In a previous paper3\ it was shown that the numerical solution of the cross flow around a circular cylinder submerged in viscoelastic fluids agreed with the experimental results obtained by James and Acosta1^The analysis was restricted to the moderate Reynolds number range (l

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In order to create a photorealistic Virtual Reality model, we have to record the appearance of the object from different directions under different illuminations. In this paper, we propose a method that renders photorealistic images from a small amount of data. First, we separate the images of the object into a diffuse reflection component and a specular reflection component by using linear polarizers. Then, we estimate the parameters of the reflection model for each component. Finally, we compress the difference between the input images and the rendered images by using wavelet transform. At the rendering stage, we first calculate the diffuse and specular reflection images from the reflection parameters, then add the difference decompressed by inverse wavelet transform into the calculated reflection images, and finally obtain the photorealistic image of the object.

A high-order feedforward neural architecture, called pi t -sigma (π t σ ) neural network, is proposed for lossy digital image compression and reconstruction problems. The π t σ network architecture is composed of an input layer, a single hidden layer, and an output layer. The hidden layer is composed of classical additive neurons, whereas the output layer is composed of translated multiplicative neurons (π t -neurons). A two-stage learning algorithm is proposed to adjust the parameters of the π t σ network: first, a genetic algorithm (GA) is used to avoid premature convergence to poor local minima; in the second stage, a conjugate gradient method is used to fine-tune the solution found by GA. Experiments using the Standard Image Database and infrared satellite images show that the proposed π t σ network performs better than classical multilayer perceptron, improving the reconstruction precision (measured by the mean squared error) in about 56%, on average.

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