ORCID Profile
0000-0001-8579-5444
Current Organisations
Charles Darwin University
,
Sejong University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: MDPI AG
Date: 25-09-2022
DOI: 10.3390/MI13101593
Abstract: The COVID-19 pandemic, caused by a new coronavirus, has affected economic and social standards as governments and healthcare regulatory agencies throughout the world expressed worry and explored harsh preventative measures to counteract the disease’s spread and intensity. Several academics and experts are primarily concerned with halting the continuous spread of the unique virus. Social separation, the closing of borders, the avoidance of big gatherings, contactless transit, and quarantine are important methods. Multiple nations employ autonomous, digital, wireless, and other promising technologies to tackle this coronary pneumonia. This research examines a number of potential technologies, including unmanned aerial vehicles (UAVs), artificial intelligence (AI), blockchain, deep learning (DL), the Internet of Things (IoT), edge computing, and virtual reality (VR), in an effort to mitigate the danger of COVID-19. Due to their ability to transport food and medical supplies to a specific location, UAVs are currently being utilized as an innovative method to combat this illness. This research intends to examine the possibilities of UAVs in the context of the COVID-19 pandemic from several angles. UAVs offer intriguing options for delivering medical supplies, spraying disinfectants, broadcasting communications, conducting surveillance, inspecting, and screening patients for infection. This article examines the use of drones in healthcare as well as the advantages and disadvantages of strict adoption. Finally, challenges, opportunities, and future work are discussed to assist in adopting drone technology to tackle COVID-19-like diseases.
Publisher: MDPI AG
Date: 06-07-2021
DOI: 10.3390/ELECTRONICS10141618
Abstract: This work describes an optimum utilization of hybrid photovoltaic (PV)—wind energy for residential buildings on its occurrence with a newly proposed autonomous fuzzy controller (AuFuCo). In this regard, a virtual model of a vertical axis wind turbine (VAWT) and PV system (each rated at 2 kW) are constructed in a MATLAB Simulink environment. An autonomous fuzzy inference system is applied to model primary units of the controller such as load forecasting (LF), grid power selection (GPS) switch, renewable energy management system (REMS), and fuzzy load switch (FLS). The residential load consumption pattern (4 kW of connected load) is allowed to consume energy from the grid and hybrid resources located at the demand side and classified as base, priority, short-term, and schedulable loads. The simulation results identify that the proposed controller manages the demand side management (DSM) techniques for peak load shifting and valley filling effectively with renewable sources. Also, energy costs and savings for the home environment are evaluated using the proposed controller. Further, the energy conservation technique is studied by increasing renewable conversion efficiency (18% to 23% for PV and 35% to 45% for the VAWT model), which reduces the spending of 0.5% in energy cost and a 1.25% reduction in grid demand for 24-time units/day of the simulation study. Additionally, the proposed controller is adapted for computing energy cost (considering the same load pattern) for future demand, and it is exposed that the PV-wind energy cost reduced to 6.9% but 30.6% increase of coal energy cost due to its rise in the Indian energy market by 2030.
Publisher: MDPI AG
Date: 02-08-2023
DOI: 10.3390/EN16155761
Abstract: This manuscript presents a thorough review of unitized regenerative fuel cells (URFCs) and their importance in Remote Area Power Supply (RAPS). In RAPS systems that utilize solar and hydrogen power, which typically include photovoltaic modules, a proton exchange membrane (PEM) electrolyzer, hydrogen gas storage, and PEM fuel cells, the cost of these systems is currently higher compared to conventional RAPS systems that employ diesel generators or batteries. URFCs offer a potential solution to reduce the expenses of solar hydrogen renewable energy systems in RAPS by combining the functionalities of the electrolyzer and fuel cell into a single unit, thereby eliminating the need to purchase separate and costly electrolyzer and fuel cell units. URFCs are particularly well-suited for RAPS applications because the electrolyzer and fuel cell do not need to operate simultaneously. In electrolyzer mode, URFCs function similarly to stand-alone electrolyzers. However, in fuel cell mode, the performance of URFCs is inferior to that of stand-alone fuel cells. The presented review summarizes the past, present, and future of URFCs with details on the operating modes of URFCs, limitations and technical challenges, and applications. Solar hydrogen renewable energy applications in RAPS and challenges facing solar hydrogen renewable energy in the RAPS is discussed in detail.
Publisher: MDPI AG
Date: 22-03-2023
DOI: 10.3390/SU15065575
Abstract: In this paper, a novel hybrid Maximum Power Point Tracking (MPPT) algorithm using Particle-Swarm-Optimization-trained machine learning and Flying Squirrel Search Optimization (PSO_ML-FSSO) has been proposed to obtain the optimal efficiency for solar PV systems. The proposed algorithm was compared with other well-known methods viz. Perturb & Observer (P& O), Incremental Conductance (INC), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CSO), Flower Pollen Algorithm (FPA), Gray Wolf Optimization (GWO), Neural-Network-trained Machine Learning (NN_ML), Genetic Algorithm (GA), and PSO-trained Machine Learning. The proposed algorithm was modelled in the MATLAB/Simulink environment under different operating conditions, for ex le, with step changes in temperature, solar irradiance, and partial shading. The proposed algorithm improved the efficiency up to 0.72% and reduced the settling time up to 76.4%. The findings of the research highlight that PSO_ML-FSSO is a potential approach that outperforms all other well-known algorithms tested herein for solar PV systems.
Publisher: Hindawi Limited
Date: 19-10-2023
DOI: 10.1155/2023/6696446
Publisher: MDPI AG
Date: 17-07-2022
DOI: 10.3390/S22145335
Abstract: A miniaturized four-element antenna of 20 mm × 20 mm with edge-to-edge distance of 4.9 mm between the array antennas operating from 4.6–8.6 GHz is investigated in this article. The antenna consists of 4 × integrated dipole driven elements, and complementary split ring resonator (CSRR) metacells are loaded on the both sides of each dipole arms. The loaded meta-couplers magnetically couple to dipole drivers, and the induced resonance effect improves the 10-dB impedance bandwidth (IBW) to 60.6%. To improvise the isolation between antenna elements, metallic vias are implemented that trap electromagnetic (EM)-surface waves to condense into the ground. So, the meta-couplers induce electromagnetic (EM)-propagation as surface wave trapments for radiation and decouple near-field condensed currents, acting as couplers/decouplers. The maximum isolation achieved is −22.5 dB without any external decoupling network. The ersity parameters indicate good attributes in isotropic, indoor, and outdoor channel environments with an envelope correlation coefficient (ECC) 0.165 and realized gain of 5.5 dBi with average radiation efficiency of 80–90% in the desired operating bands. An equivalent circuit model using lumped components is designed for the proposed four-element antenna. For validation, a prototype antenna is fabricated and measured to be implemented in 5G applications, which shows good correlation with the full-wave simulated results.
Publisher: MDPI AG
Date: 28-07-2022
DOI: 10.3390/ELECTRONICS11152371
Abstract: In this research article, a dual-polarized compact bow-tie-shaped irregular fractal antenna with a dual metasurface (DMS) for directional radiation applications is designed using a dual-mode simple feeding mechanism. A short-circuited strip linked between the impedance transformation feed and the radiating patch activates the induced coupling modes, which are capacitive (C-mode) and inductive (L-mode), respectively. The C-mode antenna operates at 2.39–2.53 GHz, whereas the L-mode antenna operates at 2.88–4.49 GHz. It comprises a DMS positioned at 0.22λ∘ from the antenna with 0.016λ∘ separation and a 4 × 4 array of checkerboard type hole-injected tiny unit cells on each metasurface. A rectangular cavity-backed slot was employed as the ground plane to emulsify the reflected energy waves from the DMS, in order to start the coupling process with the boresight radiation, resulting in high gain and suppressed backward radiations. The gain in C-mode was 6.74 dBi, and the gain in L-mode was 7.7 dBi. For validation, a miniaturized metasurface antenna with the overall size of 0.32λ∘ × 0.32λ∘ × 0.22λ∘ (where λ∘ is the free-space wavelength at 2.45 GHz) was fabricated and measured. The measured outcomes highlight its potential for 4G/5G wireless applications.
No related grants have been discovered for Mohammed H. Alsharif.