小波分析及其在时变滤波和信号分离中的应用

利用小波变换具有的多时频分辨率特性，对地震勘探和测井信号这类非平衡信号进行分析，可以充分利用信号的时间－频率特性。文中介绍了小波变换的基本概念及其在地震勘探信号的时变滤波和声波全波列测井信号的信号分离中的应用，实例表明，小波变换在地球物理信号中具有广阔的应用前景。图５参７     

A New Matlab De-noising Algorithm for Signal Extraction

The goal of a de-noising algorithm is to reconstruct a signal from its noise-corrupted observations. Perfect reconstruction is seldom possible and performance is measured under a given fidelity criterion. In a recent work, the authors addressed a new Matlab algorithm for de-noising. A key method of the algorithm is selecting an optimal basis from a library of wavelet bases for ideal de-noising. The algorithm with an optimal basis from a library of wavelet bases for de-noising was created through making use of Matlab＇s Wavelet Toolbox. The experimental results show that the new algorithm is efficient in signal de-nosing.     

Use of a neural network to link AFM etch patterns to plasma parameters

Plasmas play a critical role in depositing thin films or etching fine patterns while manufacturing integrated circuits. A new model for plasma diagnosis is presented. This was accomplished by linking atomic force microscopy (AFM) to plasma parameters using a neural network. Experimental AFM data were collected during the etching of silicon oxynitride films in C₂F₆ inductively coupled plasma. Surface roughness of etched patterns was characterized by means of discrete wavelet transformation. This led to the construction of three vertical (type I), diagonal (type II), and horizontal (type III) wavelet coefficient-based models. The performance of diagnosis models was evaluated in terms of the prediction and recognition accuracies. Both accuracies were optimized as a function of the number of hidden neurons. Comparisons revealed that the type I model yielded the largest recognition and the smallest prediction error. This was demonstrated even under stricter monitoring conditions. More improved diagnosis is expected by enhancing AFM resolution.     

一种新的混合相位反褶积方法

在地震资料处理中,常用预测反褶积方法提高地震资料的分辨率,它具有两个基本的假设条件:①地震子波是最小相位;②反射系数序列是随机过程。上述假设条件与实际地震子波的混合相位存在较大差别。为此,笔者在借鉴前人研究的基础上,提出了一种新的混合相位反褶积方法。其实现过程为:①根据地震记录的自相关和地震子波自相关之间的关系,由最小二乘法拟合出地震子波的振幅谱,进而计算出地震子波的自相关;②利用计算出来的地震子波自相关设计一个最小相位滤波器,然后加一个延迟量于自相关函数的主对角线上,将最小相位反滤波器分解为一个长度很短的滤波器,再与一个长度较长的滤波器进行褶积,可得到一系列的混合相位子波和混合相位滤波器;③将得到的混合相位滤波器和原始地震道进行褶积,选取一定的判别准则,以选出最佳的混合相位滤波器以及相应的混合相位子波。通过模型试算和实际资料处理可知,分时窗反褶积的效果要好于单时窗反褶积,混合相位反褶积效果好于最小相位反褶积。在分时窗求取地震子波时,为了得到更好的反褶积效果,要注意时窗宽度与算子长度之间的关系。     

连续小波变换VLSI实现综述

小波变换是信号处理、图像压缩和模式识别等诸多领域中一个非常有效的数学分析工具。然而，实时小波变换计算量大，需要专用硬件来实现。连续小波变换的VLSI实现在处理速度、功耗及适用频率范围方面部具有较明显的优势，且实现方法灵活。本文对近年来有关该领域的研究情况作了综合评述，讨论了其中存在的问题，并指出了今后的若干发展方向，特别是瞬时缩展电路技术是实现低电压低功耗小波变换芯片的重要途经之一。     

一种空间自适应小波门限去噪算法

提出了一种空间自适应小波门限去噪算法,该算法在小波域对含噪小波系数做两次自适应去噪,两次自适应门限分别基于最大似然(ML)方差估计和最大后验概率(MAP)方差估计.仿真结果表明,该算法与其它自适应门限去噪算法相比,去噪后的图象具有更高的峰值信噪比(PSNR).     

基于区域生长法的医学图像分割研究

针对基于区域的分水岭分割算法中通常存在的严重过分割现象，通过两点措施来改进：其一，以梯度图像的浮点活动图像取代梯度图像进行分水岭变换，使边缘定位更准确；其二，以基于面积和对比度控制的合并小区域准则，有效抑制过分割现象，同时满足感兴趣区的分割要求。实验表明该算法简单有效，能够得到符合人类视觉系统特性的分割结果，边缘定位精确度较高。     

解析小波及在振动分析中的应用

本文在希尔伯特变换和小波定义的基础上,提出解析小波及解析小波变换,分析了它的一个重要性质即对信号可实现正交滤波、提取包络。最后给出了小波在轴承振动共振解调应用的实例。     

Wavelet packet based channel equalization

Recently, considerable amount of attention is being given to the field of wavelets and wavelet packets. It has found numerous applications in signal representation, image compression and applied mathematics. In this paper, we present a channel equalization method based on wavelet packets. The proposed equalizer structure is based on the fact that for sufficiently narrowband sequences, a non-ideal channel can be modelled as an attenuation and delay. If the data sequence is used to modulate a set of narrowband wavelet packets, then no equalization is required at the receiver end. The equalization problem reduces to that of determining the delay introduced by the channel for each of the wavelet packets. A minimum square variance algorithm for adaptively choosing the delay has been proposed. This algorithm has been shown to perform as desired analytically in a simple delay channel case. Simulations have been used to study its performance in the non-ideal channel’s case and the results corroborate theoretical predictions.     

直接基于灰度图象的多尺度角点检测方法

在计算机视觉领域中角点特征的提取具有相当重要的地位，因为角点包含了丰富的信息。本文提出了一种新的利用微分几何和小波变换的多尺度角点检测方法，它具有以下特点：一是无须提取边缘图象，而是直接在灰度图象上提取角点。二是由于利用了小波变换技术从多个尺度上进行角点的提取，所以它既较为有效的抑制了由于噪音等原因造成的诸多错误角点位置的出现，又能得到较为精确的有用角点位置。最后通过实验证明了本算法有效性。