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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Ashraf Abuelhaija
Applied Science Private University
Computer Science & IT Electrical & Electronics Engineering

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Dual tuned 1H/31P quadrature microstripline-based transmit/receive switch for 7 Tesla magnetic resonance imaging Ashraf Abuelhaija; Gameel Saleh
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2177-2183

Abstract

A dual tuned transmit/receive (T/R) Switch for 7 tesla magnetic resonance imaging (MRI) that is based on concentric microstripline (MSL) coupler is introduced. The proposed switch is designed using two concentric MSL quadrature couplers on the top and bottom faces of the switch. The switch can be used to handle two frequency signals to/from two radio frequency (RF) coils. In this article, a 1H/31P atomic nuclei are excited. The two MSLs on the upper face of the switch are designed to transmit 298 and 120.6 MHz signals into RF coils, whereas each of the identical upper and lower MSLs are used to receive these signals from the RF coils. This switch can be used to transmit/receive signals from two RF coils at the same time, one work with 1H and the second with 31P atomic nuclei, and without any tuning. The proposed switch has been designed and simulated using the electromagnetic microwave studio computer simulation technology (CST). It demonstrates good matching (≈17 dB), low insertion loss (≈0.3) and high isolation (>70) for the 1H and 31P magnetic resonance signals at transmit mode. During receive mode, It demonstrates good matching (>20 dB), low insertion loss (≈0.2) and high isolation (>70) for the 1H and 31P magnetic resonance signals.
Swarm robotics:design and implementation Ashraf Abuelhaija; Ayham Jebrein; Tarik Baldawi
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (858.538 KB) | DOI: 10.11591/ijece.v10i2.pp2173-2181

Abstract

This project presents a swarming and herding behaviour using simple robots. The main goal is to demonstrate the applicability of artificial intelligence (AI) in simple robotics that can then be scaled to industrial and consumer markets to further the ability of automation. AI can be achieved in many different ways; this paper explores the possible platforms on which to build a simple AI robots from consumer grade microcontrollers. Emphasis on simplicity is the main focus of this paper. Cheap and 8 bit microcontrollers were used as the brain of each robot in a decentralized swarm environment were each robot is autonomous but still a part of the whole. These simple robots don’t communicate directly with each other. They will utilize simple IR sensors to sense each other and simple limit switches to sense other obstacles in their environment. Their main objective is to assemble at certain location after initial start from random locations, and after converging they would move as a single unit without collisions. Using readily available microcontrollers and simple circuit design, semiconsistent swarming behaviour was achieved. These robots don’t follow a set path but will react dynamically to different scenarios, guided by their simple AI algorithm.
Two-section branch-line hybrid couplers based broadband transmit/receive switch Ashraf Abuelhaija; Gameel Saleh
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2600-2607

Abstract

This article introduces a broadband microstripline-based transmit/receive switch for 7-Tesla magnetic resonance imaging. The designed switch aims to handle a signal of multiple frequencies to/from a multi-tuned radio-frequency coil that resonates at frequencies corresponding to the speed of precession of a wide range of atomic X-nuclei, at the same time and without tuning. These include 1H, 23Na, 13C, 31P, 19F, and 7Li used in magnetic resonance spectroscopy as a measure to the existence of many diseases. The fundamental and third harmonic center frequencies of the switch are adjusted to resonate at two broadbands covering a wide range of atomic X-nuclei. Two section branch-line hybrid couplers with phase inverters are designed to build the broadband switch. The designed switch used the minimum trace widths of transmission lines that reveal a compact size without increasing the heat and then the loss beyond specific values. The couplers and the switch S-parameters exhibited good return loss (<-10 dB), high isolation (<-40 dB), less insertion loss (<1 dB) and two clear wide bands covering many atomic X-nuclei used in diagnosis, at the same time and without the need for any tuning circuit during operation.
A machine learning assisted eight-element metamaterial antenna array for non-invasive breast cancer detection Muneera Altayeb; Sanaa Salama; Duaa Zyoud; Ashraf Abuelhaija
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.12510

Abstract

Breast cancer remains one of the most prevalent cancers and a leading cause of cancer-related mortality among women worldwide, highlighting the need for accurate and non-invasive early detection methods. This study presents an integrated breast cancer diagnosis system that combines a microwave imaging platform composed of eight metamaterial-based antenna elements with machine learning (ML) techniques for breast tissue classification. The proposed non-ionizing approach utilizes backscattered S-parameters collected from the antenna array over the 3-7 GHz frequency range to identify tumor masses with diameters of 20 mm, 25 mm, and 30 mm. A dataset comprising 32,048 samples was generated from the measured S-parameter responses and categorized into four classes: healthy breast tissue and tumor cases with diameters of 20 mm, 25 mm, and 30 mm. Three ML classifiers, namely adaptive boosting (AdaBoost), gradient boosting (GB), and random forest (RF), were trained and evaluated using metrics including classification accuracy (CA), Matthews correlation coefficient (MCC), F1-score, recall, and precision. Experimental results demonstrate the effectiveness of the proposed framework, with AdaBoost achieving the best performance and an accuracy of 99.8%, outperforming the other classifiers. The findings confirm the potential of combining metamaterial-based microwave imaging with ML techniques for accurate, non-invasive, and early breast cancer diagnosis.
Co-Authors Ayham Jebrein Duaa Zyoud Gameel Saleh Muneera Altayeb Sanaa Salama Tarik Baldawi