The codes of the physics-based non-local dual-domain network (PND-Net) for metal artifact reduction are submitted here.
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This work contains data gathered by a series of sensors (PM 10, PM 2.5, temperature, relative humidity, and pressure) in the city of Turin in the north part of Italy (more precisely, at coordinates 45.041903N, 7.625850E). The data has been collected for a period of 5 months, from October 2018 to February 2019. The scope of the study was to address the calibration of low-cost particulate matter sensors and compare the readings against official measures provided by the Italian environmental agency (ARPA Piemonte).
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The database contains the raw range-azimuth measurements obtained from mmWave MIMO radars (IWR1843BOOST http://www.ti.com/tool/IWR1843BOOST) deployed in different positions around a robotic manipulator. Data has been collected inside a workspace environment. The data is used to train a machine learning model for the detection of a human operator placed in different positions (see the image). The Python code examples (on github link) use the data for federated learning.
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