Micro-expression is a facial movement that humans make when they are trying to hide their true emotions. It has the typical characteristics of short duration and small amplitude. Concerning the problems of the difficulty in recognition and the unsatisfactory recognition effect of micro-expression, a micro-expression recognition algorithm based on Convolutional Block Attention Module (CBAM) and Dual Path Networks (DPN), namely CBAM-DPN, was proposed. Firstly, data fusion of typical micro-expression datasets was performed. Then, the change values of pixels in the sequence frames were analyzed to determine the position of the apex frame, after that, image enhancement was performed to the apex frame. Finally, based on the CBAM-DPN network, the features of the enhanced micro-expression apex frame was effectively extracted, and a classifier was constructed to recognize the micro-expression. The Unweighted F1-score (UF1) and Unweighted Average Recall (UAR) of the model after optimization can reach 0.720 3 and 0.729 3 respectively, which are improved by 0.048 9 and 0.037 9 respectively compared with those of the DPN model, and are improved by 0.068 3 and 0.078 7 respectively compared with those of the CapsuleNet model. Experimental results show that the CBAM-DPN algorithm combined with the advantages of CBAM and DPN can enhance the information extraction ability of small features, and effectively improve the performance of micro-expression recognition.

The traditional feature selection methods always remove redundant features by using correlation measures, and it is not considered that there is a large amount of noise in a high-dimensional correlation matrix, which seriously affects the feature selection result. In order to solve the problem, a feature selection method based on Random Matrix Theory (RMT) was proposed. Firstly, the singular values of a correlation matrix which met the random matrix prediction were removed, thereby the denoised correlation matrix and the number of selected features were obtained. Then, the singular value decomposition was performed on the denoised correlation matrix, and the correlation between feature and class was obtained by decomposed matrix. Finally, the feature selection was accomplished according to the correlation between feature and class and the redundancy between features. In addition, a feature selection optimization method was proposed, which furtherly optimize the result by comparing the difference between singular value vector and original singular value vector and setting each feature as a random variable in turn. The classification experimental results show that the proposed method can effectively improve the classification accuracy and reduce the training data scale.

With the growing demand of big data computing, the processing speed of the cluster needs to be improved rapidly. However, the processing performance of the existing big data framework can not satisfy the requirement of the computing development gradually. As the framework of the storage is distributed, the placement of data to be processed has become one of the key factors affecting the performance of the cluster. Firstly, the current distributed file system structure was introduced. Then the popular data placement algorithms were summarized and classified according to different optimization goals, such as network load balance, energy saving and fault tolerance. Finally, future challenges and research directions in the area of storage framework and algorithms were presented.

For flame recognition problem, the traditional recognition methods based on physical signal are easily affected by the external environment. Meanwhile, most of the current methods based on feature extraction of flame image are less discriminative to different scene and flame type, and then have lower recognition precision if the flame scene and type change. To overcome this drawback, a new fast recognition method for flame image was proposed by introducing colorspace information into Scale Invariant Feature Transform (SIFT) algorithm. Firstly, the feature descriptors of flame were extracted by SIFT algorithm from the frame images which were obtained from flame video. Secondly, the local noisy feature points were filtered by introducing the feature information of flame colorspace, and the feature descriptors were transformed into feature vectors by means of Bag Of Keypoints (BOK). Finally, Extreme Learning Machine (ELM) was utilized to establish a fast flame recognition model. Experiments were conducted on open flame datasets and real-life flame images. The results show that for different flame scenes and types the accuracy of the proposed method is more than 97%, and the recognition time is just 2.19 s for test set which contains 4301 images. In addition, comparing with the other three methods such as support vector machine based on entropy, texture and flame spread rate, support vector machine based on SIFT and fire specialty in color space, ELM based on SIFT and fire specialty in color space, the proposed method outperforms in terms of recognition accuracy and speed.

Support Vector Machine (SVM) has good performance in dealing with dimensionality disaster, over fitting and nonlinearity, which other traditional situation assessment algorithms does not have. However SVM has low efficiency when dealing with large-scale data. To effectively confront the challenge of handling big data, a MapReduce-based SVM (MR-SVM) situation assessment algorithm was proposed. Considering the characteristics of SVM algorithm, the parallelization and parameter selection of SVM based on MapReduce programming was implemented by designing procedures of map function and reduce function. The performances of MR-SVM and SVM were compared on Hadoop platform, MR-SVM had lower efficiency than SVM when dealing with small-scale data, but much better performance when dealing with large-scale data. SVM had an exponential growth on training time with the growth of data scalability while MR-SVM has slow growth. The experiment results show that MR-SVM solves the problem of data scalability, therefore it is suitable for situation assessment in big data environment.

Focusing on the mismatch occurred in template matching when using Generalized Hough Transform (GHT) algorithm to extract the target shape from the rotated image, an improved orientation-invariant generalized Hough transform algorithm based on U-chord curvature was proposed. Firstly, the modified R-table with orientation-invariant performance was constructed by using features of U-chord curvature and displacement vectors of edge points of the template shape; secondly, the information such as the displacement vector was achieved by calculating the curvature of edge points as an index to lookup the constructed R-table; finally, the possible locations of reference points were calculated according to the information. The point with maximum voting was the location of the target shape of the image. When the target shape of the image is rotated by 0°, 2°, 4°, 5° and 6° individually, the sharper peaks occur in the target shape position of all the rotation images by using the proposed algorithm. The simulation results show that the Improved Generalized Hough Transform (I-GHT) algorithm has high stability in rotation and noise conditions.

In order to solve the problem that selected accuracy stopping criterion can only be applied in the scenario of batch mode-based active learning, an improved stopping criterion for single-labeling mode was proposed. The matching relationship between each predicted label and the corresponding real label existing in a pre-designed number of learning rounds was used to approximately estimate and calculate the selected accuracy. The higher the match quality was, the higher the selected accuracy was. Then, the variety of selected accuracy could be monitored by moving a sliding-time window. Active learning would stop when the selected accuracy was higher than a pre-designed threshold. The experiments were conducted on 6 baseline data sets with active learning algorithm based on Support Vector Machine (SVM) classifier for indicating the effectiveness and feasibility of the proposed criterion. The experimental results show that when pre-designing an appropriate threshold, active learning can stop at the right time. The proposed method expands the applications of selected accuracy stopping criterion and improves its practicability.

In order to solve the problem of auditing the data protected by Secure Sockets Layer/Transport Layer Security (SSL/TLS) protocol on the Internet, a plaintext gathering method for network data protected by SSL/TLS protocol based on the principles of man-in-the-middle was proposed. A data gatherer was connected between the server and the client in series, which was able to get the encryption key by modifying handshake messages during SSL/TLS handshake, so as to decrypt the secure data and then gather its plaintext. Compared with the existing gathering method based on the principles of proxy server, the proposed method has a shorter transmission delay, a larger SSL throughput and a smaller memory occupation. Compared with the existing gathering method in which the gatherer possesses the server's private key, the proposed method has a wider application scope, and also has the advantage of being unaffected by packet losses on the Internet. The experimental results show that the proposed method has a decrease in transmission delay of about 27.5% and an increase in SSL throughput of about 10.4% compared with the method based on the principles of proxy server. The experimental results also show that the SSL throughput of the proposed method approaches the ideal maximum value.

Concerning the problem of key collision and the storage space of matrices of seeded-key in Combined Public Key (CPK), a method of coefficient remapping was proposed and the rules of selecting the elements of seeded matrices were designed. Firstly, in the phase of identification mapping, the binary bit streams were produced, and they were divided into coefficient sequence and row sequence. Then the coefficient sequence was remapped according to the remapping rules, which could avoid that the coefficient was zero. So the storage space of the matrices was reduced by the coefficient remapping. Secondly, in the generation step of seeded-key matrix, based on the coefficient remapping, some rules were specified to choose elements to create matrices of seeded-key to ensure that the generated keys were exclusive. Finally, the elements of the matrices were selected according to the row sequence and the increasing column sequence. Then the public key and the private key were generated on the basis of the coefficient sequence and the selected elements. The theoretical analysis results suggest that the proposed scheme can optimize the storage of matrices and solve the key collision problem.

In view of the low delivery ratio of conventional probabilistic routing in opportunistic networks, an improved routing algorithm based on History Meeting Predictability Routing (HMPR) was put forward. The algorithm was primarily based on the contact duration and the meeting frequency of history information of nodes, and predicted the utility of packets successfully delivered to the destination. Through comparing the utility value, nodes could determine packets whether to be forwarded from them to next hop nodes. The simulation results show that, compared with traditional epidemic routing and probabilistic routing, the proposed routing scheme has better performance in the delivery ratio of packets, the average delay time and the average buffer time.

To select the optimal threshold for image segmentation, a new method based on local complexity and local difference was proposed. Firstly, the local grayscale features of a given image were generated, including local complexity and local difference. Next, the new feature matrix was constructed using local feature fusion. Then, an automatic threshold was defined based on the mean and standard deviation of feature matrix, and the image transition region was extracted. Finally, the optimal grayscale threshold was obtained by calculating the grayscale mean of transition pixels, and the binary result was yielded. The experimental results show that, the proposed method performs well in transition region extraction and thresholding, and it is reasonable and effective. It can be an alternative to traditional methods.

In distributed wireless communication and Coordinated Multi-Point (CoMP) system, the deterioration of channel quality seriously affect the system receptivity of edge users, and therefore the coordinated coding processing is used to improve the quality of reception. A coordinated transmission and joint pre-coding method was proposed in this paper. Coordinated Remote Antenna Units (RAU) were processed jointly. According to the state information of channel, different pre-coding rules were adopted by each RAU to transmit data to the same user. A Maximal-Ratio Combining (MRC) algorithm was used to calculate the received signals of terminals. The simulation results show that the proposed method can effectively decrease the Bit Error Rate (BER) and improve the transmission reliability.

A robust 3D reconstruction method based on image sequence was presented. First, an optimal parameter estimation of two images was applied. Then, along with the number of images increasing, the sparse bundle adjustment was adopted to reduce the minimum geometric error of the measurements of image coordinates. Finally, the 3D structure and camera parameters were processed with a global optimization method to improve the robustness of the reconstruction. The experiment results demonstrate that the proposed method can effectively improve the accuracy and robustness of reconstruction, and factually represent 3D model of objects.

Recently, Lin, Tang and Wang proposed a multi-prime RSA based on a star architecture of key distribution and made use of it to construct a centralized group key management scheme. According to several main security requirements of group key management, from the perspective of cryptographic engineering practice and applying computational number theory, four kinds of attacks against this scheme were proposed: a ring idempotent attack, a chosen plaintext attack，an attack of extracting high order integer roots, and a collusion attack based on the elliptic curve factoring method and Chinese remainder theorem. The mathematical analysis and cryptanalysis indicate that under certain conditions these attacks can be realized efficiently, and it is the characteristic of "without rekeying the key server's encryption exponent" that causes such security risks.

Establishing a mapping relationship between image visual features and semantics can be used to reduce "semantic gap". A novel mapping method for image semantics and visual features was presented. In this method, image semantic information could be captured by adding users relevance feedback, and then a decision table of visual features and semantics was constructed. Knowledge reduction of rough set theory was used to reduce the redundant visual features according to semantics, by doing that a mapping relationship between image visual features and its semantics was established. The experimental results indicate that amount of visual features irrelevant to image semantics can be reduced greatly, the complexity and cost of semantic classification are reduced and the accuracy of classification is better.

After formally defining the examination and approval of business, a formal dynamic behavior model for examining and approving business, called EABWF-net, was established. The model was different from traditional process-oriented model. It centered on the examination and approval role taking the message as roam mechanism and rule as flow logic of business. The model overcomed the limitation of traditional modeling method that lacks process suppleness and system flexibility. Furthermore it can explain the examination and approval business clearer and more directly.

In mobile agent systems, in order to make mobile agents(MA) cooperate well, and to locate mobile agents quickly, it is necessary to design an efficient and reliable MA locating protocol. It should meet the requirements proposed by traditional distributed systems, like transparency, scalability and efficiency, and maintain the consistency of the information concerning the MA environment. A new MA locating protocol called DRSP(Distributed Registration Search Protocol) was proposed to meet all the requirements above, and the specific details in DRSP were described. Its consistency was proved by a search program.