2026-10-15T01:02:40Z https://www.scidb.cn/oai

10.57760/sciencedb.29527 2026-03-13T15:58:20Z

2026-03-13 ECT-Mat: An Experimental Mutual Capacitance Dataset for Material Sensing and Inverse Problem Benchmarking doi:10.57760/sciencedb.29527 en We introduce ECT-Bench, the ffrst large-scale, experimentally acquired dataset for planar Electrical Capacitance Tomography (ECT) designed to evaluate both imaging and dynamic tracking algorithms. The dataset contains 54,739 high-resolution mutual capacitance matrices acquired via a custom 3×3 electrode array. Taking advantage of a uniffed robotic pipeline, data was collected under comprehensive spatial variables, covering contact and multi-distance non-contact conditions (Z-axis permutations), alongside continuous spatio-temporal trajectories. Three representative materials (glass, resin, wood) and geometric shapes (circle, triangle, square) are thoroughly documented. Each sample couples raw capacitance signals with strict positional metadata and ground-truth object labels. Furthermore, ECT-Bench provides quantitative reconstructions comparing traditional physical solvers (Linear Back Projection, Landweber) against data-driven deep learning (CNNs). Unlike existing simulation-based benchmarks, ECT-Bench captures genuine soft-ffeld characteristics, dielectric variability, positional uncertainty, and hardware noise. By openly releasing all sensor fabrication ffles, acquisition codes, and baselines, ECT-Bench establishes a standardized, rigorous foundation for bridging the gap between numerics and practical real-world touchless sensing.  Electrical Capacitance Tomography; touchless material sensing; inverse problem research; Material recognition Duanpeng Shi Yuliang Wang Xu Li Tengchen Sun Huaping Liu Di Guo PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank