Download Report IEEE DataPort : Multi-Sensor Dataset of Ultrasonic and mmWave for Material Classification (MatSense2025) - 2025
CSV by MOHAMMED SADIQ, SHAHEEN ABDULKAREEM, AHMAD AL-KHALIL
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Format: CSV Publisher: IEEE DataPort Publication Date of the Electronic Edition: 12/16/2025
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ISBN: 10.21227/8ddw-ax75
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Description
This dataset details a publicly accessible of reflected signals from ultrasonic and millimetre-wave (mmWave) sensors used for material classification. The dataset was gathered using two sensing methods: the URM09 ultrasonic sensor operating at 40 kHz and the DFRobot mmWave C4001 radar sensor operating at 24 GHz. Six common materials were chosen for data collection: wood, plastic, metal, glass, cardboard, and asbestos. Each material was tested at three thicknesses and three sizes (25 cm × 25 cm, 20 cm × 20 cm, and 15 cm × 15 cm). All measurements were taken from a fixed distance of 55 cm in a controlled indoor setting with foam-lined enclosures to reduce external noise. The raw analogue signals were processed with Savitzky-Golay filtering and sliding window techniques. Time-domain features (mean, standard deviation, energy, RMS, kurtosis, skewness) and frequency-domain features (spectral centroid, spectral bandwidth, dominant frequency) were extracted from each sample. For URM09 the dataset includes 1,789 refined data points across all six material-thickness combinations and 565 records for six material-only classification. On the other hand, For C4001 the dataset includes 1,504 refined data points across all five material-thickness combinations and 470 records for five material-only classification. This dataset supports research in robotics, industrial inspection, non-destructive testing, and sensor fusion applications where contactless material identification is essential.
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