摘要
近年来提出的压缩感知(CS)理论指出可以从很少的采样点中以很大的概率准确重建原始的未知稀疏信号。在压缩感知的过程中,测量矩阵在数据采样和信号重建环节中发挥着至关重要的作用,本文详细介绍了现在几种常用的随机测量矩阵和两种实时性比较强的测量矩阵,并且提出了随机测量矩阵在工超声波工业检测系统中应用必须克服的技术难题,为压缩感知理论在超声波工业检测系统中的应用指明了一个可能的方向,在技术难题的解决方案上,最后用实验说明了从探头的发射的超声波信号进行改变的可行性。
Recent theory of Compressed Sensing(CS)suggests that exact recovery of an unknown sparsesignal can be achieved from few measurements with overwhelming probability.In the process ofCompressed Sensing,random measurement matrix play a crucial role in the data sampling and signalconstruction.In this paper,we introduce several commom random measurement matrix and twomeasurement matrix of strong real-time,and the technical problems in the way of appling the theory ofCompressed Sensing in the ultrasoud imaging system.So that this paper points out a possible direction forlater jobs.On the solution of technical problems,Finally we make experiment to illustrates the feasibilityof the change of the ultrasonic signal from the probe.
作者
戴光智
Dai Guangzhi(Shenzhen Polytechnic,Shenzhen Guangdong 510640,China)
出处
《科技通报》
北大核心
2017年第7期71-76,93,共7页
Bulletin of Science and Technology
基金
广东省自然科学基金资助项目(10451805501006279
S2011010004487)
中国博士后科学基金资助项目(2012M511551)
关键词
超声波成像
测量矩阵
压缩感知
信号重构
ultrasound imaging
random measurement matrix
compressed sensing
signal reconstruction