Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/4092
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYiğit, Enes-
dc.contributor.authorÖzkaya, Umut-
dc.contributor.authorDuysak, Huseyin-
dc.date.accessioned2023-05-30T21:11:48Z-
dc.date.available2023-05-30T21:11:48Z-
dc.date.issued2020-
dc.identifier.issn2148-2683-
dc.identifier.urihttps://doi.org/10.31590/ejosat.802719-
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1136048-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/4092-
dc.description.abstractIn this study, radar signals were analyzed to classify grain surface types by using machine learning methods. Radar backscatter signals were recorded using a vector network analyzer between 18-40 GHz. A total of 5681 measurements of A scan signals were collected. The proposed method framework consists of two parts. First Order Statistical features are obtained by applying Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) on backscatter signals in the first part of the framework. Classification process of these features was carried out with Support Vector Machine (SVM). In the second part of the proposed framework, two dimensional matrices in complex form were obtained by applying Short Time Fourier Transform (STFT) on the signals. Gray-Level Co-Occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) were obtained and feature extraction process was completed. Classification process was carried out with DVM. 10-k cross validation was applied. The highest performance was achieved with STFT+GLCM+SVM.en_US
dc.language.isoenen_US
dc.relation.ispartofAvrupa Bilim ve Teknoloji Dergisien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRadaren_US
dc.subjectMeasurementen_US
dc.subjectMachine Learningen_US
dc.subjectClassificationen_US
dc.subjectRadaren_US
dc.subjectÖlçümen_US
dc.subjectMakine Öğrenmesien_US
dc.subjectSınıflamaen_US
dc.titleGrain Surface Classification via Machine Learning Methodsen_US
dc.typeArticleen_US
dc.identifier.doi10.31590/ejosat.802719-
dc.departmentKTÜNen_US
dc.identifier.volume0en_US
dc.identifier.issueEjosat Özel Sayı 2020 (ICCEES)en_US
dc.identifier.startpage54en_US
dc.identifier.endpage59en_US
dc.institutionauthor-
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.trdizinid1136048en_US
dc.ktun-updatektunupdateen_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeArticle-
item.cerifentitytypePublications-
crisitem.author.dept02.04. Department of Electrical and Electronics Engineering-
Appears in Collections:TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections
Files in This Item:
File SizeFormat 
10.31590-ejosat.802719-1319042.pdf945.13 kBAdobe PDFView/Open
Show simple item record



CORE Recommender

Page view(s)

50
checked on Jun 17, 2024

Download(s)

20
checked on Jun 17, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.