Enabling antenna design with nano-magnetic materials using machine learning

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

doi:10.1109/nmdc.2015.7439256

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…The conductivity of MWCNT is 10 e 4, relative permeability is 1.2, and relative permittivity is 120. Based on References , the dielectric values are used to design the shield material and are designed for interaction with the experimental scenario.…”

Section: Sar Reduction Methods Based On Shield Materialsmentioning

confidence: 99%

“…The first type of composite proposed is magneto‐dielectric nanocomposites (MDNCs). They are flexible to design a substrate and proved to be numerically efficient to increase the performance of the antenna . The MDNC is proposed for twin purposes namely: miniaturization of the antenna and for SAR reduction.…”

Section: Sar Reduction Methods Based On Shield Materialsmentioning

confidence: 99%

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An investigation on specific absorption rate reduction materials with human tissue cube for biomedical applications

Stephen

Hemanth

2019

Int J RF Microw Comput Aided Eng

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The objective of this study is to investigate the impact of human exposure due to electromagnetic fields and to reduce the specific absorption rate (SAR). This work focuses on the implementation of different electromagnetic shield materials such as conductive materials, dielectric materials, ferrite materials, and metamaterials that are currently used for SAR reduction. The experimental analysis is performed with the global system for mobile communication (GSM) dipole antenna operating at 900 MHz as its radiation source. The designed dipole is placed in front of a cube with similar dielectric properties of human tissue. The computation, implementation, and evaluation are performed by using the method of moments. Further calculation of SAR reduction factor (SRF), SAR 1 g, gain, directivity, and beamwidth are estimated for 15 different shield materials. There is a need for an ideal shield material that could reduce SAR and does not compromise the performance of the radiation structure. This work investigates and proposes the nanomaterials and composites as suitable shield materials for SAR reduction. This novel approach of implementation of nanomaterials has proved to increase the SRF over 90% than conventional reduction materials. In addition, gain and directivity are also increased by 50% than the existing shield materials. The currently used materials are compared with the proposed nanomaterials, and the results are analyzed. The results indicate that the nanomaterials and its composites are ideal shield material for SAR reduction.

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Section: Sar Reduction Methods Based On Shield Materialsmentioning

confidence: 99%

Section: Sar Reduction Methods Based On Shield Materialsmentioning

confidence: 99%

An investigation on specific absorption rate reduction materials with human tissue cube for biomedical applications

Stephen

Hemanth

2019

Int J RF Microw Comput Aided Eng

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“…The results show that SVM has a higher convergence rate and better computational efficiency than an ANN. Bayesian Regularization algorithms have been implemented to design a planar inverted F-antenna where the ML algorithm has been used to minimize the error and accelerate the cycle time for the new material synthesis with fewer simulations time [22]. In [23], authors have used linear regression (LR) methods to evaluate the feasibility of antenna design, a heuristic algorithm-enhanced ANN to model the embedded antenna, and then a multi-fidelity neural network to model and optimize the antenna design.…”

Section: Literature Reviewmentioning

confidence: 99%

A comparative analysis of machine learning approach for optimizing antenna design

Shakya,

Kube,

Zhou

2023

Int. J. Microw. Wireless Technol.

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With the increasing demand for smarter antenna design in advanced technology applications, well-designed antennas have been an important factor in enhancing system performance. Most traditional antenna design requires multiple iterations and extensive testing to produce a final product. Machine learning (ML) algorithms have been used as an alternative to predict the optimal design parameters, but the outcome depends highly on the ML model efficiency. With recent development in machine learning algorithms and the availability of data for antenna design, we investigated different machine learning algorithms for optimizing the output strength of three basic antennae by analyzing the signal strength of the antenna for various antenna parameters. Different regression-based ML models were used to learn the behaviors and efficiency of three different antennas and to predict the output strength (S11) for different ranges of frequencies. The experiment compared and analyzed these ML regression algorithms for three different antennas: shot antenna, patch antenna, and bowtie antenna. In addition, the paper also provides comparison of ensemble ML models for performance analysis using the best three ML algorithms from the preliminary study. This study optimizes antenna parameters and quicker and smarter antenna design procedure using ML algorithms as compared to traditional design methods.

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“…The optimization of design parameters of a PIFA with magneto dielectric nano‐composite substrate using ANNs trained with the BR algorithm was presented in Reference 147. The model was trained on two databases obtained from CST simulations.…”

Section: Predicting Antenna Parameters With Machine Learning Modelsmentioning

confidence: 99%

“…Working with the same antenna, the algorithm used in Reference 147 has been further optimized in 148 for the same input and output parameters. In addition, a reverse technique has been also addressed using ML, for which the corresponding design space of possible material parameters can be generated based on given antenna parameters.…”

Section: Predicting Antenna Parameters With Machine Learning Modelsmentioning

confidence: 99%

A review on the design and optimization of antennas using machine learning algorithms and techniques

Misilmani

Naous

Khatib

2020

Int J RF Microw Comput Aided Eng

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This paper presents a focused and comprehensive literature survey on the use of machine learning (ML) in antenna design and optimization. An overview of the conventional computational electromagnetics and numerical methods used to gain physical insight into the design of the antennas is first presented. The major aspects of ML are then presented, with a study of its different learning categories and frameworks. An overview and mathematical briefing of regression models built with ML algorithms is then illustrated, with a focus on those applied in antenna synthesis and analysis. An in‐depth overview on the different research papers discussing the design and optimization of antennas using ML is then reported, covering the different techniques and algorithms applied to generate antenna parameters based on desired radiation characteristics and other antenna specifications. Various investigated antennas are sorted based on antenna type and configuration to assist the readers who wish to work with a specific type of antennas using ML.

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Enabling antenna design with nano-magnetic materials using machine learning

Cited by 10 publications

References 8 publications

An investigation on specific absorption rate reduction materials with human tissue cube for biomedical applications

An investigation on specific absorption rate reduction materials with human tissue cube for biomedical applications

A comparative analysis of machine learning approach for optimizing antenna design

A review on the design and optimization of antennas using machine learning algorithms and techniques

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