基于空间平滑的矩阵分解算法

本文研究了协方差矩阵分解算法解相关的问题,提出了一种修正的矩阵分解算法-基于空间平滑的矩阵分解（SSMD）算法。SMMD算法能在低信噪比条件下的估计相干信源,且估计精度比原修正的算法高,计算量比原修正算法小,最后通过大量的计算机仿真实验来说明矩阵修正算法的性能。     

Closed-loop automatic tuning of PID controller for nonlinear systems

In this paper, a robust control system is first proposed which is suitable for the control of a class of nonlinear systems. A parallel connection of a relay to a proportional integral derivative (PID) controller collectively forms the robust controller. The relay ensures robust control by providing a high feedback gain, but it also induces a control chattering phenomenon. Instead of viewing chattering as an undesirable yet inevitable feature, the chattering signals are used as natural excitation signals for identifying an equivalent PID controller using the recursive least squares algorithm. No other explicit input signal is required. Analysis is provided on the stability properties of the control scheme. Simulation results for the level control of fluid in a spherical tank using the scheme are presented.     

A review of error estimation and adaptivity in the boundary element method

When using the boundary element method, the accuracy of the numerical solution depends critically on the discretization of the boundary into elements (panels). The distribution of the panels is one of the most important decisions taken when analyzing a problem, but still the vast majority of users employ empirical guidelines to distribute the panels. This paper reviews the various adaptive schemes that have been proposed for boundary elements. Numerical results are obtained for infinite fluid flow problems and free surface problems and are used to assess the reliability and effectiveness of each method.     

彩色等离子体显示屏中运动失真现象的研究

本文研究了等离子体显示屏在显示动态的画面时产生运动失真现象的原因，并在比较国内外各种解决方法的基础上提出了一种优化的ADS驱动方式。在优化的ADS驱动方式基础上开发的14时彩色等离子体原型样机表明优化的ADS驱动方式在视觉闪烁感和运动图像质量方面都有了很大的提高。     

数字混沌扩频序列的盲估计

混沌序列具有非线性、宽带类噪声、大的码族、任意长的周期且容易产生的特性,因此在扩频通信系统中很有实用价值。本文将文献[1]中对伪随机扩频序列盲估计的方法推广应用到混沌扩频通信中,并对其不足提出了改进方法。在只知道扩频码周期而无其它任何先验知识的条件下,利用特征值分析的方法可以对数字混沌直扩序列进行盲估计,仿真实验证明了该方法的有效性。     

Bioprocess applications of model-based estimation techniques

The last decade has seen the development of a number of approaches for estimating those variables which are difficult to measure on-line in industrial process situations. Whilst a range of techniques is available, a common element is the use of process knowledge in the form of a system model. In the case of bioprocess systems, although a large range of models has been presented in the literature, their use in estimation schemes on an industrial scale has been limited. A number of reasons can be identified for their low level of utilisation. Of particular significance is the uncertainty which exists in quantifying system performance and the process-model mismatch which inevitably results. The level of ‘pre-defined model’ uncertainty, together with the knowledge gained during the course of the fermentation, serves to dictate estimator structure. The paper considers a range of estimation strategies and contrasts, through industrial applications, their performance characteristics and utility.     

European intercomparison on electron spin resonance identification of irradiated foodstuffs

The results of a co-trial organized by the Community Bureau of Reference on the use of Electron Spin Resonance spectroscopy for the identification of irradiated food in 21 laboratories are presented. The trial was qualitative on beef and trout bones, sardine scales, pistachio nut shells, dried grapes, and papaya, and quantitative on poultry bones. There was no difficulty in identifying irradiated meat bones, dried grapes, and papaya. In the case of fish bones there is a need for further kinetic study on different species. Identification of irradiation in pistachio nuts is more complicated and additional research is needed before further trials. All laboratories were able to distinguish between chicken bones irradiated at 1 to 3 kGy or 7 to 10 kGy although there was a partial overlap between the results.     

Improved Estimates for the Accuracy of Small Disjuncts

Learning systems often describe a target class as a disjunction of conjunctions of conditions. Recent work has noted that small disjuncts, i.e., those supported by few training examples, typically have poor predictive accuracy. One model of this accuracy is provided by the Bayes-Laplace formula based on the number of training examples covered by the disjunct and the number of them belonging to the target class. However, experiments show that small disjuncts associated with target classes of different relative frequencies tend to have different error rates. This note defines the context of a disjunct as the set of training examples that fail to satisfy at most one of its conditions. An empirical adaptation of the Bayes-Laplace formula is presented that also makes use of the relative frequency of the target class in this context. Trials are reported comparing the performance of the original formula and the adaptation in six learning tasks.     

Spectral estimation of video signals

Spectrum analyzers are ubiquitous in laboratory work concerning one dimensional signals. This is because linear operators are best examined in the frequency domain. Linear operators, such as linear filters, DCT coders, line shufflers, etc., dominate also the video systems scenario. Their frequency domain study is as appropriate and informative as it is in the case of their one-dimensional counterparts. This paper considers the problems associated with the introduction of two well-known spectral estimation techniques, the periodogram and AR estimates, to the context of television signals. The potential for application of spectral estimation to video problems is exemplified by a number of applications related to the fields of enhanced quality television and HDTV. Special attention is paid to the computational aspects, whose effective solution conditions the practical applicability of the proposed spectral estimation techniques.R. Rinaldo is currently at the Department of Electrical and Computer Engineering of the University of California, Berkeley.