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Simulation of Neural Network-Multicriterion Optimization Image Reconstruction Technique (NN-MOIRT) for imaging using a 32-channel Brain ECVT sensor
Handayani N.a,b, Hidayanti K.F.b, Baidillah M.R.c, Arif I.a, Khotimah S.N.a, Haryanto F.a, Taruno W.P.c
a Department of Physics, Institut Teknologi Bandung, Bandung, Indonesia
b Department of Physics, UIN Sunan Kalijaga, Yogyakarta, Indonesia
c CTech Labs PT Edwar Technology, Jl. Jalur Sutera Blok 23 BC, Tangerang, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© Published under licence by IOP Publishing Ltd.NN-MOIRT has been proposed earlier as an alternative for existing image reconstruction algorithms that can accurately show a volumetric image in a cylindrical sensor vessel. The Brain ECVT sensor has a different shape and dimensions compared with a common ECVT sensor, which has a cylindrical shape. Using a different sensor changes the image reconstruction algorithm parameters. Thus, the image reconstruction algorithm should be modified to be able to properly make a reconstruction. In this study, a simulation of the reconstruction of an image from a 32-channel Brain ECVT sensor using NN-MOIRT was conducted. The simulation was performed by varying the position and number of objects in the helmet-shaped Brain ECVT sensor. The alpha parameter (penalty factor) was varied from 10 to 150 with the number of iterations from 1 to 200. The RMSE (root mean square error) was calculated based on the difference between the permittivity distribution of the objects and the reconstructed image. It was found that the NN-MOIRT algorithm is more convergent and more stable for image reconstruction than the ILBP algorithm.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Cylindrical shapes,Image reconstruction algorithm,Image reconstruction techniques,Multi-criterion optimization,Number of iterations,Permittivity distributions,Reconstructed image,RMSE (root mean square error)[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1742-6596/1127/1/012008[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]