Machine Learning Based Localization and Classification with Atomic Magnetometers - Shape and Position

<div>Datasets created with an atomic magnetometer performing magnetic induction imaging, and later analysed with machine learning.</div><div><br></div><div><div>In this dataset, object's position is randomly varied in the (x,y) plane, and four different shapes are used.</div><div>Shape 01: 50 mm x 50 mm x 3 mm Aluminium square</div><div>Shape 02: 50 mm diameter x 3 mm Aluminium disk</div><div>Shape 03: 50 mm x 25 mm x 3 mm Aluminium rectangle</div><div>Shape 04: 50 mm base isosceles triangle, 50 mm height.</div><div>Data are arranged per shape; position is indicated in the file's name, in mm.</div><div>Four classes of data are collected: Radius (R), Phase (P), in-phase (X), and out-of-phase (Y). </div><div><br></div><div><div>Blind datasets: Information removed from filename.</div><div>Details are reported in the file: Shape_Position_blind_files.pdf</div></div><div><br></div><div>For further details, see the paper published in Physical Review Letters: https://doi.org/10.1103/PhysRevLett.120.033204.</div>