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dc.contributor.authorKoziel, Slawomir
dc.contributor.authorMAHOUTİ, Peyman
dc.contributor.authorBelen, Aysu
dc.contributor.authorTari, Ozlem
dc.contributor.authorBELEN, MEHMET ALİ
dc.contributor.authorKARAHAN, SERDAL
dc.date.accessioned2023-10-10T13:10:18Z
dc.date.available2023-10-10T13:10:18Z
dc.date.issued2023
dc.identifier.citationMAHOUTİ P., Belen A., Tari O., BELEN M. A., KARAHAN S., Koziel S., "Data-Driven Surrogate-Assisted Optimization of Metamaterial-Based Filtenna Using Deep Learning", ELECTRONICS, cilt.12, sa.7, 2023
dc.identifier.issn2079-9292
dc.identifier.otherav_2d763916-1296-44b8-bbaf-4336fc54407f
dc.identifier.othervv_1032021
dc.identifier.urihttp://hdl.handle.net/20.500.12627/190473
dc.identifier.urihttps://doi.org/10.3390/electronics12071584
dc.identifier.urihttps://avesis.istanbul.edu.tr/api/publication/2d763916-1296-44b8-bbaf-4336fc54407f/file
dc.description.abstractIn this work, a computationally efficient method based on data-driven surrogate models is proposed for the design optimization procedure of a Frequency Selective Surface (FSS)-based filtering antenna (Filtenna). A Filtenna acts as a module that simultaneously pre-filters unwanted signals, and enhances the desired signals at the operating frequency. However, due to a typically large number of design variables of FSS unit elements, and their complex interrelations affecting the scattering response, FSS optimization is a challenging task. Herein, a deep-learning-based algorithm, Modified-Multi-Layer-Perceptron (M2LP), is developed to render an accurate behavioral model of the unit cell. Subsequently, the M2LP model is applied to optimize FSS elements being parts of the Filtenna under design. The exemplary device operates at 5 GHz to 7 GHz band. The numerical results demonstrate that the presented approach allows for an almost 90% reduction of the computational cost of the optimization process as compared to direct EM-driven design. At the same time, physical measurements of the fabricated Filtenna prototype corroborate the relevance of the proposed methodology. One of the important advantages of our technique is that the unit cell model can be re-used to design FSS and Filtenna operating various operating bands without incurring any extra computational expenses.
dc.language.isoeng
dc.subjectTemel Bilimler (SCI)
dc.subjectBilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği
dc.subjectSinyal İşleme
dc.subjectBilgisayar Bilimleri
dc.subjectBilgi Güvenliği ve Güvenilirliği
dc.subjectTemel Bilimler
dc.subjectMühendislik ve Teknoloji
dc.subjectİstatistiksel ve Doğrusal Olmayan Fizik
dc.subjectGenel Mühendislik
dc.subjectElektrik ve Elektronik Mühendisliği
dc.subjectMühendislik (çeşitli)
dc.subjectBilgi sistemi
dc.subjectBilgisayar Bilimi Uygulamaları
dc.subjectBilgisayar Bilimi (çeşitli)
dc.subjectGenel Bilgisayar Bilimi
dc.subjectFizik Bilimleri
dc.subjectBİLGİSAYAR BİLİMİ, BİLGİ SİSTEMLERİ
dc.subjectBilgisayar Bilimi
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectMÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK
dc.subjectMühendislik
dc.subjectFİZİK, UYGULAMALI
dc.subjectFizik
dc.titleData-Driven Surrogate-Assisted Optimization of Metamaterial-Based Filtenna Using Deep Learning
dc.typeMakale
dc.relation.journalELECTRONICS
dc.contributor.departmentYıldız Teknik Üniversitesi , Uygulamalı Bilimler Fakültesi , Havacılık Elektroniği
dc.identifier.volume12
dc.identifier.issue7
dc.contributor.firstauthorID4312662


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