小波分析方法在电力系统短期负荷预测中的应用

本文将对目前基于小波分析方法的电力系统短期负荷预测的一些理论与存在的问题进行初步的探讨，提出使用小波分析对基于不同频段的负荷进行分类，然后对分类后的不同频段的负荷使用人工神经网络进行训练预测的一种改进负荷预测方法，并尝试提出小波理论在短期负荷预测方向应用的建议。     

基于反对称双正交小波分解系数的模极大值的信号快速重构

因为反对称双正交小波具有近似微分算子性质,该文用它对信号作无下采样(Un-decimated)的小波变换,以此获得信号在各尺度下与信号的边缘点相对应的局部模极大值。在只保留这些局部模极大值(即边缘)点信息的条件下,利用高斯函数直接拟合各级小波系数,然后再作小波逆变换就可恢复原信号。实验表明,与现有的采用二进小波变换的方法比较,该方法更加简便快捷。     

视频中适应光照可变情况下的人脸识别方法

提出一种结合使用自适应直方图均衡(AHE)、Gabor滤波器及局部三值模式(LTP)描述器进行视频中上下左右和正面光照条件下识别人脸的方法。首先,使用AHE对来自YaleB与CMU-PIE数据库的人脸图片进行降噪处理。然后用Gabor滤波器进行卷积,提取出相应的Gabor特征图,针对每一个Gabor特征图利用LTP描述器提取出局部邻域关系模式。最后由这些模式的区域直方图形成的序列来描述人脸。YaleB人脸库以及CMUPIE人脸库验证该方法的有效性。     

Optimal wavelet denoising for phonocardiograms

Phonocardiograms (PCGs), recordings of heart sounds, have many advantages over traditional auscultation in that they may be replayed and analysed for spectral and frequency information. PCG is not a widely used diagnostic tool as it could be. One of the major problems with PCG is noise corruption. Many sources of noise may pollute a PCG including foetal breath sounds if the subject is pregnant, lung and breath sounds, environmental noise and noise from contact between the recording device and the skin. An electronic stethoscope is used to record heart sounds and the problem of extracting noise from the signal is addressed via the use of wavelets and averaging. Using the discrete wavelet transform, the signal is decomposed. Due to the efficient decomposition of heart signals, their wavelet coefficients tend to be much larger than those due to noise. Thus, coefficients below a certain level are regarded as noise and are thresholded out. The signal can then be reconstructed without significant loss of information in the signal content. The questions that this study attempts to answer are which wavelet families, levels of decomposition, and thresholding techniques best remove the noise in a PCG. The use of averaging in combination with wavelet denoising is also addressed. Possible applications of the Hilbert transform to heart sound analysis are discussed.     

基于小波变换的不规则网格的多分辨率分析

Lounsbery 提出了一种三角形网格多分辨率分析方法，但该方法只能应用于规则的三角形网格，且包含了重新网格化的过程。为了解决该问题，基于小波变换，该文扩展了Lounsbery的方法。该算法直接对不规则网格进行渐进压缩，得到了不同分辨率的网格。在此过程中还可以基于三角形网格的连接信息，对三角形网格进行优化，使之更加规则，从而使该文算法得到了改善。将该文算法与以前的算法进行了比较，结果表明，该文算法速度快，效果良好，有一定的实用性。     

一种基于小波变换的小接地电流系统单相接地故障选线方法

提出了一种基于小波变换的配电网单相接地故障选线方法。该方法中的选线判据采取区段内小波系数线性叠加后再比较的方法，充分地利用了故障信息，更明显的突出了区分故障状态和正常运行状态的特征量，从而达到了准确选线的目的。EMTP仿真分析的结果表明，该方法是十分有效的。     

Wavelet-based AE characterization of composite materials

This paper addresses an application of recently developed wavelet-based signal processing technique on a composite fracture behavior study. Here, the wavelet methodology is introduced, and a complete procedure of wavelet-based acoustic emission (AE) analysis methods developed by the author are demonstrated. In this research, the AE signals from glass fiber reinforced (GFR) composites are collected during the standard quasi-static tensile testing and transformed by the Daubechies discrete wavelets using the programs developed by the author. Based on AE tests, the fracture behaviors are studied for the material. In the study of GFR composite fracture behavior, classical linear fracture mechanics method is used for comparison. The exponential constant m value used to determine the relationship between stress and stress intensity factor are compared relative to the classical fracture mechanics and the AE techniques. In order to demonstrate the advantage of the wavelet-based AE techniques, the conventional AE analysis is also provided side-by-side for comparison. The results verify that the wavelet-based method better approximates residual strength relative to the classical AE techniques.In this paper, the results of the wavelet-based AE analysis of glass fiber composites are presented. The results indicate that wavelets-based signal processing is an efficient tool in the analysis and the transformation of AE signals.     

Novel wavelet-based QIM data hiding technique for tamper detection and correction of digital images

This paper proposes a tamper detection and correction technique using semi-fragile data hiding that aims to achieve high perceptual quality of images at the user-end even after malicious modifications. A binary signature and an image digest are embedded by modulating integer wavelet coefficients using dither modulation based quantization index modulation. Half-toning technique is used to obtain image digest from the low-resolution version of the host image itself. Decoder extracts the binary signature from the watermarked image for tamper detection, while the extracted image digest is used to correct the tamper region. Unlike previously proposed techniques, this novel approach distinguishes malicious changes from various common image processing operations more efficiently and also correct tapered regions effectively. Experimental results show that the proposed technique provides a superior performance in terms of probability of miss and false alarm as well as in tamper correction, compared to several existing semi-fragile watermarking techniques.     

Signal smoothness estimation in hölder spaces

Previous research has shown that wavelet method can be used to estimate the Besov smoothness of a function (signal). This paper describes an algorithm that is based on the magnitudes of the wavelet coefficients and linear regression model to estimate the smoothness of different signals of one and two-dimensional in the Hölder spaces. Computational results show that the Holder smoothness of the general two-dimensional image is between 0.2 and 0.7. We compare our results with those in Besov smoothness spaces and discuss the smoothness relations between these two function spaces.     

Compactly Supported Biorthogonal Wavelet Bases on the Body Centered Cubic Lattice

In this work, we present a family of compact, biorthogonal wavelet filter banks that are applicable to the Body Centered Cubic (BCC) lattice. While the BCC lattice has been shown to have superior approximation properties for volumetric data when compared to the Cartesian Cubic (CC) lattice, there has been little work in the way of designing wavelet filter banks that respect the geometry of the BCC lattice. Since wavelets have applications in signal de‐noising, compression, and sparse signal reconstruction, these filter banks are an important tool that addresses some of the scalability concerns presented by the BCC lattice. We use these filters in the context of volumetric data compression and reconstruction and qualitatively evaluate our results by rendering images of isosurfaces from compressed data.