Ahmed O. Elmeligy’s research while affiliated with American University of Sharjah and other places

The use of cellular networks for massive machine-type communications (mMTC) is an appealing solution due to the wide availability of cellular infrastructure. Estimating the number of devices (network load) is vital for efficient allocation of the available resources, especially for managing the random access channel (RACH) of the network. This paper considers a two-priority RACH and proposes two network load estimators: a maximum likelihood (ML) estimator and a reduced complexity (RCML) variant. The estimators are based on a novel model of the random access behavior of the devices coupled with a flexible analytical framework to calculate the involved probabilities. Monte Carlo simulations demonstrate the accuracy of the proposed estimators for different network configurations.

The market share of electric vehicles (EVs) is increasing due to their low greenhouse gas (GHG) emissions and reduced running costs. However, range anxiety and charging downtime are some challenges slowing down the adoption of this new technology. Dynamic wireless charging (DWC) aims to minimize the problems above by allowing the EVs to charge while in motion. With DWC, optimizing the locations of the lanes is crucial to maximizing their utilization while minimizing the capital and operational expenditure of the system. Additionally, the extra load that DWC incurs on the power grid must be considered to avoid grid overload. In this work, a realistic traffic simulation is built. A new methodology is proposed to optimally deploy DWC lanes in the simulated road network and allocate distributed generation (DG) resources to support the power grid. The model is implemented for a large road network within Sharjah, UAE. Results reveal that, for a 30% EV penetration level, a DWC system is economically feasible and profitable to deploy to address the anticipated EV charging demand within the traffic network under consideration.

Three-dimensional printing is an innovative technology that has gained prominence in recent years due to its attractive features such as affordability, efficiency, and quick production. The technology is used to produce a three-dimensional model by depositing materials in layers using specific printers. In the medical field, it has been increasingly used in various specialties, including neurosurgery, cardiology, and orthopedics, most commonly for the pre-planning of complex surgeries. In addition, it has been applied in therapeutic treatments, patient education, and training wof medical professionals. In the field of obstetrics and gynecology, there is a limited number of studies in which three-dimensional printed models were applied. In this review, we aim to provide an overview of three-dimensional printing applications in the medical field, highlighting the few reported applications in obstetrics and gynecology. We also review all relevant studies and discuss the current challenges and limitations of adopting the technology in routine clinical practice. The technology has the potential to expand for wider applications related to women's health, including patient counseling, surgical training, and medical education.