Blind restoration of linearly degraded discrete signals by Gibbssampling

This paper addresses the problem of simultaneous parameter estimation and restoration of discrete-valued signals that are blurred by an unknown FIR filter and contaminated by additive Gaussian white noise with unknown variance. Assuming that the signals are stationary Markov chains with known state space but unknown initial and transition probabilities, Bayesian inference of all unknown quantities is made from the blurred and noisy observations. A Monte Carlo procedure, called the Gibbs sampler, is employed to calculate the Bayesian estimates. Simulation results are presented to demonstrate the effectiveness of the method     

Hybrid fullband cellular automaton/Monte Carlo approach for fastsimulation of charge transport in semiconductors

We present a fullband cellular automaton (CA) code for simulation of electron and hole transport in Si and GaAs. In this implementation, the entire Brillouin zone is discretized using a nonuniform mesh in k-space, and a transition table is generated between all initial and final states on the mesh, greatly simplifying the final state selection of the conventional Monte Carlo algorithm. This method allows for fully anisotropic scattering rates within the fullband scheme, at the cost of increased memory requirements for the transition table itself. Good agreement is obtained between the CA model and previously reported results for the velocity-field characteristics and high field distribution function, which illustrate the potential accuracy of the technique. A hybrid CA/Monte Carlo algorithm is introduced which helps alleviate the memory problems of the CA method while preserving the speed up and accuracy     

Efficient simulation of digital transmission over WSSUS channels

This paper proposes an efficient Monte Carlo method which reduces computation for digital communication simulations over a multipath Rayleigh fading, wide-sense-stationary uncorrelated-scattering (WSSUS) channel. An equivalent discrete-time channel representation, which can be realized by a FIR filter with time-variant tap gains, is employed. In the proposed method, the tap gains are generated from a linear transformation of a set of orthogonal zero-mean complex Gaussian random processes. By the central limit theorem, each random process is approximated by summing a finite number of randomly generated phasors (Monte Carlo approximation). When compared with the tap gain generation method, which approximates the physical channel by the Monte Carlo approximation first and then generates the tap gain values, the proposed method demonstrates a considerable reduction in the required simulation time as well as improved accuracy under similar conditions     

Evaluating the KTI Monte Carlo Method for System Reliability Calculations

This paper describes and analyses the Kumamoto, Tanaka, Inoue (KTI) Monte Carlo method for estimating the reliability R of an s-coherent system by bracketing it between deterministic lower and upper bounds, and then positioning R between the bounds as a weighted average of the structure functions of Monte Carlo generated k-vectors. The procedure is illustrated with both a 2-out-of-3 system and a larger example. Some known alternatives are discussed: bracketing R as in KTI, but without Monte Carlo; exact methods; the Esary-Proschan min-cut lower bounds; and Monte Carlo that samples component reliabilities instead of successes or failures. Although KTI has interesting set-theoretic features and is apparently both variance reducing and s-unbiased, each of the alternatives mentioned above is more useful than KTI because it is easier to develop and uses existing general purpose software.     

The effect of potential obstacles on charge transfer in imagesensors

Numerical methods are presented to investigate charge transfer in charge-coupled devices (CCDs) when potential barriers or wells occur. A Monte Carlo simulation of electron thermal diffusion and field-aided drift is used to determine the time scale for charge transfer. The Monte Carlo approach is useful for exploring new problems, but it requires considerable amounts of computer time. A quicker technique, that of the mean first passage time, is introduced. This method reduces the solution of the carrier continuity equation for charge transfer to the evaluation of a double integral that yields the characteristic time τ for e^-tτ/. This provides the leading or dominant time dependence of the carrier continuity equation's solution. Numerical examples are presented to show how τ varies with the size and location of the potential obstacle. The mean first passage time approach permits rapid estimates of the effects of potential obstacles on charge transfer in CCD's. These estimates are in excellent agreement with the results of the Monte Carlo simulations     

基于蒙特卡罗法的虚拟仪器测量不确定度应用

本文把蒙特卡罗法应用于虚拟仪器测量不确定度评定中,对今后虚拟仪器精度领域的研究是一个借鉴。对虚拟仪器的测量不确定度进行评定和验证是个极其重要的问题,采用蒙特卡罗法重要思想就是,确定一个统计量,其数学期望正好等于所要求的值,样本方差等于理论方差的估计值。     

Testing Goodness-of-Fit for the Singly Truncated Normal Distribution Using the Kolmogorov-Smirnov Statistic

This paper proposes the singly truncated normal distribution as a model for estimating radiance measurements from satellite-borne infrared sensors. These measurements are made in order to estimate sea-surface temperatures which can be related to radiances. Maximum-likelihood estimation is used to provide estimates for the unknown parameters. In particular, a procedure is described for estimating clear radiances in the presence of clouds and the Kolmogorov-Smirnov statistic is used to test goodness-of-fit of the measurements to the singly truncated normal distribution. Tables of quantile values of the Kolmogorov-Smirnov statistic for several values of the truncation point are generated from Monte Carlo experiments. Finally a numerical example using satellite data is presented to illustrate the application of the procedures.     

双轴向列相Fréedericksz转变的Monte Carlo模拟

通过Monte Carlo方法研究了在磁场诱导下的双轴向列相液晶的Fréedericksz转变,在模拟的过程中采用了London色散力近似。考虑3种不同的排列方式,通过改变3个分子轴的磁化率和磁场方向,讨论了9种不同的Fréedericksz转变并分别给出了相应的阈值磁场,通过计算机的模拟实现了短轴u和v的转变,长轴w和短轴u的转变以及长轴w和短轴v的转变。模拟结果表明,在给定假设的情况下满足单一弹性常数近似。研究结果对发展双轴液晶显示器件有一定的理论指导意义。     

Monte Carlo estimation of hot carrier noise at millimeter- and submillimeter-wave frequencies

This paper deals with the use of Monte Carlo experiments for investigating noise phenomena associated with hot carrier transport in semiconductors. In the first part of the paper procedures and problems associated with the design and interpretation of such experiments are discussed. The use of a Monte Carlo experiment is then demonstrated by estimating the velocity fluctuation spectrum of electrons in GaAs. Significant new results and insights are obtained. In particular, two new Spectral peaks are discovered and explained in terms of underlying physical processes, certain simple intuitive assumptions often made in noise theory are shown to be unjustified, and a criterion for the minimum flight time needed for estimates of "conventional" diffusion coefficients is obtained.     

Efficient Evaluation of System Reliability by Monte Carlo Method

This paper presents a new Monte Carlo method to estimate the reliability of a large complex system represented by a reliability block diagram or by a fault tree. Two binary functions are introduced; one dominates the system structure function and the other is dominated by the structure function. These functions can be constructed easily by using part of path sets and cut sets of the system. Through the use of these binary functions, two variance-reducing techniques (control variate and importance sampling) are applied to the Monte Carlo evaluation of the system reliability. We prove that the new Monte Carlo method gives a reliability estimate with a smaller variance than that of the crude Monte Carlo method.     

Nonlinear image recovery with half-quadratic regularization

One popular method for the recovery of an ideal intensity image from corrupted or indirect measurements is regularization: minimize an objective function that enforces a roughness penalty in addition to coherence with the data. Linear estimates are relatively easy to compute but generally introduce systematic errors; for example, they are incapable of recovering discontinuities and other important image attributes. In contrast, nonlinear estimates are more accurate but are often far less accessible. This is particularly true when the objective function is nonconvex, and the distribution of each data component depends on many image components through a linear operator with broad support. Our approach is based on an auxiliary array and an extended objective function in which the original variables appear quadratically and the auxiliary variables are decoupled. Minimizing over the auxiliary array alone yields the original function so that the original image estimate can be obtained by joint minimization. This can be done efficiently by Monte Carlo methods, for example by FFT-based annealing using a Markov chain that alternates between (global) transitions from one array to the other. Experiments are reported in optical astronomy, with space telescope data, and computed tomography.     

An Improved Importance Sampling Method for Digital Communication System Simulations

The use of importance sampling techniques can substantially reduce the variance of probability of error estimates obtained by Monte Carlo type simulations of digital communication systems. However, the advantages of importance sampling diminish as the memory length of the system becomes greater. This paper presents a modified importance sampling method which achieves substantial variance reduction for systems with memory.     

基于蒙特卡洛方法的线性混合效应状态空间模型

针对线性混合效应状态空间模型中的状态估计问题,提出了一种新的统计推断方法,在假设总体参数已知及个体随机效应未知的情况下,通过卡尔曼滤波算法与序贯蒙特卡洛算法的结合,实现了对模型中状态的估计。最终在实际模型产生的模拟数据的基础上,通过文中所提算法与卡尔曼滤波算法的实例比较,验证了该方法的有效性。     

Design metrics for gate oxide leakage characterisation in nano-CMOS transistors

In this paper, we propose a set of novel metrics, of primary interest to modelling and design engineers, for characterising the impact of gate oxide tunnelling current in nanoscale CMOS (nano-CMOS) devices. We concentrate on three distinct quantities: (i) steady-state on current (I_ON ); (ii) steady-state off current (I_OFF ); and (iii) effective tunnelling capacitance during transitions (C ^t _eff ). We define C ^t _eff as the change in tunnelling current with respect to the rate of change of input voltage, which represents the capacitive load of the transistor due to tunnelling. This concisely encapsulates both qualitative as well as quantitative information about the swing in tunnelling current during state transitions, while simultaneously accounting for the transition rate. We also investigate, via Monte Carlo simulations, the effect of process and design variations on the metrics. To the best of our knowledge, this is the first ever work that quantifies gate leakage during state transitions through the use of C ^t _eff      

On the theory of importance sampling applied to the analysis ofdetection systems

Detection systems are designed to operate with optimal or nearly optimal probability of a wrong decision. Analytical solutions of the performance of these systems have been very difficult to obtain. Monte Carlo simulations are often the most tractable method of estimating performance. However, in systems with small probability of error, this technique requires very large amounts of computer time. A technique known as importance sampling substantially reduces the number of simulation trials needed, for a given accuracy, over the standard Monte Carlo method. The theory and application of the importance sampling method in Monte Carlo simulation is considered in a signal detection context. A general method of applying this technique to the optimal detection problem is given. Results show that in cases examined, the gain is approximately proportional to the inverse of the error probability. Applications of the proposed method are not limited to optimum detection systems; analysis, leading to a measure of the gain in using this biasing scheme, shows that in all optimal systems considered, less than 100 trials is needed to achieve estimates with 45% confidence, even for extremely small error probabilities     

Estimation in a random censoring model with incomplete information:exponential lifetime distribution

Analysis of methods and simulation results for estimating the exponential mean lifetime in a random-censoring model with incomplete information are presented. The instant of an item's failure is observed if it occurs before a randomly chosen inspection time and the failure is signaled. Otherwise, the experiment is terminated at the instant of inspection during which the true state of the item is discovered. The maximum-likelihood method (MLM) is used to obtain point and interval estimates for item mean lifetime, for the exponential model. It is demonstrated, using Monte Carlo simulation, that the MLM provides positively biased estimates for the mean lifetime and that the large-sample approximation to the log-likelihood ratio produces accurate confidence intervals. The quality of the estimates is slightly influenced by the value of the probability of failure to signal. Properties of the Fisher information in the censored sample are investigated theoretically and numerically     

Inference Based on Type-II Hybrid Censored Data From a Weibull Distribution

A hybrid censoring scheme is a mixture of type-I and type-II censoring schemes. This article presents the statistical inferences on Weibull parameters when the data are type-II hybrid censored. The maximum likelihood estimators, and the approximate maximum likelihood estimators are developed for estimating the unknown parameters. Asymptotic distributions of the maximum likelihood estimators are used to construct approximate confidence intervals. Bayes estimates, and the corresponding highest posterior density credible intervals of the unknown parameters, are obtained using suitable priors on the unknown parameters, and by using Markov chain Monte Carlo techniques. The method of obtaining the optimum censoring scheme based on the maximum information measure is also developed. We perform Monte Carlo simulations to compare the performances of the different methods, and we analyse one data set for illustrative purposes.     

一种数据驱动的工业报警自适应阈值预测方法

化工过程常常工作在多个稳定状态。传统的报警阈值只是针对单个模式而设定,当变量从一个稳定状态到另一个稳定状态时,会产生误报警和漏报警,于是提出了一种报警阈值自适应预测方法。首先,通过历史数据得到各个阶段的带宽系数和贝叶斯估计的样本信息。其次,为了更新模型参数,在过渡过程采用基于蒙特卡罗方法的贝叶斯参数参数估计方法,利用后验分布函数的均值和方差,并在稳定过程采用递推迭代公式更新均值和方差。针对整个过程得到自适应的报警阈值,以此减小产生误报警和漏报警的数量。最后,通过一个工业实例数据验证了方法的有效性。     

Multimode power modeling and maximum-likelihood estimation

It is known that circuits exhibit multiple modes of power consumption due to various factors such as the presence of many feedback (or sequential) elements, RAM, large size, etc. Previous power-estimation techniques have largely ignored this fact. For example, Monte Carlo simulation-based power estimators tend to produce estimates for the average power consumption that corresponds only to the most probable power mode of the circuit. This can be a cause for trouble later in the design step. The aim of this paper is twofold. First, an algorithm is proposed that estimates the total number of power modes of a circuit based on simulated data. This is then followed by a maximum-likelihood estimation procedure that produces the average values of the power modes along with their probabilities of occurrence. Theoretical ideas are supported by experimental results for ISCAS '85 benchmark circuits and a large industrial circuit. The proposed method is shown to perform well by capturing the multiple power modes for both large and small circuits even when the number of simulated samples is small while the Monte Carlo estimator does not. We conclude with a note that the proposed method is also applicable to other model selection problems in VLSI.     

基于运动学参数的高速高机动目标跟踪自适应IMM算法

提出一种基于运动学参数的自适应IMM算法,进行高速高机动目标跟踪.该算法利用交互输出的结果,计算目标的运动学参数,进而实时估算出机动目标的转弯率,利用估计值自适应地使模型与目标实际运动状态匹配.文中还将该算法与其他两种IMM算法进行了比较.蒙特卡罗仿真结果表明,这种算法跟踪目标的稳定性和精确性均优于其他两种算法,有利于高速高机动目标的实时跟踪,具有较好的可实现性.