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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).
This dataset contains high-frequency ultrasound measurements acquired from controlled experiments on microplastic microspheres of different materials and size ranges. The data support research on ultrasound-based detection, material identification, and size estimation of microplastic particles. Raw data The raw data consist of three-dimensional tensors representing the spatial and temporal structure of the recorded ultrasound signals, with dimensions corresponding to the lateral scan coordinates (x, y) and time (t). These tensors were acquired over a defined scan area for samples containing microplastic microspheres. The raw radio-frequency (RF) ultrasound signals are provided in MATLAB (.mat) format and are stored in the raw_data.zip archive. These files contain the original, unprocessed measurements recorded during the experiments. Processed and labeled data Particle-specific signals isolated using a peak-based extraction procedure are stored in the all_labeled_signals.csv file. This file contains signal representations derived from the raw measurements together with associated material and particle size labels. Each signal entry is assigned a unique particle identifier, which enables signals originating from the same particle to be grouped and traced back to the corresponding raw measurements. Intended use The dataset is intended to support the development, evaluation, and benchmarking of machine-learning methods for microplastic characterization using ultrasound.
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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.
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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.
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.
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.
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