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naviiidz/hfus-mp-characterization: v1.0.0
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This is a duplicate software repository release — the same high-frequency ultrasound microplastic characterization code as entry 1060.
This software release contains the code used for ultrasound-based characterization of microplastic particles, including material identification and particle size estimation using high-frequency ultrasound signals. The repository includes data processing pipelines, peak-based signal extraction routines, feature computation, and implementations of classical machine-learning methods as well as a one-dimensional convolutional neural network (1D-CNN) for material classification. Particle size estimation is implemented using material-specific machine-learning models trained on extracted acoustic features. The code is designed to operate with the accompanying Zenodo dataset and supports reproducible evaluation of ultrasound-based microplastic detection, classification, and size estimation workflows. This release corresponds to version v1.0.0 and is linked to the associated npj Emerging Contaminants publication.
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naviiidz/hfus-mp-characterization: v1.0.0
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This is a software repository release accompanying a research paper on using high-frequency ultrasound to identify and size microplastic particles. The code supports machine-learning classification and size estimation workflows for ultrasound-based microplastic detection.
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This is a dataset release containing high-frequency ultrasound measurements from controlled microplastic experiments, supporting the companion research on ultrasound-based microplastic identification and size estimation (related to entries 1060 and 1074).
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Researchers developed a compact high-frequency ultrasonic backscattering platform — integrating a Difrascope UT transducer, peristaltic pump, and MATLAB/Arduino control — and applied it to detect and estimate the concentration of suspended microplastic particles in marine water samples without optical systems or complex sample preparation. The M-Scan processing pipeline, which included artifact suppression, cross-correlation enhancement, and peak detection, produced frame-by-frame particle event counts enabling quantitative microplastic monitoring in situ.
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This is a duplicate data repository entry — the same publicly compiled US plastic packaging and microplastics modeling dataset as entry 1068.
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Researchers applied ultrasonic imaging to quantify microplastic concentrations in liquids, exploring acoustic methods as a non-destructive detection alternative to conventional spectroscopic approaches.
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