Multifeature-Assisted Neuro-transfer Function Surrogate-Based EM Optimization Exploiting Trust-Region Algorithms for Microwave Filter Design

This article proposes a multifeature-assisted neuro-transfer function (neuro-TF) surrogate-based electromagnetic (EM) optimization technique exploiting trust-region algorithms for microwave filter design. The proposed optimization technique addresses the situation where the response of the starting...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on microwave theory and techniques Jg. 68; H. 2; S. 531 - 542
Hauptverfasser:	Feng, Feng, Na, Weicong, Liu, Wenyuan, Yan, Shuxia, Zhu, Lin, Ma, Jianguo, Zhang, Qi-Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Computational modeling Design optimization Design specifications Electromagnetic (EM) optimization feature Feature extraction Filter design (mathematics) Linear programming microwave filter Microwave filters neuro-transfer function (neuro-TF) Optimization Optimization techniques Parameter identification Parameter modification Specifications Transfer functions trust region
ISSN:	0018-9480, 1557-9670
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	This article proposes a multifeature-assisted neuro-transfer function (neuro-TF) surrogate-based electromagnetic (EM) optimization technique exploiting trust-region algorithms for microwave filter design. The proposed optimization technique addresses the situation where the response of the starting point is far away from the design specifications. We propose to utilize multiple feature parameters to help move the passband of the filter response into the range of design specifications. The pole-zero-based neuro-TF is introduced in this article to help extract the multiple feature parameters when the feature parameters of filter responses are not explicitly identified. Furthermore, we propose to derive new optimization objective functions to involve the multiple feature parameters. A new trust-region updating formulation for the modified optimization objective functions is derived to guarantee the optimization convergence. With the assistance of multiple feature parameters, the proposed surrogate-based EM optimization has a better capability of avoiding local minima and can reach the optimal EM solution faster than the surrogate-based EM optimizations without feature assistance. Three examples of EM optimizations of microwave filters are used to demonstrate the proposed technique.
AbstractList	This article proposes a multifeature-assisted neuro-transfer function (neuro-TF) surrogate-based electromagnetic (EM) optimization technique exploiting trust-region algorithms for microwave filter design. The proposed optimization technique addresses the situation where the response of the starting point is far away from the design specifications. We propose to utilize multiple feature parameters to help move the passband of the filter response into the range of design specifications. The pole-zero-based neuro-TF is introduced in this article to help extract the multiple feature parameters when the feature parameters of filter responses are not explicitly identified. Furthermore, we propose to derive new optimization objective functions to involve the multiple feature parameters. A new trust-region updating formulation for the modified optimization objective functions is derived to guarantee the optimization convergence. With the assistance of multiple feature parameters, the proposed surrogate-based EM optimization has a better capability of avoiding local minima and can reach the optimal EM solution faster than the surrogate-based EM optimizations without feature assistance. Three examples of EM optimizations of microwave filters are used to demonstrate the proposed technique.
Author	Na, Weicong Yan, Shuxia Zhu, Lin Feng, Feng Zhang, Qi-Jun Ma, Jianguo Liu, Wenyuan
Author_xml	– sequence: 1 givenname: Feng orcidid: 0000-0002-3569-8782 surname: Feng fullname: Feng, Feng email: fengfeng@doe.carleton.ca organization: Department of Electronics, Carleton University, Ottawa, Canada – sequence: 2 givenname: Weicong orcidid: 0000-0001-9775-5124 surname: Na fullname: Na, Weicong email: weicongna@bjut.edu.cn organization: Faculty of Information Technology, Beijing University of Technology, Beijing, China – sequence: 3 givenname: Wenyuan surname: Liu fullname: Liu, Wenyuan email: liuwenyuan@sust.edu.cn organization: College of Electrical and Information Engineering, Shaanxi University of Science and Technology, Xi'an, China – sequence: 4 givenname: Shuxia orcidid: 0000-0002-1991-3526 surname: Yan fullname: Yan, Shuxia email: tjuysx@163.com organization: School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, China – sequence: 5 givenname: Lin surname: Zhu fullname: Zhu, Lin email: zhulin@tcu.edu.cn organization: School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin, China – sequence: 6 givenname: Jianguo surname: Ma fullname: Ma, Jianguo email: mjg@gdut.edu.cn organization: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China – sequence: 7 givenname: Qi-Jun orcidid: 0000-0001-7852-5331 surname: Zhang fullname: Zhang, Qi-Jun email: qjz@doe.carleton.ca organization: Department of Electronics, Carleton University, Ottawa, Canada
BookMark	eNp9kMtOAyEYhYnRxHp5AONmEtdUYIYWllVbNbGa6LieMMM_I810qMB4ewYfWmqNCxeGBeFwzn_59tB2ZztA6IiSIaVEnubzPB8yQuWQSc4ooVtoQDkfYzkak200IIQKLDNBdtGe94v4zDgRA_Q579tgalChd4An3hsfQCe30DuLg1Odr8Els76rgrFd8tA7ZxsVAJ8pH33TeXK3CmZpPtT3__Rt1VoTTNckuet9wPfQrPVJ21hnwtPSJ7V1ydxUzr6qF0hmpg2xwQV403QHaKdWrYfDn3sfPc6m-fkVvrm7vD6f3OAq5TJgWVeipEwIXpcsoykt05ITrXVGZcZ1yaoMiIaRqkXKtRgTqkstVTxV1DVL99HJpu7K2ecefCgWtnddbFmwNNKThEgRXeONK87qvYO6qEz4XjNyMW1BSbFGX6zRF2v0xQ_6mKR_kitnlsq9_5s53mQMAPz6hWSZpDz9AvdylQU
CODEN	IETMAB
CitedBy_id	crossref_primary_10_1109_TMTT_2022_3163745 crossref_primary_10_1109_TMTT_2024_3400152 crossref_primary_10_1038_s41598_024_73323_w crossref_primary_10_3390_s21030851 crossref_primary_10_1109_TMTT_2023_3297403 crossref_primary_10_1007_s10470_020_01733_7 crossref_primary_10_1109_TMTT_2022_3176885 crossref_primary_10_1038_s41598_022_04810_1 crossref_primary_10_1109_LMWC_2022_3150312 crossref_primary_10_1109_LMWT_2023_3329783 crossref_primary_10_1109_ACCESS_2021_3105811 crossref_primary_10_1109_MMM_2024_3486596 crossref_primary_10_1109_MMM_2024_3486599 crossref_primary_10_1002_ett_4579 crossref_primary_10_1109_MMM_2024_3387036 crossref_primary_10_1109_MMM_2021_3095990 crossref_primary_10_1109_ACCESS_2022_3226323 crossref_primary_10_1109_TMTT_2020_3034906 crossref_primary_10_1109_TMTT_2022_3219072 crossref_primary_10_1109_ACCESS_2020_3036600 crossref_primary_10_1038_s41598_022_17661_7 crossref_primary_10_1109_ACCESS_2021_3104338 crossref_primary_10_1109_TMTT_2021_3061566 crossref_primary_10_1088_1361_6463_ad6fb1 crossref_primary_10_1109_TMTT_2021_3061128 crossref_primary_10_1002_jnm_2879 crossref_primary_10_1109_TMTT_2023_3341584 crossref_primary_10_1109_TMTT_2023_3275232 crossref_primary_10_1109_LMWT_2024_3374792 crossref_primary_10_1109_TMTT_2021_3088870 crossref_primary_10_3390_mi11090831 crossref_primary_10_1109_LMWT_2023_3276746 crossref_primary_10_1109_TMTT_2022_3197751 crossref_primary_10_1109_TMTT_2021_3105677 crossref_primary_10_1109_TMTT_2022_3208898 crossref_primary_10_1109_LMWC_2021_3101258 crossref_primary_10_1002_jnm_3154 crossref_primary_10_1038_s41598_022_20728_0 crossref_primary_10_1109_TMTT_2022_3207761 crossref_primary_10_1109_MMM_2024_3486588 crossref_primary_10_1007_s10489_024_05853_9 crossref_primary_10_1109_TMTT_2024_3484760 crossref_primary_10_1109_MCOM_001_2400209 crossref_primary_10_1109_TMTT_2024_3374371 crossref_primary_10_1002_mmce_23034 crossref_primary_10_3390_electronics11223705 crossref_primary_10_1109_TAP_2023_3341226 crossref_primary_10_1002_mmce_23032
Cites_doi	10.1049/iet-map.2015.0246 10.1109/TMTT.2015.2504096 10.1109/TMTT.2013.2278148 10.1109/APMC.2013.6694945 10.1109/LMWC.2018.2884643 10.1109/TMTT.2005.854190 10.1109/TMTT.2015.2504099 10.1109/TMAG.2011.2174349 10.1109/MMM.2015.2514188 10.1007/BF01197433 10.1109/TMTT.2016.2605666 10.1109/TMTT.2009.2032476 10.1109/TMTT.2014.2315781 10.1109/MMM.2008.929554 10.1109/TMTT.2003.814318 10.1109/TMTT.2005.845685 10.1109/61.772353 10.1109/TMTT.2011.2182655 10.1109/TMTT.2004.842482 10.1109/TMTT.2003.820901 10.1109/TMTT.2015.2495180 10.1109/TAP.2013.2254695 10.1109/TMTT.2015.2431675 10.1109/MWSYM.2016.7539963 10.1109/TMTT.2008.2011243 10.1109/TMTT.2003.820904 10.1109/22.808989 10.1049/iet-map.2017.0238 10.1109/TMAG.2006.872018 10.1109/TMTT.2010.2078890 10.1109/TMTT.2003.820900 10.1109/TMTT.2006.882894 10.1109/TMTT.2003.820892 10.1109/TMTT.2003.820897 10.1109/TMTT.2010.2090169 10.1049/iet-map.2011.0138 10.1049/iet-map.2010.0607 10.1109/TMTT.2006.872811 10.1109/TMTT.2017.2650904
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020
DBID	97E RIA RIE AAYXX CITATION 7SP 8FD L7M
DOI	10.1109/TMTT.2019.2952101
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library (IEL) (UW System Shared) CrossRef Electronics & Communications Abstracts Technology Research Database Advanced Technologies Database with Aerospace
DatabaseTitle	CrossRef Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts
DatabaseTitleList	Technology Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1557-9670
EndPage	542
ExternalDocumentID	10_1109_TMTT_2019_2952101 8924915
Genre	orig-research
GrantInformation_xml	– fundername: National Natural Science Foundation of China grantid: 61601323 funderid: 10.13039/501100001809 – fundername: Scientific Research Project of Tianjin Education Commission grantid: 2017KJ088 – fundername: Natural Science Foundation of Tianjin City grantid: 18JCQNJC00900 funderid: 10.13039/501100006606 – fundername: China Postdoctoral Science Foundation grantid: 2019M650404 funderid: 10.13039/501100002858 – fundername: National Natural Science Foundation of China grantid: 61901010 funderid: 10.13039/501100001809 – fundername: Natural Sciences and Engineering Research Council of Canada grantid: RGPIN-2017-06420 funderid: 10.13039/501100000038
GroupedDBID	-~X .GJ 0R~ 29I 3EH 4.4 5GY 5VS 66. 6IK 85S 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IAAWW IBMZZ ICLAB IDIHD IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS RXW TAE TAF TN5 VH1 VJK VOH AAYXX CITATION 7SP 8FD L7M
ID	FETCH-LOGICAL-c359t-9fc8b12885fb24131b3b50ddd41945db2c4e0de6af835d8701dbd9a9a9ce0dd23
IEDL.DBID	RIE
ISICitedReferencesCount	70
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000526546100011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0018-9480
IngestDate	Mon Jun 30 10:18:10 EDT 2025 Tue Nov 25 02:39:26 EST 2025 Tue Nov 18 21:58:05 EST 2025 Wed Aug 27 06:27:55 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	2
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c359t-9fc8b12885fb24131b3b50ddd41945db2c4e0de6af835d8701dbd9a9a9ce0dd23
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-1991-3526 0000-0002-3569-8782 0000-0001-9775-5124 0000-0001-7852-5331
PQID	2352190098
PQPubID	106035
PageCount	12
ParticipantIDs	crossref_citationtrail_10_1109_TMTT_2019_2952101 ieee_primary_8924915 crossref_primary_10_1109_TMTT_2019_2952101 proquest_journals_2352190098
PublicationCentury	2000
PublicationDate	2020-02-01
PublicationDateYYYYMMDD	2020-02-01
PublicationDate_xml	– month: 02 year: 2020 text: 2020-02-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on microwave theory and techniques
PublicationTitleAbbrev	TMTT
PublicationYear	2020
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref34 ref12 ref37 ref15 ref36 ref14 ref31 ref30 ref33 ref11 ref32 ref10 ref2 ref1 ref39 ref17 ref38 ref16 ref19 ref18 morro (ref21) 2005; 53 ref24 ref23 ref26 ref25 ref20 ref22 zhang (ref35) 2018; 12 ref28 cao (ref27) 2009; 57 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5
References_xml	– ident: ref33 doi: 10.1049/iet-map.2015.0246 – ident: ref30 doi: 10.1109/TMTT.2015.2504096 – ident: ref18 doi: 10.1109/TMTT.2013.2278148 – ident: ref20 doi: 10.1109/APMC.2013.6694945 – ident: ref38 doi: 10.1109/LMWC.2018.2884643 – ident: ref13 doi: 10.1109/TMTT.2005.854190 – ident: ref28 doi: 10.1109/TMTT.2015.2504099 – ident: ref9 doi: 10.1109/TMAG.2011.2174349 – ident: ref6 doi: 10.1109/MMM.2015.2514188 – ident: ref39 doi: 10.1007/BF01197433 – ident: ref31 doi: 10.1109/TMTT.2016.2605666 – volume: 57 start-page: 2727 year: 2009 ident: ref27 article-title: A new training approach for parametric modeling of microwave passive components using combined neural networks and transfer functions publication-title: IEEE Trans Microw Theory Techn doi: 10.1109/TMTT.2009.2032476 – ident: ref19 doi: 10.1109/TMTT.2014.2315781 – ident: ref3 doi: 10.1109/MMM.2008.929554 – ident: ref10 doi: 10.1109/TMTT.2003.814318 – volume: 53 start-page: 1130 year: 2005 ident: ref21 article-title: Fast automated design of waveguide filters using aggressive space mapping with a new segmentation strategy and a hybrid optimization algorithm publication-title: IEEE Trans Microw Theory Techn doi: 10.1109/TMTT.2005.845685 – ident: ref37 doi: 10.1109/61.772353 – ident: ref17 doi: 10.1109/TMTT.2011.2182655 – ident: ref12 doi: 10.1109/TMTT.2004.842482 – ident: ref23 doi: 10.1109/TMTT.2003.820901 – ident: ref7 doi: 10.1109/TMTT.2015.2495180 – ident: ref26 doi: 10.1109/TAP.2013.2254695 – ident: ref34 doi: 10.1109/TMTT.2015.2431675 – ident: ref36 doi: 10.1109/MWSYM.2016.7539963 – ident: ref8 doi: 10.1109/TMTT.2008.2011243 – ident: ref2 doi: 10.1109/TMTT.2003.820904 – ident: ref1 doi: 10.1109/22.808989 – volume: 12 start-page: 82 year: 2018 ident: ref35 article-title: An enhanced cognition-driven formulation of space mapping for equal-ripple optimization of microwave filters publication-title: IET Microw Antennas Propag doi: 10.1049/iet-map.2017.0238 – ident: ref25 doi: 10.1109/TMAG.2006.872018 – ident: ref32 doi: 10.1109/TMTT.2010.2078890 – ident: ref22 doi: 10.1109/TMTT.2003.820900 – ident: ref15 doi: 10.1109/TMTT.2006.882894 – ident: ref4 doi: 10.1109/TMTT.2003.820892 – ident: ref11 doi: 10.1109/TMTT.2003.820897 – ident: ref14 doi: 10.1109/TMTT.2010.2090169 – ident: ref16 doi: 10.1049/iet-map.2011.0138 – ident: ref5 doi: 10.1049/iet-map.2010.0607 – ident: ref24 doi: 10.1109/TMTT.2006.872811 – ident: ref29 doi: 10.1109/TMTT.2017.2650904
SSID	ssj0014508
Score	2.548113
Snippet	This article proposes a multifeature-assisted neuro-transfer function (neuro-TF) surrogate-based electromagnetic (EM) optimization technique exploiting...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	531
SubjectTerms	Algorithms Computational modeling Design optimization Design specifications Electromagnetic (EM) optimization feature Feature extraction Filter design (mathematics) Linear programming microwave filter Microwave filters neuro-transfer function (neuro-TF) Optimization Optimization techniques Parameter identification Parameter modification Specifications Transfer functions trust region
Title	Multifeature-Assisted Neuro-transfer Function Surrogate-Based EM Optimization Exploiting Trust-Region Algorithms for Microwave Filter Design
URI	https://ieeexplore.ieee.org/document/8924915 https://www.proquest.com/docview/2352190098
Volume	68
WOSCitedRecordID	wos000526546100011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1557-9670 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014508 issn: 0018-9480 databaseCode: RIE dateStart: 19630101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NaxsxEBWO6SE59MspdZMWHXoqlb2fXumYJjY91E5ItuDbstJo24XYLuu18yfyozMjy6bQUih7EYu0LLyV5s3OmxnGPppSx0aaVGQSlEgAIqEp_q4zJeMSObSsXNeSb9lsJudzddNhnw-5MNZaJz6zAxq6WD6szIZ-lQ0lOQuUUX6UZaNdrtYhYpCkgT91cQMnch_BDAM1zKd5TiIuNYgUWivf_2Vvg1xTlT9OYmdeJi_-78VesueeRvKLHe6vWMcuX7OT34oL9tijy62trKvcKRAGAhS4q8YhWsdXbcMnaNcIG363aZoV_VMTX9CwAR9P-TUeJwufp8mdWK8mkTTPKU9D3FqSMvOL-x-rpm5_LtYc-S-fksDvodxaPqkpEM-vnETklH2fjPPLr8L3XhAmTlUrVGWkRtsl00pT6C3UsU4DAEhClaSgI5PYAOyorJDCAW76EDSoEi-D9yGK37DucrW0bxkvQ1tBGRltZZqMcJhJlegAvXJ8QAzQZ8EejcL4wuTUH-O-cA5KoAoCsCAACw9gn306LPm1q8rxr8k9Quww0YPVZ-d7yAu_b9dFhHwUKVKg5Lu_rzpjxxF53E63fc66bbOx79kzs23rdfPBfZJP_4_iqg
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fb9MwED5NAwn2wK-BKAzwA08Ib07irPbjgFVDtAVBkPYWxT4HKm3tlKbjn-CP5s51KyQQEsqLFdlRpC_2fZf77g7gpW9c4Y0v5dCglRoxl47j725oTdEQhzZt7FoyHk6n5vzcftqB19tcmBBCFJ-FQx7GWD4u_Ip_lR0ZdhY4o_xGqXWu1tla25iBLlU6d2kLa7OJYWbKHlWTqmIZlz3MLdmr1AFmY4ViW5U_zuJoYEZ3_-_V7sGdRCTFyRr5-7AT5g9g77fygvvwM2bXtiHW7pQEBEOKItbjkH1krKETI7JsjI74suq6Bf9Vk2_ItKE4nYiPdKBcpkxNEeV6M5ZJi4ozNeTnwGJmcXLxbdHN-u-XS0EMWExY4vejuQ5iNONQvHgXRSIP4evotHp7JlP3BemL0vbStt44sl6mbB0H3zJXuFIhos6sLtHlXgeF4bhpicQhbfsMHdqGLk_3MS8ewe58MQ-PQTRZaLHJvQum1Mc0HBqrnSK_nB5QIA5AbdCofSpNzh0yLurooihbM4A1A1gnAAfwarvkal2X41-T9xmx7cQE1gAONpDXaecu65wYKZEkZc2Tv696AbfOqsm4Hr-ffngKt3P2v6OK-wB2-24VnsFNf93Plt3z-Hn-AupU5fE
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Multifeature-Assisted+Neuro-transfer+Function+Surrogate-Based+EM+Optimization+Exploiting+Trust-Region+Algorithms+for+Microwave+Filter+Design&rft.jtitle=IEEE+transactions+on+microwave+theory+and+techniques&rft.au=Feng%2C+Feng&rft.au=Weicong+Na&rft.au=Liu%2C+Wenyuan&rft.au=Yan%2C+Shuxia&rft.date=2020-02-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=0018-9480&rft.eissn=1557-9670&rft.volume=68&rft.issue=2&rft.spage=531&rft_id=info:doi/10.1109%2FTMTT.2019.2952101&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9480&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9480&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9480&client=summon