Mohamed I. Mahmoud

Design and Implementation of a Fast General Purpose Fuzzy Processor

In this paper, a Fast Fuzzy processor FP is proposed. This processor, which is implemented using FPGA, has four inputs and one output with 8-bits width for each. The proposed processor is synthesized, functionally verified and implemented using Xilinx Integrated Software Environment ISE and is tested using Xilinx Spartan 3E starter kit. A PC Graphical User Interface GUI is programmed using C# programming language to select and download the parameters of the processor through the serial port communication. The proposed processor is experimentally tested through water sprinkler system example. The experimental results approve the excellent performance of the proposed processor.

REBTAM: reliable energy balance traffic aware data reporting algorithm for object tracking in multi-sink wireless sensor networks

Recently, Multi-sink Wireless Sensor Networks (WSNs) have received more and more attention due to their significant advantages over the single sink WSNs such as improving network throughput, balancing energy consumption, and prolonging network lifetime. Object tracking is regarded as one of the key applications of WSNs due to its wide real-life applications such as wildlife animal monitoring and military area intrusion detection. However, many object tracking researches usually focus on how to track the location of objects accurately, while few researches focus on data reporting. In this work, we propose an efficient data reporting method for object tracking in multi-sink WSNs. Due to the limited energy resource of sensor nodes, it seems especially important to design an energy efficient data reporting algorithm for object tracking in WSNs. Moreover, the reliable data transmission is an essential aspect that should be considered when designing a WSN for object tracking application, where the loss of data packets will affect the accuracy of the tracking and location estimation of a mobile object. In addition, congestion in WSNs has negative impact on the performance, namely, decreased throughput, increased per-packet energy consumption and delay, thus congestion control is an important issue in WSNs. Consequentially, this paper aims to achieve both minimum energy consumption in reporting operation and balanced energy consumption among sensor nodes for WSN lifetime extension. Furthermore, data reliability is considered in our model where the sensed data can reach the sink node in a more reliable way. Finally, this paper presents a solution that sufficiently exerts the underloaded nodes to alleviate congestion and improve the overall throughput in WSNs. This work first formulates the problem as 0/1 Integer Linear Programming problem, and proposes a Reliable Energy Balance Traffic Aware greedy Algorithm in multi-sink WSNs (REBTAM) to solve the optimization problem. Through simulation, the performance of the proposed approach is evaluated and analyzed compared with the previous work which is related to our topic such as DTAR, NBPR, and MSDDGR protocols.

Resource aware and reliable data reporting algorithm for object tracking in WSNs

Object tracking have become one of the major applications of Wireless Sensor Networks (WSNs) due to its wide real-life applications such as wildlife animal monitoring and military area intrusion detection. Many recent articles have been dedicated to localization of objects; however, few of these articles were concentrated on the reliability of network data reporting along with objects localization. In this work, an efficient data reporting method is proposed for object tracking in WSNs. Energy is considered as one of the most critical resources for WSN. Data transmission from the nodes to the sink along with the minimum energy path could be one of the solutions to minimize the overall network energy consumption. However, this might lead to unbalanced energy among sensor nodes resulting in, energy hole problem. Moreover, the reliable data transmission is an essential aspect that should be considered when designing a WSN for object tracking application, where the loss of data packets will affect the accuracy of the tracking and location estimation of a mobile object. Furthermore, due to the limited memory resources of sensor nodes, full utilization of such resources with less buffer overflow remains as a one of main consideration when a WSN application is designed. Consequently, this paper aims to achieve both minimum energy consumption in reporting operation and balanced energy consumption among sensor nodes for WSN lifetime extension. In addition, data reliability is considered in our model where, the sensed data can reach the sink node in a more reliable way. Finally, buffer space is considered in to reduce the packet loss and energy consumption due to the retransmission of the same packets. This work first formulates the problem as 0/1 Integer Linear Programming (ILP) problem, and proposes SWARM intelligence to solve the optimization problem. Through simulation, the performance of proposed method to report information about the detected objects to the sink is compared with the previous work such as LR-based object tracking algorithm, EBRP, ACO, TADR, SEB, and CLR-Routing.

Direct Adaptive Control of Process Control Benchmark Using Dynamic Recurrent Neural Network

In this article, we develop a direct adaptive control scheme based on Dynamic Recurrent Neural Network (DRNN) for a process control benchmark. The DRNN is represented in a general nonlinear state space form for producing the control action that force the system output to a desired trajectory. The control algorithm can be implemented without a priori knowledge of the controlled system. Indeed, the weights of the DRNN controller are adjusted on-line using the truncated Back Propagation Through Time (BPTT) method. Unlike the approaches in the literature, the learning signal of the network weights is generated by a control error estimator stage in the developed controller. Finally, the developed controller is applied to a laboratory flow control system with two experimental scenarios.

Swarm Intelligence Based Reliable and Energy Balance Routing Algorithm for Wireless Sensor Network

Energy is an extremely crucial resource for Wireless Sensor Networks (WSNs). Many routing techniques have been proposed for finding the minimum energy routing paths with a view to extend the network lifetime. However, this might lead to unbalanced distribution of energy among sensor nodes resulting in, energy hole problem. Therefore, designing energy-balanced routing technique is a challenge area of research in WSN. Moreover, dynamic and harsh environments pose great challenges in the reliability of WSN. To achieve reliable wireless communication within WSN, it is essential to have reliable routing protocol. Furthermore, due to the limited memory resources of sensor nodes, full utilization of such resources with less buffer overflow remains as a one of main consideration when designing a routing protocol for WSN. Consequently, this paper proposes a routing scheme that uses SWARM intelligence to achieve both minimum energy consumption and balanced energy consumption among sensor nodes for WSN lifetime extension. In addition, data reliability is considered in our model where, the sensed data can reach the sink node in a more reliable way. Finally, buffer space is considered to reduce the packet loss and energy consumption due to the retransmission of the same packets. Through simulation, the performance of proposed algorithm is compared with the previous work such as EBRP, ACO, TADR, SEB, and CLR-Routing.

Reference Speed Tracking Control for DC Motor Drives

AbstractThis paper presents the theoretical design and practical application of a modified controller to a DC motor for reference speed tracking control of DC drives. In the proposed technique, the motor performance can be controlled for input and external disturbances such as required speed trajectory tracking and load torque changes, respectively. A feed forward controller is used to achive motor speed tracking for the refernce speed changes. An optimal feed back controller is used to eliminate the speed effect due to the load torque changes. An additional feedback control loop of the motor load torque is developed to ensure an intensity fast speed stabilization even at large load disturbances. The effectiveness of the proposed control scheme is demonstrated by using a laboratory drive system with an on-line IBM PC computer and intensity fast speed stabilization even at large load disturbances. The effestiveness of the proposed control scheme is demonstrated by using a laboratory drive system with an on...