IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

In this paper, an algorithm to hide information in an image using QR code
technology is presented. QR Code “QUICK RESPONSE CODE” is a two-dimensional
array that can include different types of data and was first developed in 1993 for the
Japanese Toyota Corporation for the purpose of tracking products through production and
marketing. The researchers paid great attention to QR code technology especially in the
field of information security. The proposed algorithm in this paper hides the secret text in
the image in a random way by generating random positions and using the LSB “Least
Significant Bit” method. After that, the random positions are saved in the QR code file, and
this is the hiding stage. In the extraction stage, the QR code file is read first to extract the
positions where the secret text has been hidden. After that, the secret text is read according
to the LSB method. The algorithm was implemented using the C-Sharp programming
language and Microsoft Visual Studio 2019 development environment. After conducting
experiments on a number of images and extracting results based on the PSNR “Peak
signal-to-noise ratio “test method, the results were good and the algorithm is very strong
against a brute force attack. This algorithm can be used in building authentication systems.

Massive Multiple-Input Multiple-Output (MIMO) is an extension of
the conventional MIMO in the wireless systems which improves both of the
access density and the spectral efficiency by adding a massive number of
antenna array at the base station (BS). Massive MIMO increases the spectral
efficiency by using the beamforming. Besides, the beamforming in massive
MIMO improves the energy efficiency by focusing the energy in the desired
direction instead of the omnidirectional propagation. In this paper, we propose
and discuss different beamforming objectives in both the uplink and the
downlink channels. These proposed objectives can be either use the
beamforming of the desired signal without nulling the interference or use the
beamforming with interference nulling. The beamforming with nulling
objectives have better performance than those without nulling but this leads to a
higher computational complexity as well. The results of this paper show and
compare the performance of these objective including the spectral efficiency
and energy efficiency as well as the computational complexity.

Blockchain technology relies on cryptographic techniques that
provide various advantages,
as trustworthiness, collaboration,
such
organization, identification, integrity, and transparency. Meanwhile, data
analytics refers to the process of utilizing techniques to analyze big data and
comprehend the relationships between data points to draw meaningful
conclusions. The field of data analytics in Blockchain is relatively new, and few
studies have been conducted to examine the challenges involved in Blockchain
data analytics. This article presents a systematic analysis of how data analytics
affects Blockchain performance, with the aim of investigating the current state
of Blockchain-based data analytics techniques in research fields and exploring
how specific features of this new technology may transform traditional business
methods. The primary objectives of this study are to summarize the significant
Blockchain techniques used thus far, identify current challenges and barriers in
this field, determine the limitations of each paper that could be used for future
development, and assess the extent to which Blockchain and data analytics have
been effectively used to evaluate performance objectively. Moreover, we aim to
identify potential future research paths and suggest new criteria in this
burgeoning discipline through our review.

Recently, Deep Learning (DL) has accomplished enormous prosperity in various
areas, like natural language processing (NLP), image processing, different medical issues and
computer vision. Both Machine Learning (ML) and DL as compared to traditional methods,
can learn and make better and enhanced use of datasets for feature extraction. This paper is
divided into three parts. The first part introduces a detailed information about different
characteristics and learning types in terms of learning problems, hybrid learning problems,
statistical inference and learning techniques; besides to an exhausted historical background
about feature learning and DL. The second part is about the major architectures of DL with
mathematical equations and clarified examples. These architectures include Autoencoders
(AEs), Generative Adversarial Networks (GANs), Deep Belief Networks (DBNs),
Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Recursive
Neural Networks. The third part of this work represents an overview with detailed explanation
about different applications and use-cases. Finally, the fourth part is about hardware/ software
tools used with DL.

Conventional voting activities are often replaced by electronic voting (EV) in
light of the quick expansion of the Internet. For a variety of reasons, various nations have
lately switched to EV rather than conventional voting. Different EV systems were
presented up to this point. In both practical and theoretical fields, on the other hand,
there is no perfect solution. To meet such objectives, the researchers strive for preserving
cryptographic primitives when developing high-efficiency e-voting schemes. The concept
of fog computing was developed to improve network infrastructure to satisfy the demands
of large amounts of data the same time as also increasing the efficiency of the processing
power. Also, it has been created as well to address concerns with Cloud computing, like
the distribution environment complexity, real-time response, mobility, and IoT
application location awareness. The concentration of this study was on a complete review
regarding the systems of EVs through various scholars as a platform to detect flaws or
problems in the deployment of extremely secure EV systems. In addition, nations having
a history of EV system adoption were examined. A concept for future work on establishing
a safe EV system depends on problems discovered in numerous works.

Exoskeleton robots help users with mechanical forces by
recognizing their intentions, and they require a lot of energy efficiency, a lot of
load capacity, and a good fit. A basic one degree of freedom (DOF)
construction was devised in this work, which was mostly used in the knees of
exoskeleton robots. The exoskeleton is a small robotic device used for knee
injury training. It is a nonlinear mathematical model with many mechanical
factors that might vary and produce uncertainty, as well as external
disturbances that can be utilized to monitor control. The transitioning process is
frequently organized using tracking differentiator TD to resolve the conflict
between system speed and overshoot. An active disturbance rejection control
(ADRC) with a modified tracking differentiator is described to tackle these
challenges, enhance control accuracy, and reduce settling time for exoskeleton
modified trajectory differentiator (MTD).Simulation tests showed that (MTD)
reduced the tracking error by 36%, when compared with the improved TD1 and
37.5% for Hans TD2 at uncertainty case . Despite the presence of several model
uncertainties, the suggested training knee exoskeleton robot system using the
MTD-ADRC was able to achieve the necessary target value. Control design and
analysis can be done with Matlab and Simulink

The fifth generation (5G) of mobile technology is emerging as a advance
communication network, delivering elevated speeds, coverage and reliability, this
technology like other technologies not clear from drawbacks and limitations, 5G mobile
system like other mobile systems 4G, 3G and 2G affected by interference and signal quality
is going to be decreased because of higher number of C-Node-Bs which is because higher
frequency and smaller coverage, in this paper, interference effect has been studied with
both types cochannel and adjacent channel, best case and worst cases has been presented
simulated and calculated the signal to interference ratio vs cluster size for different
pathloss exponents , models and techniques of minimizing this effect has been studied
starting with 5G cell planning, then a proposed network using optical fiber between C-
Node-Bs, the proposed network model will minimize the interference effect to about
negligible level, results shown in this paper about total length of fiber optic cables and
cluster size, also, an example with best cluster size has been presented.

Since the need for larger data rates and wireless system radio networks has
increased, several organizations in this industry have started to develop and implement
their 5G mobile technology scenarios. Since mobile telecommunications' quick expansion
has motivated companies to constantly plan and work from the first generation to the fourth
generation of mobile technologies. MIMO systems and beamforming antenna arrays are
expected to be very important. In the 5G wireless communication systems to be utilized
after 2020, when they are paired with massive MIMO systems. In this paper, the main goal
is to determine the benefits of beamforming techniques in massive MIMO systems in order
to increase system throughput and reduce interference, thus eliminating and resolving the
various technical challenges that are presented by the implementation of massive MIMO
system architectures. The goal of this work is to contribute to the development of a 5G
mobile system's C-Node-B base station transceiver. The suggested design makes use of a
(64 x 64 MIMO) system and optimum the beamforming technology, MIMO parameters, and
the primary fundamental parameters in beamforming.

Semantic segmentation refers to labeling each pixel in the scene to its belonging
object. It is a critical task for many computer vision applications that requires scene
understanding because It attempts to mimic human perceptual grouping. Despite the
unremitting efforts in this field, it is still a challenge and preoccupies of researchers.
Semantic segmentation performance improved using deep learning rather than traditional
methods. Semantic segmentation based on deep learning models requires capturing local
and global context information, where deep learning models usually can extract one of
them but is challenging to integrate between them. Deep learning based on attention
mechanisms can gather between the capturing of local and glopal information, so it is
increasingly employed in semantic segmentation. This paper gives an introductory survey
of the rising topic attention mechanisms in semantic segmentation. At first, it will discuss
the concept of attention and its integration with semantic segmentation requirements. Then,
it will review deep learning based on attention mechanisms in semantic segmentation.

The lower limb amputees are increasing day by day. This has led to an
increase in research in the field of prosthetic knee. In this work, a prosthetic knee was
designed and developed to assist human movements and more quality of life for millions
of individuals who have lost lower limbs. The dynamic model and parameter
identification of a two degree of freedom (2-DOF) joint prosthetic knee is derived
according to the Lagrangian dynamic approach. The two controllers Backstepping and
Adaptive Backstepping are adopted to control the system. Stability analysis and
controller design dependent on Lyapunov theory are assessed to prove a tracking of a
desired trajectory. From the results, found that the quantitative comparison between the
two controllers, showed significant improvement in results in position tracking. To
comparison between Backstepping control and Adaptive Backstepping control, at the
control action consumptions. It was found that the position error of the prosthetic knee
in Backstepping control is by 9% at link 1 (thigh) and 7.4% at link 2 (shank) compared
with desired trajectory, while in Adaptive Backstepping control is by 1.16% at link 1 and
1.65% at link 2 compared with desired trajectory. When comparing between
Backstepping control and Adaptive Backstepping control, the improvement rate was 7.84
at link 1 and 5.75 at link 2 , the proposed Adaptive Backstepping control, it may be
concluded, is more robust against this perturbation and to deal with uncertainty.
Therefore, the controller is built in a MATLAB environment, and its performance and
robustness are assessed.

Compression of images process is a step in the image processing. It
is concerned with the transmission and storage of digitally created images.
Fractal coding is a potential image and video compression approach with good
reconstruction fidelity and relatively large compression ratios, because of its
simplicity and great performance; fractal image compression (FIC) is a
particularly popular approach in image compression applications. However, it
has a significant disadvantage in the form of a long encoding time. This is
because encoding any small bit necessitates a massive similarity search in the
original data As a result; the FIC search time is reduced while the quality of the
reconstructed images is maintained acceptable level in many introduced paper
and other still a study topic in progress. Fractal images are images that are
self-similar in that each individual part is the same as the total. This paper will
discusses many attempts for more author that working on image and video
compression using fractal compression technique based on various approach
and with each discuss focuses on the main parameter of compression such
compression ratio (CR), peak signal to noise ratio (PSNR) and encoding time,
as well as the details of data set the used for testing also writing with each
technique to creating fractal video and image compression.

argumentation has become an attraction recently, because it is widely used in
decision-making, at 1994 Dung invented a new argumentation model, called
Argumentation Framework AF. This system investigates assaults of arguments, and it also
works away on attributes, this model is designed to take care of argument attacks among
them, without paying attention to how the sentences are formulated or arranged, and
identify the supporting and supporting arguments. It is also possible for a group of experts,
to evaluate arguments to resolve the debate about the current problem, by determining the
extent to which a particular argument affects the other by attacking it, This framework was
a comprehensive new system, called the gaming argumentation framework (GAF), It helps
make decisions about the current problems, through making Claims and Attack
Determinations (CAD) to arguments, and after that, putting the result of those CAD to
game theory, with 2 players for the purpose of achieving final results, which are helpful
for decision-makers, in making decisions concerning current problems. The present paper
gives a proposed system, using the GAF to build the dynamic model based on the gaming
argumentation framework (DGAF); it as works as the GAF by adding the feedback to suit
all possible conditions, and by making a companion between them, the argumentation and
the game theory. Since the foreign exchange market depends on changing conditions, it
was a case study.

Data theory coding is an excellent and well-known branch of study
that has produced various crucial solutions to the insoluble challenges of safe
data transfers. Last improvements in detecting error techniques have resulted in
a significant increase in the use of low-density parity-check (LDPC) code to
address critical concerns connected to secure data transfer. Until now, decent
efforts have been performed on LDPC codes that target low complexity, high
performance, and low bit error rate goals. The final aim of this review is to
provide a recent literature understanding of modern improvements previously
mentioned and in LDPC encoding and decoding (applicative and theoretical)
techniques. A comparative scan of many remarkable LDPC decoding
algorithms, 5G standard requirements, popular power management methods,
and low-energy LDPC design studies is also shown. Lastly, conclusions are
presented by outlining key study results, current concerns, and general thoughts
on new research directions possibilities.

Temperature control system is a widely applied process since
temperature plays a major role in our life starting from room temperature
conditioning to various industrial and medical applications. In this paper, a
control algorithm is proposed for controlling the temperature of a certain
process. Analysis is performed to verify the feasibility of the proposed control
algorithm. A simulation is performed using MATLAB software to show the
performance of the proposed control algorithm and a practical implementation
is performed using Arduino to investigate the validity of the analysis. The
results show the ability of the proposed controller to achieve the desired results
which confirms the validity of the proposed controller and mathematical
analysis.

Recently, the growth of data transmission through various networks has the
necessity for an elevated level of security. Encryption is one of the essential technologies
for protecting and ensuring the integrity of IoT devices. Secure communication among
constrained devices is critical during data transmission from the client to the server
devices. Lightweight cipher algorithms are defined as a secure solution for devices with
limited computational functions and memory. On the other hand, most lightweight
algorithms suffer from a trade-off between complexity and speed to produce a robust
cipher algorithm. This paper evaluates the effectiveness of an image encryption technique
that uses a Lightweight GIFT algorithm and logistic map equation-based dynamic DNA
coding to create a secure, lightweight cipher algorithm for IoT devices. When we
employed dynamic DNA coding by the binary bit method, we observed that the developed
approach is more secure and has a high level of randomness due to the results.
Additionally, the correlation between nearby pixels is approximately zero; there is no
association between the two images. Therefore, the developed approach achieves a
higher encryption efficiency when compared to the original algorithm.