An ultrafast and high accuracy calculation method for gas radiation characteristics using artificial neural network

Cui, Juntao; Zhang, Jianqi; Dong, Cunquan; Liu, Delian; Huang, Xi

doi:10.1016/j.infrared.2020.103347

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…GRNN achieves a good balance between the high classification accuracy and speed for both solid and gaseous materials datasets (Figures 5 and 6, respectively). The corresponding computational efficiency is proven in [39] for all kinds of gas molecules supported by a spectral database. The results show that the multivariate discriminative model of LDA and GRNN, in association with THz spectroscopy, provides a cost-effective and low-time-consuming alternative to the commonly used models in the literature for material classification, suggesting a commercial and regulatory potential.…”

Section: Performance Of Classification Techniquesmentioning

confidence: 99%

Signal Processing and Machine Learning Techniques for Terahertz Sensing: An overview

Helal

Sarieddeen

Dahrouj

et al. 2022

IEEE Signal Process. Mag.

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Following the recent progress in Terahertz (THz) signal generation and radiation methods, joint THz communications and sensing applications are being proposed for future wireless systems. Towards this end, THz spectroscopy is expected to be carried over user equipment devices to identify material and gaseous components of interest. THz-specific signal processing techniques should complement this re-surged interest in THz sensing for efficient utilization of the THz band. In this paper, we present an overview of these techniques, with an emphasis on signal pre-processing (standard normal variate normalization, min-max normalization, and Savitzky-Golay filtering), feature extraction (principal component analysis, partial least squares, t-distributed stochastic neighbor embedding, and nonnegative matrix factorization), and classification techniques (support vector machines, k-nearest neighbor, discriminant analysis, and naive Bayes). We also address the effectiveness of deep learning techniques by exploring their promising sensing and localization capabilities at the THz band. Lastly, we investigate the performance and complexity trade-offs of the studied methods in the context of joint communications and sensing (JCAS). We thereby motivate the corresponding use-cases, and present a handful of contextual future research directions.

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Section: Performance Of Classification Techniquesmentioning

confidence: 99%

Signal Processing and Machine Learning Techniques for Terahertz Sensing: An overview

Helal

Sarieddeen

Dahrouj

et al. 2022

IEEE Signal Process. Mag.

View full text Add to dashboard Cite

show abstract

“…GRNN achieves a good balance between the high classification accuracy and speed for both solid and gaseous materials datasets (Figures 5 and 6, respectively). The corresponding computational efficiency is proven in [42] for all kinds of gas molecules supported by a spectral database. The results show that the multivariate discriminative model of LDA and GRNN, in association with THz spectroscopy, provides a cost-effective and low-time-consuming alternative to the commonly used models in the literature for material classification, suggesting a commercial and regulatory potential.…”

Section: Performances Of Classification Techniquesmentioning

confidence: 99%

Signal Processing and Machine Learning Techniques for Terahertz Sensing: An Overview

Helal¹,

Sarieddeen²,

Dahrouj³

et al. 2021

Preprint

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Following the recent progress in Terahertz (THz) signal generation and radiation methods, joint THz communications and sensing applications are shaping the future of wireless systems. Towards this end, THz spectroscopy is expected to be carried over user equipment devices to identify material and gaseous components of interest. THz-specific signal processing techniques should complement this re-surged interest in THz sensing for efficient utilization of the THz band. In this paper, we present an overview of these techniques, with an emphasis on signal pre-processing (standard normal variate normalization, min-max normalization, and Savitzky-Golay filtering), feature extraction (principal component analysis, partial least squares, t-distributed stochastic neighbor embedding, and nonnegative matrix factorization), and classification techniques (support vector machines, k-nearest neighbor, discriminant analysis, and naive Bayes). We also address the effectiveness of deep learning techniques by exploring their promising sensing capabilities at the THz band. Lastly, we investigate the performance and complexity trade-offs of the studied methods in the context of joint communications and sensing; we motivate the corresponding usecases, and we present few future research directions in the field.

show abstract

Deep learning based rapid calculation approach for gas radiation characteristics considering foreign gas collision broadening

Cui

Zhang

Liu

et al. 2021

Infrared Physics & Technology

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An ultrafast and high accuracy calculation method for gas radiation characteristics using artificial neural network

Cited by 6 publications

References 33 publications

Signal Processing and Machine Learning Techniques for Terahertz Sensing: An overview

Signal Processing and Machine Learning Techniques for Terahertz Sensing: An overview

Signal Processing and Machine Learning Techniques for Terahertz Sensing: An Overview

Deep learning based rapid calculation approach for gas radiation characteristics considering foreign gas collision broadening

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