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Noise reduction techniques are conceptually very similar regardless of the signal being processed, howevera priori knowledge of the characteristics of an expected signal can mean the implementations of these techniques vary greatly depending on the type of signal.
This results in length and time-consuming data preparation and computation.
Firstly, we developed a source program; then by using the input data from previous researcher.The transmission loss can be calculated.
The calculated data is then compared with the published data in order to verify the developed source code.

The water samples were collected every 30 min, filtered and measured the color reduction by UV-VIS spectrophotometer.
This data supported the XRD results of showing that hydrothermal method provided a very fine TiO2 nanoparticle.
The data indicates that nanoparticle synthesized by hydrothermal method contained a higher specific surface area and pore volume rather than nanoparticles synthesized by sol-gel method.
This data confirms the results from XRD and TEM.
The analytical data indicated that the anatase phase of TiO2 can be synthesized at the low temperature (of < 400°C) by hydrothermal method.

The 2D laser range finder provides the depth information of the scanning plane, but cannot obtain the depth information of the whole 3D structure from scan data without additional device such as tilting module.
The most important feature of CALOS is that the laser data are used as the 3D points.
The relative position between the sensors is estimated using the fact that some points of the scanned data are on the pattern plane.
If the motion of the system is estimated, the scan data are transformed into a common coordinate.
[2] Andrea Bonarini, Matteo Matteucci and Marcello Restelli, Automatic Error Detection and Reduction for an Odometric Sensor based on Two Optical Mice, Proceedings of the 2005 IEEE International Conference on Robotics and Automation Barcelona, Spain, April 2005

If it’s too long, separation can’t act effectively because the reduction of swirling intensity at the second half of the part.
The simulated data is listed in table 1.
Tab.1 Gas parameters under different environmental temperature T1(K) 283.15 293.15 298.15 303.15 308.15 313.15 T2(K) 190.5 197.2 200.6 203.9 207.3 210.7 V2(m/s) 410.7 417.9 421.4 424.9 428.4 431.9 T3(K) 231 239.2 243.3 259.8 252.7 255.5 V3(m/s) 314 319.5 322.3 324.9 327.6 330.3 Fig.3 The relations of the parameters-T2&T3-T1(left), V2&V3-T1(right) According to the data given in table 1, the relations of parameters and T1 are shown in figure 3.
The simulated data is listed in table 2.
The simulated data is listed in table 3.

Structural data of the compounds formed in the system of Zr-O № Compound Prototype Pearson symbol, sp. gr Unit cell parameters, [nm] Note Source Zr-О system 1 g-ZrO2 CaF2 cF12, Fmm a=0.509 a=0.5272 T=2400°С [6] 2 b-ZrO2 HgI2 tP6, P42/nmc a=0.364 c=0.527 T=1250°С [6] 3 a-ZrO2 ZrO2 mP12, P21/с mP12, P21/с a=0.51505 b=0.52116 c=0.53173 b= 99.230° [6] a = 0.51415 b = 0.52056 c = 0.53128 β = 99.30 T<1205°С [9,10] 4 d-ZrO2 - Рbст a=0.5007 b=0.5227 c=0.5058 At T=600°С and P=6 GPa [6] 5 δ-ZrO2 - oP16, Pbcm a=0.50364 b=0.52546 c=0.50855 3<Р<12.5 GPa [11] 6 ε-ZrO2 PbCl2 oP12, Pnma a = 0.562 b = 0.3347 c = 0.6503 Р> 12.5 GPa [11,12] 7 ω-ZrO2 MnF2 tP12 Pm2 a = 0.5046 c = 0.5219 3.5 < Р < 15 GPa [11] Oxygen with transition metals can give compounds with an ordered structure formed from solid solutions; in the systems of metal - oxygen numerous phases of variable composition are formed - from suboxides to peroxide compounds. 
According to the data presented in the reference book [6], phase diagram of Y-Zr system refers to the eutectic type, without the formation of intermetallic compounds and solid solutions.
Structural data of the compounds formed in the system of Y-O № Compound Prototype Pearson symbol, sp. gr Unit cell parameters, [nm] Note Source 1 g-Y2О3 Mn2О3 сI80, Ia3 a=1,0605 [6] a=1,0605 a=1,0601 a=1,0604 High-temperature [13] 2 a-Y2О3 Sm203 mC30, sp.gr.
Structural data of the compounds formed in the system of ZrO2–YO1.5 № Compound Prototype Pearson symbol, sp. gr Unit cell parameters, [nm] Note Source 1 δ-Zr3Y4O12 Zr3Y4O12 hR5 R a = 0.97345 c = 0.91092 [16] a = 0.9738 c = 0.9115 [17] a = 0.9723 c = 0.909 [18] 2 ZrY6O11 At Т > 1750°С [15] 3 Zr2Y2O7 ТÎ1100-2530°С [15] 4 α- (Y1-ХZrХ)2O3+X Mn2O3 cI80, Ia 0.85Such yttrium ions remaining in the material volume cease to have a stabilizing effect on the crystal lattice of zirconium dioxide, it leads to a reduction of the stability level of its high-temperature modification [27].

The data about caustic magnesia provided by the manufacturer are: size distribution 90 %  < 30 mm, calcination temperature 750 °C and the content as follows: MgO (73.0 %), CaO (4.0 %), SiO2 (4.0 %), Fe2O3 (3.0 %) and Al2O3 (1.0 %).
Analyzing the obtained results of samples with caustic magnesia, it can be seen that by reducing the amount of magnesium chloride, also value of compressive strength reduces (for example, reduction of 50 % MgCl2 from the mixes IV-4 to IV-6 resulted in 17 % lower compressive strength value).
Describing the obtained data from lightweight concrete samples using dolomite waste materials, it can be seen that the use of three different fraction particles of porous glass ceramics gives higher values of compressive strength (from 3.1 to 4.85 MPa samples I-LV-L1 and I-LV-L2; from 7.65 to 8.13 MPa samples II-RU-L1 and II-RU-L2; from 12.65 to 16.6 MPa samples III-UZB-L1 and III-UZB-L2).
The test of thermal conductivity was done by testing two groups of samples: using dolomite waste material and porous ceramics granules, using caustic magnesia and synthetic foaming agent (see data of Table 2 and Table 3).
After analysis of the obtained data, the following conclusions can be drawn.

These mathematical models are able to: a. describe and interpret experimental data; b. offer control in real time and / or analyze air quality; c. manage the accidental release and evaluate risk areas; [3] reports that the modeling of dispersion is the main tool used to simulate the air pollutant transport and to identify areas that are unsuitable for installation of new industries.
This can be evidenced in Fig. 7, which shows the concentration profile of the pollutant obtained from numerical and experimental data reported by [5] in a distant crossline, at 2000 meters from the emission source.
Figure 7: Comparison between the numerical concentration profile with the experimental data reported by [5], 2000 meters from the entrance, at y = 0.
Conclusions From the results obtained in the simulations, it can be concluded that: • The proposed mathematical model was able to evaluate the flow around the chimney of a thermal power plant, and to assess the behavior of a pollutant dispersion plume; • The plume represents the pollutant concentration gradient, which undergoes small scattering before touching the ground after several meters away from the emission source; • The pollutant concentration profile calculated by the model shows that because of the dispersion, there is a rapid reduction in the concentration of the pollutant; this behavior shows good agreement with the data reported in the literature.

In the end, the mathematical simulation of the compressor were given and the theoretical optimization study predicted that a large reduction in frictional loss could be possibly achieved, which brings about an improvement in EER of the compressor of about 8.55%.
Obviously, as data is processed, the objective function decreases, from which it can be deduced that the energy efficiency ratio improves, especially when the effective iteration is less than 30 times.

Use of halogen/organic free medium, drastic reduction in excess amount of alkali precursors and short synthesis time makes this methodology much more versatile.
Pristine ETS-4 and Sr-ETS-4 materials were characterized with DRS-UV-Vis spectra (data not shown).

A set of image processing procedures were applied such as background subtraction and noise reduction.
Therefore, a method for acquiring and performing 3D radiography image may be beneficial in order to do quick assessment without performing complex data manipulation and formation.