2017
DOI: 10.1049/iet-spr.2016.0307
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Reconstruction and basis function construction of electromagnetic interference source signals based on Toeplitz‐based singular value decomposition

Abstract: In this study, the authors propose a novel method, namely Toeplitz-based singular value decomposition (or TL-SVD for short) for the reconstruction and basis function construction of electromagnetic interference (EMI) source signals. Given a specific EMI source signal, they first construct a Toeplitz type data matrix. By applying singular value decomposition (SVD) to the constructed matrix, they obtain a set of singular values, which are further divided into two parts, corresponding to the clear and noisy compo… Show more

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Cited by 10 publications
(3 citation statements)
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“…IEMD Algorithm. Many researchers have employed the EMD method, which is an adaptive algorithm to perform the reconstruction [22], classification [23], and denoising [24] of a signal. However, the use of CSI to generate peak and lowest envelopes has some disadvantages such as overtones and subtones.…”
Section: Description Of Iemd-iwfdmentioning
confidence: 99%
“…IEMD Algorithm. Many researchers have employed the EMD method, which is an adaptive algorithm to perform the reconstruction [22], classification [23], and denoising [24] of a signal. However, the use of CSI to generate peak and lowest envelopes has some disadvantages such as overtones and subtones.…”
Section: Description Of Iemd-iwfdmentioning
confidence: 99%
“…Common methods for signal processing includes: independent component analysis [1], principle component analysis (PCA) [2], wavelet transform [3], Hilbert Huang transform [4], subspace learning [5][6][7], empirical mode decomposition [5,[7][8][9][10][11], and clustering [12][13][14][15]. To all of these methods, it is always crucial to determine how to select and extract features from a signal, especially from those high-dimension signals.…”
Section: Introductionmentioning
confidence: 99%
“…It has successfully extended from theoretical research to a variety of applications, such as signal classification [1], image and signal denoising [2,[31][32][33][34], blind sources separation [3] and so on. So far, most denosing literatures about sparse representation are image denoising [4]- [6], and signal denoising is rarely studied.…”
Section: Introductionmentioning
confidence: 99%