基于小波leaders的海杂波时变奇异谱分布分析

海杂波的奇异谱分析不仅能从理论上揭示海洋表面的动力学机理,同时也是对海探测雷达的关键技术之一.本文提出基于小波leaders的海杂波时变奇异谱分析方法,将时间信息引入海杂波的奇异谱分析之中,从而实现动态的解析描述海杂波随时间变化的奇异谱特性.在理论上,通过信号自身加窗,将时间信息引入传统的奇异谱(或称多重分形谱),实现了对海杂波时变奇异谱分布分析;在算法上,充分利用了小波leaders技术对于多种奇异性的提取能力(包括chirp奇异性和cusp奇异性),通过对时变奇异性指数和时变尺度函数的Legendre变换,实现对海杂波时变奇异谱分布的计算;在应用部分,采用经典的多重分形模型——随机小波序列(RWC)以及三级海态条件下连续波多普勒体制雷达海杂波进行仿真分析,实验结果表明:1)基于小波leaders的奇异谱分布能跟踪海杂波的时变尺度特性,有效展示其时变奇异性谱分布;2)算法具有较好的负矩特性和统计收敛性.该方法能为复杂非线性系统及随机多重分形信号分析提供参考.     

Inferring mixed-culture growth from total biomass data in a wavelet approach

It is shown that the presence of mixed-culture growth in batch fermentation processes can be very accurately inferred from total biomass data by means of the wavelet analysis for singularity detection. This is accomplished by considering simple phenomenological models for the mixed growth and the more complicated case of mixed growth on a mixture of substrates. The main quantity provided by the wavelet analysis is the Hölder exponent of the singularity that we determine for our illustrative examples. The numerical results point to the possibility that Hölder exponents can be used to characterize the nature of the mixed-culture growth in batch fermentation processes with potential industrial applications. Moreover, the analysis of the same data affected by the common additive Gaussian noise still lead to the wavelet detection of the singularities although the Hölder exponent is no longer a useful parameter.     

Wavelet Leaders: A new method to estimate the multifractal singularity spectra

Wavelet Leaders is a novel alternative based on wavelet analysis for estimating the Multifractal Spectrum. It was proposed by Jaffard and co-workers improving the usual wavelet methods. In this work, we analyze and compare it with the well known Multifractal Detrended Fluctuation Analysis. The latter is a comprehensible and well adapted method for natural and weakly stationary signals. Alternatively, Wavelet Leaders exploits the wavelet self-similarity structures combined with the Multiresolution Analysis scheme. We give a brief introduction on the multifractal formalism and the particular implementation of the above methods and we compare their effectiveness. We expose several cases: Cantor measures, Binomial Multiplicative Cascades and also natural series from a tonic-clonic epileptic seizure. We analyze the results and extract the conclusions.     

Discrete wavelet approach to multifractality

A new and simple method is presented to study local scaling properties of measures defined on regular and fractal supports. The method, based on a discrete wavelet analysis (WA), complements the well-known multifractal analysis (MA) extensively used in many physical problems. The present wavelet approach is particularly suitable for problems where the multifractal analysis does not provide conclusive results, as e.g. in the case of measures corresponding to Anderson localized wave-functions in one-dimension. Examples of different types of measures are also discussed which illustrate the usefulness of the WA to classify non-multifractal measures according to additional characteristic exponents, which can not be obtained within the MA.     

On the use of biorthogonal interpolating wavelets for large-eddy simulation of turbulence

Recent work in the field of turbulence modelling has demonstrated the benefits of the wavelet-based multiresolution analysis technique as a tool for the formulation of the large-eddy simulation (LES) equations. In this formalism, the LES equations are obtained by projecting the Navier–Stokes equations onto a hierarchy of wavelet spaces. This paper investigates the use of biorthogonal interpolating wavelets as a basis for this projection, placing special emphasis on the wavelet-based differential operators that define this mapping. A detailed analysis of their convergence properties is presented and compared to those of their orthogonal counterpart, the Daubechies wavelets. Based on this study, we highlight the weaknesses of the unlifted interpolating wavelet representation for LES sub-grid modelling. Finally, we establish a link between the unlifted framework and the sampling-based LES approach recently proposed in the literature.     

The time-singularity multifractal spectrum distribution

Although the multifractal singularity spectrum revealed the distribution of singularity exponent, it failed to consider the temporal information, therefore it is hard to describe the dynamic evolving process of non-stationary and nonlinear systems. In this paper, we aim for a multifractal analysis and propose a time-singularity multifractal spectrum distribution (TS-MFSD), which will hopefully reveal the spatial dynamic character of fractal systems. Similar to the Wigner–Ville time-frequency distribution, the time-delayed conjugation of fractal signals is selected as the windows function. Furthermore, the time-varying Holder exponent and the time-varying wavelet singularity exponent are deduced based on the instantaneous self-correlation fractal signal. The time-singularity exponent distribution i.e. TS-MFSD is proposed, which involves time-varying Hausdorff singularity spectrum distribution, time-varying large deviation multifractal spectrum and time-varying Legendre spectrum distribution, which exhibit the singularity exponent distribution of fractal signal at arbitrary time. Finally, we studied the algorithm of the TS-MFSD based on the wavelet transform module maxima method, analyzed and discussed the characteristic of TS-MFSD based on Devil Staircase signal, stochastic fractional motion and real sea clutter.     

Joint multifractal analysis based on wavelet leaders

Mutually interacting components form complex systems and these components usually have long-range cross-correlated outputs. Using wavelet leaders, we propose a method for characterizing the joint multifractal nature of these long-range cross correlations; we call this method joint multifractal analysis based on wavelet leaders (MF-X-WL). We test the validity of the MF-X-WL method by performing extensive numerical experiments on dual binomial measures with multifractal cross correlations and bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. Both experiments indicate that MF-X-WL is capable of detecting cross correlations in synthetic data with acceptable estimating errors. We also apply the MF-X-WL method to pairs of series from financial markets (returns and volatilities) and online worlds (online numbers of different genders and different societies) and determine intriguing joint multifractal behavior.     

Multifractal estimates of monofractality in RR-heart series in power spectrum ranges

Two popular estimators of multifractal properties: the Wavelet Transform Modulus Maxima method and Multifractal Detrended Fluctuation Analysis are applied to investigate signals consisting of normal RR-series in 39 healthy subjects and 90 patients suffering from systolic dysfunction of the left ventricle. However, differently from standards for multifractal analysis the scaling is performed separately in intervals corresponding to standard power spectral bands: low (LF), very low (VLF) and ultra low frequencies (ULF). Tests on fractional Brownian motions (fBm) are done to quantify properties of the estimators as detectors of monofractality in LF, VLF and ULF bands. Arguments are given that multifractal analysis of RR-series performed in these bands has a physiological meaning. The increased activation of the sympathetic nervous system caused by heart disease is detected evidently only by analysis in LF. The transition in multifractal characteristics between diurnal and nocturnal activity takes place when the analysis moves from LF and VLF to ULF. Only in ULF, the diurnal heart rate variability can be approximated by fBm with a self-similarity index of H=0.20.     

基于双树复小波变换的非平稳时间序列去趋势波动分析方法

多重分形去趋势波动分析是研究非平稳时间序列非均匀性和奇异性的有效工具, 针对该方法中趋势项难以确定的问题, 提出一种基于双树复小波变换的方法, 实现了非平稳信号的多重分形自适应去趋势波动分析. 利用双树复小波变换提取信号的多尺度趋势和波动信息, 通过小波系数的希尔伯特变换确定每个时间尺度不重叠子区间的长度, 使多重分形分析具有信号自适应性及较高的计算效率. 以具有解析形式分形特征的倍增级联信号和分数布朗运动时间序列为例验证本文方法的有效性, 所得结果与解析解相吻合. 与传统的多项式去趋势多重分形方法相比, 本文方法根据信号自身特点自适应地确定信号的趋势和不重叠等长度子区间长度, 所得结果更加精确. 对倍增级联信号时间序列取不同的长度, 验证了算法的稳定性. 分别与基于极大重叠离散小波变换和离散小波变换多重分形方法进行比较, 表明本文方法具有更精确的结果和更快的运算速度.     

Multiresolution analysis of fluctuations in non-stationary time series through discrete wavelets

We illustrate the efficacy of a discrete wavelet based approach to characterize fluctuations in non-stationary time series. The present approach complements the multifractal detrended fluctuation analysis (MF-DFA) method and is quite accurate for small size data sets. As compared to polynomial fits in the MF-DFA, a single Daubechies wavelet is used here for detrending purposes. The natural, built-in variable window size in wavelet transforms makes this procedure well suited for non-stationary data. We illustrate the working of this method through the analysis of binomial multifractal model. For this model, our results compare well with those calculated analytically and obtained numerically through MF-DFA. To show the efficacy of this approach for finite data sets, we also do the above comparison for Gaussian white noise time series of different sizes. In addition, we analyze time series of three experimental data sets of tokamak plasma and also spin density fluctuations in 2D Ising model.     

Statistics of biophysical signal characteristics and state specificity of the human EEG

The arguments are given that local exponents obtained in multifractal analysis by two methods: wavelet transform modulus maxima (WTMM) and multifractal detrended fluctuation analysis (MDFA) allow to separate statistically hearts of healthy people and subjects suffering from reduced left ventricle systolic function (NYHA I–III class). Proposed indices of fractality suggest that a signal of human heart rate is a mixture of two processes: monofractal and multifractal ones.     

Statistics of event by event fluctuations

Following Hwa and Wu [R.C. Hwa, Y. Wu, Phys. Rev. C 60 (1999) 0544904], we characterize the fluctuation behavior of the hadron density produced during quark-hadron phase transition, as modeled by a 2D Ising model. Using a recently developed discrete wavelet based approach, the scaling behavior is studied at temperatures below, at and above T_c. At T_c, we find the Hurst exponent H?1, as observed in a recent experimental finding [L. Qin, M. Ta-chung, Phys. Rev. D 72 (2005) 014011]. However, as compared to the R/S analysis, which yields only the Hurst exponent, our local approach finds a correlation behavior and multifractal properties at temperatures below, at and above T_c. We find evidence for a transition from Brownian to fractional Browian motion near T_c. The correlation behavior compares well with the results obtained from a continuous wavelet based average wavelet co-efficient method, as well as with Fourier power spectral analysis.     

Detection of meso-micro scale surface features based on microcanonical multifractal formalism

Synthetic aperture radar(SAR) is an effective tool to analyze the features of the ocean. In this paper, the microcanonical multifractal formalism is used to analyze SAR images to obtain meso-micro scale surface features. We use the Sobel operator to calculate the gradient of each point in the image, then operate continuous variable scale wavelet transform on this gradient matrix. The relationship between the wavelet coefficient and scale is built by linear regression. This relationship decides the singular exponents of every point in the image which contain local and global features. The manifolds in the ocean can be revealed by analyzing these exponents. The most singular manifold, which is related to the streamlines in the SAR images, can be obtained with a suitable threshold of the singular exponents. Experiments verify that application of the microcanonical multifractal formalism to SAR image analysis is effective for detecting the meso-micro scale surface information.     

A wavelet-based method for multifractal image analysis. I. Methodology and test applications on isotropic and anisotropic random rough surfaces

We generalize the so-called wavelet transform modulus maxima (WTMM) method to multifractal image analysis. We show that the implementation of this method provides very efficient numerical techniques to characterize statistically the roughness fluctuations of fractal surfaces. We emphasize the wide range of potential applications of this wavelet-based image processing method in fundamental as well as applied sciences. This paper is the first one of a series of three articles. It is mainly devoted to the methodology and to test applications on random self-affine surfaces (e.g., isotropic fractional Brownian surfaces and anisotropic monofractal rough surfaces). Besides its ability to characterize point-wise regularity, the WTMM method is definitely a multiscale edge detection method which can be equally used for pattern recognition, detection of contours and image denoising. Paper II (N. Decoster, S.G. Roux, A. Arnéodo, to be published in Eur. Phys. J. B 15 (2000)) will be devoted to some applications of the WTMM method to synthetic multifractal rough surfaces. In paper III (S.G. Roux, A. Arnéodo, N. Decoster, to be published in Eur. Phys. J. 15 (2000)), we will report the results of a comparative experimental analysis of high-resolution satellite images of cloudy scenes. Received 8 July 1999     

Linearization effect in multifractal analysis: Insights from the Random Energy Model

The analysis of the linearization effect in multifractal analysis, and hence of the estimation of moments for multifractal processes, is revisited borrowing concepts from the statistical physics of disordered systems, notably from the analysis of the so-called Random Energy Model. Considering a standard multifractal process (compound Poisson motion), chosen as a simple representative example, we show the following: (i) the existence of a critical order q^∗ beyond which moments, though finite, cannot be estimated through empirical averages, irrespective of the sample size of the observation; (ii) multifractal exponents necessarily behave linearly in q, for q>q^∗. Tailoring the analysis conducted for the Random Energy Model to that of Compound Poisson motion, we provide explicative and quantitative predictions for the values of q^∗ and for the slope controlling the linear behavior of the multifractal exponents. These quantities are shown to be related only to the definition of the multifractal process and not to depend on the sample size of the observation. Monte Carlo simulations, conducted over a large number of large sample size realizations of compound Poisson motion, comfort and extend these analyses.