Widrow-cellular neural network and optoelectronic implementation

A new type of optoelectronic cellular neural network has been developed by providing the capability of coefficients adjusment of cellular neural network (CNN) using Widrow based perceptron learning algorithm. The new supervised cellular neural network is called Widrow-CNN. Despite the unsupervised CNN, the proposed learning algorithm allows to use the Widrow-CNN for various image processing applications easily. Also, the capability of CNN for image processing and feature extraction has been improved using basic joint transform correlation architecture. This hardware application presents high speed processing capability compared to digital applications. The optoelectronic Widrow-CNN has been tested for classic CNN feature extraction problems. It yields the best results even in case of hard feature extraction problems such as diagonal line detection and vertical line determination.     

Randomization helps to perform independent tasks reliably

This paper is about algorithms that schedule tasks to be performed in a distributed failure‐prone environment, when processors communicate by message‐passing, and when tasks are independent and of unit length. The processors work under synchrony and may fail by crashing. Failure patterns are imposed by adversaries. Linearly‐bounded adversaries may fail up to a constant fraction of the processors. Weakly‐adaptive adversaries have to select, prior to the start of an execution, a subset of processors to be failure‐prone, and then may fail only the selected processors, at arbitrary steps, in the course of the execution. Strongly adaptive adversaries have a total number of failures as the only restriction on failure patterns. The measures of complexity are work, measured as the available processor steps, and communication, measured as the number of point‐to‐point messages. A randomized algorithm is developed, that attains both ??(n log*n) expected work and ??(n log*n) expected communication, against weakly‐adaptive linearly‐bounded adversaries, in the case when the numbers of tasks and processors are both equal to n. This is in contrast with performance of algorithms against strongly‐adaptive linearly‐bounded adversaries, which has to be Ω(n log n/log log n) in terms of work. © 2003 Wiley Periodicals, Inc. Random Struct. Alg., 2004     

基于加载算法的室内无线信道OFDM接收机的性能

本文研究了无线局域网中使信号频谱得到优化的一种简单OFDM子载波加载算法。此算法基于边缘自适应（Margin Adaptive）加载准则及其在室内无线信道上的性能。     

Predictive control of periodic-review production inventory systems with deteriorating items

In this paper a predictive control strategy is applied to a periodic-review dynamic inventory system with deteriorating items. Given the current inventory level, we determine the optimal production rates to be implemented at the beginning of each of the following periods over the control horizon. The effectiveness of this approach is the use of future information of the inventory target level and the desired production rate, which are available, along the fixed horizon. The deterioration coefficient may be known or unknown and both cases are considered. In the case where it is unknown, the self-tuning predictive control is applied. The proposed control algorithms are illustrated by simulations.     

半参数方差误差模型的估计

本文综合近邻权函数法及最小二乘法，用两阶段最小二乘估计的方法得到了半参数EV模型中参数的估计量及其强相合性，渐近正态性。同时也得到了非参数函数的估计量及其强相合性，一致强相合性。     

基于非锐化掩模的实时自适应图像增强算法

针对传统的非锐化掩模算法的局限性,依据图像中各像素点及以其为中心的若干相邻像素点的均方差值,提出了自适应图像增强算法的原理,并分析其硬件实现方法,然后给出了该算法与其它算法应用于图像增强的对比结果,最后在FPGA（field programmable gate array）实验板上进行验证.实验结果表明,此算法有效的增强图像的细节区域,防止图像边缘区域出现过冲现象,抑制图像平坦区域的噪声放大.因此,该算法取得了良好的视觉效果,硬件实现简单,适合于实时条件下图像的增强.     

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。     

COPART:一种面向约束条件的自适应软硬件划分算法

本文介绍一种将自适应算法、遗传算法和列表调度算法结合起来应用于软硬件划分问题的算法COPARTART，并提出了一种基于约束条件的开销系数自适应调整方法，该方法所获得的划分结果能够良好体现设计目标。     

遗传算法的改进及其在电力系统无功优化中的应用

针对无功优化的动态、多目标、多约束以及非线性特点,提出了基于简单遗传算法的改进算法,在简单遗传算法的基础上,添加了自适应控制和适应度排序思想,并对算法终止准则进行了改进。以IEEE14节点标准测试系统为例对该算法进行了有效性验证。仿真结果表明,该方法对电力系统的无功优化效果良好。     

稀疏阵列天线综合的遗传算法优化

通过遗传算法优化选择常规满阵中一定比例的工作单元,使阵列在欠饱和状态下达到窄波束低副瓣的要求。以单元的工作状态和激励幅度为优化参量,波束宽度和副瓣电平为适值函数,通过适值缩放和多种群进化竞争使遗传进化稳定地收敛到全局最优。详细分析了稀疏阵的单元布局、稀疏率及单元激励对优化阵列特性的影响。