小波变换轮廓术的测量范围研究

利用小波“脊”处的小波系数来提取变形条纹中的相位信息可以在很大程度上抑制条纹图中有用的基频分量与零频和其它谐波频率分量的混叠,弥补了傅里叶变换轮廓术的不足。从离散信号频域分析角度,推导了变形条纹小波变换的频谱描述形式,讨论了其测量范围,包括结构条件和抽样条件。结果表明,只有在无周期内瞬时频谱混叠,即任意位置处物体瞬时高度变化满足h/xx=b<1/3条件时,和不存在抽样引起的周期间瞬时频谱混叠的抽样条件下(即一个周期内的抽样点数m≥4时),小波变换轮廓术才能正确恢复被测物体的三维面型。计算机模拟和实验验证了该结论。     

基于经典小波变换的布里渊光时域反射计光信号处理

布里渊光时域反射计结构的布里渊散射分布式光纤传感器检测的是自发散射光,信号非常微弱,而且频带在几十兆赫兹以上,难以应用普通相干解调方法。针对传感散射光信号特点,提出基于经典(Morlet)小波变换的光相干检测方法。首先采用微波电光调制产生频移参考光和自发布里渊散射光进行相干检测,再应用经典小波变换进行散射光信号的幅度解调,得到信噪比改善的布里渊散射传感光信号。给出了数值模拟和实验数据处理结果,表明经典小波变换能较好地处理布里渊光时域反射计检测信号。并和基于希尔伯特(Hilbert)变换的传感信号处理方法进行了对比,发现此方法优于基于希尔伯特变换的信号处理。     

简单有效的运动汽车投影阴影分割算法

在实时的车型识别系统中,由于光照的影响,需要一种简单快速有效的方法将汽车车体与其阴影分割开。利用阴影的光谱属性,同时根据阴影的几何特征及阴影区域内的点和汽车的空间位置、形状等相关特点,提出一种基于小波变换多分辨率特性的阴影分割算法,该算法能有效地分割出阴影与目标之间的分界线。先利用阴影的光谱属性对阴影进行粗分割,然后利用小波变换的多尺度性对候选阴影点进行特征提取,从而获取最后的阴影分界线。该方法不需要事先确定光源的方向、车体的彩色信息和背景纹理信息,能有效地分割任何颜色、任何背景纹理下、任何光源方向下的运动汽车投影阴影。实验表明,该方法定位准确,处理速度快,抗噪能力强,为进一步的车型识别提供基础。     

煤粉燃烧过程中相转变点的小波识别

相变点的识别对研究煤粉的低温共熔和结焦有重要意义.本文分别对活性炭和加入SiO2的活性炭混合物的DSC曲线求导得到的DDSC曲线进行小波分析,提取DDSC曲线的近似部分和细节部分,得到更多关于相变的定量信息,再结合其TG,DTG曲线进行分析,实现对以上两种样品燃烧过程中相变点的识别;并将该方法运用到煤粉燃烧过程的相变分析中,对相变点后的烧结形态的场发射扫描电镜分析结果表明该方法是可行的.     

基于小波变换的岩石超声波检测信号的研究

介绍了小波变换的基本概念和特性.通过对岩石超声波检测信号的小波分析和处理,表明该方法在奇异信号检测和局部化分析方面具有优异特性.     

激光主动探测中回波信号的小波去噪方法

在分析激光主动探测中回波信号的噪声特性和小波变换去噪原理的基础上，提出了一种基于最大信噪比准则的小渡阈值去噪方法。首先用最大信噪比准则对小波变换系数进行阈值选取，然后采用软阂值方法对小波系数进行量化处理后再重构。仿真结果表明最大信噪比准则小波去噪方法改善信噪比效果十分显著，检测下限达到-16．2dB。证明了该方法在激光主动探测系统回波信号检测中的有效性。     

An effective noise removal technique for recovering the shapes of diamond abrasive grains in SLM images degraded by clustered spike noise

A new noise-removal technique is applied to scanning laser microscopic (SLM) images to remove clustered spike noise in the images and to recover the shapes of diamond abrasive grains degraded by the noise. For achievement of this purpose, noise points in the SLM image are accurately detected by taking advantage of their properties in the space and spatial-frequency regions. The noise points are removed by a method of smoothing that is based on linear interpolation; that is, their pixel values are replaced by the interpolated values of their non-noise neighboring points. Noise-point information in the space region is acquired from image segmentation based on pixel classification, while noise-point information in the frequency region is derived from redundant wavelet decomposition for the SLM image. Fisher's linear discriminant method is used to yield the two noise-point images. The degraded grain shapes in the SLM images at different noise levels are satisfactorily recovered with a single iteration of smoothing without losses in sharp edges although a single smoothing needed four interpolations. Thus, the present noise-removal technique is shown to be effective for recovering the original shapes of the grains in every SLM image.     

Phase recovery from fringe patterns using the continuous wavelet transform

Interferometry is well established as an optical technique in which a measurand is encoded as the phase of a periodically varying intensity pattern. In view of the inherent accuracy of interferometry, many methods have been developed to retrieve the phase from images of the fringe pattern. Our focus in this paper is one such technique—the continuous wavelet transform (CWT). We begin by reviewing the CWT and the space–spatial–frequency localisation properties of wavelets. We show that a path which follows the maximum modulus of the CWT (the wavelet ridge) gives the instantaneous fringe frequency as a function of spatial displacement. The phase is automatically and trivially obtained, without discontinuities, by integration. Examples of practical wavelets are given and algorithms to isolate the wavelet ridge reviewed.     

基于复小波包分形理论的爬壁机器人故障检测

通过研究爬壁式机器人的控制和运动特征，提出一种基于复小波包分形理论的故障检测方法．利用复小波包的平移不变性，将爬壁式机器人传感器输出信号分解成独立的频谱，并进行阀值处理和重构，从而有效去除高频噪音并提取故障的特征频率；依据信号分形维数的多尺度不变性，在嵌入维数空间，采用维数最大距离法，确定复小波包域故障信号的关联雏数．仿真实验表明，爬壁式机器人在各种异常工作模式下的故障信号关联维数能比较真实地反映其故障状态空间，同时也验证了故障信号的关联维数低于正常信号的关联维数作为故障发生与否的定量判据的正确性．     

Homogeneous approximation property for continuous wavelet transforms

The homogeneous approximation property (HAP) for frames is useful in practice and has been developed recently. In this paper, we study the HAP for the continuous wavelet transform. We show that every pair of admissible wavelets possesses the HAP in L² sense, while it is not true in general whenever pointwise convergence is considered. We give necessary and sufficient conditions for the pointwise HAP to hold, which depends on both wavelets and functions to be reconstructed.