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Letter

GeSi Nanocrystals Photo-Sensors for Optical Detection of Slippery Road Conditions Combining Two Classification Algorithms

1
National Institute of Materials Physics, 405A Atomistilor Street, 077125 Magurele, Romania
2
Academy of Romanian Scientists, 54 Splaiul Independentei, 050094 Bucharest, Romania
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(21), 6395; https://doi.org/10.3390/s20216395
Received: 30 September 2020 / Revised: 29 October 2020 / Accepted: 5 November 2020 / Published: 9 November 2020
(This article belongs to the Section Optical Sensors)
One of the key elements in assessing traffic safety on the roads is the detection of asphalt conditions. In this paper, we propose an optical sensor based on GeSi nanocrystals embedded in SiO2 matrix that discriminates between different slippery road conditions (wet and icy asphalt and asphalt covered with dirty ice) in respect to dry asphalt. The sensor is fabricated by magnetron sputtering deposition followed by rapid thermal annealing. The photodetector has spectral sensitivity in the 360–1350 nm range and the signal-noise ratio is 102–103. The working principle of sensor setup for detection of road conditions is based on the photoresponse (photocurrent) of the sensor under illumination with the light reflected from the asphalt having different reflection coefficients for dry, wet, icy and dirty ice coatings. For this, the asphalt is illuminated sequentially with 980 and 1064 nm laser diodes. A database of these photocurrents is obtained for the different road conditions. We show that the use of both k-nearest neighbor and artificial neural networks classification algorithms enables a more accurate recognition of the class corresponding to a specific road state than in the case of using only one algorithm. This is achieved by comparing the new output sensor data with previously classified data for each algorithm and then by performing an intersection of the algorithms’ results. View Full-Text
Keywords: optical sensor; photodetection of reflected light from asphalt; road conditions detection sensor; road safety; smart roads; k-nearest neighbor algorithm; artificial neural networks optical sensor; photodetection of reflected light from asphalt; road conditions detection sensor; road safety; smart roads; k-nearest neighbor algorithm; artificial neural networks
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MDPI and ACS Style

Palade, C.; Stavarache, I.; Stoica, T.; Ciurea, M.L. GeSi Nanocrystals Photo-Sensors for Optical Detection of Slippery Road Conditions Combining Two Classification Algorithms. Sensors 2020, 20, 6395. https://doi.org/10.3390/s20216395

AMA Style

Palade C, Stavarache I, Stoica T, Ciurea ML. GeSi Nanocrystals Photo-Sensors for Optical Detection of Slippery Road Conditions Combining Two Classification Algorithms. Sensors. 2020; 20(21):6395. https://doi.org/10.3390/s20216395

Chicago/Turabian Style

Palade, Catalin, Ionel Stavarache, Toma Stoica, and Magdalena Lidia Ciurea. 2020. "GeSi Nanocrystals Photo-Sensors for Optical Detection of Slippery Road Conditions Combining Two Classification Algorithms" Sensors 20, no. 21: 6395. https://doi.org/10.3390/s20216395

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