Approximate channel identification via δ‐signed correlation

A method of approximate channel identification is proposed that is based on a simplification of the correlation estimator. Despite the numerical simplification (no multiplications or additions are required, only comparisons and an accumulator), the performance of the proposed estimator is not significantly worse than that of the standard correlation estimator. A free (user selectable) parameter moves ‘smoothly’ from a situation with small sum‐squared channel estimation error but hard‐to‐identify channel peaks, to one with a larger sum‐squared estimation error but easy‐to‐identify channel peaks. The proposed estimator is shown to be biased and its behaviour is analysed in a number of situations. Applications of the proposed estimator to sparsity detection, symbol timing recovery and to the initialization of blind equalizers are suggested. Copyright © 2002 John Wiley & Sons, Ltd.     

水利工程建设项目的风险分析

水利工程建设中潜藏着众多风险，对风险进行有效分析是工程顺利建设的重要保证。水利工程风险分析是由风险识别、估算、评价、管理、控制决策等组成的科学体系。明确了水利工程中风险的定义，识别了水利工程的风险因素，论述了风险估算和评价的方法，并提出对工程风险进行管理和控制，以保证工程建设顺利进行。     

Empirical Evaluations of Usage-Based Reading and Fault Content Estimation for Software Inspections

This thesis describes six empirical investigations of two techniques applied to software inspections, namely reading techniques and fault content estimation techniques. The first part of the thesis presents a series of experiments of a novel reading technique called usage-based reading. The second part investigates fault content estimation applied to the software inspection process. The estimation methods used are capture-recapture and curve fitting.     

双谱估计在导引头信号检测与处理中的应用

双谱估计是一种应用广泛的高阶谱估计。将双谱估计应用于超宽带导引头信号检测与处理系统中,为导引头信号处理中的平滑和滤波增添了一种新的方法。仿真结果表明该方法是有效的。     

非线性系统Wiener模型辨识

本文采用不同幅值的三电平伪随机m序列作为输入信号,辨识了离散非线性系统Wiener 模型的线性脉冲响应函数和非线性增益系数.同时讨论了参数估计的统计特性及其区间估 计,并附有仿真结果.     

局部散射源参数估计的非线性算子方法

本文利用单次快摄数据的两个非线性算子估计局部散射源中心波达方向和角扩展,与多维参数搜索和其他低复杂性的局部散射源参数估计算法不同,非线性算子方法给出了单次快摄的局部散射源参数估计的闭式解,无需进行多维搜索和子空间分解。     

Cost estimates to guide pre-selection of processes

A process is a method of shaping, joining or surface-treating a material. Process selection has three steps. The first is to identify from the ‘menu’ of all available processes, the subset, which can give a chosen material the desired shape with the desired detail, precision and finish. The second is to choose, from among these, the ones that will do so at the lowest cost. The final step is to investigate the most promising processes in depth, exploring considerations such as availability, in-house experience, safety and environmental issues. The first two steps can be thought of as process pre-selection. Components have to be assembled and finished to create products. Here too, the ability to rank by cost, however crudely, helps guide pre-selection. Cost models are reviewed from the perspective of material and process selection. An approximate model is useful provided it has generality—that is, it must allow comparison of very different processes. Many approaches fail in this. One that works, based on resource consumption, is developed here and its use for selection is illustrated. It has been implemented as part of a tool that allows rapid pre-selection from a database of 112 processes.     

一种空间自适应小波门限去噪算法

提出了一种空间自适应小波门限去噪算法,该算法在小波域对含噪小波系数做两次自适应去噪,两次自适应门限分别基于最大似然(ML)方差估计和最大后验概率(MAP)方差估计.仿真结果表明,该算法与其它自适应门限去噪算法相比,去噪后的图象具有更高的峰值信噪比(PSNR).     

定容气田开发规划概算法

基于物质平衡方程、产能方程和递减方程的结合,提出了定容气田开发规划的概算法.该法可用于预测地层压力、井底流动压力、井口流动压力和稳产年限.实际应用表明,本文提供的方法是实用有效的.     

A semiparametric model for compositional data analysis in presence of covariates on the simplex

Compositional data occur as natural realizations of multivariate observations comprising element proportions of some whole quantity. Such observations predominate in disciplines like geology, biology, ecology, economics and chemistry. Due to unit sum constraint on compositional data, specialized statistical methods are required for analyzing these data. Dirichlet distributions were originally used to study compositional data even though this family of distribution is not appropriate (see Aitchison, 1986) because of their extreme independence properties. Aitchison (1982) endeavored to provide a viable alternative to existing methods by employing Logistic Normal distribution to analyze such constrained data. However this family does not include the Dirichlet class and is therefore unable to address the issue of extreme independence. In this paper generalized Liouville family is investigated to model compositional data which includes covariates. This class permits distributions that admit negative or mixed correlation and also contains non-Dirichlet distributions with non-positive correlation and overcomes deficits in the Dirichlet class. Semiparametric Bayesian methods are proposed to estimate the probability density. Predictive distributions are used to assess performance of the model. The methods are illustrated on a real data set.