got simultaneously with this method, so it could reduce calculation time in the process of system identification. Considering the inaccuracy of the determinant ratio method, a model validation method was proposed to enhance the identification ability. At last, the simulation indicates that the expended determinant ratio method effectively estimates the system order and parameters.

Binary Signed Digit (BSD) representation of an integer is widely used in computer arithmetic, cryptography and digital signal processing. An integer of length n bits can have several BSD representations. In this paper, the authors studied the properties of the number of BSD representation of an integer, and presented two improved nonrecursion algorithms. They can rapidly calculate the exact number of BSD representations of an integer of a certain length, and the storage requirements get reduced.

It is often difficult to classify Electrocardiogram （ECG） waveform automatically due to high similarity. A new feature representation of ECG waveform was proposed — window-slope method. In this method, an ECG waveform was divided into different windows in a plane, and the slope of maximum and minimum amplitude in a window was extracted as feature information to perform similarity analysis. The experimental results show that the method can not only reduce the dimension, but also can enlarge the difference between different types of waveforms under distance-based classification. The classification accuracy and efficiency can be improved by using the method; meanwhile the sensitivity and specificity of classification can be stabilized at a higher level.

The traditional ontology matching methods that use the ontology's structure to find the matches do not really make good use of the ontology's structural feature, which leads to considerable computation redundancies during the entire matching process. Therefore, a modified method named TARA was proposed to improve the matching process in this paper. The method firstly casted matching process by strictly using the ontology's structural information, and then a re-match process was applied to overcome the inevitable defect that caused by the matching process before. The experimental results show that the method has good performances in both recall and precision.