提升格式下几种去噪方法的比较

本文比较了四种去噪声的效果,利用提升格式设计的灵活性,通过预算子或更新算子的设计最优匹配特定的信号,比较了软阈值函数、硬阈值函数、比例萎缩LAWML法和小波域中值滤波四种去噪方法.     

基于概率判断函数的分类探讨——以玻璃分类为例

分类问题在基因、化学、地质等领域都有着广泛的应用,在数据满足正态分布的前提下,利用一元正态分布函数的概率密度函数比值构造概率判断函数,建立数学模型,在默认阈值为α=0.5的前提下,对未知类型进行判断,并通过不同阈值下的正负谬误做误差估计,确定最优的阈值.以高钾类、铅钡类玻璃的风化、无风化四种数据为研究对象,利用概率判断函数对模型进行验证.结果表明,模型能准确的对待测的高钾类、铅钡类样本进行分类.在误差分析方面,通过改变阈值进行灵敏度分析,对于高钾类、铅钡类无风化问题,当阈值α=0.65时,误差最小.     

基于改进阈值的马田系统在医疗智能诊断中的应用

人工智能与医疗数据的融合可以加速疾病诊断过程,提高诊断精度,挖掘诊断过程关键指标,改善医疗工作流程.以医疗决策者偏好为前提,提出基于二次损失函数改进阈值的马田系统算法.通过正交表与信噪比对指标进行优化,降低模型复杂度;通过对阈值的改进,提高模型的灵敏度,满足医疗工作者的决策偏好.将此方法应用于UCI乳腺癌医学数据及三甲医院哮喘临床数据,并与其他改进阈值的马田系统算法及智能算法对比分析.结果表明,改进阈值的马田系统算法识别灵敏度高,简化诊断指标,训练耗时少,是一种更为有效的医疗智能诊断方法.     

具有非线性感染力的一类SIR传染病模型的阈值分析

文章针对一类SIR传染病模型进行了改进,考虑了非线性感染力对阈值的影响.主要分四种情形对非线性感染力下的传染病阈值进行了计算与分析.对结果分析可知,传染病的传播阈值与非线性感染力有着密切关系,同时,免疫率μ对传染病阈值λ_c也起着非常关键的决定性作用.     

基于四点插值细分小波的SAR图像去噪

针对SAR图像去噪过程中存在降低相干斑与保持有效细节这一矛盾,提出了一种基于四点插值细分小波的SAR图像去噪算法,该方法将小波和细分方法相融合,将四点插值细分规则应用到细分小波中,提出了图像去噪的新方法.该算法先用四点插值细分小波对原始图像进行分解,然后用Bayes自适应阈值及阈值函数对图像进行去噪,最后对去噪的小波系数进行重构,并通过等效视数、边缘保持指数等评价指标对去噪结果进行了评价.实验结果表明,算法的等效视数、边缘保持指数都有所提高,去噪效果得到了优化.     

一种改进灰狼优化算法研究及应用

针对灰狼算法易陷入局部最优、收敛精度不高、收敛速度慢等缺点,提出一种改进的灰狼算法.引入莱维飞行,扩大搜索范围,增强全局搜索能力,避免陷入局部最优;引入贪婪原理,提升种群优良性以提高算法收敛精度;引入自适应收敛因子,加快收敛速度;引入动态权重策略,制约全局搜索与局部搜索的相互影响.将改进算法与其他四种算法作对比,实验表明,改进算法在收敛速度与收敛精度上都有更好的性能.最后,应用于图像多阈值分割中,采用GWO-Otsu法可以克服传统Otsu法在多阈值分割时计算量大,实时性差的特点,不但能够取得最优解,且明显缩减计算时间.     

基于改进小波阈值去噪算法的心电信号处理及仿真

为更好地滤除心电信号处理过程中基线漂移、肌电以及工频干扰等噪声,提出了一种新阈值函数去噪算法.通过仿真确定了最佳的小波函数类型和分解层数;改进算法克服了传统的阈值函数在信号处理中存在不连续性或恒定偏差的问题;利用双曲线函数实现向原函数的快速逼近,避免了去噪后的波形失真和振荡现象的发生;具有更少的参数,调节方便,计算量小;注重对较小系数的处理,提高重构信号的精度.利用MIT-BIH心律失常数据库中的105号数据进行验证.算法能有效地滤除噪声干扰,相比于其他方法,其重构信号的信噪比和均方误差均有极大的改善,去噪效果更好.     

关于亚纯函数唯一性的一些结果

本文研究了涉及重值的具有四个公共小函数的亚纯函数的唯一性问题.利用关于小函数的Nevanlinna第二基本定理精简形式及处理小函数的有关方法,改进了有关结果.利用Gundersen处理公共值对的方法,对具有五个IM公共值对和四个IM公共小函数对的亚纯函数的情况作了进一步讨论,推广改进了相关结果.     

一种改进的随机Hough变换检测圆的方法

针对随机Hough变换检测圆准确率低的问题,提出了一种改进的随机Hough变换算法.算法利用原标准随机Hough变换的检测结果,确定出合适的阈值,将检测结果中平均误差超过此阈值的圆视为错检圆,并将这些圆上的点取出,重新进行随机Hough变换,直到正确检测出所有圆.模拟数据和真实图像的数值实验都表明改进算法的检测准确率明显提高.     

基于改进遗传算法的自动阈值图像分割方法

图像分割技术在图像分析和图像识别上具有重要意义.传统自适应遗传算法有可能使问题求解陷入局部最优解,而求得错误的图像分割阈值.为了得到最优的图像分割阈值,对交叉率和变异率公式进行了重构,使得交叉率和变异率在任何情况下都不为零.同时,以最大二维熵函数作为适应度函数,采用选择、交叉变异等遗传操作作搜索最优分割阈值.仿真实验表明,该方法可以有效地提高图像分割精度和计算速度.     

Limit Theorems for Risk Estimate in Models with Non-Gaussian Noise

The problem of constructing an estimate of a signal function from noisy observations, assuming that this function is uniformly Lipschitz regular, is considered. The thresholding of empirical wavelet coefficients is used to reduce the noise. As a rule, it is assumed that the noise distribution is Gaussian and the optimal parameters of thresholding are known for various classes of signal functions. In this paper a model of additive noise whose distribution belongs to a fairly wide class, is considered. The mean-square risk estimate of thresholding is analyzed. It is shown that under certain conditions, this estimate is strongly consistent and asymptotically normal.     

Bessel变换的一个下界

Bessel逆问题在物理、化学和工程学等诸多领域有重要应用.解决线性逆问题的传统方法不适合处理具有奇异性曲线边缘的二元函数.鉴于切波对这一类函数的最优表示能力,相关文献采用切波方法研究Bessel逆问题,构造了目标函数的切波域值估计器,得到了它在函数空间V中积分均方差收敛阶的上界.在此基础上利用统计理论给出其最小最大风险的一个下界,证明了在估计Bessel逆问题时此估计器是最优的.     

Wavelet Thresholding in Fixed Design Regression for Gaussian Random Fields

Journal of Fourier Analysis and Applications - In this paper, we consider block thresholding wavelet estimators of spatial regression functions on stationary Gaussian random fields observed over a...     

Asymptotically optimal wavelet thresholding in models with non-Gaussian noise distributions

The problem of nonparametric estimation of a signal function by thresholding the coefficients of its wavelet decomposition is considered. In models with various noise distributions, asymptotically optimal thresholds and orders of the loss functions are calculated on the basis of probabilities of errors in the calculation of wavelet coefficients.     

Optimally thresholded realized power variations for Lévy jump diffusion models

Thresholded Realized Power Variations (TPVs) are one of the most popular nonparametric estimators for general continuous-time processes with a wide range of applications. In spite of their popularity, a common drawback lies in the necessity of choosing a suitable threshold for the estimator, an issue which so far has mostly been addressed by heuristic selection methods. To address this important issue, we propose an objective selection method based on desirable optimality properties of the estimators. Concretely, we develop a well-posed optimization problem which, for a fixed sample size and time horizon, selects a threshold that minimizes the expected total number of jump misclassifications committed by the thresholding mechanism associated with these estimators. We analytically solve the optimization problem under mild regularity conditions on the density of the underlying jump distribution, allowing us to provide an explicit infill asymptotic characterization of the resulting optimal thresholding sequence at a fixed time horizon. The leading term of the optimal threshold sequence is shown to be proportional to Lévy’s modulus of continuity of the underlying Brownian motion, hence theoretically justifying and sharpening selection methods previously proposed in the literature based on power functions or multiple testing procedures. Furthermore, building on the aforementioned asymptotic characterization, we develop an estimation algorithm, which allows for a feasible implementation of the newfound optimal sequence. Simulations demonstrate the improved finite sample performance offered by optimal TPV estimators in comparison to other popular non-optimal alternatives.     

A new multi-threshold image segmentation approach using state transition algorithm

Thresholding plays an important role in image segmentation and image analysis. In this paper, the normalized histogram of an image is fitted by a linear combined normal distribution functions and each normal distribution function represents a class of pixels, whereas the parameters like the mean, the variance and the weights in the fitting function are undetermined. By transforming the fitting problem into a nonlinear and non-convex optimization problem, the state transition algorithm (STA) which is a new global optimization method is used to choose the optimal parameters of the fitting function. The effectiveness of proposed approach in multilevel thresholding problems is tested by several experimental results. By comparing with OTSU, particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE) algorithm, it has shown that STA has competitive performance in terms of both optimization results and thresholding segmentation.     

A nonparametric regression cross spectrum for multivariate time series

We consider dependence structures in multivariate time series that are characterized by deterministic trends. Results from spectral analysis for stationary processes are extended to deterministic trend functions. A regression cross covariance and spectrum are defined. Estimation of these quantities is based on wavelet thresholding. The method is illustrated by a simulated example and a three-dimensional time series consisting of ECG, blood pressure and cardiac stroke volume measurements.     

Chaos in discrete learning systems

Departing from the customary view of the sigmoid thresholding functions as smooth nonlinearities introduced into multilayer perceptron networks to enable continuously differentiable gradient descents toward optimal solutions minimizing some error norm, here a different more fundamental point of view is advanced: the intrinsic local dynamics of the network become that of the quadratic map of the chaos theory. This new insight aids understanding of important supervised learning algorithms such as the widely used Backpropagation scheme.     

Data compression on the sphere using multiscale radial basis function approximation

We propose two new approaches for efficiently compressing unstructured data defined on the unit sphere. Both approaches are based upon a meshfree multiscale representation of functions on the unit sphere. This multiscale representation employs compactly supported radial basis functions of different scales. The first approach is based on a simple thresholding strategy after the multiscale representation is computed. The second approach employs a dynamical discarding strategy, where small coefficients are already discarded during the computation of the approximate multiscale representation. We analyse the (additional) error which comes with either compression and provide numerical experiments using topographical data of the earth.     

The Discrete Wavelet Transform in S

Abstract

The theory of wavelets has recently undergone a period of rapid development. We introduce a software package called wavethresh that works within the statistical language S to perform one- and two-dimensional discrete wavelet transforms. The transforms and their inverses can be computed using any particular wavelet selected from a range of different families of wavelets. Pictures can be drawn of any of the one- or two-dimensional wavelets available in the package. The wavelet coefficients can be presented in a variety of ways to aid in the interpretation of data. The package's wavelet transform “engine” is written in C for speed and the object-oriented functionality of S makes wavethresh easy to use. We provide a tutorial introduction to wavelets and the wavethresh software. We also discuss how the software may be used to carry out nonlinear regression and image compression. In particular, thresholding of wavelet coefficients is a method for attempting to extract signal from noise and wavethresh includes functions to perform thresholding according to methods in the literature.