Thermal performance of different pond cells under actual climatic conditions in Qatar

Experimental work was undertaken to study the thermal performance of three different pond cells under actual climatic conditions of Qatar. The first cell was an open pond. In the second unit, a finned radiator, painted black, was placed over the pond. In the third cell, the water was contained in a polyethylene bag. Experimental results indicated that the exposed pond cell came out best, when considered in terms of how quickly the temperature of the stored water could be reduced, and how low that temperature could be. The water in the exposed pond was cooled directly (by evaporation and radiation to sky) whereas in the case of the finned radiator cell, heat had to be transferred from the stored water to the radiator, and then from the radiator to the surrounding. In the case of water-in-bag cell, evaporation was eliminated and radiative exchange with the sky was reduced due to the partial transparency of the polyethylene bag (in the infrared region).     

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.     

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     

A SOCIOLOGICAL PERSPECTIVE ON THE REORDERING PROBLEM IN MULTIPATH ROUTING

The term multipath routing means using multiple paths concurrently to transport data over network. The main problem of this routing scheme is the difference among the delays of selected paths, which causes reordering of a single flow's packets. In this paper, this problem is analyzed through a sociological perspective. We show that reordering problem is not inherently related to multipath routing, rather caused by the dominant capitalist view of the problem. then, the problem is addressed through a Marxism perspective. We theoretically prove that by this perspective, there exists a routing scheme that minimizes latency and also the requirement of buffering at receiver.