Geostatistical Analysis of Spatial Distribution of Salt Bed Thickness

The data on salt bed thickness distribution are processed using the semi-variogram analysis. Two- and three-dimensional maps of bed thickness distribution, as well as the error maps are presented.     

Characterization and lime treatment of olive mill wastewater

Seventeen olive mill wastewater (OMW) samples were characterized. For characterization of the samples, amount of total, fixed, volatile, suspended, volatile-suspended solids, COD, oil-grease, polyphenol, volatile phenol, nitrogen and reducing sugar were determined. Effects of lime treatment on the waste samples were investigated. Reduction of contents of the samples treated with the lime was described. The effect of the addition of lime on an artificial phenolic mixture was also examined.     

Long-wavelength monolithic VCSEL arrays with high optical output power

Two-dimensional vertical-cavity surface-emitting laser (VCSEL) arrays at 1.55 /spl mu/m wavelength with parallel operation of single- or multimode devices are presented. 3/spl times/3 VCSEL arrays show maximum output powers beyond 30 mW and conversion efficiencies as high as 22%.     

The impact of a realistic packet traffic model on the performance of surveillance wireless sensor networks

It is quite common to see that classical periodic or Poisson packet traffic models are used for evaluating the performance of wireless sensor networks (WSNs). However, these models may not be appropriate for modeling the data traffic resulting from a particular application. Furthermore, they may be overestimating the performance of a WSN. In this paper, we show the significance of using a realistic and application-specific packet traffic model by comparing the performance of a well-known WSN protocol under the Surveillance WSN packet traffic model (SPTM), as well as under periodic and binomial traffic models. A packet traffic framework specific to surveillance applications is proposed which is then used for deriving SPTM analytically. In order to be adaptable and flexible, SPTM incorporates a probabilistic and parametric sensor detection model. Simulation results show that to employ an application-specific packet traffic model has significant impact on the performance evaluation of the WSN and ordinary traffic models may overestimate the capacity of the WSN.     

A multilayer incremental neural network architecture for classification

A new multilayer incremental neural network (MINN) architecture and its performance in classification of biomedical images is discussed. The MINN consists of an input layer, two hidden layers and an output layer. The first stage between the input and first hidden layer consists of perceptrons. The number of perceptrons and their weights are determined by defining a fitness function which is maximized by the genetic algorithm (GA). The second stage involves feature vectors which are the codewords obtained automaticaly after learning the first stage. The last stage consists of OR gates which combine the nodes of the second hidden layer representing the same class. The comparative performance results of the MINN and the backpropagation (BP) network indicates that the MINN results in faster learning, much simpler network and equal or better classification performance.     

Modelling of the spring-in phenomenon in curved parts made of a thermosetting composite

In this study, a simple two step finite element model is developed to predict the spring-in of C-sections parts made of AS4/8552 composite. The development of resin properties throughout the MRCC were derived using Group Interaction Modelling and the mechanical properties of the composite predicted by using micromechanical models. Important mechanisms during manufacturing that are effective in the formation of residual stresses and shape distortions are defined. The finite element method implemented is composed of two steps before and after the vitrification of the resin. Vitrification is treated as a point at which the material suddenly changes from the rubbery to glassy state with constant properties in each state. The spring-in angles predicted by the finite element analysis are compared to the angles measured on C-section specimens of various lay-ups and thicknesses. The correlation is good showing the validity of the assumptions adopted.     

PNS modules for the synthesis of parallel self-organizing hierarchical neural networks

The PNS module is discussed as the building block for the synthesis of parallel, self-organizing, hierarchical, neural networks (PSHNNs). The PNS module contains three submodules (units), the first two of which are created as simple neural network constructs and the last of which is a statistical unit. The first two units are fractile in nature, meaning that each such unit may itself consist of a number of parallel PNS modules in a fractile fashion. Through a mechanism of statistical acceptance or rejection of input vectors for classification, the sample space is divided into a number of regions. The input vectors belonging to each region are classified by a dedicated set of PNS modules. This strategy results in considerably higher accuracy of classification and better generalization as compared to previous neural network models. If the delta rule network is used to generate the first two units, each region approximates a linearly separable region. In this sense, the total system becomes similar to a piecewise linear model. The various regions are determined nonlinearly by the first and third units of the PNS modules.