Dehazing algorithms based on image training pairs are difficult to deal with the problems of insufficient training sample pairs in remote sensing images, and have the model with weak generalization ability, therefore, a remote sensing image dehazing method based on cascaded Generative Adversarial Network (GAN) was proposed. In order to solve the missing of paired remote sensing datasets, U-Net GAN (UGAN) learning haze generation and Pixel Attention GAN (PAGAN) learning dehazing were proposed. In the proposed method, UGAN was used to learn how to add haze to the haze-free remote sensing images with the details of the images retained by using unpaired clear and haze image sets, and then was used to guide the PAGAN to learn how to correctly dehazing such images. To reduce the discrepancy between the synthetic haze remote sensing images and the dehazing remote sensing images, the self-attention mechanism was added to PAGAN. By the generator, the high-resolution detail features were generated by using cues from all feature locations in the low-resolution image. By the discriminator, the detail features in distant parts of the images were checked whether they are consistent with each other. Compared with the dehazing methods such as Feature Fusion Attention Network (FFANet), Gated Context Aggregation Network (GCANet) and Dark Channel Prior (DCP), this cascaded GAN method does not require a large number of paired data to train the network repeatedly. Experimental results show this method can remove haze and thin cloud effectively, and is better than the comparison methods on both visual effect and quantitative indices.

Aiming at the problem that the Bidirectional Long Short-Term Memory network-Conditional Random Field (BiLSTM-CRF) based event extraction model can only obtain the semantic information of character granularity, and the upper limit of the model is low due to the low dimensionality of learnable features, a Chinese emergency event extraction method based on named entity recognition task feedback enhancement was proposed by taking the Chinese public emergency event data in open field as the research object, namely FeedBack-Lattice-Bidirectional Long Short-Term Memory network-Conditional Random Field (FB-Latiice-BiLSTM-CRF). Firstly, the Lattice mechanism was integrated with Bidirectional Long Short-Term Memory network (BiLSTM) as the sharing layer of the model to obtain the semantic features of words in sentences. Secondly, the named entity recognition auxiliary task was added to jointly learn and mine entity semantic information. At the same time, the output of the named entity recognition task was fed back to the input end, and the word segmentation results corresponding to the entities were extracted as the external input of the Lattice mechanism, so as to reduce the computing overhead brought by the large number of self-formed words of the mechanism and further enhance the extraction of entity semantic features. Finally, the total loss of the model was calculated by the maximum Gaussian likelihood estimation method to maximize the homoscedasticity uncertainty, so as to solve the problem of loss imbalance caused by multi-task joint learning. Experimental results show that FB-Latiice-BiLSTM-CRF has the accuracy of 81.25%, the recall of 76.50%, and the F1 value of 78.80% on the test set, which are 7.63, 4.41 and 5.95 percentage points higher than those of the benchmark model, respectively, verifying the effectiveness of the improvement performing to the benchmark model.

To overcome the shortcoming that the Iterative Closest Point (ICP) algorithm is easy to fall into local optimum, an auto-registration method of ground based building point clouds based on line features and ICP algorithm was proposed. Firstly, the plane segmentation was performed on point clouds based on normal consistency. Secondly, the outlines of point clusters were extracted by alpha-shape algorithm, and the feature line segments were obtained by the splitting and fitting process. Then, the feature line pairs were taken as the registration primitives, and the angle and distance between line pairs were used as similarity measures for same-name feature matching in order to achieve the coarse registration of building cloud points. Finally, with the coarse registration result as the initial value, the ICP algorithm was used to realize the fine registration of building point clouds. Two sets of partially overlapping building point clouds were used to carry out the experiments. The experimental results show that the proposed coarse-to-fine registration method can effectively improve the dependency of ICP algorithm on initial value and realize the effective registration of partially overlapping building point clouds.

With the help of Service-Oriented Architecture (SOA), the remote sensing image processing algorithms can be abstracted into a set of component services. Then, flexible business requirements can be met through service selection and composition. In order to get the service components that meet the user’s Quality of Service (QoS) requirements for combination， QoS of all services should be firstly obtained. Indeed, the QoS of a service is unknown to its users who have never invoked the service before. Hence, many research work have been proposed to predict the missing QoS. However, these existing methods seldom take the temporal factors into consideration, which may decrease the prediction accuracy. In order to resolve the issue, a new QoS model based on time slice was firstly proposed by considering temporal factors. Furthermore, a time-aware QoS prediction method based on Collaborative Filtering (CF) was proposed. The experiments results on the WS-DREAM real dataset show that， the proposed time-aware QoS prediction method can obtain smaller Mean Square Error (MAE) and Root Mean Square Error (RMSE).In addition, some parameters may affect the time-aware QoS prediction performance. Thus, a set of experiments and analysis with various parameter combinations were carried out, which provides a certain reference for parameter selection.

In view of the fact that facial beautification has been widely studied,but the lack of effective beautification image quality evaluation methods limits the further development of beautification technology,a no-reference evaluation method for facial beautification image quality was proposed. In this method,the facial cognition and perception were combined with the facial beautification technologies to unearth the quality representation of beautified images. Firstly,a facial beautification image database was constructed,the facial image was decomposed to three areas:skin,eyes and mouth. Then,facial aesthetic features were extracted from five aspects:skin color,smoothness,illumination,grayscale difference and sharpness. Finally,Support Vector Regression(SVR)was used to train the facial beautification quality model and predict the quality of the beautified image. The experimental results show that the proposed method achieves 0. 920 5 and 0. 900 9 respectively in the Pearson linear correlation coefficient and Spearman RankOrder Correlation Coefficient(SROCC) on the proposed database,which are higher than those of image quality evaluation methods BIQI(Blind Image Quality Indices),and NIQE(Natural Image Quality Evaluation).

Because of the uncertainty and complexity of remote sensing image data, it is difficult for traditional unsupervised algorithms to create an accurate classification model for them. Pattern recognition methods based on fuzzy set theory can express the fuzziness of data effectively. In these methods, type-2 fuzzy set can better describe inter-class hybrid uncertainty. Furthermore, semi-supervised method can use prior knowledge to deal with the generalization problem of algorithm to data. Therefore, a remote sensing image classification method based on Semi-Supervised Adaptive Interval Type-2 Fuzzy C-Means (SS-AIT2FCM) was proposed. Firstly, by integrating the semi-supervised and evolution theory, a novel fuzzy weight index selection method was proposed to improve the robustness and generalization of the adaptive interval type-2 fuzzy C-means clustering algorithm. The proposed algorithm was more suitable for the classification of remote sensing data with severe spectral aliasing, large coverage areas and abundant features. In addition, by performing soft constrained supervision on small number of labeled samples, the iterative process of the algorithm was optimized and guided, and the greatest expression of the data was obtained. In the experiments, SPOT5 multi-spectral remote sensing image data of the Summer Palace in Beijing and Landsat TM multi-spectral remote sensing image data of the Hengqin Island in Guangdong were used to compare the results of the existing fuzzy classification algorithms and SS-AIT2FCM. The experimental results show that the proposed method obtains more accurate classification and clearer boundaries of classes, and has good data generalization ability.

The rapid development of the network makes online news become an important source of acquiring information. It has a significant impact on the usability of Web sites whether the information published on the Web sites can reflect the current focus of attention or whether the latest progress of the event on the Web sites can be timely updated. In this paper, the clue development trend on the same subject related to the information on Web sites was obtained from the topic clue sentences identified by Conditional Random Field (CRF) model. The time range of topic clues can be inferred by clue development trends obtained, and further the effective range of Web information is estimated. On this basis, combined with the timeliness the information is published and the freshness of the content on Web sites, the timeliness of Web information can be evaluated reasonably. The experimental results show that the proposed method has a good effect on the timeliness evaluation of information on Web sites.

Aiming at the excessively long time consumption of round function in block cipher implementation, a new software pipelining realization method of Advanced Encryption Standard (AES) algorithm based on Reconfigurable Cipher Stream Processor (RCSP) was proposed. The operations of round function were divided into several pipelining segments. The different pipelining segments corresponded to different cipher resources. The instruction level parallelism was developed to accelerate the execution speed of round function by executing different pipelining segments of multiple round functions in parallel. The execution efficiency of block cipher algorithm was improved. The separation processes of pipelining segments and software pipelining mapping methods of AES algorithm were analyzed with the computing resources of single cluster, two clusters and four clusters of RCSP. The experimental results show that, the proposed software pipelining realization method, which makes different data fragments of one block or multiple blocks processed in parallel, can not only improve the performance of a block serial execution, but also improve the performance of multiple blocks parallel execution by developing the parallelism between the blocks.

High reliability is an important issue in the development of high performance network-on-chip router. Concerning the problem that the ports of the router whose virtual channel is dynamic allocated are prone to fail at present, a design of fault-tolerant router based on port fault granularity partition was proposed. Firstly, a fault and congestion model for ports based on granularity partition was established combining with the specialty of the virtual channel dynamic allocation and faults' characteristics. Then, the related fault-tolerant circuit was designed on the basis of the model combining with the real-time fault detection methods, an adjacent port sharing module was added and a fault-tolerant read/write point control logic circuit was designed. Finally, a fault-tolerant and congestion mitigation scheme was put forward based on the design. The experiments shows that the proposed router can maintain fault tolerant properties under various port failure modes with little performance degradation, and it has high ratio of performance improvement and area overhead.

In order to meet the performance and throughput requirements of distributed systems, the asynchronous message communication is a common strategy. However, this strategy can not solve the consistency problem of the distributed system. In order to solve this problem, this paper proposed the establishment of consistency guarantee platform. Firstly, the system fulfilled idempotency and strong consistency between business data and message production/consumption records. Secondly, a message monitoring strategy was established. And it could be decided whether a message was correct or the compensation/idempotent operation was needed, according to the monitoring rules and production/consumption records, in order to realize the eventual consistency of the distributed system based on message communication. Lastly, the Separation of Concerns (SoC) and horizontal segmentation methods were adopted in design and realization of this platform. Experiments and analyses have shown the better performance of this distributed message communication, comparing to the asynchronous communication. This platform could timely check and handle the inconsistency and thus achieve the eventual consistency, i.e. the final eventual consistency of the whole system. Also the platform design could easily be adopted to multiply business systems, which means this platform is not only superior-performed but also economic.

The formation rules of microblog new words are extremely complex with high degree of dispersion, and the extracted results by using traditional C/NC-value method have several problems, including relatively low accuracy of the boundary of identified new words and low detection accuracy of new words with low frequency. To solve these problems, a method of integrating heuristic rules, modified C/NC-value method and Conditional Random Field (CRF) model was proposed. On one hand, heuristic rules included the abstracted information of classification and inductive rules focusing on the components of microblog new words. The rules were artificially summarized by using Part Of Speech (POS), character types and symbols through observing a large number of microblog documents. On the other hand, to improve the accuracy of the boundary of identified new words and the detection accuracy of new words with low frequency, traditional C/NC-value method was modified by merging the information of word frequency, branch entropy, mutual information and other statistical features to reconstruct the objective function. Finally, CRF model was used to train and detect new words. The experimental results show that the F value of the proposed method in new words detection is improved effectively.

The data acquired by the satellite positioning receiver in the trajectory correction projectile must be filtered in real-time to predict the point. The calculation of traditional filtering method is time-consuming, and is difficult to meet the requirements of real-time filtering. A kind of extended Kalman filtering algorithm based on polynomial fitting was proposed. The data of projectile flight in the time interval was replaced by the fitting interpolation data. In this way the filter frequency could be reduced. Simulation results show that the computation time of the proposed method can be reduced by 7/8 compared to traditional extended Kalman filtering without reducing the filtering precision, and the real-time performance is improved. This method can provide important reference for the research of key technology of trajectory correction projectile. At the same time, the method can be applied to other filtering algorithms, and has a strong portability.

The parameter distribution of unvoiced/voiced Line Spectrum Frequency (LSF) has differences. In order to improve the quantization performance of LSF parameters in vocoder, an unvoiced/voiced mode codebook design algorithm based on Cell Evenness (CE) was presented by using the difference between unvoiced/voiced LSF parameters distribution and CE. Firstly, the optimal amount ratio of unvoiced/voiced LSF parameters participating in the codebook training was deduced according to CE. Then the specified number of atypia LSF parameters were eliminated from unvoiced speech. The final codebook was retrained. The experimental results show that, compared with the shared codebook algorithm under the same bit-rate condition, the average spectrum distortion of the proposed algorithm was reduced by 2.5%, the mean opinion score was increased by 2.3% and the storage of codebook was reduced by 21.1%. The proposed algorithm is also adapted to the vocoder without unvoiced/voiced symbol transmission and the algorithm is also adapted to the vocoder without unvoiced/voiced symbol transmission.

Superword Level Parallelism (SLP) is a vector parallelism exploration approach for basic block. With loop unrolling, more parallel possibility can be explored. Simultaneously too much exploring paths are brought in. In order to solve the above problem, an optimized SLP method with subsection constraints was proposed. Redundant elimination on segmentation was used to obtain homogeneous segments. Inter-section exploring method based on SLP was used to restrain exploring paths and reduce the complexity of the algorithm. And finally pack adjustment was used to deal with the situation of overlap on memory access. The experimental results show that the vectorization capability of SLP is enhanced; for the test serial program, the average speedup of vectorized version is close to 2.

This paper proposed a new dynamic software watermarking algorithm based on stack-state relations in order to overcome the weakness of the existing software watermarking algorithms. The watermark was hidden in the stack-state relations generated by execution of the program and extracted by recognizing the relationship of stack states in runtime. The paper gave the concept of stack-state transition graph, and explained its properties. It also gave detailed description of the software watermark embedding and extraction algorithms, and explained it through a specific example. The analysis and experiments show that the algorithm does not affect the host program significantly, and has the ability to resist various attacks.

Software security measurement is critical to the development of software and improvement of software security. Based on the entropy and attack surface proposed by Manadhata et al. (MANADHATA P K, TAN K M C, MAXION R A, et al. An approach to measuring a system's attack surface, CMU-CS-07-146. Pittsburgh: Carnegie Mellon University, 2007; MANADHATA P K, WING J M. An attack surface metric. IEEE Transactions on Software Engineering, 2011, 37(3): 371-386), a method of software security measurement was used to assess the threat of the software's resources and provide the threat weight of these resources. Based on the threat weight, the attack surface metric was calculated for determining whether a software product is secure in design, or in what aspect the software product can be improved. The method is demonstrated in a case to show that, when using the method, the probable security threats can be found as early as possible to prevent from producing the software products that may have vulnerabilities, and the directions for the improvement of software security are pointed out clearly.

Poor adaptability and slow convergence rate are the two main disadvantages of the existing precision test methods. To solve this problem, an elementary functions precision test method based on continued-fraction approximation was proposed by analyzing Unit in the Last Place (ULP) error and comparing several different functions true values calculations. The different calculations were analyzed and tested in the following two ways: time complexity and convergence degree. The experimental results show that the precision test method based on continued-fraction approximation is more effective, less complex and achieves faster convergence.

Compared to verbal predicates，the structure between nominal predicates and their roles in Semantic Role Labeling (SRL) is more flexible and complex. In this paper, some new word-related and syntactic features were explored from various nominal predicate-specific features to capture the structure information for nominal SRL. The experimental results show that the proposed nominal SRL system achieved the performance of 73.99 in F1-measure on gold parse trees and gold predicates, and outperformed the state-of-the-art nominal SRL. However, the performance dropped to 57.16 in F1-measure on automatic parse trees and automatic predicates. Finally, the training data were augmented with verbal SRL instances to examine whether nominal SRL could benefit from verbal instances. The experimental result show, however, adding verbal SRL instances does indeed improve the performance of nominal SRL, although the improvement is not statistically significant.

The PMI has been recommended as a new information protection infrastructure,to provide privilege management service and to get an access control mechanism which is fit for most of actual transact patterns and free of the development and management of concrete applications.The advantages of using PMI technology instead of application-embedded privilege management as before were introduced in distributed Web applications.The possibility of its realization was discussed and the scheme of its realization was proposed.

A fast border-detection algorithm named local border search algorithm was presented here, and applied to an on-line surface inspection system for cold rolled strips. Based on the last search result, a local search was used to position the cold rolled strip border and increased the algorithms speed greatly. Test with images collected from real world data shows that local border search algorithm is about 20 to 200 times faster than the traditional gray level gradient threshold algorithm, and can detect borders more accurately. The algorithm can also be used in the search of other image border.