Abdalla Abdelrahman

Abdalla Abdelrahman
Queen's University | QueensU · Department of Electrical & Computer Engineering

Doctor of Philosophy

About

14
Publications
7,140
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78
Citations
Additional affiliations
March 2014 - March 2018
Queen's University
Position
  • Research Assistant

Publications

Publications (14)
Article
Driver profiling is the real-time process of detecting driving behaviors and computing a driver’s expected risk based on detected behaviors. Predicting risk based solely on the inclusion of detected behaviors may not be accurate because this method of predicting ignores the environmental (e.g., weather conditions, traffic density level) context of...
Article
Full-text available
Driver behavior profiling has been gaining increased attention due to its relevance in many applications. For instance, car insurance telematics and fleet management entities have been recently using smartphones' embedded sensors, On-Board Diagnostics II (OBDII) units and other on-board IoT devices to collect data on vehicles' behavior and evaluate...
Article
Current route planning systems report to the driver routes based on expected travel time and distance. However, these systems do not provide individualized routing options. With the current routing systems lacking the provision of individualized routing choices, a routing framework which provides a personalized route option not solely based on time...
Conference Paper
Full-text available
In the last decade, naturalistic driving studies (NDSs) have offered researchers an unprecedented way to study the behavior of drivers through the deployment of on-board vehicles' sensors and cameras. The ability to determine the dominant driving risk factors can play an essential role in shaping transportation policies and education programs for d...
Article
Full-text available
Background/Purpose: The peritoneal membrane of long-term peritoneal dialysis (PD) patients is characterized by morphological and microvascular changes. It is said that lactate-based peritoneal dialysate is implicated in the development of these changes. The aim of this study is to compare the effects of long-term exposure to glucose-based, lactate-...
Article
Objective Metformin continues to be the safest and most widely used antidiabetic drug. In spite of its well-known benefits; metformin use in end-stage renal disease (ESRD) patients is still restricted. Little has been reported about the effect of peritoneal dialysis (PD) on metformin clearance and the phantom of lactic acidosis deprives ESRD patien...
Patent
Full-text available
A system and method for linearizing a power amplifier using digital predistortion technique is provided including processing circuitry, the processing circuitry configured to apply a digital predistortion function based on a weighted static polynomial function, a weighted dynamic polynomial function and a threshold parameter which splits the nonlin...
Conference Paper
Full-text available
In this paper, two multi-basis weighted memory polynomial models are proposed for radio frequency power amplifiers' behavioral modelling. In these models, the conventional memory polynomial function of the generalized and hybrid memory polynomial models is replaced by a weighted version of it. Experimental validation was performed on a power amplif...
Article
Full-text available
In this paper, a novel weighted memory polynomial based-model is proposed for wireless transmitters and radio frequency power amplifiers' behavioral modeling and predistortion. The new model introduces an instantaneous-power dependent weight function on the static and dynamic terms of the conventional memory polynomial model. Experimental validatio...

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Projects

Projects (2)
Project
The success of intelligent transportation systems (ITS) relies on their ability to provide drivers and municipalities with accurate real-time information. Crowdsourcing has been shown effective in navigation systems where traffic congestion information is updated by drivers. Robust Crowdsensing for Intelligent Road Services presents a framework to collect crowd-based information, monitoring road conditions and hazards; and, driver-based information, including driving style, preferences, skills and experience, to build representative driver profiles. Our proposed system, iDriveSense, integrates sensor technologies available in both the vehicles and the driver smartphones to provide advanced, robust localization and accurate monitoring of vehicle dynamics and driver behavior. Robustness is achieved through calibration and cross-referencing on two levels: a single-driver level and a cloud multi-driver level. Moreover, we design efficient route selection algorithms based on driver preferences, supported by road conditions monitored and reported in real time. This includes learning route preferences based on monitoring both the routes taken by drivers, and the drivers’ competence levels on different road types.
Archived project