Abstract Iris is a desirable biometric representation of an individual for security-related applications. However the iris segmentation and normalization process is challenging due to the presence of eye lashes that occlude the iris, the dilation of pupils due to different light illumination and several other uncontrolled factors. In this work, we enhanced Daugman method to locate the iris and normalized it... Iris is a desirable biometric representation of an individual for security-related applications. However the iris segmentation and normalization process is challenging due to the presence of eye lashes that occlude the iris, the dilation of pupils due to different light illumination and several other uncontrolled factors. In this work, we enhanced Daugman method to locate the iris and normalized it from Polar to Cartesian coordinate. The algorithm is tested using iris images from CASIA database and MMU database. The percentage detection on MMU iris database is 99% and that of CASIA is 98%. Our approach is feasible to produce an iris template for identity identification and biometric watermarking application. Read More Read Less

Abstract Department of Computer Science Faculty of Science Minia University, El-Minia Egypt... Department of Computer Science Faculty of Science Minia University, El-Minia Egypt Read More Read Less

Abstract In this work, we developed a hybrid-learning based model for intelligent control of autonomous mobile robots. In the proposed model, we used the Q-learning technique for navigation and the inter-process communication (IPC) for message passing between the robots. First, we developed a simulation program based on multi-agent paradigm that adopts these techniques for controlling the movements of the mobile robot... In this work, we developed a hybrid-learning based model for intelligent control of autonomous mobile robots. In the proposed model, we used the Q-learning technique for navigation and the inter-process communication (IPC) for message passing between the robots. First, we developed a simulation program based on multi-agent paradigm that adopts these techniques for controlling the movements of the mobile robot and for collision avoidance. Second, we conducted experiments on three robots from the Robix Company and results show a good performance of the proposed method. At the motion control level, the Q-Learning technique is trained to perform motor control that moves one of the robots from the resource point to the destination point and back. The communication between the robots is carried out by using the inter-process communication technique (IPC). Read More Read Less

Abstract In this work, we developed a hybrid-learning based model for intelligent control of autonomous mobile robots.In the proposed model, we used the Q-learning technique for navigation and the inter-process communication (IPC) for message passing between the robots. First, we developed a simulation program based on multi-agent paradigm that adopts these techniques for controlling the movements of the mobile robot and... In this work, we developed a hybrid-learning based model for intelligent control of autonomous mobile robots.In the proposed model, we used the Q-learning technique for navigation and the inter-process communication (IPC) for message passing between the robots. First, we developed a simulation program based on multi-agent paradigm that adopts these techniques for controlling the movements of the mobile robot and for collision avoidance. Second, we conducted experiments on three robots from the Robix Company and results show a good performance of the proposed method. At the motion control level, the Q-Learning technique is trained to perform motor control that moves one of the robots from the resource point to the destination point and back. The communication between the robots is carried out by using the inter-process communication technique (IPC). Read More Read Less

Abstract Provision of quality of service (QoS) in an ad hoc network is a challenging task. In this paper, we propose a QoS aware routing for Time Division Multiple Access (TDMA) based ad hoc networks. Our protocol tries to identify multiple paths each of which is capable of providing the QoS in terms of the number of time slots at... Provision of quality of service (QoS) in an ad hoc network is a challenging task. In this paper, we propose a QoS aware routing for Time Division Multiple Access (TDMA) based ad hoc networks. Our protocol tries to identify multiple paths each of which is capable of providing the QoS in terms of the number of time slots at its own or by combining it to a group of paths. Our protocol incorporates a procedure to determine the available time slots in a localized and distributed fashion. We evaluate the performance of the protocol through simulations. While evaluating the performance, we focus on the number of paths that are able to satisfy the QoS and the QoS success ratio. Read More Read Less

Abstract Iris is a desirable biometric representation of an individual for security-related applications. However the iris segmentation and normalization process is challenging due to the presence of eye lashes that occlude the iris, the dilation of pupils due to different light illumination and several other uncontrolled factors. In this work, we enhanced Daugman method to locate the iris and normalized it... Iris is a desirable biometric representation of an individual for security-related applications. However the iris segmentation and normalization process is challenging due to the presence of eye lashes that occlude the iris, the dilation of pupils due to different light illumination and several other uncontrolled factors. In this work, we enhanced Daugman method to locate the iris and normalized it from Polar to Cartesian coordinate. The algorithm is tested using iris images from CASIA database and MMU database. The percentage detection on MMU iris database is 99% and that of CASIA is 98%. Our approach is feasible to produce an iris template for identity identification and biometric watermarking application. Read More Read Less

Abstract The aim of this research has been to introduce a learning algorithm based on market concepts for grid resource allocation and resolve some incompetence with current mechanisms. Grid resource allocation could be considered as a double auction in which grid resource manager acts as an auctioneer and jobs and resource owners act as buyers (resource consumers) and sellers. In our... The aim of this research has been to introduce a learning algorithm based on market concepts for grid resource allocation and resolve some incompetence with current mechanisms. Grid resource allocation could be considered as a double auction in which grid resource manager acts as an auctioneer and jobs and resource owners act as buyers (resource consumers) and sellers. In our novel algorithm resource allocation is based on activity history of each participant in the auction. Bids in the auctions are calculated in each round, based on proportion of former bid fluctuations and prior activity of each buyer and deadline issues of each buyer’s job. As the same, asks in the auctions are calculated in each round, based on proportion of former ask fluctuations and prior activity of each seller probability of failure for each seller (resource owner) which is calculated by Poisson function. Practical evaluation results demonstrate that market equilibrium in our mechanism is improved in comparison with other mechanisms. Also essence of learning features in this novel algorithm increases competition incentive in participants which do selfishly in every round of an auction to maximize their utility. Read More Read Less