The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Reconstruction of Electrical Impedance Tomography Using Fish School Search, Non-Blind Search, and Genetic Algorithm
|
|
Author(s): Valter A. F. Barbosa (Universidade Federal de Pernambuco, Brazil), Reiga R. Ribeiro (Universidade Federal de Pernambuco, Brazil), Allan R. S. Feitosa (Universidade Federal de Pernambuco, Brazil), Victor L. B. A. Silva (Escola Politécnica da Universidade de Pernambuco, Brazil), Arthur D. D. Rocha (Escola Politécnica da Universidade de Pernambuco, Brazil), Rafaela C. Freitas (Escola Politécnica da Universidade de Pernambuco, Brazil), Ricardo E. Souza (Universidade Federal de Pernambuco, Brazil)and Wellington P. Santos (Universidade Federal de Pernambuco, Brazil & Escola Politécnica da Universidade de Pernambuco, Brazil)
Copyright: 2019
Pages: 18
Source title:
Biotechnology: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-8903-7.ch082
Purchase
|
Abstract
Electrical Impedance Tomography (EIT) is a noninvasive imaging technique that does not use ionizing radiation, with application both in environmental sciences and in health. Image reconstruction is performed by solving an inverse problem and ill-posed. Evolutionary Computation and Swarm Intelligence have become a source of methods for solving inverse problems. Fish School Search (FSS) is a promising search and optimization method, based on the dynamics of schools of fish. In this article the authors present a method for reconstruction of EIT images based on FSS and Non-Blind Search (NBS). The method was evaluated using numerical phantoms consisting of electrical conductivity images with subjects in the center, between the center and the edge and on the edge of a circular section, with meshes of 415 finite elements. The authors performed 20 simulations for each configuration. Results showed that both FSS and FSS-NBS were able to converge faster than genetic algorithms.
Related Content
|
Shweta Arun Avhad.
© 2023.
21 pages.
|
|
Majorie Moraa Nyasani, Victor Odhiambo Shikuku.
© 2023.
10 pages.
|
|
Prashant Kumar, Sunil Kumar Verma.
© 2023.
12 pages.
|
|
Sunil Kumar Verma, Prashant Kumar.
© 2023.
21 pages.
|
|
Kannadhasan S., Nagarajan R..
© 2023.
10 pages.
|
|
Kondapalli Vamsi Krishna, Sompalli Bhavana, Koushik Koujalagi, Alok Malaviya.
© 2023.
44 pages.
|
|
Bela Khiratkar, Shankar Mukundrao Khade, Abhishek Dutt Tripathi.
© 2023.
10 pages.
|
|
|