Showing 3 results for Khalid
Shamil Alnajjar, Prof. Dr. Khalid K. Mohammed,
Volume 20, Issue 3 (September 2024)
Abstract
This work presents an analysis and design of the two barrier-quantum well asymmetric spacer tunnel layer (QW-ASPAT) diodes for implantable rectenna circuits application. The RF and DC characteristic of a 10×10μm2 QW-ASPAT devices based on GaAs and In0.53Ga0.47As platform was simulated and extracted by using SILVACO atlas software. The highest extracted curvature coefficient, kv value of the both QW-ASPAT devices at zero bias was about 33V-1 compared with the standard structure GaAs/InGaAs was about 13V-1. The effects of changing in the thickness of the thin AlAs-barrier, the well width, and the spacer layer are fully investigated on the non-linear relationship between current and voltage of these diodes. A CV simulation was carried out, and it was found that the addition of the quantum-well layer between spacers and barrier reduced the junction capacitance of the QW-ASPAT device when compared with standard devices. The cut-off frequency of the proposed QW-GaAs and QW-InGaAs devices are 26GHz and 46GHz respectively. Finally, we conclude that the QW-ASPAT device is the best structure and can be used for microwave rectifiers in the miniaturized integrated rectenna systems.
Malik Khalid , Baharuddin Ismail , Chanuri Charin, Arnawan Hasibuan , Abd Alazeez Almaleeh,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
This paper presents a comprehensive research endeavor focused on evaluating the influence of renewable energy, particularly wind power, on power quality within the context of Jordan's electrical grid. The escalating global demand for energy, coupled with the imperative to curb greenhouse gas emissions, has propelled the rapid adoption of renewable energy sources. Against this backdrop, the study aims to meticulously analyze the effects of wind energy projects on power quality parameters such as voltage fluctuations, harmonics, and power factor. Through an extensive methodology comprising data collection, rigorous analysis, and advanced simulation techniques, actionable insights are provided into the seamless integration of renewable energy into existing grid infrastructures. In this work, power quality parameters like Total Harmonic Distortion, flickers, power frequency, Crest factor, and voltage unbalance are measured at Al-Tafilah Governorate, Jordan. The significance of this study lies in its contribution to the development of strategies and guidelines essential for policymakers, engineers, and stakeholders. By fostering a deeper understanding of the interplay between renewable energy and power quality, the findings aim to facilitate the establishment of a sustainable and resilient energy system in Jordan. Beyond mitigating climate change and enhancing energy security, this research underscores the pivotal role of renewable energy in ushering in a greener, cleaner future for generations to come.
Humairah Mansor, Shazmin Aniza Abdul Shukor, Razak Wong Chen Keng, Nurul Syahirah Khalid,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Building fixtures like lighting are very important to be modelled, especially when a higher level of modelling details is required for planning indoor renovation. LIDAR is often used to capture these details due to its capability to produce dense information. However, this led to the high amount of data that needs to be processed and requires a specific method, especially to detect lighting fixtures. This work proposed a method named Size Density-Based Spatial Clustering of Applications with Noise (SDBSCAN) to detect the lighting fixtures by calculating the size of the clusters and classifying them by extracting the clusters that belong to lighting fixtures. It works based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), where geometrical features like size are incorporated to detect and classify these lighting fixtures. The final results of the detected lighting fixtures to the raw point cloud data are validated by using F1-score and IoU to determine the accuracy of the predicted object classification and the positions of the detected fixtures. The results show that the proposed method has successfully detected the lighting fixtures with scores of over 0.9. It is expected that the developed algorithm can be used to detect and classify fixtures from any 3D point cloud data representing buildings.
