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An EEMD Signal Processing Method Realized via GPU and CPU

Xiaojun Zhu

Abstract


Ensemble empirical mode decomposition (EEMD) restrains the possible mode mixing problems in traditional empirical mode decomposition (EMD) during the decomposition process. It can be realized via auxiliary analysis by adding several groups of white noises into the original signal. However, it significantly reduces the decomposition efficiency for signals. In order to eliminate the influence of mode mixing and improve decomposition efficiency, this paper proposes an EEMD parallel computation model based on GPU+CPU. The core idea of this method is to carry out logic operation namely iteration control of decomposition with CPU and execute high-capacity numerical computation with GPU. Experiments are conducted for simulation signal and EEG signal respectively. The results demonstrate that the computation velocity associated with this model is 5-8 times higher than that of serial computation model based on CPU.

Keywords


EEMD, CUDA, GPU and CPU, EEG, Parallel Computation.

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