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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.
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.
More Papers Like This
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.
Dataset for high-frequency ultrasound–based microplastic identification and size estimation
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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).
Dataset for high-frequency ultrasound–based microplastic identification and size estimation
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Researchers created a labeled dataset of high-frequency ultrasound signals from microplastic microspheres of different materials and sizes to support machine-learning-based detection methods. The dataset enables AI models to identify and size microplastic particles non-invasively, which could improve real-time monitoring of microplastics in water and food systems.
High-frequency ultrasound combined with deep learning enables identification and size estimation of microplastics
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Researchers developed a high-frequency ultrasound method combined with deep learning — using a 1D convolutional neural network on spectral and temporal echo features — to identify microplastic material type and estimate particle size, offering a faster and non-destructive alternative to FTIR and Raman spectroscopy.
Detección De Microplásticos En El Medio Marino Mediante Backscattering Ultrasónico De Alta Frecuencia
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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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