基于CCD采集的Mach-Zehnder干涉条纹图的处理算法

Mach-Zehnder干涉仪适用于研究气体密度迅速变化的状态。由于气体折射率的变化与其密度的变化成正比,而折射率的变化将使通过气体的光线有不同的光程,因此可通过干涉臂变化对干涉条纹图像效果的影响得到气体密度。实测中,采用图像采集卡和CCD来接收Mach-Zehnder干涉仪产生的条纹图像,再通过计算机对条纹图像的条纹间距进行处理,从而得到气体密度的变化状态。从光干涉理论出发,对Mach-Zehnder干涉条纹图像特征进行了分析,建立了Mach-Zehnder干涉条纹的数学模型,并根据此模型设计了处理Mach-Zehnder干涉条纹图像的算法。算法包括图像的预处理(即图像的噪声提取)、图像的二值化及图像的细化。     

BRD96N光调制吸收增强现象的实验研究

通过光谱响应特性实验和记录／读出图像实验，研究了基因变异型细菌视紫红质（BR_D96N）分子膜对单色光的光调制特性.发现BR_D96N分子膜在550nm—600nm范围内对调制光有吸收增强的现象，且对此范围内不同波长的单色光其调制程度有差异.利用曲线拟和方法发现550nm—600nm吸收增强的变化过程分为快过程和慢过程，其对应的时间常数分别为30s和5min.利用强度调制器的吸收强度与图像灰度之间的关系，分析了560nm—600nm范围内出现图像反转的实验现象. 关键词： 细菌视紫红质D96N分子膜 光谱响应 吸收增强现象 图像反转现象     

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。     

Reply to “Comment on papers by K. Shanahan that propose to explain anomalous heat generated by cold fusion”, E. Storms, Thermochim. Acta, 2006

Dr. E. Storms has published a Letter [E. Storms, Comment on papers by K. Shanahan that propose to explain anomalous heat generated by cold fusion, Thermochim. Acta, 2006] in which he argues that in a sequence of recent papers, the apparent excess heat signal claimed by Dr. Shanahan to arise from a calibration constant shift is actually true excess heat. In particular he proposes that the mechanisms proposed that foster the proposed calibration constant shifts cannot occur as postulated for several reasons. As well, he proposes Shanahan has ignored the extant data proving this. Because this Letter may lend unwarranted support to acceptance of cold fusion claims, these erroneous arguments used by Storms need to be answered.     

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。     

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.