Aug 2026· Photoacoustics· Vol 51, pp. 100868· 1 citation· 58 references
Medicine
Abstract
In photoacoustic spectroscopy (PAS) gas sensors, existing concentration inversion methods typically rely on either a single scalar or a feature vector. In principle, the complete signal waveform contains richer concentration-related information, and its utilization is expected to further improve inversion accuracy, which remains unverified to date. To achieve this, this study proposes an end-to-end method based on a one-dimensional convolutional neural network (1D-CNN) that directly maps the full second-harmonic (2f) waveform to gas concentration. Experimental validation with chloroform (CHCl3) showed strong predictive robustness under stochastic waveform perturbations, achieving a coefficient of determination (R2) of 0.9999; among the noise-augmented evaluation samples, 95.95% exhibited absolute errors below 1 ppm, and 61.8% exhibited relative errors below 1%. This method autonomously extracts concentration information from the full waveform and establishes a complex nonlinear mapping to gas concentration. Furthermore, this framework may provide a strategy for concentration inversion in other gas sensors employing second-harmonic wavelength modulation spectroscopy (WMS-2f) detection.
An interferometric cryogenic temperature sensor, featuring anti-electromagnetic interference and chemical corrosion resistance, provides increasing opportunities for precise monitoring in spacecraft, biomedicine, and cryobiology. However, in existing sensing systems operating over a wide dynamic range, spectral overlap...
Junling Hu, Meiyu Cai, Sa Zhang et al.· Optics Letters· 0 citations
Comparative experiments demonstrate that the TCN model outperforms state-of-the-art methods including SpecNet, VECTOR, LSTM, Bi-LSTM, GAN, and CNN + GRU in identifying authentic Raman peaks, and significantly reduces computational cost.
Kang-Wen Yang, Yuan E. Long, Yu-Xin Zhang et al.· Spectrochimica Acta Part A -...· 0 citations
Spectral noise limits the reliability of deep-learning ellipsometry (DLE), particularly under short measurement times required for high-throughput materials characterization. Here, we propose an adaptive denoising framework that integrates a Mixture-of-Experts (MoE) module into a U-Net architecture for spectroscopic el...
Yuki Yamamoto, R. Iwayama, Y. Ikarashi et al.· Machine Learning: Science an...· 0 citations
Comparative experiments on computational complexity and inference time further demonstrate that ST-FMA significantly reduces model complexity while maintaining high inference speed, confirming its strong feasibility for practical engineering applications.
Da Geng, Yonghao Shan, Yuan Liu et al.· Measurement science and tech...· 0 citations
Light inherently carries multidimensional information, including intensity, polarization, and spectrum. Employing a miniaturized device to simultaneously, instantaneously, and accurately capture the multidimensional information of incident light in a single exposure holds significant applications across numerous fields...
Hui-Ming Luo, Jie Deng, Jing Zhou et al.· Light: Science & Application...· 4 citations
Monitoring the internal flow stability of supersonic inlets is critical for flight safety but faces dual challenges: acquiring high-fidelity data under harsh environments and inferring global flow states from sparse measurements. To address these challenges, this article proposes a “sensor-to-algorithm” closed-loop fra...
Yong-Kai Zhu, Jia-Wei Liu, Rui Li et al.· IEEE Sensors Journal· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
