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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Visconti, Paolo
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Computer Science & IT Electrical & Electronics Engineering

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Environmental odor detection and classification with electronic nose system Macías-Quijas, Ricardo; Velázquez, Ramiro; Del-Valle-Soto, Carolina; Lizut, Rafal; Visconti, Paolo; Lay-Ekuakille, Aimé
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9046

Abstract

A prototype of an electronic nose (e-nose) system integrating a set of general-purpose gas sensors, an electronic module, and signal processing and classification methods has been designed and implemented to detect certain environmental odors that might pose a risk to human health. The proposed device explores the filter diagonalization method (FDM), an advanced signal processing technique for accurate spectral estimation, to detect the presence of odors together with random forest (RF), a popular machine learning algorithm, to classify the features of such spectra. Experimental results show that the proposed FDM-RF approach can recognize the targeted odors with an accuracy of 96.4%.
A novel stretched-compressed exponential low-pass filter and its application to electrocardiogram signal denoising de Fazio, Roberto; Al-Naami, Bassam; Rawash, Yahia; Al-Hinnawi, Abdel-Razzak; Al-Zaben, Awad; Visconti, Paolo
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp230-245

Abstract

The study investigates a novel stretched-compressed exponential low-pass (SCELP) filter to denoise electrocardiogram (ECG) signals. As an extension of Gaussian filter and unlike other denoising filters, the SCELP filter utilizes the stretched-compressed exponential function (SCEF) in the convolution kernel, being the Gaussian function its particular case. A MATLAB implementation is provided with a single parameter (β), which allows to modify the filter strength, to increase the signal-to-noise ratio (SNR) and reduce the mean squared error (MSE). The SCELP filter’s advantages over traditional denoising filters (i.e., Gaussian, Mittag–Leffler, and Savitzky-Golay filters) were assessed on 100 ECG signals, 50 normal and 50 abnormal (affected by sleep apnea), provided by the PhysioNet dataset. The SCELP filter’s efficacy in rejecting noise was evaluated as the β parameter varies, quantifying the filters' performance in terms of mean SNR and MSE to determine the optimal β value. The obtained results showed that the SCELP filter's best performances are achieved for β equal to = 1.6 (i.e., 16.9508 dB and 13.7574 dB SNR values, and 0.01025 and 0.01178 MSE values for normal and abnormal ECGs, respectively). Furthermore, the SCELP filter was tested on ECG signals with added white noise; compared to Gaussian, Mittag–Leffler, and Savitzky-Golay filters, the SCELP filter yields better performance regarding SNR (16.495 and 14.940 dB) and MSE (0.0106 and 0.0114) values, for normal and abnormal ECGs, respectively, suggesting its applicability for ECG signals' denoising.
Co-Authors Al-Hinnawi, Abdel-Razzak Al-Naami, Bassam Al-Zaben, Awad de Fazio, Roberto Del-Valle-Soto, Carolina Lay-Ekuakille, Aimé Lizut, Rafal Macías-Quijas, Ricardo Rawash, Yahia Velazquez, Ramiro