In the current large-scale data retrieval tasks, learning to hash methods can learn compact binary codes, which saves storage space and can quickly calculate the similarity in Hamming space. Therefore, for approximate nearest neighbor search, hashing methods are often used to improve the mechanism of fast nearest neighbor search. In most current hashing methods, the offline learning models are used for batch training, which cannot adapt to possible data changes appeared in the environment of large-scale streaming data, resulting in reduction of retrieval efficiency. Therefore, the adaptive hash functions were proposed and learnt in online hashing methods, which realize the continuous learning in the process of inputting data and make the methods can be applied to similarity retrieval in real-time. Firstly, the basic principles of learning to hash and the inherent requirements to realize online hashing were explained. Secondly, the different learning methods of online hashing were introduced from the perspectives such as the reading method, learning mode, and model update method of streaming data under online conditions. Thirdly, the online learning algorithms were further divided into six categories, that is, categories based on passive-aggressive algorithms, matrix factorization technology, unsupervised clustering, similarity supervision, mutual information measurement, codebook supervision respectively. And the advantages, disadvantages and characteristics of these algorithms were analyzed. Finally, the development directions of online hashing were summarized and discussed.

In order to solve the index calculation problems of high complexity, strong completeness and low efficiency in the filed of financial foreign exchange market monitoring, a novel distributed computing framework for foreign exchange market monitoring based on Spark big data structure was proposed. Firstly, the business characteristics and existing technology framework for foreign exchange market monitoring were analyzed and summarized. Secondly, the foreign exchange business features of single-market multi-indicator and multi-market multi-indicator were considered. Finally, based on Spark's Directed Acyclic Graph (DAG) job scheduling mechanism and resource scheduling pool isolation mechanism of YARN (Yet Another Recourse Negotiator), the Market-level DAG (M-DAG) model and the market-level resource allocation strategy named M-YARN (Market-level YARN) model were proposed, respectively. The experimental results show that, the performance of the proposed distributed computing framework for foreign exchange market monitoring improves the performance by more than 80% compared to the traditional technology framework, and can effectively guarantee the completeness, accuracy and timeliness of foreign exchange market monitoring indicator calculation under the background of big data.

In order to solve the problems of the difficulty to extract features on the smooth regions of mesh models and the impossibility to recognize the feature vertices distributed only along one specific direction by the existing feature detection methods, a direction-perception method of feature recognition on mesh models was proposed. Firstly, the changes of the normal vectors of the mesh vertex adjacent surfaces were detected in x, y and z directions separately. With a suitable threshold set, if the change of a normal vector of the mesh vertex adjacent surfaces exceeded the threshold in any direction, the vertex would be recognized as a feature vertex. Then, concerning the problem that the existing mesh model feature detection algorithms cannot recognize the terraced field structure only distributed along the z-axis of three-dimensional medical model, the algorithm detected the change of normal vectors of the mesh vertex adjacent surfaces just along the z-axis direction, and recognized the vertex as a terraced field structure vertex once the change of the vertex exceeds the threshold. The abnormal terraced field structures were separated from the normal structures of the human body successfully. The experimental results show that, compared with the dihedral angle method, the proposed method can identify the features of the mesh model better under the same conditions. The proposed method solves the problem that the dihedral angle method cannot effectively identify the feature vertices on the smooth regions without obvious broken lines, and also solves the problem that the existing mesh model feature detection algorithms cannot distinguish the abnormal terraced field structures from the normal human body structures due to the lack of the direction detection ability, and establishes a base for the following digital geometry processing of the medical model.

Aiming at the low prediction effect caused by single data source in the current RMB exchange rate forecast research, a forecast technology based on Internet public opinion intensity was proposed. By comparing and analyzing various data sources, the forecast error of RMB exchange rate was effectively reduced. Firstly, the Internet foreign exchange news data and historical market data were fused, and the multi-source text data were converted into the computable vectors. Secondly, five feature combinations based on sentiment feature vectors were constructed and compared, and the feature combination embedded with intensity of Internet public opinion was given as the input of forecast models. Finally, a temporal sliding window of foreign exchange public opinion data was designed, and an exchange rate forecast model based on machine learning was built. Experimental results show that feature combination embedded with Internet public opinion outperforms the feature combination without public opinion by 9.8% and 16.2% in Root Mean Squared Error (RMSE) and Mean Squared Error (MAE). At the same time, the forecast model based on Long Short-Term Memory network (LSTM) is better than that based on Support Vector Regression (SVR), Decision Tree regression (DT) and Deep Neural Network (DNN).

Focused on the time-consuming and laborious problem of data annotation in review text sentiment analysis, a new automatic data annotation method was proposed. Firstly, the sentiment tendency of the review text was calculated based on the sentiment dictionary. Secondly, the review text was automatically annotated by using the weak tagging information of the user and the sentiment tendency based on the dictionary. Finally, Support Vector Machine (SVM) was used to classify the sentiment of the review text. The proposed method reached 77.2% and 77.8% respectively in the accuracy of sentiment classification on two types of data sets, which were 1.7 percentage points and 2.1 percentage points respectively higher than those of the method only based on user rating. The experimental results show that the proposed method can improve the classification effect in movie reviews sentiment analysis.

To increase the coding speed of the quality Scalable High efficiency Video Coding (SHVC), a new intra prediction algorithm based on quality SHVC was proposed. Firstly, the potential depth was predicted by using inter-layer correlation, and the depths with low possibility were eliminated. Secondly, for the likely depth, Inter-Layer Reference (ILR) mode was used to code and examine the residual distribution by using distribution fitting to determine whether the residuals follow a Laplace distribution. If the residual followed the Laplace distribution, intra prediction was skipped. Finally, the depth residual coefficient of depth coding was tested to determine whether to satisfy the depth of early termination, if the condition was met, the code process would be terminated to improve the coding speed. The experimental results show that the proposed algorithm can improve the coding speed by 79% with negligible coding loss.

Since it is difficult to denoise and simplify a three dimensional point cloud data by a same parameter, a new unified algorithm based on the Extended Surface Variation based Local Outlier Factor (ESVLOF) for denoising and simplification of scattered point cloud was proposed. Through the analysis of the definition of ESVLOF, its properties were given. With the help of the surface variability computed in denoising process and the default similarity coefficient, the parameter γ which decreased with the increase of surface variation was constructed. Then the parameter γ was used as local threshold for denoising and simplifying point cloud. The simulation results show that this method can preserve the geometric characteristics of the original data. Compared with traditional 3D point-cloud preprocessing, the efficiency of this method is nearly doubled.

In order to achieve unified management and remote communication of measuring equipment in car body-in-white online visual inspection station, a measurement and control system for the car body-in-white detection was designed to improve the working efficiency. Using STM32F407 as the core, μC/OS-Ⅱ and LwIP were transplanted to build a Web server, and the Web server was set up to realize remote communication. Multithreaded tasks were established to achieve the information interaction between serial port and net port. By analyzing the data security issue in the process of data's routing and discussing the phenomenon of packet loss on transmitting, a solution was proposed. 2D normalized cross-correlation method was used to realize the image 2D positioning, and enhome the processing speed. The experimental results show that the system can provide remote communication function, reduce the cost, and improve the efficiency of equipment management.

With the continuous development of enterprise marketing, the enterprise abbreviation has been widely used. Nevertheless, as one of the main sources of unknown words, the enterprise abbreviation can not be effectively identified. A methodology on predicting enterprise abbreviation based on constitution pattern and Conditional Random Field (CRF) was proposed. First, the constitution patterns of enterprise name and abbreviation were summarized from the perspective of linguistics, and the Bi-gram algorithm was improved by a combination of lexicon and rules, namely CBi-gram. CBi-gram algorithm was used to realize the automatic segmentation of the enterprise name and improve the recognition accuracy of the company's core word. Then the enterprise type was subdivided by CBi-gram, and the abbreviation rule sets were collected by artificial summary and self-learning method to reduce noise caused by unsuitable rules. Besides, in order to make up the limitations of artificial building rules on abbreviations and mixed abbreviation, the CRF was introduced to generate enterprise abbreviation statistically, and word, tone and word position were used as characteristics to train model as supplementary. The experimental results show that the method exhibites a good performance and the output can fundamentally cover the usual range of enterprise abbreviations.

Focusing on the routing selection of end-to-end key agreement in Quantum Key Distribution (QKD) network, an optimal routing selection algorithm of end-to-end key agreement based on the Dijkstra algorithm was designed. Firstly, the unavailable links in the QKD networks were eliminated based on the strategy of choosing the available paths. Secondly, based on the strategy of choosing the shortest paths, the Dijkstra algorithm was improved to find out all the shortest paths with the least key consumption. Finally, according to the strategy of choosing the optimal path, the optimal path with the highest network service efficiency was selected from the shortest paths. The analysis results show that, the proposed algorithm solves the problems such as the optimal path is not unique, the best path is not the shortest, the optimal path is not optimal, and so on.The proposed algorithm can reduce the key consumption of end-to-end key agreement in QKD network, and improve the efficiency of network services.

To solve the problem of Fine Particulate Matter (PM2.5) concentration prediction, a PM2.5 concentration prediction model was proposed. First, through introducing the comprehensive meteorological index, the factors of wind, humidity, temperature were comprehensively considered; then the feature vector was conducted by combining the actual concentration of SO2, NO2, CO and PM10; finally the Least Squares Support Vector Machine (LS-SVM) prediction model was built based on feature vector and PM2.5 concentration data. The experimental results using the data from the city A and city B environmental monitoring centers in 2013 show that, the forecast accuracy is improved after the introduction of a comprehensive weather index, error is reduced by nearly 30%. The proposed model can more accurately predict the PM2.5 concentration and it has a high generalization ability. Furthermore, the author analyzed the relationship between PM2.5 concentration and the rate of hospitalization, hospital outpatient service amount, and found a high correlation between them.

In conventional permutation and confusion based image encryption algorithm, there usually exists some problems such as inefficient permutation and difficult to resist known or chosen plaintext attack. To solve these problems, an image encryption algorithm based on maze permutation and Logistic mapping was proposed, where Depth First Search (DFS) maze permutation was used to product permutation efficiently. In order to resist known or chosen plaintext attack, the plaintext image Message Digest Algorithm 5 (MD5) digest was bound with the user key to generate maze starting coordinates, Logistic chaotic map parameters and initial values which drive Logistic maps to generate random numbers. These random numbers were used to determine maze node probing directions and participate in image confusion to make all encryption stages tight coupled with the plaintext image. Experiments show the proposed algorithm has better performance in encryption quality and it can resist known or chosen plaintext attack with high security.

Object-oriented analysis of polarimetric Synthetic Aperture Radar (SAR) has been used commonly, while the polarimetric decomposition is still based on pixel, which is inefficient to extract polarimetric information. A object-based method was proposed for polarimetric decomposition. The coherent matrix of object was constructed by weighted iteration of scattering coefficient of similarity, and the convergence of coherent matrix was analyzed, therefore polarimetric information could be obtained through the coherent matrix of object instead of pixel， which can improve the efficiency of obtaining polarimetric features. To more fully reflect the terrain target, spatial features of object were extracted. After feature selection, polarimetric SAR image classification experiments using Support Vector Machine (SVM) demonstrate the effectiveness of the proposed method.

Bursty topics detection on microblog is an import branch of online public opinion analysis, and has attracted much attention from international scholars. In this paper, a new approach of calculating users' influence was proposed based on the analysis of users' behavior characteristics. Combining the user influence with text features and propagation features, this paper defined a concept named Bursty which is used to judge if a word was a burst word. Being judged by Bursty, burst words can be extracted from microblog corpus. Hierarchical clustering algorithm was introduced to cluster the burst words and chose appropriate burst word clusters to describe bursty topics on microblog in order to realize bursty topics detection on microblog. In experiments, the precision, recall and F-measure reached 63.64%,87.5% and 74% respectively. The method is proved effective on bursty topic detection based on mass microblog data.

To avoid the limitations of the traditional fuzzy rule based on Genetic Algorithm (GA), a calculation method of fuzzy control rule which contains weight coefficient was presented. GA was used to find the best weight coefficient which calculate the fuzzy rules. In this method, different weight coefficients could be provided according to different input levels, the correlation and symmetry of the weight coefficients could be used to assess all the fuzzy rules and then reduce the influence of the invalid rules. The performance comparison experiments show that the system which consists of these fuzzy rules has small overshoot, short adjustment time, and practical applications in fuzzy control. The experiments of different stimulus signals show that the system which consists of these fuzzy rules doesnt rely on stimulus signal as well as having a good tracking effect and stronger robustness.

In order to enhance the performance of the synchronization and demodulation, a Very high frequency (VHF) Data Broadcast (VDB) signal demodulation algorithm based on the solution of differential equation was proposed. This algorithm eliminated the synchronization performance deterioration caused by the frequency offset. And frame synchronization, bit synchronization, frequency offset estimation and correction could be completed within a single set of synchronization symbols. The simulation results show that the method is effective to enhance the VDB signal demodulation performance.

To deal with the channel uncertainty of the correlated block fading channels, a joint estimation-decoding approach based on Factor Graph Expectation Maximization (FGEM) algorithms was proposed. In the receiver, a message passing method on factor graph was adopted to jointly estimate the channel and decode the message. EM algorithm was used to remove the effect of loops on the convergence of message passing. It also solved the calculation problem of Gaussian mixture message. The calculation of message passing was simplified by the Kalman forward-backward algorithm, which resulted in reduced complexity in joint estimation-decoding. The simulation results show that the proposed algorithm can improve the accuracy of the channel estimation and improve the decoding performance.

The current disadvantages of Mobile Ad Hoc Network (MANET) test method include simple model, high cost and difficult duplicate. In this paper, a large-scale MANET hybrid emulation testing method based on NS2 (LHEN) was proposed to solve these problems. By making use of simulation function of NS2, the authors could complete encapsulation and decapsulation of real packets and virtual packets with Tap agent, thus they could achieve the communication between virtual environment and real environment through network objects and NS2 real-time scheduler. A real node movement could be emulated by controlling network wireless signal strength, and then a real network environment was established. Finally, the authors constructed a large scale MANET respectively for contrast experiments through method of hybrid emulation and simulation. The experimental results show that the performance is almost consistent and mean difference value is lower than 18.7%, which means LHEN is able to be applied in some indicators test and verification for a large scale MANET.

The trust management models in Peer-to-Peer (P2P) network mainly have two problems. For one thing, the different influences on value of trust between short-term trading and long-term trading are usually ignored. For another, the lack of the specific risk analysis on trading resources exists. Consequently, focusing on the quality of different nodes and its opposite risk value, this paper introduced the concept of risk factor with setting up its value and proposed a trust management model based on evaluation of value-at-risk with changing time. From the simulation results, a higher efficiency on resisting malicious actions in P2P network is achieved, and it has confirmed to select better traders effectively with a deeply quantitative analysis of trade resources through the model.

For Direction-Of-Arrival (DOA) estimation of signal in additive noise, the traditional Multiple Signal Classification (MUSIC) algorithm cannot process the coherent signal with fewer snapshots. The searching scope of estimated DOA was considered as redundant dictionary. Consequently, the estimated DOA was taken as some elements in the dictionary, and could be represented sparsely by the dictionary. Then, this problem was thrown into the Second Order Cone (SOC) constraints and an efficient estimation algorithm using a single snapshot was developed. This constrained problem could be depicted as a standard SOC form and be solved by the SeDuMi, an optimization toolbox. The simulation results show that the proposed algorithm has a few advantages over the existing subspace method including one single snapshot to be needed, no requirement for the number of source signals, ability to work with coherent and non-coherent signals.

Concerning the clock skew and clock drift problem in wireless sensor networks, some different methods of synchronization time on synchronization accuracy were studied. With the principle of clock synchronization of cluster-shaped network structure, an optimized clock basis algorithm was put forward. And the Kalman filtering method was applied to adjust the nodes’ clock deviation in optimized recursive way. Compared with the general synchronization algorithm of cluster-shaped, the proposed algorithm can not only improve the synchronization accuracy, but also reduce the energy consumption of synchronous nodes. The simulation results show that the algorithm can accurately describe the synchronization precision and is more efficient for clock synchronization.