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Wireless Feature Extraction System for Cloud-Dust Based Intelligent System by Using Embedded Wavelet Packet Method

机译:嵌入式小波包方法的云尘智能系统无线特征提取系统

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In the paper,a wireless feature extraction system for cloud-dust based intelligent system using embedded wavelet packet method is developed.In order to decrease the massive transmission data form receivers and transmitters of wireless sensors network,we reduce the data dimensions of sensors using embedded feature extraction method.The massive transmission data will be reduced via the process of wavelet packet decomposition and reconstruction.So it decreases the transmission loading of general wireless sensors network.Considering computing time of feature extraction,the circuit hardware of wavelet packet is designed in the paper.It effectively reduces the computing time of feature extraction.Therefore,the wireless feature extraction system using embedded wavelet packet method makes the transmission of wireless sensors network work effectively.At last,we prove the data reduction of transmission,the shorter computing time of feature extraction and the computing accuracy of wavelet packet hardware by the experiments.It can greatly improve wireless sensors network with large number of nodes more efficient transmission,and the sampling rate of sensors nodes,without causing the transmission delay and data errors.So it will make wireless sensors network more applications with higher precision,higher computing speed,lower power and lower cost in the future.
机译:本文开发了一种基于嵌入式小波包方法的云尘智能系统无线特征提取系统。为了减少无线传感器网络收发器的海量传输数据,我们减小了嵌入式传感器的数据量。通过提取小波包分解和重构过程来减少海量传输数据,从而减轻了一般无线传感器网络的传输负担。考虑到特征提取的计算时间,在小波包设计中设计了电路硬件。从而有效地减少了特征提取的计算时间。因此,采用嵌入式小波包方法的无线特征提取系统使无线传感器网络的传输有效地进行。最后,我们证明了传输的数据减少,缩短了传感器的计算时间。小波包特征提取与计算精度通过实验硬件,可以大大提高无线传感器网络的节点数量,提高传输效率,提高传感器节点的采样率,而不会造成传输时延和数据错误,从而使无线传感器网络具有更高的精度。 ,更高的计算速度,更低的功耗以及未来的低成本。

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