有色噪声的扩散过程模型

研究使用基于随机微分方程的扩散过程模型产生有色噪声.首先给出Markov扩散过程的平稳分布,该分布给出了扩散过程模型中的漂移系数、扩散系数和有色噪声概率密度分布之间的关系;选择扩散过程模型中的扩散系数为x的一次幂,其系数决定了所生成噪声的相关特性;数值算法使用Milstein高阶法.以三元独立积构造的G-分布雷达杂波为例进行仿真分析,验证所提出方法的准确性和有效性.     

基于随机非线性微分方程的振荡器相位噪声研究

根据振荡器电路的时变非线性特性 ,运用一种通用的相位噪声理论 ,通过对噪声源随机过程建模 ,求解具有严格数学意义的随机非线性微分方程 ,得到一个常数 c来描述时间抖动和频谱扩散。分别用基于随机非线性微分方程和线性时变的方法求解 ,结果表明线性时变得到的相位噪声频谱在基频附近分布的能量之和超过载波能量 ,在物理意义上有一定不足 ;而文中的相位噪声分析结果表明相位噪声只改变能量的分布并不能使能量显著增加 ,得到的结果为设计电路时减少相位噪声影响提供了思路。     

基于随机微分方程模型的股票预测

金融时间序列是复杂的动态系统,是非线性、混沌和时变的[1],影响金融市场的因素众多,它们之间的相互作用复杂多变。为了更合理的解释经济运行的规律,本文提出了随机微分方程模型,该模型利用进化算法拟合时间序列数据,在常微分方程基础上加入随机项,模拟系统的运作。     

相关噪声中随机信号的Robust因果Wiener滤波

本文研究了相关噪声中随机信号的Robust因果Wiener滤波问题,讨论了极大极小对策的鞍点解存在的条件及其特点,证明了在一定的条件约束下,对最不利功率谱密度(LFPSD's)构造的Wiener滤波器就是极大极小Robust Wiener滤波器。本文给出了求解LFPSD's的数值方法,并例举了计算机仿真结果。     

不同类型噪声作用下振荡器的相位噪声分析

相位噪声是振荡器最重要的性能指标.文中从描述振荡器的非线性微分方程出发,提出将噪声作为非线性微分方程的一项,通过建立随机非线性微分方程来分析振荡器的相位噪声,为振荡器的相位噪声提出了一种新的分析方法.用这种方法,在相同强度下,针对白噪声分别为加性和乘性的情形,分析其产生的相位噪声,得出了乘性噪声产生的相位噪声远大于加性噪声所产生的相位噪声的结论.     

一种低频有色噪声的自适应对消模型

文中讨论了使用自适应噪声对消抵制有色噪声的原理、特点,提出了在特定情况下 一种改进的对消模型及其原理,并结合ＬＭＳ（Ｌｅａｓｔ Ｍｅａｎ Ｓｑｕａｒｅｓ）和ＲＬＳ（Ｒｅｃｕｒｓｉｖｅ Ｌｅａｓｔ Ｓｑｕａｒｅｓ）滤波器的实现,给出了计算实例。     

不同类型噪声作用下的随机共振

在对随机共振研究中,当系统参数和信号一定时,主要讨论白噪声对共振特性的影响,但是在实际系统中存在的噪声并非理想白噪声。为了更真实的反应实际系统,本文针对双稳系统,分别讨论了限带白噪声、窄带白噪声、高频白噪声、高次谐波作用下的随机共振特性。信号设定的频率为0.01Hz,所加噪声为理想白噪声,其频率范围为0～2.5Hz。仿真结果表明,双稳系统发生随机共振不仅与系统参数、信号幅度、噪声强度有关,而且与噪声的频谱分布有关。本文的结论拓展了随机共振的研究范围,对其应用有一定实际意义。     

采用双通道卷积神经网络构建的随机脉冲噪声深度降噪模型

为提高对随机脉冲噪声(RVIN)图像的降噪效果,该文提出一种被称为双通道降噪卷积神经网络(D-DnCNN)的RVIN深度降噪模型。首先,提取多个不同阶对数差值排序(ROLD)统计值及1个边缘特征统计值构成描述图块中心像素点是否为RVIN噪声的噪声感知特征矢量。其次,利用预先训练好的深度置信网络(DBN)预测模型实现特征矢量到噪声标签的映射,完成对噪声图像中噪声点的检测。再次,在噪声检测标签的指示下采用Delaunay三角剖分插值算法快速修复噪声像素点从而获得初步复原图像。最后,将初步复原图像作为参考图像与噪声图像联接(concatenate)后输入D-DnCNN模型后获得残差图像,将参考图像减去残差图像即可获得降噪后图像。实验数据表明：D-DnCNN模型在各个噪声比例下的降噪效果均显著超过了现有的经典开关型RVIN降噪算法,与普通的单通道RVIN深度降噪模型相比也有较大幅度提升。     

极细沟道NMOSFET中的大幅度随机电报信号噪声

研究了极细沟道NMOSFET器件的随机电报信号噪声(RTS)的特征.首次在室温下观测到了大幅度(大于60%)的RTS,发现当器件工作在弱反型区时,RTS幅度基本与温度和栅压无关.对RTS的动力学机制的分析及数值模拟表明,载流子数涨落与迁移率涨落引起的RTS的幅度随着沟道宽度的减小而增加,当沟道宽度减小至40nm以下时,由荷电陷阱对沟道载流子散射而产生的迁移率涨落对细沟道中RTS幅度的影响起主导作用.     

噪声密度不敏感的随机采样椒盐噪声滤波算法

中值滤波系列算法在处理被不同密度椒盐噪声污染的细节图像和平坦图像时,降噪性能不一致。本文借鉴开关中值滤波和压缩感知的思想,提出了随机采样滤波算法去除椒盐噪声。算法以噪声检测为基础,将被椒盐噪声污染的图像分为疑似噪声像素和信号像素,随机采样仅对信号像素采样。然后,利用正交匹配追踪算法重构出被污染前的图像,替代了中值滤波对噪声像素的估计。由于随机采样滤波基于压缩感知理论,对稀疏信号的重构具有最少测量次数的条件,因此随机采样点的数量具有一定的浮动空间,表现为对噪声密度不敏感。以被不同噪声密度污染图像的纹理、平坦局部区域进行验证,实验表明,当噪声密度在一定范围内变化时,算法可以实现对噪声密度不敏感。在高密度噪声污染的情况下,相较于中值滤波系列算法,随机采样滤波算法具有更好的细节保留能力和滤波能力。对标准测试图像进行了全局滤波,不同噪声密度具有一致的滤波效果,与自适应滤波算法相比,随机采样滤波算法在处理包含密集边缘特征的区域时更具备优势。      

Stochastic Differential Equations for Modeling of High Maneuvering Target Tracking

In this paper, we propose a new adaptive single model to track a maneuvering target with abrupt accelerations. We utilize the stochastic differential equation to model acceleration of a maneuvering target with stochastic volatility (SV). We assume the generalized autoregressive conditional heteroscedasticity (GARCH) process as the model for the tracking procedure of the SV. In the proposed scheme, to track a high maneuvering target, we modify the Kalman filtering by introducing a new GARCH model for estimating SV. The proposed tracking algorithm operates in both the non‐maneuvering and maneuvering modes, and, unlike the traditional decision‐based model, the maneuver detection procedure is eliminated. Furthermore, we stress that the improved performance using the GARCH acceleration model is due to properties inherent in GARCH modeling itself that comply with maneuvering target trajectory. Moreover, the computational complexity of this model is more efficient than that of traditional methods. Finally, the effectiveness and capabilities of our proposed strategy are demonstrated and validated through Monte Carlo simulation studies.     

基于随机微分的射频噪声干扰技术研究

根据随机微分与射频噪声干扰信号处理的内在联系,将随机微分引入到雷达噪声干扰信号处理领域,对射频噪声干扰信号进行了系统地分析。首先建立了射频噪声干扰信号通过雷达中频滤波器后所满足的福克尔一普朗克方程,然后利用群移傅立叶变换（Motion—Group Fourier Transform,MGFT）将此偏微分方程化成了齐次线性微分方程组,最后得到了射频噪声干扰信号通过雷达中频滤波器后的概率密度函数。     

基于随机微分的相位噪声统计特性

根据随机微分与噪声信号处理的内在联系,对相位噪声信号进行了系统的分析。建立了相位噪声通过滤波器后所满足的福克尔-普朗克方程,利用群移傅里叶变换(MGFT)给出了方程的解,得到了相位噪声幅度和相位的联合概率密度函数。     

Using Stochastic Differential Equation for Verification of Noise in Analog/RF Circuits

Today’s analog/RF design and verification face significant challenges due to circuit complexity, process variations and short market windows. In particular, the influence of technology parameters on circuits, and the issues related to noise modeling and verification still remain a priority for many applications. Noise could be due to unwanted interaction between the circuit elements or it could be inherited from the circuit elements. In addition, manufacturing disparity influence the characteristic behavior of the manufactured circuits. In this paper, we propose a methodology for modeling and verification of analog/RF designs in the presence of noise and process variations. Our approach is based on modeling the designs using stochastic differential equations (SDE) that will allow us to incorporate the statistical nature of noise. We also integrate the device variation due to 0.18μ m fabrication process in an SDE based simulation framework for monitoring properties of interest in order to quickly detect errors. Our approach is illustrated on nonlinear Tunnel-Diode and a Colpitts oscillator circuits.     

Analysis of Random Mobility Models with Partial Differential Equations

In this paper, we revisit two classes of mobility models which are widely used to represent users' mobility in wireless networks: random waypoint (RWP) and random direction (RD). For both models, we obtain systems of partial differential equations which describe the evolution of the users' distribution. For the RD model, we show how the equations can be solved analytically both in the stationary and transient regime, adopting standard mathematical techniques. Our main contributions are 1) simple expressions which relate the transient duration to the model parameters and 2) the definition of a generalized random direction model whose stationary distribution of mobiles in the physical space corresponds to an assigned distribution.     

一种求解随机期望值模型的改进差分进化算法

针对随机期望值模型的特点,将随机模拟技术和松弛选择策略相结合,提出了一种改进的差分进化算法.并将该算法用于求解报童问题等模型,数值结果表明了算法的有效性和可行性.     

Compact Noise Models for MOSFETs

A physical understanding of both intrinsic and extrinsic noise mechanisms in a MOSFET is developed. Intrinsic noise mechanisms fundamental to device operation include channel thermal noise, induced gate noise, and induced substrate noise. While the effect of channel thermal noise is observable at zero drain-to-source voltage, the induced gate and substrate noise do not manifest themselves under these conditions. However, the attendant fluctuations in the channel charge are observable by the passage of electric current through the device. Extrinsic noise mechanisms manifested due to structural evolution of the MOSFET include the distributed gate resistance noise, distributed substrate resistance noise, bulk charge effects, substrate current supershot noise, gate current noise, excess channel noise, and$hbox1/f$noise. Where available, compact noise models covering these noise mechanisms are explained. Also, where possible, methods of suppression of these mechanisms are highlighted. A survey of current public domain MOS models is presented, and a lack of comprehensive coverage of noise models is noted. Open areas of MOSFET noise research in the sub-hundred-nanometer regime are also highlighted. With suitable adaptation, noise concepts elucidated in the context of MOS transistors have a much wider applicability to the operation of HEMTs, JFETs, MESFETs, and other field-effect devices.     

Differential Equations for Trigger and Multivibrator

Journal of Communications Technology and Electronics - Differential equations are derived for a symmetric transistor trigger and multivibrator. It is shown that the equations can be derived using...     

基于高阶偏微分方程的非线性去噪算法

提出了一种将小波分析与高阶偏微分方程相结合的非线性方法。这种方法利用小波分析的时频局部性和高阶偏微分方程的平缓图像轮廓的特性对图像进行处理,不仅很好地抑制了噪声,而且使图像保留了尽可能多的细节,看起来更平滑自然。实验结果表明了该方法的有效性。     

Stochastic Models for Multistage Cell Classification Systems

Probabilistic models for multistage cell classification systems are described. A simple finite Markov chain models classification events which occur as a cell passes through the system. The state space consists of various identities assigned to the cell, including true celi type and identities assigned by classifiers. Effects of throughput rate, data buffer capacity, and classifier processing rate on system performance are predicted by another model composed of a network of single server queues. Markov and queue models are interrelated in that classification events at one processor (modeled by the Markov chain) govern arrival rates of other processors. In turn, the queue model predicts the probability that a cell wili be missed due to fmite data buffer capacity. The miss event is modeled by the Markov chain as a possible classification outcome. Application of the models is illustrated for a multistage gynecologic flow prescreening system with slit-scan processing in the first stage and two dimensional image processing in the second. Results predict system sensitivity as a function of first stage false alann rate and abnormal cell occurrence rate.