VLSI Implementation of Forward Error Control Technique for ATM Networks

In asynchronous transfer mode (ATM) networks, fixed length cells of 53 bytes are transmitted. A cell may be discarded during transmission due to buffer overflow or a detection of errors. Cell discarding seriously degrades transmission quality. The quality degradation can be reduced by employing efficient forward error control (FEC) to recover discarded cells. In this paper, we present the design and implementation of decoding equipment for FEC in ATM networks based on a single parity check (SPC) product code using very‐large‐scale integration (VLSI) technology. FEC allows the destination to reconstruct missing data cells by using redundant parity cells that the source adds to each block of data cells. The functionality of the design has been tested using the Model Sim 5.7cXE Simulation Package. The design has been implemented for a 5 ° 5 matrix of data cells in a Virtex‐E XCV 3200E FG1156 device. The simulation and synthesis results show that the decoding function can be completed in 81 clock cycles with an optimum clock of 56.8 MHz. A test bench was written to study the performance of the decoder, and the results are presented.     

化工软测量技术研究进展

详细说明了软测量技术的含义以及软测量系统的组成。在此基础上，介绍了软测量技术中基于机理分析建模、应用状态观测器建模、应用统计分析建模、神经元网络、模糊技术、以及支持向量机的智能建模等软测量建模理论方法和最新研究成果，之后介绍了小波分析、推断控制算法在软测量数据处理中的应用，最后介绍了基于虚拟仪器开发平台的软测量系统实现技术。     

Revising deductive knowledge and stereotypical knowledge in a student model

A user/student model must be revised when new information about the user/student is obtained. But a sophisticated user/student model is a complex structure that contains different types of knowledge. Different techniques may be needed for revising different types of knowledge. This paper presents a student model maintenance system (SMMS) which deals with revision of two important types of knowledge in student models: deductive knowledge and stereotypical knowledge. In the SMMS, deductive knowledge is represented by justified beliefs. Its revision is accomplished by a combination of techniques involving reason maintenance and formal diagnosis. Stereotypical knowledge is represented in the Default Package Network (DPN). The DPN is a knowledge partitioning hierarchy in which each node contains concepts in a sub-domain. Revision of stereotypical knowledge is realized by propagating new information through the DPN to change default packages (stereotypes) of the nodes in the DPN. A revision of deductive knowledge may trigger a revision of stereotypical knowledge, which results in a desirable student model in which the two types of knowledge exist harmoniously.     

基于双因子认证技术的网络身份识别

本文提出了一种基于动态双因子认证技术的网络身份识别方法,用户每次登录的口令是利用系统时间和用户ID通过MD5加密算法计算得到,其通过MD5加密后生成的登录口令是随机的,这将进一步提高开放网络环境下身份识别的可靠性和安全性。同时,本文还提出网络身份识别技术可以应用到各类网站的服务器上,用来完成对用户身份的识别,以提高网络系统的安全性。     

应用遗传神经网络研究低渗透储层成岩储集相—以胜利渤南油田三区沙河街组为例

以渤南油田三区沙河街组为例,应用遗传人工神经网络模式识别方法,开展了低渗透储层成岩储集相的研究。该工作是在储集层沉积相、成岩作用研究的基础上,选用流动层带指标、孔隙度、渗透率、粒度中值、泥质含量、孔喉半径均值和变异系数等7项参数,采用神经网络模式识别方法,通过建立遗传神经网络的学习及预测模型,对渤南油田三区沙河街组进行了成岩储集相识别,识别出4类成岩储集相:不稳定组分强溶解次生孔隙成岩储集相、碳酸盐胶结物溶解次生孔隙成岩储集相、强压实强胶结残余粒间孔成岩储集相和极强压实强胶结致密成岩储集相。Ⅰ类储集相的储集性能最好,Ⅳ类最差,为非储层或差储层。     

地震勘探技术在焉耆盆地勘探开发中的合理应用探讨

地震勘探技术应用可以分为以二维地震研究构造为主的地震勘探初级阶段、以地震地层学为主的地震勘探中级阶段、以三维地震油藏静态描述为主的地震勘探高级阶段、以地震储层研究为主的开发地震妆级阶段和以四维地震进行油藏监测为主的开发地震高级阶段。针对5个不同阶段的地质条件和地质任务，选择使用合理的地震勘探技术是有效提高勘探开发效益的重要途径。焉耆盆地的勘探开发体现了以地震勘探为主的思路，在勘探开发过程中重视了针对不同勘探开发阶段和不同的地质 地震技术，因此，在早期勘探开发中取得了很好的经济效益。     

数据通信网综合网管的设计分析

结合TMN电信管理网的体系结构．对数据通信网综合网管的结构和功能进行了分析。     

软交换技术的发展及其应用

软交换是下一代网络NGN的核心技术，本文从软交换的概念入手，介绍了软交换的基本功能及其优点．软交换的网络系统结构、软交换的对外接口等内容，并探讨了软交换技术的应用。     

基于软交换的NAT/防火墙穿透技术研究

通过对目前几种解决NAT(network addres stranslation)／防火墙穿逢方案的分析，提出了一种较易实现的穿透技术，并给出具体实现方案。     

Wavefront reduction using graphs,neural networks and genetic algorithm

This paper presents an algorithm for nodal numbering in order to obtain a small wavefront. Element clique graphs are employed as the mathematical models of finite element meshes. A priority function containing five vectors is used, which can be viewed as a generalization of Sloan's function. These vectors represent different connectivity properties of the graph models. Unlike Sloan's algorithm, which uses two fixed coefficients, here, five coefficients are employed, based on an evaluation by artificial neural networks. The networks weights are obtained using a simple genetic algorithm. Examples are included to illustrate the performance of the present hybrid method. Copyright © 2004 John Wiley & Sons, Ltd.