一种增强型冲突分解算法

冲突分解算法是一种能有效改善无线信道多址接入协议的方式。本文对传统的冲突分解算法——树形冲突分解算法和先到先服务冲突分解算法进行了深入研究和仿真，探讨了这两种算法的优点和不足之处，提出了一种增强型的冲突分解算法（Enhanced Splitting Algorithm，ESA）。该算法继承了树形冲突分解算法和先到先服务算法的优点，不仅考虑了碰撞分组产生的时间，还考虑了分组的产生间隔，从而有效地提高了冲突分解算法的性能。仿真结果显示ESA算法是一种可行的算法。     

数据挖掘技术在有线电视领域的应用

数据挖掘是近年来在数据库技术、统计学、人工智能、神经网络等学科的基础上发展起来的一门交叉学科，数据挖掘技术已经成功地应用于商业领域，随之又迅速地扩展到社会保险、医学、电信等领域，探讨和分析数据挖掘技术在有线电视领域的应用，并给出了系统体系结构和挖掘算法。     

POWER OPTIMIZATION OF FINITE STATE MACHINE BASED ON GENETIC ALGORITHM

Using state assignment to minimize power dissipation and area for finite state machines is computationally hard.Most of published results show that the reduction of switching activity often trades with area penalty.In this paper,a new approach is proposed.Experimental results show a significant reduction of switching activity without area penalty compared with previous publications.     

采用多级技术的DBF雷达CCAB算法

数字波束形成算法中,CCAB算法在较高信干噪比条件下具有一定优势。但是计算时需要求逆矩阵,在阵元数较多的情形下运算量极大,很难实时实现,严重制约了其进一步的应用。提出了一种基于原有CCAB算法的多级计算方法,可以有效地减少运算量。实验表明,该方法虽然其信噪比性能比普通CCAB算法有所下降,但是仍能在干扰方向上形成较深的零陷并有效地抑制干扰,能够进一步应用于实际工程中。     

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。     

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).     

Nonparametric estimation and adaptive control in a class of finite Markov decision chains

We consider a class of Markov decision processes withfinite state and action spaces which, essentially, is determined by the following condition: The state space isirreducible under the action of any stationary policy. However, except by this restriction, the transition law iscompletely unknown to the controller. In this context, we find a set of policies under which thefrequency estimators of the transition law are strongly consistent and then, this result is applied to constructadaptive asymptotically discount-optimal policies.Dedicated to Professor Truman O. Lewis, on the occasion of his sixtieth birthdayThis research was supported in part by the Third World Academy of Sciences (TWAS) under Grant TWAS RG MP 898-152, and in part by the Consejo Nacional de Ciencia y Tecnología (CONACYT) under Grant A128CCOEO550 (MT-2).     

Approximation bounds for quadratic maximization and max-cut problems with semidefinite programming relaxation

In this paper,we consider a class of quadratic maximization problems.For a subclass of the problems,we show that the SDP relaxation approach yields an approximation solution with the ratio is dependent on the data of the problem with α being a uniform lower bound.In light of this new bound,we show that the actual worst-case performance ratio of the SDP relaxation approach (with the triangle inequalities added) is at least α δd if every weight is strictly positive,where δd ＞ 0 is a constant depending on the problem dimension and data.     

Fitting the control parameters of a genetic algorithm: An application to technical trading systems design

This paper studies the problem of how changes in the design of the genetic algorithm (GA) have an effect on the results obtained in real-life applications. In this study, focused on the application of a GA to the tuning of technical trading rules in the context of financial markets, our tentative thesis is that the GA is robust with respect to design changes. The optimization of technical trading systems is a suitable area for the application of the GA metaheuristic, as the complexity of the problem grows exponentially as new technical rules are added to the system and as the answer time is crucial when applying the system to real-time data. Up to now, most of GAs applications to this subject obviated the question of possible “design dependence” in their results. The data we report, based on our experiments, do not allow us to refute the hypothesis of robustness of the GA to design implementation, when applying to technical trading systems tuning.     

Pattern Search Method for Discrete <Emphasis Type="Italic">L</Emphasis><Subscript>1</Subscript>–Approximation

We propose a pattern search method to solve a classical nonsmooth optimization problem. In a deep analogy with pattern search methods for linear constrained optimization, the set of search directions at each iteration is defined in such a way that it conforms to the local geometry of the set of points of nondifferentiability near the current iterate. This is crucial to ensure convergence. The approach presented here can be extended to wider classes of nonsmooth optimization problems. Numerical experiments seem to be encouraging. This work was supported by M.U.R.S.T., Rome, Italy.