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Off-axis integrated cavity output spectroscopy (OA-ICOS) allows the laser to be reflected multiple times inside the cavity, increasing the effective absorption path length and thus improving sensitivity. However, OA-ICOS systems are affected by various types of noise, and traditional filtering methods offer low processing efficiency and perform limited feature extraction. Deep learning models enable us to extract important features from large-scale, complex spectral data and analyze them efficiently and accurately.

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As breath nitric oxide (NO) is a biomarker of respiratory inflammation, reliable techniques for the online detection of ppb-level NO in exhaled breath are essential for the noninvasive diagnosis of respiratory inflammation. Here, we report a breath NO sensor based on the multiperiodic spectral reconstruction neural network. First, a spectral reconstruction method that transforms a spectrum from the wavelength domain to the intensity domain is proposed to remove noise and interference signals from the spectrum.

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We present a semi-analytical model that can accurately explain the working principle behind the recently reported electrically injected InGaAs/GaAs monolithic nano-ridge lasers and more importantly show how the model can be used to study the effect of device parameters on the spectral behavior, the slope efficiency and the threshold gain. We show that mode beating between the fundamental mode and a higher order mode is fundamental in the operation of these lasers. Analytical expressions for codirectional mode coupling are used in developing the round-trip laser model.

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Electric-field oscillations are now experimentally accessible in the THz-to-PHz frequency range. Their measurement delivers the most comprehensive information content attainable by optical spectroscopy - if performed with high sensitivity. Yet, the trade-off between bandwidth and efficiency associated with the nonlinear mixing necessary for field sampling has so far strongly restricted sensitivity in applications such as field-resolved spectroscopy of molecular vibrations.

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Deep learning is a double-edged sword. The powerful feature learning ability of deep models can effectively improve classification accuracy. Still, when the training samples for each class are limited, it will not only face the problem of overfitting but also significantly affect the classification result.

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