Stochastic properties of tumor growth with coupling between non-Gaussian and Gaussian noise terms

Dynamical behavior of a tumor-growth model with coupling between non-Gaussian and Gaussian noise terms is investigated. The departure from the Gaussian noise can not only reduce the probability of tumor cells in the active state, induce the minimum of the average tumor-cell population to move toward a smaller non-Gaussian noise, but also decrease the mean first-passage time. The increase of white-noise intensity can increase the tumor-cell population and shorten the mean first-passage time, while the coupling strength between noise terms has opposite effects, and the noise correlation time has a very small effect.

Upper bound for the time derivative of entropy for a stochastic dynamical system with double singularities driven by non-Gaussian noise

A stochastic dynamical system with double singularities driven by non-Gaussian noise is investigated. The Fokker Plank equation of the system is obtained through the path-integral approach and the method of transformation. Based on the definition of Shannon＇s information entropy and the Schwartz inequality principle, the upper bound for the time derivative of entropy is calculated both in the absence and in the presence of non-equilibrium constraint. The present calculations can be used to interpret the effects of the system dissipative parameter, the system singularity strength parameter, the noise correlation time and the noise deviation parameter on the upper bound.

Stationary properties of a single-mode laser system with non-Gaussian and Gaussian noise

A single-mode laser system with non-Gaussian and Gaussian noise is investigated. The stationary mean value and the normalized variance of the laser intensity are numerically calculated under the condition that the stationary probability distribution function （SPDF） is derived. The SPDF as a function of the laser intensity exhibits a maximum, The maximum becomes smaller with the increase of the correlation intensity or the non-Gaussian parameter, where the later is a measure of the deviation from the Gaussian characteristic. The maximum becomes larger as the correlation time increases. The laser intensity stationary mean value decreases with the increase of the correlation intensity or the non-Gaussian parameter while increases with the correlation time increasing. The laser intensity normalized variance increases with the increase of the correlation intensity or the non-Gaussian parameter while decreases as the correlation time increases.

Ballistic diffusion induced by non-Gaussian noise

In this letter,we have analyzed the diffusive behavior of a Brownian particle subject to both internal Gaussian thermal and external non-Gaussian noise sources.We discuss two time correlation functions C（t） of the non-Gaussian stochastic process,and find that they depend on the parameter q,indicating the departure of the non-Gaussian noise from Gaussian behavior：for q ≤ 1,C（t） is fitted very well by the first-order exponentially decaying curve and approaches zero in the longtime limit,whereas for q 〉 1,C（t） can be approximated by a second-order exponentially decaying function and converges to a non-zero constant.Due to the properties of C（t）,the particle exhibits a normal diffusion for q ≤ 1,while for q 〉 1 the non-Gaussian noise induces a ballistic diffusion,i.e.,the long-time mean square displacement of the free particle reads 〈[x（t）-]2∝t2.

Effects of non-Gaussian noise on a calcium oscillation system

We investigate the effects of the non-Gaussian colored noise on a calcium oscillation system using stochastic simulation methods. It is found that the reciprocal coefficient of variance R has a maximum （Rmax） with increasing noise intensity Q. The non-Gaussian noise parameter q has an important effect on the system. For some values of q （e.g., q = 0.9, q = 1.0）, R has a maximum with increasing correlation time t. Non-Gaussian noise induced spikes are more regular than Gaussian noise induced spikes when q is small and Q has large values. The R has a maximum with increasing q. Therefore, non-Gaussian noise could play more effective roles in the calcium oscillation system.

ELEMENTARY BIFURCATIONS FOR A SIMPLE DYNAMICAL SYSTEM UNDER NON-GAUSSIAN LVY NOISES

Nonlinear dynamical systems are sometimes under the influence of random fluctuations. It is desirable to examine possible bifurcations for stochastic dynamical systems when a parameter varies.A computational analysis is conducted to investigate bifurcations of a simple dynamical system under non-Gaussian a-stable Levy motions, by examining the changes in stationary probability density functions for the solution orbits of this stochastic system. The stationary probability density functions are obtained by solving a nonlocal Fokker-Planck equation numerically. This allows numerically investigating phenomenological bifurcation, or P-bifurcation, for stochastic differential equations with non-Gaussian Levy noises.

Effects of non-Gaussian noise on the dynamical properties of a logistic system

The dynamical properties of a tumor cell growth system described by the logistic system with coupling between non- Gaussian and Gaussian noise terms are investigated. The effects of the nonextensive index q on the stationary properties and the transient properties are discussed, respectively. The results show that the nonextensive index q can induce the tumor cell numbers to decrease greatly in the case of q 〉 1. Moreover, the switch from the steady stable state to the extinct state is speeded up as the increases of q, and the tumor cell numbers can be more obviously restrained for a large value of q. The numerical results are found to be in basic agreement with the theoretical predictions.

Performance Analysis of Outage Probability and Error Rate of Square M-QAM in Mobile Wireless Communication Systems over Generalized α-μ Fading Channels with Non-Gaussian Noise

This paper derives new and exact closed-form expressions for the average symbol error rate(SER) of square M-ary quadrature amplitude modulation(M-QAM) in wireless communication systems over theα-μfading channels subject to an additive non-Gaussian noise. The obtained expressions take into account static and mobile wireless receivers. In addition, a closed-form expression for the outage probability in mobile networks is obtained. Please note that all derived expressions in this paper a valid for integer and non-integer values of the fading parameters. Analytical results are presented to study the impact of noise shaping parameter, severity of fading, and mobility on the average SER. Monte-Carlo simulations results are also provided to validate the accuracy of the analytical results.

Generation of Non-Gaussian Random Vibration Excitation Signal for Reliability Enhancement Test

During environment testing, the time histories of some dynamic environments follow non-Gaussian distribution. It is always assumed that the random vibration simulated follows Gaussian distribution, because the traditional digital random vibration control system can only supply the random vibration excitation signal of Gaussian. Yo simulate the real environment of product, a method is developed in this paper that can generate non-Gaussian random signal with specified power spectrum density （PSD）, skewness and kurtosis by shot noise. In this way, non-Gaussian random vibration can be produced on traditional electrodynamic shaker. It solves the problems of spectral valley and energy shortage in low frequency on omni-axis shaker. At last, the wavelet is used to analyze the non-Gaussian signal

Analytical Fitting Functions of Finite Sample Discrete Entropies of White Gaussian Noise

In order to find the convergence rate of finite sample discrete entropies of a white Gaussian noise(WGN), Brown entropy algorithm is numerically tested.With the increase of sample size, the curves of these finite sample discrete entropies are asymptotically close to their theoretical values.The confidence intervals of the sample Brown entropy are narrower than those of the sample discrete entropy calculated from its differential entropy, which is valid only in the case of a small sample size of WGN. The differences between sample Brown entropies and their theoretical values are fitted by two rational functions exactly, and the revised Brown entropies are more efficient. The application to the prediction of wind speed indicates that the variances of resampled time series increase almost exponentially with the increase of resampling period.

Improved Non-Local Means Algorithm for Image Denoising

Image denoising technology is one of the forelands in the field of computer graphic and computer vision. Non-local means method is one of the great performing methods which arouse tremendous research. In this paper, an improved weighted non-local means algorithm for image denoising is proposed. The non-local means denoising method replaces each pixel by the weighted average of pixels with the surrounding neighborhoods. The proposed method evaluates on testing images with various levels noise. Experimental results show that the algorithm improves the denoising performance.

A MODEL FOR WHITE NOISE ANALYSIS IN P-ADIC NUMBER FIELDS

We propose a simple model for white noise analysis in the p-adic case based on the p-adic analogue of the space L(2)(R(infinity), d mu).

NEW APPROACH TO HARMONIC RETRIEVAL IN NOISE

This paper addresses the harmonic retrieval problem in non-Gaussian ARMA noise. A hybrid ESPRIT approach using second-and third-order statistics is proposed. First, third-order statistics are used to identify the AR part of the non-Gaussian noise process, then the noisy measurements are filtered by AR polynomial, finally, the harmonic signal parameters are estimated. Simulation examples show the effectiveness and high resolution of the new approach.

A variational formulation for physical noised image segmentation

Image segmentation is a hot topic in image science. In this paper we present a new variational segmentation model based on the theory of Mumford-Shah model. The aim of our model is to divide noised image, according to a certain criterion, into homogeneous and smooth regions that should correspond to structural units in the scene or objects of interest. The proposed region-based model uses total variation as a regularization term, and different fidelity term can be used for image segmentation in the cases of physical noise, such as Gaussian, Poisson and multiplicative speckle noise. Our model consists of five weighted terms, two of them are responsible for image denoising based on fidelity term and total variation term, the others assure that the three conditions of adherence to the data, smoothing, and discontinuity detection are met at once. We also develop a primal-dual hybrid gradient algorithm for our model. Numerical results on various synthetic and real images are provided to compare our method with others, these results show that our proposed model and algorithms are effective.

基于MCC的后端优化方法及其在ORB-SLAM2中的应用

自主定位和环境感知是机器人实现复杂任务的前提,视觉同时定位与建图(VSLAM)技术是有效解决方案。VSLAM中,传感器误差和环境噪声等影响定位和建图精度,造成累计误差。后端优化是VSLAM中消除累计误差的关键环节,现有后端优化算法通常以高斯噪声为前提,属于MSE标准下的后端算法。然而,由于图像的非凸特性和真实场景中产生的非高斯噪声,高斯噪声假设并不总成立,导致现有算法在真实场景中运行时性能下降。鉴于此,利用最大互相关熵(MCC)标准在处理非高斯噪声方面的优势,提出了一种基于MCC标准的后端优化方法,并将所提出方法应用于ORB-SLAM2框架,以测试所提出的方法在定位和建图精度方面的性能。最后,在EuRoC和KITTI公开数据集上进行实验,结果表明,无论是室内还是室外,所提方法在大部分序列中性能优于原ORB-SLAM2中基于Huber的后端优化算法以及基于Cauchy的后端优化算法。

非高斯噪声下基于CKMC-CKF的发电机动态状态估计

准确的动态状态估计对于电力系统实时运行状态的监测至关重要。针对同步发电机中非高斯噪声导致状态估计器性能下降的实际情况,提出一种基于基于柯西核最大相关熵(Cauchy kernel maximum correntropy, CKMC)的容积卡尔曼滤波(cubature KF,CKF)算法(简称CKMC-CKF算法)。首先,建立CKMC目标函数,采用2种加权局部相似度来更新噪声协方差矩阵,从而降低不良数据的权重;其次,利用线性化回归方程统一目标函数中的状态和测量误差,并通过定点迭代法获得最佳估计状态。最后,以IEEE 39节点系统为算例分析验证所提出方法的有效性。与CKF和最大熵CKF相比,CKMC-CKF在非高斯噪声环境下具有更好的估计精度和更强的鲁棒性。

Multiple enhancements of rate oscillations by non-Gaussian noise in NO reduction with CO on Pt(100)

The effect of the non-Gaussian colored noise(NGN) on the dynamics of nonlinear systems has attracted increasing attention.This work numerically studied the effect of a particular kind of the NGN,mainly in terms of its departure from Gaussian noise,on the rate oscillations(RO) in the catalytic reduction of NO with CO on Pt(100) surfaces.It was found that power spectrum density changes non-monotonically and the signal-to-noise ratio shows several peaks with increasing departure,demonstrating the presence of"departure-induced multiple resonance".Since the departure of the NGN determines the probability distribution and hence denotes the type of the noise,the phenomenon of"departure-induced multiple resonance"not only gives multiple enhancements of the RO by virtue of the departure of the NGN,but also implies that,besides Gaussian noise,various other types of noise may enhance the RO in the surface catalytic reaction systems.

基于AR-HMM在线能量调整的语音增强方法

针对单通道语音增强技术对非平稳噪声的跟踪不准确、噪声抑制效果较差的问题,本文提出一种基于在线能量调整的语音增强方法.该方法以归一化临界带能量为特征,采用高斯混合模型对背景噪声进行分类,利用对应类型噪声的自回归隐马尔可夫模型(Auto-Regressive Hidden Markov Model,AR-HMM)和纯净语音的AR-HMM,在最小均方误差准则下估计语音和噪声的功率谱.考虑到非平稳环境中训练集和测试集的差异性,需在线调整语音模型和噪声模型中的能量,语音模型的能量调整采用迭代的期望最大化算法;噪声模型的能量调整则利用的是模型训练过程中的能量重估方法,并以最小值控制的递归平均算法确定噪声能量调整的初始值.在ITU-T G.160标准下对算法进行性能测试,测试结果表明,本文方法对非平稳噪声的跟踪效果较好,对噪声衰减量较大,收敛时间较短.

改进自适应ADMCC-HCKF算法及在SINS/CNS/GNSS中的应用

针对传统容积卡尔曼滤波器(CKF)在非高斯噪声下滤波精度下降以及传统最大相关熵(MCC)算法收敛速度较慢的问题,提出了一种改进的自适应相关熵高阶容积卡尔曼滤波(ADMCC-HCKF)算法。该方法依据MCC迭代过程的误差变化自适应调整核宽大小,核宽能够改变核参数对输入数据的敏感性,从而提高算法收敛速度及对非高斯噪声的处理能力。基于非高斯噪声环境,搭建SINS/CNS/GNSS组合导航实验,研究结果表明,改进的ADMCC-HCKF算法相比传统HCKF和基于常规MCC的HCKF算法具有更强的鲁棒性,在降噪性能及对非高斯噪声的适应性角度均有所提升的同时,滤波精度较HCKF算法提高了9.63%。

Non-Gaussian noise-enhanced stochastic rate oscillations in CO oxidation on nanometer-sized Pd particles

The effect of non-Gaussian colored noise(NGN),mainly its departure q from the Gaussian noise,on the optimal ISRO of CO oxidation on nanometer-sized Pd particles was studied.It was found that the ISRO in the absence of external noise can still be enhanced when the NGN is applied.Specifically,the ISRO varies with changing q and becomes more regular at an appropriate q value,and when q is optimal,the ISRO becomes the most regular.Because the departure q from the Gaussian noise determines the probability distribution function and hence may denote the types of noise,this result shows that different types of external noise can enhance the ISRO of CO oxidation,and non-Gaussian noise may enhance the ISRO more greatly than the Gaussian noise.Therefore,non-Gaussian noise could play more effective roles in the catalytic process of CO oxidation on nanometer-sized Pd particles.