Abstract
Digital signal processing is critical during the use of sensor-clouds, and the data information acquired by sensors is inevitably noisy. The Variational Mode Decomposition (VMD) algorithm can be used to reduce noise on the signal. The selection of the modal decomposition number of the VMD algorithm and the selection of the modal components of the reconstructed signal affect the effect of signal noise reduction. In this paper, an adaptive variational mode decomposition scheme is proposed. It adaptively selects the modal decomposition number through the established modal decomposition number and the input signal sample entropy model, selects the optimal modal component according to the sample entropy threshold, and reconstructs the signal after the selected modal component is processed by data smoothing. The results show that compared with traditional variational mode decomposition, empirical mode decomposition algorithms, and other algorithms, the effect of adaptive variational mode decomposition in filtering out signal noise is analyzed. The proposed adaptive VMD (A-VMD) algorithm can effectively filter out the noise of the signal obtained by the sensor.
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Acknowledgment
This work was funded by the Applied Basic Research Program of Qinghai Province (Grant Number 2020-ZJ-724) and the Shanghai Natural Science Foundation (Grant Number 21ZR1461700).
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Huo, Z., Jia, G., Fang, W., Chen, W., Zhang, W. (2023). A-VMD: Adaptive Variational Mode Decomposition Scheme for Noise Reduction in Sensor-Cloud. In: Wang, G., Choo, KK.R., Wu, J., Damiani, E. (eds) Ubiquitous Security. UbiSec 2022. Communications in Computer and Information Science, vol 1768. Springer, Singapore. https://doi.org/10.1007/978-981-99-0272-9_33
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DOI: https://doi.org/10.1007/978-981-99-0272-9_33
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