Artificial Neural Network Model for Evaluating Parameters of Reflection-Asymmetric Samples From Reference-Plane-Invariant Measurements

A technique based on artificial neural network (ANN) is proposed to extract the electromagnetic properties of reflection-asymmetric samples from reference-plane-invariant (RPI) scattering parameter measurements. It first determines reference plane transformation distances and then extracts the mater...

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Published inIEEE transactions on instrumentation and measurement Vol. 72; pp. 1 - 8
Main Authors Hasar, Ugur C., Ozturk, Hamdullah, Ertugrul, Mehmet, Barroso, Joaquim J., Ramahi, Omar M.
Format Journal Article
LanguageEnglish
Published New York IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Artificial neural networks (ANNs)
Asymmetry
bianisotropic
Biomedical measurement
Calibration
Economic indicators
Electromagnetic properties
Electromagnetics
Evaluation
Invariants
material characterization
Material properties
Mathematical models
Metamaterials
metamaterials (MMs)
Neurons
reference-plane-invariant (RPI)
reflection-asymmetric
S parameters
Scattering parameters
Slabs
Waveguides
Online AccessGet full text
ISSN0018-9456
1557-9662
DOI10.1109/TIM.2023.3273664

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Abstract A technique based on artificial neural network (ANN) is proposed to extract the electromagnetic properties of reflection-asymmetric samples from reference-plane-invariant (RPI) scattering parameter measurements. It first determines reference plane transformation distances and then extracts the material properties. The number of neurons in the hidden layer of the ANN model was evaluated subject to accuracy and time constraints. We examined the conformity of the dataset of the ANN model and the required time for the training process by considering different numbers of neurons in the selected hidden layer. <inline-formula> <tex-math notation="LaTeX">S </tex-math></inline-formula>-parameter waveguide measurements at the <inline-formula> <tex-math notation="LaTeX">X </tex-math></inline-formula>-band (8.2-12.4 GHz) of two bianisotropic metamaterial (MM) slabs, as reflection-asymmetric samples, composed of square-shaped split ring resonators (SRRs) and asymmetrically positioned into their measurement cells were used to validate the ANN model and evaluate the effectiveness of the proposed method in extracting the electromagnetic properties.
AbstractList A technique based on artificial neural network (ANN) is proposed to extract the electromagnetic properties of reflection-asymmetric samples from reference-plane-invariant (RPI) scattering parameter measurements. It first determines reference plane transformation distances and then extracts the material properties. The number of neurons in the hidden layer of the ANN model was evaluated subject to accuracy and time constraints. We examined the conformity of the dataset of the ANN model and the required time for the training process by considering different numbers of neurons in the selected hidden layer. <inline-formula> <tex-math notation="LaTeX">S </tex-math></inline-formula>-parameter waveguide measurements at the <inline-formula> <tex-math notation="LaTeX">X </tex-math></inline-formula>-band (8.2-12.4 GHz) of two bianisotropic metamaterial (MM) slabs, as reflection-asymmetric samples, composed of square-shaped split ring resonators (SRRs) and asymmetrically positioned into their measurement cells were used to validate the ANN model and evaluate the effectiveness of the proposed method in extracting the electromagnetic properties.
A technique based on artificial neural network (ANN) is proposed to extract the electromagnetic properties of reflection-asymmetric samples from reference-plane-invariant (RPI) scattering parameter measurements. It first determines reference plane transformation distances and then extracts the material properties. The number of neurons in the hidden layer of the ANN model was evaluated subject to accuracy and time constraints. We examined the conformity of the dataset of the ANN model and the required time for the training process by considering different numbers of neurons in the selected hidden layer. [Formula Omitted]-parameter waveguide measurements at the [Formula Omitted]-band (8.2–12.4 GHz) of two bianisotropic metamaterial (MM) slabs, as reflection-asymmetric samples, composed of square-shaped split ring resonators (SRRs) and asymmetrically positioned into their measurement cells were used to validate the ANN model and evaluate the effectiveness of the proposed method in extracting the electromagnetic properties.
Author Ertugrul, Mehmet
Ramahi, Omar M.
Barroso, Joaquim J.
Ozturk, Hamdullah
Hasar, Ugur C.
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10.1016/j.sna.2013.09.007
10.1109/TMTT.2020.3018712
10.1088/0957-0233/19/5/055706
10.1109/TMTT.2008.2002229
10.1017/CBO9780511812651
10.3923/jas.2013.133.139
10.1364/JOSAA.33.000954
10.1109/TMTT.2008.2011242
10.1063/1.1695439
10.3390/s19214766
10.1177/0142331217721968
10.1109/TIM.2019.2963580
10.1109/22.57336
10.1016/j.sna.2018.09.005
10.1109/TIM.2021.3062676
10.1109/TMTT.2017.2756964
10.1109/TMTT.2016.2644639
10.1103/PhysRevE.79.026610
10.1109/TIM.2020.2988329
10.1109/72.88168
10.1016/j.sna.2013.11.032
10.1109/TMTT.2022.3157718
10.1109/TMTT.2017.2772864
10.1109/ACCESS.2019.2940723
10.1109/TMTT.2015.2431685
10.1109/TMTT.1979.1129778
10.1109/TMTT.2007.906473
10.1088/0957-0233/17/6/R01
10.1109/TEMC.2011.2156416
10.1109/TIE.2020.3032870
10.1109/OJAP.2021.3121177
10.1163/156939311797164756
10.1109/TIM.1970.4313932
10.1109/TMTT.2016.2606389
10.1007/s10762-011-9869-3
10.1002/0470020466
10.1109/PROC.1974.9382
10.1109/TAP.2020.2979292
10.1109/LMWC.2009.2020045
10.1049/el:19700354
10.1109/22.552032
10.2528/PIER09061008
10.1109/TIM.2020.3047490
10.1109/TIM.2021.3126011
10.1109/TMTT.2009.2027160
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References ref13
ref12
ref15
ref14
ref11
ref10
ref17
ref16
ref19
ref18
(ref46) 2015
Dean (ref50)
ref45
ref47
ref41
ref44
ref43
ref49
ref8
ref7
ref9
ref4
ref3
ref6
ref5
ref40
ref35
ref34
(ref48) 2021
ref37
ref36
ref31
ref30
ref33
ref32
ref2
ref1
ref39
ref38
ref24
Erhan (ref51) 2010; 11
ref23
ref26
ref25
ref20
ref22
ref21
Haykin (ref42) 1996
ref28
ref27
ref29
References_xml – ident: ref17
  doi: 10.1364/JOSAB.30.001058
– ident: ref26
  doi: 10.1016/j.sna.2013.09.007
– ident: ref32
  doi: 10.1109/TMTT.2020.3018712
– ident: ref19
  doi: 10.1088/0957-0233/19/5/055706
– ident: ref18
  doi: 10.1109/TMTT.2008.2002229
– volume-title: Neural Networks: A Comprehensive Foundation
  year: 1996
  ident: ref42
– ident: ref43
  doi: 10.1017/CBO9780511812651
– ident: ref49
  doi: 10.3923/jas.2013.133.139
– ident: ref44
  doi: 10.1364/JOSAA.33.000954
– ident: ref10
  doi: 10.1109/TMTT.2008.2011242
– ident: ref35
  doi: 10.1063/1.1695439
– ident: ref40
  doi: 10.3390/s19214766
– ident: ref2
  doi: 10.1177/0142331217721968
– ident: ref3
  doi: 10.1109/TIM.2019.2963580
– ident: ref8
  doi: 10.1109/22.57336
– ident: ref33
  doi: 10.1016/j.sna.2018.09.005
– volume-title: CST Microwave Studio
  year: 2021
  ident: ref48
– ident: ref22
  doi: 10.1109/TIM.2021.3062676
– ident: ref30
  doi: 10.1109/TMTT.2017.2756964
– ident: ref37
  doi: 10.1109/TMTT.2016.2644639
– ident: ref36
  doi: 10.1103/PhysRevE.79.026610
– ident: ref21
  doi: 10.1109/TIM.2020.2988329
– ident: ref45
  doi: 10.1109/72.88168
– ident: ref27
  doi: 10.1016/j.sna.2013.11.032
– ident: ref34
  doi: 10.1109/TMTT.2022.3157718
– ident: ref13
  doi: 10.1109/TMTT.2017.2772864
– ident: ref31
  doi: 10.1109/ACCESS.2019.2940723
– ident: ref28
  doi: 10.1109/TMTT.2015.2431685
– ident: ref47
  doi: 10.1109/TMTT.1979.1129778
– ident: ref15
  doi: 10.1109/TMTT.2007.906473
– ident: ref5
  doi: 10.1088/0957-0233/17/6/R01
– ident: ref29
  doi: 10.1109/TEMC.2011.2156416
– ident: ref41
  doi: 10.1109/TIE.2020.3032870
– ident: ref1
  doi: 10.1109/OJAP.2021.3121177
– ident: ref25
  doi: 10.1163/156939311797164756
– ident: ref6
  doi: 10.1109/TIM.1970.4313932
– start-page: 1223
  volume-title: Proc. Adv. Neural Inf. Process. Sys.
  ident: ref50
  article-title: Large scale distributed deep networks
– ident: ref11
  doi: 10.1109/TMTT.2016.2606389
– ident: ref16
  doi: 10.1007/s10762-011-9869-3
– ident: ref4
  doi: 10.1002/0470020466
– ident: ref7
  doi: 10.1109/PROC.1974.9382
– ident: ref38
  doi: 10.1109/TAP.2020.2979292
– ident: ref20
  doi: 10.1109/LMWC.2009.2020045
– ident: ref14
  doi: 10.1049/el:19700354
– ident: ref9
  doi: 10.1109/22.552032
– volume-title: MATLAB Neural Network Toolbox Release 2019b
  year: 2015
  ident: ref46
– ident: ref39
  doi: 10.2528/PIER09061008
– ident: ref12
  doi: 10.1109/TIM.2020.3047490
– ident: ref23
  doi: 10.1109/TIM.2021.3126011
– ident: ref24
  doi: 10.1109/TMTT.2009.2027160
– volume: 11
  start-page: 625
  year: 2010
  ident: ref51
  article-title: Why does unsupervised pretraining help deep learning?
  publication-title: J. Mach. Learn. Res.
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Snippet A technique based on artificial neural network (ANN) is proposed to extract the electromagnetic properties of reflection-asymmetric samples from...
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SubjectTerms Artificial neural networks
Artificial neural networks (ANNs)
Asymmetry
bianisotropic
Biomedical measurement
Calibration
Economic indicators
Electromagnetic properties
Electromagnetics
Evaluation
Invariants
material characterization
Material properties
Mathematical models
Metamaterials
metamaterials (MMs)
Neurons
reference-plane-invariant (RPI)
reflection-asymmetric
S parameters
Scattering parameters
Slabs
Waveguides
Title Artificial Neural Network Model for Evaluating Parameters of Reflection-Asymmetric Samples From Reference-Plane-Invariant Measurements
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