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The server take out the corresponding data from the data queue of receiving and to extract In accordance with the key rules, then to reduce of information in corresponding positions.
The data structure of DS is a two byte DS= (A, R), where A is a finite set of data nodes, R is a finite set A relationship.
Linear table is a data structure, it can be expressed as DS= (A, R), A is a collection of n nodes with a0，a1，……，an-1, R has only one relation , (N={(ai-1ai) |1Definition 4: Data structure of compressed storage.There are two main types methods of compressing data matrix in traditional: random sparse matrix compression and special shape matrix (symmetric matrix and diagonal matrix) compression.
Corresponding to this,the server take out the corresponding data from the received data queue,in accordance with the rules and to extract key,then the reduction of information in corresponding positions,finally realizes the identity information authenticity, integrity verification of communication subject.Can effectively prevent man in the middle attack and reduce the amount of data transmission, greatly reduce the transmission time for the information.

The results indicated a significant improvement in the pest-control decision-making processes, with a significant reduction in the total areas for pest control application, including more than 68% reduction for the P. oleivora pest and over 92% reduction for the P. latus pest.
The conventional approach for monitoring and controlling these pests consists of data processing and analysis from the samples collected in the field, normally grouped into operational sub-areas.
Data Capture and Processing Field sampling is carried out according to the established sampling protocol.
The field samples records are then transferred to a related database, where a set of data validation routines are executed, including coordinate verification, pest control measurement validation, as well as other validation functions.
The L. phoenicis pest had a 28.50 % reduction in the pest control area with the proposed approach.

Field Data Collection Design of the Interface Based on Data Collected.
Disposed Data processing Do/Data processing Wait Do/Check event queue Receive Entry/ Receive data Receive events Stop Fig. 4 Bus data processing state diagram Data Processing Processing time for data update.
Data filtering.
State updating Waiting processing State checking …… Data_control_disable(); Check_data_state(); Data_control_enable(); …… Fig. 5 Control updates and sample status updates Reliability of Data Processing Data overflow.
T P FI Thermometer Pressure gauge Flow meter Moisture tester Pipeline pumps Fig. 6 Pipeline diagram Thus the final data reduction is the original model, that is, users need the data in units of a pipeline data organization unit [9].

Results (data not shown) indicated that CaCO3 quickly increased the pH values and masked the possible neutralisation effect of added substrates.
Substrate materials used in this study were able to promote bacterial sulphate reduction and metal precipitation.
Sulphate reduction was highest in flasks with ethanol supplement and lowest in flasks with no SRB source i.e. anaerobic sludge.
Sulphate reduction and metal precipitation in different test conditions.
Metal and sulphate reduction vs. redox potential.

The program SB8001 was used to extract the data from the Symphonie Data Acquisition unit and display the data as Noise Reduction Coefficients.
Table 1 shows the results of the porosity testing and the values of the noise reduction coefficient at the octave band frequencies required to calculate the average noise reduction coefficient.
However, its noise reduction coefficient was reduced at frequencies below 900 Hz.
Based on experimental data, the NRC was low at frequencies below 900 Hz, but rose significantly after 900 Hz.
An increase in porosity increases the noise reduction coefficient for the higher than 900 Hz frequency range, but decreases the noise reduction coefficient in the lower than 900 Hz range.

Research on utilizing Environmental Friendly Materials of Barrier Board for Particle Reduction of High-turbidity Raw Water Jian-Liang Chen1, a, Yi-Kuo Chang2,Yun-Hwei Shen 1, Kun-Liao Chen 1,b and Wun-Jiun Guo 1 1 Department of Resources Engineering, National Cheng Kung University, Tainan City 701, Taiwan 2 Department of Safety Health and Environmental Engineering, Central Taiwan University of Science and Technology, Taichung 406, Taiwan a1969liang@gmail.com, b c44514451@gmail.com Keywords: high-turbidity raw water, environmental friendly materials, barrier board treatment, particle reduction.
The rectangular container made of commercial PE material will be used for turbidly reduction, the size of which is 70cm × 47cm × 45cm, side view as show in Fig. 1.
Guide the water in the turbidly reduction flowing through the Orifices Wall and Baffle Board.
The data listed below explains that when the water quality is It justifies that significant efficiency of grain settling can be achieved along with the increase of accumulation percentage and the reduction of flow rate as well as longer staying time.

Besides real time data transmission, the BAN system can also store data in its onboard memory.
The data is saved in binary file format and then exported to ASCII file before opening the data file in third party software applications such as Excel.
The data acquisition software of the BAN system transferred real-time data to the computer and plot three linear acceleration graphs in time domain.
Total of 50 sets of data is collected.
More data will be collected in our future work.

Flood reduction requires water flow detention or deceleration of during the storm water season.
This study investigated the reduction of water flow in Yom basin (a sub-basin of Chao Phraya basin) using the synthetic unit hydrograph to synthesize deluge parameters.
The relationship between volume (S) and height (H) of the detention pond system was found to be S=18866224.8H1.15 based on the analysis of elevation data using Geographic Information System (GIS).
Normal elevation of the water surfaec is 40 m (MSL). 4Correlation between water elevation-land-storage volumes The areal data was analyzed by means of the SWAT model [8] and MapWindow [9] based on DEM [10] and a map of scale 1:50000.
The parameters and areal data enabled us to find a correlation between volume and height of the swamp studied, i.e., S=18866224.8H1.15m3.

Then, select the attribute of maximum importance into reduction attribute set, until reduction set and all the attribute dependence of original information table are consistent.
Combine the same row and get reduction table with reduction set.
Detailed solution process is said: (1)Initialize the reduction set is empty; (2)Calculate all the importance of condition attribute which is not in reduction set, and rank; (3)Select condition attribute of maximum importance into reduction attribute set, then judge dependence of reduction set at this time.
Given all training data is fitted without errors by linear function in precision ω, and considering allowable fitting error, introduce slack variables ζi≥0 and ≥0, then said: (6) Then, the optimization target is said: (7) Where, Ω>0, is punishment degree of going beyond error ω sample.
Aiming at possible uncertainty of information in air combat, RS theory is introduced, reduction of knowledge is used to predigest index of TA.

As FRF is sensitive to the changes of the structural physical parameter, it is often used as the original data for structural damage identification.
Wu[1] uses the former 200 data of FRF from experiment as input to the BP neural network to identify the damage of a 3-layer building.
Using all available vibration transmissibility data, let us form matrix [H(w)]M×N which has M rows of vibration transmissibility, each with N frequency points.
Structural damage detection using artificial neural networks and measured FRF data reduced via principal componet projection.
Imregun combined neural network and reduced FRF techniques for slight damage detection using measured response data.