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For smooth flow geometries, such as the plane Poiseuille flow which is studied here, the method combines the advantages of a high accuracy, exponentially fast convergent with mesh refinement and spectral approximation with those of an efficient numerical implementation well suited for a vector/parallel computer architecture.
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Its implementation is well suited for a parallel computing environment.
Our algorithm takes advantage of a non-linear model of neural tuning in a computationally inexpensive implementation that is well suited for mobile, low-power prosthetic systems.
The NU-DESC has been validated against more extensive tests and impresses mainly with its simple implementation, making it well suited for routine use in a PACU [ 13, 16].
The methods do not require either smoothing parameters or bootstrap resampling for their implementation and so are well suited to practical work.
Nevertheless, this work has been performed several times [1 3] using standard PC, and the obtained processing rate is around 10 images/s; the implementation is still not well suited for embedded applications and does not use parallelism.
We conclude that the proposed design offers greatly improved memory and computational efficiency compared to a naïve implementation of the EWMA's difference equation, and that it is well suited for implementation in digital hardware.
Hardware implementations of Spiking Neural Networks are numerous because they are well suited for implementation in digital and analog hardware, and outperform classic neural networks.
New implementations that are particularly well suited for use with the discrepancy principle are described.
The Karplus Strong algorithm is particularly well suited for implementation on a microcontroller due to its simple arithmetic.
The Reduced Instruction Set Computer (RISC) is an architecture particularly well suited for implementation as a single-chip VLSI computer.
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