基于提升框架的整数小波变换

提升框架可以从一个新的角度来构造小波滤波器,它的核心是确定预测算子和更新算子。该文 引进了多项式局部拟合方法来求取预测算子和更新算子,对每步提升下取整,就实现了整数小波变换。仿真结果表明,该方法应用于二维数字图像,在一定失真的前提下,具有较高的压缩比,而且小波系数与尺度系数都是整数,不需要量化步骤。因此,它对于整数数据压缩具有重要的意义。     

Algorithms for designing wavelets to match a specified signal

Algorithms for designing a mother wavelet /spl psi/(x) such that it matches a signal of interest and such that the family of wavelets {2/sup -(j/2)//spl psi/(2/sup -j/x-k)} forms an orthonormal Riesz basis of L/sup 2/(/spl Rscr/) are developed. The algorithms are based on a closed form solution for finding the scaling function spectrum from the wavelet spectrum. Many applications require wavelets that are matched to a signal of interest. Most current design techniques, however, do not design the wavelet directly. They either build a composite wavelet from a library of previously designed wavelets, modify the bases in an existing multiresolution analysis or design a scaling function that generates a multiresolution analysis with some desired properties. In this paper, two sets of equations are developed that allow us to design the wavelet directly from the signal of interest. Both sets impose bandlimitedness, resulting in closed form solutions. The first set derives expressions for continuous matched wavelet spectrum amplitudes. The second set of equations provides a direct discrete algorithm for calculating close approximations to the optimal complex wavelet spectrum. The discrete solution for the matched wavelet spectrum amplitude is identical to that of the continuous solution at the sampled frequencies. An interesting byproduct of this work is the result that Meyer's spectrum amplitude construction for an orthonormal bandlimited wavelet is not only sufficient but necessary. Specific examples are given which demonstrate the performance of the wavelet matching algorithms for both known orthonormal wavelets and arbitrary signals.     

Construction of a Morlet Wavelet Power Spectrum

The Morlet wavelets transform (MWT) is an efficient means of detecting and analyzing transient signals. However, ordinary iterative processes that calculate the MWT are time-consuming. In addition, when the MWT is applied to construct a wavelet power spectrum on a linear frequency axis, the peak response appears at a value lower than the actual signal frequency. In this work, formulae that produce a fast MWT and Morlet power spectrum (MPS) scheme without iterative processes are derived. Also, we discuss in detail why the frequency slant phenomenon occurs. To avert this phenomenon, the transform kernel of the MWT is modified to facilitate the construction of an equal-amplitude Morlet wavelet transform. The modified Morlet power spectrum produces the peak responses roughly proportional to the squared input amplitudes at the accurate signal component frequencies.     

QAM信号的波特率估计

将传统的信号处理工具———傅里叶变换与小波变换相结合,提出了一种QAM信号波特率提取的新方法。对QAM信号作2次连续小波变换,其小波变换的结果在码元跳变点处出现明显的峰值,其他处都为零。然后再作FFT,则一次谐波谱线(即第2根峰值谱线)的位置恰好位于f=fb处,因此,通过检测第2根峰值谱线位置即可估计出QAM信号的波特率。对高斯白噪声中QAM信号的波特率提取进行了仿真并给出了部分实验结果。     

认知无线电中基于小波变换和干扰温度估计的频谱感知

干扰温度是认知无线电领域中引入的一种衡量频段上干扰强度的量.本文提出了基于小波变换和干扰温度估计的频谱感知策略.该思想首先利用小波变换分析无线宽带系统中信号功率谱密度变化的奇异点,将整个宽带频谱分为几个子区域,实现对无线传播环境的"粗感知";再运用多窗谱估计与奇异值分解(MTM-SVD)算法进行处理,获得每个子区域的干扰温度值;最后与干扰温度门限比较判决,从而得到适合认知无线电系统应用的频谱空洞,完成对无线传播环境的"细感知".     

Phaselets of framelets

Phaselets are a set of dyadic wavelets that are related in a particular way such that the associated redundant wavelet transform is nearly shift-invariant. Framelets are a set of functions that generalize the notion of a single dyadic wavelet in the sense that dyadic dilates and translates of these functions form a frame in L/sup 2/(IR). This paper generalizes the notion of phaselets to framelets. Sets of framelets that only differ in their Fourier transform phase are constructed such that the resulting redundant wavelet transform is approximately shift invariant. Explicit constructions of phaselets are given for frames with two and three framelet generators. The results in this paper generalize the construction of Hilbert transform pairs of framelets.     

基于空间-频率的DOA估计研究

在阵列信号处理中,提取回波信号中的空间频率,可以得到目标的方位信息。该文正是利用小 波变换的时间-频率特性,对回波信号进行时间-空间处理,从而对目标方位进行了估计。文中给出了计算机仿真结果,并与基于短时傅里叶变换的估计结果进行了比较。     

一种自适应整数小波变换方法

本文给出了一种自适应整数小波变换方法。构造整数小波变换的方法通常是由提升结构得到。本文也正是基于一种具有完全重构的自适应提升结构而得到自适应整数小波变换。G.Piella给出的自适应提升结构,由于它严格限制更新步骤中滤波器系数之和为1,使得不易于用它构造整数变换。为了得到整数变换,本文将它推广到更一般的情形。由这种自适应提升结构得到的自适应整数变换对图像中的边缘点和均匀区域有区别地对待,而且对整数信号进行变换没有舍入误差。这些性质在数字图像数据压缩中有重要应用。     

基于小波变换的非均匀采样信号频谱的研究

该文提出基于小波变换的非均匀采样信号频谱的检测方法,给出变换函数关系使得非均匀采样信号满足小波变换的两个基本条件。文中说明了小波的非均匀化过程,从均匀小波得到非均匀小波,以非均匀小波分析非均匀采样信号,得到非均匀采样信号的频谱。文中还说明了非均匀小波变换的抗混叠的原理以及对信号频谱的检测方法,最后给出实验结果。理论和实验表明,非均匀采样信号的小波变换方法是一种行之有效的非均匀采样信号的频率检测方法,使用该方法处理信号可以得到准确的频率估计效果。     

基于语谱能量的音素边界检测

根据音素发音时语谱结构的变化提出了一种基于语谱能量的音素边界检测方法。该方法首先根据语谱结构变化特点将信号频域划分为高频、中频、低频3个区域,并以语音帧间语谱能量向量的欧氏距离为判别依据分别对3个区域进行音素边界检测,然后对3个区域检测的边界分别进行二次筛选,最后将3个区域的边界信息融合,得到音素边界检测结果,相对于基于音素属性的边界检测方法,计算复杂性大大降低,边界检测率提高了3．95％。     

一种基于多重统计量分析的稳健语音信息隐藏算法

本文提出了一种基于多重统计量分析的小波域语音信息隐藏算法.该算法首先将载体语音信号分成若干包含相同采样点的帧,利用短时能量以及过零率找出属于浊音段的帧分别进行多尺度离散小波分解,提取小波分解后的低频系数;然后对低频系数进行分组并计算各组系数的能量、绝对值方差等统计量的值,根据各组统计值的比较及嵌入的秘密信息比特值,采用不改变或者适当调节各组统计值大小关系的方法来隐藏信息.该算法只在语音的浊音段嵌入信息,充分考虑了人耳的听觉特性.实验结果表明:算法可以盲检测,对加噪、低通滤波、重采样、重量化等攻击均具有良好的稳健性.     

基于关键帧和小波变换的盲视频水印算法

针对现有视频水印算法在抗几何攻击能力方面的一些不足,提出一种基于关键帧和小波变换的盲视频水印算法.依据视频帧间差欧氏距离提取视频关键帧并分块,结合人类视觉系统特性,从关键帧不同的块中选择亮度敏感性和纹理敏感性最高的块构成三维体块,进行三维小波变换,将置乱的水印图像以不同的嵌入强度嵌入到小波系数的低频区域,利用阈值门限实现水印的盲提取.结果表明,该算法提高了水印嵌入容量,对于针对视频水印的攻击具有较好的不可见性和稳健性.     

间歇混沌合成1/f噪声的相关特性分析

本文利用非线性随机微分方程来合成间歇混沌信号,针对该信号表现出的1/f噪声特征,在不同消失矩的小波基下进行相关特性分析.仿真结果发现,在功率谱的中间频段内,该信号的功率谱密度表现出典型的1/f噪声特性,其小波变换系数方差与相应的小波尺度呈对数线性关系;且在该频段内,部分尺度下该间歇性信号的小波变换系数的相关性随小波基的消失矩的增大而减小,在另一部分尺度下该相关性则随着消失矩的增大而增大.实验结果表明,随小波消失矩的增大,并非在所有尺度下小波变换对该间歇性信号均具有去相关作用.论文讨论了小波变换系数的方差和尺度的关系,详细分析了小波变换系数的相关性随小波消失矩的变化趋势.     

Multiresolution scene-based video watermarking using perceptualmodels

We present a watermarking procedure to embed copyright protection into digital video. Our watermarking procedure is scene-based and video dependent. It directly exploits spatial masking, frequency masking, and temporal properties to embed an invisible and robust watermark. The watermark consists of static and dynamic temporal components that are generated from a temporal wavelet transform of the video scenes. The resulting wavelet coefficient frames are modified by a perceptually shaped pseudorandom sequence representing the author. The noise-like watermark is statistically undetectable to thwart unauthorized removal. Furthermore, the author representation resolves the deadlock problem. The multiresolution watermark may be detected on single frames without knowledge of the location of the frames in the video scene. We demonstrate the robustness of the watermarking procedure to several video degradations and distortions     

Frequency-Domain Design of Overcomplete Rational-Dilation Wavelet Transforms

The dyadic wavelet transform is an effective tool for processing piecewise smooth signals; however, its poor frequency resolution (its low Q-factor) limits its effectiveness for processing oscillatory signals like speech, EEG, and vibration measurements, etc. This paper develops a more flexible family of wavelet transforms for which the frequency resolution can be varied. The new wavelet transform can attain higher Q-factors (desirable for processing oscillatory signals) or the same low Q-factor of the dyadic wavelet transform. The new wavelet transform is modestly overcomplete and based on rational dilations. Like the dyadic wavelet transform, it is an easily invertible 'constant-Q' discrete transform implemented using iterated filter banks and can likewise be associated with a wavelet frame for L₂(R). The wavelet can be made to resemble a Gabor function and can hence have good concentration in the time-frequency plane. The construction of the new wavelet transform depends on the judicious use of both the transform's redundancy and the flexibility allowed by frequency-domain filter design.     

基亏小波变换的语音特征参数提取

将小波变换的多分辨率特性用于改进Mel频率倒谱系数MFCC的前端处理中,给出了一种新的语音特征参数——小波MFCC。其特点在于采用小波变换、分层FFT和频率合成代替原来MFCC中的FFT部分,使频谱分辨率提高了一倍。试验证明,小波MFcc特征参数在较大词汇量情况下,其识别率优于MFCC特征参数的结果。     

A simple method to build oversampled filter banks and tight frames.

This paper presents conditions under which the sampling lattice for a filter bank can be replaced without loss of perfect reconstruction. This is the generalization of common knowledge that removing up/downsampling will not lose perfect reconstruction. The results provide a simple way of building oversampled filter banks. If the original filter banks are orthogonal, these oversampled banks construct tight frames of l2 (Z(n)) when iterated. As an example, a quincunx lattice is used to replace the rectangular one of the standard wavelet transform. This replacement leads to a tight frame that has a higher sampling in both time and frequency. The frame transform is nearly shift invariant and has intermediate scales. An application of the transform to image fusion is also presented.     

胶合板声发射信号的小波包特征提取及神经网络模式识别

为识别胶合板的不同损伤类型,将小波包时频分析与能量谱相结合,提出基于时频和频段能量占比的胶合板损伤声发射信号特征提取方法。研究得出胶合板基体开裂信号以膨胀波和弯曲波模式并举,频谱较宽,能量主要集中在小波能量谱的第一、二、三、四和七频段;分层信号频率单一,幅值较高,并以膨胀波为主;纤维断裂主要以弯曲波模式为主,频率较低;脱胶信号波形为膨胀波和弯曲波的混合型,以弯曲波为主,能量多集中于第一、二、三、四频段。用小波包提取的能量占比作为由BP神经网络构成的智能化模式分类器的输入样本,对4种声发射信号进行识别,正确率达到92．6％。     

步进频率MMW雷达小波变换目标成像方法

提出一种改进的调制型高斯小波分析方法，探索了毫米波雷达步进频率信号小波变换当中小波尺度与目标距离的对应关系，可进行任意距离范围和步进的功率谱分析，并提出目标分阶段的成像方法。该方法与一般小波变换相比减少了运算量，同时获得较为精确的一维距离像，从而更有利于目标定位与跟踪。