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Öğe Analysis of C-shaped compact microstrip antennas using deep neural networks optimized by Manta Ray foraging optimization with Lévy-Flight Mechanism(2021) Biçer, Mustafa BerkanIn recent years, microstrip antennas have become a popular research subject with the increasing use of mobile technologies. With the development of neural networks, the design and analysis of microstrip antennas are carried out quickly with high accuracy. However, optimizing the weight matrices and bias vectors of deep neural learning models is an important challenge for engineering problems. This study presents a deep neural network-based (DNN-based) neural model to estimate the gain and scattering parameter (S11) of C-shaped compact microstrip antennas (CCMAs). For this purpose, the S11 and gain values of 324 CCMAs with different physical and electrical properties were obtained using full-wave electromagnetic simulation software based on the finite integration technique (FIT). The data related to 324 CCMAs were used for the training and testing process. The improved manta ray foraging optimization (MRFO) algorithm based on the Lévy-flight (LF) mechanism was used to optimize the connection weights matrices and bias vectors. The MRFO-optimized model has estimation success for training and testing data as 0.925 and 0.922, in terms of R2 score, respectively. The estimated resonant frequencies using the trained model are compared with the studies in the literature, and an average percentage error (APE) of 0.933% is obtained.Öğe Design and fabrication of rectangular microstrip antenna with various flexible substrates(Institute of Electrical and Electronics Engineers Inc., 2021) Biçer, Mustafa Berkan; Aydin, E.A.In addition to being small, light, practical, and cheap to manufacture, microstrip antennas are also exceedingly difficult to obtain the most suitable electrical parameters such as resonance frequency, bandwidth, return loss, gain, efficiency, and standing wave ratio. To achieve this, researchers are trying different physical structures and applying optimization techniques to them in order to obtain the most suitable radiation power and shape in different sizes and materials. Especially at high frequencies, the dielectric property of the material used can change all the parameters of microstrip antennas and affect the antenna performance to a great extent. The purpose of this study is to investigate the impacts of the physical structure and electrical properties of various textile materials and obtaining the most suitable material. For this purpose, textile-based wearable rectangular microstrip antenna designs were carried out on three different resonant frequency bands, which are widely used with different textile products such as felt, photo paper, and fiberglass, and their performances were examined. The proposed antennas on felt, photographic paper, and fiberglass substrates, were designed and manufactured. The feeding line and radiating and ground planes were formed using conductive (copper) tape. The operating frequency range of the antenna was chosen between 2 GHz and 10 GHz, and the simulated gain of the antenna was obtained as 5.31 dB. The measurement S11results demonstrate that the results are in good agreement with the simulated ones. The proposed antenna allows continuous monitoring of patients at high risk of cancer. © 2021 IEEE.Öğe Diagnosis of COVID-19 using deep CNNs and particle swarm optimization(Springer Science and Business Media Deutschland GmbH, 2022) Gürcan, Ömer Faruk; Atıcı, Uğur; Biçer, Mustafa Berkan; Doğan, OnurCoronavirus pandemic (COVID-19) is an infectious illness. A newly explored coronavirus caused it. Currently, more than 112 million verified cases of COVID-19, containing 2,4 million deaths, are reported to WHO (February 2021). Scientists are working to develop treatments. Early detection and treatment of COVID-19 are critical to fighting disease. Recently, automated systems, specifically deep learning-based models, address the COVID-19 diagnosis task. There are various ways to test COVID-19. Imaging technologies are widely available, and chest X-ray and computed tomography images are helpful. A publicly available dataset was used in this study, including chest X-ray images of normal, COVID-19, and viral pneumonia. Firstly, images were pre-processed. Three deep learning models, namely DarkNet-53, ResNet-18, and Xception, were used in feature extraction from images. The number of extracted features was decreased by Binary Particle Swarm Optimization. Lastly, features were classified using Logistic Regression, Support Vector Machine, and XGBoost. The maximum accuracy score is 99.7% in a multi-classification task. This study reveals that pre-trained deep learning models with a metaheuristic-based feature selection give robust results. The proposed model aims to help healthcare professionals in COVID-19 diagnosis. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Öğe A novel 3D printed curved monopole microstrip antenna design for biomedical applications(Springer, 2021) Biçer, Mustafa Berkan; Aydin, Emine AvsarThis paper proposes a novel and compact monopole microstrip antenna design with a three-dimensional (3D) printed curved substrate for biomedical applications. A curved substrate was formed by inserting a semi-cylinder structure in the middle of the planar substrate consisting of polylactic acid. The antenna was fed with a microstrip line, and a partial ground plane was formed at the bottom side of the substrate. The copper plane with two triangular slots is arranged on the curved semi-cylinder structure of the substrate. The physical dimensions of the radiating plane and ground plane were optimally determined with the use of the sparrow search algorithm to provide a wide-10 dB bandwidth between 3 and 12 GHz. A total of six microstrip antennas having different parameters related to physical dimensions were designed and simulated to compare the performance of the proposed antenna with the help of full-wave electromagnetic simulation software called CST Microwave Studio. The proposed curved antenna was fabricated, and a PNA network analyzer was used to measure the S-11 of the proposed antenna. It was demonstrated that the measured S-11 covers the desired frequency range.Öğe A novel coplanar waveguide-fed compact microstrip antenna for future 5g applications(Univ North, 2020) Biçer, Mustafa BerkanIn this study, a coplanar waveguide-fed compact microstrip antenna design for applications operating at higher 5G bands was proposed. The antenna with the compact size of 8 x 12.2 mm(2) on FR4 substrate, having the dielectric constant of 4.3 and the height of 1.55 mm, was considered. The dimensions of the radiating patch and ground plane were optimized with the use of artificial cooperative search (ACS) algorithm to provide the desired return loss performance of the designed antenna. The performance analysis was done by using full-wave electromagnetic package programs based on the method of moment (MoM) and the finite integration technique (FIT). The 10 dB bandwidth for return loss results obtained with the use of the computation methods show that the proposed antenna performs well for 5G applications operating in the 24.25 - 27.50 GHz, 26.50 - 29.50 GHz, 27.50 - 28.35 GHz and 37 - 40 GHz frequency bands.
