Tugce ToprakAlper Mustafa SelverMustafa SeçmenE. Yesim ZoralToprak, TugceZoral, E. YesimYesim Zoral, E.Alper Selver, M.Selver, M. AlperSecmen, Mustafa2025-10-06202113036203, 130006321303-62031300-063210.3906/ELK-2002-1012-s2.0-85101003102https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101003102&doi=10.3906%2FELK-2002-101&partnerID=40&md5=1db1d508e55a11981e28afc8d981a0b1https://gcris.yasar.edu.tr/handle/123456789/9094https://doi.org/10.3906/elk-2002-101https://doi.org/10.3906/ELK-2002-101Object classification using late-time resonant scattering electromagnetic signals is a significant problem found in different areas of application. Due to their unique properties spherical objects play an essential role in this field both as a challenging target and a resource of analytical late-time resonant scattering electromagnetic signals. Although many studies focus on their detailed analysis the challenges associated with target classification by resonant late-time resonant scattering electromagnetic signals from multilayer spheres have not been investigated in detail. Moreover existing studies made the simplifying assumption that the objects having (one or more) layers constitute equal permeability values at the core and coatings. However especially for metamaterials magneto-dielectric inclusions require consideration of magnetic properties as well as dielectric ones. In this respect this study shows that the utilization late-time resonant scattering electromagnetic signals of magnetic spheres provide diverse information and features which result in superior object classification performance. For this purpose first time-domain late-time resonant scattering electromagnetic signals are generated numerically for single and multilayer radially symmetrical dielectric and magnetic spheres. Then by using emerging deep learning tools particularly convolutional neural networks trained with spheres having different material properties a high multilayer object classification performance is achieved. Furthermore by incorporating the frequency characteristics of the late-time resonant scattering electromagnetic signals to the classification process through Fourier transform and convolutional neural network layers for feature extraction a convolutional neural network with long short term memory algorithm is developed. The outcome of the proposed algorithm design is shown to be particularly successful even in the case of limited available data on challenging targets. This extended strategy is also shown to outperform modern data augmentation and transfer learning techniques in terms of accuracy as well as the computational cost. © 2021 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessDeep Learning, Long Short-term Memory, Scattered Signals, Target Classification, Transfer Learning, Classification (of Information), Convolution, Convolutional Neural Networks, Multilayer Neural Networks, Multilayers, Network Layers, Spheres, Time Domain Analysis, Transfer Learning, Classification Process, Computational Costs, Electromagnetic Signals, Frequency Characteristic, Learning Techniques, Object Classification, Simplifying Assumptions, Target Classification, Deep LearningClassification (of information), Convolution, Convolutional neural networks, Multilayer neural networks, Multilayers, Network layers, Spheres, Time domain analysis, Transfer learning, Classification process, Computational costs, Electromagnetic signals, Frequency characteristic, Learning techniques, Object classification, Simplifying assumptions, Target Classification, Deep learningDeep LearningScattered SignalsTransfer LearningTarget ClassificationLong Short-Term MemoryArticle