On the Memory Cost of EMD Algorithm

Hsu Wen Vincent Young, Yu Chuan Lin, Yung Hung Wang

研究成果: 雜誌貢獻期刊論文同行評審

4 引文 斯高帕斯(Scopus)


Empirical mode decomposition (EMD) and its variants are adaptive algorithms that decompose a time series into a few oscillation components called intrinsic mode functions (IMFs). They are powerful signal processing tools and have been successfully applied in many applications. Previous research shows that EMD is an efficient algorithm with computational complexity O(n) for a given number of IMFs, where n is the signal length, but its memory is as large as (13+mimf)n, where mimf is the number of IMFs. This huge memory requirement hinders many applications of EMD. A physical or physiological oscillation (PO) mode often consists of a single IMF or the sum of several adjacent IMFs. Let mout denote the number of PO modes and, by definition, mOut≤ mimf. In this paper, we will propose a low memory cost implementation of EMD and prove that the memory can be optimized to (2+mout)n without aggravating the computational complexity, while gives the same results. Finally, we discuss the optimized memory requirements for different noise-assisted EMD algorithms.

頁(從 - 到)114242-114251
期刊IEEE Access
出版狀態已出版 - 2022


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