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Reduction of the sliding space has a decisive effect on the decrease of the phone size.
Especially, the semi-auto sliding mechanism of this type of the phone can be continuously applied to a QWERTY type of the keypad for a smartphone, in the future; hence, problems such as the inconvenience in entering data using the small keypad of the phone can be solved.
Besides, under this design environment, the width of an FPCB is between 15 mm and 20 mm, and the width is on an upward trend because of the massive amounts of data being transferred.

Nevertheless, the reliability of bioimpedance analysis still is the focus of much controversy as there are much fewer data available and less detailed research have been conducted.
The fourth block was the development of data acquisition (DAQ) and graphical user-interface (GUI) system.
After digitization, it was fed to a UART-to-USB converter to convert the bioimpedance data to a USB formatted bit-stream data.
The data stream was then transmitted to a personal computer (PC) and a state-of-the-art GUI system stores it automatically in a temporary data file specified by the user for future reference and further analysis.
Elimininating the subject 6’s data due to the defect of the duplex ultrasound in finger assessment, the result revealed that the developed prototype has an overall accuracy of 75.65 % for the ten data being collected from the five subjects.

For signal processing, wavelet transform is widely used in the application of multi-resolution editing, signal filtering for noise, edge detection, marking reduction, data storage, compression, recognition, enhancement and synthesis of speech [4, 12].
One second of time response signal, sampled at 20 kHz were acquired in each data.
National Instrument (NI) is the component to collect data and DasyLab is the also known as Data Acquisition System Laboratory to save the data.
(a) Raw data for no leak condition after injection Hamming chirp signal.
(a) Raw data for leak condition after injection Hamming chirp signal.

Kernel CCA is a kind of CCA nonlinear extension, the input data is nonlinear transformed through the kernel function.
Similar to traditional nonlinear method, kernel CCA is particularly suitable for the analysis of two related data set.
Formula (9) makes three input data set canonical correlated.
We call (9) as canonical correlation analysis on three data sets.
CIKCCA is used in color image recognition, whose input data sets are a color image’s preprocessed red, green, blue color component.

The sampling data were off-line analyzed and processed.
Its general objectives are (1) data reduction and (2) interpretation.
The k principal components can then replace the initial p variables, and the original data set consisting of n measurements on k principal components. 　
Data Disposal and Analysis of characteristic information.
Based on the principal component analysis, the data in Table 1 are processed.

Test data Test of ice storage process starts at 19:00 on August 19, 2010 and ends at 3:30on August 20, 2010, the total time of thermal storage is 8 hours (ice storage time is 7.5 hours).
Detection data in ice storage process shown in table 2.The total ice storage capacity is 18513 kWh, the total power consumption during ice storage is 6268.4kWh.
Detection data in ice discharge process is shown in table3.
The data can be used to comparing the economy of cold source
(4) The test can provide reference data for compared to ice storage air-conditioning system and traditional air-conditioning system and especially for the optimum design and economic operation of other ice storage systems in Yangzhou. 

This method can make new data to add to the specific position of the board simply.
If one data of bit boards is 1, Boolean condition of the data is true.
If one data of bit boards is 0, Boolean condition of the data is false.
This representation can improve the efficiency of reading and storing data, and judge the connection and threat of the stones in evaluating conveniently.
But there is more to iterative deepening than just a reduction of storage space.

Assume x(t) (t =1, 2, …, N) is a set of stationary random sample data, then, the following equation can be created
The end effect may cause divergence on data ends of the local mean function line and the local envelope function line and finally lead to serious distortion of decomposition results.
In addition, the singular value of a matrix also has such characteristics as scale invariance, rotational invariance and dimensionality reduction and compression [5].
The sampling frequency is 2048Hz and the rotation rate is 6000 rpm. 10 sets of data are extracted randomly from such 4 types of data respectively to be sample data and the remaining data are used as test data.
The local mean decomposition and its application to EEG perception data.

First, establish a comprehensive evaluation index system, then list indicator data matrix X.
Processing the raw data into the naturalization analyze data which is a mean of 0 and variance of 1, the original data matrix X is then converted to standardized covariance matrix S.
Substitute the original data of each unit into the equation to calculate the value of comprehensive evaluation, then analyze and compare them.
b) The raw data was made standardized by using SPSS software through the process of Analyze-Descriptive Statistics-Descriptive, standardized result is shown in the matrix below.
c) Call SPSS software through the process of Analyze-Data Reduction-Factor to calculate the Covariance matrix which is Correlation coefficient matrix.

As a case study, we select 30 provinces of mainland China except for Tibet considering the availability of data.
Detailed data for energy consumption come from National Bureau of Energy Statistics of China (2010), while the data for GDP, forest area and, population are from China Statistical Yearbook 2010.
Lacking of sufficient data on the environmental capacity, the forest area is used instead.
However, we modeled the two-stage optimization only based on data of year 2009 considering the simplicity for calculating.
This is worth exploring although it may produce new problems such like allocation criteria, data processing, etc. in the future research.