This paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSO
learning algorithm is more effective and robust than genetic learning algorithm; thi

An adaptive nonlinear neural controller to reduce the nonlinear flutter in 2-D wing is proposed in the paper. The nonlinearities in the system come from the quasi steady aerodynamic model and torsional spring in pitch direction. Time domain simulations are used to examine the dynamic aero elastic instabilities of the system (e.g. the onset of flutter and limit cycle oscillation, LCO). The structure of the controller consists of two models :the modified Elman neural network (MENN) and the feed forward multi-layer Perceptron (MLP). The MENN model is trained with off-line and on-line stages to guarantee that the outputs of the model accurately represent the plunge and pitch motion of the wing and this neural model acts as the identifier. Th

Abstract

Zigbee is considered to be one of the wireless sensor networks (WSNs) designed for short-range communications applications. It follows IEEE 802.15.4 specifications that aim to design networks with lowest cost and power consuming in addition to the minimum possible data rate. In this paper, a transmitter Zigbee system is designed based on PHY layer specifications of this standard. The modulation technique applied in this design is the offset quadrature phase shift keying (OQPSK) with half sine pulse-shaping for achieving a minimum possible amount of phase transitions. In addition, the applied spreading technique is direct sequence spread spectrum (DSSS) technique, which has

<p>The demand for internet applications has increased rapidly.  Providing quality of service (QoS) requirements for varied internet application is a challenging task. One important factor that is significantly affected on the QoS service is the transport layer. The transport layer provides end-to-end data transmission across a network. Currently, the most common transport protocols used by internet application are TCP (Transmission Control Protocol) and UDP (User Datagram Protocol). Also, there are recent transport protocols such as DCCP (data congestion control protocol), SCTP (stream congestion transmission protocol), and TFRC (TCP-friendly rate control), which are in the standardization process of Internet Engineering Task

In the last two decades, networks had been changed according to the rapid changing in its requirements. The current Data Center Networks have large number of hosts (tens or thousands) with special needs of bandwidth as the cloud network and the multimedia content computing is increased. The conventional Data Center Networks (DCNs) are highlighted by the increased number of users and bandwidth requirements which in turn have many implementation limitations. The current networking devices with its control and forwarding planes coupling result in network architectures are not suitable for dynamic computing and storage needs. Software Defined networking (SDN) is introduced to change this notion of traditional networks by decoupling control and

Mobile Wireless sensor networks have acquired a great interest recently due to their capability to provide good solutions and low-priced in multiple fields. Internet of Things (IoT) connects different technologies such as sensing, communication, networking, and cloud computing. It can be used in monitoring, health care and smart cities. The most suitable infrastructure for IoT application is wireless sensor networks. One of the main defiance of WSNs is the power limitation of the sensor node. Clustering model is an actual way to eliminate the inspired power during the transmission of the sensed data to a central point called a Base Station (BS). In this paper, efficient clustering protocols are offered to prolong network lifetime. A kern

<p> Traditionally, wireless networks and optical fiber Networks are independent of each other. Wireless networks are designed to meet specific service requirements, while dealing with weak physical transmission, and maximize system resources to ensure cost effectiveness and satisfaction for the end user. In optical fiber networks, on the other hand, search efforts instead concentrated on simple low-cost, future-proofness against inheritance and high services and applications through optical transparency. The ultimate goal of providing access to information when needed, was considered significantly. Whatever form it is required, not only increases the requirement sees technology convergence of wireless and optical networks but

      Wireless sensor network (WSN) security is an important component for protecting data from an attacker. For improving security, cryptography technologies are divided into two kinds: symmetric and asymmetric. Therefore, the implementation of protocols for generating a secret key takes a long time in comparison to the sensor’s limitations, which  decrease network throughput because they are based on an asymmetric method. The asymmetric algorithms are complex and decrease network throughput. In this paper, an encryption symmetric secret key in wireless sensor networks (WSN) is proposed. In this work, 24 experiments are proposed, which are encryption using the AES algorithm in the cases of 1 key, 10 keys, 25 keys, and 50 keys. I

This paper presents a proposed neural network algorithm to solve the shortest path problem (SPP) for communication routing. The solution extends the traditional recurrent Hopfield architecture introducing the optimal routing for any request by choosing single and multi link path node-to-node traffic to minimize the loss. This suggested neural network algorithm implemented by using 20-nodes network example. The result shows that a clear convergence can be achieved by 95% valid convergence (about 361 optimal routes from 380-pairs). Additionally computation performance is also mentioned at the expense of slightly worse results.