Yazar "Bicer, Mustafa Berkan" seçeneğine göre listele

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    3D-Printed Antenna Design Using Graphene Filament and Copper Tape for High-Tech Air Components
    (Sae Int, 2023) Aydin, Emine Avsar; Bicer, Mustafa Berkan; Mert, Mehmet Erman; Ozgur, Ceyla; Mert, Basak Dogru
    Additive manufacturing (AM) technologies can produce lighter parts; reduce manual assembly processes; reduce the number of production steps; shorten the production cycle; significantly reduce material consumption; enable the production of prostheses, implants, and artificial organs; and produce end-user products since it is used in many sectors for many reasons; it has also started to be used widely, especially in the field of aerospace. In this study, polylactic acid (PLA) was preferred for the antenna substrate because it is environmentally friendly, easy to recycle, provides convenience in production design with a three-dimensional (3D) printer, and is less expensive compared to other available materials. Copper (Cu) tape and graphene filament were employed for the antenna patch component due to their benefits. The comprehensive comparative analysis between a full-wave model and a 3D-printed prototype of the antenna via the CST Microwave Studio program was demonstrated here. The surface characterization was achieved with scanning electron microscope and energy dispersive X-ray (SEM-EDX) and X-ray diffractometer (XRD) analysis. The homogeneous Cu and oxidized graphene (GO) were detected. The weight percent of carbon (C) and oxygen (O) on the graphene surface was 59.82% and 40.18%, respectively. The Cu (111), Cu (200), and Cu (220) peaks were determined on the Cu tape. The GO (011) peak was seen in the XRD spectra of the graphene sheet. The simulation and measurement comparisons are quite satisfactory. The antennas, produced using a conventional 3D printer, will be beneficial for various applications in aeronautics and astronautics.
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    Analysis and synthesis of L- and T-shaped flexible compact microstrip antennas using regression-based machine learning approaches
    (Walter De Gruyter Gmbh, 2023) Bicer, Mustafa Berkan
    The purpose of this study was to analyze and synthesize L-shaped compact microstrip antennas (LCMA) and T-shaped compact microstrip antennas using regression-based machine learning algorithms (TCMA). This was accomplished by simulating 3808 LCMAs and 900 TCMAs operating at UHF and SHF frequencies with different physical and electrical characteristics. The acquired data was utilized to create a data set containing the antennas' physical and electrical characteristics, as well as their resonant frequencies in the TM010 mode. Four baseline regression models and seven machine learning models were developed to determine the resonance frequency of antennas and the values of the physical parameters required for a particular frequency. To examine the efficacy of machine learning models, three-dimensional LCMAs and TCMAs were created using polylactic acid (PLA) and felt-based flexible substrates, as well as copper tape. The results illustrate the feasibility of using machine learning models for LCMA and TCMA analysis and synthesis.
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    Analyzing equilateral triangle compact microstrip antennas using Gaussian process regression for telemedicine and mobile biomedical imaging systems
    (Springer, 2023) Bicer, Mustafa Berkan; Aydin, Emine Avsar
    Antennas are vital in the internet of things (IoT) for enabling telemedicine and healthcare communication between devices and networks. They receive and transmit signals, extending range, improving efficiency, and reducing power consumption. Antennas are versatile and can be integrated into devices or added as external modules. Their flexibility and adaptability are important in applications involving humans, as they can bend and conform to the shape of the body. Overall, antennas are a crucial and adaptable component of IoT technology. The first thing that needs to be done is to determine the frequency at which the antenna should operate for the problem at hand and design an antenna that can work at those resonant frequencies. In this study, equilateral triangular-shaped compact microstrip antennas (ETMAs) were chosen, and their resonance frequencies were calculated using the Gaussian process regression method (GPR). For this purpose, 630 ETMA were simulated, and a dataset was created utilizing the antenna characteristics and resonant frequencies. Support vector machines (SVM), artificial neural networks (ANN), and GPR models were trained on the obtained data set. To validate the performance of the trained models, two ETMAs with an outer length of 50 mm and an inner slot length of 5 mm were fabricated utilizing polylactic acid (PLA) and felt-based substrates with copper tape as the conducting material. The accuracy of the resonant frequency estimation using the GPR approach for the fabricated antennas is 2.833% and 1.706% for the PLA- and felt-based antennas, respectively, when compared to the measurement results. The GPR model trained in this study has an accuracy of 0.470% and 0.662% when compared to simulations in the literature and measurement results, respectively. In addition, one of the designed antennas is in wearable form, and the other is PLA, produced with a low-cost 3D printer, allowing continuous monitoring of patients with high cancer risk. In this article, an easier and cheaper microstrip patch antenna that can be used for imaging and telemedicine applications is designed with a copper band on one flexible and one rigid substrate, and its performance is analyzed experimentally.
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    Deep Learning-based Classification of Breast Tumors using Raw Microwave Imaging Data
    (Gazi Univ, 2024) Bicer, Mustafa Berkan; Eliiyi, Ugur; Tursel Eliiyi, Deniz
    Breast cancer is the leading type of malignant neoplasm disease among women worldwide. Breast screening makes extensive use of powerful techniques such as x-ray mammography, magnetic resonance imaging, and ultrasonography. While these technologies have numerous benefits, certain drawbacks such as the use of low-energy ionizing x-rays, a lack of specificity for malignant tissues, and cost, have motivated researchers to investigate novel imaging and detection modalities. Microwave imaging (MWI) has been extensively studied due to its low-cost structure and ability to perform measurements using non-ionizing electromagnetic waves. This study proposes a novel convolutional neural network (CNN) model for detecting and classifying tumor scatterers in MWI simulation data. To accomplish this, 10001 different numerical breast models with tumor scatterers of varying numbers and positions were developed, and the simulation results were derived using the synthetic aperture radar (SAR) technique. The presented CNN structure was trained using 8000 pieces of simulation data, and the remaining data were used for testing, achieving accuracy rates of 99.61% and 99.75%, respectively. The proposed model is compared to three state-of-the-art models on the same dataset in terms of classification performance. The results demonstrate that the proposed model effectively performs effectively well in detecting and classifying tumor scatterers.
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    Design of an octagonal-shaped curved sensor antenna for dielectric characterization of liquids
    (Elsevier Sci Ltd, 2023) Bicer, Mustafa Berkan
    In this study, an octagonal microstrip antenna with a 3D-printed curved-shaped substrate was proposed as a microwave sensor for measuring the dielectric constants of liquids. During the development of the sensor, an octagonal radiating plane was placed on the curved structure by adding an empty semi-cylindrical structure to the planar substrate. The 30 x 30 x 1 mm3 substrate was designed using PLA material with a dielectric constant of 2.41, and the conductive planes were formed using copper tape. The sensor was analyzed using full-wave electromagnetic computation software, and the reflection coefficients were obtained through simulations. Using simulation-obtained reflection coefficient data, the multi-layer perceptron (MLP) was utilized to estimate the dielectric constant. Then, the sensor was manufactured using a 3D printer, and the reflection coefficient measurements of the antenna were performed by filling the curved structure with liquids of varying charac-teristics. The MLP, which has an APE of 0.20 % for training and 1.52 % for testing, made predictions with an APE of 0.172 % for the simulation of the five materials considered and 1.524 % for the measurement data. It has been observed that the variable dielectric constant results in differences in the resonant frequency and amplitude. The good agreement between simulation and measurement results demonstrates that the proposed sensor can be utilized effectively for measuring the dielectric constants of liquids.
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    Electromagnetic and Chemical Analysis and Performance Comparison of Inset-fed Rectangular Microstrip Antennas
    (Wiley-V C H Verlag Gmbh, 2023) Aydin, Emine Avsar; Bicer, Mustafa Berkan; Mert, Mehmet Erman; Mert, Basak Dogru
    A simple method for creating lightweight and inexpensive microstrip patch antennas using reduced graphene oxide or acetylene black added epoxy resin was developed. The biggest goal in the method is optimizing the appropriate chemicals and production processes for producing the materials with the designed properties. Five examples of an inset-fed microstrip patch antenna operating at approximately 2.0-12.0 GHz were designed based on the antenna's basic analytical formula. Their models were created in a 3D electromagnetic simulation environment. After examining the performance results of the design, the appropriate design models were produced with both 3D printer technology and wet-chemical methods, and the experimental results were compared with the simulation results. The produced reduced graphene oxide or acetylene black added samples ' structure was illuminated with scanning electron microscope images, FTIR and Raman spectroscopy analysis. The measured S-11 characteristics of the antennas provide better performance as compared to the simulated results. The measured S-11 parameters for the two and three frequency bands fell substantially below -10 dB. As a result of the dielectric constants of the materials and the fabrication of the radiation plane, horizontal shifts were detected in the measurement outcomes.