Computer
Hayder I. Mutar; Muna M. Jawad
Abstract
Wireless Sensor Networks (WSNs) have become the most cost- effective monitoring solution due to their low cost, despite their major drawback of limited power due to dependence on batteries. Each Sensor Node (SN) is clustered in a particular location and forms a network by self-organizing. They often ...
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Wireless Sensor Networks (WSNs) have become the most cost- effective monitoring solution due to their low cost, despite their major drawback of limited power due to dependence on batteries. Each Sensor Node (SN) is clustered in a particular location and forms a network by self-organizing. They often operate in some of the world's most unusual or dangerous conditions. Networking errors, memory and processor limitations, and energy constraints all pose problems for WSN developers. Many problems in WSNs are expressed as multivariate optimization problems that are solved using biologically inspired techniques. Particle swarm optimization (PSO) is an easy, algorithmically sound, and robust optimization technique. It has been used to address problems like Clustering, data routing, Cluster Head (CH) collection, and data collecting in WSNs. This paper presents a brief analysis of WSN studies in which the PSO algorithm was used as the primary or secondary algorithm for enhancing lifespan of WSNs, focusing on results that show energy efficiency in the sensors, extending the network's life.
Computer
Noor Abdul Khaleq Zghair; Ahmed S. Al-Araji
Abstract
A mobile robot's major purpose is to get to its destination by traveling over an optimum path defined by various parameters such as time, distance, and the robot's safety from any impediments in its path. As a result, the backbone of the autonomous mobile robot is path planning and obstacle avoidance. ...
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A mobile robot's major purpose is to get to its destination by traveling over an optimum path defined by various parameters such as time, distance, and the robot's safety from any impediments in its path. As a result, the backbone of the autonomous mobile robot is path planning and obstacle avoidance. Several algorithms for path planning and obstacle avoidance have been presented by various researchers, each with its own set of benefits and drawbacks. This paper focuses on two parts; the first part finds the short and smooth collision-free path for a mobile robot to navigate in a static environment based on two proposed hybrid algorithms. The first hybrid is between Firefly Algorithm (FA) and Modify Chaotic Particle Swarm Optimization (MCPSO), namely (HFACPSO), while the other hybrid is between Genetic Algorithm (GA) and MCPSO, namely (HGACPSO).The second part suggests an algorithm planner for improving the efficiency of the route-planning algorithm with moving obstacle avoidance by adjusting the velocity or re-planning the path for the mobile robot. To demonstrate the effectiveness of the proposed algorithms in terms of the shortest path length and collision-free, as well as obtaining optimal or near-optimal wheel velocities with the minimum number of iterations. The proposed hybrid (FAMCPSO) algorithm provides enhancement on the path length equal to (0.82%) compared to the firefly algorithm (FA). Moreover, the hybrid (GAMCPSO) algorithm enhancement on the path length equals (0.67%) compared to the genetic algorithm (GA). All methods are simulated in a static and dynamic obstacle environment using MATLAB 2018b.
Afrah Salman Dawood; Mohammed Najm Abdullah
Abstract
Being able to send different types of data (i.e. text, audio, or video) through thenetwork is the most important aspect of networks. Different networks have different issuesand restrictions while sending data. These restrictions are basically the QoS (Quality ofService) metrics and security. The recent ...
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Being able to send different types of data (i.e. text, audio, or video) through thenetwork is the most important aspect of networks. Different networks have different issuesand restrictions while sending data. These restrictions are basically the QoS (Quality ofService) metrics and security. The recent Software-Defined Networking (SDN) that aimsto separate the control plane from the data plane can be applied where Businessrequirements are not responsible for the way the network is configured; instead, it is theresponsibility of the high-level business policies and objectives. SDN gives preferabletechniques for centralized dynamic management and control configurations. In this work,a proposed model has been estimated and discussed to promote QoS requirements in somesuggested topologies. Adaptive Resource Management (ARM) and control to send differenttypes of data through different hosts have been investigated. The intended requirementsare basically the capacity and delay of traffic metrics sent through different hosts throughthe network. It produces a mathematical model and implementation for three proposedalgorithms to enhance the quality of a sample video sent from source host to destinationhost by Visible Light Communication (VLC)-media player in three different topologies.These algorithms (statistical, MOGA, and PSO) have been implemented using Mininetemulator, FNSS tool, PULP, and network libraries; with two types of controllers whichare Floodlight and OVS under Linux operating system and in python programminglanguage.
Assist. Prof. Dr. Hazem I. Ali; Mr. Mustafa I. Abd
Volume 15, Issue 1 , April 2015, , Page 18-34
Abstract
Abstract- In this work, the design of three types of robust controllers is presented to control the magnetic levitation system. These controllers are: basic H∞ controller, robust Genetic Algorithm (GA) based PID (GAPID) controller and robust Particle Swarm Optimization (PSO) based PID (PSOPID) controller. ...
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Abstract- In this work, the design of three types of robust controllers is presented to control the magnetic levitation system. These controllers are: basic H∞ controller, robust Genetic Algorithm (GA) based PID (GAPID) controller and robust Particle Swarm Optimization (PSO) based PID (PSOPID) controller. In the second and third controllers, the GA and PSO methods are used to tune the parameters of PID controller subject to multi-objective cost function (H∞ constraints and time domain specifications). The use of GA and PSO methods is used to simplify the design procedure and to overcome the difficulty of the resulting high order controller of the basic H∞ controller. The ability of the proposed controllers in compensating the system with a wide range of system parameters change is demonstrated by simulation using MATLAB 7.14.
Assist. Prof. Dr. Hazem I. Ali
Volume 14, Issue 1 , April 2014, , Page 1-9
Abstract
Abstract – In this paper, the design of robust blood glucose controller in diabetes using H-infinity technique is presented. The Particle Swarm Optimization (PSO) method is used to tune the specific structure controller parameters subject to H-infinity constraints. The Bergman model is used to represent ...
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Abstract – In this paper, the design of robust blood glucose controller in diabetes using H-infinity technique is presented. The Particle Swarm Optimization (PSO) method is used to tune the specific structure controller parameters subject to H-infinity constraints. The Bergman model is used to represent the artificial pancreas. This model is one of the more widely used models of the effect of insulin infusion and glucose inputs on the blood glucose concentration. The results show the effectiveness of the designed controller in controlling the behavior of glucose deviation to a sudden rise in the blood glucose. The proposed controller can effectively attenuate the blood glucose deviation to 0.15. This value of attenuation makes the proposed controller superior to the other controllers in previous works. Matlab 7.11 is used to demonstrate the simulation results.
Dr. Hazem I. Ali
Volume 12, Issue 1 , June 2012, , Page 32-41
Abstract
This paper presents an application of a robust deadbeat controller for permanent magnet
stepper motor. This approach has been considered in order to assure robust stability and
performance (disturbance rejection, reference tracking) with the presence of system
parameters uncertainty. The Particle ...
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This paper presents an application of a robust deadbeat controller for permanent magnet
stepper motor. This approach has been considered in order to assure robust stability and
performance (disturbance rejection, reference tracking) with the presence of system
parameters uncertainty. The Particle Swarm Optimization (PSO) is used to tune the
controller parameters by minimizing the cost function subject to H-infinity constraints.
It is shown that the designed deadbeat controller presents simple, low order, and robust
position control for a permanent magnet stepper motor. A two-phase motor is
considered in this paper.