To solve the problems of color distortion and local detail information loss caused by the histogram expansion of High Dynamic Range (HDR) image generated by single image, an imaging algorithm of high dynamic range image based on luminance partition fusion was proposed. Firstly, the luminance component of normal exposure color image was extracted, and the luminance was divided into two intervals according to luminance threshold. Then, the luminance ranges of images of two intervals were extended by the improved exponential function, so that the luminance of low-luminance area was increased, the luminance of high-luminance area was decreased, and the ranges of two areas were both expanded, increasing overall contrast of image, and preserving the color and detail information. Finally, the extended image and original normal exposure image were fused into a high dynamic image based on fuzzy logic. The proposed algorithm was analyzed from both subjective and objective aspects. The experimental results show that the proposed algorithm can effectively expand the luminance range of image and keep the color and detail information of scene, and the generated image has better visual effect.

In order to solve the problem of misjudgment which due to emotion point to an unknown and missing hidden view in traditional emotion classification method, a text sentiment classification method based on emotional role modeling was proposed. The method firstly identified evaluation objects in the text, and it used the measure based on local semantic analysis to tag the sentence emotion which had potential evaluation object. Then it distinguished the positive and negative polarity of evaluation objects in this paper by defining its emotional role. And it let the tendency value of emotional role integrate into feature space to improve the feature weight computation method. Finally, it proposed the concept named "features converge" to reduce the dimension of model. The experimental results show that the proposed method can improve the effect and accuracy of 3.2% for text sentiment classification effectively compared with other approaches which tend to pick the strong subjective emotional items as features.

The traditional spacetime twodimensional parameter estimation has many shortcomings, such as high computational complexity, poor robustness and generalization, and slow convergence speed. According to the spacetime equivalence and that the spatial and time domain processing algorithms can be transformed into each other, a suitable fitness function was derived, the improved particle swarm algorithm was used to search the arrival angle and frequency of signal, and the search results were classified with Kmeans clustering algorithm. Using particle swarm algorithms feature, such as global convergence, parallelism, can improve the algorithms searching capabilities. The computer simulation shows that the proposed method has better statistics and convergence performance than traditional methods.

A new threshold function under non-Gaussian noise background was presented to overcome the limitations of wavelet threshold algorithm under the Gaussian noise background. The shortcomings of conventional function, such as discontinuity of hard threshold function and the invariable dispersion of soft threshold function, can be solved. The new function which employed high order power function was put forward based on Garrote threshold. First, the signal with a class of non-Gaussian noise was decomposed by wavelet. Secondly, each high frequency wavelet coefficient was quantified based on new threshold function. Thirdly, signal was reconstructed by the low frequency coefficients of wavelet decomposition and quantified high frequency coefficients. The simulation results under non-Gaussian noise background indicate that the new threshold function gets higher Signal-to-Noise Ratio (SNR) gains and lower minimum Mean Square Error (MSE) compared to the soft and hard threshold, two types of improved threshold and Garrote threshold.

In order to improve the storage efficiency of Hadoop Distributed File System (HDFS) and its load balance ability, this paper presented an improved solution named Noah to replace the original multiple-replication strategy. Noah introduced a coding module to HDFS. Instead of adopting the multiple-replication strategy by the original system, the module encoded every data block of HDFS into a greater number of data sections (pieces), and saved them dispersedly into the clusters of the storage system in distributed fashion. In the case of cluster failure, the original data would be recovered via decoding by collecting any 70% of the sections, while the dynamic replication strategy also worked synchronously, in which the amount of copies would dynamically change with the demand. The experimental results in analogous clusters of storage system show the feasibility and advantages of new measures in proposed solution.