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Open AccessArticle

Highly Sensitive Hydrogen Sensing Based on Tunable Diode Laser Absorption Spectroscopy with a 2.1 μm Diode Laser

by Tiantian Liang, Shunda Qiao, Xiaonan Liu and Yufei Ma

Chemosensors 2022, 10(8), 321; https://doi.org/10.3390/chemosensors10080321 - 11 Aug 2022

Cited by 40 | Viewed by 6176 | Correction

As a new form of energy, hydrogen (H₂) has clean and green features, and the detection of H₂ has been a hot topic in recent years. However, the lack of suitable laser sources and the weak optical absorption of H₂ limit the research concerning its detection. In this study, a continuous-wave distributed feedback (CW-DFB) diode laser was employed for sensing H₂. Tunable diode laser absorption spectroscopy (TDLAS) was adopted as the detection technique. The strongest H₂ absorption line, located at 4712.90 cm⁻¹ (2121.83 nm, line strength: 3.19 × 10⁻²⁶ cm⁻¹/cm⁻² × molec), was selected. We propose a H₂-TDLAS sensor based on the wavelength modulation spectroscopy (WMS) technique and a Herriott multipass gas cell (HMPC) with an optical length of 10.13 m to achieve a sensitive detection. The WMS technique and second harmonic (2f) demodulation technique were utilized to suppress system noise and simplify the data processing. The 2f signal of the H₂-TDLAS sensor, with respect to different H₂ concentrations, was measured when the laser wavelength modulation depth was at the optimal value of 0.016 cm⁻¹. The system’s signal-to-noise ratio (SNR) and minimum detection limit (MDL) were improved from 248.02 and 0.40% to 509.55 and 0.20%, respectively, by applying Daubechies (DB) wavelet denoising, resulting in 10 vanishing moments. The Allan variance was calculated, and the optimum MDL of 522.02 ppm was obtained when the integration time of the system was 36 s. Full article

(This article belongs to the Special Issue Gas Detection Sensors for On-Chip Applications)

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17 pages, 5974 KB

Open AccessArticle

The Optimal Selection of Mother Wavelet Function and Decomposition Level for Denoising of DCG Signal

by Young In Jang, Jae Young Sim, Jong-Ryul Yang and Nam Kyu Kwon

Sensors 2021, 21(5), 1851; https://doi.org/10.3390/s21051851 - 6 Mar 2021

Cited by 57 | Viewed by 7404

Abstract

The aim of this paper is to find the optimal mother wavelet function and wavelet decomposition level when denoising the Doppler cardiogram (DCG), the heart signal obtained by the Doppler radar sensor system. To select the best suited mother wavelet function and wavelet decomposition level, this paper presents the quantitative analysis results. Both the optimal mother wavelet and decomposition level are selected by evaluating signal-to-noise-ratio (SNR) efficiency of the denoised signals obtained by using the wavelet thresholding method. A total of 115 potential functions from six wavelet families were examined for the selection of the optimal mother wavelet function and 10 levels (1 to 10) were evaluated for the choice of the best decomposition level. According to the experimental results, the most efficient selections of the mother wavelet function are “db9” and “sym9” from Daubechies and Symlets families, and the most suitable decomposition level for the used signal is seven. As the evaluation criterion in this study rates the efficiency of the denoising process, it was found that a mother wavelet function longer than 22 is excessive. The experiment also revealed that the decomposition level can be predictable based on the frequency features of the DCG signal. The proposed selection of the mother wavelet function and the decomposition level could reduce noise effectively so as to improve the quality of the DCG signal in information field. Full article

(This article belongs to the Special Issue Sensors for Vital Signs Monitoring)

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14 pages, 8114 KB

Open AccessArticle

Extraction of Quasi-Monochromatic Gravity Waves from an Airglow Imager Network

by Chang Lai, Wei Li, Jiyao Xu, Xiao Liu, Wei Yuan, Jia Yue and Qinzeng Li

Atmosphere 2020, 11(6), 615; https://doi.org/10.3390/atmos11060615 - 10 Jun 2020

Cited by 2 | Viewed by 3481

Abstract

An algorithm has been developed to isolate the gravity waves (GWs) of different scales from airglow images. Based on the discrete wavelet transform, the images are decomposed and then reconstructed in a series of mutually orthogonal spaces, each of which takes a Daubechies (db) wavelet of a certain scale as a basis vector. The GWs in the original airglow image are stripped to the peeled image reconstructed in each space, and the scale of wave patterns in a peeled image corresponds to the scale of the db wavelet as a basis vector. In each reconstructed image, the extracted GW is quasi-monochromatic. An adaptive band-pass filter is applied to enhance the GW structures. From an ensembled airglow image with a coverage of 2100 km × 1200 km using an all-sky airglow imager (ASAI) network, the quasi-monochromatic wave patterns are extracted using this algorithm. GWs range from ripples with short wavelength of 20 km to medium-scale GWs with a wavelength of 590 km. The images are denoised, and the propagating characteristics of GWs with different wavelengths are derived separately. Full article

(This article belongs to the Special Issue Gravity Waves in the Atmosphere)

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12 pages, 2523 KB

Open AccessArticle

Lifting Wavelet Transform De-noising for Model Optimization of Vis-NIR Spectroscopy to Predict Wood Tracheid Length in Trees

by Ying Li, Brian K. Via, Qingzheng Cheng and Yaoxiang Li

Sensors 2018, 18(12), 4306; https://doi.org/10.3390/s18124306 - 6 Dec 2018

Cited by 9 | Viewed by 3246

Abstract

The data analysis of visible-near infrared (Vis-NIR) spectroscopy is critical for precise information extraction and prediction of fiber morphology. The objectives of this study were to discuss the de-noising of Vis-NIR spectra, taken from wood, to improve the prediction accuracy of tracheid length in Dahurian larch wood. Methods based on lifting wavelet transform (LWT) and local correlation maximization (LCM) algorithms were developed for optimal de-noising parameters and partial least squares (PLS) was employed as the prediction method. The results showed that: (1) The values of tracheid length in the study were generally high and had a great positive linear correlation with annual rings (R = 0.881), (2) the optimal de-noising parameters for larch wood based Vis-NIR spectra were Daubechies-2 (db2) mother wavelet with 4 decomposition levels while using a global fixed hard threshold based on LWT, and (3) the Vis-NIR model based on the optimal LWT de-noising parameters (

R_{c}^{2}

= 0.834,

RMSEC

= 0.262,

{RPD}_{c}

= 2.454) outperformed those based on the LWT coupled with LCM algorithm (LWT-LCM) (

R_{c}^{2}

= 0.816,

RMSEC

= 0.276,

{RPD}_{c}

= 2.331) and raw spectra (

R_{c}^{2}

= 0.822,

RMSEC

= 0.271,

{RPD}_{c}

= 2.370). Thus, the selection of appropriate LWT de-noising parameters could aid in extracting a useful signal for better prediction accuracy of tracheid length. Full article

(This article belongs to the Section Remote Sensors)

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21 pages, 1680 KB

Open AccessArticle

Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task

by Noor Kamal Al-Qazzaz, Sawal Hamid Bin Mohd Ali, Siti Anom Ahmad, Mohd Shabiul Islam and Javier Escudero

Sensors 2015, 15(11), 29015-29035; https://doi.org/10.3390/s151129015 - 17 Nov 2015

Cited by 137 | Viewed by 11707

Abstract

We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20), Symlets (sym1–sym20), and Coiflets (coif1–coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using “sym9” across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions. Full article

(This article belongs to the Section Biosensors)

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