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Rouabeh Hanene

Mlle Rouabeh Hanene
Post-DOC

Electrical Engineering Department
ENIS
Sfax University
 
Address: City Center M'dhilla 2170 Gafsa 0 Tunisia
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Rouabeh Hanene

Publication


Hanene Rouabah,Chokri Abdelmoula,Mohamed Masmoudi

The Realization of a Neural Network Controller for Vehicle-Type Mobile Robot Navigation , 2012 International Conference on Design & Technology of Integrated Systems in Nanoscale Era , 0000-00-00
[Abstract]
Abstract
Abstract— This work is part of improving the autonomous navigation of mobile robots. In an environment where many obstacles in form of wall are present, the robot must detect obstacles around it, steer to the nearest and track its deviations keeping a desired distance fixed. A joint use of information issued from the three sensors installed on the platform: in front, on the left and on the right are used to determine the proper motion for the robot at each position allowing it to navigate autonomously. The effectiveness of Neural Networks in mobile robot control is important in their learning abilities and their capacity to treat noisy data. Keywords-Mobile Robot; Wall Following; Neural Networks

Hanene Rouabah, Chokri Abdelmoula, Mohamed Masmoudi

Behavior Control of a Mobile Robot Based on Fuzzy Logic and Neuro Fuzzy Approaches for Monitoring Wall , 2012 International Conference on Design & Technology of Integrated Systems in Nanoscale Era , 0000-00-00
[Abstract]
Abstract
Abstract— This work describes the design and development of controllers based on artificial intelligence tried on a newly design of a mobile robot type-vehicle to control behavior for monitoring wall. Two approaches have been optimized and developed to control the robot: The first one is based on Fuzzy logic. This control algorithm combines the different sensory information and provides a suitable control command allowing the mobile robot to follow the wall deviations. The second approach consists of applying a hybrid-type Neuro-Fuzzy ANFIS controller for the same task. This controller combines the advantages of Fuzzy logic and Neural Networks. Simulations results are presented and implemented with VHDL using ANFIS architecture. Keywords- Mobile Robot; Wall Following; Fuzzy Logic; Neural Networks; Neuro-Fuzzy; ANFIS

Hanene Rouabeh, Chokri Abdelmoula, Mohamed Masmoudi

VHDL based Hardware Architecture of a High Performance Image Edge Detection Algorithm , International Journal of Computer Applications (0975 – 8887) Volume 91 – No.12, April 2014 , 0000-00-00
[Abstract]
Abstract
ABSTRACT This article presents the software and hardware implementation of a low cost and high performance image edge detection algorithm. This algorithm will be used as part of a complete vision based driver assistance system. The main challenge consists in realizing a real-time implementation of edge detection algorithm that contributes in increasing the performance of the whole system. The software implementation of the developed algorithm using MATLAB tool is discussed in this paper, as well as the hardware architecture developed using VHDL language. Test results for both implementations were presented and compared to other edge detection operators. Computational time and other features comparison have shown the effectiveness of the proposed approach. General Terms Computer science, Image processing, VHDL

Chokri Abdelmoula, Hanen Rouabeh, Mohamed Masmoudi

Behavior Control of a New Designed Mobile Robot Based on Fuzzy Logic and Neuro Fuzzy Approaches for MonitoringWall , International Journal of Intelligent Engineering and Systems, Vol.6, No.3, pp: 17-26, 2013 , 0000-00-00
[Abstract]
Abstract
Abstract: This work describes the design and development of controllers based on artificial intelligence applied to a newly designed mobile robot type-vehicle to control behavior for monitoring wall. Two approaches have been developed and optimized to achieve this task. The first one is based on Fuzzy logic. This control algorithm combines different sensory information and provides a suitable control command allowing the mobile robot to follow the wall deviations. The second approach consists of the application of a hybrid-type Neuro-Fuzzy ANFIS controller for the same task. An important feature of this approach is that the controller combines the advantages of both Fuzzy logic and Neural Networks.The simulation results are presented and implemented with VHDL using ANFIS architecture.