To address the problem of bandwidth resource management in Web applications, a prediction method for bandwidth requirement and Quality of Service (QoS) of Web applications based on network simulation was proposed. A modeling framework and formal specification were presented for Web services, a simplified parallel workload model was adopted, the model parameters were extracted from Web application access logs by means of automated data mining, and the complex network transmission process was simulated by using network simulation tool. As a result, the bandwidth requirement and changes on QoS were able to be predicted under different workload intensities. A classic benchmark system named TPC-W was used to evaluate the accuracy of prediction results. Theoretical analysis and simulation results show that compared with traditional linear regression prediction, network simulation can stably simulate real system, the predicted average relative error for total request number and total byte number is 4.6% and 3.3% respectively. Finally, with different bandwidth scaling schemes simulated and evaluated based on the TPC-W benchmark system, the results can provide decision support for resource management of Web applications.

In massive Multiple-Input-Multiple-Output (MIMO) system, as the increse of antenna number, traditional detection algorithms have lower performance, higher complexity, and they are not suitable for high order modulation. To solve the problem, based on the idea of shadow domain, a search tree detection algorithm combining Quadratic Programming (QP) and Branch and Bound (BB) algorithm was proposed. Firstly, with QP model constructed, the unreliable symbols from solution vector of first-order QP algorithm were extracted; then, BB search tree algorithm was applied to the unreliable symbols for the optimal solution; meanwhile three pruning strategies were proposed to reach a compromise between complexity and performance. The simulation results show that the proposed algorithm increases 20 dB performance gain compared with the traditional QP algorithm in 64 Quadrature Amplitude Modulation (QAM) and increases 21 dB performance gain compared with QP algorithm in 256 QAM. Meanwhile, applying the same pruning strategies, the complexity of the proposed algorithm is reduced by about 50 percentage points compared with the traditional search tree algorithm.

To solve the problem that the text regions can not be extracted well in low contrast images by traditional Maximally Stable Extremal Regions (MSER) method, a novel scene text detection method based on edge enhancement was proposed. Firstly, the MSER method was effectively improved by Histogram of Oriented Gradients (HOG), the robustness of MSER method was enhanced to low contrast images and MSER was applied in color space. Secondly, the Bayesian model was used for the classification of characters, three features with translation and rotation invariance including stroke width, edge gradient direction and inflexion point were used to delete non-character regions. Finally, the characters were grouped into text lines by geometric characteristics of characters. The proposed algorithm's performance on standard benchmarks, such as International Conference on Document Analysis and Recognition (ICDAR) 2003 and ICDAR 2013, was evaluated. The experimental results demonstrate that MSER based on edge enhancement in color space can correctly extract text regions from complex and low contrast images. The Bayesian model based classification method can detect characters from small sample set with high recall. Compared with traditional MSER based method of text detection, the proposed algorithm can improve the detection rate and real-time performance of the system.

Most existing cloud storage systems are based on the model, which leads to a full dataset scan for multi-dimensional queries and low query efficiency. A KD-tree and R-tree based multi-dimensional cloud data index named KD-R index was proposed. KD-R index adopted two-layer architecture: a KD-tree based global index was built in the global server and R-tree based local indexes were built in local server. A cost model was used to adaptively select appropriate R-tree nodes to publish into global KD-tree index. The experimental results show that, compared with R-tree based global index, KD-R index is efficient for multi-dimensional range queries, and it has high availability in the case of server failure.

For the Hough transform method for ellipses detection is slow and needs huge storage, a fast randomized algorithm was presented. The algorithm randomly sampled two edge-points and searched for the third point to determine the center coordinate of an ellipse by the characteristics of pole chord, and then solved the elliptic systems obtained by coordinate transformation to get the rest parameters of the ellipse. At the time of determining a candidate ellipse by a verification process, only the points in the enclosing rectangle of the ellipse were selected. And the authenticity of a potential ellipse was confirmed by a special cumulative function. The experimental results demonstrate that the proposed method has the advantages of high speed and accuracy as well as strong resistance to partial occlusion.

To improve the quality of video surveillance outdoors and to automatically acquire the weather situations, a method to recognize weather situations in outdoor images is presented. It extracted such parameters as power spectrum slope, contrast, noise, saturation as features to realize the multi-classification of weather situations with Support Vector Machine (SVM). Then a decision tree was constructed in accordance with the distance between these features. The experimental results on WILD image base and our image set of eight hundred samples show that the proposed method can recognize sunny, overcast, foggy weather more than 85%, and recognize rainy weather more than 75%.

A novel algorithm for the creation of classifier ensemble based on fuzzy clustering was introduced. The algorithm got the distribution characteristics of the training sets by fuzzy clustering and sampled different training dataset to train different individual classifiers. Then the algorithm adjusted every sample's weight to get more classifiers through evaluating the quality of the classifier until certain termination condition was satisfied. The algorithm was tested on the UCI benchmark data sets and compared with two other classical algorithms: AdaBoost and Bagging. Results show that the new algorithm is more robust and has higher accuracy.