Ground penetrating radar response from voids: A demonstration using a simple model

A simple Ricker wavelet model can be used to illustrate some fundamental properties of the ground penetrating radar (GPR) response from both air- and water-filled voids. Reflections from the top and the bottom of a void overlap significantly, and generate one common characteristic of a void response: a “bright spot”, analogous to the “bright spot” observed in seismic exploration. For time delays equal to about half the pulsewidth, the reflected wavelets superimpose to yield maximum reflection amplitudes. The top reflected wavelet becomes completely separated from the bottom reflected wavelet when the void time delay exceeds twice the wavelet pulsewidth. The two wavelet reflections can be individually identified at earlier time delays, approximately equal to the wavelet pulsewidth. This is still substantial and explains why it is difficult to use GPR to infer void thicknesses, especially for air-filled voids.     

On the statistical decorrelation of the 2D discrete wavelet transform coefficients of a wide sense stationary bivariate random process

We present a second order statistical analysis of the 2D Discrete Wavelet Transform (2D DWT) coefficients. The input images are considered as wide sense bivariate random processes. We derive closed form expressions for the wavelet coefficientsʼ correlation functions in all possible scenarios: inter-scale and inter-band, inter-scale and intra-band, intra-scale and inter-band and intra-scale and intra-band. The particularization of the input process to the White Gaussian Noise (WGN) case is considered as well. A special attention is paid to the asymptotical analysis obtained by considering an infinite number of decomposition levels. Simulation results are also reported, confirming the theoretical results obtained. The equations derived, and especially the inter-scale and intra-band dependency of the 2D DWT coefficients, are useful for the design of different signal processing systems as for example image denoising algorithms. We show how to apply our theoretical results for designing state of the art denoising systems which exploit the 2D DWT.     

Voice conversion using General Regression Neural Network

The objective of voice conversion system is to formulate the mapping function which can transform the source speaker characteristics to that of the target speaker. In this paper, we propose the General Regression Neural Network (GRNN) based model for voice conversion. It is a single pass learning network that makes the training procedure fast and comparatively less time consuming. The proposed system uses the shape of the vocal tract, the shape of the glottal pulse (excitation signal) and long term prosodic features to carry out the voice conversion task. In this paper, the shape of the vocal tract and the shape of source excitation of a particular speaker are represented using Line Spectral Frequencies (LSFs) and Linear Prediction (LP) residual respectively. GRNN is used to obtain the mapping function between the source and target speakers. The direct transformation of the time domain residual using Artificial Neural Network (ANN) causes phase change and generates artifacts in consecutive frames. In order to alleviate it, wavelet packet decomposed coefficients are used to characterize the excitation of the speech signal. The long term prosodic parameters namely, pitch contour (intonation) and the energy profile of the test signal are also modified in relation to that of the target (desired) speaker using the baseline method. The relative performances of the proposed model are compared to voice conversion system based on the state of the art RBF and GMM models using objective and subjective evaluation measures. The evaluation measures show that the proposed GRNN based voice conversion system performs slightly better than the state of the art models.     

基于小波一阶灰度矩向量和神经网络的旋转机械故障诊断的研究

在旋转机械轴系振动故障模拟试验的基础上,对大量故障模拟试验数据进行计算,建立了典型故障的小波一阶灰度矩向量样本,将其作为概率神经网络的输入进行故障诊断研究。结果表明,基于一阶灰度矩向量的概率神经网络可实现对训练样本100%的正确识别率,对陌生样本的正确识别率也超过75%。可见,概率神经网络综合了Bayes分类器和神经网络的优势,利用概率神经网络融合信号的一阶灰度矩向量特征实现旋转机械轴系故障模式识别是一种可行有效的方法。     

平整机轧制力阶跃奇异点的小波检测与分析

轧制力阶跃奇异点是对平整机轧制力控制系统进行故障诊断和系统分析的一类重要数据,采用小波分析方法对平整机轧制力控制系统阶跃奇异点进行检测、定位和分析.对奇异点检测的小波分析方法进行探讨,确定了用于轧制力阶跃奇异点检测的小波基函数.通过采用小波阶跃奇异点检测方法对某平整机实际轧制力数据进行阶跃奇异点的检测和定位,论证了该方法的正确性.     

基于小波分析的油气管道缺陷磁记忆检测研究

金属磁记忆检测技术是目前唯一能对铁磁性构件早期损伤进行诊断的无损检测手段,能检测识别油气管道早期损伤以及以应力集中为特征的裂纹缺陷。小波分析由于具有良好的时频局部性,能很好地克服傅里叶变换的不足,得到广泛的应用。针对目前金属磁记忆检测技术以过零点作为缺陷判定准则存在的不足,利用小波尺度谱和再分配的尺度谱对磁记忆信号进行分析。试验结果表明,小波再分配尺度谱能有效应用于管道缺陷的磁记忆检测之中。     

基于虚拟仪器的表面肌电信号特征提取算法的研究

提出一种基于虚拟仪器的表面肌电信号的特征提取算法。该方法利用虚拟仪器丰富的函数功能,针对肌电信号的非平稳性特征,应用积分阈值法首先去除静息电位,保留最有价值的信号部分,然后利用小波包变换的方法对肌电信号进行小波包分解,根据其投影到不同频段上小波包系数能量的不同,利用能量较大的几组系数重构肌电信号。实验结果表明:该方法能有效地去除静息电位及噪声信号,且保留了肌电信号的细节信息,为肌电信号的模式识别创造了良好的条件。该研究依据虚拟仪器平台,为创建表面肌电信号实时控制机械臂系统提供了研究基础,具有潜在的工程应用价值。     

几种常用遥感影像融合方法分析及初步试验

本文对目前在影像融合过程中最常用的融合方法进行了简单归纳和介绍,并运用Brovey变换、主成分变换、小波变换、IHS变换等融合方法对某一试验区SPOT5卫星的全色和多光谱数据进行融合处理,并对融合结果进行了定量分析对比,得出对于实验数据来说最适合的融合方法.     

基于小波变换对GPS信号去噪的分析

小波分解能够精细地把信号划分到不同的频带范围内,因此可对含噪信号在不同频带范围内的特征进行信噪分离.本文从GPS精密测量、导航领域的信号去噪角度,探讨了基于小波分析的信噪分离方法,并结合具体实例,说明小波分析对GPS信号消噪处理的实用性及有效性.     

Identification of rod dynamics under influence of Active Magnetic Bearing

The purpose of this paper is to apply the wavelet transform algorithm to identify the magnetic damping and magnetic stiffness coefficients of the drive rod with which a set of 4-pole Active Magnetic Bearing (AMB) is equipped. By taking advantage of time–frequency analysis feature, the ridge curve of rod free response after wavelet transformation can be extracted to find the natural frequency of the rod/AMB system. In other words, due to the influence of magnetized field by the AMB, the stiffness of the rod dynamics is not linear any more and can be estimated from the curve of the amplitude versus frequency by wavelet transformation. On the other hand, the nonlinear damping coefficients can be estimated from the derivative of amplitude versus amplitude by wavelet transformation of rod free vibration. It is found that the nonlinear magnetic damping coefficients are up to 2nd-order in polynomial and the stiffness coefficient is mainly of 3rd-order respectively. In addition, the identified 2nd-order damping coefficient is negative and hence implies that under specific rod displacement and speed, the dynamic of the rod/AMB system in axial direction is unstable.