Bézier样条曲线改进的近似弧长参数化方法

提出了Bézier样条曲线利用分割技术近似弧长参数化的一种方法，并给出了相应的算法。通过求出曲线上所谓的‘最坏点’并在相应点处进行分割。可得到两条Bézier样条曲线。让这两条Bézier样条曲线具有与它们的近似弧长成比例的权，并对所得到的新的Bézier样条曲线进行同样的工作最终可得到一条由多条Bézier样条曲线所构成的新曲线。将这多条Bézier样条曲线合并成为一条Bézier样条曲线并通过节点插入技术将所得Bézier样条曲线转化为B一样条曲线的形式可得到全局参数域，其中各条Bézier曲线在全局参数域中所占子区间的长度与它们的权成比例，这样使得到了一条近似弧长参数化曲线。     

基于Shear-warp的交互式海量数据场体绘制算法

提出了基于min-max Octree快速分类Shear-warp的交互式海量数据场体绘制算法.主要包括根据海量数据的特征,快速读入数据以及设计适当的不透明度传递函数;建立Summed-Area表与min-max Octree数据结构,并对体数据进行快速分类,然后,利用分类的结果进行快速体绘制.实验证明该方法不仅效率高,而且显示效果好.     

数字电路网上虚拟实验实时连续仿真技术

随着远程教学的发展,远程虚拟实验的需求日益强烈.远程虚拟电路实验是远程虚拟实验的一个重要应用.远程虚拟电路实验系统开发的难点在于仿真必须具有真实性、连续性、实时交互性.对此,论述了时间片分割策略,把整个电路仿真过程按时间轴分割成若干时间片,再通过状态重置和交互响应机制实现连续仿真.在此基础上,设计并实现了支持连续仿真的可交互的远程虚拟数字电路实验平台.     

隐写术的研究与应用

近几年来,隐写术领域已经成为了信息安全的焦点.因为每个Web站点都依赖多媒体,如音频、视频和图像.隐写术这项技术可以将秘密信息嵌入到数字媒介中而不损坏它的载体的质量.第三方既觉察不到秘密信息的存在,也不知道存在秘密信息.因此密钥、数字签名和私密信息都可以在开放的环境(如Internet,或者内联网)中安全的传送.主要介绍隐写术的基本原理以及各方面的应用.     

提高电梯运行舒适感的有效途径

本文全面论述了影响电梯运行舒适感的原因，并提出了从电气和机械两个方面解决这些问题的办法。     

关于网络课堂的构建

优化教学手段、改善教学技术是保持高校设计艺术教育先进性的体现。利用网络实现课堂教学的互补也是新时期对教师提出的一项要求。本文介绍了构筑在“你我家”网络真实社区（http：／／www．niwojia．com）平台上的专业合作，交流组织“艺术设计联盟”（http：／／design．niwojia．com）的形成背景、发展历程和主要特点。     

Anisotropic nanowire growth via a self-confined amorphous template process: A reconsideration on the role of amorphous calcium carbonate

Calcium carbonate crystals with various morphologies have been found in a variety of biospecimens and artificially synthesized structures. Usually, the diversity in morphology can be attributed to different types of interactions between the specific crystal faces and the environment or the templates used for the growth of CaCO₃ crystals. On the other hand, isotropic amorphous calcium carbonate (ACC) has been recognized as the precursor of other crystalline calcium carbonate forms for both in vivo and in vitro systems. However, here we propose a self-confined amorphous template process leading to the anisotropic growth of single-crystalline calcite nanowires. Initiated by the assembly of precipitated nanoparticles, the calcite nanowires grew via the continuous precipitation of partly crystallized ACC nanodroplets onto their tips. Then, the crystalline domains in the tip, which were generated from the partly crystallized nanodroplets, coalesced in the interior of the nanowire to form a single-crystalline core. The ACC domains were left outside and spontaneously formed a protective shell to retard the precipitation of CaCO₃ onto the side surface of the nanowire and thus guided the highly anisotropic growth of nanowires as a template.

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A Simple Model to Describe Different Types of Exchange Bias Training Effect

The exchange bias (EB) training effect, referring to the exchange field (H _E) and/or the coercivity (H _C) decreasing with the magnetic cycle (n), is often accompanied with EB. Usually, the EB training effect has different types, showing that H _C1 (coercive field at the descending branch) and H _C2 (coercive field at the ascending branch) change with n differently. In order to understand the origin producing the training type, a phenomenological model is therefore proposed. According to this model, how H _C1 (or H _C2) changes with n is determined by the change of pinning magnetization at the descending (or ascending) branch during the training process. For verifying the validity of our model, various experimental training results with respect to different exchange-biased systems are selected for fitting and all the fitting results are nearly perfect.     

Shadow compensation and illumination normalization of face image

This study proposes a novel shadow compensation and illumination normalization method under uncontrolled light conditions. First, we decompose the face image into two images based on the Lambertian theory, which corresponds to the large- and small-scale features, respectively. Then, the threshold minimum-and-maximum filter on the small-scale features to smooth the shadow edge is applied. After that, the robust Principal Component Analysis and some normalization methods are used to remove the shadow and normalize the face image on the large-scale features. In the end, the normalized face image is obtained by combining both results from the large- and small-scale features. Our main contribution is that a more reliable shadow compensation approach is found, which can get a better normalized face image. Experiments on the Extended Yale B, CMU-PIE and FRGC 2.0 (Face Recognition Grand Challenge) face datasets show that not only the recognition performance is significantly improved, but also much better visual quality is achieved.