This paper presents a better understanding of the use of finite integration techniques (FIT) and finite element method (FEM) in different types of microstrip antennas in order to determine which numerical method gives relatively more accurate results. Although the theoretical formulation based on Maxwell's equations of both FEM and FIT are approached from different aspects in the literature, there is still a lack of comparison of the same antenna type using different numerical methods employing FEM and FIT. Therefore, in this study, FEM and FIT were applied to two different types of microstrip antennas, and their simulation and experimental results was compared. For the first antenna demonstration, a multilayer structure was chosen to achieve one of the significant parameters. Then, a microstrip antenna with a compact structure was used in the second demonstration. Using these two antennas, the accuracy of FEM and FIT in different structures were compared and all simulated return loss and gain results were verified by the measured results. The experimental demonstrations show that FEM performs better for both types of microstrip antennas while FIT provides an adequate result for two‐layer microstrip antennas.

This 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.

No abstract

No abstract

Brain 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.