基于小波变换的心音信号降噪方法

为了弥补传统阈值函数在消噪过程中存在的不足,得到高信噪比的心音信号更好地进行心音分析,本文提出一种新的阈值函数。该函数通过灵活调节参数a和m的大小,更好地对染噪心音信号小波分解的每一层高频系数进行阈值量化。仿真中,应用传统的软、硬阈值函数及新阈值函数分别对大量的标准心音信号进行消噪处理,并对消噪效果进行了比较分析,同时将新阈值函数应用到实测心音信号消噪中。结果表明,新阈值函数能有效地消除噪声和保留心音信号的特征,具有较强的实用价值。     

Embolic Doppler ultrasound signal detection using discrete wavelet transform

Asymptomatic circulating emboli can be detected by Doppler ultrasound. Embolic Doppler ultrasound signals are short duration transient like signals. The wavelet transform is an ideal method for analysis and detection of such signals by optimizing time-frequency resolution. We propose a detection system based on the discrete wavelet transform (DWT) and study some parameters, which might be useful for describing embolic signals (ES). We used a fast DWT algorithm based on the Daubechies eighth-order wavelet filters with eight scales. In order to evaluate feasibility of the DWT of ES, two independent data sets, each comprising of short segments containing an ES (N=100), artifact (N=100) or Doppler speckle (DS) (N=100), were used. After applying the DWT to the data, several parameters were evaluated. The threshold values used for both data sets were optimized using the first data set. While the DWT coefficients resulting from artifacts dominantly appear at the higher scales (five, six, seven, and eight), the DWT coefficients at the lower scales (one, two, three, and four) are mainly dominated by ES and DS. The DWT is able to filter out most of the artifacts inherently during the transform process. For the first data set, 98 out of 100 ES were detected as ES. For the second data set, 95 out of 100 ES were detected as ES when the same threshold values were used. The algorithm was also tested with a third data set comprising 202 normal ES; 198 signals were detected as ES.     

Application of the robust discrete wavelet transform to signal detection in underwater sound

This paper applies the notion of robust discrete wavelet transform (robust DWT) to recursive density estimation as a solution to detecting multipath signals underwater. Though there are no obvious distribution assumptions that can be made to model underwater noise, a recursive density estimator of the initial background noise reconstructed by the discrete wavelet transform (DWT) can be established as an empirical model. Observations that are identified as an outlier are then flagged as potential signals. In a multipath environment, where many signal components arrive with arbitrary delays, the DWT's lack of translation invariance is a problem for processing multipath signals. We adopt a robust DWT as a solution. Using an iterative algorithm in the Zak domain, a scaling function can be turned to a robust one. By designing a robust scaling function we can obtain a robust DWT. The performance of the robust DWT, exhibiting much better shift invariance than the conventional DWT, is illustrated. Utilizing the robust DWT reconstruction to establish the recursive density estimator of the underwater background noise, the ability to detect a multipath signal in an underwater environment is improved compared to that of the conventional DWT.     

基于小波时频分析的心音异常信号检测

利用小波时频分析知识,结合Choi－Williams分布及重排对正常心音信号和异常心音信号进行能量分布分析,运用matlab编程可以计算得到正常心音和异常心音信号不同的时频分布结构,从而可以很清晰的区分心音信号是否正常。时频分布操作简单,信号蕴含信息很强,可以通过连续观测结果分析,清楚的发现异常心音信号出现的时刻,从而进行及早的预判和相应治疗,对于心脏相关疾病的预防具有很好的指导作用。     

Fast text location based on discrete wavelet transform

The paper describes a texture-based fast text location scheme which operates directly in the Discrete Wavelet Transform (DWT) domain. By the distinguishing texture characteristics encoded in wavelet transform domain, the text is fast detected from complex background images stored in the compressed format such as JPEG2000 without full decompress. Compared with some traditional character location methods, the proposed scheme has the advantages of low computational cost, robust to size and font of characters and high accuracy. Preliminary experimental results show that the proposed scheme is efficient and effective.     

Fast implementation of the continuous wavelet transform withinteger scales

We describe a fast noniterative algorithm for the evaluation of continuous spline wavelet transforms at any integer scale m. In this approach, the input signal and the analyzing wavelet are both represented by polynomial splines. The algorithm uses a combination of moving sum and zero-padded filters, and its complexity per scale is O(N), where N is the signal length. The computation is exact, and the implementation is noniterative across scales. We also present examples of spline wavelets exhibiting properties that are desirable for either singularity detection (first and second derivative operators) or Gabor-like time-frequency signal analysis     

Multiscale corner detection by using wavelet transform

A multiscale corner detection algorithm based on the wavelet transform of contour orientation is proposed. It can utilize both the information of the local extrema and modulus of transform results to detect corners and arcs effectively. The ramp-width of contour orientation profile, which can be computed using the transformed modulus of two scales, reveals the difference between corner and arc and is utilized in the determination of corner points. The experimental results have shown that the detector is more effective than both the single- and multiple-scale detectors. They also demonstrate that the detector is insensitive to boundary noise. In addition, the proposed method is more efficient than the other multiscale corner detector because it operates on fewer number of scales, which can be implemented by a fast transform algorithm.     

基于小波变换的图像边缘检测

介绍了利用小波变换进行图像边缘检测的原理与方法。基于小波变换的模极大值原理,利用不同尺度小波变换后的不同方向获取图像的高频信息,并通过小波系数的模极值点与过零点,检测出图像在四个方向上的模极大值,得到该位置模的局部最大值。仿真测试表明,利用小波变换进行图像边缘检测可以较好的检测图像边缘的细节特征,取得了很好的效果。     

基于小波变换的图像边缘检测

介绍了利用小波变换进行图像边缘检测的原理与方法。基于小波变换的模极大值原理,利用不同尺度小波变换后的不同方向获取图像的高频信息,并通过小波系数的模极值点与过零点,检测出图像在四个方向上的模极大值,得到该位置模的局部最大值。仿真测试表明,利用小波变换进行图像边缘检测可以较好的检测图像边缘的细节特征,取得了很好的效果。     

Spike detection using the continuous wavelet transform

This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution.     

基于小波变换的电力系统谐波检测的研究

小波变换在电力系统谐波中的应用,与谐波本身的特性是直接相关的,要得到实时性和精确性都较高的检测效果就需要对电力系统的谐波特点,小波在这方面的应用原理有深刻的理解。文中对此在作了深入分析的基础上,还重点研究了采用小波变换研究谐波检测的主要因素,用仿真验证其影响效果。最后针对目前的研究成果论述小波在谐波检测应用中的发展,为小波变换在分析有效、精确、可靠的电力谐波检测方法提供研究思路。     

基于小波变换的QRS波群检测

提出了一种基于小波多分辨分析的算法,对心电信号进行特征提取和识别。通过小波变换对常规心电图信号进行分解去噪和特征提取,并利用动态自适应阈值和删除多检点,补偿漏检点对QRS波检测进行优化。实验结果表明该方法在QRS波形不失真的情况下,提高了一部分MIT-BIH数据库信号中QRS波识别的准确率,并且对于较低准确率的心电信号的原因进行了分析。     

Fast wavelet transforms based on the extended lapped transform

A new family of wavelet transforms (WTs) based on the extended lapped transform (ELT) is introduced. A new optimisation criterion for the ELT butterfly angles is presented, leading to ELT-based WTs with virtually the same performance but a much lower computational complexity than the standard maximally-regular WTs.<>     

小波变换在图像边缘检测中的应用

目前,被广泛使用的经典边缘检测算子有Sobel算子,Prewitt算子,Roberts算子,Log算子,Canny算子等等。这些算子的核心思想是图像的边缘点是相对应于图像灰度值梯度的局部极大值点。然而,当图像中含有噪声时这些算子对噪声都比较敏感,使得将噪声作为边缘点。由于噪声的干扰,不能检测出真正的边缘。一个拥有良好属性的的边缘检测算法是每个研究者的追求。利用小波交换的特点,设计了三次B样条平滑滤波算子。通过利用这个算子,对利用小波变换来检测图像的边缘进行了一定的研究和理解。     

基于小波变换的呼吸音降噪新方法研究

为了对临床采集的含噪声呼吸音信号进行降噪,探讨了基于小波多分辨率分解和重构的两种方法:基于小波的自适应阈值(AWT)降噪方法和基于平稳-非平稳滤波技术(ST-NST)降噪方法。AWT将呼吸音作为噪声先行去除,通过参数调解,对采集信号每一层高频小波系数自适应的进行阈值量化;ST-NST通过平稳-非平稳滤波技术将呼吸音和心音分离到两个空间,分别通过重构两个空间的小波系数,重构出呼吸音。通过对标准信号及对临床采集呼吸音提取实验,ST-NST对正常人支气管呼吸音降噪、AWT对哮喘病人哮鸣音降噪分别都有很好的效果。以上两种方法,具有较强的实用价值,可以得到高信噪比的呼吸音信号,为后续呼吸音特征提取和分类提供了基础。     

DOPPLER PARAMETER EXTRACTION OF MOVING TARGETS IN SAR IMAGING BY WAVELET TRANSFORM

In this paper, the shortages of Wigner-Ville Distribution(WVD) double-linearity in extracting the Doppler parameters of moving-targets are discussed, especially in multi-point moving-target imaging processing based on the spectrum characteristics of moving-target echo signals in Synthetic Aperture Radar (SAR) imaging processing and the properties of WVD. Combined with the characteristics of Continuous Wavelet Transform (CWT), the responsibility and advantages of CWT in multi-point moving-target Doppler parameter extraction are analyzed. Finally a kind of multi-point moving-target Doppler parameter extracting algorithm based on CWT are developed, and the computer stimulating results demonstrate the correctness of the algorithm.     

基于小波变换的语音基音周期检测

语音信号是一种非平稳信号,基音周期是语音信号最重要的参数之一,传统的基音检测方法存在一些缺陷.小波变换鲁棒性强、能很好地反映信号的时频特性,非常适合处理非平稳信号.为准确提取基音频率,提出了一种基于小波变换的基音周期检测方法.检测前在小波域上用Teager能量算子分离出语音信号的浊音段,然后对浊音段采用空域相关函数降噪...     

Fast detection of multi-circle with randomized Hough transform

Randomized Hough transform （RHT） is an effective method for circle detection. But when dealing with multi-circle complex images, the random sampling will bring lots of invalid accumulations and result in a large number of calculations. In this paper, by selecting three points of the candidate circle, a fast detection algorithm of multi-circle with randomized Hough transform is presented. Experimental results demonstrate that the proposed scheme can quickly detect multiple circles, and has a strong robustness.     

Fast codeword search algorithm using wavelet transform and partialdistance search techniques

A new fast codeword search algorithm for vector quantisers is presented. This algorithm performs a fast search in the wavelet domain of codewords using the partial distance search technique. Simulation results show that the algorithm has only 2% of the arithmetic complexity of the exhaustive search method     

基于小波变换的红外图像弱小目标检测研究

针对红外图像弱小点目标检测中遇到的难点,即信噪比低、帧间相对运动和强噪声干扰等问题,给出了一种帧间膨胀累积提高信噪比的方法,并且提出将l/f噪声白化和白噪声去除同步处理的新思路,大量的仿真实验证明,该方法能够很好地抑制噪声对图像的影响,大大提高信噪比,降低误检率。