With the rapid development of Internet technology, many traditional auctions are gradually replaced by electronic auctions, and the security privacy protection problem in them becomes more and more concerned. Concerning the problems in the current electronic bidding and auction systems, such as the risk of the privacy of bidder being leaked, the expensive cost of third-party auction center is expensive, and the collusion between third-party auction center and the bidder, a sealed-bid auction scheme based on blockchain smart contract technology was proposed. In the scheme, an auction environment without third-party was constructed by making full use of the features of the blockchain, such as decentralization, tamper-proofing and trustworthiness; and the security deposit strategy of the blockchain was used to restrict the behaviors of bidders, which improved the security of the electronic sealed-bid auction. At the same time, Pedersen commitment was used to protect auction price from being leaked, and Bulletproofs zero-knowledge proof protocol was used to verify the correctness of the winning bid price. Security analysis and experimental results show that the proposed auction scheme meets the security requirements, and has the time consumption of every stage within the acceptable range, so as to meet the daily auction requirements.

Smart contract is one of the core technologies of blockchain,and its security and reliability are very important. With the popularization of blockchain application,the number of smart contracts has increased explosively. And the vulnerabilities of smart contracts will bring huge losses to users. However,the current research focuses on the semantic analysis of Ethereum smart contracts,the modeling and optimization of symbolic execution,and does not specifically describe the process of detecting smart contract vulnerabilities using symbolic execution technology,and how to detect common vulnerabilities in smart contracts. Based on the analysis of the operation mechanism and common vulnerabilities of Ethereum smart contract,the symbol execution technology was used to detect vulnerabilities in smart contracts. Firstly,the smart contract control flow graph was constructed based on Ethereum bytecode,then the corresponding constraint conditions were designed according to the characteristics of smart contract vulnerabilities,and the constraint solver was used to generate software test cases to detect the common vulnerabilities of smart contracts such as integer overflow,access control,call injection and reentry attack. The experimental results show that the proposed detection scheme has good detection effect, and has the accuracy of smart contract vulnerability detection up to 85% on 70 smart contracts with vulnerabilities in Awesome-Buggy-ERC20-Tokens.

User reviews contain a wealth of user opinion information which has great reference value to potential customers and merchants. Opinion targets and opinion words are core objects of user reviews, so the automatic extraction of them is a key work for user review intelligent applications. At present, the problem is solved mainly by supervised extraction method, which depends on high quality labeled samples to train the model. And traditional manual labeling method is time-consuming, laborious and costly. Crowdsourcing calculation provides an effective way to build a high-quality training sample set. However, the quality of the labeling results is uneven due to some factors such as knowledge background of the workers. To obtain high-quality labeling samples at a limited cost, an adaptive crowdsourcing labeling method based on professional level evaluation of workers was proposed to construct a reliable dataset of opinion target-opinion words. Firstly, high professional level workers were digged out with small cost. And then, a task distribution mechanism based on worker reliability was designed. Finally, an effective fusion algorithm for labeling results was designed by using the dependency relationship between opinion targets and opinion words, and the final reliable results were generated by integrating the labeling results of different workers. A series of experiments on real datasets show that the reliability of high quality opinion target-opinion word dataset built by the proposed method can be improved by about 10%, compared with GLAD (Generative model of Labels, Abilities, and Difficulties) model and MV (Majority Vote) method when the cost budget is low.

To deal with weak edge, intensity inhomogeneity and low contrast that may appear in finger-vein images, a new segmentation algorithm based on even-symmetric Gabor filter and level set method was proposed. Firstly, the even-symmetric Gabor filter was used to filter the finger-vein image through 8 different orientations; secondly, finger-vein image based on the 8 filtered results was reconstructed to obtain the high quality image with significantly improved gray contrast between target and background; finally, the level set algorithm combining local features and global features was applied to segment finger-vein image. Compared with the level set algorithm proposed by Li, et al. (LI C, HUANG R, DING Z, et al. A variational level set approach to segmentation and bias correction of images with intensity inhomogeneity. MICCAI'08: Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II. Berlin: Springer, 2008: 1083-1091), and Legendre Level Set (L2S) algorithm, the percentage of Area Difference (AD) of the proposed algorithm decreased by 1.116% and 0.370% respectively, and the Relative Difference Degree (RDD) reduced by 1.661% and 1.379% respectively. The experimental results show that the proposed algorithm can achieve better results compared with traditional level set image segmentation algorithms that only consider local information or global information.

In order to estimate the State-Of-Charge (SOC) of automobile power lithium-ion battery online, an Unscented Kalman Filtering (UKF) algorithm was proposed combined with neural network. First of all, Thevenin circuit was treated as an equivalent circuit, the state space representation of the battery model was established and the least square method was applied to identify the parameters of model. Then on this basis, the neural network algorithm was expected to fit the functional relationships between SOC of battery and model parameters respectively. After many experiments, the convergence curve of the neural network algorithm was determined. The proposed method was more accurate than the traditional curve fitting. In addition, the Extended Kalman Filtering (EKF) principle and the UKF principle were introduced separately and some tests were designed including the validation experiment of battery equivalent circuit model, the test experiment of SOC and the convergence experiment of the algorithms. The experimental results show that, the proposed method which can be used for SOC estimation online has higher estimation precision and stronger environmental adaptability than simple extended Kalman filtering algorithm under different conditions, its maximum error is less than 4%. Finally, the proposed algorithm combining UKF and neural network has better convergence and robustness, which can be used to solve the problems of inaccurate estimation of initial value and cumulative error effectively.

To study the influence of atmospheric space environment on satellite communications, the weather conditions were divided into clear sky, cloudy and foggy weather, rainy and snowy weather. The satellite channel propagation characteristics were researched under different weather conditions. The Rice model, C.Loo model and Suzuki model were established respectively. The three-state Markov process was introduced into the satellite channel model, which could describe the transformation of the channel states caused by the weather change. The statistical characteristics of the satellite channel three-state Markov model were analyzed. Finally, the satellite channel three-state Markov model was designed and simulated based on the sum of sinusoid method and the measured data of meteorological satellite cloud image. The simulation results show that the proposed model can be used in the simulation of actual satellite channel propagation characteristics.

A method to remove Scale Invariant Feature Transform (SIFT) mismatches based on Canonical Correlation Analysis (CCA) was presented to improve the quality of feature matching when the feature points locate in some similar structures of one image. At first, SIFT matching algorithm was used to get the initial matching pairs. Then, a line was fitted based on the linear relation between the canonical correlation components. The mismatches were removed by thresholding the distances from the points to the line. Furthermore, the influence of each remained match on the collineartiy degree was analyzed to indicate false matches. The experimental results show that the proposed algorithm can remove mismatches efficiently and keep more correct matches.

By analyzing the security of a certificateless signature scheme without bilinear pairing proposed by Wang Y, et al. (WANG Y, DU W. Security analysis and improvement of certificateless signature scheme without bilinear pairing. Journal of Computer Applications, 2013, 33(8): 2250-2252), the result that the scheme can not resist forgery attack was pointed out and an improved scheme was proposed. The improved scheme enhanced the relationship of parameters in signature algorithm to resist forgery attack. The results of security analysis show that the improved scheme is proved to be existentially unforgeable against adaptive chosen message and identity attacks in random oracle model. The improved scheme is more efficient than the existing schemes for avoiding bilinear pairings and inverse operation.

According to the feature of high-dimensional data, the number of variables is usually larger than the sample size and the data are often heterogeneous, a robust and effective feature selection method was proposed by using the dimensional reduction technique of variable selection and the modal regression based estimation method. The estimation algorithm was given by using Local Quadratic Algorithm (LQA) and Expectation-Maximum (EM) algorithm, and the selection method of the parameter adjustment was also discussed. Data analysis of the simulation shows that the proposed method is overall better than the least square and median regression based regularized method. Compared with the existing methods, the proposed method has higher prediction ability and stronger robustness especially for the non-normal error distribution.

The Chase2 algorithm is one of the soft-decision decoding algorithms of Turbo Product Code (TPC). The conventional Chase2 algorithm needs a large amount of computation to calculate the Euclidean distance and search for competing codeword, so it is complex to implement in the hardware. Therefore, on the basis of the conventional Chase2 algorithm, the correlation was substituted as the metric for the Euclidean distance and the process of searching for competing codeword was simplified to reduce the decoding complexity; and the soft-output value was adjusted when there was no competing codeword to improve the coding gain. The simulation results show that, compared with the conventional algorithm, the modified algorithm has better performance and faster decoding speed, and it is suitable for hardware implementation.

To synthesize facial pose and expression images simultaneously from one image, a tensor-based subspace projection method for synthesizing multi-pose and expression face images was proposed. Firstly, the forth order texture tensor and shape tensor were created from the feature annotated images respectively. Then a tucker tensor decomposition technique was applied to build projection subspaces (person, expression, pose and feature subspaces). Core tensors, expressions, poses and feature subspaces were organized into a new tensor properly which was used for synthesizing new facial poses and expressions. The proposed method took full advantage of the intrinsic relationship among the facial affected various factors. The experimental results show that the proposed method can synthesize different facial expressions with kinds of poses of the face using a known facial expression and pose image.

The construction of digital fingerprint embedding and acquisition scheme for anti-rotation, anti-scaling and anti-distortion attack can improve the anti-attack capability of the digital fingerprint. The designed spatial-DCT (Discrete Cosine Transform) domain combinational embedding scheme of digital fingerprint provided the construction of differential characteristic point, on which the digital fingerprint embedding and acquisition algorithm was proposed. And an attack parameter recognition algorithm with high accuracy was presented. The simulation results show that the accuracy of attack recognition algorithm can be the order of sub-pixel and can resist the scaling attack with parameter larger than 0.5, any rotation attack with angle less than 45° and any distortion attack with angle less than 25°. In addition, the effect of the proposed scheme would not decrease with the increase of the rotation and distortion angle. The proposed scheme improves the robustness of the digital fingerprint and enables the digital fingerprint system to resist the removal and CTP (cutting, trimming, pasting) attack as well as RSD (rotation, scaling, distoration) attack.

Mining spatial association rules can be used to discover the special spatial relationship between spatial predicate and nonspatial predicate in the spatial database.A multiple minimum supports-based algorithm for the discovery of spatial association rules was proposed,aiming at the road information of downtown area, and the general method of mining spatial association rules in GIS was presented.The algorithm can find the spatial association rules which users are interested in from city road geographical information database.Through the application in the city road planning and management system,the algorithm is proved to be effective and viable.

The performance of DiffServ in wireless ad hoc networks was evaluated by simulation. The simulation results show that bandwidths obtained by high and low priority traffics were not consistent with their WRR(Weighted Round Robin) weight ratios. Combined with simulation trace, this phenomenon is caused by MAC mechanism. We conclude that it is impossible to proportionally distribute resource only using DiffServ at network layer in wireless ad hoc networks.