File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Applying Deep Learning Approach to the Far-Field Subwavelength Imaging Based on Near-Field Resonant Metalens at Microwave Frequencies

TitleApplying Deep Learning Approach to the Far-Field Subwavelength Imaging Based on Near-Field Resonant Metalens at Microwave Frequencies
Authors
KeywordsImaging
Training
InterferenceTraining data
Data models
Optical switches
Issue Date2019
PublisherInstitute of Electrical and Electronics Engineers: Open Access Journals. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639
Citation
IEEE Access, 2019, v. 7, p. 63801-63808 How to Cite?
AbstractIn this paper, we utilize the deep learning approach for the subwavelength imaging in far-field, which is realized by the near-field resonant metalens at microwave frequencies. The resonating metalens consisting of split-ring resonators (SRRs) are equipped with the strong magnetic coupling ability and can convert evanescent waves into propagating waves using the localized resonant modes. The propagating waves in the far-field are utilized as the input of a trained deep convolutional neural network (CNN) to realize the imaging. The training data for establishing the deep CNN are obtained by the EM simulation tool. Besides, the white Gaussian noise is added into the training data to simulate the interference in the real application scenario. The proposed CNN composes of three pairs of convolutional and activation layers with one additional fully connected layer to realize the recognition, i.e., the imaging process. The feasibility of utilizing the trained deep CNN for imaging is validated by numerical benchmarks. Distinguished from the subwavelength imaging methods, the spatial response and Green's function need not be measured and evaluated in the proposed method.
Persistent Identifierhttp://hdl.handle.net/10722/278142
ISSN
2023 Impact Factor: 3.4
2023 SCImago Journal Rankings: 0.960
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYAO, HM-
dc.contributor.authorLi, M-
dc.contributor.authorJiang, L-
dc.date.accessioned2019-10-04T08:08:18Z-
dc.date.available2019-10-04T08:08:18Z-
dc.date.issued2019-
dc.identifier.citationIEEE Access, 2019, v. 7, p. 63801-63808-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10722/278142-
dc.description.abstractIn this paper, we utilize the deep learning approach for the subwavelength imaging in far-field, which is realized by the near-field resonant metalens at microwave frequencies. The resonating metalens consisting of split-ring resonators (SRRs) are equipped with the strong magnetic coupling ability and can convert evanescent waves into propagating waves using the localized resonant modes. The propagating waves in the far-field are utilized as the input of a trained deep convolutional neural network (CNN) to realize the imaging. The training data for establishing the deep CNN are obtained by the EM simulation tool. Besides, the white Gaussian noise is added into the training data to simulate the interference in the real application scenario. The proposed CNN composes of three pairs of convolutional and activation layers with one additional fully connected layer to realize the recognition, i.e., the imaging process. The feasibility of utilizing the trained deep CNN for imaging is validated by numerical benchmarks. Distinguished from the subwavelength imaging methods, the spatial response and Green's function need not be measured and evaluated in the proposed method.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers: Open Access Journals. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639-
dc.relation.ispartofIEEE Access-
dc.rightsIEEE Access. Copyright © Institute of Electrical and Electronics Engineers (IEEE): OAJ.-
dc.subjectImaging-
dc.subjectTraining-
dc.subjectInterferenceTraining data-
dc.subjectData models-
dc.subjectOptical switches-
dc.titleApplying Deep Learning Approach to the Far-Field Subwavelength Imaging Based on Near-Field Resonant Metalens at Microwave Frequencies-
dc.typeArticle-
dc.identifier.emailLi, M: minli@hku.hk-
dc.identifier.emailJiang, L: jianglj@hku.hk-
dc.identifier.authorityJiang, L=rp01338-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ACCESS.2019.2915263-
dc.identifier.scopuseid_2-s2.0-85066425302-
dc.identifier.hkuros306191-
dc.identifier.volume7-
dc.identifier.spage63801-
dc.identifier.epage63808-
dc.identifier.isiWOS:000469944800001-
dc.publisher.placeUnited States-
dc.identifier.issnl2169-3536-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats