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The Iraqi Journal of Computers, Communications, Control, and Systems Engineering (IJCCCE) is a quarterly engineering journal issued by the University of Technology /Baghdad, aiming to enrich the knowledge in computer, communication, and control fields.
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Journal Information

Publisher: University of Technology

Email:  ijccce@uotechnology.edu.iq

Editor-in-chief: Professor Dr. Amjad J. Humaidi

Print ISSN: 1811-9212

Online ISSN: 2617-3352

A Convolutional Neural Network for Detecting COVID-19 from Chest X-ray Images

Basma Wael Abdullah; Hanaa Mohsin Ahmed

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2022, Volume 22, Issue 3, Pages 1-14
DOI: https://doi.org/10.33103/uot.ijccce.22.3.1

since the global pandemic of COVID-19 has spread out, the use of Artificial Intelligence to analyze Chest X-Ray (CXR) image for COVID-19 diagnosis and patient treatment is becoming more important. This research hypothesized that using COVID19 radiographic changes in the X-Ray images. Artificial Intelligence (AI) systems may extract certain graphical elements regarding COVID-19 and offer a clinical diagnosis ahead of pathogenic test; therefore, saving vital time for disease prevention. Employing 2614 CXR radiographs from Kaggle data collection of verified COVID-19 cases and healthy persons, a new Convolutional Neural Network (CNN) model that is inspired by the Xception architecture was presented for the diagnosis of coronavirus pneumonia infected patients. The suggested technique reached an average validation accuracy of 0.99, precision of 0.95, recall of 0.92, and F1-score of 0. 95. Finally, such findings revealed that the Deep Learning (DL) technique has the potential to decrease frontline radiologists' stress, enhance early diagnosis, treatment, and isolation; therefore, aid in epidemic control.

Meerkat Clan-Based Feature Selection in Random Forest Algorithm for IoT Intrusion Detection

Adil Yousef Hussein; Ahmed T. Sadiq

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2022, Volume 22, Issue 3, Pages 15-24
DOI: https://doi.org/10.33103/uot.ijccce.22.3.2

Hackerscanconductmoredestructivecyber-attacksthankstotherapidspread of Internet of Things (IoT) devices, posing significant security risks for users. Through a malicious process, the attacker intended to exhaust the capital of the target IoT network. Researchers and company owners are concerned about the reliability of IoT networks, which is taken into account because it has a significant impact on the delivery of facilities provided by IoT systems and the security of user groups. The intrusion prevention system ensures that the network is protected by detecting malicious activity. In this paper, the focus is on predicting attacks and distinguishing between normal network use and network exploitation for intrusion and network attack and we will use Swarm Intelligence (SI) which is one of the types of artificial intelligence (AI) that we harness to choose features to determine the task of them and specifically we will use an algorithm Meerkat Clan (MCA) for this purpose, as this research suggested a modified IDS in machine learning (ML) based IoT environments to identify features and these features will be input into Random Forest algorithm. The IoTID20 dataset is used where nominal traits are removed, so the final dataset contains 79 traits. The data set contains three categories: the label that identifies whether it is a natural use or exploitation, the category that characterizes the type of exploitation, and the subcategory that describes that exploitation more accurately. The number of trees in a random forest (RF) classifier for binary, class, and subclass will be determined by the experiment. The trained classifier is then tested and the approach achieves 100% accuracy for binary target prediction, 96.5% for category and accuracy ranges of 83.7% for sub-category target prediction. The proposed system is evaluated and compared with previous systems and its performance is shown through the use of confusion matrix and others.

An Efficient Electronic Payment Using Biometric Authentication

Ahmed Abdul Karim Talib; Aymen Dawood Salman

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2022, Volume 22, Issue 3, Pages 25-33

Traditional identification techniques for electronic payments, such as the Personal Identification Number (PIN), are becoming outdated and unsafe, while mobile payments are becoming more popular and widely used. It presents a risk to issuers since there is no reliable consumer verification method available, and the lack of safe and reliable e-payment systems is one of the key issues restricting progress. As a result, efforts have been made to develop and maintain a unified payment system that is well-organized, efficient, dependable, and secure. This system avoids the need for physical cash while also still satisfying all payment and identification requirements, a safe and trustworthy method is required for the country's successful adoption of an e-payment system. This article focuses on the future of online payment and the security problems through using effective biometric authentication technologies to provide a trustworthy authentication method for an e-payment system.

Robust Sensor Fault Estimation for Control Systems Affected by Friction Force

Ruaa Hameed Ahmed; Montadher Sami Shaker

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2022, Volume 22, Issue 3, Pages 34-47
DOI: https://doi.org/10.33103/uot.ijccce.22.3.4

The paper presents an observer-based estimation of sensor fault for control systems affected by friction force. In such systems, the non-linearity of friction force leads to deteriorating sensor fault estimation capability of the observer. Hence, the challenge is to design an observer capable of attaining robust sensor fault estimation while avoiding the effects of friction. To overcome the highlighted challenge, an Unknown Input Observer (UIO) is designed to decouple the effects of friction as well as to estimate the state and sensor fault.The benefit of proposing UIO is to guarantee robust sensor fault estimation despite the highly non-linear disturbance in the form of friction. The gains of the UIO are computed through a singlestep linear matrix inequality. Finally, an inverted pendulum simulation is presented to demonstrate the novel approach's performance effectiveness.



Index Terms—Robust fault estimation; Fault-Tolerant control; unknown input observer; Friction force; estimation/decoupling approach.
Index Terms—Robust fault estimation; Fault-Tolerant control; unknown input observer; Friction force; estimation/decoupling approach.

RCAE_BFV: Retrieve Encrypted Images using Convolution AutoEncoder and BFV

Emad M. Alsaedi; Alaa Kadhim Farhan

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2022, Volume 22, Issue 3, Pages 48-61
DOI: https://doi.org/10.33103/uot.ijccce.22.3.5

Content-Based Image Retrieval (CBIR) is an actual application in computer vision, which retrieves similar images from a database. Deep Learning (DL) is essential in many applications, including image retrieval applications. However, encryption techniques are used to protect data privacy because these data are vulnerable to being viewed by unauthorized parties while being transmitted over unsecured channels.
This paper includes two parts for images retrieval. In the first part, features of all images of a Canadian Institute for Advanced Research CIFAR-10 dataset were extracted and stored on the Server-side. In the second part, the Brakerski/Fan-Vercauteren (BFV) homomorphic encryption scheme method for encrypting an image sent by the client-side. First, their decryption and image features are extracted depending on the trainer model when they arrive on the server-side. Then an extracted features are compared with stored features using the Cosine Distance method, and then the server encrypts the retrieved images and sends them to the client-side. Deep-learning results on plain images were 97% for classification and 96.7% for retriever images. At the same time, TheNational Institute of Standards and Technology (NIST ) test was used to check the security of BFV when applied to CIFAR-10 dataset.

Intelligent Parameter Tuning using Deep Q-network in Adaptive Queue Management Systems

Ayman Basheer Yousif; Hassan Hassan Jaleel; Ghaida Muttasher

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2022, Volume 22, Issue 3, Pages 62-71
DOI: https://doi.org/10.33103/uot.ijccce.22.3.6

Network traffic has risen in recent years to the point that it is obviously and astonishingly in 2020, with the increase predicted to double in the following days. Up to 23 Teraa bit every month is an incredible amount. The Active Queue Management (AQM) algorithm is one of the most significant study areas in network congestion control; nevertheless, new self-learning network management algorithms are needed on nodes to cope with the huge quantity of traffic and minimize queuing latency, used reinforcement learning for automatic adaptive parameter with the AQM algorithm for effective network management, and present a novel AQM algorithm that focuses on deep reinforcement learning to deal with latency and the trade-off between queuing delay and throughput; choose Deep Q-Network (DQN) as the foundation for our scheme and equate it with Random Early Detection (RED) Results based on Network simulation (NS3) simulation suggest that the DQN algorithm has good and better results were obtained from RED, where the difference reached a drop rate of 2%, and this percentage is considered good, in addition to the percentage of throughput and the packet transfer rate of 3% is better in the proposed algorithm.

Collision Prediction Based on Vehicular Communication System

Abdulqader Falhi Jabbar; Rana Fareed Ghani; Asia Ali Salman

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2022, Volume 22, Issue 3, Pages 72-80
DOI: https://doi.org/10.33103/uot.ijccce.22.3.7

Road traffic accidents are one of the leading causes of mortality globally. Reducing the number of traffic-related incidents has become a serious socio-economic and public health problem, given the ever-increasing number of cars on the road. As a result, this paper proposes an intelligent vehicle prediction communication mechanism that alerts drivers to any autos that may be overtaking or bypassing the targeted vehicle. The primary goal of this paper is to leverage modern Internet of Things (IoT) and wireless sensor technologies to predict any potential accident that may occur as a result of car accidents. This paper proposes the Collision Prediction of a Moving Vehicle (CPMV) system. The information acquired by CPMV will alert the driver to divert the vehicle in a reasonable amount of time before any harm occurs. It redirects the inbound object that emitted the Ultrasound signal which was received by the vehicle, to a safe location. The proposed system predicts collision between vehicles through Wi-Fi and Bluetooth, using a set of sensors with a precision of 360 degrees and a distance of collision prediction of one meter and at a speed of 200-300 revolutions per minute. The python programming language was utilized to code the programs that control the vehicle during the implementation of this project. The Raspberry Pi 4 is utilized as the controller to examine the vehicle’s spatial data. The test results showed that using this application to deal with an approaching object can be a successful strategy in the three proposed scenarios at different angles and directions.

A Framework for Predicting Airfare Prices Using Machine Learning

Heba Mohammed Fadhil; Mohammed Najm Abdullah; Mohammed Issam Younis

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2022, Volume 22, Issue 3, Pages 81-96
DOI: https://doi.org/10.33103/uot.ijccce.22.3.8

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Trees (DT), K- nearest neighbor (KNN), and Logistic Regression (LR), have been used to identify the parameters that allow for effective price estimation. These approaches were tested on a data set of an extensive Indian airline network. When it came to estimating flight prices, the results demonstrate that the Decision tree method is the best conceivable Algorithm for predicting the price of a flight in our particular situation with 89% accuracy. The SGD method had the lowest accuracy, which was 38 %, while the accuracies of the KNN, NB, ADA, and LR algorithms were 69 %, 45 %, and 43 %, respectively. This study's presented methodologies will allow airline firms to predict flight prices more accurately, enhance air travel, and eliminate delay dispersion.

Hybridized Dimensionality Reduction Method for Machine Learning based Web Pages Classification

Thabit Sulaiman Sabbah

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2022, Volume 22, Issue 3, Pages 97-110
DOI: https://doi.org/10.33103/uot.ijccce.22.3.9

Feature space high dimensionality is a well-known problem in text classification and web mining domains, it is caused mainly by the large number of vocabularies contained within web documents. Several methods were applied to select the most useful and important features over the years; however, the performance of such methods is still improvable from different aspects such as the computational cost and accuracy. This research presents an enhanced cosine similarity-based hybridization of two efficient feature selection methods for higher classification performance. The reduced feature sets are generated using the Random Projection (RP) and the Principal Component Analysis (PCA) methods, individually, then hybridized based on the cosine similarity values between features’ vectors. The performance of the proposed method in terms of accuracy and F-measure was tested on a dataset of web pages based on several term weighting schemes. As compared to relevant methods, results of the proposed method show significantly higher accuracy and f-measure performance based on less feature set size.

Unmasking Deepfakes Based on Deep Learning and Noise Residuals

Wildan J. Jameel; Suhad M. Kadhem; Ayad R. Abbas

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2022, Volume 22, Issue 3, Pages 111-117
DOI: https://doi.org/10.33103/uot.ijccce.22.3.10

The main reason for the emergence of a deepfake (deep learning and fake) term is the evolution in artificial intelligence techniques, especially deep learning. Deep learning algorithms, which auto-solve problems when giving large sets of data, are used to swap faces in digital media to create fake media with a realistic appearance. To increase the accuracy of distinguishing a real video from fake one, a new model has been developed based on deep learning and noise residuals. By using Steganalysis Rich Model (SRM) filters, we can gather a low-level noise map that is used as input to a light Convolution neural network (CNN) to classify a real face from fake one. The results of our work show that the training accuracy of the CNN model can be significantly enhanced by using noise residuals instead of RGB pixels. Compared to alternative methods, the advantages of our method include higher detection accuracy, lowest training time, a fewer number of layers and parameters.

Automatic Quality of Experience Measuring for Video Conference in Real-Time

Roaa E. Alhassany; Rana F. Ghani

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2022, Volume 22, Issue 3, Pages 118-127
DOI: https://doi.org/10.33103/uot.ijccce.22.3.11

In recent years, especially with COVID-19, video conference applications have become very important. Millions of peoples around the world have become to communicate with each other through using video conference applications. The most critical factor in the performance success of video conference applications is the user's perception of the quality of the experience. In this work, an Extreme Learning Machine (ELM) model was proposed for predicting video quality of experience. The proposed system extracts several features from videos that have a significant impact on the quality of the experience. The model performance was validated with unseen data. Spearman’s Rank Correlation Coefficient (SRCC), Root Mean Square Error (RMSE), Pearson’s Linear Correlation Coefficient (PLCC) metrics have been used to measure the accuracy of the model and correlation. The results demonstrate that the proposed model had better performance than models used by the previous researchers that were used for predicting video QoE in terms of precision, correlation, and running time.

Optimizing Artificial Neural Networks Using Levy- Chaotic Mapping on Wolf Pack Optimization Algorithm for Detect Driving Sleepiness

Sarah Saadoon Jasim; Alia Karim Abdul Hassan; Scott Turner

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2022, Volume 22, Issue 3, Pages 128-136
DOI: https://doi.org/10.33103/uot.ijccce.22.3.12

Artificial Neural Networks (ANNs) are utilized to solve a variety of problems in many domains. In this type of network, training and selecting parameters that define networks architecture play an important role in enhancing the accuracy of the network's output; Therefore, Prior to training, those parameters must be optimized. Grey Wolf Optimizer (GWO) has been considered one of the efficient developed approaches in the Swarm Intelligence area that is used to solve real-world optimization problems. However, GWO still faces a problem of the slump in local optimums in some places due to insufficient diversity. This paper proposes a novel algorithm Levy Flight- Chaotic Chen mapping on Wolf Pack Algorithm in Neural Network. It efficiently exploits the search regions to detect driving sleepiness and balance the exploration and exploitation operators, which are considered implied features of any stochastic search algorithm. Due to the lack of dataset availability, a dataset of 15 participants has been collected from scratch to evaluate the proposed algorithm's performance. The results show that the proposed algorithm achieves an accuracy of 99.3%.

SNOW3G Modified by using PLL Algorithms in Magic Cube

Rana Mohammed Zaki; Hala Bahjat Abdul wahab

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2022, Volume 22, Issue 3, Pages 137-146
DOI: https://doi.org/10.33103/uot.ijccce.22.3.13

Thomas and Patrik are working on a stream cipher called SNOW 3G. In 2006, it was chosen as the centerpiece of a new set of confidentiality and integrity algorithms for the Universal Mobile Telecommunications System (UMTS). In 2008, Böhm published an article named "Statistical Evaluation of Stream Cipher SNOW 3G." He put the randomness of the SNOW 3G key stream generator to the test. As a randomness test tool, Böhm uses the NIST test statistics package, which consists of three kinds of tests: lengthy key stream data, short key stream data, and initialization vector data. Only the short key stream set of data failed eight random chance test results out of three kinds of tests, according to the report's findings. The SNOW 3G suggestions, he claims, fail due to a flaw with in cipher's initialization. In this paper, we use the PLL algorithm to modify the SNOW 3G algorithm for key initialization and generation keystream. We employ the same itself Böhm employed. The modify SNOW 3G algorithm exceed whole of the statistic exam in a experiment. The findings show that PLL has an effect on algorithm randomness.

The Techniques of Based Internet Key Exchange (IKE) Protocol to Secure Key Negotiation

Zainab Kareem Mahyob; Raheem AbdulSahib Ogla; Suhiar Mohammed Zeki

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2022, Volume 22, Issue 3, Pages 147-154
DOI: https://doi.org/10.33103/uot.ijccce.22.3.14

The Internet is a massive network that connects millions of users from all over the world and the data transmitted via it needs great protection, especially since that are in the age of big data. To solve part of this problem, IPsec was utilized, which is a set of protocols necessary to offer security to units of the Internet in general and the IP layer in particular. It is mostly based on major exchange protocols. The most frequent mechanism for transferring key materials and establishing security linkages between two entities is Internet Key Exchange (IKE). In the present work, it is proposed to use a public key that works together with Diffie-Hellman cryptography and the main advantages of a single-stage contribution (as opposed to the two-stage in standard IKE) it is better in terms of improved transfer and time (more time for the corresponding negotiation) to make the proposed IKE more secure with Simple account constraints

Performance Evaluation of Fiber Bragg Grating Strain Sensor Integrating in WDM Communication System

Mohammed Ali Yaseen; A. K. Abass

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2020, Volume 20, Issue 2, Pages 74-78

In this paper, a fiber Bragg grating (FBG) strain sensor is simulated utilizing OptiGrating software. Then the proposed sensor is integrated into the wavelength division multiplexing (WDM) communication system via OptiSystem software in order to evaluate its performance as a strain sensor. The proposed WDM system has 4–channels with NRZ modulation format and 100 GHz frequency spacing. According to the results, the degradation in the receiving signal reaching the limitation value (6 for Q–factor and 10E–9 for BER) at applied strain ranging from 170 μ –to– 180 μ.

Web Based E-Hospital Management System

Muayad Sadik Croock; Salih Mahdi Al-Qaraawi; Ridhab Sami Abd-Ali

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2018, Volume 18, Issue 1, Pages 11-28

Hospital Management System (HMS) is a computerizing scheme used to simplify several efforts of the working in a hospital. The system deals with all the requirements of different hospitals to offer efficient services to patients. In addition, it stores the patients’ information for long term for historical records. In this paper, we design and implement a web based HMS for group of hospitals in Iraq. The system provides an efficient link between the hospital’s departments including the laboratories, radiology, pharmacy and the consultants. The proposed system gives simple access to basic data, thus enabling the management to take better decisions on time. The website is designed using PHP, HTML and CSS environments, while the database is built utilizing MySQL version 5.7.11. As a case study, two hospitals (Margan in Babylon and Ibn-Alnafees in Baghdad) have been considered. The results show that the proposed system has achieved high performance and accuracy in terms of provided health services, data management and time consuming.

Design and Implementation of Autonomous Quadcopter using SITL Simulator

Hasan M. Qays; Bassim A. Jumaa; Ayman D. Salman

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2020, Volume 20, Issue 1, Pages 1-15

In recent years Quadcopter has been used in many applications such as military, security & surveillance, service delivery, disaster rescue and much more due to its flexibility of flying. In this paper, Quadcopter will be used for mail delivery between many locations that is received from the end user. The Quadcopter will execute an autonomous flight using the concept of companion PC. Raspberry PI 3 (RPI3) will control the Quadcopter by command the controller of the drone (Pixhawk) by using DroneKit-Python API to send MAVLink messages to the Ardupilot. This concept is useful to perform an additional task to the autopilot and provide such a smart capability like image processing and path planning which cannot be done by the flight controller alone. Basically, the idea has been stimulated and the code has been tested by using the SITL Simulator with MAVProxy under Ubuntu environment. The result of controlling the Quadcopter using Python script was excellent and give a motivation to implement the same script on a real Quadcopter. The implementation on real Quadcopter was perfect as it has given the same behavior as the SITL drone in the simulation.

Implement Wireless Transceiver System Based On Convolutional Coding; Aided by Soft-Bit Decoding

Israa Hazem Ali

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2018, Volume 18, Issue 1, Pages 49-55

The main aim with any modern digital communication systems is to provide error free data transmission. To achieve this aim, error control coding techniques are applied with these systems. In this paper, digital communication system was implemented based on convolutional code, aided by soft-bit decoding for error free transmission. Fixed length source encoder was represented with this work, and statistically described the output of the source encoder and save this description at the receiver as a priori knowledge about the source encode. These a priori knowledge were exploited at the receiver to improve the performance of the proposed system in term of BER by conceal the errors occurred through the transmission of information over AWGN channel, aided the convolutional encoder. Significant improvements were obtained with this proposed system.

Positively Invariant Sets in Sliding Mode Control Theory with Application to Servo Actuator System with Friction*

Dr. Shibly Ahmed AL-Samarraie

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2010, Volume 10, Issue 1, Pages 121-134

Abstract:
In this paper two invariant sets are derived for a second order nonlinear affine system using a sliding mode controller. If the state started in these sets, it will not leave it for all future time. The invariant set is found function to the initial condition only, from which the state bound is estimated and used when determining the gain of the sliding mode controller. This step overcomes an arithmetic difficulty that consists of calculating suitable controller gain value that ensures the attractiveness of the switching manifold. Also, by using a differentiable form for the approximate signum function in sliding mode controller formula, the state will converge to a positively invariant set rather than the origin. The size of this set is found function to the parameters that can be chosen by the designer, thus, it enables us to control the size of the steady state error. The sliding mode controller is designed to the servo actuator system with friction where the derived invariant sets are used in the calculation of the sliding mode controller gain. The friction model is represented by the major friction components; Coulomb friction, the Stiction friction, and the viscous friction. The simulation results demonstrate the rightness of the derived sets and the ability of the differentiable sliding mode controller to attenuate the friction effect and regulate the state to the positively invariant set with a prescribed steady state error.

IoT Monitoring System Based on MQTT Publisher/Subscriber Protocol

Narges A-hussein; Ayman D. Salman

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2020, Volume 20, Issue 3, Pages 75-83

In the past years, the scene has witnessed huge technological progress which made our lives simpler and flixper. After Wi-Fi and cellular communications networks’ improvements, with the parallel optimization of numerous embedded devices, momentum has risen globally and today gave us a concept that is the IoT or the Internet of Things. Where several contemporary technologies have been utilized and the developers have been advancing structures to collect data from sensor systems that may be sent to any part of the world over the Internet. The Internet of Things can be used for many purposes like controlling, tracking and managing systems. In this study, we presented the work of the MQTT internet routing protocol to exchange sensor information between two different devices. The IoT platform is about monitoring temperature and humidity in a smart home based on an MQTT protocol which makes this connection possible. However, the MQTT protocol works in publishing/ subscribing mode. The proposed work used an Esp8266NodeMCU as a publisher and Raspberry pi3 model (B) as a subscriber. Also, it used a dht11 sensor to measure the temperatures and humidity. The measurements were collected from sensors and alternated between the two devices through the MQTT broker (server). Node-Red and ThingSpeak designed as a website to share the data.

Smart Autonomous Wheelchair Controlled by Voice Commands-Aided by Tracking System

Farah F. Alkhalid; Bashra Kadhim Oleiwi

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2019, Volume 19, Issue 1, Pages 82-87

This study focuses on the design of an autonomous wheelchair based
smart driving features for disabled persons. The movement directions and
position tracking of the wheelchair are controlled and localized by pre-defined
voice commands and global positioning system (GPS), respectively. Arduino
microcontroller based on speaker dependent voice recognition module and
tracking system based on quad-board SIM808 has been used to help the
wheelchair navigation. The experimental tests of the proposed system have been
done and given satisfactory results in controlling the wheelchair and making a
call on demand. Hence the proposed system is a simple, easy to use and lowcost
hardware for designing.

FPGA-Based Multi-Core MIPS Processor Design

Sarah M. Al-sudany; .Ahmed S. Al-Araji1; Bassam M. Saeed

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2021, Volume 21, Issue 2, Pages 16-35

This research presents a study for multicore Reduced Instruction Set
Computer (RISC) processor implemented on the Field Programmable Gate
Array(FPGA).The Microprocessor without- Interlocked Pipeline Stages (MIPS)
processor is designed for the implementation of educational purposes, as well as it is
expected that this prototype of processor will be used for multimedia or big data
applications. 32- bit MIPS processor was designed by using Very High speed Hardware
Description Language (VHDL). Pipelined MIPS processor contains three parts that are :
data path 32-bit MIPS pipeline, control unit, and hazard unit. The single cycle MIPS
system was subdivided into five pipeline stages to achieve the pipeline MIPS processor.
The five parts include: instruction fetch (IF), Instruction Decode (ID), execution (EXE),
memory (MEM) and Write Back (WB). Three types of hazard: data hazard , control
hazard and strctural hazard are resolved. Certain components in the pipelined stage for
the design processor were iterated for four core SIMD pipelined processors. The MIPS is
developed using Xilinx ISE 14.7 design suite. The designed processor was implemented
successfully on Xilinx Virtex-6 XC6VLX240T-1FFG1156 FPGA. The total power
analysis of multi-core MIPS processor is obtanined 3.422 watt and the clock period was
7.329 ns (frequency: 136.444MHz).

Indoor Localization System Using Wi-Fi Technology

Noor Abdul Khaleq Zghair; Muayad Sadik Croock; Ali Abdul Razzaq Taresh

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2019, Volume 19, Issue 2, Pages 69-77

Recently, indoor localization has witnessed an increase in interest,
due to the potential wide range of using in different applications, such as
Internet of Things (IoT). It is also providing a solution for the absence of Global
Positioning System (GPS) signals inside buildings. Different techniques have
been used for performing the indoor localization, such as sensors and wireless
technologies. In this paper, an indoor localization and object tracking system is
proposed based on WiFi transmission technique. It is done by distributing
different WiFi sources around the building to read the data of the tracked
objects. This is to measure the distance between the WiFi receiver and the
object to allocate and track it efficiently. The test results show that the proposed
system is working in an efficient way with low cost.

Visible Light Fidelity Technology: Survey

Kawther Dawood Salman; Ekhlas Kadum Hamza

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2021, Volume 21, Issue 2, Pages 1-15

Wireless Fidelity (Wi-Fi) is particularly popular today. Every place has
hotspots to access the internet via Wi-Fi, including homes, offices, colleges, and other
public places. This increased number of users, and hence the use of bandwidth, has led to
radio spectrum congestion. Thus, in the year of 2011, Light-Fidelity (Li-Fi) was
introduced, which applies a visible light region for data transmission, to solve this radio
crisis problem. This visible part of the spectrum was 10,000 times big compared to the
part utilized in the Wi-Fi radio. Furthermore, Li-Fi was a sub-set of Visible Light
Communication (VLC), using Light-Emitting Diode (LED) bulbs for transmitting data
utilizing light-medium. This paper reviews the differences between Li-Fi technology and
wireless types. It Also focuses on the architecture, components, functioning, modulation
technologies, and applications of Li-Fi.

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