Nagi H. Al-Ashwal

Work place: Department of Electrical Engineering, Ibb University, Ibb City, Yemen



Research Interests: Computer Vision, Pattern Recognition, Image Processing


Nagi H. Al-Ashwal has a B.Sc. in Electrical Engineering from Sana’a University, Sana’a, Yemen (1997), a M.Sc. (2003) and Ph.D. (208) in Computer Engineering from Assiut University, Egypt. He joined the Electrical Engineering Department, at the University of Ibb, Ibb, Yemen in 2018 as an Assistant Professor and became Associate Professor in November 2013. Since September 2021, he has been a visiting scholar in the School of Electrical of the Engineering and Science in the American University in Cairo, Egypt. He worked as a head of the Electrical Engineering Department in the Faculty of Engineering of the University of Ibb from 2009-2011 and as a Dean of the Faculty of Engineering at the University of Ibb from 2015-2021. His research interests are Computer Vision, Image processing and pattern recognition. He has many publications in these fields.

Author Articles
Design of 28/38-GHz Dual-Band Millimeter Wave Antenna based on SIW for Future Cellular Communication Systems

By Khaled A. M. Al Soufy Nagi H. Al-Ashwal Faisal S. Al-Kamali Redhwan Saad Majed A. AL-Sayadi

DOI:, Pub. Date: 8 Oct. 2023

The millimeter wave (mmWave) band has gained significant attention due to its potential to cater to the rapidly increasing wireless data rates. Due to the reduced wavelength in mmWave communications, it is possible to implement large antenna arrays at both the transmitter and the receiver. Designing small antennas in the mmWave range presents many challenges, which is the main aim of this paper. The aim of this work is to proposed an efficient design of a dual-band mmWave antenna, with the dimension of 26.5mm×7.0mm×0.254mm, for future cellular communication systems using a substrate integrated waveguide (SIW). The elements of the proposed antenna consist of SIW cavity with one longer longitudinal slot and another shorter engraved slot in one of the conducting planes (1×2) for 28 GHz and 38 GHz, respectively. The substrate duroid 5880/Rogers are used with a loss tangent and dielectric constant of 0.003 and 2.2, respectively. The CST Microwave Studio, an industry-standard software, was utilized to conduct the simulation results. The proposed antenna's performance was evaluated by analyzing its gain, radiation pattern, and return loss at the frequencies of 28 GHz and 38 GHz. Furthermore, it is compared with other relative works. The single antenna element was able to attain an impedance bandwidth (S11< -10 dB) of 1.32 GHz and 3.1 GHz, with a satisfactory gain of 6.1 dBi and 5.81 dBi at 28 GHz and 38 GHz, respectively. The results indicate that the designed antenna can attain consistent and adjustable dual-frequency performance, making it a viable option for future cellular communication systems.

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Design of Automatic Number Plate Recognition System for Yemeni Vehicles with Support Vector Machine

By Farhan M. Nashwan Khaled A. M. Al Soufy Nagi H. Al-Ashwal Majed A. Al-Badany

DOI:, Pub. Date: 8 Aug. 2023

Automatic Number Plate Recognition (ANPR) is an important tool in the Intelligent Transport System (ITS). Plate features can be used to provide the identification of any vehicle as they help ensure effective law enforcement and security. However, this is a challenging problem, because of the diversity of plate formats, different scales, rotations and non-uniform illumination and other conditions during image acquisition. This work aims to design and implement an ANPR system specified for Yemeni vehicle plates. The proposed system involves several steps to detect, segment, and recognize Yemeni vehicle plate numbers. First, a dataset of images is manually collected. Then, the collected images undergo preprocessing, followed by plate extraction, digit segmentation, and feature extraction. Finally, the plate numbers are identified using Support Vector Machine (SVM). When designing the proposed system, all possible conditions that could affect the efficiency of the system were considered. The experimental results showed that the proposed system achieved 96.98% and 99.19% of the training and testing success rates respectively.

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