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The parallelization is achieved by utilizing the parallel processing capabilities of the CUDA architecture of a low cost NVIDIA GPU card.
In consideration with the imperfect networked environment with signal transmission delay and data packet dropout, a fuzzy NCS via dynamic output feedback is established by utilizing the parallel distributed compensation (PDC) technique.
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Attention is focused on the design of a fuzzy state feedback controller, which is implemented by utilizing the so-called parallel distributed compensation scheme.
This paper presents a new MPC algorithm based on multiplexed MPC that can take advantage of the full potential of an existing FPGA design by utilizing the provided 'free' parallel computational channels arising from such pipelining.
This analysis method was successfully applied to simply supported rectangular plates reinforced with multi-parallel beams by utilizing the power flow coupling relationships at the beam plate junctions and the zero intensity conditions at the plate edges.
By utilizing the designed fuzzy state observer, a serial-parallel estimation model is established.
The periodicity introduced by the parallel distribution of rib-stiffeners is considered by utilizing the space harmonic series to express the solutions.
In this paper, we propose a parallel implementation of the Wang Landau algorithm on computers of shared memory architectures by utilizing the OpenMP API for distributed computing.
We implement the SMCPP in a fully parallel way based on CUDA, which is a technology that can increase the computing performance dramatically by utilizing the hundreds of cores in graphics processing unit (GPU).
Pursue further research by utilizing the Internet.
The NVIDIA compute unified device architecture (CUDA) platform [7] is used in order to access the computational capability provided by the GPU, which enables a programmer to utilize the parallel processing functionality of NVIDIA GPUs.
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