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• W3185374219 startingPage "21038" @default.
• W3185374219 abstract "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) <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>3</sup> 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." @default.
• W3185374219 created "2021-08-02" @default.
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• W3185374219 date "2021-09-15" @default.
• W3185374219 modified "2023-09-30" @default.
• W3185374219 title "Planar Near-Field Electric Field Sensor Array Applications Facilitated by Neural Networks" @default.
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• W3185374219 doi "https://doi.org/10.1109/jsen.2021.3099984" @default.
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