In order to improve the speech enhancement effect of small microphone array, a better method was proposed for small-array speech enhancement by combining the Array Crosstalk Resistant Adaptive Noise Cancellation (ACRANC) method with the BeamForming (BF) method. Firstly, ACRANC subsystems were constructed to obtain multiple channels of enhanced speech signals. Then, the proposed Adaptive Mode Control (AMC) algorithm and the Delay And Sum (DAS) beamforming method were applied to the enhanced speech signals for further improving the enhancement effect of multi-channel speech signals. The computational complexity of the proposed method was estimated, and it was verified that the proposed method was able to be realized in real-time with common chips. Experimental results in actual environments show that the speech enhancement effect of the proposed method is higher than that of the ACRANC method and thus the method has some advantages.

In order to improve the denoising performance of dual mini micro-array speech enhancement system in multi-noise environment, an improved generalized sidelobe canceller speech enhancement algorithm for dual mini micro-array was proposed. According to the structure characteristics of the dual mini micro-array, firstly, an improved coherent filtering algorithm based on noise cross-power spectrum estimation was used to eliminate the weak correlation noise between microphones with long distances. Secondly, the strong correlation noise between microphones with short distances was eliminated by using a generalized sidelobe cancelling algorithm. Finally, the minima-controlled recursive averaging based sub-band spectrum subtraction was used to eliminate the residual noise in different spectrum bands purposefully. Experimental results show that the proposed algorithm achieves better score in perceptual evaluation of speech quality than existing dual mini micro-array speech enhancement algorithms under multi-noise environment, and improves the suppression effect of dual mini micro-array speech enhancement system on complex noise to a certain extent.

In order to solve the low speech recognition rate in noise environment, and the difficulty of traditional beamforming algorithm in dealing with spatial noise problem, an improved Minimum Variance Distortionless Response (MVDR) beamforming method based on dual micro-array was proposed. Firstly, the gain of micro-array was increased by diagonal loading, and the computational complexity was reduced by the inversion of recursive matrix. Then, through the modulation domain spectrum subtraction for further processing, the problem that music noise was easily produced by general spectral subtraction was solved, effectively reducing speech distortion, and well suppressing the noise. Finally, the Convolution Neural Network (CNN) was used to train the speech model and extract the deep features of speech, effectively solve the problem of speech signal diversity. The experimental results show that the proposed method achieves good recognition effect in the CNN trained speech recognition system, and has the speech recognition accuracy of 92.3% in F16 noise environment with 10 dB signal-to-noise ratio, means it has good robustness.

In order to deal with the highly computational complexity of static attribute reduction when the data increasing dynamically in incomplete hybrid decision system, an incremental attribute reduction method was proposed for incomplete hybrid data with variable precision. The important degrees of attributes were measured by conditional entropy in the variable precision model. Then the incremental updating of conditional entropy and the updating mechanism of attribute reduction were analyzed and designed in detail when the data is dynamically increased. An incremental attribute reduction method was constructed by heuristic greedy strategy which can achieve the dynamical updating of attribute reduction of incomplete numeric and symbolic hybrid data. Through the experimental comparison and analysis of five real hybrid datasets in UCI, in terms of the reduction effects, when the incremental size of the Echocardiogram, Hepatitis, Autos, Credit and Dermatology increased to 90%+10%, the original number of attributes is reduced from 12, 19, 25, 17, 34 to 6, 7, 10, 11, 13, which is accounted for 50.0%, 36.8%, 40.0%, 64.7%, 38.2% of the original attribute set; in terms of the execution time, the average time consumed by the incremental algorithm in the five datasets is 2.99, 3.13, 9.70, 274.19, 50.87 seconds, and the average time consumed by the static algorithm is 284.92, 302.76, 1062.23, 3510.79, 667.85 seconds. The time-consuming of the incremental algorithm is related to the distribution of the instance size, the number of attributes, and the attribute value type of the data set. The experimental results show that the incremental attribute reduction algorithm is significantly superior to the static algorithm in time-consuming, and can effectively eliminate redundant attributes.

In recent years, meteorological data increases dramatically, and the amount of data has been TB-per-hour-level. The traditional relational database and file storage system have troubles in the massive data storage and management, thus large-scale and heterogeneous meteorological data cannot also be used effectively in meteorological business. Furthermore, it would be also difficult for scientific researchers to efficiently explore the huge amount of heterogeneous meteorological data. In order to tackle these problems, researchers have developed many types of distributed computing frameworks based on MapReduce and HBase, etc., which provide an effective way to exploit large-scale meteorological data. The distributed computing and storing techniques have been tested separately in applications of meteorology field. However, to our best knowledge, these techniques have not been carefully studied jointly. Therefore, a new 3D storm tracking method based on the combination of MapReduce and Hbase was studied by using a large amount of weather radar data accumulated in recent years. Moreover, based on the original Rest interface, a series of distributed service interfaces were implemented for exploring a variety of point, line and surface data. Compared with the performance of the standard single data storage and access interface based on Rest, the proposed method has better comprehensive performance, and the efficiency is improved about 100%. A practical application for tracking 3D storm in Zhujiang River urban agglomeration from 2007 to 2009 was used to further validate the performance of the proposed method.

To balance the contradiction between accuracy and resource cost during constructing an automatic system for collecting massive well-classified Web pages, a cascaded and low-consuming online method for large-scale Web page category acquisition was proposed, which utilizes a cascaded strategy to integrate online and offline Web page classifiers so as to take full of use of their advantages. An online Web page classifier trained by features in the anchor text was used as the first-level classifier, and then the confidence of the classification results was computed by the information entropy of the posterior probability. The second-level classifier was triggered when the confidence is larger than the predefined threshold obtained by Multi-Objective Particle Swarm Optimization (MOPSO). The features were extracted from the downloaded Web pages by the secondary classifier, then they were classified by an offline classifier pre-trained by Web pages. In the comparison experiments with single online classification and single offline classification, the proposed method dramatically increased the F1 measure of classification by 10.85% and 4.57% respectively. Moreover, compared with the single online classification, the efficiency of the proposed method did not decrease a lot (less than 30%), while the efficiency was improved about 70% compared with single offline classification. The results demonstrate that the proposed method not only has a more powerful classification ability, but also significantly reduces the computing overhead and bandwidth consumption.

In order to get better speech enhancement effect for hearing aids when used in the environment with non-stationary or multiple noise, which will lead a sharp decline effect of user experience, a Coherent Filter Generalized Sidelobe Canceller (CF-GSC) speech enhancement algorithm based on small size microphone array was proposed. Aiming at the weak correlation noise which caused by the waves, fans and other approximate white noise, as well as the strong correlation noise caused by the point or other competitive sources, coherent filtering and traditional Generalized Sidelobe Canceller (GSC) structure were utilized to remove weak correlation and strong correlation noise separately, the Voice Activity Detection (VAD) algorithm was also applied during this process. The simulation results show that the proposed algorithm can obtain enhancement effect by almost 2 dB compared with the improved coherent filter and traditional generalized sidelobe canceller method under the environment of a variety of noise, meanwhile, the speech intelligibility also gets obviously improved.

There are some deficiencies in traditional two-step algorithm for under-determined blind source separation, such as the value of K is difficult to be determined, the algorithm is sensitive to the initial value, noises and singular points are difficult to be excluded, the algorithm is lacking theory basis, etcetera. In order to solve these problems, a new two-step algorithm based on the potential function algorithm and compressive sensing theory was proposed. Firstly, the mixing matrix was estimated by improved potential function algorithm based on multi-peak value particle swarm optimization algorithm, after the sensing matrix was constructed by the estimated mixing matrix, the sensing compressive algorithm based on orthogonal matching pursuit was introduced in the process of under-determined blind source separation to realize the signal reconstruction. The simulation results show that the highest estimation precision of the mixing matrix can reach 99.13%, and all the signal reconstruction interference ratios can be higher than 10dB, which meets the reconstruction accuracy requirements well and confirms the effectiveness of the proposed algorithm. This algorithm is of good universality and high accuracy for under-determined blind source separation of one-dimensional mixing signals.

With the growth of cloud technology, distributed application platform develops towards elasticity resources and dynamic migration environment. The refinement of distributed application access control policies was associated with resources and environment, which also needs to improve performance to adapt to the dynamics. Although present access control space policies conflict analysis methods could be used in the conflict analysis of distributed application access control policies refinement. The granularity of its calculating unit is too fine to make batter performance. In this article, the authors designed a conflict analysis algorithm used in distributed application access control policies refinement, the conflict analysis algorithm was based on recursive calculation the intersection of sets and the calculation unit of the algorithm was permission assignment unit which improved computing granularity. The experimental results and analysis show that the proposed algorithm has better performance, and fits the needs of improving computing performance of cloud platform access control policies refinement.

Based on the original definition and classification of Support Vector (SV) and confidence vector, this paper put forward a method of data tendency measure mining in dynamic association rules, according to the characteristic of rules with time changing. First, taking advantage of tendency measure threshold to eliminate useless rules, the item sets candidates can be reduced. Second, producing the dynamic association rule, this method found out valuable rules and improved the mining quality. Finally, by analyzing a transaction database that is characterized by the tendency of changes and cycles, the analytical results verify the validity of the proposed method.

The Standard Particle Filter (SPF) is a typical method of solving the tracking problem of non-linear/non-Gaussian model system. However, updating process strictly depends on parameters selection, and it cannot handle the changes in curve topology. In regard to this, a new particle filter target tracking algorithm based on geometric active contours was proposed, which made a good deal with the changes of curve topology using level set theory. The algorithm improved the resampling techniques and increased the diversity of particles. The simulation results indicate that the proposed method can effectively improve the state estimation precision with more flexibility.

Along with the development of computer technology , traditional computer forensics model could not meet the requirements for safety. The new forensic model was proposed here. Camparing with traditional computer forensic model, the major differenced between these two models lies on the distributed structure and the mechanism of dynamical data gathering. With this two characteristics, forensics system based on the new model could gather real-time evidences dynamically in a distributed system, and save this evidences in a safe place in time. So unauthorised deletion ,change to evidences could be detected and prevented. Then the stored evidences could be used for further analysis and review.