Combining Swaps and Node Weights in an Adaptive Greedy Approach for the Maximum Clique Problem

In this work, the NP-hard maximum clique problem on graphs is considered. Starting from basic greedy heuristics, modifications and improvements are proposed and combined in a two-phase heuristic procedure. In the first phase an improved greedy procedure is applied starting from each node of the graph; on the basis of the results of this phase a reduced subset of nodes is selected and an adaptive greedy algorithm is repeatedly started to build cliques around such nodes. In each restart the selection of nodes is biased by the maximal clique generated in the previous execution. Computational results are reported on the DIMACS benchmarks suite. Remarkably, the two-phase procedure successfully solves the difficult Brockington-Culberson instances, and is generally competitive with state-of-the-art much more complex heuristics.     

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。     

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.     

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。     

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。     

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。