Aiming at the performance degradation of transmission performance of Transmission Control Protocol (TCP) in wireless network caused by the loss packet retransmission mechanism triggered by packet loss, an Adaptive transmission mechanism based on Forward Error Correction (AdaptiveFEC) was proposed. In the mechanism, the transmission performance of TCP was improved by the avoidance of triggering TCP loss packet retransmission mechanism, which realized by reducing data segment loss with forward error correction. Firstly, the optimal redundant segment ratio in current time was selected according to the current network status and the data transmission characteristics of the current connection. Then, the network status was estimated by analyzing the data segment sequence number in the TCP data segment, so that the redundant segment ratio was dynamically updated according to the network. Large number of experiment results show that, in the transmission environment with a round-trip delay of 20 ms and a packet loss rate of 5%, AdaptiveFEC can increase the transmission rate of TCP connection by 42% averagely compared to static forward error correction mechanism, and the download speed can be twice as much as the original speed with the proposed mechanism applied to file download applications.

Multi-label feature selection has been widely used in many domains, such as image classification and disease diagnosis. However, there usually exist missing labels in the label space of data in practice, which destroys the structure and correlation between labels, so that the learning algorithms are difficult to exactly select important features. To address this problem, a Multi-label Feature Selection based on Label-specific feature with Missing Labels (MFSLML) algorithm was proposed. Firstly, the label-specific feature for each class label was obtained via sparse learning method. At the same time, the mapping relations between labels and label-specific features were constructed based on linear regression model, and were used to recover the missing labels. Finally, experiments were performed on 7 datasets with using 4 evaluation metrics. Experimental results show that compared to some state-of-the-art multi-label feature selection algorithms, such as multi-label feature selection algorithm based Max-Dependency and Min-Redundancy (MDMR) and the Multi-label Feature selection with Missing Labels via considering feature interaction (MFML), MFSLML can increase the average precision by 4.61-5.5 percentage points. It can be seen that MFSLML achieves better classification performance.

Concerning the problem of privacy leak of personal location when querying the nearest neighbor location in social network, a geo-indistinguishability mechanism was used to add random noise to the location data, and a privacy budget allocation method was proposed. First, the spatial regions were divided into grids, and the personalized privacy budget allocation was performed according to the location hits of user in different regions. Then, in order to solve the problem of low hit rate of the neighbor query in the disturbance location dataset, a Combined Incremental Neighbor Query (CINQ) algorithm was proposed to expand the search range of the demand space, and the combination query was used to filter out the redundancy data. Simulation results show that compared with the SpaceTwist algorithm, the CINQ algorithm had the query hit rate increased by 13.7 percentage points. Experimental results verify that the CINQ algorithm effectively solves the problem of low query hit rate caused by the location disturbance of the query target, and it is suitable for neighbor queries for disturbed locations in social network applications.

In the process of traditional impact assessment of engineering change propagation for complex products,the joint effect of different association relationships among parts on change propagation is not comprehensively considered. Therefore,the multiplex network theory was applied to the modeling of complex product. Firstly,to explore the impact of different association relationships on engineering change propagation,the parts of a complex product and their functional, behavioral and structural association relationships were abstracted to a multiplex network. Then,the improved all-around distance index was used for evaluating the importance degrees of nodes in the multiplex network. Finally,the Susceptibility-Infection-Susceptibility(SIS)model was used to quantitatively assess the impact of engineering change propagation. To verify the effectiveness of the proposed method,the impact assessment of engineering change propagation of a type of diesel engine was taken as an example. Results demonstrate that:the multiplex network can fully describe complex products with different types of association relationships;the improved all-around distance index increases the ability to distinguish the importance degrees of nodes with the same coreness;the change in high-important parts will cause the avalanche propagation of engineering change throughout the product range.

The current consistency detection methods of software design and implementation require a large number of template sets and are difficult to generalize. In order to solve these problems, a consistency analysis method of software design and implementation based on control flow was proposed. Firstly, the pseudocode of the design document and the source code of the program were converted into the intermediate representations with the same features, and the design feature and the implementation feature were respectively extracted from the intermediate representations. The features include the function call relationship which can reflect the system structure and the control flow information which can reflect the internal structure of the function. Then, the design feature model and the implementation feature model were respectively established according to the design feature and the implementation feature. Finally, the similarity of the feature model was measured by calculating the feature similarity, so as to obtain the consistency detection result. Experimental results show that this method can correctly detect the inconsistent function call relationship when the function call relationship realized by the software is inconsistent with the design, and can correctly detect the inconsistency of the internal structure of the function when the function call relationship realized by the software is consistent with the design, with the accuracy reached 92.85%. This method can effectively obtain the consistency detection results without any template set, and has superior generality.

The basic Artificial Bee Colony (ABC) algorithm has some shortcomings such as slow convergence, low precision and easily getting trapped in local optimum. To overcome these issues, an improved ABC algorithm with enhanced exploitation ability was proposed. On one hand, the obtained optimum solution was directly introduced into the search equations of employed bees in two different ways and guided the employed bees to perform neighborhood search, which enhanced the exploitation or local search ability of the algorithm. On the other hand, the search was performed by the combination of the current solution and its random neighborhood in the search equations of onlooker bees, which improved the global optimization ability of the algorithm. The simulation results on some common benchmark functions show that in convergence rate, precision, and global optimization or exploration ability, the proposed ABC algorithm is generally better than the other similar improved ABC algorithms such as global best ABC (ABC/best) algorithm, and some ABC algorithms with hybrid search strategy such as ABC algorithm with Variable Search Strategy (ABCVSS) and Multi-Search Strategy Cooperative Evolutionary (ABCMSSCE).

In order to reduce the harm caused by bursty events in social media, a multi-feature based microblog bursty events detection algorithm was proposed. The algorithm combines text emotion filtering and user influence calculation methods. Firstly, the microblog text with negative emotion was obtained through noise filtering and emotion filtering. Then the proposed user influence calculation method was combined with the burst word extraction algorithm to extract the characteristics of burst words. Finally, a cohesive hierarchical clustering algorithm was introduced to cluster bursty word sets, and extract bursty events from them. In the experimental test, the accuracy is 66.84%, which proves that the proposed method can effectively detect bursty events.

Interpolation algorithms are usually used to estimate the channel frequency response value at the data location in Long Term Evolution-Advanced (LTE-A) system. Concerning the problem that traditional Linear Minimum Mean Square Error (LMMSE) algorithm needs to obtain channel statistical properties in advance and it suffers from a high computational complexity due to an inversion matrix operation, an improved LMMSE channel estimation interpolation algorithm was proposed. Firstly, the pilots were interpolated to add virtual pilots, which improved the performance of the algorithm. Secondly, an approximate estimation method of autocorrelation matrix and Signal-to-Noise Ratio (SNR) was given by using the fact that channel energy in the time domain is more concentrated. Finally, a sliding window method was adopted to further simplify the algorithm complexity to complete the LMMSE interpolation in frequency domain. The simulation results show that the overall performance of the proposed algorithm is better than that of linear interpolation method and Discrete Fourier Transform (DFT) interpolation method, and it has similar Bit Error Rate (BER) and Mean Squared Error (MSE) performance with the traditional LMMSE interpolation algorithm. Furthermore, it reduces the complexity by 98.67% compared with traditional LMMSE estimator without degrading the overall BER and MSE performance, so it is suitable for practical engineering applications.

Aiming at the motif finding problem in Chromatin Immunoprecipitation Sequencing (ChIP-Seq) datasets of Next-Generation Sequencing (NGS), a new motif finding algorithm based on Fisher's exact test, called FisherNet, was proposed. Firstly, Fisher's exact test was used to calculate the P values of all k-mers, some of which were selected as motif seeds. Secondly, the position weight matrix of the initial motif was constructed. Finally, the position weight matrix was employed to scan all k-mers for obtaining the final motif. The ChIP-Seq datasets of mouse Embryonic Stem cells (mESC), mouse erythrocytes, human lymphoblastic lines and the ENCODE database were used for verifying. The verification results show that, the accuracy and calculation speed of the proposed algorithm are higher than those of other common motif finding algorithms, and it can find more than 80% of core motifs for known transcription factors and their co-factors. The proposed algorithm can be applied to large-scale sequencing datasets while ensuring high accuracy.

According to the influence of Doppler frequency shift on the detection of Physical Random Access Channel (PRACH) signals, the signal detection algorithms were were divided into medium speed, high speed, ultra-high speed modes, and they were improved respectively. In the medium speed mode, a preamble detection algorithm based on frequency offset correction was proposed. In the high speed mode, an multi-sliding window peak detection algorithm was proposed. In the ultra-high speed mode, a frequency offset compensation preamble detection algorithm based on integer subcarrier was proposed. The simulation results show that, in different scenarios, when the PRACH signals are transmitted through the Additive White Gaussian Noise (AWGN) channel, and the false alarm rate performance of the receiver is improved by at least 3.8 dB, when the PRACH signals are transmitted through the Extend Typical Urban model (ETU) channel, the false alarm rate performance is improved by at least 1 dB. Compared with the frequency domain correlation detection algorithm, the proposed algorithms can improve the probability of successful detection of preamble signals and reduce the random access delay.

In order to deeply understand and fully grasp the research progress of similarity join query technology of big data and to promote its wide application in image clustering, entity resolution, similar document detection, similar trajectory retrieval, a comprehensive survey was conducted on similarity join query technology of big data. Firstly, the basic concepts of similarity join query were introduced; then intensive study on the big data similarity join research works for different data types, such as set, vector, spatial data, probabilistic data, string and graph was elaborated, their advantages and disadvantages were analyzed and summarized. Finally, some challenging research problems and future research priorities in big data similarity join query were pointed out.

Focusing on the difficulties in communication networks test and optimization since some key technologies like carrier aggregation applied in communication networks, and the shortage of Long Term Evolution (LTE) air-interface analyzer in domestic market, a Long Term Evolution-Advanced (LTE-A) air-interface analyzer design scheme was proposed, which supports 3GPP R10/11 protocol standards and LET-A key technologies like carrier aggregation. Firstly, the physical and logical architecture of LTE-A air-interface analyzer was introduced, and the relationship between the two, the function of each module in the physical and logical architecture was illustrated, then an implementation scheme of carrier aggregation in the instrument was designed. At the same time, in order to meet the demand of new technologies in communication network as well as the requirements of users and base station equipment test, a scheme which could support multi-user in the case of multi-carrier and multi-cell for the analyzer was proposed. The application of the scheme, can accelerate the commercialization of carrier aggregation in communication network, speed up the network deployment process and shorten the network construction cycle, and it will play an indispensable role in communication network operation and maintenance.

In view of the problem of computational redundancy due to the repeated computation of resource mapping positions in the traditional de-resource mapping method of Long Term Evolution-Advanced (LTE-A) physical layer, a new architecture of Physical Downlink Shared channel (PDSCH) de-resource mapping method was proposed, which provides support for the related physical layer processing of the LTE-A air interface analyzer. Firstly, before to the mapping of the physical downlink signal and the channel de-resource, the resource indexes of each signal and channel in single antenna port 0 mode, transmit diversity mode, single-stream beamforming, and dual-stream beamforming were generated; and then, the time-frequency location of the resource was directly located according to the resource index; finally, the PDSCH de-resource mapping module was put in the entire LTE-A link level simulation platform, and the simulations were given in four transmission modes, and the corresponding bit error rate and throughput comparison chart was obtained, which provides a theoretical reference to final hardware implementation. At the same time, compared with the de-resource mapping module under the traditional architecture, it shows that the de-resource mapping module under the new architecture costs 33.33% less time than the traditional computation mapping simulation, which reduces the de-resources and device resource consumption when de-resources mapping.

Aiming at the problem of decision error caused by similarity collision in evidence theory, a new combination rule for evidence theory was proposed. Firstly, the features of focal-element sequence in evidence were extracted and converted into a sort matrix to reduce similarity collision. Secondly, the weight of each evidence was determined based on sort matrix and information entropy. Finally, the Modified Average Evidence (MAE) was generated based on the evidence set and evidence weight, and the combination result was obtained by combing MAE for n-1 times by using Dempster combination rule. The experimental results on the online dataset Iris show that the F-Score of average-based combination rule, similarity-based combination rule, evidence distance-based combination rule, evidence-credit based combination rule and the proposed method are 0.84, 0.88, 0.88, 0.88 and 0.91. Experimental results show that the proposed method has higher accuracy of decision making and more reliable combination results, which can provide an efficient solution for decision-making based on evidence theory.

The single feature based vehicle license plate localization algorithms are hard to be adapted to the complex environment. In order to solve the problem, a multi-feature fusion algorithm was proposed, which made use of multi-features such as edge, color and texture. The localization process was divided into two phases: Hypothesis Generation (HG) and Hypothesis Verification (HV). In HG, feature point detection algorithm and mathematical morphology were used as the primary techniques, and the character texture and color information of vehicle license plate were extracted as the features to generate the candidates. In HV, gray projection technology and constant feature of vehicle license plate were used to verify the candidates from HG, then the correct license plate was located. The experimental results show that the proposed algorithm can achieve the localization success ratio of 96.6% and the precision of 95.4% in the testing image set in real environment. Moreover, the rationality and validity of the multi-feature fusion algorithm are verified.

With the rapid development of Internet, huge amount of textual information is accessible on the Internet. The task of reliable person-person relation extraction from Web page has become an import research topic in the field of information extraction. To address this problem, this work implemented a blocked person relation recognition system and adopted abundant of features, i.e., bag-of-word, relevant frequency, Dependency Tree (DT), Named Entity Recognition (NER) features, etc. A series of experiments were conducted to select out optimal feature set and classification algorithm for each relation type to improve the performance. This system was performed on two tasks in China Conference of Machine Learn Competition (CCML Competition) of 2015, to recognize person relation from single or a set of news titles in Chinese (Task1 and Task2, respectively). For these two tasks, this system achieved the MacroF1 score of 75.68% and 76.58%, respectively and ranked the 1st on both tasks.

As an important part of the urban vehicle network, bus vehicle network provides supports for a wide range of urban-vehicle communication network due to cyclical movement law. However, the complex urban road environment brings great challenges to highly efficient and reliable routing protocols for bus vehicle network. In bus vehicle network with the characteristics of cyclical movement, a new protocol named SRMHR (Single & Realmending-Multi Hop Routing) was proposed to ensure the single hop link's life time and multi-hop submission probability in limited delay. According to the signal propagation attenuation model and vehicle mobility model, a single hop selection mechanism and a multi-hop delay probability forwarding mechanism were proposed to ensure the reliability and effectiveness of bus-assistant forwarding. On the urban traffic simulation platform, using real road traffic data of slight adjustment, the performance of signal attenuation model, single hop selection mechanism and light correction model under different traffic densities were tested. The results prove the validity of each link of the scheme. Comparison with SF (Spray and Focus) and SW (Spray and Wait) proves that SRMHR protocol has a higher successful rate of data transmission and lower delivery delay.

Human-computer interaction technology based on virtual hand is a research focus of virtual reality. The key technologies were studied, the modeling method of polygon mesh was used and a realistic geometric model of virtual hand was established. The data acquisition module of data glove based on Virtools was developed and a method of data conversion based on initial value was proposed, the phenomenon of jump was solved when the virtual hand begin to move. For the fingers outreach motion, the method of constraint value was used, and the matching consistency of virtual hand with the real hand was increased. At last, for the interaction operation of virtual hand with virtual objects, a crawl algorithm based on effective threshold angle was put forward. Verified by Virtools platform, high fidelity of the virtual hand model is got, the shape of the hand changes naturally. Meanwhile, the interaction operation of virtual hand to virtual objects is implemented and it has a strong practicability.

Aiming at the problem that the memory and computational capability is insufficient while using Eclat algorithm to mine frequent itemsets from massive dataset, a parallel mining algorithm based on Map/Reduce framework, called MREclat(MapReduce Eclat), was proposed. Firstly, MREclat algorithm converted the horizontal database into a vertical one. Secondly, it redistributed the converted dataset according to the first item of each frequent 2-itemset and load-balance was taken into consideration while distributing datasets. Then, all the frequent itemsets prefixed by the same item were computed in each computing node. Finally, MREclat algorithm collected the result of each computing node and generated the whole frequent itemsets. In this paper, the idea of MREclat was introduced and the performance of the algorithm was studied. The experimental results show that MREclat algorithm is twice as efficient as PEclat algorithm, and the speedup performance of MREclat algorithm is 64% higher than that of PEclat.

In the traditional false data filtering schemes of Wireless Sensor Networks (WSN), only the false data reports are filtered, while the compromised nodes still can continuously inject false data to WSN which wastes network resources. In order to cut off the source of false data generating, a new false data filtering scheme based on trust management mechanism was proposed. The conspiracy forged false data generated from multiple captured nodes were limited within a cluster by using clustering method, then the trust management mechanism was used to detect whether the node was compromised, hence isolated the compromised node. The analysis results show that the proposed scheme can not only filter false data effectively, but also isolate compromised nodes, and has a strong ability of tolerating compromised nodes.

An Improved Genetic Algorithm (IGA) was proposed for the inverse kinematics problem solution of upper limb kinematic chain which had high degree of freedom and was too complex to be solved by using geometric, algebraic, and iterative methods. First, the joint-units of upper limb kinematic chain and its mathematical modeling were constructed by using Denavit-Hartenberg (D-H) method, then population diversity and initialization were completed based on simulating human being population, and the adaptive operators for crossover and mutation were designed. The simulation results show that the IGA can search the high precise solutions and avoid prematurity convergence or inefficient searching in later stage with larger probability than standard genetic algorithm.

The methods of image super-resolution via sparse representation achieve good quality image reconstruction, but the CPU-based implementation of the methods hardly satisfies the requirement of real-time video super-resolution because of high computational complexity. Then, the method of real-time video super-resolution via sparse representation based on GPU acceleration was proposed. It focused on optimizing data parallel processing and improving resource utilization of GPU, including utilizing queues for video sequences, improving memory concurrent access rates, employing Principal Component Analysis (PCA) dimensionality reduction and optimizing dictionary querying operation. As a result, compared with the CPU-based implementation, the speed of data processing is increased two orders of magnitude, and the speed of playing a video with the size of 669×546 reaches 33 frames per second.

To solve the low classification accuracy and poor interpretability of selected features in traditional credit risk evaluation, a new model using Sparse Bayesian Learning (SBL) to evaluate personal credit risk (SBLCredit) was proposed in this paper. The SBLCredit utilized the advantages of SBL to get as sparse as possible solutions under the priori knowledge on the weight of features, which led to both good classification performance and effective feature selection. SBLCredit improved the classification accuracy of 4.52%, 6.40%, 6.26% and 2.27% averagely when compared with the state-of-the-art K-Nearest Neighbour (KNN), Nave Bayes, decision tree and support vector machine respectively on real-world German and Australian credit datasets. The experimental results demonstrate that the proposed SBLCredit is a promising method for credit risk evaluation with higher accuracy and fewer features.

Similar string search algorithms based on Trie tree need to compute active-node set of a node by editing distance threshold. A large number of redundant computation leads to a high time and space complexity. A new algorithm named New-Trie-Stack was proposed, which utilized the symmetrical properties of active-node set and the dynamic programming method to improve the performance. It could avoid the redundancy cost on active-node set computing and storing; moreover, active-node sets were pruned. The experimental results show that New-Trie-Stack algorithm has lower time complexity and space complexity.

The Peer-to-Peer (P2P) Distributed Hash Table (DHT) protocol is concise, and can be understood easily, but implementing and deploying a component like Chord with all functions in practice is very difficult and complicated because of the mismatch between popular imperative language and distributed architecture. To resolve these problems, a P2P DHT protocol based on Bloom system was proposed. Firstly, the distributed logic programming language's key elements of Bloom system were expounded. Secondly, a minimal distributed system was designed. Thirdly, a Chord prototype system was implemented through defining persistent, transient, asynchronous communicating and periodic collections and designing several algorithms for finger table maintaining, successor listing, stabilization preseving and so on. The experimental results show that the prototype system can finish full functions of Chord, and compared to traditional languages, 60% of the code lines can be saved. The analysis indicates such a high degree of uniformity between final code of the algorithm and the DHT protocol specification makes it more readable and reusable, and helpful for further understanding the specific protocol and relative applications.

In the hybrid wired/wireless network, the traditional Transmission Control Protocol (TCP) versions in wired network simply ascribe packet loss to congestion, which causes unnecessary performance degradation. To solve this problem, a new adaptive control algorithm based on fuzzy theory was proposed. It selected new network status parameters, and used fuzzy loss differentiating method to make comprehensive evaluation out of network status, and it was based on feedback theory method, finally built an adaptive control model, i.e. getting the evaluation result set, then yielding the Transmission Performance Index (TPI) by summing up the result sets weighting elements, which entered into next evaluation cycle as one of the input factors and also adjusted the factors weights. The simulation results show this algorithm better reflects the real congestion status of hybrid network, has better network adaptability and performs better than current main TCP mechanisms. This algorithm, on the background of multi-parameters and using fuzzy methods, makes new explorations of hybrid network congestion and its adaptive control research.

In order to solve the two key problems of the short text classification, very sparse features and strong context dependency, a new method based on latent Dirichlet allocation was proposed. The generated topics not only discriminate contexts of common words and decrease their weights, but also reduce sparsity by connecting distinguishing words and increase their weights. In addition, a short text dataset was constructed by crawling titles of Netease pages. Experiments were done by classifying these short titles using K-nearest neighbors. The proposed method outperforms vector space model and topic-based similarity.

Precipitation data quality of regional automatic weather stations determines the missed alarm and false probability of weather warning, but it is difficult to verify the validity of the data by only relying on space and time correlation of automatic data. Precipitation of weather radar can verify automatic weather station data. However, it is difficult to determine the coverage of weather stations by radar station due to heterogeneous and complex geographical environment of radar station, which results in constant error-checking. This paper built a coverage matching algorithm of radar stations and automatic weather stations by using the layer volume scan mode and the radar beam occlusion model of weather radar. Automatic stations and radar stations matching software based on the algorithm can effectively support Automatic Weather Station (AWS) precipitation data inspection.

This paper studied on the operational scheduling problem in a cross-docking center of a single receiving and a single shipping door with finite temporary storage. A dynamic programming model was built with the objective to minimize the costs including additional handing, temporary storage and truck replacement cost. A cultural algorithm with two layer evolutionary mechanism was proposed to solve the problem. The evolution of population space adopted genetic algorithm, and the belief space received good individual from population space to form knowledge which was used to guide evolution in turn. Numerical experiments under small and big scale situations prove the validity of proposed cultural algorithm.

In this paper, a Zero Forcing BeamForming (ZFBF) precoding scheme based on two-level codebook was proposed. The defect, that the performance of ZFBF precoding scheme was worse with fewer feedback bits in the multi-user Multiple Input Multiple Output (MIMO) system upon limited feedback, was well overcome by this scheme. In the proposed scheme, unitary matrix was combined with random vector codebook. The best beamforming vector in the one-level codebook was searched firstly and then the Signal to Interference and Noise Ratio (SINR) corresponding with the best beamforming vector in the disturbance range of orthogonal vector was calculated. Finally, the user's feedback information was decided by comparing the SINR calculated with the SINR threshold value set by system. At last, the codebook was adaptively changed and ZFBF precoding was finished by the station, according to the user's feedback information. The simulation results show that every user's average number of feedback bits is significantly reduced and a better sum rate performance with a lower feedback rate is gained. The codebook searching process is simplified and the sum rate performance is improved by this scheme successfully.