IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

This paper presents a moving object tracker for monitoring system which
can be used in a smart city. Kernel density estimation (KDE) algorithm has been used
for representing a background model, while a minimum distance between the current
image and the background has been used to extract the foreground. Also, morphological
operations are carried out to remove the noise regions and to filter out ambiguous areas.
The performance has been evaluated by determining the true, false, and miss detections
of an object area. The optimal results have been obtained by adjusting the
morphological operation sequence to be (close > thicken) combination by which the
true-hits are 14 out of 16 while miss-number is 2 and zero false-hits, While, the
percentage hit ratio was 87.5% (14 out of 16). Also, the salt noise introduction in video
reduces the hit number from 14 to 11 when it increases from zero to 0.5 percent of the
total frame pixels. The accepted absolute error ratio (in morphological properties of the
matched object) is kept at 0.05 for all tests. The implementation has been built by using
a combination of two platforms, ISE 14.6(2013) and Matlab(2013a) platforms, to
avoid the size weakness of XC3S700A-FPGA board.

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.

Wireless sensor networks undergo tremendous applications to be utilized
for. In this paper, we propose a wireless ad hoc sensor network architecture that does
not depend on a centralized unit to urban city’s vehicular control, by applying different
sensors distribution for main and side streets. On this architecture, we define and
evaluate through simulation the effectiveness of our work against the traditional fixed-
time traffic light model. Traffic lights coordination is addressed in master and local
controllers by executing green-wave algorithm at our architecture. Simulation results
show that this architecture achieves great reduction in total waiting time on the city been
projected

This paper proposes a cognitive neural controller to guide a
nonholonomic mobile robot during continuous and non-continuous trajectory tracking
and to navigate through static obstacles with collision-free and minimum tracking error.
The structure of the controller consists of two layers; the first layer is a neural network
topology that controls the mobile robot actuators in order to track a desired path based
on back-stepping technique and posture identifier. The second layer of the controller is
cognitive layer that collects information from the environment and plans the optimal
path. In addition to this, it detects if there is any obstacle in the path so it can be avoided
by re-planning the trajectory using particle swarm optimization (PSO) technique. The
stability and convergence of control system are proved by using the Lyapunov criterion.
Simulation results and experimental work show the effectiveness of the proposed
cognitive neural control algorithm; this is demonstrated by minimizing tracking error
and obtaining the smooth torque control signal, especially when the robot navigates
through static obstacles with collision-free and the external disturbances applied.
.

The main core of this paper is to design an experimental method for
estimating of the nonlinearity, calibrating and testing of the different types of
thermocouples temperature sensors (J, K, T, S and R) using multi-layer perceptron
(MLP) neural network based on slice genetic (SG) optimization learning algorithm.
Temperature sensor has a nonlinearity behavior nature in its output response but it
requires a linear behavior output with accepts approximation in accuracy level, noise
and measurement errors. Therefore, neural network topology is proposed with five main
steps algorithm to reduce the effected noise and minimize the measured errors. Matlab
simulation results and laboratory work (LabVIEW) validate the preciously of the
proposed cognitive neural linearization algorithm in terms of calculating the
temperature from the different types of thermocouples temperature sensors and
minimizing the error between the actual temperature output and neural linearization
temperature output as well as overcoming the problem of the over learning in the
linearization model with the minimum number of fitness evaluation for the learning
algorithm..

Steganalysis is the technique of analyzing a stego-image to determine
whether it has embedded data or not. More deliberately steganalysis, it can be achieved
by coding a program that examines the stego-image structure and measures its statistical
properties. This paper presents a novel steganalysis algorithm by detecting the sequence
occurrence distribution (SOD) of cover/setgo-image using three types of statistical
randomness properties tests: Frequency, Serial and Poker. Where hidden a 2.4Х10-7%
distortion of covering image in multiple-LSB (MLSB), the difference achieved
detection between cover-stage images as; frequency is 0.91362828; serial is 3.45887
and poker is 160.6455. Also, this proposed algorithm can point to the occurrences of the
sequence which is affected by the embedded message, then implemented it by using 8-
bit pair code and made by Xilinx-spartan-3A XC3S700AFPGA, with 50 MHz internal
clock.

This paper demonstrates the effect of color image transmission through
AWGN channel using binary phase shift key modulation (BPSK) system and
transmission of compressed color image through AWGN. On the other hand, the
transmission consumes a large amount of channel and needs the processing, therefore
utilizes compression techniques to reduce the size of the original color image to
facilitate the transmission compressed color image through AWGN and to obtain the
channel optimization. In this paper, a simple hybrid lossy color image compression
transmission through AWGN using convolutional coding with Viterbi decoding system
is proposed. It is based on combining effective techniques, started by wavelet transform
that decompose the image signal followed by polynomial approximation model of linear
based to compress approximation image band. The error caused by applying polynomial
approximation is coded using bit plane slice coding, whereas the absolute moment block
truncation coding exploited to coded the detail sub bands. Then, the compressed
information encoded using LZW, run length coding and Huffman coding techniques,
the compressed information is entered to the channel coding to coded the information
using convolutional coding and modulation using BPSK to transmit through channel
and added AWGN, the received signal is demodulated and decoded using Viterbi
decoding, the result compressed data passed to source decoding to reconstruct the
compressed image. The test results indicated that the proposed system can produce a
balance between the compression performance and preserving the image quality, and
also simulations results observed that with increase in signal to noise ratio (SNR) values
the bit error rate (BER) values decrease.