In view of the high cost as well as the limitation of retrieval function of the existing searchable encryption schemes based on blockchain to realize result verification and fair payment， a multi-keyword ranked search scheme supporting verification and fair payment was proposed based on blockchain. In the proposed scheme， the Cloud Service Provider （CSP） was used to store the encrypted index tree and perform search operations， and a lookup table including verification certificates was constructed to assist the smart contract to complete the verification of retrieval results and fair payment， which reduced the complexity of smart contract execution and saved time as well as expensive cost. In addition， the index of balanced binary tree structure was constructed by combining vector space model and Term Frequency-Inverse Document Frequency （TF-IDF）， and the index and query vectors were encrypted by using secure K -nearest neighbor， which realized the multi-keyword ranked search supporting dynamic update. Security and performance analysis show that the proposed scheme is secure and feasible in the blockchain environment and under the known ciphertext model. Simulation results show that the proposed scheme can achieve result verification and fair payment with acceptable cost.

The consensus problem of the two-layer multi-agent systems subjected to cyber-attacks was studied. Aiming at the two-layer multi-agent systems composed of the leaders' layer and the followers' layer, the situation as the following was given:the neighboring agents in the leaders' layer were cooperative, the adjacent agents in the followers' layer were cooperative or competitive, and there was a restraining relationship between some corresponding agents in the leaders' layer and the followers' layer. The consensus problem among the nodes of leaders' layer, followers' layer and two-layer multi-agent systems subjected to cyber-attack was discussed. Based on the related knowledge such as Linear Matrix Inequality (LMI), Lyapunov stability theory and graph theory, the sufficient criteria for consensus between the nodes in the leaders' layer multi-agent system, bipartite consensus between the nodes in the followers' layer multi-agent system and node-to-node bipartite consensus between the nodes in the two-layer multi-agent systems were given. Finally, the numerical simulation examples were given, and the consensus of the two-layer multi-agent systems subjected to cyber-attack was realized, which verified the validity of the proposed criteria.

Aiming at the credibility problem, a secure multi-party blockchain auditable signature scheme was proposed. In the proposed scheme, the trust vector with timestamp was introduced, and a trust matrix composed of multi-dimensional vector groups was constructed for regularly recording the trustworthy behavior of participants, so that a credible evaluation mechanism for the participants was established. Finally, the evaluation results were stored in the blockchain as a basis for verification. On the premise of ensuring that the participants are trusted, a secure and trusted signature scheme was constructed through secret sharing technology. Security analysis shows that the proposed scheme can effectively reduce the damages brought by the malicious participants, detect the credibility of participants, and resist mobile attacks. Performance analysis shows that the proposed scheme has lower computational complexity and higher execution efficiency.

In the process of network traffic classification, the traditional model has poor classification on minority classes and cannot be updated frequently and timely. In order to solve the problems, a network Traffic Classification Model based on Ensemble Learning (ELTCM) was proposed. First, in order to reduce the impact of class imbalance problem, feature metrics biased towards minority classes were defined according to the class distribution information, and the weighted symmetric uncertainty and Approximate Markov Blanket (AMB) were used to reduce the dimensionality of network traffic features. Then, early concept drift detection was introduced to enhance the model's ability to cope with the changes in traffic features as the network changed. At the same time, incremental learning was used to improve the flexibility of model update training. Experimental results on real traffic datasets show that compared with the Internet Traffic Classification based on C4.5 Decision Tree (DTITC) and Classification Model for Concept Drift Detection based on ErrorRate (ERCDD), the proposed ELTCM has the average overall accuracy increased by 1.13% and 0.26% respectively, and the classification performance of minority classes all higher than those of the models. ELTCM has high generalization ability, and can effectively improve the classification performance of minority classes without sacrificing the overall classification accuracy.

Aiming at the problems of color distortion and noise in night image dehazing based on Dark Channel Prior (DCP) and atmospheric scattering model method, a Conditional Generated Adversarial Network (CGAN) dehazing algorithm based on Msmall-Patch training (MP-CGAN) was proposed. Firstly, UNet and Densely connected convolutional Network (DenseNet) were combined into a UDNet (U Densely connected convolutional Network) as the generator network structure. Secondly, Msmall-Patch training was performed on the generator and discriminator networks, that was, multiple small penalty regions were extracted by using the Min-Pool or Max-Pool method for the final Patch of the discriminator. These regions were degraded or easily misjudged. And, severe penalty loss was proposed for these regions, that was, multiple maximum loss values in the discriminator output were selected as the loss. Finally, a new composite loss function was proposed by combining the severe loss function, the perceptual loss and the adversarial perceptual loss. On the test set, compared with the Haze Density Prediction Network algorithm (HDP-Net), the proposed algorithm has the PSNR （Peak Signal-to-Noise Ratio） and SSIM （Structural SIMilarity index） increased by 59% and 37% respectively； compared with the super-pixel algorithm, the proposed algorithm has the PSNR and SSIM increased by 59% and 48% respectively. The experimental results show that the proposed algorithm can reduce the noise artifacts generated during the CGAN training process, and improve the night image dehazing quality.

In order to solve the image degradation in the non-uniform dust environment with multiple scattering lights, a slices reconstruction method for single image dedusting was proposed. Firstly, the slices along the depth orientation were produced based on McCartney model in dust environment. Secondly, the joint dust detection method was used to detect dust patches in the slices where non-dust areas were reserved but the dust zones were marked as the candidate detected areas of the next slice image. Then, an image was reconstructed by combining these non-dust areas of each slice and the dust zone of the last slice. Finally, a restored image was obtained by a fast guided filter which was applied to the reconstructed area. The experimental results prove that the proposed restoration method can effectively and quickly get rid of dust in the image, and lay the foundation of object detection and recognition work based on computer vision in dust environment.

Regarding to the resource allocation and partition starting time in the Integrated Modular Avionics (IMA), a Unified Modeling Language (UML) model of partition configuration and initialization was proposed based on the case study of VxWorks 653 partition operating system. The proposed model including classes diagram and initial sequence diagram for partition, was established to facilitate the analysis of the mechanism of partition configuration and starting/initialization. The contents and function of partition configuration in the processes of resources allocation, operating system compilation and partition initialization, were discussed in detail, as well as the differences between "cold start" and "warm start" mode. A platform was set up for testing the startup times of the two kinds of startup modes, and the test results showed that the time of cold start was 148 ms, and warm start time was 8.5 ms. Furthermore, the applicable scenarios for cold start and warm start mode were discussed. The policies of partition configuration and application software initialization were proposed based on the starting time. The mode of partition start and time of partition initialization should be fully considered when establishing the partition main time frame and identifying the health management policy. The designed policies can be applicable to other partition system design in high security applications.

In order to improve the decoding quality of depth map video in Distributed Multi-view Video plus Depth (DMVD) coding, a new non-uniform quantization scheme based on the sub-band layer and sub-band coefficients was proposed in wavelet domain Distributed Video Coding (DVC). The main idea was allocating more bits to pixels belong to the edge of depth map and consequently improving the quality of the depth map. According to the distribution characteristics of the wavelet coefficients of depth map, the low frequency wavelet coefficients of layer- N kept the uniform quantization scheme, while the high frequency wavelet coefficients of all layers used the non-uniform quantization scheme. For the high frequency wavelet coefficients around "0", larger quantization step was adopted. As the amplitude of the high frequency wavelet coefficients increased, the quantization step decreased, with finer quantization and the quality of the edge was improved consequently. The experimental results show that, for "Dancer" and "PoznanHall2" depth sequence with more edges, the proposed scheme can achieve up to 1.2 dB in terms of the Rate-Distortion (R-D) performance improvement by improving the quality of edges; for "Newspaper" and "Balloons" depth sequences with less edges, the proposed scheme can still get 0.3 dB of the R-D performance.

The standard Particle Swarm Optimization (PSO) suffers from the premature convergence problem and the slow convergence speed problem when solving complex optimal problems, so a Heterogenous PSO with Multi-strategy parallel learning (MHPSO) was presented. Firstly two new learning strategies, named local disturbance learning strategy and Gaussian subspace learning strategy respectively, were proposed to maintain the population's diversity and jump out from the local optima. And an efficient and stable strategy pool was constructed by combing the above two strategies with the existed one (MBB-PSO); Secondly, a simpler and more effective strategy change mechanism was proposed, which could guide particles when to change the learning strategy. The experimental study on a set of classical test functions show that the proposed approach improves the solution accuracy and convergence speed greatly, and has a superior performance in comparison with several other improved PSO algorithms, such as APSO (Adaptive Particle Swarm Optimization).

For the shortage of big data and incremental data processing in static algorithm, the dynamic rule extraction algorithm based on rough-decision tree was constructed to diagnose rotating machinery faults. Through the combination of rough set with decision tree, the sample selections were made by the method of incremental sampling. Through dynamic reduction, decision tree construction, rules extraction and selection, matching, four steps of loop iteration process, dynamic rule extraction was achieved, which improved the credibility of the extracted rules. Meanwhile, by applying the algorithm to the dynamic problem: rotating machinery fault diagnosis, the effectiveness of the algorithm was verified. Finally, the efficiency of the algorithm was compared with static algorithm and incremental dynamic algorithm. The result demonstrates that the proposed algorithm can obtain more implied information in the most streamlined way.

It is difficult to understand all the relations between firewall rules. Poorly-organized rules may cause the problem that firewall could not filter packets correctly. In order to solve this problem, an analysis method of inclusion relations between firewall rules based on set theory was proposed. Based on the inclusion relations in set theory, the proposed method analyzed and classified the relations between firewall rules without considering the actions of rules. The proposed method simplified the process of analysis relations between firewall rules, and it was implemented by using a functional programming language, Haskell. The whole Haskell codes were concise, which also were easy to maintain and expand. The experimental results show that, with regard to medium scale sets of rules, the proposed method can analyze the inclusion relations between firewall rules rapidly and effectively. The proposed method also provides an important basis for the succeeding rules conflict detection.

After analyzing a classical Radio Frequency Identification (RFID) indoor positioning system named LANDMARC, an improved RFID indoor positioning algorithm based on reference tags' credibility and deviation correction was proposed to enhance the positioning accuracy. This algorithm introduced reference tags to assist in positioning process. After checking the credibility of all nearest neighbour reference tags, a small number of incredible tags were discarded. Then the system did deviation correction of positioning aiming at these final selected nearest neighbor reference tags. At last, the position of the tag to be positioned was calculated. The experimental results show that, compared with the original LANDMARC, this algorithm improves positioning accuracy and is suitable for the application of indoor objects.

Low Density Parity Check Code (LDPC) is a kind of (n, k) linear block codes. The conventional encoding method can complete the encoding work when the length of the codes is short, but as the codes become longer, the memory of computer is hard to bear when still using the common encoding methods. To solve this problem, two kinds of effective encoding and decoding schemes were proposed. Firstly, different from the traditional data parity bit decoding, the proposed data reconciliation scheme used side information and syndrome produced by the initial data to employ joint decoding. Secondly, the parity matrix was stored in a way that only the positions of 1 in the form of the cross circular list were recorded, which could greatly save memory space. At last, C implementation could improve effectiveness of the codes. Length of a block of codes in this experiment was 105. The Bit Error Ratio (BER) of codes was converged above 1.0dB, only in need of 4 seconds to decode one block, the code rate could reach 24.85kb/s when the decoder was converged. The results show that the proposed schemes have strong timeliness.

Due to the mutability of unstrained or handwritten digits, most algorithms in previous study either forfeited easy implementation for high accuracy, or vice versa. This paper proposed a new feature descriptor named Distance Distribution Histogram (DDH) and adapted Shape Accumulate Histogram (SAH) feature descriptor based on shape context which was not only easy to implement, but also was robust to noise and distortion. To make hybrid features more comprehensive, some other adapted topological features were combined. The new congregated features were complementary as they were formed from different original feature sets extracted by different means. What's more, they were not complicate. Meanwhile, three Support Vector Machine (SVM) with different feature vector were used as classifier and their results were integrated to get the final classification. The average accurate rate of several experiments based on self-established data sets, MNIST and USPS is as high as 99.21%, which demonstrates that the proposed algorithm is robust and effective.

The common computing resources in the Integrated Modular Avionics (IMA) system provide the hosted applications with temporal and spatial partitioning platform. The platform for applications development should comply with the ARINC 653 specification which defines the application executive interfaces for partitioning operation system. By porting and developing the Board Support Package (BSP) and AFDX network driver, for the first time, an IMA platform solution for hosted application development was achieved based on the C2K, a Commercial Off-The-Shelf (COTS) single board computer. The functionality and performance of the COTS based platform are similar to the popular common computing resources in IMA of modern civil transportation aircraft, offering a development platform for hosted applications development and debug at a pretty lower costs.

In this paper, an adaptive color image enhancement method was proposed: Firstly, color image was transformed from RGB to HSV color space and the H component was kept invariable, while the illumination component of brightness image could be estimated through Adaptive Bidimensional Empirical Mode Decomposition (ABEMD); Secondly, reflection component was figured out by the method of center/surround Retinex algorithm, and the illumination and reflection components were controlled through Gamma emendation and Weber's law and processed with weighted average method; Thirdly, the S component was adjusted adaptively based on characteristics of the whole image, and then image was transformed back to RGB color space. The method could be evaluated by subjective effects and objective image quality assessment, and the experiment results show that the proposed algorithm is better in mean value, square variation, entropy and resolution than MSR algorithm and Meylan's algorithm.

The role of common knowledge of pricing game in electronic marketplaces was studied. By simply changing the demand function and allocation function, seller Agents can acquire the common knowledge about the constantly changing market, rather than inferred individual knowledge. Simulation results indicated that seller Agents tends to be more coordinated in their pricing behaviour and became more intelligent in concerning the problem of whether to cooperate or compete in a long term. Results also shows that common knowledge can improve market effectiveness.