In this paper, we study a cancer invasion model both theoretically and numerically. The model is a nonstationary, nonlinear system of three coupled partial differential equations modeling the motion of cancer cells, d...
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In this paper, we study a cancer invasion model both theoretically and numerically. The model is a nonstationary, nonlinear system of three coupled partial differential equations modeling the motion of cancer cells, degradation of the extracellular matrix, and certain enzymes. We first establish existence of global classical solutions in both two- and three-dimensional bounded domains, despite the lack of diffusion of the matrix-degrading enzymes and corresponding regularizing effects in the analytical treatment. Next, we give a weak formulation and apply finite differences in time and a Galerkin finite element scheme for spatial discretization. The overall algorithm is based on a fixed-point iteration scheme. Our theory and numerical developments are accompanied by some simulations in two and three spatial dimensions.
We consider Anderson extrapolation to accelerate the (stationary) Richardson iterative method for sparse linear systems. Using an Anderson mixing at periodic intervals, we assess how this benefits convergence to a pre...
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We consider Anderson extrapolation to accelerate the (stationary) Richardson iterative method for sparse linear systems. Using an Anderson mixing at periodic intervals, we assess how this benefits convergence to a prescribed accuracy. The method, named alternating Anderson-Richardson, has appealing properties for high-performance computing, such as the potential to reduce communication and storage in comparison to more conventional linear solvers. We establish sufficient conditions for convergence, and we evaluate the performance of this technique in combination with various preconditioners through numerical examples. Furthermore, we propose an augmented version of this technique.
This paper proposes a novel algebraic integer (AI) based multi-encoding of Daubechies-4 and -6 2-D wavelet filters having error-free integer-based computation. Digital VLSI architectures employing parallel channels ar...
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This paper proposes a novel algebraic integer (AI) based multi-encoding of Daubechies-4 and -6 2-D wavelet filters having error-free integer-based computation. Digital VLSI architectures employing parallel channels are proposed, physically realized and tested. The multi-encoded AI framework allows a multiplication-free and computationally accurate architecture. It also guarantees a noise-free computation throughput the multi-level multi-rate 2-D filtering operation. A single final reconstruction step (FRS) furnishes filtered and down-sampled image outputs in fixed-point, resulting in low levels of quantization noise. Comparisons are provided between Daubechies-4 and -6 designs in terms of SNR, PSNR, hardware structure, and power consumptions, for different word lengths. SNR and PSNR improvements of approximately 30% were observed in favour of AI-based systems, when compared to 8-bit fixed-point schemes (six fractional bits). Further, FRS designs based on canonical signed digit representation and on expansion factors are proposed. The Daubechies-4 and -6 4-level VLSI architectures are prototyped on a Xilinx Virtex-6 vcx240t-1ff1156 FPGA device at 282 MHz and 146 MHz, respectively, with dynamic power consumption of 164 mW and 339 mW, respectively, and verified on FPGA chip using an ML605 platform.
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