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Taher F, Hamadi HA, Alzaidi MS, Alhumyani H, Elkamchouchi DH, Elkamshoushy YH, Haweel MT, Sree MFA, Fatah SYA. Design and Analysis of Circular Polarized Two-Port MIMO Antennas with Various Antenna Element Orientations. Micromachines (Basel) 2023; 14:mi14020380. [PMID: 36838080 PMCID: PMC9959551 DOI: 10.3390/mi14020380] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 01/29/2023] [Accepted: 01/30/2023] [Indexed: 06/01/2023]
Abstract
This article presents the circularly polarized antenna operating over 28 GHz mm-wave applications. The suggested antenna has compact size, simple geometry, wideband, high gain, and offers circular polarization. Afterward, two-port MIMO antenna are designed to get Left Hand Circular Polarization (LHCP) and Right-Hand Circular Polarization (RHCP). Four different cases are adopted to construct two-port MIMO antenna of suggested antenna. In case 1, both of the elements are placed parallel to each other; in the second case, the element is parallel but the radiating patch of second antenna element are rotated by 180°. In the third case, the second antenna element is placed orthogonally to the first antenna element. In the final case, the antenna is parallel but placed in the opposite end of substrate material. The S-parameters, axial ratio bandwidth (ARBW) gain, and radiation efficiency are studied and compared in all these cases. The two MIMO systems of all cases are designed by using Roger RT/Duroid 6002 with thickness of 0.79 mm. The overall size of two-port MIMO antennas is 20.5 mm × 12 mm × 0.79 mm. The MIMO configuration of the suggested CP antenna offers wideband, low mutual coupling, wide ARBW, high gain, and high radiation efficiency. The hardware prototype of all cases is fabricated to verify the predicated results. Moreover, the comparison of suggested two-port MIMO antenna is also performed with already published work, which show the quality of suggested work in terms of various performance parameters over them.
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Affiliation(s)
- Fatma Taher
- College of Technological Innovation, Zayed University, Dubai 19282, United Arab Emirates
| | - Hussam Al Hamadi
- College of Engineering and IT, University of Dubai, Dubai 14143, United Arab Emirates
| | - Mohammed S. Alzaidi
- Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Hesham Alhumyani
- Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Dalia H. Elkamchouchi
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Yasser H. Elkamshoushy
- Electrical Engineering Department, Faculty of Engineering, Pharos University, Alexandria 21311, Egypt
| | - Mohammad T. Haweel
- Electrical Engineering Department, Shaqra University, Riyadh 17454, Saudi Arabia
- Electronics and Communication Engineering Department, Al-Madinah Higher Institute for Engineering and Technology, Giza 12947, Egypt
| | - Mohamed Fathy Abo Sree
- Department of Electronics and Communications Engineering, Arab Academy for Science, Technology and Maritime Transport, Cairo 11865, Egypt
| | - Sara Yehia Abdel Fatah
- Deparment of Electronics and Communication, Higher Institute of Engineering and Technology, EI-Tagammoe EI-Khames, Cairo 11835, Egypt
- Department of Electrical Engineering, Faculty of Engineering, Egyptian Chinese University, Cairo 11771, Egypt
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Taher F, M. Abdelwahab K, M. Emara H, Shoaib M, El-shafai W, El-samie FEA, T. Haweel M. Audio Security from a Chaotic Map Perspective.. [DOI: 10.2139/ssrn.4367690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Taher F, Shoaib MR, Emara HM, Abdelwahab KM, Abd El-Samie FE, Haweel MT. Efficient framework for brain tumor detection using different deep learning techniques. Front Public Health 2022; 10:959667. [PMID: 36530682 PMCID: PMC9752904 DOI: 10.3389/fpubh.2022.959667] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/31/2022] [Indexed: 12/03/2022] Open
Abstract
The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals in medical imaging for the medical diagnosis of several symptoms. In this paper, transfer-learning-based models in addition to a Convolutional Neural Network (CNN) called BRAIN-TUMOR-net trained from scratch are introduced to classify brain magnetic resonance images into tumor or normal cases. A comparison between the pre-trained InceptionResNetv2, Inceptionv3, and ResNet50 models and the proposed BRAIN-TUMOR-net is introduced. The performance of the proposed model is tested on three publicly available Magnetic Resonance Imaging (MRI) datasets. The simulation results show that the BRAIN-TUMOR-net achieves the highest accuracy compared to other models. It achieves 100%, 97%, and 84.78% accuracy levels for three different MRI datasets. In addition, the k-fold cross-validation technique is used to allow robust classification. Moreover, three different unsupervised clustering techniques are utilized for segmentation.
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Affiliation(s)
- Fatma Taher
- College of Technological Innovative, Zayed University, Abu Dhabi, United Arab Emirates
| | - Mohamed R. Shoaib
- Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Heba M. Emara
- Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt,*Correspondence: Heba M. Emara
| | | | - Fathi E. Abd El-Samie
- Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt,Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Mohammad T. Haweel
- Department of Electrical Engineering, Shaqra University, Shaqraa, Saudi Arabia
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Haweel MT, Zahran O, Abd El-Samie FE. Polynomial FLANN Classifier for Fetal Cardiotocography Monitoring. 2021 38th National Radio Science Conference (NRSC) 2021. [DOI: 10.1109/nrsc52299.2021.9509832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
- Mohammad T. Haweel
- Shaqra University,Electrical Engineering Department,Dawadmi,Riyadh,Saudi Arabia
| | - O. Zahran
- Menoufia University,Faculty of Electronic Engineering,Egypt
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Haweel MT, Zahran O, El-Samie FEA. Adaptive Polynomial Method for Solving Third-Order ODE With Application in Thin Film Flow. IEEE Access 2021; 9:67874-67889. [DOI: 10.1109/access.2021.3072944] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Haweel MT, Abd El-Samie FE, Zahran O. Polynomial Series FLANN for Nonlinear Equalization. Menoufia Journal of Electronic Engineering Research 2019; 28:78-82. [DOI: 10.21608/mjeer.2019.76768] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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