And another use of FPGA is that provide DSP original data. DSP is used as digital filters used by sampling at the foreside and at the export correspondingly.The original data used by DSP is taken from the first adder in the circle. After the DSPs disposal,the output data are sent to guidance system.

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With the technological development, more and more original data shall be acquired and processed and a large amount of intermediate data and result data will be produced in processing. Pure digital data have developed to multimedia data such as sound and image ones, so that there are more demands for advanced data management. Moreover, the system calls for higher integration degree even on-line real-time processing in many cases, and its integration with network and distributed processing need considering as well.

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Then the reconstructed data are analyzed by multiple statistical indexes such as T2 or SPE to determine if the supervised process exceeds the normal control limits. If fault occurs, then the supervising program will alarm to prompt that the process has abnormal states. Because it is hard to get the inverse mapping from the kernel space to the original space after the original data are nonlinear mapped by the KPCA method, hence it is difficult to diagnose how the fault happened.

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They first organize and analyze a set of data with the stem-and-leaf diagram. They point out that the diagram is used for a small number of data and the original data can be retained in the diagram. To compare two different set of data of the same nature, they propose the use of a back to back stem-and-leaf diagram.

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Network information processing transfers the majority of processing and computing works into WSN based on original data collection. A mass of incomprehensible original data is extracted and inferred into meaningful and consultable data to some certain users. Meanwhile, by network information processing, the data transmission account and latency are decreased; the energy efficiency and data accuracy are increased.

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Data mining is an analytic skill that can analyze and find out the potential information among the original data and materials. Under data mining, the way used most frequently for prospecting original materials is decision tree of classification.According this divinable model, business can find out the relation between all attributes such as customers, products and regions etc, so that they can formulate some business management strategies like product-marketing base on these relations.

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Furthermore, it can not define indiscernibility relationship when default data is included in rough data. In this dissertation, a new rough set model, which can be used to process original data which include default data, fuzzy data, or incomplete data, is proposed based on clustering methods.

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Presence of multiples leads to poor quality in main stages of offshore seismic data processing, such as velocity analysis, stacking and imaging. Three basic multiple attenuating methods are reviewed; and a more effective multiple attenuator is achieved by joining them. As the first step, shot or receiver records are extrapolated by wave-equation datum algorithms. The extrapolation will establish a model of waterbottom multiples. Waterbotton multiples will then be removed by subtracting the extrapolated records from the original records. After that, predictive deconvolution is used to attenuate the multiples created by source ghost. Finaly, RADON transformation is adopted to separate peg-leg multiples and residual water-bottom multiples from the original data in the τ-p domain.

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The method is realized via the following steps: first to apply Isomap or LLE to get the embeddings of the original data set in the low dimensional space; then to obtain support vectors, which are the most significant and intrinsic data for the final classification result, by using support vector machine on these low dimensional embedding data; subsequently to get support vectors in the original high dimensional space based on the corresponding labels of the obtained low dimensional support vectors; finally to apply support vector machine again on these high dimensional support vectors to gain the final classification discriminant function.

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