Different clustering algorithms are used to design their own strategies. However,each technology has certain limitations when it executes a particular dataset. An adequate choice of Discrimination Information Method(DIM)can ensure the document clustering. To solve these problems,a DIM of Document Clustering based on Consensus and Classification (DCCC) was proposed. Firstly,Clustering by DIM (CDIM) was used to solve the generation of initial clustering for dataset,and two initial cluster sets were generated by two different CDIMs. Then,two initial cluster sets were initialized again by different parameter methods,and a consensus was established by using the relationship between the cluster label information,so as to maximize the sum of documents' discrimination number. Finally,Discrimination Text Weight Classification(DTWC)was chosen as text classifier to assign new cluster label to the consensus,the base partitions were altered by training the text classifier,and the final partition was obtained based on the predicted label information. Experiments on 8 network datasets for clustering verification by BCubed's precision and recall index were carried out. Experimental results show that the clustering results of the proposed consensus and classification method are superior to those of comparison methods.

Concerning the problem that a straightforward implementation of the experience-replay procedure based on python data-structures may lead to a performance bottleneck in Deep Q Network (DQN) related applications, a design scheme of a universal experience-replay module was proposed to provide high performance. The proposed module consists of two software layers. One of them, called the "kernel", was written in C++, to implement fundamental functions for experience-replay, achieving a high execution efficiency. And the other layer "wrapper", written in python, encapsulated the module function and provided the call interface in an object-oriented style, guaranteeing the usability. For the critical operations in experience-replay, the software structure and algorithms were well researched and designed. The measures include implementing the priority replay mechanism as an accessorial part of the main module with logical separation, bringing forward the samples' verification of "get_batch" to the "record" operation, using efficient strategies and algorithms in eliminating samples, and so on. With such measures, the proposed module is universal and extendible. The experimental results show that the execution efficiency of the experience-replay process is well optimized by using the proposed module, and the two critical operations, the "record" and the "get_batch", can be executed efficiently. The proposed module operates the "get_batch" about 100 times faster compared with the straightforward implementation based on python data-structures. Therefore, the experience-replay process is no longer a performance bottleneck in the system, meeting the requirements of various kinds of DQN-related applications.

Focused on the issue that the mass small file system developed based on HDFS (Hadoop Distributed File System), SMDFS (Mass Small Distributed File System), is not compatible with POSIX (Portable Operating System Interface of UNIX) constraints, a POSIX compatible technology based on local cache and an efficient metadata management technology based on temporary data cache were proposed. Firstly, the data storage area was set to realize the redirection of the file flow in the read-write mode, and then an asynchronous thread pool model was established to synchronize the data in temporary cache, thereby completing all POSIX-related file operations from the user layer to the storage layer. In addition, with the help of the metadata cache of the skip list structure, the efficiency of metadata operations such as the List directory was optimized. The test results show that, compared to the Linux client of HDFS, the performance of random read improves ten times more, the sequential read and sequential write improves about three to four times. The performance of random write can reach 20% of the local file system. Besides, the List operation efficiency of the directory improves about 10 times. However, due to the additional switching of kernel-mode and user-mode introduced by FUSE (Filesystem in Userspace), the Linux client of SMDFS3.0 has a performance penalty of about 50% compared to Java interface.

In matrix permutation-based Block Compressive Sensing (BCS), matrix permutation strategy is introduced, which makes the complex sub-blocks and sparse sub-blocks change to the middle level of sparsity and reduces the blocking artifacts when sampling with the single sampling rate. However there is still a problem of poor sparsity balance among blocks. In order to get better reconstruction effect, a Ripple Matrix Permutation-based sparsity balanced BCS (BCS-RMP) algorithm was proposed. Firstly, an image was pre-processed by matrix replacement before sampling, and the sparsity of each sub-block of the image was equalized by ripple permutation matrix. Then, a same measurement matrix was used to sample the sub-blocks and reconstruct them on the decoding side. Finally, the final reconstructed image was obtained by inverse transformation of reconstruction results by the ripple permutation inverse matrix. The simulation results show that, compared with the existing matrix replacement algorithms, the proposed ripple matrix permutation algorithm can effectively improve the quality of image reconstruction, and it can reflect the details more accurately when choosing appropriate sub-block size and sampling rate.

In view of the problem that the traditional channel estimation algorithm based on Discrete Cosine Transform (DCT) does not eliminate the noise in the cyclic prefix length, a new method of Orthogonal Frequency Division Multiplexing (OFDM) system channel estimation based on wavelet de-noising and DCT interpolation was proposed. First the method of Least Squares (LS) was used to preliminarily estimate channel for received pilot signal, then the results estimated by LS method were processed through discrete wavelet thresholding denoising, finally the noise of the cyclic prefix length was handled again by DCT interpolation algorithm to further reduce the influence of noise. The simulation on Matlab 2012 platform, compared with the traditional channel estimation algorithm based on DCT, under the conditions of the same Bit-Error-Rate (BER), the Signal-to-Noise Rate (SNR) performance of the proposed algorithm improved about 1 dB; under the conditions of the same Mean-Square-Error (MSE), the SNR performance of the proposed algorithm improved about 2 dB.The simulation results show that the proposed algorithm can not only reduce the influence of Additive White Gaussian Noise (AWGN), but also improve the accuracy of channel estimation effectively, and the proposed algorithm has better performances than the channel estimation algorithm based on DCT.

The existing evaluation methods for node importance in complex network mainly focus on undirected-unweighted complex networks, and can not reflect objectively the reality of some real world status. Focusing on the problems such as the limited scope of evaluation indexes and not enough comprehensive evaluation results in the undirected-weighted and directed-weighted networks, and the node importance evaluation method in undirected-unweighted networks based on mutual information was used for reference, a new evaluation method based on mutual information that is suitable for the undirected-weighted and directed-weighted networks was proposed. In this method, each edge was regarded as a flow of information, the structure characteristics of the corresponding complex networks and the definition method of "amount of information" were considered, then the amount of information was calculated as the node importance evaluation index. The analyses of the instance network show that the proposed algorithm can more detailed describe the differences between nodes in the directed-weighted network under the premise of guaranteeing estimation accuracy. In the evaluation of the ARPA (Advanced Research Project Agency) network nodes, the first five most important nodes number that were evaluated from the proposed algorithm and the previous indexes were especially close, so the algorithm's ability of finding the core nodes was highlighted. The proposed algorithm provides a certain theoretical help for evaluating the core nodes in the undirected-weighted and directed-weighted networks and improving the network invulnerability ability quickly and accurately.

Traditional Linear Minimum Mean Square Error (LMMSE) channel estimation was required to know the statistical characteristics of the channel. However, these characteristics are usually unknown in practical applications. Aiming at the uncertainty of wireless channel statistics, taking the time-domin channel sparsity of the energy distribution into consideration, this article proposed an improved LMMSE channel estimation algorithm based on Least Squares (LS) estimation. The algorithm began with the highest confidence degree subcarrier, making the adjacent subcarrier channel estimation value as the current subcarrier real response to compute the weighting coefficient, then to complete channel response of the multiple channels by the method of weighted average. This algorithm avoided the complicated operation of the matrix inversion and decomposition, and might be done effectively and easily. The experimental results show that the performance of the improved algorithm is better than LS and the SVD-LMMSE (Singular Value Decomposition-Linear Minimum Mean Square Error) channel estimation, and the Bit Error Ratio (BER) is close to traditional LMMSE algorithm.

For the inaccurate problem of the estimation of the blood flow velocity which is caused by the clutter signal in ultrasound Color Flow Imaging (CFI), this paper proposed a clutter suppression method based on dynamic region polynomial regression and Singular Value Decomposition (SVD), called ARS algorithm. First, according to the time-domain characteristics and the energy intensity of the echo signal, this method adopted the dynamic partitioning method to distinguish the range of signal; then, according to the divided range, polynomial regression method or SVD method was selected to dynamically reject the clutter signal. This paper made a simulation to compare the proposed method with the projection initialized Infinite Impulse Response (IIR) filter, the non-stationary filter, the regression filter and the SVD algorithm. The experimental results show that the proposed method can completely reject the interference of tissue motion (the velocity is almost zero in the tissue area and the clutter-to-blood ratio is about 5.427 dB after the clutter suppressing is implemented), the estimated maximum blood flow velocity (0.968 m/s) is close to the theoretical value and the blood flow distributes uniformly, the integrity of the blood flow velocity profile can be better maintained and the achieved blood flow velocity map illustrates the authentic flow velocity and high image quality.

Now the integer Discrete Cosine Transform (DCT) algorithm of H.264 can not apply to Distributed Video Coding (DVC) framework directly because of its high complexity. In view of this, the authors presented a integer DCT algorithm and transform radix generating method based on fixed long step quantization which length was 2x (x was a plus integer). The transform radix in H.264 could be stretched. The authors took full advantage of this feature to find transform radix which best suits for working principle of hardware, and it moved the contracted-quantized stage from coder to decoder to reduced complexity of coder under the premise of "small" transform radix. In the process of "moving", this algorithm guaranteed image quality by saturated amplification for DCT coefficient, guaranteed reliability by overflow upper limit, and improved compression performance by reducing radix error. The experimental results show that, compared with corresponding module in H.264, the quantization method of this algorithm is convenient for bit-plane extraction. And it reduces calculating work of contracted-quantized stage of coder to 16 times of integer constant addition under the premise of quasi-lossless compression, raises the ratio of image quality and compression by 0.239. This algorithm conforms to DVC framework.

In view of speckle noise in ultrasound image, there are some disadvantages of traditional anisotropic diffusion methods, such as in-sufficient noise suppression and edge details preservation. A de-speckling method based on Mallat-Zhong Discrete Wavelet Transform (MZ-DWT) wavelet was proposed. The method used MZ-DWT wavelet and Expectation Maximization (EM) algorithm as the discrimination factor between homogeneous and edge regions, making it more accurately to control diffusion intensity and rate and achieving the noise suppression and details preservation. The experimental results show that, the proposed algorithm can better de-speckle while preserving image details and the performance of the method is better than the traditional anisotropic diffusion methods.

When hybrid dynamic load balancing algorithm is applied to cloud computing, some problems will occur, such as that frequent exchange of sites information leads to low processing efficiency and algorithm is lack of fault-tolerant mechanism. Hence this paper proposed a load balancing algorithm based on multi-layer and fault- tolerant mechanism. The algorithm mixed the advantages of centralized and distributed methods. By organizing neighbor sites, the sites information exchange was controlled within a range of neighbor sites. When site scheduled tasks, it appended the load information of itself and its neighbors to the job transfer request. The method resolved network business and low efficiency of servers which was caused by broadcasting load information frequently. The algorithm achieved load balancing in cloud computing and minimum response time. The method introduced fault-tolerant mechanism to enhance the scalability of cloud system. The experimental results show that the load balancing strategy of cloud computing based on multi-layer and fault-tolerant mechanism is superior to traditional algorithms more than 20% in task distribution time and response time. Besides, it surpasses the traditional one in the stability of the algorithm.

Concerning the impact on network performance of the mobile nodes and the constantly changing data transmission rate, the authors proposed a new backoff scheme for IEEE802.15.4, which used Probability Judgment based on Network Load and Exponentially Weighted Moving Average (PJNL_EWMA) method. According to a realtime monitoring of current network status by probability judgment of network load, this method dynamically adjusted backoff exponent by EWMA when Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) began. Compared with the IEEE802.15.4 standard protocol and MBS (Memorized Backoff Scheme)+EWMA algorithm, the simulation experiments on NS2 platform show that the PJNL_EWMA algorithm not only improves the throughput of the network, but also reduces the packet loss rate and the collision ratio, significantly improving the network performance.

To solve the detection problem of selfish behavior in IEEE802.15.4 Wireless Sensor Network (WSN), an improved Sliding Window Non-parameter Cumulation Sum (SWN-CUSUM) algorithm based on statistics was proposed to decrease the detection delay. By tracing the delay characteristic sequence between successful transmissions, the algorithm could distinguish if there was a selfish behavior in the WSNs. The NS2 simulation tool was conducted to validate the feasibility of the proposed algorithm. The experimental results show that the improved algorithm not only weakens the impact of the threshold on the performance of the algorithm, but also reduces the size of sliding window used to detect selfish behavior, and the improved algorithm makes improvement in the calculation and the detection delay than the primitive SWN-CUSUM algorithm, so the improved algorithm can detect effectively and rapidly the selfish behavior of nodes in IEEE802.15.4 WSNs.

The coverage problem is a basic problem in the wireless sensor networks, which indicates the Quality of Service (QoS) of sensing by wireless sensor networks. A lot cover blind areas and cover redundancies will be produced, when the nodes are deployed initially in the networks. A hybrid algorithm was proposed to deploy the heterogeneous network nodes reasonably to improve the coverage ratio and reduce the cost of the nodes,which introduced the ε-target constraint method based on Particle Swarm Optimization (PSO) and Fish Swarm Algorithm (FSA). The swarm hybrid algorithm firstly set up the concept of individual center, to quickly search the best solution domain of the individuals' locations, introducing the idea of the cluster behavior and tracing cauda behavior into the PSO, and then used the PSO to find the optimized speed and optimized location of the individuals. The simulation results show that the swarm hybrid algorithm is better than the standard PSO and the standard FSA in pursuing the balance and optimization between the coverage ratio and the cost of the networks.

Concerning the high packet delay and frequent transmission bottleneck in Wireless Sensor Network (WSN) with one-sink node, a multi-sink wireless sensor network model and its Time Division Multiple Access (TDMA) scheduling algorithm based on Genetic Algorithm (GA) were proposed. The algorithm divided the whole sensor network into some small sensor networks according to the number and position of the sink nodes, and adopted GA to optimize the slot allocation result. The simulation results show that, the TDMA time slot allocation method based on genetic algorithm is better in the length of time slot allocation frame, the average of packet delay and the average energy consumption than that of graph coloring algorithm.

The retina vessel enhancement of retina angiography image is considered as an essential factor to improve diagnosis. Therefore, this paper proposed a multi-scale vessel enhancement method for retinal image based on Hessian matrix. Besides, the strategy and process of applying this method were provided. This method was tested on DRIVE database and its results were compared with other methods using the same database. The result shows a considerable accuracy as other methods while obtaining better robustness.

To study the fairness-optimization problem with different user-rate in non-regenerative relay network, a power allocation algorithm based on user-required rate proportional fairness was proposed. Because the source could not receive the messages of the user's hope rate, a pre-average allocation fairness algorithm was proposed to overcome the disadvantage of the prior algorithm. The simulation results show that the two algorithms realize goal of minimizing the difference between user-rate and required rate and save the resource in network with assuring the users' requirement of Quality of Service (QoS).

To resolve many existing problems in integrity-based remote attestation of trusted platform, an abstract model of property-based remote attestation was put forward, in which the property of platforms was validated by the trusted third party. Then the model was described and validated explicitly by a logic security language. This new method enriches the security semantic of platform attestation and is more adapt to the impartial and open network environment.