Planar Near-Field Electric Field Sensor Array Applications Facilitated by Neural Networks

• Published in: IEEE sensors journal Vol. 21; no. 18; pp. 21038 - 21049
• Main Authors: Drummond, Zachary D., Claytor, Kevin E., Adelman, Ross N., Allee, David R., Hull, David M.
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; deep learning; Dielectrics; ECT; Electric fields; Electrical capacitance tomography; Image sensors; Imaging; inverse problem; Inverse problems; LF electric fields; neural network; Neural networks; Object recognition; Permittivity; Sensor arrays; Sensors; Tomography; Two dimensional models
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2021.3099984

Electrical capacitance tomography is employed for various process tomography applications, typically with circular imaging regions (e.g., to estimate fluid levels in plastic pipes). Typical state-of-the-art implementations focus on circular or cylindrical sensor arrays. In contrast, this research explores using a planar 2D array of electric-field sensors to image volumes composed of various dielectric materials. The array is designed to be used with very-low-frequency electric fields, which are desirable due to their ability to differentiate between various non-conducting objects. D-dot sensors (i.e., charge induction sensors) are used as the electric-field sensing element. In this research, imaging regions of interest are modeled as a composition of (25 cm) 3 voxels of dielectric material with randomized relative permittivities. Neural networks are utilized as the inversion algorithm to map measured E-field distortions to the voxels' relative permittivities. Three applications are explored in a simulated environment: 1) predicting relative permittivities of the entire (pseudo-3D) imaging region from one measurement of electric-field distortions (modeled in free space), 2) imaging regions arbitrarily large (in two dimensions) using the planar array as an imaging kernel, and 3) repeating application (1) in a model of a practical, real-world imaging scenario both with and without interfering material. Application (3) is performed with a real-world experimental setup using a room-sized "E-field Cage" meant to generate a uniform electric field. This work showcases a new electric-field imaging modality using a planar 2D D-dot sensor array paired with a DNN-based inversion algorithm.