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The fading is leading to the delay spread of the transmitted data and causing to the inter-symbol interference (ISI) at the receiver.
(a) (b) Figure. 2 (a) Experimental setup of channel measurement and (b) Flow chart of data processing.
Furthermore, we can describe a flow chart of the data processing as illustrated in Fig. 2 (b).
Firstly, the simulation data is generated by using computer simulation as well as the computational measured channel data.
Sharma, Diversity: A fading reduction technique, International Journal of Advanced Research in Computer Science and Software Engineering, 2 (2012), 58-61

The purpose of the present study is to investigate the effect of blockage at the leading edge of cavity by using PIV which is to collect the u and v velocity profile data for each blockage in the cavity.
In Fig. 1, one example of image was included and also the validation data with previous researcher with the same experiment environment without any blockage to prove the experiment result [6].
(a) (b) Fig. 2 (a) Example of cavity image with blockage and (b) Validation data compared with S.M.
At leading edge, there are no clear different data to compare in the cavity but at the above, the vortex seems to be high for any kind of blockages.
Mean velocity profile data have been gathered throughout the cavity within four sections.

Thermo-physical properties were obtained by parametric fitting of time-temperature data obtained from cooling curve.
We can conclude that the driving force for grain growth is reduction in grain boundary area.
Ni amount Thermal Diffusivity (mm2.s-1) Specific Heat Capacity (J.kg-1.K-1) Density (kg.m-3) Thermal Conductivity (W.m-2.K-1) 0% 20.45 452 7897 73 20% 5.20 460 7933 19 40% 2.66 460 8169 10 2% 17.60* 453* 7900* 63* *Values have been computed from the regression of table values data.

The magnitude of true stress when a specimen fractured has been precisely determined from the measured data of a drawing force and the cross sectional area of the draw-bent specimen after fracture.
It causes serious thickness reduction to sheet metals and very often leads to fracture.
A potentiometer is attached to the hydraulic cylinder A, and sends displacement data to a computer.

Line loss rate forecast can help supply companies develop reasonable loss reduction and energy efficiency goals.
This article based on less information needs in gray system model, and with high accuracy, as well as how much of the raw data is not the demanding requirements of characteristics [6].
Gray relational analysis by the geometric relationship of the system data sequence to analyze the degree of correlation between the various factors in the system [7], The use of gray relational grade to determine the line loss rate greater impact variable and as the neural network input variables .
Grey Forecasting Model For Line Loss A.The basic model Gray prediction model GM (1,1) is frequently used ,and the model building process as follows: (1)Accumulated generating operation for the original data sequence: (1) (2) (2)The establishment of differential equations (3) (4) And in the formula (5) (3) a, b, back to the original differential equations: (6) Regressive to get the original data: (7) B.Correction Model In order to improve prediction accuracy, combining the initial data with the predicted results optimized.
The application of double BP neural network combined forecasting model in real-time data predicting)[J].

The research presented herein tests the hypothesis that cumulative damage in the material under random dynamic compression will result in a reduction in the overall stiffness as well as an increase in the overall damping of the element.
The paper tests the hypothesis that cumulative damage in the material under random dynamic compression will result in a reduction in the overall stiffness as well as an increase in the overall damping of the structure.
The effectiveness of establishing the level of damage by quantifying the modal characteristics obtained from measuring the frequency response function (FRF) is critical and often depends of the type of structure being studied as well as the quality of the experimental data.
Relative Static Stiffness (%) . 0 Figure 5: Shift in the stiffness and correlation between static and dynamic stiffness Fig. 7 shows the average FRF data set as well as the corresponding natural frequencies (black dot) for a typical experiment (box 3).

Removal of coal-fired pollutants using a novel composite collector in a wet electrostatic precipitator Jian Zhao1, a, Jingcai Chang2,b, Jiaqiu Song2c,Aiping Tao3,d, Mingfeng Gao3,e 1 China Energy Conservation and Emission Reduction Co.
The new wet ESP device acts in synergy with WFGD and SCR systems for controlling coal-fired pollutants emissions and solves the adverse impacts caused by wet flue gas desulfurization (WFGD) and selective catalytic reduction (SCR) systems at the same time.
The data shown in Fig.5 (b) indicated that different emitters experienced significant differences in collection efficiency based on discharge energy, especially with shorter gas treatment time.
The data indicated that the characteristic of barbed wires served as emitter electrodes was superior to that of diamond-shaped wires.

First, probabilistic cyclic stress-strain model and linear heteroscedastic probabilistic cyclic strain-life model are founded based on the fatigue test data.
The fatigue life follows lognormal distribution, the lognormal is often much easier to use, and some researchers found, more often than not, lognormal provides a better fit than the Weibull to real fatigue data[2].
This paper presents a probabilistic cyclic stress-strain model and a linear heteroscedastic probabilistic cyclic strain-life model[5] based on the fatigue test data of turbine disk material GH4133.
All the test data are between in the curves with μ = ±3, it was accounted for that Eq.11 could simulate the distribution of cycle stress-strain curve very well.
The probabilistic cyclic stress-strain model and linear heteroscedastic probabilistic cyclic strain-life model of GH4133 are founded based on the fatigue test data.

In addition, principal components analysis (PCA) is used to extract the main features of the wavelet order spectrum and reduce the volume of data.
Using few main components, it can keep most the original data set intact without mutual relationships to achieve the simplifying data
Fig. 1 The flow chart of wavelet order spectrum method Fig. 2 Wavelet order spectrum The wavelet order spectrum method with PCA, through the selection of principal components as principal features for the fault diagnosis, reduces data dimensions of the wavelet order spectrum.
The SOM neural network learning algorithm can automatically find it’s similarities between input data, with the configuration of input which close to the output layer neurons, and thus forms the input data which can be selectively provided to the reaction grid.
In addition, through the PCA data reduction process, training time can be reduced to 85 seconds .

However, the material properties of boron steel and other key process parameters, such as friction coefficient and interfacial heat transfer coefficient, which affect the sheet thickness reduction and die quenching quite significantly need to be determined first for using as the input data in the finite element simulations.
The platform contains stretch components, swaging components, heating components and data acquisition instruments.
The friction coefficient was 0.54 obtained according to the experiment data.
In subsequent finite element simulations, the friction coefficients were substituted in as the input data.
The experiments conducted with the developed platforms were quite smooth and the experimental data measured were also without singular points.