Polarized laser speckle images produced by calibrated polystyrene microspheres suspensions: comparison between backscattering and transmission experimental configurations

Loutfi, Hadi; Pellen, Fabrice; Jeune, Bernard; Brun, Guy Le; Abboud, Maher

doi:10.1088/1555-6611/acde6f

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Despite successful predictions, the study acknowledged inherent uncertainties in the models. Loutfi et al [39] used calibrated polystyrene microsphere suspensions to produce polarized laser speckle images, detecting helicity flipping as an indicator of transitioning from simple to multiple scattering with changing scatterer concentrations. The study suggested that speckle grain size is a more effective parameter than the degree of light polarization for tracking changes in the scattering regime for transmitted scattered photons.…”

Section: Related Workmentioning

confidence: 99%

Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity

Parra,

Ahmad,

Sendra

et al. 2024

Chemosensors

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Turbidity is one of the crucial parameters of water quality. Even though many commercial devices, low-cost sensors, and remote sensing data can efficiently quantify turbidity, they are not valid tools for the classification it. In this paper, we design, calibrate, and test a novel optical low-cost sensor for turbidity quantification and classification. The sensor is based on an RGB light source and a light detector. The analyzed samples are characterized by turbidity values from 0.02 to 60 NTUs, and have four different sources. These samples were generated to represent natural turbidity sources and leaves in the marine areas close to agricultural lands. The data are gathered using 64 different combinations of light, generating complex matrix data. Machine learning models are compared to analyze this data, including training, validation, and test datasets. Moreover, different alternatives for data preprocessing and feature selection are assessed. Concerning the quantification of turbidity, the best results were obtained using averaged data and principal components analyses in conjunction with exponential gaussian process regression, achieving an R2 of 0.979. Regarding the classification of the turbidity, an accuracy of 91.23% is obtained with the fine K-Nearest-Neighbor classifier. The cases in which data were misclassified are characterized by turbidity values lower than 5 NTUs. The obtained results represent an improvement over the current solutions in terms of turbidity quantification and a completely novel approach to turbidity classification.

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Section: Related Workmentioning

confidence: 99%

Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity

Parra,

Ahmad,

Sendra

et al. 2024

Chemosensors

View full text Add to dashboard Cite

show abstract

Machine Learning-Based Approach towards Identification of Pharmaceutical Suspensions Exploiting Speckle Pattern Images

Bello,

Coghe,

Gerbasi

et al. 2024

Sensors

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Parenteral artificial nutrition (PAN) is a lifesaving medical treatment for many patients worldwide. Administration of the wrong PAN drug can lead to severe consequences on patients’ health, including death in the worst cases. Thus, their correct identification, just before injection, is of crucial importance. Since most of these drugs appear as turbid liquids, they cannot be easily discriminated simply by means of basic optical analyses. To overcome this limitation, in this work, we demonstrate that the combination of speckle pattern (SP) imaging and artificial intelligence can provide precise classifications of commercial pharmaceutical suspensions for PAN. Towards this aim, we acquired SP images of each sample and extracted several statistical parameters from them. By training two machine learning algorithms (a Random Forest and a Multi-Layer Perceptron Network), we were able to identify the drugs with accurate performances. The novelty of this work lies in the smart combination of SP imaging and machine learning for realizing an optical sensing platform. For the first time, to our knowledge, this approach is exploited to identify PAN drugs.

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Polarized laser speckle images produced by calibrated polystyrene microspheres suspensions: comparison between backscattering and transmission experimental configurations

Cited by 2 publications

References 23 publications

Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity

Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity

Machine Learning-Based Approach towards Identification of Pharmaceutical Suspensions Exploiting Speckle Pattern Images

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