scholarly journals Plasmonic Mid-Infrared Filter Array-Detector Array Chemical Classifier Based on Machine Learning

ACS Photonics ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 648-657
Author(s):  
Jiajun Meng ◽  
Jasper J. Cadusch ◽  
Kenneth B. Crozier
Keyword(s):  
Machine Learning ◽  
Detector Array ◽  
Array Detector ◽  
Mid Infrared ◽  
Filter Array

Microplastic adulteration in homogenized fish and seafood - a mid-infrared and machine learning proof of concept

2021 ◽  
pp. 119985
Author(s):  
Stephanie Owen ◽  
Samuel Cureton ◽  
Mathew Szuhan ◽  
Joel McCarten ◽  
Panagiota Arvanitis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Proof Of Concept ◽  
Mid Infrared

scholarly journals Design of mid-infrared filter array based on plasmonic metal nanodiscs array and its application to on-chip spectrometer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hwa-Seub Lee ◽  
Gyu-Weon Hwang ◽  
Tae-Yeon Seong ◽  
Jongkil Park ◽  
Jae Wook Kim ◽  
...  
Keyword(s):  
Nanoimprint Lithography ◽  
Filter Design ◽  
Reconstruction Technique ◽  
Filter Function ◽  
Promising Alternative ◽  
Mid Infrared ◽  
Spectrum Reconstruction ◽  
Filter Array ◽  
On Chip ◽  
Multispectral Filter Array

AbstractMid-infrared wavelengths are called the molecular fingerprint region because it contains the fundamental vibrational modes inherent to the substances of interest. Since the mid-infrared spectrum can provide non-destructive identification and quantitative analysis of unknown substances, miniaturized mid-infrared spectrometers for on-site diagnosis have attained great concern. Filter-array based on-chip spectrometer has been regarded as a promising alternative. In this study, we explore a way of applying a pillar-type plasmonic nanodiscs array, which is advantageous not only for excellent tunability of resonance wavelength but also for 2-dimensional integration through a single layer process, to the multispectral filter array for the on-chip spectrometer. We theoretically and experimentally investigated the optical properties of multi-periodic triangular lattices of metal nanodiscs array that act as stopband filters in the mid-infrared region. Soft-mold reverse nanoimprint lithography with a subsequent lift-off process was employed to fabricate the multispectral filter array and its filter function was successfully extracted using a Fourier transform infrared microscope. With the measured filter function, we tested the feasibility of target spectrum reconstruction using a Tikhonov regularization method for an ill-posed linear problem and evaluated its applicability to the infrared spectroscopic sensor that monitors an oil condition. These results not only verify that the multispectral filter array composed of stopband filters based on the metal nanodiscs array when combined with the spectrum reconstruction technique, has great potential for use to a miniaturized mid-infrared on-chip spectrometer, but also provide effective guidance for the filter design.

Monolithically integrated near-infrared and mid-infrared detector array for spectral imaging

2007 ◽  
Vol 50 (2-3) ◽  
pp. 211-216 ◽  
Author(s):  
S.V. Bandara ◽  
S.D. Gunapala ◽  
D.Z. Ting ◽  
J.K. Liu ◽  
C.J. Hill ◽  
...  
Keyword(s):  
Near Infrared ◽  
Infrared Detector ◽  
Spectral Imaging ◽  
Detector Array ◽  
Mid Infrared ◽  
Monolithically Integrated ◽  
Infrared Detector Array

Monolithically integrated near-infrared and mid-infrared detector array

2005 ◽  
Author(s):  
S. V. Bandara ◽  
S. D. Gunapala ◽  
D Z. Ting ◽  
J. K. Liu ◽  
C. J. Hill ◽  
...  
Keyword(s):  
Near Infrared ◽  
Infrared Detector ◽  
Detector Array ◽  
Mid Infrared ◽  
Monolithically Integrated ◽  
Infrared Detector Array

scholarly journals Machine Learning Mid‐Infrared Spectral Models for Predicting Modal Mineralogy of CI/CM Chondritic Asteroids and Bennu

2021 ◽  
Author(s):  
L. B. Breitenfeld ◽  
A. D. Rogers ◽  
T. D. Glotch ◽  
V. E. Hamilton ◽  
P. R. Christensen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mid Infrared ◽  
Spectral Models ◽  
Infrared Spectral

Detector Array for Measuring Far-Field Power Density Distribution of Mid-Infrared Laser

2010 ◽  
Vol 37 (2) ◽  
pp. 521-525 ◽  
Author(s):  
杨鹏翎 Yang Pengling ◽  
冯国斌 Feng Guobin ◽  
王振宝 Wang Zhenbao ◽  
王群书 Wang Qunshu ◽  
冯刚 Feng Gang ◽  
...  
Keyword(s):  
Density Distribution ◽  
Power Density ◽  
Infrared Laser ◽  
Detector Array ◽  
Far Field ◽  
Mid Infrared ◽  
Power Density Distribution

Antarctic cloud detection and classification from far and mid infrared downwelling radiance spectra: performances optimization and results

2020 ◽  
Author(s):  
Davide Magurno ◽  
Tiziano Maestri ◽  
William Cossich ◽  
Gianluca Di Natale ◽  
Luca Palchetti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Far Infrared ◽  
Mixed Phase ◽  
Radiative Properties ◽  
High Spectral Resolution ◽  
Ice Clouds ◽  
Mid Infrared ◽  
Clear Sky ◽  
Threat Score ◽  
Mixed Phase Clouds

<p>This work aims at determining the best performing mid and far-infrared (MIR and FIR) joint spectral interval to identify and classify clouds in the Antarctic region by mean of a machine learning algorithm.</p><p>About 1700 spectral-resolved radiances, collected during 2013 by the ground based Radiation Explorer in the Far InfraRed-Prototype for Applications and Development, REFIR-PAD (Palchetti et al., 2015) at Dome C, Antarctic Plateau, are selected in coincidence with the co-located with backscatter and depolarization profiles derived from a tropospheric lidar system (Ricaud et al., 2017) to pre-classify clear sky, ice clouds, or mixed phase clouds.</p><p>A machine learning cloud identification and classification algorithm named CIC (Maestri et al., 2019), trained with a pre-selected set of REFIR-PAD spectra, is applied to this dataset by assuming that no other information than the spectrum itself is known.</p><p>The CIC algorithm is applied by considering different spectral intervals, in order to maximize the classification results for each class (clear sky, ice clouds, mixed phase clouds). A CIC "threat score" is defined as the classification true positives divided by the sum of true positives, false positives, and false negatives. The maximization of the threat score is used to assess the algorithm performances that span from 58% to 96% in accordance with the selected interval. The best performing spectral range is the 380-1000 cm<sup>-1</sup>. The result, besides suggesting the importance of a proper algorithm calibration in accordance with the used sensor, highlights the fundamental role of the FIR part of the spectrum.</p><p>The calibrated CIC algorithm is then applied to a larger REFIR-PAD dataset of about 90000 spectra collected from 2012 to 2015. Some results of the full dataset cloud classification are also presented.</p><p>The present work contributes to the preparatory studies for the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission that has recently been selected as ESA’s 9<sup>th</sup> Earth Explorer mission, scheduled for launch in 2026. </p><p> </p><p>References:</p><p><span>Maestri, T., Cossich, W., and Sbrolli, I., 2019: Cloud identification and classification from high spectral resolution data in the far infrared and mid-infrared, Atmos. Meas. Tech., 12, pp. 3521 - 3540</span></p><p><span>Palchetti, L., Bianchini, G., Di Natale, G., and Del Guasta, M., 2015: Far infrared radiative properties of water vapor and clouds in Antarctica. Bull. Amer. Meteor. Soc., 96, 1505–1518, doi: http://dx.doi.org/10.1175/BAMS-D-13-00286.1.</span></p><p><span>Ricaud, P., Bazile, E., del Guasta, M., Lanconelli, C., Grigioni, P., and Mahjoub, A., 2017: Genesis of diamond dust, ice fog and thick cloud episodes observed and modelled above Dome C, Antarctica, Atmos. Chem. Phys., 17, 5221–5237, https://doi.org/10.5194/acp-17-5221-2017.</span></p>

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