Machine Leaning Based Wavelength Modulation Spectroscopy for Rapid Gas Sensing

Zhang, Wanlu; Zhang, Rui; Fu, Yalei; Enemali, Godwin; Si, Jingjing; Liu, Chang

doi:10.1109/i2mtc50364.2021.9459850

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…The demodulation methods reduce data throughput, enabling uninterrupted communication between the DAQ system and the high-level processors. Most recently, neural networks are deployed within the SoC to extract spectral features, such as the peaks of the harmonics or the path-integrated absorbances shown in figure 8(c) [92,95]. The neural networks enable very fast computation, which facilitates real-time WMS measurement and image visualisation.…”

Section: Soc-sp Daq Schemesmentioning

confidence: 99%

Perspectives on instrumentation development for chemical species tomography in reactive-flow diagnosis

Liu

McCann

2023

Meas. Sci. Technol.

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Chemical species tomography (CST) has been deployed in a wide range of applications in the last two decades for multi-dimensional measurement of gaseous flow fields. CST offers unique capability for spatiotemporally resolved imaging of multiple thermochemical parameters. It is fundamentally robust, highly sensitive, and adaptable to industrial processes and large-scale combustion systems. The instrumentation methods used to implement CST measurements are critical in determining the physical and chemical variables that may be imaged by CST. In this perspective, we have three main objectives: a) discuss recent advances in CST instruments from the viewpoint of optics and electronics; b) highlight the on-going challenges for systems to address the ever-increasing requirements on temporal and spatial resolutions; and c) consider potential developments for next-generation CST instrumentation.

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Section: Soc-sp Daq Schemesmentioning

confidence: 99%

Perspectives on instrumentation development for chemical species tomography in reactive-flow diagnosis

Liu

McCann

2023

Meas. Sci. Technol.

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“…Unsupervised machine learning techniques, such as those used for clustering and dimensionality reduction, have allowed for numerical modeling of physical processes without the need for initial assumptions of the underlying physics. In atomic, molecular, and optical (AMO) spectroscopy, machine learning techniques have been used for regression problems such as absorbance measurement [ 5 , 6 ], signal restoration [ 7 , 8 ], density estimation [ 9 ] and quantum state reconstruction [ 10 ]. Furthermore, applications have been found in classification problems for the identification of light sources [ 11 , 12 ], near infrared spectroscopy [ 13 ], and electron microscopy [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Application of kernel principal component analysis for optical vector atomic magnetometry

McKelvy,

Novikova,

Mikhailov

et al. 2023

Mach. Learn.: Sci. Technol.

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Vector atomic magnetometers that incorporate electromagnetically induced transparency (EIT) allow for precision measurements of magnetic fields that are sensitive to the directionality of the observed field by virtue of fundamental physics. However, a practical methodology of accurately recovering the longitudinal angle of the local field through observations of EIT spectra has not been established. In this work, we address this problem of angle determination with an unsupervised machine learning algorithm utilizing nonlinear dimensionality reduction. The proposed algorithm was developed to interface with spectroscopic measurements from an EIT-based atomic rubidium magnetometer and uses kernel principal component analysis (KPCA) as an unsupervised feature extraction tool. The resulting KPCA features allow each EIT spectrum measurement to be represented by a single coordinate in a new reduced dimensional feature space, thereby streamlining the process of angle determination. A supervised support vector regression (SVR) machine was implemented to model the resulting relationship between the KPCA projections and field direction. If the magnetometer is configured so that the azimuthal angle of the field is defined with a polarization lock, the KPCA-SVR algorithm is capable of predicting the longitudinal angle of the local magnetic field within 1 degree of accuracy and the magnitude of the absolute field with a resolution of 70 nT. The combined scalar and angular sensitivity of this method make the KPCA-enabled EIT magnetometer competitive with conventional vector magnetometry methods. © 2023. All rights reserved.

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“…Unsupervised machine learning techniques, such as those used for clustering and dimensionality reduction, have allowed for numerical modeling of physical processes without the need for initial assumptions of the underlying physics. In atomic, molecular, and optical (AMO) spectroscopy, machine learning techniques have been used for regression problems such as absorbance measurement [5], [6], signal restoration [7], [8], density estimation [9] and quantum state reconstruction [10]. Furthermore, applications have been found in classification problems for the identification of light sources [11], [12] and near infrared spectroscopy [13].…”

Section: Introductionmentioning

confidence: 99%

Application of Kernel Principal Component Analysis for Optical Vector Atomic Magnetometry

McKelvy¹,

Novikova²,

Михайлов³

et al. 2023

Preprint

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<p> An unsupervised machine learning algorithm utilizing nonlinear dimensionality reduction has been developed to interface with spectroscopic measurements from an atomic rubidium optical magnetometer. The magnetometer uses Electromagnetically Induced Transparency (EIT) optical spectroscopy with atomic vapor cells in order to observe magneto-sensitive spin transitions of the 87Rb atom. The Rb EIT measurements result in the observation of seven transmission peaks associated with the available two-photon resonances between the Rb D1 line Zeeman sublevels. The developed algorithm correlates the waveform of the EIT spectra and the directionality of the local magnetic field using kernel principal component analysis (KPCA). The analysis, implemented with a radial basis function (RBF) kernel trick, was found to neatly cluster the EIT spectra according to the direction of the local magnetic field in the reduced dimensional feature space. The KPCA projections demonstrated a smooth curve in a three-dimensional feature space, and the resulting curve was modeled with a support vector regression (SVR) machine. We found that if the direction of the magnetic field is undefined initially, the measurement accuracy is better than 3 degrees in both azimuthal and longitudinal angles with respect to the real direction of the magnetic field. If the magnetometer is configured so that the azimuthal angle of the field is defined, the KPCA- SVR algorithm is capable of predicting the longitudinal angle of the local magnetic field within 1 degree of accuracy. These results suggest that the dimensionality reduction methods may be a powerful tool for approaching regression problems in optical spectroscopy. </p>

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Machine Leaning Based Wavelength Modulation Spectroscopy for Rapid Gas Sensing

Cited by 4 publications

References 13 publications

Perspectives on instrumentation development for chemical species tomography in reactive-flow diagnosis

Perspectives on instrumentation development for chemical species tomography in reactive-flow diagnosis

Application of kernel principal component analysis for optical vector atomic magnetometry

Application of Kernel Principal Component Analysis for Optical Vector Atomic Magnetometry

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