大学形象及其塑造

大学形象是大学无形的财富，她由自然形象、历史形象、品牌形象、校长形象、教师形象、校园文化形象构成，既是学校走向成功的结果，更是学校走向新的成功的更大的前提。文章就如何塑造大学形象提出了建设性意见。     

Kerberos认证系统的分析和改进

首先介绍Kerberos的认证过程，特别是分析Kerberos认证协议的局性性，然后采用RSA加密算法的一种Yaksha改进算法，给出一种公钥认证协议，这种协议由用户验证自己产生的时间戳，这样就解决了时间同步的问题，并且克服了Kerberos的某些局限性。     

狭缝式高速摄影机胶片的计算机判读

根据狭缝摄影机胶片的成象原理,运用数字图象处理技术,对狭缝式高速摄影机胶片判读工作的计算机化进行研究.应用结果表明,该技术先进实用,极大地提高了判读的精度和效率,将是这种胶片判读技术的发展方向.     

数码相机引入科技照像实验

介绍了数码相机的系统结构和工作原理，通过与传统相机的对比分析，在科技照相实验中应该使用数码相机。     

液体可视图象序列中杂质小目标的检测方法

在分析红外图象和视图象差异的基础上，研究了一种适用于可视图像序列的运动小目标检测算法。受目标影响的象素点具有较弱的时间相关性，即相对普通象素点有较大的时间方差，根据此特征，用管道更新的方法产生新方差图象序列，再用管道更新的方法累积，增加信噪比，然后用基于统计均值的自适应门限方法分割出目标，最后将原图象的梯度倒数作为象素点强度的加权，有效地抑制灰度突变边界的传感器噪音，保存目标点。     

使用超声估计肌肉疲劳的初步研究

表面肌电信号把神经肌肉活动和肌肉收缩联系起来，已被视为一种估计肌肉疲劳比较客观的方法，但也存在一些不足。另一方面，超声已广泛应用于医学诊断和研究，在骨骼肌方面也有不少研究报道，但还未见文献报道超声用于肌肉疲劳的估计。本文介绍一套自行开发的新的同步连续采集B超图像、力矩和肌电信号的采集系统，并利用它对肱二头肌的肌肉疲劳进行了估计。试验表明在30秒的肌肉疲劳过程中，肌肉厚度会以非线性的方式增加。     

First-principles study on the origin of optical transitions to be associated with F colour centers for PbWO4 crystals

The electronic structures of PbWO₄ crystals containing F type color centers with the lattice structure optimized are studied within the framework of the fully relativistic self-consistent Direc–Slater theory, using a numerically discrete variational (DV-Xα) method. The calculated results show that F and F⁺ centers have donor energy level in forbidden band. Their optical transition energy are 1.84 eV, 2.21 eV, respectively, which corresponds to the 680 nm, 550 nm absorption bands. It predicts that the 680 nm, 550 nm absorption bands originate form the F and F⁺ centers in PbWO₄ crystals.     

高固体分羟基丙烯酸树脂的合成

以过氧化二苯甲酰（BPO）／过氧化二异丙苯（DCP）（质量比为2：3）为复合引发剂，二甲苯为溶剂，选用适量含羟基单体和分子量调节剂，以减缓树脂合成聚合反应中的自动加速现象，合成了分子量为3000-4000，多分散性指数d〈2的高固体分羟基丙烯酸树脂．该树脂与缩二脲多异氰酸酯（HDI）的配漆实验证明，所得漆膜鲜映性好、丰满度高、色泽好、雾影值低、综合性能较好．     

Mathematical topographical correction of XPS images using multivariate statistical methods

For rough heterogeneous samples, the contrast observed in XPS images may result from both changes in elemental or chemical composition and sample topography. Background image acquisition and subtraction are frequently utilized to minimize topographical effects so that images represent concentration variations in the sample. This procedure may significantly increase the data acquisition time. Multivariate statistical methods can assist in resolving topographical and chemical information from multispectral XPS images. Principal component analysis (PCA) is one method for identification of the highest correlation/variation between the images. Topography, which is common to all of the images, will be resolved in the first most significant component. The score of this component contains spatial information about the topography of the surface, whereas the loading is a quantitative representation of the topography contribution to each elemental/chemical image. The simple‐to‐use self‐modelling mixture analysis (Simplisma) method is a pure variable method that searches for the source of most differences in the data and therefore has the potential to distinguish between chemical and topographical phases in images. The mathematical background correction scheme is developed and validated by comparing results to the experimental background correction for samples with differing degrees of topography. Copyright © 2004 John Wiley & Sons, Ltd.     

Widrow-cellular neural network and optoelectronic implementation

A new type of optoelectronic cellular neural network has been developed by providing the capability of coefficients adjusment of cellular neural network (CNN) using Widrow based perceptron learning algorithm. The new supervised cellular neural network is called Widrow-CNN. Despite the unsupervised CNN, the proposed learning algorithm allows to use the Widrow-CNN for various image processing applications easily. Also, the capability of CNN for image processing and feature extraction has been improved using basic joint transform correlation architecture. This hardware application presents high speed processing capability compared to digital applications. The optoelectronic Widrow-CNN has been tested for classic CNN feature extraction problems. It yields the best results even in case of hard feature extraction problems such as diagonal line detection and vertical line determination.