基于面元素重排的三维网格模型数据隐藏算法

目的 为了解决目前三维数据隐藏算法不能兼顾无失真和盲提取的问题，提出一种新的完全无失真的三维网格模型数据隐藏盲算法。方法 首先使用混沌逻辑映射选择嵌入与提取模式，保证数据的安全性。然后利用面元素重排，完全不会造成三维模型失真的性质，通过不同嵌入模式规则对三角面元素进行重排，以嵌入秘密数据。接收端则可根据相应的提取模式规则提取秘密数据。结果 仿真结果与分析表明，该算法不会对三维模型造成任何失真，嵌入容量为每顶点2比特，且能抵抗仿射变换攻击、噪声攻击和平滑攻击等。结论 这种三维数据隐藏盲算法无失真，容量大、安全性高、鲁棒性强，适用于三维载体不容修改的情形，如军事、医学、秘密通信和版权保护等。     

一种变步长D-Rake多用户检测器

在时变多径衰落信道下,接收到的CDMA信号功率变化较大,此时D-Rake盲自适应多用户检测器性能显著下降,将变步长LMS算法与基于主分量的相干合并引入到D-Rake(DecorrelatingRake)检测器中,构成一种变步长D-Rake,称之为VD-Rake(Variablestep-sizeDecorrelatingRake)检测器。该检测器能克服原D-Rake检测器对信号功率变化较敏感等缺点,有效地改善了D-Rake检测器的性能。     

高密度互连印制板

本文阐述了RCC材料与化学蚀刻法制作高密度互连印制板的工序和详细方法,意在探寻最为经济、简单而又行 之有效的工艺。     

黄河流域委属大河控制站站网布局分析检验与调整

简要论述了黄河流域委属大河控制站的基本情况，通过资料分析，按“直线原则”计算了黄河大河控制站布站数目的上、下限，并调查分析落实了各河段近期和中长期治理开发对水资料的需求，确定各河段测站数目的采用值；对照现有站数，结合测站位置，功能，测验，生活条件等，逐段逐站进行审查，综合评估，决定测站的撤，迁，留，并对密度不足的河段提出了设站意见。     

南水北调西线工程可调水量分析

在受水区缺水形势严峻的情况下，引水工程可调水量的确定主要与三个因素有关：一是引水河流及引水坝址径流量；二是坝址上下游用水要求；三是引水水库工程规模。在考虑各种因素影响后，提出各引水坝址可调水量占坝比径流量的比例在70％左右比较合适，按现阶段推荐的两种方案，可调水量可达160亿－170亿m^3.     

南水北调西线工程水环境影响分析

南水北调西线工程调水后，引水坝址下游局部河段水量明显减少，但影响范围和程度十分有限。随着两岸环保力度的加大，排污量的减少，径流量减少对局部江段水质的不利影响，可得到控制。预估调水后下游水质不会发生类别变化。     

基于运动矢量的视频图象数字水印算法

数字水印技术是近年发展起来的一种用于数字产品版权保护和真伪性论证的新兴技术。与静止图象相比，视频水印技术要满足盲检的要求。本文根据运动矢量的特征值ρl，提出了一种视频图象水印新算法。实验结果表明，所提出的水印算法简单、快速，能满足视频编码的实时性要求；算法与现有的视频压缩标准有很好的兼容性；水印的提取具有盲检功能，无需原图象；水印的嵌入不影响I帧的图象质量，与嵌入水印前的原压缩图象相比，嵌入水印后的视频图象信噪比损失很小。     

利用独立分量分析法去除地震噪声

 独立分量分析（ICA）作为盲源分离（BSS）的一种新方法,是分解观测数据中独立信息的有力工具。以往的ICA算法一般假设噪声可以忽略不计，而实际的观测数据中又常常包含一些加性噪声。对于加性噪声的影响不能忽略的情况下，改进的ICA算法首先利用非零时间滞后协方差，应用两步特征值分解法(EVD)可成功地去除部分加性噪声的影响；再利用ICA算法就能更好地分离出原信号。本文通过对地震理论模型和实际资料的试验，说明改进的ICA算法能够有效地克服加性噪声对常规ICA算法的影响，能够分离出地震资料中的有效信号，从而实现利用独立分量分析压制地震资料噪声的目的。     

Three-dimensional simulation with a CFD tool of the airflow phenomena in single floor double-skin facade equipped with a venetian blind

Nowadays different kinds of double-skin facades are developed and used in new architectural projects. The aim of these facades is, on the one hand, to increase internal comfort and, on the other hand, to decrease energy consumption. In order to optimise the overall performance of the double-skin façades, their detailed behaviour needs to be better understood. The prediction of the airflow within the channel (between the two glazings) is very important for understanding of the double-skin facades behaviour, especially in summer conditions. A comprehensive modelling of a compact double-skin facade equipped with a venetian blind and forced ventilation is proposed here. The modelling is done using the CFD (computational fluid dynamics) approach to assess the air movement within the ventilated facade channel. Three-dimensional airflow is modelled using a homogeneous porous media representation, in order to reduce the size of the mathematical model. A parametric study is proposed here, analysing the impact of three parameters on the airflow development: slat tilt angle, blind position and air outlet position. The distance between the blind and the external glazing was found to have a major impact on the velocity profiles inside the double-skin facade channel.     

A genetic‐based neuro‐fuzzy controller for blind equalization of time‐varying channels

This paper presents a neuro‐fuzzy network (NFN) where all its parameters can be tuned simultaneously using genetic algorithms (GAs). The approach combines the merits of fuzzy logic theory, neural networks and GAs. The proposed NFN does not require a priori knowledge about the system and eliminates the need for complicated design steps such as manual tuning of input–output membership functions, and selection of fuzzy rule base. Although, only conventional GAs have been used, convergence results are very encouraging. A well‐known numerical example derived from literature is used to evaluate and compare the performance of the network with other equalizing approaches. Simulation results show that the proposed neuro‐fuzzy controller, all parameters of which have been tuned simultaneously using GAs, offers advantages over existing equalizers and has improved performance. From the perspective of application and implementation, this paper is very interesting as it provides a new method for performing blind equalization. The main contribution of this paper is the use of learning algorithms to train a feed‐forward neural network for M‐ary QAM and PSK signals. This paper also provides a platform for researchers of the area for further development. Copyright © 2008 John Wiley & Sons, Ltd.