Fluorescent dye labeled bacteriophages--a new tracer for the investigation of viral transport in porous media: 1. Introduction and characterization

A new method for the study of pathogen transport in porous media is presented. The method is based on conjugation of fluorescent dyes to target bacteriophages and application of the modified bacteriophages for tracer studies. We demonstrate that the relevant transport determining properties of Rhodamine and several fluorescein-labeled phages are practically identical to those of the native bacteriophages. The advantages of the proposed method relative to direct enumeration of bacteriophages by plaque forming unit method, turbidity, fluorescent microspheres, and other alternative tracers are discussed. Notable advantages include simple quantitation by optical methods, unbiased signals even when virus aggregates are formed, and the ability to decouple inactivation kinetics from transport phenomena. Additionally, the signal reflects the removal and transport of the studied microorganism and not a surrogate.     

Development of an interferometer for measurement of the diffusion coefficient of miscible liquids

A common-path interferometer (CPI) system was developed to measure the diffusivity of transparent liquid pairs by real-time visualization of the concentration gradient profile. The CPI is an optical technique that can be used to measure changes in the gradient of the refractive index of transparent materials. The CPI is a shearing interferometer that shares the same optical path from a laser light source to the final imaging plane. Molecular diffusivity of liquids can be determined by use of physical relations between changes in the optical path length and the liquid phase properties. The data obtained by this interferometer are compared with similar results from other techniques. This demonstrates that the instrument is reliable for measurement of the diffusivity of miscible liquids and allows the system to be compact and robust. It can also be useful for studies in interface dynamics as well as other applications in a low-gravity environment.     

A joint compression-discrimination neural transformation applied to target detection.

Many image recognition algorithms based on data-learning perform dimensionality reduction before the actual learning and classification because the high dimensionality of raw imagery would require enormous training sets to achieve satisfactory performance. A potential problem with this approach is that most dimensionality reduction techniques, such as principal component analysis (PCA), seek to maximize the representation of data variation into a small number of PCA components, without considering interclass discriminability. This paper presents a neural-network-based transformation that simultaneously seeks to provide dimensionality reduction and a high degree of discriminability by combining together the learning mechanism of a neural-network-based PCA and a backpropagation learning algorithm. The joint discrimination-compression algorithm is applied to infrared imagery to detect military vehicles.     

4H-SiC钒离子注入层的特性

研究了钒注入4H-SiC形成半绝缘层的方法和特性，注入层的离子浓度分布由蒙特卡罗分析软件TRIM模拟提取. 采用一种台面结构进行I-V测试. 钒注入层的电阻率与4H-SiC层的初始导电类型关系很大，常温下钒注入p型和n型SiC的电阻率分别为1.2E9～1.6E10Ω·cm和2.0E6～7.6E6Ω·cm.     

Interactive Selective and Adaptive Clustering for Detection of Microcalcifications in Mammograms

Estevez, L., Kehtarnavaz, N., and Wendt, R. III, Interactive Selective and Adaptive Clustering for Detection of Microcalcifications in Mammograms,Digital Signal Processing6(1996), 224–232.This paper presents a clustering algorithm, called interactive selective and adaptive clustering (Isaac), to assist radiologists in looking for small clusters of microcalcifications in mammograms. Isaac is developed to identify suspicious microcalcification regions which are missed by other classification techniques due to false positive samples in the feature space. It comprises two parts: (i) selective clustering and (ii) interactive adaptation. The first part reduces the number of false positives by identifying the microcalcification subspace or domains in the feature space. The second part allows the radiologist to improve results by interactively identifying additional false positive or true negative samples. Clinical evaluations of mammograms indicate the potential of using this algorithm as an effective tool to bring microcalcification areas to the attention of the radiologist during a routine reading session of mammograms.     

Skin cancer detection by spectroscopic oblique-incidence reflectometry: classification and physiological origins

Data obtained from 102 skin lesions in vivo by spectroscopic oblique-incidence reflectometry were analyzed. The participating physicians initially divided the skin lesions into two visually distinguishable groups based on the lesions' melanocytic conditions. Group 1 consisted of the following two cancerous and benign subgroups: (1) basal cell carcinomas and squamous cell carcinomas and (2) benign actinic keratoses, seborrheic keratoses, and warts. Group 2 consisted of (1) dysplastic nevi and (2) benign common nevi. For each group, a bootstrap-based Bayes classifier was designed to separate the benign from the dysplastic or cancerous tissues. A genetic algorithm was then used to obtain the most effective combination of spatiospectral features for each classifier. The classifiers, tested with prospective blind studies, reached statistical accuracies of 100% and 95% for groups 1 and 2, respectively. Properties that related to cell-nuclear size, to the concentration of oxyhemoglobin, and to the concentration of deoxyhemoglobin as well as the derived concentration of total hemoglobin and oxygen saturation were defined to explain the origins of the classification outcomes.     

Handwriting style classification

This paper describes an independent handwriting style classifier that has been designed to select the best recognizer for a given style of writing. For this purpose a definition of handwriting legibility has been defined and a method implemented that can predict this legibility. The technique consists of two phases. In the feature-extraction phase, a set of 36 features is extracted from the image contour. In the classification phase, two nonparametric classification techniques are applied to the extracted features in order to compare their effectiveness in classifying words into legible, illegible, and middle classes. In the first method, a multiple discriminant analysis (MDA) is used to transform the space of extracted features (36 dimensions) into an optimal discriminant space for a nearest mean based classifier. In the second method, a probabilistic neural network (PNN) based on the Bayes strategy and nonparametric estimation of probability density function is used. The experimental results show that the PNN method gives superior classification results when compared with the MDA method. For the legible, illegible, and middle handwriting the method provides 86.5% (legible/illegible), 65.5% (legible/middle), and 90.5% (middle/illegible) correct classification for two classes. For the three-class legibility classification the rate of correct classification is 67.33% using a PNN classifier.Received: 6 September 2002, Accepted: 19 September 2002, Published online: 6 June 2003     

SiC外延工艺中的气体流体模型

构建了热壁CVD法生长α-SiC外延的气体流体力学模型,并通过COMSOL模拟软件计算对基座的几何形状进行设计.结果表明基座的几何形状改变,影响衬底表面的气流分布;基座有一定的倾斜角度,可以使其表面气流分布均匀,有利于得到高质量的α-SiC外延片.