Enabling antenna design with nano-magnetic materials using machine learning

Carmine Gianfagna; Madhavan Swaminathan; P. Markondeya Raj; Rao Tummala; Giulio Antonini

doi:10.1109/NMDC.2015.7439256

Enabling antenna design with nano-magnetic materials using machine learning

Carmine Gianfagna, Madhavan Swaminathan, P. Markondeya Raj, Rao Tummala, Giulio Antonini

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Scopus citations

Abstract

A machine learning approach to design with magneto dielectric nano-composite (MDNC) substrate for planar inverted-F antenna (PIFA) is presented. A new mixing rule model has been developed. A database of material properties has been created using several particle radius and volume fraction. A second database built with antenna simulations has been developed to complete the machine learning dataset. It is shown that, starting from particle radius and volume fraction of the nano-magnetic material, it is possible to calculate the antenna parameters like gain, bandwidth, radiation efficiency, resonant frequency, and viceversa with good precision by using machine learning techniques.

Original language	English (US)
Title of host publication	2015 IEEE Nanotechnology Materials and Devices Conference, NMDC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467393621
DOIs	https://doi.org/10.1109/NMDC.2015.7439256
State	Published - Mar 22 2016
Event	10th IEEE Nanotechnology Materials and Devices Conference, NMDC 2015 - Anchorage, United States Duration: Sep 12 2015 → Sep 16 2015

Publication series

Name	2015 IEEE Nanotechnology Materials and Devices Conference, NMDC 2015

Conference

Conference	10th IEEE Nanotechnology Materials and Devices Conference, NMDC 2015
Country/Territory	United States
City	Anchorage
Period	9/12/15 → 9/16/15

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials

Access to Document

10.1109/NMDC.2015.7439256

Cite this

Gianfagna, C., Swaminathan, M., Raj, P. M., Tummala, R., & Antonini, G. (2016). Enabling antenna design with nano-magnetic materials using machine learning. In 2015 IEEE Nanotechnology Materials and Devices Conference, NMDC 2015 Article 7439256 (2015 IEEE Nanotechnology Materials and Devices Conference, NMDC 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NMDC.2015.7439256

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title = "Enabling antenna design with nano-magnetic materials using machine learning",

abstract = "A machine learning approach to design with magneto dielectric nano-composite (MDNC) substrate for planar inverted-F antenna (PIFA) is presented. A new mixing rule model has been developed. A database of material properties has been created using several particle radius and volume fraction. A second database built with antenna simulations has been developed to complete the machine learning dataset. It is shown that, starting from particle radius and volume fraction of the nano-magnetic material, it is possible to calculate the antenna parameters like gain, bandwidth, radiation efficiency, resonant frequency, and viceversa with good precision by using machine learning techniques.",

author = "Carmine Gianfagna and Madhavan Swaminathan and Raj, {P. Markondeya} and Rao Tummala and Giulio Antonini",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 10th IEEE Nanotechnology Materials and Devices Conference, NMDC 2015 ; Conference date: 12-09-2015 Through 16-09-2015",

year = "2016",

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doi = "10.1109/NMDC.2015.7439256",

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Gianfagna, C, Swaminathan, M, Raj, PM, Tummala, R & Antonini, G 2016, Enabling antenna design with nano-magnetic materials using machine learning. in 2015 IEEE Nanotechnology Materials and Devices Conference, NMDC 2015., 7439256, 2015 IEEE Nanotechnology Materials and Devices Conference, NMDC 2015, Institute of Electrical and Electronics Engineers Inc., 10th IEEE Nanotechnology Materials and Devices Conference, NMDC 2015, Anchorage, United States, 9/12/15. https://doi.org/10.1109/NMDC.2015.7439256

Enabling antenna design with nano-magnetic materials using machine learning. / Gianfagna, Carmine; Swaminathan, Madhavan; Raj, P. Markondeya et al.
2015 IEEE Nanotechnology Materials and Devices Conference, NMDC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. 7439256 (2015 IEEE Nanotechnology Materials and Devices Conference, NMDC 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Enabling antenna design with nano-magnetic materials using machine learning

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AU - Swaminathan, Madhavan

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AU - Antonini, Giulio

PY - 2016/3/22

Y1 - 2016/3/22

N2 - A machine learning approach to design with magneto dielectric nano-composite (MDNC) substrate for planar inverted-F antenna (PIFA) is presented. A new mixing rule model has been developed. A database of material properties has been created using several particle radius and volume fraction. A second database built with antenna simulations has been developed to complete the machine learning dataset. It is shown that, starting from particle radius and volume fraction of the nano-magnetic material, it is possible to calculate the antenna parameters like gain, bandwidth, radiation efficiency, resonant frequency, and viceversa with good precision by using machine learning techniques.

AB - A machine learning approach to design with magneto dielectric nano-composite (MDNC) substrate for planar inverted-F antenna (PIFA) is presented. A new mixing rule model has been developed. A database of material properties has been created using several particle radius and volume fraction. A second database built with antenna simulations has been developed to complete the machine learning dataset. It is shown that, starting from particle radius and volume fraction of the nano-magnetic material, it is possible to calculate the antenna parameters like gain, bandwidth, radiation efficiency, resonant frequency, and viceversa with good precision by using machine learning techniques.

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M3 - Conference contribution

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T3 - 2015 IEEE Nanotechnology Materials and Devices Conference, NMDC 2015

BT - 2015 IEEE Nanotechnology Materials and Devices Conference, NMDC 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th IEEE Nanotechnology Materials and Devices Conference, NMDC 2015

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ER -

Gianfagna C, Swaminathan M, Raj PM, Tummala R, Antonini G. Enabling antenna design with nano-magnetic materials using machine learning. In 2015 IEEE Nanotechnology Materials and Devices Conference, NMDC 2015. Institute of Electrical and Electronics Engineers Inc. 2016. 7439256. (2015 IEEE Nanotechnology Materials and Devices Conference, NMDC 2015). doi: 10.1109/NMDC.2015.7439256

Enabling antenna design with nano-magnetic materials using machine learning

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