Discrete wavelets transform for signal denoising in capillary electrophoresis with electrochemiluminescence detection

Discrete wavelets transform (DWT). was applied to noise on removal capillary electrophoresis-electrochemiluminescence (CE-ECL) electropherograms. Several typical wavelet transforms, including Haar, Daublets, Coiflets, and Symmlets, were evaluated. Four types of determining threshold methods, fixed form threshold, rigorous Stein's unbiased estimate of risk (rigorous SURE), heuristic SURE and minimax, combined with hard and soft thresholding methods were compared. The denoising study on synthetic signals showed that wave Symmlet 4 with a level decomposition of 5 and the thresholding method of heuristic SURE-hard provide the optimum denoising strategy. Using this strategy, the noise on CE-ECL electropherograms could be removed adequately. Compared with the Savitzky-Golay and Fourier transform denoising methods, DWT is an efficient method for noise removal with a better preservation of the shape of peaks.

Using wavelet transform to study the Lipschitz local singular exponent in wall turbulence

In this paper, wavelet,transform is introduced to study the Lipschitz local singular exponent for characterising the local singularity behavior of fluctuating velocity in wall turbulence. I, is found that the local singular exponent is negative when the ejections and sweeps of coherent structures occur in a turbulent boundary layer.

Phase unwrapping algorithms, respectively, based on path-following and discrete cosine transform

This paper describes a path-following phase unwrapping algorithm and a phase unwrapping algorithm based on discrete cosine transform (DCT) which accelerates the Computation and suppresses the propagation of noise. Through analysis of fringe pattern with serious noises simulated in mathematic model, we make a contrast between path-following algorithm and DCT algorithm. The advantages and disadvantages or analytical fringe pattern are also given through comparison of two algorithms. Three-dimensional experimental results have been given to prove the validity of these algorithms. Despite DCT phase unwrapping technique robustness and speed in some cases, it cannot be unwrapping inconsistencies phase. The path-following algorithm can be used in automation analysis of fringe patterns with little influence of noise. (c) 2007 Elsevier GmbH. All rights reserved.

The application of spline wavelet transform to the determination of electroosmotic mobility in capillary electrophoresis

The rule of current change was studied during capillary electrophoresis (CE) separation process while the conductivity of the sample solution was different from that of the buffer. Using a quadratic spline wavelet of compact support, the wavelet transforms (WTs) of capillary electrophoretic currents were performed. The time corresponding to the maximum of WT coefficients was chosen as the time of current inflection to calculate electroosmotic mobility. The proposed method was suitable for different CE modes, including capillary zone electrophoresis, nonaqueous CE and micellar electrokinctic chromatography. Compared with the neutral marker method, the relative errors of the developed method for the determination of electroosmotic mobility were all below 2.5%. (C) 2002 Elsevier Science B.V. All rights reserved.

Comparison of wavelet transform methods - The compression of IR spectral data

In this paper, an introduction of wavelet transform and multi-resolution analysis is presented. We describe three data compression methods based on wavelet transform for spectral information,and by using the multi-resolution analysis, we compressed spectral data by Daubechies's compactly supported orthogonal wavelet and orthogonal cubic B-splines wavelet, Using orthogonal cubic B-splines wavelet and coefficients of sharpening signal are set to zero, only very few large coefficients are stored, and a favourable data compression can be achieved.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.

多尺度分解与相位反褶积提高海上地震资料分辨率和信噪比

Offshore seismic exploration is full of high investment and risk. And there are many problems, such as multiple. The technology of high resolution and high S/N ratio on marine seismic data processing is becoming an important project. In this paper, the technology of multi-scale decomposition on both prestack and poststack seismic data based on wavelet and Hilbert-Huang transform and the theory of phase deconvolution is proposed by analysis of marine seismic exploration, investigation and study of literatures, and integration of current mainstream and emerging technology. Related algorithms are studied. The Pyramid algorithm of decomposition and reconstruction had been given by the Mallat algorithm of discrete wavelet transform In this paper, it is introduced into seismic data processing, the validity is shown by test with field data. The main idea of Hilbert-Huang transform is the empirical mode decomposition with which any complicated data set can be decomposed into a finite and often small number of intrinsic mode functions that admit well-behaved Hilbert transform. After the decomposition, a analytical signal is constructed by Hilbert transform, from which the instantaneous frequency and amplitude can be obtained. And then, Hilbert spectrum. This decomposition method is adaptive and highly efficient. Since the decomposition is based on the local characteristics of the time scale of data, it is applicable to nonlinear and non-stationary processes. The phenomenons of fitting overshoot and undershoot and end swings are analyzed in Hilbert-Huang transform. And these phenomenons are eliminated by effective method which is studied in the paper. The technology of multi-scale decomposition on both prestack and poststack seismic data can realize the amplitude preserved processing, enhance the seismic data resolution greatly, and overcome the problem that different frequency components can not restore amplitude properly uniformly in the conventional method. The method of phase deconvolution, which has overcome the minimum phase limitation in traditional deconvolution, approached the base fact well that the seismic wavelet is phase mixed in practical application. And a more reliable result will be given by this method. In the applied research, the high resolution relative amplitude preserved processing result has been obtained by careful analysis and research with the application of the methods mentioned above in seismic data processing in four different target areas of China Sea. Finally, a set of processing flow and method system was formed in the paper, which has been carried on in the application in the actual production process and has made the good progress and the huge economic benefit.

Identification of Nonlinear Systems Through Time-Frequency Filtering Technique

In the previous paper, a class of nonlinear system is mapped to a so-called skeleton linear model (SLM) based on the joint time-frequency analysis method. Behavior of the nonlinear system may be indicated quantitatively by the variance of the coefficients of SLM versus its response. Using this model we propose an identification method for nonlinear systems based on nonstationary vibration data in this paper. The key technique in the identification procedure is a time-frequency filtering method by which solution of the SLM is extracted from the response data of the corresponding nonlinear system. Two time-frequency filtering methods are discussed here. One is based on the quadratic time-frequency distribution and its inverse transform, the other is based on the quadratic time-frequency distribution and the wavelet transform. Both numerical examples and an experimental application are given to illustrate the validity of the technique.

Burst event detection in wall turbulence by WVITA method

Wavelet Variable Interval Time Average (WVITA) is introduced as a method incorporating burst event detection in wall turbulence. Wavelet transform is performed to unfold the longitudinal fluctuating velocity time series measured in the near wall region of a turbulent boundary layer using hot-film anemometer. This unfolding is both in time and in space simultaneously. The splitted kinetic of the longitudinal fluctuating velocity time series among different scales is obtained by integrating the square of wavelet coefficient modulus over temporal space. The time scale that related to burst events in wall turbulence passing through the fixed probe is ascertained by maximum criterion of the kinetic energy evolution across scales. Wavelet transformed localized variance of the fluctuating velocity time series at the maximum kinetic scale is put forward instead of localized short time average variance in Variable Interval Time Average (VITA) scheme. The burst event detection result shows that WVITA scheme can avoid erroneous judgement and solve the grouping problem more effectively which is caused by VITA scheme itself and can not be avoided by adjusting the threshold level or changing the short time average interval.

Extended self-similar scaling law of multi-scale eddy structure in wall turbulence

The longitudinal fluctuating velocity of a turbulent boundary layer was measured in a water channel at a moderate Reynolds number. The extended self-similar scaling law of structure function proposed by Benzi was verified. The longitudinal fluctuating velocity, in the turbulent boundary layer was decomposed into many multi-scale eddy structures by wavelet transform. The extended self-similar scaling law of structure function for each scale eddy velocity was investigated. The conclusions are I) The statistical properties of turbulence could be self-similar not only at high Reynolds number, but also at moderate and low Reynolds number, and they could be characterized by the same set of scaling exponents xi (1)(n) = n/3 and xi (2)(n) = n/3 of the fully developed regime. 2) The range of scales where the extended self-similarity valid is much larger than the inertial range and extends far deep into the dissipation range,vith the same set of scaling exponents. 3) The extended selfsimilarity is applicable not only for homogeneous turbulence, but also for shear turbulence such as turbulent boundary layers.

Time-Frequency Analysis of Nonlinear Systems: The Skeleton Linear Model and the Skeleton Curves

The joint time-frequency analysis method is adopted to study the nonlinear behavior varying with the instantaneous response for a class of S.D.O.F nonlinear system. A time-frequency masking operator, together with the conception of effective time-frequency region of the asymptotic signal are defined here. Based on these mathematical foundations, a so-called skeleton linear model (SLM) is constructed which has similar nonlinear characteristics with the nonlinear system. Two skeleton curves are deduced which can indicate the stiffness and damping in the nonlinear system. The relationship between the SLM and the nonlinear system, both parameters and solutions, is clarified. Based on this work a new identification technique of nonlinear systems using the nonstationary vibration data will be proposed through time-frequency filtering technique and wavelet transform in the following paper.

小波变换方法评价曲线的分形特征

应用小波变换对Kiesswetter工线和3种方法生成的分数维布朗运动（FBm）进行了分析，验证了该方法计算分形维数具有较高的精度。在宽广的分形维数范围内，与其他7种计算方法比较表明，小波变换方法的精确性和一致性都最好。小波变换为进一步分辨确定性信号、分形特征的信号或完全随机性的信号提供了一种有效工具，为评价精糙表面形貌的分形特征提供了前提条件。

光学干涉图像处理中基于质量权值的离散余弦变换解包裹相位

为减少噪声对相位恢复过程的影响,快速得到正确的解包裹相位,提出了一种改进的相位解包裹方法——加权离散余弦变换解包裹算法。该方法把离散余弦变换和标识相位数据好坏的质量权值结合起来,兼有速度快和可靠度高的优势。为验证此算法,对模拟和实验得到的包裹相位图添加随机噪声和散粒噪声,同时采用加权与非加权离散余弦变换算法进行处理,所得到的解包裹结果与未加噪声的解包裹相位值进行比较,结果表明,通过加权离散余弦变换算法恢复的相位图比非加权离散余弦变换算法所恢复的相位图更接近于理想值,而且两种算法的运行速度基本相同,这证明提

频谱泄漏对正弦相位调制干涉测量精度的影响何

在正弦相位调制(SPM)干涉仪中,若调制频率或者采样频率发生变化将使干涉信号出现频谱泄漏,减小了谐波分量的幅值,在测量结果中引入了误差。对频谱泄漏的产生及其对测量精度的影响进行了理论分析,获得了频谱泄漏引入测量误差的计算方法。实验测得频率漂移量在-0.3～0.3 Hz内,得到的频谱泄漏引入的误差为0.3～7.9 nm,当超出这个范围时,频谱泄漏误差将迅速增长。实验结果与模拟分析结果一致。