Research Article
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Year 2025, Volume: 13 Issue: 4, 991 - 998, 01.12.2025
https://doi.org/10.36306/konjes.1662027

Abstract

References

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  • I. Bayrakli, "A portable N2O sensor based on quartz-enhanced photoacoustic spectroscopy with adistributed-feedback quantum cascade laser for medical and atmospheric applications," Optical and Quantum Electronics 53, 642 (2021).
  • I. Bayrakli, and H. Akman, "Ultrasensitive, real-time analysis of biomarkers in breath using tunable external cavity laser and off-axis cavity-enhanced absorption spectroscopy," Journal of Biomedical Optics 20, 037001 (2015).
  • I. Bayrakli, "External cavity quantum cascade lasers without anti-reflection coating with intracavity and extracavityacoustic-optic frequency shifter for fast standoff detection," Optics and Laser Technology 148, 107747 (2022).
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  • I. Bayrakli, and E. Eken, "Compact laser spectroscopy-based sensor using a transformer-based model for analysis of multiple molecules," Applied Optics 63, 6941-6947 (2024).
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  • S. Hochreiter, "Long Short-term Memory," Neural Computation MIT-Press (1997).
  • J. Chung, C. Gulcehre, K. Cho, and Y. Bengio. "Empirical evaluation of gated recurrent neural networks on sequence modeling," arXiv preprint arXiv:1412.3555 (2014).
  • L. Ze, et al., "Swin transformer: Hierarchical vision transformer using shifted windows," In Proceedings of the IEEE/CVF international conference on computer vision, 10012-10022. (2021).
  • C. Nicolas, et al., "End-to-end object detection with transformers," In European conference on computer vision," Cham: Springer International Publishing, 213-229 (2020).
  • J. L. Ba, "Layer normalization," arXiv preprint arXiv:1607.06450 (2016).
  • H. Kaiming, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," In Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778 (2016).
  • Bayrakli, and E. Eken, "Compact laser spectroscopy-based sensor using a transformer-based model for analysis of multiple molecules," Applied Optics, 63, 6941-6947 (2024).
  • I. Bayrakli, and E. Eken. "A novel breath molecule sensing system based on deep neural network employing multiple-line direct absorption spectroscopy," Optics and Laser Technology 158 108918 (2023).

EXTRACTING A MUCH WIDER ABSORPTION SPECTRAL RANGE FROM A NARROWER RANGE USING TRANSFORMER MODEL

Year 2025, Volume: 13 Issue: 4, 991 - 998, 01.12.2025
https://doi.org/10.36306/konjes.1662027

Abstract

Obtaining a wide absorption spectral range rapidly at high resolution plays a crucial role in scientific research and practical applications. The main motivation of this work is to investigate the rapid acquisition of a very wide spectral range at high resolution by using a much narrower absorption spectral range. To accomplish this, we proposed a novel transformer-based approach that produces a wider absorption spectral range (100 cm−1 ) with high resolution in milliseconds from a narrow absorption spectral range (2 cm−1 ). A distributed feedback quantum cascade laser (DFB QCL) together with a multi-pass cell was used to obtain the absorption lines. To evaluate the performance of the sensor, N2O was selected as the target molecule.

References

  • Y. Matsuoka, et al., "External-cavity quantum cascade laser using intra-cavity out-coupling," Optics Letters 43, 3726-3729 (2018).
  • I. Bayrakli, "A portable N2O sensor based on quartz-enhanced photoacoustic spectroscopy with adistributed-feedback quantum cascade laser for medical and atmospheric applications," Optical and Quantum Electronics 53, 642 (2021).
  • I. Bayrakli, and H. Akman, "Ultrasensitive, real-time analysis of biomarkers in breath using tunable external cavity laser and off-axis cavity-enhanced absorption spectroscopy," Journal of Biomedical Optics 20, 037001 (2015).
  • I. Bayrakli, "External cavity quantum cascade lasers without anti-reflection coating with intracavity and extracavityacoustic-optic frequency shifter for fast standoff detection," Optics and Laser Technology 148, 107747 (2022).
  • Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 25 (2012).
  • J. Redmon, "You only look once: Unified, real-time object detection," In Proceedings of the IEEE conference on computer vision and pattern recognition (2016).
  • F. Hosein, and H. Abbasimehr, "A novel combined approach based on deep Autoencoder and deep classifiers for credit card fraud detection," Expert Systems with Applications 217, 119562 (2023).
  • I. Bayrakli, and E. Eken, "Compact laser spectroscopy-based sensor using a transformer-based model for analysis of multiple molecules," Applied Optics 63, 6941-6947 (2024).
  • Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE 86, 2278-2324 (1998).
  • A. Vaswani, "Attention is all you need," Advances in Neural Information Processing Systems (2017).
  • D.P. Kingma, and B. Jimmy "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980 (2014).
  • T. Tieleman, "Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA:Neural networks for machine learning," 4, 2 (2012).
  • J. L. Elman, "Finding structure in time. Cognitive science," 14, 2 (1990).
  • S. Hochreiter, "Long Short-term Memory," Neural Computation MIT-Press (1997).
  • S. Hochreiter, "Long Short-term Memory," Neural Computation MIT-Press (1997).
  • J. Chung, C. Gulcehre, K. Cho, and Y. Bengio. "Empirical evaluation of gated recurrent neural networks on sequence modeling," arXiv preprint arXiv:1412.3555 (2014).
  • L. Ze, et al., "Swin transformer: Hierarchical vision transformer using shifted windows," In Proceedings of the IEEE/CVF international conference on computer vision, 10012-10022. (2021).
  • C. Nicolas, et al., "End-to-end object detection with transformers," In European conference on computer vision," Cham: Springer International Publishing, 213-229 (2020).
  • J. L. Ba, "Layer normalization," arXiv preprint arXiv:1607.06450 (2016).
  • H. Kaiming, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," In Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778 (2016).
  • Bayrakli, and E. Eken, "Compact laser spectroscopy-based sensor using a transformer-based model for analysis of multiple molecules," Applied Optics, 63, 6941-6947 (2024).
  • I. Bayrakli, and E. Eken. "A novel breath molecule sensing system based on deep neural network employing multiple-line direct absorption spectroscopy," Optics and Laser Technology 158 108918 (2023).
There are 22 citations in total.

Details

Primary Language English
Subjects Photonic and Electro-Optical Devices, Sensors and Systems (Excl. Communications)
Journal Section Research Article
Authors

Enes Eken 0000-0002-7534-6247

İsmail Bayraklı 0000-0002-4512-8783

Publication Date December 1, 2025
Submission Date March 20, 2025
Acceptance Date July 8, 2025
Published in Issue Year 2025 Volume: 13 Issue: 4

Cite

IEEE E. Eken and İ. Bayraklı, “EXTRACTING A MUCH WIDER ABSORPTION SPECTRAL RANGE FROM A NARROWER RANGE USING TRANSFORMER MODEL”, KONJES, vol. 13, no. 4, pp. 991–998, 2025, doi: 10.36306/konjes.1662027.