Based On Adaptive Lifting Wavelet Process Of Data Compression Research

Due to the requirement of safe and efficient manufacture in industrial process, the quantity of measured process data has exploded with the development of computer and sensor technology. Abundant data need to be stored because they bring a lot of useful information that will benefit many sorts of the running and controlling tasks. However, these data inevitably carry much redundant and irrelevant information resulted from the measuring and the computing errors and even out of some human factors. Moreover, with the development of the network technique, the remote and network-based monitoring and controlling strategies become popular in the industry production process. And they both require large amount of the process data transmitted in real time via the network in order to share the data resources. Thus it is necessary to develop the real-time process data compression technique to reserve the major characteristic information of the process, to save the data storing space, as well as to reduce the data stream and to improve the speed and the efficiency of the data transmission. Accordingly, it is necessary to develop the data compression technique which would serve the mill-wide automation system and improve the performance concerning precision and real time in automatic technology.On the foundation of wavelet theory, we research SPIHT algorithm and its characteristics. We know SPIHT algorithm is the improvement of ZEW algorithm. SPIHT algorithm constructs two different kinds of zero trees .So it can make use of fall of wavelet coefficient. Considering all the advantages, we decide to extend the 2-D algorithm to the 1-D process data compression. But it has some shortcomings in making coder simpler, differentiating the importance of low and high frequency subbands and increasing the efficiency of encoder. The low frequency subband has more energy and then more important. The high frequency subband which describes the signal details is less important. In order to distinguish the importance and to transfer more information in the same bits, we should preprocess the high frequency subband. Meanwhile during the industry process, unexpected things which would express by the different frequency process data could happen. So we should analyse the transient part of the signal exactly. During normal station, the process data which always in stationary is better described by compact support wavelet. According to stationary signal, transient signal need to be described by long support wavelet. Due to the different stuff, we introduce a new adaptive lifting scheme.This thesis is made up of the following parts:Chapter one discusses the technological background and the significance of the process data compression. And then we conclude the research project.Chapter two introduces the wavelet theory and how the lift scheme constructs and works. At last, we discuss the more advantage of the lift scheme than the first general wavelet.Chapter three introduces the method of the process data compression which is exist before and analyses the advantages of all these method as well as disadvantages.In chapter four, according to the characteristic of 1-D signal and the effect we want to realize, we introduce the SPIHT in data compression based a new adaptive lifting scheme. And during the research, we have improved the SPIHT algorithm. We program the algorithm by matlab and have reached the purpose basically.At the end of the thesis, we summarized the system's function, usage and give some instructions for further work.