Parameters Extraction of the Au/SnO 2 -Si(n)/Al p-n Junction Solar Cell Using Lambert W Function

Belgacem, Chokri Hadj; El-Amin, A. A.

doi:10.1007/s12633-014-9216-0

Cited by 11 publications

(1 citation statement)

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“…Another proposed method for feature extraction on ECG biometrics applications is the frequency-based approach. Analysis of the frequency domain based on the Fourier transform has been conducted in research [12][13]. Other researchers propose the time-frequency domain method for feature extraction, as reported in [14][15].…”

Section: Introductionmentioning

confidence: 99%

Biometric systems based on ECG using ensemble empirical mode decomposition and Variational Mode decomposition

Hadiyoso¹,

Wijayanto²,

Rizal³

et al. 2020

J Appl Eng Science

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Electrocardiogram (ECG) based biometric is challenging to be developed with the aim of high-security access. This biometric system is more diffi cult to falsify, compared to the conventional biometric systems. From previous proposed studies, there is still a gap to improve the accuracy of the system. Therefore in this study, a new protocol is proposed to improve the performance of the ECG biometric system compared to previously reported studies. This study decomposes the ECG signals using a method based on empirical mode decomposition (EMD) based, which are Variational Mode Decomposition (VMD) and Ensemble Empirical Mode Decomposition (EEMD). These two methods are the development of the EMD method to overcome one main problem of EMD. That is, the EMD method generates oscillations with the same time scales, which stored in different decomposition levels. A private ECG dataset, recorded using one lead ECG signal from 11 subjects, is used in this study. ECG signals from each person are then segmented into ten windows to become training data and test data. VMD and EEMD methods are used to decompose ECG signals into fi ve sub-signals. Feature extraction based on statistical calculations is applied at each level of decomposition to obtain the characteristics of the ECG signal. Mean, variance, skewness, kurtosis, and entropy are evaluated as predictors. Support vector machines and 10-fold cross-validation are used to validate the performance of the proposed method. Our simulations demonstrate that the proposed method outperforms several previous studies and achieves an accuracy of up to 98.2%.

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