Deep spectral CNN for laser induced breakdown spectroscopy
نویسندگان
چکیده
This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn (1) disentangle from the sources of sensor uncertainty (i.e., pre-process) and (2) get qualitative quantitative measures chemical content sample given signal calibrate). Once CNN is trained, it can accomplish either task through single feed-forward pass, with real-time benefits without any additional side information requirements including dark current, system response, temperature detector-to-target range. Our experiments demonstrate that proposed method outperforms existing approaches used by Mars Science Lab for pre-processing calibration remote sensing observations rover, ‘Curiosity’.
منابع مشابه
Laser Induced Breakdown Spectroscopy
Laser-induced breakdown spectroscopy (LIBS) is a powerful analytical technique that can be used for the detection and characterization of materials. In LIBS, a focused laser beam is used to generate a plasma plume on the surface of solid and liquid samples or inside the sample volume of gases, liquids, and aerosols. Each excited atom in the plasma emits a unique set of spectral lines, particula...
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Abstract Laser induced breakdown spectroscopy is a fast non-contact technique for the analysis of the elemental composition of any sample. Our focus is to advance this technique into a new regime where we use pulse energies below 100 μJ. This regime is referred to as microLIBS or μLIBS. Preliminary pulse emission scaling of Na in latent fingerprints has been investigated for ∼ 130 fs, 266 nm pu...
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ژورنال
عنوان ژورنال: Spectrochimica Acta Part B: Atomic Spectroscopy
سال: 2021
ISSN: ['0584-8547', '1873-3565']
DOI: https://doi.org/10.1016/j.sab.2021.106125