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Mahmoud Elnaggar

dblp:206/3180 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2019
0000-0003-4168-3568ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1Software engineering, systems software and programming languages · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Vehicular, aerial and satellite networks · 33% Physical-layer communications · 33% Wireless sensing and localization · 33%
Artificial intelligence
1 paper
Autonomous driving · 100%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Vehicular, aerial and satellite networks
connected vehicles
0.412019
Characterizing Uncertainties of Wireless Channels in Connected Vehicles · MobiCom 2019
Physical-layer communications › channel modeling › channel characterization
wireless channel characterization
0.412019
Characterizing Uncertainties of Wireless Channels in Connected Vehicles · MobiCom 2019

Methods — techniques the papers use, named apart from their topics

multipath signal mapping · 0.8
YearPublicationVenuePosition
2019 Implementable Discrete-Time $L_{1}$ Adaptive Control for a Cart Inverted Pendulum System
abstract
This paper discusses the implementation of the L1adaptive control for the inverted pendulum system. The proposed design has two merits. First, it is able to reject the system disturbance and uncertainties. Second, it ensures the overall system stability. Both the continuous-time L1adaptive control and the discrete-time L1adaptive control are designed. Simulation results show that the discrete-time L1adaptive control maintains the system stability in implementation with a guaranteed performance.
Mahmoud Elnaggar, Ahmed Lasheen
CoDIT1
2019 Characterizing Uncertainties of Wireless Channels in Connected Vehicles
abstract
The performance of autonomous cars can be greatly enhanced through wireless coordination. However, mobility has traditionally been a challenge for wireless networks due to rapid fluctuation of the signal quality. Current control systems handle this challenge by slowing down the vehicle to preserve safety. However, in this research, we demonstrate that we can robustly characterize the channel quality by mapping the multipath signals to the dynamics of the physical environment, thus controlling the trajectory of the mobile agent to a safe efficient motion path. This allows mobile systems to realize the performance benefits of wireless coordination while providing safety.
Elahe Soltanaghai, Mahmoud Elnaggar, Katie Kleeman, Kamin Whitehouse, Cody H. Fleming
MobiCom2