arXiv subjects

Mohamed A. Khamis

Publications and source records attributed to Mohamed A. Khamis.

3 recordsLinked to original sources

arXiv2025

Pioneering Advancements of 2D Graphene: Energy and Electronics applications

This review explores the synthesis, characterization, and potential applications of graphene, a two-dimensional material with exceptional properties. Graphene's versatility in energy and electronics applications is highlighted, with its high conductivity and huge surface area facilitating improved energy storage capabilities in supercapacitors and batteries. In electronics, graphene is revolutionizing the industry by enabling the development of flexible displays, high-speed transistors, and enhanced thermal management systems. The integration of graphene into composite materials presents opportunities for stronger, lighter, and more conductive materials. The study provides a comprehensive overview of graphene's current and future impact on technology, emphasizing its transformative potential in energy solutions and electronic advancements. In the energy sector, graphene's integration into batteries, energy storage systems, capacitors, fuel cells, and renewable energy technologies signifies a leap forward in efficiency, capacity, and sustainability. In the electronics sector, graphene's unique characteristics are utilized in RFID, sensors, and EMI shielding, leading to communication, security, and device miniaturization advancements. The study underscores graphene's potential to spearhead future innovations, reinforcing its status as a pivotal material in the ongoing technological evolution.

cond-mat.mes-hall↗

arXiv2021

Markov Switching Model for Driver Behavior Prediction: Use cases on Smartphones

Several intelligent transportation systems focus on studying the various driver behaviors for numerous objectives. This includes the ability to analyze driver actions, sensitivity, distraction, and response time. As the data collection is one of the major concerns for learning and validating different driving situations, we present a driver behavior switching model validated by a low-cost data collection solution using smartphones. The proposed model is validated using a real dataset to predict the driver behavior in short duration periods. A literature survey on motion detection (specifically driving behavior detection using smartphones) is presented. Multiple Markov Switching Variable Auto-Regression (MSVAR) models are implemented to achieve a sophisticated fitting with the collected driver behavior data. This yields more accurate predictions not only for driver behavior but also for the entire driving situation. The performance of the presented models together with a suitable model selection criteria is also presented. The proposed driver behavior prediction framework can potentially be used in accident prediction and driver safety systems.

cs.LG↗

arXiv2018

MARL-FWC: Optimal Coordination of Freeway Traffic Control Measures

The objective of this article is to optimize the overall traffic flow on freeways using multiple ramp metering controls plus its complementary Dynamic Speed Limits (DSLs). An optimal freeway operation can be reached when minimizing the difference between the freeway density and the critical ratio for maximum traffic flow. In this article, a Multi-Agent Reinforcement Learning for Freeways Control (MARL-FWC) system for ramps metering and DSLs is proposed. MARL-FWC introduces a new microscopic framework at the network level based on collaborative Markov Decision Process modeling (Markov game) and an associated cooperative Q-learning algorithm. The technique incorporates payoff propagation (Max-Plus algorithm) under the coordination graphs framework, particularly suited for optimal control purposes. MARL-FWC provides three control designs: fully independent, fully distributed, and centralized; suited for different network architectures. MARL-FWC was extensively tested in order to assess the proposed model of the joint payoff, as well as the global payoff. Experiments are conducted with heavy traffic flow under the renowned VISSIM traffic simulator to evaluate MARL-FWC. The experimental results show a significant decrease in the total travel time and an increase in the average speed (when compared with the base case) while maintaining an optimal traffic flow.

eess.SY↗