Optimal signal reconstruction based on time-varying weighted empirical mode decomposition

Kızılkaya, Aydın; Elbi, Mehmet Dogan

doi:10.1016/j.compeleceng.2016.12.006

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Specifically, in the case of Fourier analysis, components in a signal are defined in terms of sine and cosine waves. The EMD defines components in terms of IMFs, which are sets of symmetric mono frequency signals, or the narrow-band signals that represent the corresponding frequency components of signals [ 17 , 18 , 19 , 20 ].…”

Section: Theoretical Framework Of Emd Filtering Methodsmentioning

confidence: 99%

Novel EMD-Based Extraction Approach of Electric Field Fringe Impacts on a Micro Capacitive Displacement Sensor

2018

Micromachines

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This paper presents an EMD (empirical mode decomposition)-based extraction approach to decouple the electric field fringe component impacts on the nonlinearity errors of a micro encoder-like capacitive displacement sensor. A calculated capacitance model built under Maxwell’s equations against the impacts of electric field fringe component indicates that signals of this sensor are all in periodic co-sinusoidal waveforms. Applying the proposed EMD scheme, signals are decomposed into sets of intrinsic mode functions (IMFs) and a residual, in which a fundamental component represents all the features of sensor signal and reserves the local information. Interpreting sensor information from the extracted component instead of the original signal drastically diminishes the impacts of electric field fringe effect. Results from a test bench shows that after applying the EMD-based extraction approach, waveform errors were decreased from over 4.18% to less than 0.89%. Nonlinearity of the interpreted displacement was decreased from 1.54% to 0.29% for 8 mm stroke.

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Section: Theoretical Framework Of Emd Filtering Methodsmentioning

confidence: 99%

Novel EMD-Based Extraction Approach of Electric Field Fringe Impacts on a Micro Capacitive Displacement Sensor

2018

Micromachines

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“…In (Weng and Barner, 2008), authors utilize a linear and a bidirectional weighting for the reconstruction of the denoised signal. Authors in (Kizilkaya and Elbi, 2017a, 2017b; Kizilkaya et al, 2016) developed an optimal signal reconstruction algorithm in which time-varying weights are determined for each of the oscillation modes.…”

Section: Introductionmentioning

confidence: 99%

Component-scaled signal reconstruction for enhanced noise filtration

Singh

Moore

2022

Journal of Vibration and Control

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This research introduces a procedure for signal denoising based on linear combinations of intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). The method, termed component-scaled signal reconstruction, employs the standard EMD algorithm, with no enhancements to decompose the signal into a set of IMFs. The problem of mode mixing is leveraged for noise removal by constructing an optimal linear combination of the potentially mixed IMFs. The optimal linear combination is determined using an optimization routine with an objective function that maximizes and minimizes the information and noise, respectively, in the denoised signal. The method is demonstrated by applying it to a computer-generated voice sample and the displacement response of a cantilever beam with local stiffness nonlinearity. In the first application, the noise is introduced into the sample manually by adding a Gaussian white-noise signal to the signal. In the second application, the response of the entire beam is filmed using two 1-megapixel cameras, and the three-dimensional displacement field is extracted using digital image correlation. The noise in this application arises entirely from the images captured. The proposed method is compared to existing EMD, ensemble EMD, and LMD based denoising approaches and is found to perform better.

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Parameter free and reliable signal denoising based on constants obtained from IMFs of white Gaussian noise

2017

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Optimal signal reconstruction based on time-varying weighted empirical mode decomposition

Cited by 5 publications

References 19 publications

Novel EMD-Based Extraction Approach of Electric Field Fringe Impacts on a Micro Capacitive Displacement Sensor

Novel EMD-Based Extraction Approach of Electric Field Fringe Impacts on a Micro Capacitive Displacement Sensor

Component-scaled signal reconstruction for enhanced noise filtration

Parameter free and reliable signal denoising based on constants obtained from IMFs of white Gaussian noise

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