层叠加权中值滤波器

利用层叠滤波器的阈值分解特性，提出了一种基于阈值分解结构的滤波器－层叠加权中值滤波器。该滤波器结构简单，易于并行处理和通过ＶＬＳＩ实现。图象处理仿真实验表明，其具有良好的滤波效果。     

Windows95和NT下的网络游戏开发(下)

其中所有的参数都取自ＥｎｕｍＳｅｓ－ｓｉｏｎｓ,系统每收到一个回复的消息便调用一次ＥｎｕｍＳｅｓｓｉｏｎ,并在结构ＬＰＤＰＳＥ－ＳＳＩＯＮＤＥＳＣ中添入适当的值。以下是ＥｎｕｍＳｅｓｓｉｏｎｓＣａｌｌｂａｃｋ的程序实例。ＢＯＯＬＦＡＲＰＡＳＣＡＬＥｎｕｍｓｅｓｓｉｏｎ（ＬＰＤＰＳＥＳＳＩＯＮＤＥＳＣｌｐＤＰＧａｍｅＤｅｓｃ,ＬＰＶＯＩＤｌｐＣｏｎｔｅｘｔ,ＬＰＤＷＯＲＤｌｐｄｗＴｉｍｅｏｕｔ,ＤＷＯＲＤｄｗＦｌａｇｓ）｛ＬＯＮＧｉｌｎｄｅｘ：ＨＷＮＤｈｗｎｄ＝（ＨＷＮＤ）ｌｐＣｏｎ－ｔｅｘｔ;／…     

克隆粒子群算法的镜像层叠滤波器的优化设计

层叠滤波器是一种具有层叠性和阈值分解性的滑动窗非线性数字滤波器.层叠滤波器的优化设计过程关键就是对正布尔函数优化.本文利用了克隆粒子群优化算法的全局寻优能力和免疫系统的免疫信息处理机制,改善了粒子群优化算法摆脱局部极值点的能力,提高了算法进化过程中的收敛速度和精度.仿真实验的结果也表明,该算法在正布尔函数的优化效果得到了显著改善,收敛加快,试验结果表明该算法设计的层叠滤波器具有良好的细节保持能力和去噪声能力,有效地改善了滤波性能.     

改进克隆选择算法的层叠滤波器的优化设计

层叠滤波器优化设计的核心是正布尔函数最优化的问题.为了提高优化速度并对层叠滤波器进行全局优化设计,提出了一种改进的克隆选择算法.该算法引入多克隆算子和记忆单元及保留群体同时进化的思想.多克隆算子中的个体克隆规模根据个体的亲和度浓度自适应变化;重组操作在父代记忆个体与子代变异后的记忆个体之间展开,避免了近亲繁殖;保留群体的变异保证了群体的多样性.实验结果证明,该算法优化的层叠滤波器能在较短的时间内得到较好的滤波结果.     

中文WINDOWS 95／NT／3.x对中文 DOS汉字输入法的全面自动接管和超前接 …

该文在简要介绍了目前我国在实现中文ＷＩＮＤＯＷＳ９５／ＮＴ／３．ｘ接管中文ＤＯＳ汉字输入法的现状,分析了中文ＷＩＮＤＯＷＳ９５／ＮＴ／３．Ｘ全面、自动、超前接管中文ＤＯＳ汉字输入法的可行性,在此基础上提出了一个实现中文ＷＩＮＤＯＷＳ９５／ＮＴ／３．Ｘ全面、自动、超前接管中文ＤＯＳ汉字输入法的新算法,最后给出了一 个新算法演示程序。     

OLE在多媒体开发中的应用

本文介绍了ＷＩＮＤＯＷＳ环境中对象链接和嵌入技术的一般机制和原理；介绍了ＶＩＳＵＡＬＦＯＸＰＲＯ高级数据库管理系统的ＯＬＥ技术和它对超文本数据的处理功能。并用实例说明了用ＶＩＳＵＡＬＰＯＸＰＲＯ开发多媒体数据管理系统的一种技术和方法。     

用于语音合成的PSOLA算法简介

本文介绍了基于波形编辑的语音合成中，用于改善合成语音的自然度的ＰＳＯＬＡ算法的一般过程，及具体的ＴＤ－ＰＳＯＬＡ，ＬＰ－ＰＳＯＬＡ和ＦＤ－ＰＳＯＬＡ算法。分析了时域和频域的算法分别在宽带和窄带条件下的谱特性和它们之间的关系，最后比较了这三种算法的适用范围及其在语音合成中的应用。     

模糊控制在现场总线节点控制器上的实现

简要介绍了当今流行的 Ｌ Ｏ Ｎ Ｗ Ｏ Ｒ Ｋ Ｓ现场总线技术，详细介绍了如何模糊控制算法与 Ｌ Ｏ Ｎ Ｗ Ｏ Ｒ Ｋ Ｓ现场总线网络系统紧密结合，充分利用节点中ｎｅｕｒｏｎ－ ｃｈｉｐ 微处理器及其外设的各种资源，构成工业现场级的模糊控制节点。实际证明该控制器控制效果良好。     

WINDOWS下自动演示程序的制作

本文给出一种ＷＩＮＤＯＷＳ下自动演示程序的制作方法，此法综合利用了ＷＩＮＤＯＷＳ中的Ｗｒｉｔｅ、Ｐａｉｎｔｂｒｕｓｈ、ＷＯＲＤ的ＭＳＤｒａｗ等现成工具及ＷＩＮＤＯＷＳ下的剪贴板、ＤＤＥ、多任务等功能，只需进行简单的编程实现了ＷＩＮＤＯＷＳ下应用软件的自动演示。     

Windows NT网络教学应用中的技巧

１引言 ＷＩＮＤＯＷＳ ＮＴ ４．０作为一个高性能的３２位多任务、多用户的网络操作系统,由于其界面的友好性和强大而直观的管理功能,使得用户可以较快地掌握和构造一套基于ＷＩＮ－ＤＯＷＳ ＮＴ的网络环境。作为 ＷＩＮＤＯＷＳ家族的成员之一,ＷＩＮＤＯＷＳ ＮＴ被设计成为可以最好地发挥 ＷＩＮＤＯＷＳ ９８优势的网络操作系统。我校计算机系多媒体机房在 １９９８年建成,采用了基于ＷＩＮＤＯＷＳ ＮＴ的网络多媒体教学环境,由于 ＷＩＮＤＯＷＳ ９８对软件具有较好的兼容性,而 ＷＩＮＤＯＷＳＮＴ又具有较好的安全性,这种…     

0.8 /spl mu/m CMOS implementation of weighted-order statistic image filter based on cellular neural network architecture.

In this paper, a very large scale integration chip of an analog image weighted-order statistic (WOS) filter based on cellular neural network (CNN) architecture for real-time applications is described. The chip has been implemented in CMOS AMS 0.8 /spl mu/m technology. CNN-based filter consists of feedforward nonlinear template B operating within the window of 3 /spl times/ 3 pixels around the central pixel being filtered. The feedforward nonlinear CNN coefficients have been realized using programmable nonlinear coupler circuits. The WOS filter chip allows for processing of images with 300 pixels horizontal resolution. The resolution can be increased by cascading of the chips. Experimental results of basic circuit building blocks measurements are presented. Functional tests of the chip have been performed using a special test setup for PAL composite video signal processing. Using the setup real images have been filtered by WOS filter chip under test.     

基于余弦调制的二维LP-NPR滤波器组设计

二维滤波器组通常用于图像处理中,而图像子带编码希望滤波器组同时也具有线性相位（LP）特性。提出了一种基于一类新余弦调制系统的二维线性相位-近似完全重构（LP-NPR）滤波器组的设计方法,设计过程分为两个步骤：首先采用Parks-McClellan算法来计原型滤波器,再经过余弦调制能到一系列的线性相位分析和综合滤波器,从而可以构造一维LP-NPR滤波器组;然后利用树状结构来结合两个一维LP-NPR滤波器组,并运用单位模变换,可以得到二维不可分离LP-NPR滤波器组。实验结果表明此方法是可行的,所设计的滤波器组可达到令人满意的重构精度。     

堆栈滤波的递归实现

堆栈滤波器是一类能够有效滤除脉冲类噪声,同时能较好地保护图象边缘与细节的非线笥滤波器,它所具有的 阈值分解和堆栈两个重要性质保证了滤波过程可以在二进制域并行实现,虽然ＭＡＥ（平均绝对误差）准则下最优堆栈滤波器可由ＬＭＡ（最小平均绝对误差）算法给出,但直接推导得到的滤波有属于非递归型滤波器,基于此,提出了ＭＡＥ准则下最优堆栈滤波器的递归实现方法,该方法不仅采取直接递归和最递归两种方案实现递滤波器的噪     

基于自适应堆滤波的边界检测

从被噪声干扰的图象中提取边界是图象测试与分析的关键之一。通常需要先滤除图象中的噪声，再用边界检测算子求出边界。本文介绍了一种边界直接检测法，即将边界检测与噪声滤波相结合，它是基于自适应堆滤波的边界检测法。首先非线性堆滤波器用于求出图象某象素点邻域内的灰度最大值与最小值的最优估计，然后以此两估计值之差代替原象素点灰度值。最后对之二值化求出边界。本文根据最小平均绝对误差准则，采用自适应方法求解堆滤波器。这种方法类似于线性自适应滤波器的ＬＭＳ方法，先任设一初始堆滤波器，利用期望图象与合噪声图象对堆滤波器进行迭代训练，最后求出最优化的自适应堆滤波器。文章最后给出了采用自适应堆滤波法求取图象边界的试验结果，表明这种方法可以有效地抑制各种分布的噪声干扰。     

SAR image processing using adaptive stack filter

Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is filtered by a Boolean function. The Boolean function that characterizes an adaptive stack filter is optimal and is computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work the behavior of adaptive stack filters on synthetic aperture radar (SAR) data is evaluated. With this aim, the equivalent number of looks for stack filtered data are calculated to assess the speckle noise reduction capability of this filter. Then a classification of simulated and real SAR images is carried out on data filtered with a stack filter trained with selected samples. The results of a maximum likelihood classification of these data are evaluated and compared with the results of classifying images previously filtered using the Lee and the Frost filters.     

Computing the Output Distribution and Selection Probabilities of a Stack Filter from the DNF of Its Positive Boolean Function

Many nonlinear filters used in practise are stack filters. An algorithm is presented which calculates the output distribution of an arbitrary stack filter S from the disjunctive normal form (DNF) of its underlying positive Boolean function (PBF). Our algorithm avoids to enumerate the models of the PBF one by one, and thus is considerably more efficient than previous methods. The so called rank selection probabilities can be computed along the way.     

Scheduling of a single crane in batch annealing process

This paper studies a single crane scheduling problem motivated by batch annealing process in the iron and steel industry. Each coil stack placed on fixed base needs to go through two-stage processing: heating and cooling. During each stage, limited special machines (furnace and cooler) must be operated by crane, respectively. Our problem is to assign the shared machines and schedule a single crane for minimizing the last coil stack completion time (makespan). A mixed integer linear programming (MILP) model is formulated by considering both machine and crane positions. We show that the problem is NP-hard in the strong sense. Some optimal properties on the problem are derived. A two-phase algorithm is constructed for the problem. In the first phase, a fully polynomial time approximation scheme (FPTAS) is developed for the assignment subproblem. In the second phase, a heuristics is proposed for the scheduling subproblem. From an absolute performance point of view, we analyze the quality of the two-phase algorithm. We also consider special cases where some properties or algorithms are developed. In order to further verify the performance of the two-phase algorithm, we develop a lower bound on the optimal objective function. Computational experiments on the randomly generated problem instances show that the algorithm is close to the lower bound within a reasonable computation time.     

Weighted operation structures to program strengths of concrete-typed specimens using genetic algorithm

This study introduces weighted operation structures (WOS) to program engineering problems, in which each WOS adopts a fixed binary tree topology. The first WOS layer serves as the parameter input entrance. The target is produced at the eventual layer using both values and a mathematical formula. Each WOS element is operated by two front nodal inputs, an undetermined function, and two undetermined weights to produce one nodal output. This study proposes the novel concept of introducing weights into a WOS. Doing so provides two unique advantages: (1) achieving a balance between the influences of two front inputs and (2) incorporating weights throughout the generated formulas. Such a formula is composed of a certain quantity of optimized functions and weights. To determine function selections and proper weights, genetic algorithm is employed for optimization. Case studies herein focused on three kinds of concrete-typed specimen strengths: (1) concrete compressive strength, (2) deep beam shear strength, and (3) squat wall shear strength. Results showed that the proposed WOS can provide accurate results that nearly equal the results obtainable using the familiar neural network. The weighted formula, however, offers a distinct advantage in that it can be programmed for practical cases.     

基于整合概率数据关联的最优传感器序列研究

研究多传感器跟踪系统中传感器处理序列优化问题．首先根据多传感器跟踪系统中多传感器信息的处理特点,提出了基于有限随机集的序列整合概率数据关联（IPDA）滤波算法;然后,通过分析序列IPDA算法中目标生存概率的均值函数,证明了在不同传感器检测概率的多传感器系统中,目标生存概率仅依赖于传感器序列中第1个传感器,并随其检测概率的增大而增大．仿真实验验证了所得结论．     

三维环境中多机器人动态目标主动协作观测方法

动态目标的多移动机器人主动协作观测方法是指以获取较优的观测结果为目的, 对携带同构/异构观测传感器的多个机器人系统的观测数据进行有效融合并同时对其行为进行协调优化的方法. 本文主要研究了三维环境中的多机器人动态目标主动协作观测的问题. 首先, 以扩展集员估计方法(Extended set-membership filter, ESMF)为基础, 将信息融合过程与算法本身存在的集合运算环节相结合, 提出了一种高精度的多机器人观测信息融合方法. 该方法在保证较高观测精度的同时, 并没有显著增加单机器人扩展集员估计算法的计算量, 因此具有较高的实时性. 此外, 利用最优观测角度的概念, 通过引入相对速度空间(Relative velocity coordinates, RVCs), 设计了多移动机器人协调行为优化方法, 该方法可以将多机器人协调行为优化问题转化为线性规划问题, 以实现具有较高实时性的多机器人三维动态目标主动协作观测. 最后, 为了验证所研究方法的可行性与有效性, 进行了三维空间动态目标协作观测仿真实验.