111,194 research outputs found

    The optimization of profiled diffusers

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    Methods have been developed to produce profiled diffusers that create a large amount of diffusion. The methods are iterative and required the development of a new parameter to measure diffusion. Achieving scattering independent of angle has been attempted over a wide bandwidth. The methods are also applicable to other diffusion criteria. The diffusers consists of a series of wells of the same width but of different depths similar to Schroeder diffusers. Applications include concert halls, theatres, and studio monitor rooms. The new diffusers have been shown to create better, more uniform diffusion than the previous designs of Schroeder. This is due to the new designs being reliant on accurate boundary element prediction methods rather than more approximate techniques

    Networks for Nonlinear Diffusion Problems in Imaging

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    A multitude of imaging and vision tasks have seen recently a major transformation by deep learning methods and in particular by the application of convolutional neural networks. These methods achieve impressive results, even for applications where it is not apparent that convolutions are suited to capture the underlying physics. In this work we develop a network architecture based on nonlinear diffusion processes, named DiffNet. By design, we obtain a nonlinear network architecture that is well suited for diffusion related problems in imaging. Furthermore, the performed updates are explicit, by which we obtain better interpretability and generalisability compared to classical convolutional neural network architectures. The performance of DiffNet tested on the inverse problem of nonlinear diffusion with the Perona-Malik filter on the STL-10 image dataset. We obtain competitive results to the established U-Net architecture, with a fraction of parameters and necessary training data

    Parabolic and Hyperbolic Contours for Computing the Bromwich Integral

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    Some of the most effective methods for the numerical inversion of the Laplace transform are based on the approximation of the Bromwich contour integral. The accuracy of these methods often hinges on a good choice of contour, and several such contours have been proposed in the literature. Here we analyze two recently proposed contours, namely a parabola and a hyperbola. Using a representative model problem, we determine estimates for the optimal parameters that define these contours. An application to a fractional diffusion equation is presented.\ud \ud JACW was supported by the National Research Foundation in South Africa under grant FA200503230001

    The effect of integration time on fluctuation measurements: calibrating an optical trap in the presence of motion blur

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    Dynamical instrument limitations, such as finite detection bandwidth, do not simply add statistical errors to fluctuation measurements, but can create significant systematic biases that affect the measurement of steady-state properties. Such effects must be considered when calibrating ultra-sensitive force probes by analyzing the observed Brownian fluctuations. In this article, we present a novel method for extracting the true spring constant and diffusion coefficient of a harmonically confined Brownian particle that extends the standard equipartition and power spectrum techniques to account for video-image motion blur. These results are confirmed both numerically with a Brownian dynamics simulation, and experimentally with laser optical tweezers.Comment: 12 pages, 6 figures, revtex4; published in Optics Express. http://www.opticsinfobase.org/abstract.cfm?URI=oe-14-25-1251
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