ArticleJournal of electrical bioimpedance2019
Narrowband Array Processing Beamforming Technique for Electrical Impedance Tomography.
Article in Journal of electrical bioimpedance, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Electrical impedance tomography in neonates: a review.Pediatric research · 2025Review
- Impact of ISTA and FISTA iterative optimization algorithms on electrical impedance tomography image reconstruction.Journal of electrical bioimpedance · 2025Article
- Optimization of electrode positions for equalizing local spatial performance of a tomographic tactile sensor.Frontiers in robotics and AI · 2023Article
- Robust imaging using electrical impedance tomography: review of current tools.Proceedings. Mathematical, physical, and engineering sciences · 2022Review
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Authors and funding
2 authors.
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Abstract
Electrical impedance tomography (EIT) has a large potential as a two dimensional imaging technique and is gaining attention among researchers across various fields of engineering. Beamforming techniques stem from the array signal processing field and is used for spatial filtering of array data to evaluate the location of objects. In this work the circular electrodes are treated as an array of sensors and beamforming technique is used to localize the object(s) in an electrical field. The conductivity distributions within a test tank is obtained by an EIT system in terms of electrode voltages. These voltages are then interpolated using elliptic partial differential equations. Finally, a narrowband beamformer detects the peak in the output response signal to localize the test object(s). Test results show that the beamforming technique can be used as a secondary method that may provide complementary information about accurate position of the test object(s) using an eight electrode EIT system. This method could possibly open new avenues for spatial EIT data filtering techniques with an understanding that the inverse problem is more likely considered here as a source localization algorithm instead as an image reconstruction algorithm.
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