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Article Citation - WoS: 5Citation - Scopus: 6Miniaturised Antenna at a Sub-Ghz Band for Industrial Remote Controllers(inst Engineering Technology-iet, 2019) Yilmaz, Vadi Su; Bilgin, Gulsima; Aydin, Elif; Kara, AliThis study presents the design and the fabrication of a miniaturised sub-GHz antenna for remote control applications. Miniaturisation techniques were examined to identify the most appropriate topology for sub-GHz band requirements. First, the design parameters of the antenna were determined, and then, a commercial electromagnetic simulation tool was used for the design and optimisation phases. Then, measurements of the fabricated antenna were undertaken. Parametric studies with several iterations were performed to achieve the best possible results. Second, the effects of the box in which the antenna could be placed were examined as most of such antennas are enclosed by plastic boxes. For this purpose, material properties of a typical industrial box available in the market were studied initially, and the most appropriate material of the box was used in simulations. Finally, a polyamide box with appropriate size was fabricated, and the designed antenna was placed inside the box and the measurements were conducted. The measurement results show that the designed antenna provides resonance at the targeted license-free band with adequate size for industrial remote controllers.Article Citation - WoS: 3Citation - Scopus: 6Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection From Mri Images(Mdpi, 2023) Yilmaz, Vadi Su; Akdag, Metehan; Dalveren, Yaser; Doruk, Resat Ozgur; Kara, Ali; Soylu, AhmetBrain tumors have been the subject of research for many years. Brain tumors are typically classified into two main groups: benign and malignant tumors. The most common tumor type among malignant brain tumors is known as glioma. In the diagnosis of glioma, different imaging technologies could be used. Among these techniques, MRI is the most preferred imaging technology due to its high-resolution image data. However, the detection of gliomas from a huge set of MRI data could be challenging for the practitioners. In order to solve this concern, many Deep Learning (DL) models based on Convolutional Neural Networks (CNNs) have been proposed to be used in detecting glioma. However, understanding which CNN architecture would work efficiently under various conditions including development environment or programming aspects as well as performance analysis has not been studied so far. In this research work, therefore, the purpose is to investigate the impact of two major programming environments (namely, MATLAB and Python) on the accuracy of CNN-based glioma detection from Magnetic Resonance Imaging (MRI) images. To this end, experiments on the Brain Tumor Segmentation (BraTS) dataset (2016 and 2017) consisting of multiparametric magnetic MRI images are performed by implementing two popular CNN architectures, the three-dimensional (3D) U-Net and the V-Net in the programming environments. From the results, it is concluded that the use of Python with Google Colaboratory (Colab) might be highly useful in the implementation of CNN-based models for glioma detection. Moreover, the 3D U-Net model is found to perform better, attaining a high accuracy on the dataset. The authors believe that the results achieved from this study would provide useful information to the research community in their appropriate implementation of DL approaches for brain tumor detection.
