2020
DOI: 10.1109/jerm.2020.2984910
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Design and Optimization of a Slotted Monopole Antenna for Ultra-Wide Band Body Centric Imaging Applications

Abstract: There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.

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Cited by 45 publications
(24 citation statements)
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References 27 publications
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“…The printed monopole antennas, already presented for breast imaging, are relatively larger than our via-fed antenna [26,27] or, with a similar size to our antenna, have less bandwidth or pulse preserving capability [10,16]. [27] presents a large size CPW monopole antenna for UWB bodycentric imaging applications. However, [27] does not evaluate any of the time-or frequency-domain distortion analyses.…”
Section: Introductionmentioning
confidence: 92%
See 2 more Smart Citations
“…The printed monopole antennas, already presented for breast imaging, are relatively larger than our via-fed antenna [26,27] or, with a similar size to our antenna, have less bandwidth or pulse preserving capability [10,16]. [27] presents a large size CPW monopole antenna for UWB bodycentric imaging applications. However, [27] does not evaluate any of the time-or frequency-domain distortion analyses.…”
Section: Introductionmentioning
confidence: 92%
“…The large axial lengths of these antennas increase the overall size of the imaging system in practical scenarios [25]. Recently, planar printed monopole antennas have been considered for breast cancer screening systems [10,16,[25][26][27] because of their merits such as simple and low-cost structure, low profile, wide impedance bandwidth, and ease of fabrication. Additionally, designing a low profile and small-size antenna would also enable a higher number of antenna elements around the tissue; thus, more received signals can be exploited, and resolution would be enhanced [10].…”
Section: Introductionmentioning
confidence: 99%
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“…The search ranges of the design variables are large, covering all possibilities. The three key elements of SADEA include Gaussian process machine learning for surrogate modeling [38], differential evolution for evolutionary optimization [39] and the surrogate model-aware evolutionary search framework [40,41] to make surrogate modeling and evolutionary optimization The simulation results that have been presented illustrate the complexity of optimizing the proposed antenna structure. As mentioned above, standard global optimization techniques may cost a very long time (estimated 1.5 months for this particular case).…”
Section: Multi-fidelity Surrogate Model Assisted Differential Evolutimentioning
confidence: 99%
“…The search ranges of the design variables are large, covering all possibilities. The three key elements of SADEA include Gaussian process machine learning for surrogate modeling [38], differential evolution for evolutionary optimization [39] and the surrogate model-aware evolutionary search framework [40,41] to make surrogate modeling and evolutionary optimization work harmoniously for antenna design landscapes. The method uses a computationally cheap Gaussian process surrogate model to approximate the computationally expensive EM simulation model in order to reduce the number of necessary EM simulations in the optimization process.…”
Section: Multi-fidelity Surrogate Model Assisted Differential Evolutimentioning
confidence: 99%