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The Cameralink interface is adopted to be the data transmission channel between the camera and interface card, which carries out the configuration of the camera, image data acquisition, caching, image reduction and PAL format conversion, and finally accomplishes the image real-time display.This paper discusses the FPGA implementation of double bilinear interpolation algorithm, including image data frames buffer, the vertical and horizontal interpolation.
The frame rate of output data is 25Hz, with a resolution of 2048×1536 image data ;then carry out image data acquisition, reduced image and the PAL format conversion and finally accomplish the image real-time display.
Video data is output to the reduction module for image reduction through ping-pong buffer, and the resolution will be reduced to 720×576 to be conformable with the video image size of the PAL format.
Two sets of RAM are designed to buffer the two lines of original image data, as two lines data from the original image are required for the interpolation to the new line of image data.
Additionally, repeating this running mode by writing video data into ROWS_RAM1, reading video data from ROWS_RAM2, and preceding the interpolation calculation with read-out data, and so on.

Based on idea of TQM, an intelligent system may be developed to obtain quality data from different manufacturing process and discover meaningful schemas and knowledge from these data.
Some data mining and artificial intelligent technologies may be used to discover these relationships.
The role of data warehouse platform module is to integrate XML with enterprise process for realizing process data exchange among departments.
Quality assurance in machining process using data mining [C].
Hierarchical decision making for proactive quality control: System development for defect reduction in automotive coating operation [J].

The solid line represents a linear fit to the data.
Data for the isothermal sintering of alumina as reported by Nettleship [6]; 170 nm alumina at 1325°C for various times with a regression line fit to the data.
The regression straight line fits the data with a correlation of 0.9887.
As a demonstration of this simple approach to sintering data take the copper sintering data of Coble and Gupta [10].
The alumina data in Fig. 20 shows this behavior [37].

(14) Using experimental data in Fig. 1 and some above-obtained constants, lnε-ln[sinh(ασ)]and ln[sinh(ασ)]-1/Tcurves can be drawn.
The average value of constant P can be obtained from experimental data with different forming conditions.
Generally, the simulation results are coincident with the experimental data for reductions of 20% and 28%.
Generally, the simulation results are coincident with the experimental data within an error of 20%.
The simulated grain sizes generally agreed with the experimental data.

This method not only reduces the demand for data, but also ensures accuracy of the results using the sensitivity correction.
The key problem of condition assessment of distribution equipments is to obtain data of the state variables from the equipments.
The more complete and accurate the data is, the more exact the assessment results will be.
Therefore, the state data of the distribution equipments is very small.
Use these data to analysis the sensitivity of non-core status.

The calculated data show that the improvement measures, e.g. reduction of dolomite consumption and energy consumption, in Chinese Pidgeon process led to 23% decrease of the GWP for the primary magnesium production in 2009 compared with 2005.
Data Collection.
The input data of materials and energy consumption were based on investigation to China magnesium factories.
An upper bound, average and lower bound data of Pidgeon process were summarized in Table 1.
These values were compared with published data in Table 3 in order to estimate the influence of uncertainties on these impacts.

Data sources and research method Study Area.
Data and Data Sources Population.The total population refers to the permanent population of Shenzhen at the end of that year, and annual population data is sourced from 1979-2009 Shenzhen Statistical Yearbooks.
The carbon emission of land use (expressed as LC).It is mainly estimated on the basis of vegetation and soil; among of them, the vegetation carbon stock data is based on the Cat.
In this research, the measurement and test of data models are processed with Eviews 6.0 econometrics package.
Based on nominal GDP and real GDP of Shenzhen, as well as the statistical data on population over years, real GDP per capita and per capita LC could be calculated (Figure 2).

The orientation distribution functions (ODFs) of the carbon steel A and the Si steel B were calculated and analyzed by orthorhombic symmetry with the Bunge method [6] based on the measured pole figure data.
Based on the ODF data in Fig. 2 at s = 0.9 for carbon steel sheet (A), the f(g) variations of main texture components are shown in Fig. 3.
Based on the ODFs data in Fig. 5 at s=0.9 for Si steel sheet (B), the f(g) variations of main texture components are shown in Fig. 6.
Main texture component f(g) variation from ODFs data at S = 0.9 for carbon steel sheet (A) samples a through d Fig. 4.
Main texture component f(g) variation based on ODFs data at S= 0.9 Si steel sheet (B) samples a through d The good formation of the Goss texture in the carbon and Si steel sheets after asymmetric rolling is related to shear deformation during the asymmetric rolling.

Experimental data Experimental conditions.
All data were recorded in an idle mode (no-load conditions) and in a mode with load of worm-and-wheel reduction unit (M = 32 Nm).
The spectral analysis of the obtained data was carried out.
The data obtained together with data from the vibrator inverter under the same conditions are shown in Fig. 5-8.
Also, it is useful for creation of the defects data base for electromechanical equipment.

Firstly, this method preprocess original data and the decision table is formed, then attribute reduction and value reduction to delete redundant attributes by Rough Set theory, and finally extract the fault diagnosis decision-making rules. 1 Establishing fault diagnosis decision table based on Rough Set theory (1) Attribute definition In Rough Set theory, in order to better handle data, the data and knowledge we obtained must be formulated.
Because the decision is based on the finite-dimensional discrete data tables, therefore, the data should be normalization processed.
However, because there is a lot of noise in the sample data, some diagnostic rules may only data derived from a small sample, if the sample data is incomplete, the corresponding diagnostic rules will not be reliable.
Coverage is the proportion of data objects which satisfied the antecedent and the consequent.
After normalization, the data was identity to facilitate subsequent processing. 38 groups of failure data was recorded, due to limited space, they are not listed here all, after normalization and attribute reduction, we obtained the decision table as shown in Table 2.