IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

Cognitive Radio (CR) is a communication technology developed to solve the
problem of spectrum scarcity. One way to improve the throughput of CR is the use of
efficient decision rules of the fusion center to combine the collected information from
cooperative sensors and produce the right final decision. For this purpose, hard decision
rules like AND and OR and soft decision rules like Square Law Combination (SLC) and
Maximum Ratio Combination (MRC) can be combined to optimize the throughput
performance. In this paper, three decision rules, each consist of two decision stages (hard
and soft) are proposed to improve the throughput of CR in cooperative scenario. The
simulation results showed that the proposed rules enhance the throughput as compared
with traditional ones. They demonstrated that the first proposed rule enhances the
throughput by 106% and 58.9% at SNR equals -10 dB in Rayleigh fading channel over the
classical OR-SLC and the AND-SLC rules, respectively. Under the same simulation
conditions, the second proposed rule enhanced the throughput by 163% and 97.5%, while
the third proposed method enhances throughput by 210% and 135%, respectively.

The number of office transactions continually increasing ,therefore,
several techniques proposed to improve quality of service for the office work.
Improvements including electronic archiving
the
solve
didn’t
they
limited;
problem of transferring parcels. Thus, paper transactions still exist inside a
single office building and the transfer needed within a time limit. This leads to
suggesting the autonomous vehicle for solving the delivery problem. In-order-to
enable such the navigation within the indoor environment, an acceptable
localization accuracy must exist. The wireless fingerprinting with Generalized
Regression Neural Network (GRNN) for classification suggested in this
research for localization estimation. Furthermore, the estimated location fused
with the Odometer to bring more stable results. The Accuracy gained about
4.1cm which enables the vehicle to localize and navigate. ZigBee modules with
Arduino microcontrollers are the basic items of the research.

Recently, the automatic movement of mobile robots has played a very important role in
the advancement of technology. Automated mobile robot path determination is one of the most
important challenges in the science of technology. This paper proposed a path planning method for
wheeled mobile robots based on a real time calculation of a predefined distance on a certain map to
enable the mobile robot to navigate at indoor areas according to the calculated distances and angles
on the paths. The proposed system uses two wheels’ car as a prototype with two optical encoders to
determine the number of wheel’s rotations, in order to calculate the needed distances and angles
between two points on the navigation path. The system was controlled by a microcomputer Raspberry
Pi, programmed using python programming language. The experimental results show an accurate
distances and angles measurement for the navigation under a suitable condition.

The impact of laser wavelength on gold nanoparticle (Au NPs) creation is
accounted by the extraction of gold atoms from the gold specimen immersed in ethanol.
Pulsed Laser ablation in fluid procedure of the gold target was carried out by employing
Nd:YAG laser (nanosecond pulses). Characterization of accomplished gold NPs was
achieved by Atomic Force Microscopy AFM), X-Ray Diffraction (XRD) and Ultraviolet-
Visible (UV-VIS) absorption and transmission spectroscopy. The outcomes demonstrated
the attributes of the prepared NPs, contingent upon XRD, AFM. NPs gotten by 532 nm
laser have preferred properties over that accomplished by 1064 nm laser as indicated by
the highest values of intensity of shorter wavelengths.

Being able to send different types of data (i.e. text, audio, or video) through the
network is the most important aspect of networks. Different networks have different issues
and restrictions while sending data. These restrictions are basically the QoS (Quality of
Service) metrics and security. The recent Software-Defined Networking (SDN) that aims
to separate the control plane from the data plane can be applied where Business
requirements are not responsible for the way the network is configured; instead, it is the
responsibility of the high-level business policies and objectives. SDN gives preferable
techniques for centralized dynamic management and control configurations. In this work,
a proposed model has been estimated and discussed to promote QoS requirements in some
suggested topologies. Adaptive Resource Management (ARM) and control to send different
types of data through different hosts have been investigated. The intended requirements
are basically the capacity and delay of traffic metrics sent through different hosts through
the network. It produces a mathematical model and implementation for three proposed
algorithms to enhance the quality of a sample video sent from source host to destination
host by Visible Light Communication (VLC)-media player in three different topologies.
These algorithms (statistical, MOGA, and PSO) have been implemented using Mininet
emulator, FNSS tool, PULP, and network libraries; with two types of controllers which
are Floodlight and OVS under Linux operating system and in python programming
language.

This paper proposes a predictive nonlinear PID neural voltage-
tracking controller design for Proton Exchange Membrane Fuel Cell (PEMFC)
Model with an on-line auto-tuning intelligent algorithm. The purpose of the
proposed robust feedback nonlinear PID neural predictive voltage controller is
to find the optimal value of the hydrogen partial pressure action in order to
control the stack terminal voltage of the (PEMFC) model for one-step-ahead
prediction. The Chaotic Particle Swarm Optimization (CPSO) is utilized as a
stable and intelligent robust on-line auto-tuning algorithm to obtain the near-
optimal weights for the proposed controller so as to improve the performance
index of the system as well as to minimize the energy consumption. The
Simulation results demonstrated the effectiveness of the proposed controller
compared with the linear PID neural controller.

Workshop contamination can lead to changes in the characteristics of the air.
Welding process for example inside the workshop will generate different pollutants just like
fume and gases. These gases may threat the environment. Also the direct exposure of these
gases by people inside the workshop may be considered as a risk on their health. For all
reasons mentioned above air pollution monitoring system is important issue to decrease
the risk of low level of health. This paper introduces the implementation of wireless sensor
network to monitor a workshop air pollution.
The proposed system prototype contains a set of gas sensors (CO, H2, NH3, Butane,
Propane, Ethanol and NO2) which are deployed on stack and infrastructure of Wireless
sensor Network. These sensors are calibrated using appropriate calibration technologies.
They are controlled by ARDUINO based microcontroller. Also there is the main server
which Installed on raspberry pi 3 and contains the main database of the system which
supports real time management strategies by using the web interface to monitor the air
pollution in form of numbers and charts.
Sensors which controlled by ARDUINO platform are connected to the server using the
wireless technology (Wi-Fi) and the communication is done by using Message Queuing
Telemetry Transport protocol (MQTT).When the level of Carbon Monoxide gas is above
the threshold the system will send an alert email to the department of the civil defense.
The system is checked and tested in the training workshops of University of Technology to
measure the levels of harmful gases which may harm the people in the workshops.