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J.C. Chedjou, K. Kyamakya, U.A. Khan, M.A. Latif
One of the most common approaches to avoid complexity while numerically solving stiff ordinary differential equations (ODEs) is approximating them by ignoring the nonlinear terms. While facing stiff partial differential equations (PDEs) the same is done by avoiding/suppressing the nonlinear terms from the Taylor’s series expansion. By so doing, the traditional methods for solving stiff […]
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R. Dolan, G. DeSouza
The inherent massive parallelism of cellular neural networks makes them an ideal computational platform for kernel-based algorithms and image processing. General-purpose GPUs provide similar massive parallelism, but it can be difficult to design algorithms to make optimal use of the hardware. The presented research includes a GPU abstraction based on cellular neural networks. The abstraction […]
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B. G. Soos, A. Rak, J. Veres, G. Cserey
In this paper, we introduce an innovative CNN algorithm development environment that significantly assists algorithmic design. The introduced graphical user interface uses Matlab Simulink with UMF-like program description, where direct functionality accompanies better accessability. The new generation of graphical cards incorporate many general purpose graphics processing units, giving the power of parallel computing to a […]

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