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In this paper a PDE based hybrid method for image denoising is introduced. The method is a bi-stage filter with anisotropic diffusion followed by wavelet based bayesian shrinkage. Here efficient denoising is achieved by reducing the convergence time of anisotropic diffusion. As the convergence time decreases, image blurring can be restricted and will produce a better denoised image than anisotropic or wavelet based methods. Experimental results based on PSNR, SSIM and edge analysis shows excellent performance of the proposed method.

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The peristaltic motion of a fluid in which are distributed uniform rigid particles through an axisymmetric tube of arbitrary wave shape is considered for low Reynolds numbers. Solutions for the stream functions and vorticity functions are obtained in the form of asymptotic expansions regarding the ratio (ε) of the tube radius to the wavelength of the peristaltic wave to be small. We have assumed the velocity equilibration length of particulate phase to be equal to the wavelength of the peristalsis. Expressions for mean pressure gradient and shear stress are obtained. Also we have discussed phenomena like “reflux” and “trapping”. The study is particularly helpful in engineering applications such as pumping of solid-fluid mixtures by peristalsis.

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