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。     

腐蚀算法在重建一类特殊管道中的应用

针对一类特殊而不能直接对其观察的管道（如动物的血管），为了准确了解其内部特征和形态，借助计算机给出了一种用腐蚀算法来重建管道原形的方法。这种方法简单易行，而且精确度高。     

Data fitting with a spline using a real-coded genetic algorithm

To obtain a good approximation for data fitting with a spline, frequently we have to deal with knots as variables. The problem to be solved then becomes a continuous nonlinear and multivariate optimization problem with many local optima. Therefore, it is difficult to obtain the global optimum. In this paper, we propose a method for solving this problem by using a real-coded genetic algorithm. Our method can treat not only data with a smooth underlying function, but also data with an underlying function having discontinuous points and/or cusps. We search for the best model among candidate models by using the Bayes Information Criterion (BIC). With this, we can appropriately determine the number and locations of knots automatically and simultaneously. Five examples of data fitting are given to show the performance of our method.     

正交多项式的并行算法修正及其稳定性分析

通过修正求解Chebyshev多项式的Clenshaw和Forsythe算法。提出了求解一般正交多项式的两种并行算法。并通过讨论这两种并行算法矩阵形式的舍入误差，对它们的稳定性进行了分析。     

基于约束优化的联想记忆模型学习算法

本文提出了一种对称互连神经元网络的学习策略,利用全局约束优化方法确定连接权。优化过程采用了梯度下降技术。这种学习算法可以保证训练样本成为系统的稳定吸引子,并且具有优化意义上的最大吸引域。本文讨论了网络的存储容量,训练样本的渐近稳定性和吸引域大小。计算机实验结果说明了学习算法的优越性。     

TEMIC射频卡的速率自适应读卡算法

提出种基于TEMIC射频卡Manchester编码的速率自适应读卡算法,并对该算法进行了说明和示例。     

Generation and Memory for Contextual Detail.

Generation enhances item memory but may not enhance other aspects of memory. In 12 experiments, the author investigated the effect of generation on context memory, motivated in part by the hypothesis that generation produces a trade-off in encoding item and contextual information. Participants generated some study words (e.g., hot-___) and read others (e.g., hot-cold). Generation consistently enhanced item memory but did not enhance context memory. More specifically, generation disrupted context memory for the color of the target word but did not affect context memory for location, background color, and cue-word color. The specificity of the negative generation effect in context memory argues against a general item-context trade-off. A processing account of generation meets greater success. In addition, the results provide no evidence that generation enhances recollection of contextual details. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

多冗余点到点链路下OSPF扩散算法的一种改进方案

文章讨论了OSPF路由协议的扩散算法在多冗余点到点链路情况下存在的缺陷,提出了一种基于扩散接口选举的改进方案。     

Projection center calibration by motion

Camera calibration is the first step of three-dimensional machine vision. A fundamental parameter to be calibrated is the position of the camera projection center with respect to the image plane. This paper presents a method for the computation of the projection center position using images of a translating rigid object, taken by the camera itself.

Many works have been proposed in literature to solve the calibration problem, but this method has several desirable features. The projection center position is computed directly, independently of all other camera parameters. The dimensions and position of the object used for calibration can be completely unknown.

This method is based on a geometric relation between the projection center and the focus of expansion. The use of this property enables the problem to be split into two parts. First a suitable number of focuses of expansion are computed from the images of the translating object. Then the focuses of expansion are taken as landmarks to build a spatial back triangulation problem, the solution of which gives the projection center position.