In the practical applications of image classification,some categories may have no labeled training data at all. The purpose of Zero-Shot Learning(ZSL)is to transfer knowledge such as image features of labeled categories to unlabeled categories and to correctly classify the unlabeled categories. However,the existing state-of-the-art methods cannot explicitly distinguish the input image belonging to the known categories or unknown categories,which leads to a large performance gap for unlabeled categories between the traditional ZSL prediction and the Generalized ZSL(GZSL)prediction. Therefore,a method of fusing of visual error and semantic attributes was proposed to alleviate the prediction bias problem in zero-shot image classification. Firstly,a semi-supervised learning based generative adversarial network framework was designed to obtain visual error information,so as to predict whether the image belongs to the known categories. Then,a zero-shot image classification network combining semantic attributes was proposed to achieve zero-shot image classification. Finally,the performance of zero-shot image classification algorithm combining visual error and semantic attributes was tested on AwA2 (Animal with Attributes) and CUB (Caltech-UCSD-Birds-200-2011) datasets. The experimental results show that, compared to the baseline models,the proposed method can effectively alleviate the prediction bias problem,and has the harmonic index H increased by 31. 7 percentage points on AwA2 dataset and 8. 7 percentage points on CUB dataset.

Focusing on the issue that classic semi-supervised image segmentation methods have difficulty in accurately segmenting scattered or small regions, a semi-supervised segmentation algorithm based on label prior and Laplacian Coordinates (LC) was proposed. Firstly, the Laplacian coordinates model was extended, and further the relationship between unlabeled pixels and labeled pixels accurately characterized by introducing the label prior. Secondly, based on the derivation of matrix equation, the posterior probability that the pixel belongs to the label was able to be effectively estimated, thus achieving the segmentation of the image. Thanks to the introduction of the label prior, the algorithm was more robust to the segmentation of scattered and small regions. Lastly, the experimental results on several public semi-supervised segmentation datasets show that the segmentation accuracy of the proposed algorithm is significantly improved compared with that of the Laplacian coordinates algorithm, which verifies the effectiveness of the proposed algorithm.

For a Mobile Crowd Sensing (MCS) network has a large group of participants, and the acquisition and submission of tasks are almost unrestricted, so that data redundancy is high and data quality cannot be guranteed. To solve the problem, a method called Participant Reputation Evaluation Scheme (PRES) was proposed to evaluate the data quality and the reputation of participants. A participant's reputation was evaluated from five aspects:response time, distance, historical reputation, data correlation and quality of submitted data. The five parameters were quantified, and a regression equation was established by using logistic regression model to get the participant reputation after submitting data. The reputation credibility of a participant was in the interval[0.0, 1.0], and concentrated in[0.0,0.2] and[0.8, 1.0], making it easier for the group of mental perception network to choose appropriate participants, and the accuracy of the evaluation results by the crowd sensing showed that PRES was more than 90%.

Privacy preservation, a key factor in expanding the application of Wireless Sensor Network (WSN), is the current research hotspot. In view of the privacy of sensory data in WSN, Privacy-preserving Equal-Interval Approximate Query (PEIAQ) algorithm in two-tiered sensor networks based on data aggregation was proposed. Firstly, sensor node IDs and sensory data were concealed in a random vector, and then linear equations were worked out by the base station based on the random vector. As a result, a histogram containing global statistics was formed, and finally the results of approximate query were obtained. In addition, sensory data were encrypted through perturbation technique and sharing key between the sensor node and base station, which can ensure the privacy of sensory data. Simulation experiments show that the PEIAQ has a 60% decrease approximately in the traffic compared with PGAQ (Privacy-preserving Generic Approximate Query) in the query phase. Therefore, PEIAQ is efficient and costs low-energy.

To solve the problems of small fault depth and complex manual deduction in previous fault attacks on SIMON, an Algebraic Fault Attack (AFA) method was proposed. Firstly, Correct equations of full-round SIMON encryption was established based on the algebraic representation of SIMON core operation ‘&’. Then faults were injected into the internal states and two models were provided for fault representation based on whether attackers knew the exact fault information or not. Finally, a CryptoMinisat-2.9.6 solver was used for round-keys recovery. The simulation results show that the fault-known and fault-unknown model need 5 and 6 faults to recover the entire key set with single-bit faults injected in the 26th round of SIMON32/64. As for SIMON128/128, two models both need only 2 faults to recover the entire key set with n-bit length faults injected in the 65th round. Moreover, it can be found that the influencing factor of average solving time will change from fault information to computation with fault number growing.

The sensor data of sensor node is easy to be influenced by the external environment, which makes the incomplete data exist widely in the wireless sensor network and the sensor data face the serious privacy threat. Aiming at the problem of privacy leakage during the query process of incomplete data in two-tiered sensor networks, a Privacy-Preserving Incomplete data Skyline query protocol in two-tiered sensor network (PPIS) based on replacement algorithm and bucket technology was proposed. In order to realize the Skyline query for incomplete data, the value of the missing attribute was replaced to the upper bound of data field and then the incomplete data was mapped into the buckets. In order to preserve the privacy of data, the range of the bucket was transformed into a prefix encoding and then the prefix encoding was loaded into Bloom filters. Thus, the query processing could be executed by the storage node without clear text of the sensor data and real range of the bucket. In order to preserve the integrity of query results, Merkle hash tree was used to construct the integrity verification code for implementing the integrity verification of query results. Theoretical analysis and simulation experiment of real dataset has confirmed the privacy and efficiency of PPIS. Compared with existing privacy-preserving Skyline query protocols-SMQ (Secure Multidimensional Query) and SSQ (Secure Skyline Query), the proposed PPIS can save the communication cost by more than 70%.

Concerning the poor efficiency in unknown protocol frame segmentation, an unknown protocol frame segmentation algorithm based on preamble mining was proposed. Firstly, the principle of the preamble being used as the start of frame was introduced. As the cause that the existing frequent sequence mining algorithm cannot mine long preamble directly, the problems in candidate sequence selection were analyzed. Combining with the characteristics of preamble, two methods for selecting candidate sequences from the target bit streams and selecting candidate sequence based on the variation of the size of candidate sequence set were given. Secondly, an algorithm inferring the length of preamble and mining the preamble was put forward for unknown protocol frame segmentation. Finally, experiments were conducted with real bit streams captured from the Ethernet. The experimental results show that the proposed algorithm can rapidly and accurately mine the preamble sequence in the bit stream of the unknown protocol with lower space and time complexity.

Since the efficiency of rule mining and validity of recommendation are not high in personalized friends recommendation based on association rules, an improved association rule algorithm based on bitmap and hashing, namely BHA, was proposed. The mining time of frequent 2-itemsets was decreased by introducing hashing technique in this algorithm, and the irrelevant candidates were compressed to decrease the traversal of data by using bitmap and relevant properties. In addition, on the basis of BHA, a friend recommendation algorithm named STA was proposed based on similarity and trust. The problem of no displayed trust relationship in microblog was resolved effectively through trust defined by similarity of out-degree and in-degree; meanwhile, the defect of the similarity recommendation without considering users' hierarchy distance was remedied. Experiments were conducted on the user data of Sina microblog. In the comparison experiment of digging efficiency, the average minging time of BHA was only 47% of the modified AprioriTid; in the comparison experiment of availability in friend recommendation with SNFRBOAR (Social Network Friends Recommendation algorithm Based On Association Rules), the precision and recall of BHA were increased by 15.2% and 9.8% respectively. The theoretical analysis and simulation results show that STA can effectively decrease average time of mining rules, and improve the validity of friend recommendation.

There are over-fitting phenomenon and prone omissions when moving window adaptive Kernel Principal Component Analysis (KPCA) is utilized to deal with sensitive parameters or slow degradation problem. In order to solve the problem, a step dynamic auto-regression KPCA was proposed. Firstly, the initial model was established step by step drawing on dynamic data matrix. Then, the exponentially weighting rule was introduced to process real-time data and update the model based on the moving window adaptive KPCA. Finally, the algorithm complexity was analyzed and specific steps were given. The simulation data was utilized to analyze the impact of decomposition coefficient and weighting factor. The results show that, compared with the moving window adaptive KPCA, the proposed algorithm efficiency was improved by nearly 90% and the number of false positives was almost 0 in the case of appropriate parameter selection; and it could also control the adaptive ability to solve a variety of dynamic problems by adjusting the value of weighting factor. The algorithm was applied to the experimental data analysis of compressor surge and bearing fault, the result verified its ability to deal with the problem of sensitive parameter and slow degradation.

The sensitive relationships between users are important privacy information in social networks. Focusing on the issue of sensitive relationships leakage between users, an edge partitioning algorithm was proposed. Firstly, every non-sensitive edge was partitioned into some sub-edges after the sensitive edge was deleted in social networks. Secondly, every sub-edge was assigned information which belongs to the original non-sensitive edge. So every sub-edge contained part information of the original non-sensitive edge. The anonymized social network that preserves privacy was generated finally. In the comparison experiments with cluster-edge algorithm and cluster-based with constraints algorithm, the edge partitioning algorithm had a greater decrease of the probability of sensitive relationships leakage with maintaining high availability of data. The probability was decreased by about 30% and 20% respectively. As a result, the edge partitioning algorithm can effectively protect sensitive relationships in social networks.

Concerning the problem that severe signal multipath effect, low accuracy of sensor node positioning, etc. in narrow space, a new method using Weighted Centroid Localization (WCL) algorithm based on Received Signal Strength Indicator (RSSI) was proposed. The algorithm was used in scenarios with characteristics of long and narrow strip space, and it could dynamically acquire the decline index of path by RSSI and distance of neighbor beacon node signal, improve the environmental adaptation of RSSI distance detection algorithm. In addition, the algorithm based on environment improved weight coefficient of weighted centroid algorithm by introducing correction factor, which improved the accuracy of localization. Theoretical analysis and simulation results show that the algorithm has been optimized to adapt to narrow space. As compared with the Weighted Centroid Localization (WCL) algorithm, in roadway environment with the width of 3 m, 5 m, 8 m, 10 m respectively and 10 beacon nodes, positioning precision increases 22.1%, 19.2%, 16.1% and 16.5% respectively, the stability increases 23.4%, 21.5%, 18.1% and 15.4% respectively.

To solve the problem that current Decentralized Information Flow Control (DIFC) systems are unable to monitor the integration of host and network sensitive data effectively, a new design framework of DIFC system based on Software Defined Network (SDN), called S-DIFC, was proposed. Firstly, this framework used DIFC modules to monitor files and processes in host plane with fine granularity. Moreover, label mapping modules were used to block network communication and insert sensitive data labels into network flow. Meanwhile the multi-level access control of the flow with security label was implemented with SDN's controller in network plane. Finally, S-DIFC recovered security labels carried by sensitive data in DIFC system on target host. The experimental results show S-DIFC influences host with CPU performance decrease within 10% and memory performance decrease within 1.3%. Compared to Dstar system with extra time-delay more than 15 seconds, S-DIFC mitigates communication overhead of distributed network control system effectively. This framework can meet the sensitive data security requirements of next generation network. In addition, the distributed method can enhance the flexibility of monitor system.

To solve the problem of too many parameters and low prediction precision in the traditional aerodynamic 4D trajectory prediction models, an Improved Kalman Filter (IKF) algorithm was proposed to estimate the 4D trajectory, which increased the accuracy of trajectory prediction through real-time estimation of system noise. First, according to the varying direction and velocity of aircraft during flight, the velocity was shifted. Then, the prediction models were set up separately by KF and IKF. Finally, by comparing the predictive deviations in X, Y and Z directions by two algorithms, the smaller one was selected. The simulation results illustrate that the deviations respectively reduce by 17.65% and 98.03% in X and Y directions by IKF; meanwhile, KF has higher accuracy in Z direction. Besides, according to the analysis of IKF in different time interval, within the width of protection zone of arrival procedure (9.46km), the time interval could be increased to 20s.

For the vacancies on digital watermarking technology based on 2D-vector animation, this paper proposed a blind watermarking scheme which made full use of vector characteristics and the timing characteristics. This scheme adopted color values of adjacent frames in vector animation changed elements as embedded target. And it used Least Significant Bit(LSB) algorithm as embedding/extraction algorithm, which embedded multiple group watermarks to vector animation. Finally the accurate watermark could be obtained by verifying the extracted multiple group watermarks. Theoretical analysis and experimental results show that this scheme is not only easy to implement and well in robustness, but also can realize tamper-proofing. What's more, the vector animation can be played in real-time during the watermark embedding and extraction.

Code function description extraction is the basic premise of functional integration. To address the problem of common low accuracy rate of extraction of code function, the mechanism of extracting code function description based on formal semantic of Clight was proposed, and it was realized by algorithm of Clight code function description. This mechanism was strictly based on Clight natural semantics inference rules, ignoring the details of code execution in the middle, only concerned with the memory states before and after implementation. And as a functional description of the code feature description, it improved the accuracy of functional extraction and the success rate of the key area in software development.

Group key agreement is one of the important stages to carry out secure multicast communication. A group controller node switch method was given pointing to the problem of satellite network topology changed dynamically. It could adjust controlling nodes in a dynamic way. Then, both authentication and integrality mechanism were used to attest agreement messages and group keys, a group key generation and renewing method was proposed, which could improve security of agreement messages. The results of simulation and analysis show that this group key agreement protocol leads to high efficiency and security.

The advantage of precise sensor network compared to the traditional sensor network was introduced firstly. Then the traffic flow model of precise sensor network was built. To improve the interpretability of the model, the precise license plate identification data were used, and the variables such as the space travel time and the classified traffic flow were introduced. The established model was dynamic in essence. The experimental results show that the fitting accuracy is higher and the mean absolute error between fitted and standard value is less than 9 seconds, and the mean relative error is less than 5%. The model has a high degree of accuracy above 90%. Finally, the great value of precise sensor network in real traffic environment was summarized.

How to bring geometric objects of different spatial coordinates into the same spatial coordinate is a common problem during the cooperation of completing certain work. However, as related to their own security and interests, the partners both do not want to disclose their secret input. Therefore, firstly, a security protocol for distance measure of different spatial coordinates was proposed and designed. Furthermore, a security protocol for sphere-sphere relation of different spatial coordinates was developed. The correctness, security and efficiency of these two protocols were analyzed too. Having the private information of both sides protected, the problem of determining the relative position of sphere-sphere is successfully solved by using the proposed protocols.

In order to improve the information processing capacity of chaotic neural networks, associative memory performance of a time-delay symmetric global coupled neural network was investigated by using a parameter modulated control method to control the coherent parameter. It can be observed that its output can be stabilized when only partial neurons enter the periodic orbits and the output sequence of the controlled network does not contain other patterns but the stored pattern corresponding to the initial input and its reverse pattern. The experimental results suggest that the network has good tolerance and excellent correct rate so that it is fit for information processing and pattern recognition.

Security routing protocol is a key element to guarantee satellite network security. To solve the problem that most of routing protocols lack security scheme, the Elliptic Curve Pintsov-Vanstone Signature Scheme (ECPVSS) was used to attain confidentiality and authentication of packets, and trust evaluation scheme could exclude internal malicious node from the route path. Then a security routing protocol oriented to High Altitude Platform (HAP)/Low Earth Orbit (LEO) architecture was formed. The analysis results show that the proposed protocol can prevent network from some common routing attacks.

A new approach to face recognition was proposed,based on Simplified Laterally Interconnected Synergetically Self-Organizing Map(S-LISSOM) Model and a EHMM. The approach extracted face character mapped from the gray image by using the S-LISSOM Model to form the Observing Vectors. Then the faces were classified by Hidden Markov Models. The experiment results were compared with other two HMM based methods. The results show that the method proposed has a lower face recognition error rate.