Future of biological aerosol detection

Biological aerosol detection in real time is an urgent civilian and military requirement. Such detection capability will be useful in environmental monitoring, for example, in gathering information in perceived hazardous areas such as housing developments downwind of sewage treatment plants. To be truly functional, the instrument has to operate continuously, 24 h a day and 7 days a week with minimal maintenance and few false alarms. A novel concept is proposed. The system employs a rapid front-end warning/alarming mechanism based on optical technologies that provides useful information for protection decision makers. This is connected to a sample collector that feeds a slower back-end liquid chemistry system that provides analytical results to the medical personnel to assist in prophylaxis and therapy decisions. Experience gained from measuring fluorescence signals of single bacterial spores under flow cytometry (FCM) using UV excitation at 340-360 nm, was applied to concept testing of a prototype instrument, built to do the same for aerosols. This machine was capable of resolving particle size as well as fluorescence intensity of each particle under laboratory and field conditions; it was called the fluorescent aerodynamic particle sizer (FLAPS). This paper describes practical aspects of measuring biological aerosols when the results must be compared to reference samplers that provide culturable or “live” data. Treatment of particle size and fluorescence information is discussed with respect to FLAPS and reference data fidelity. Along with an objective method to evaluate FLAPS data correlation to reference data, an approach for determining limit of detection in the field is discussed. In addressing the back-end detector chemistry, we have prioritized a number of important biological characteristics that must be given to a clinician to help in prophylaxis and therapy decisions. A series of biochemical measurements are proposed to define the threat of a sample and different solutions are given to implement these tests. We predict that the future for biological detection looks promising for fluorescence in situ hybridization (FISH) techniques in identifying microorganisms. A conceptual instrument based on merging FCM and microchip-based analysis is described.     

基于FCM算法的小脑基因模糊聚类分析

探讨基因表达数据的聚类分析方法,结合一种聚类结果的评判准则,应用于胎儿小脑基因表达数据,得到了最优的聚类结果,并做出了生物学解释.利用Matlab软件进行了仿真,利用模糊聚类Xie-Beni指数得到了最优聚类数,并把每一类对应的基因标号输出到txt文件,最后进行生物学解释.得到的小脑基因最优聚类数为3类,与生物学意义比较吻合,各类中的基因功能接近.基于FCM算法的基因模糊聚类是有效的,结果具有一定生物学意义,能对生物学基因聚类有一定指导作用.     

基于T2FCM-FE-HMMs的隐马氏模型参数估计算法

把2型模糊集的思想引入到了基于模糊聚类的离散HMM参数训练中,提出了改进的T 2FCM-FE-HMM s算法。     

神经元的形态分类和识别——2010年全国研究生数学建模竞赛C题论文

根据神经元的空间几何特征参数,研究神经元的空间形态分类方法,将神经元根据几何形态分类识别.首次提出利用模糊C均值算法(FCM)建立了模糊聚类分析模型,对所给的神经元样本进行了分类,这一模型用来对神经元进行分类比较合理,且计算结果准确.同时,还利用相关分析原理分析了不同动物神经系统中同一类神经元的形态特征有着很高的相似度.     

一种改进的Fuzzy-HMMS算法及其收敛性

对D at T ran和M ichae lW agner等人提出的FCM-FE-HMM S算法作了进一步的补充和改进,提出了改进的FCM-FE-HMM S算法,并给出了算法收敛性分析,得出在给定初值的情况下该算法将收敛到一个局部最优解。     

A segmentation method for noisy photoelectric image

In order to improve the ability of noisy photoelectric image segmentation and satisfy the requirement of human visual apperception, a noisy photoelectric image segmentation method is proposed in this paper. Firstly, the basic principle of FCM algorithm is analyzed in detail. Secondly, on the basis of PCM algorithm, only such pixels affected by noise lesser that centralized in the diagonal area of two-dimensional histogram is actualized image processing, and the probability of the pixel that lie in such area is obtained. Thirdly, objective function, membership matrix and cluster centers are renovated on the basis of PCM algorithm. Finally, experiment contrast between our method and other methods is executed, the results of experiment indicate that our method has relatively better segmentation quality and faster segmentation speed.     

基于模糊均值的细菌群体趋药性复杂网络社团结构发现

复杂网络的社团发现问题是网络数据挖掘中的重要问题之一.利用基于模糊C均值的细菌群体趋药性算法最大化网络的模块度,算法中模糊C均值的初始值由群体细菌取药性算法获得.模糊C均值算法在此基础上发现复杂网络的社团结构.其创新点在于最佳模块度的寻找.实验结果表明:该算法具有对现实世界网络社团划分的可行性和有效性.     

一种机器人导航中数字路标的分割和识别方法

基于路标的移动机器人视觉导航的基础上,提出了一种基于边缘点数量统计和FCM(Fuzzy C-means)聚类的路标图像分割方法。在对分割后图像进行维纳滤波,二值化等预处理的基础上,完成了数字路标的识别。通过实验证明,该算法能实时地完成数字路标的分割和识别,有效地应用于移动机器人的视觉导航。     

基于二元语义和区间FCM的森林公园生态安全评价方法研究

由于传统的生态安全评价方法不能处理区间数和语言值,并且安全度等级划分的主观性和随意性较大,评价结果有效性差。基于二元语义和区间FCM的生态安全评价方法,将实数看成区间数的特例,语言数也转化为区间数,FCM聚类方法将区间值转化为语言值,根据最大隶属度原则,对方案集进行分类。将语言值转化为二元语义,将方案集成结果转换为二元语义,利用二元语义的有序性对方案进行排序,既能处理区间值也能处理语言值,分类标准更加客观。