2020/09/22 by Reza Rashetnia, Rashetnia, Reza, Mohammad Pour‐Ghaz +1
Computer Science · Earth and Planetary Sciences · Engineering · #Applied Physics (physics.app-ph) #Electrical and Bioimpedance Tomography #FOS: Mathematics #FOS: Physical sciences #Flow Measurement and Analysis #Geophysical and Geoelectrical Methods #Neural Networks and Applications #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.2009.10884
openalex publication_date 2020/09/22 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
In the majority of applications of electrical resistance tomography (ERT) the\nestimation problem consists of either the estimation of spatial conductivity\nchange over an existing background or the estimation of spatial distribution of\nconductivity of the entire target, including the background. In some instances\nhowever, it is possible to design the background conductivity; an example of\nsuch application is the design of ERT-based sensors where the background\nconductivity can be engineered. In such applications the natural question is\nwhether the background conductivity can be engineered in such a way to increase\nthe distinguishability and further reconstructability of the sensor. The\npresent paper, uses topology optimization to design the background conductivity\nto achieve optimal distinguishability. Then, ERT reconstructions suggest the\nenhancements of reconstructability using topology optimized sensor.\n