Hikmat N. Abdullah; Nuha Sh. Baker; Musab T. S. Al-Kaltakchi
Abstract
Cognitive Radio (CR) is a communication technology developed to solve theproblem of spectrum scarcity. One way to improve the throughput of CR is the use ofefficient decision rules of the fusion center to combine the collected information fromcooperative sensors and produce the right final decision. ...
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Cognitive Radio (CR) is a communication technology developed to solve theproblem of spectrum scarcity. One way to improve the throughput of CR is the use ofefficient decision rules of the fusion center to combine the collected information fromcooperative sensors and produce the right final decision. For this purpose, hard decisionrules like AND and OR and soft decision rules like Square Law Combination (SLC) andMaximum Ratio Combination (MRC) can be combined to optimize the throughputperformance. In this paper, three decision rules, each consist of two decision stages (hardand soft) are proposed to improve the throughput of CR in cooperative scenario. Thesimulation results showed that the proposed rules enhance the throughput as comparedwith traditional ones. They demonstrated that the first proposed rule enhances thethroughput by 106% and 58.9% at SNR equals -10 dB in Rayleigh fading channel over theclassical OR-SLC and the AND-SLC rules, respectively. Under the same simulationconditions, the second proposed rule enhanced the throughput by 163% and 97.5%, whilethe third proposed method enhances throughput by 210% and 135%, respectively.
Computer
Mohammed E. Seno; Ban N. Dhannoon; Omer K. Jasim Mohammad
Abstract
Cloud computing is an evolving and high-demand research field at theforefront of technological advancements. It aims to provide software resources andoperates based on service-oriented delivery. Within the infrastructure as a service (IaaS)framework, the cloud offers end customers access to crucial infrastructure ...
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Cloud computing is an evolving and high-demand research field at theforefront of technological advancements. It aims to provide software resources andoperates based on service-oriented delivery. Within the infrastructure as a service (IaaS)framework, the cloud offers end customers access to crucial infrastructure resources,including CPU, bandwidth, and memory. When a cloud system fails to deliver asexpected, it is referred to as an event, signifying a deviation from the anticipated service.To meet their service-level agreement (SLA) obligations, cloud service providers (CSPs)must ensure continuous access to fault-tolerant, on-demand resources for their clients,particularly during outages. Consequently, finding the most efficient ways to accomplishtasks while considering the rapid depletion of resources has become an urgent concern.Researchers are actively working to develop optimal strategies tailored to the cloudenvironment. Machine learning plays a critical role in these endeavors, serving as a keycomponent in various cloud computing platforms. This study presents a comprehensiveliterature review of current research papers that employ machine learning algorithms topropose strategies for optimizing cloud computing environments. Additionally, the surveyprovides authors with invaluable resources by extensively exploring a diverse range ofmachine learning techniques and their applications in the field of cloud computing. Byexamining these areas, researchers aim to enhance their understanding of efficientresource allocation and scheduling, addressing the challenges posed by resource scarcitywhile meeting SLA obligations.
Communication
Maysoon Hashim Ismaal; Alaa Hussein Ali; Sabah M. Thaba
Abstract
Hydrothermal preparations have been made form vanadium oxygen systems V2O5 and VO2 NPs prototype to design photodetectors. In comparison, the films' polycrystalline structure can be seen in analysis of x-ray diffraction (XRD) pattern, which has 7 and 14 peaks with crystallite sizes = 19.59 for V2O5 and, ...
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Hydrothermal preparations have been made form vanadium oxygen systems V2O5 and VO2 NPs prototype to design photodetectors. In comparison, the films' polycrystalline structure can be seen in analysis of x-ray diffraction (XRD) pattern, which has 7 and 14 peaks with crystallite sizes = 19.59 for V2O5 and, crystallite sizes = 12.92 nm, for VO2. The grains had large, neatly separated conical columnar growth combined grains throughout the surface, with some of the columnar grains coalescing in a few spots, according to the analysis from atomic force microscopy (AFM). It is revealed that the average size of particle of V2O5 =29.58 nm and for VO2 = 16 nm, with rms roughness of 6.8 nm and 21.3 nm respectively. Also, the optical energy gap of V2O5 = 2.6 eV, whereas energy gap of VO2 = 1.36 eV. In addition, it was discovered that the reflectance increased in the visible and infrared regions to register 0.09 and 0.07 respectively. The maximum values of the refractive indices for V2O5 and VO2 were 2.6 and 1.9, respectively. Two types of hetero-junction photo-detectors Ag/VO2/PSi/n-Si/Ag and Ag/V2O5/PSi/n-Si/Ag have been fabricated and characterized. The proposed results of Ag/ V2O5 /PSi/n-Si/Ag heterojunction Photo-detector at different concentrations from PMMA: Acetone responsivity was 0.7A/W at 850nm and, the remarkable detectivity = 4.1 x 1012 (1/W) .cm. Hz0.5 , while, highest values of the detectivity in Ag/VO2/PSi/n-Si/Ag = 3.3 x 1012 (1/W) .cm.Hz0.5 . at wavelength equal and greater than 850 nm
Mohammed Najm Abdullah; Mohanad J. Ahmed
Abstract
The number of office transactions continually increasing ,therefore,several techniques proposed to improve quality of service for the office work.Improvements including electronic archiving limited; they didn’t solve theproblem of transferring parcels. Thus, paper transactions still exist inside ...
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The number of office transactions continually increasing ,therefore,several techniques proposed to improve quality of service for the office work.Improvements including electronic archiving limited; they didn’t solve theproblem of transferring parcels. Thus, paper transactions still exist inside asingle office building and the transfer needed within a time limit. This leads tosuggesting the autonomous vehicle for solving the delivery problem. In-order-toenable such the navigation within the indoor environment, an acceptablelocalization accuracy must exist. The wireless fingerprinting with GeneralizedRegression Neural Network (GRNN) for classification suggested in thisresearch for localization estimation. Furthermore, the estimated location fusedwith the Odometer to bring more stable results. The Accuracy gained about4.1cm which enables the vehicle to localize and navigate. ZigBee modules withArduino microcontrollers are the basic items of the research.
Computer
Umniah Hameed Jaid; alia karim Abdulhassan
Abstract
The voice signal carries a wide range of data about the speaker, including theirphysical characteristics, feelings, and level of health. There are several uses for the estimateof these physical characteristics from the speech in forensics, security, surveillance,marketing, and customer service. ...
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The voice signal carries a wide range of data about the speaker, including theirphysical characteristics, feelings, and level of health. There are several uses for the estimateof these physical characteristics from the speech in forensics, security, surveillance,marketing, and customer service. The primary goal of this research is to identify the auditorycharacteristics that aid in estimating a speaker’s age. To this end, an ensemble featureselection model is proposed that selects the best features from a baseline acoustic featurevector for age estimation from speech. Using a feature vector that covers various spectral,temporal, and prosodic aspects of speech, an ensemble-based automatic feature selection isperformed by, first calculating the feature importance or ranks based on individual featureselection methods, then voting is applied to the resulting feature ranks to attain the topranked subset by all feature selection methods. The proposed method is evaluated on theTIMIT dataset and achieved a mean absolute error (MAE) of 5.58 years and 5.12 years formale and female age estimation
Computer
Dhulfiqar Hakeem Dhayef; Sawsan S A Al-Zubaidi; Luma A H Al-Kindi
Abstract
Cell formation plays a crucial role in the development of cellular manufacturing systems (CMS). Previous studies in this field have typically assumed that each part is associated with a single process plan. However, incorporating alternative routes offers additional flexibility in CMS design. This paper ...
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Cell formation plays a crucial role in the development of cellular manufacturing systems (CMS). Previous studies in this field have typically assumed that each part is associated with a single process plan. However, incorporating alternative routes offers additional flexibility in CMS design. This paper addresses the cell formation problem by considering alternative routes and presents a two-stage approach to address this problem. In the first stage, a Route Rank Index (RRI) is developed based on a correlation matrix to select the optimal alternative route for each part. Subsequently, a Genetic Algorithm (GA) is employed in the second stage to form part families and machine cells. The proposed approach's computational performance is evaluated using a set of generalized group technology datasets found in the existing literature. The results demonstrate that the proposed approach is highly effective and efficient when it comes to addressing the cell formation problem involving alternative routes. The ramifications of these findings in practice are substantial. Our suggested approach demonstrates its resilience and adaptability by achieving comparable or better grouping results across a wide variety of benchmark datasets. This shows the method can be used in a wide range of practical situations, including those involving matrices of varying sizes and shapes. The theoretical knowledge base on part-machine grouping strategies benefits from the comparison study. By comparing the results of our suggested method to those of well- known heuristics, we shed light on its benefits and drawbacks.
Ahlam Luaibi , Shuraiji; Z. Q. Zhu
Abstract
In this study, partitioned stator permanent magnet (PS-PM) tubular machines having single- and double-layer windings layouts have been investigated. Two configurations of PS-PM tubular machines were considered, i.e. partitioned stator surface mounted permanent magnet (PS-SPM) and interior permanent magnet ...
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In this study, partitioned stator permanent magnet (PS-PM) tubular machines having single- and double-layer windings layouts have been investigated. Two configurations of PS-PM tubular machines were considered, i.e. partitioned stator surface mounted permanent magnet (PS-SPM) and interior permanent magnet (PS-IPM) tubular machines. A comprehensive comparison has been carried out in order to investigate the impact of the winding layouts on such machines. It should be mentioned that the FE package that is used in this paper is (ANSYS Maxwell). It has been noted that irrespective of machines configurations, i.e. either PS-SPM or PS-IPM tubular machines, similar thrust force capability, higher average thrust force per magnet volume, lower thrust force ripple as well as cogging force and higher fault tolerance capability can be delivered by PS-PM tubular machines with single-layer winding compared to that with double-layer winding.
Hala Jamal Hadi; Salih Mahdi Al-Qaraawi
Abstract
Recently, the automatic movement of mobile robots has played a very important role inthe advancement of technology. Automated mobile robot path determination is one of the mostimportant challenges in the science of technology. This paper proposed a path planning method forwheeled mobile robots based ...
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Recently, the automatic movement of mobile robots has played a very important role inthe advancement of technology. Automated mobile robot path determination is one of the mostimportant challenges in the science of technology. This paper proposed a path planning method forwheeled mobile robots based on a real time calculation of a predefined distance on a certain map toenable the mobile robot to navigate at indoor areas according to the calculated distances and angleson the paths. The proposed system uses two wheels’ car as a prototype with two optical encoders todetermine the number of wheel’s rotations, in order to calculate the needed distances and anglesbetween two points on the navigation path. The system was controlled by a microcomputer RaspberryPi, programmed using python programming language. The experimental results show an accuratedistances and angles measurement for the navigation under a suitable condition.
Control
Wafeeq Sh. Hanna; Velar H. Elias; Dlawar R. Maruf
Abstract
The load forecasting is a human or computational technique foraccurate preanticipation of electrical load to enhance reliable operation andoptimal planning control of system plant for electrical energy flowing withoutfacing any economical and technical limitations, therefore appropriateestimation for ...
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The load forecasting is a human or computational technique foraccurate preanticipation of electrical load to enhance reliable operation andoptimal planning control of system plant for electrical energy flowing withoutfacing any economical and technical limitations, therefore appropriateestimation for present and future consumption cost of electrical loads which arenecessary to predict the load demand for generating near to accurate power.During advanced technology at the last few decades, artificial neuralnetworks(ANNs) have been extensively employed in electrical system, they aretrained using historical data obtained from plant station. This work is intendedto be a study of short-term load forecasting (STLF) basis for a power predictedapplied to the actual past load data displayed from Azadi station for Feb.2022were used in training and validation system of neural grid. The result wasevaluated by mean square percentage error of (32.7) for the forecastingdynamic time series method to solve the data over hours, days, and weeks inadvance, using a kind of non-linear filtering. Short-term load forecasting triedout with main stages; predicted power load data sets, network training, andforecasting. Neural network used has 3-layers: an input, a hidden, and anoutput layer. The number of hidden layer neurons can be varied for the differentnetwork performance. The active power generation faces economical andtechnical challenges, therefore appropriate evaluation of loads are muchneeded
Ahmed Majid Abdel Abbas; Muayad Sadik Croock
Abstract
Recent advances in the control applications based on hand nervesignals are able to meet the needs of users who suffer from restrictions in limbmovement and also provide high performance control for those paralyzedpeople. These signals are represented as Electromyography (EMG) signals,which are biomedical ...
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Recent advances in the control applications based on hand nervesignals are able to meet the needs of users who suffer from restrictions in limbmovement and also provide high performance control for those paralyzedpeople. These signals are represented as Electromyography (EMG) signals,which are biomedical ones, used for clinical/biomedical applications. In thiswork, a control signals generation system is proposed based on hand EMGmeasurements. The process of acquisition and processing of EMG signals isperformed by only one channel surface EMG electrodes with one EMGprocessing unit as a muscle sensor. In this work, Arduino UNO is adopted as ananalog to digital converter for these hand nerve signals to be easily analyzed inthe classification process. These signals are measured from the skin surface offorearm and biceps muscles in two suggested case studies to be used ingenerating signals based on ten muscles movements. The main features thatcrystallized this research is building a smart control algorithm which increasesthe flexibility of generating precise control signals based on contracted handmovements with high simplicity of use and the low cost. The obtained results arecompared to other systems results to show the ability of achieving 93.81%classification rate or accuracy among other systems.
Computer
Omar Nowfal MohammedTaher; Mohammed Najm Abdullah; Hassan Awheed Jeiad
Abstract
Definitely, image processing operations without advanced and expensive microprocessors consume more time, power, and larger programs. So, improving the reasonable cost of microprocessors is crucial in this situation. This paper proposes an improvement for the MIPS_32 architecture that is called a Customized ...
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Definitely, image processing operations without advanced and expensive microprocessors consume more time, power, and larger programs. So, improving the reasonable cost of microprocessors is crucial in this situation. This paper proposes an improvement for the MIPS_32 architecture that is called a Customized MIPS_32 (CMIPS_32) to enhance the capabilities of image processing (IP) operations. The proposal aims to increase throughput by minimizing the iterative fetching of instructions required by a certain IP operation into a single customized IP instruction. The architecture of MIPS_32 was developed in two phases. Firstly, the Register File, control unit, and ALU are modified to manipulate the information related to the IP operations. Secondly, two new units, the address calculation unit and the last pixel detection unit, were proposed to determine a certain image's starting and ending addresses. Furthermore, the MIPS_32 pipeline is customized to have five to six stages depending on the intensity of operation required by a certain IP instruction to decrease the number of machine clocks and the power consumed. The proposal was implemented using the Zed-Board XC7Z020CLG484-1 FPGA. The results showed that the computation speedup increased by a factor equal to the number of standard instructions required to execute the same operation performed by one of the proposed IP instructions. The CMIPS_32 consumed less power than other models that were implemented on Spartan3-XC3S1500L, Virtex5-XC5VFX30T, Virtex6-XC6VLX75T, and Virtex6-Low-Power-XC6VLX75T by 0.0138W, 0.6468W, 1.31W, and 0.7898W, respectively. Comparing the power consumed by the proposal with the GPU proved that the CMIPS_32 consumes less than the NVIDIA-GPU-GTX980 by 63.8698W.
Zainab H. Tawfiq; Sarah A. Adnan; Makram A. Fakhri; Rihab K. Hamad
Abstract
The impact of laser wavelength on gold nanoparticle (Au NPs) creation isaccounted 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 employingNd:YAG laser (nanosecond pulses). Characterization of accomplished ...
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The impact of laser wavelength on gold nanoparticle (Au NPs) creation isaccounted 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 employingNd:YAG laser (nanosecond pulses). Characterization of accomplished gold NPs wasachieved by Atomic Force Microscopy AFM), X-Ray Diffraction (XRD) and UltravioletVisible (UV-VIS) absorption and transmission spectroscopy. The outcomes demonstratedthe attributes of the prepared NPs, contingent upon XRD, AFM. NPs gotten by 532 nmlaser have preferred properties over that accomplished by 1064 nm laser as indicated bythe highest values of intensity of shorter wavelengths
Computer
Rashad N. Razak; Hadeel N. Abdullah
Abstract
Multi-Object Detection and Tracking (MODT) are essential in manyapplication fields. Still, many enhancements in the speed of detection and tracking wererequired to overcome the challenges during implementation. This paper presents a newalgorithm system for (MODT) to improve the execution time to be robust ...
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Multi-Object Detection and Tracking (MODT) are essential in manyapplication fields. Still, many enhancements in the speed of detection and tracking wererequired to overcome the challenges during implementation. This paper presents a newalgorithm system for (MODT) to improve the execution time to be robust in real-timeapplications. A background subtraction detection algorithm with a Kalman filter wasused to track and predict the object position and speed parameters. To improve theprocessing time, its needs to reduce some frames in a way that does not affect thedetection accuracy too much and instead use the prediction and the estimated valueobtained based on the Kalman filter for the tracked object. This work uses a single videocamera to show how effectively to compute and detect multiple objects concurrently; it isapplied for daytime preprocessing in an automated traffic surveillance system.Preliminary testing findings show that the suggested algorithm for this vehicle monitoringsystem is feasible and effective. It illustrates that using the suggested algorithm with asingle video camera can simultaneously watch, detect, and track several vehicles andimprove execution time. Simulation results on the built system demonstrate that theproposed system reduced the execution time to approximately 41.5% compared to thestandard background subtraction algorithm. Results indicate the proposed algorithm hasan approximate error for the position and speed of detected and tracked objects comparedwith the standard background subtraction algorithm.
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.
Zainab Mohammed Resan; Muayad Sadik Croock
Abstract
Robust and accurate indoor localization has been the goal of several researchefforts over the past decade. In the building where the GPS is not available, this projectcan be utilized. Indoor localization based on image matching techniques related to deeplearning was achieved in a hard environment. So, ...
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Robust and accurate indoor localization has been the goal of several researchefforts over the past decade. In the building where the GPS is not available, this projectcan be utilized. Indoor localization based on image matching techniques related to deeplearning was achieved in a hard environment. So, if it wanted to raise the precision ofindoor classification, the number of image dataset of the indoor environment should be aslarge as possible to satisfy and cover the underlying area. In this work, a smartphonecamera is used to build the image-based dataset of the investigated building. In addition,captured images in real time are taken to be processed with the proposed model as a testset. The proposed indoor localization includes two phases the first one is the offlinelearning phase and the second phase is the online processing phase. In the offline learningphase, here we propose a convolutional neural network (CNN) model that sequences thefeatures of image data from some classis's dataset composed with a smartphone camera.In the online processing phase, an image is taken by the camera of a smartphone in real–time to be tested by the proposed model. The obtained results of the prediction can appointthe expected indoor location. The proposed system has been tested over variousexperiments and the obtained experimental results show that the accuracy of the predictionis almost 98.0%.
Computer
Zainab Hashim; Hanaa Mohsin; Ahmed Alkhayyat
Abstract
Handwritten signature identification is a process that determines an individual’s true identity by analyzing their signature. This is an important task in various applications such as financial transactions, legal document verification, and biometric systems. Various techniques have been developed ...
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Handwritten signature identification is a process that determines an individual’s true identity by analyzing their signature. This is an important task in various applications such as financial transactions, legal document verification, and biometric systems. Various techniques have been developed for signature identification, including feature-based methods and machine learning-based methods. However, verifying handwritten signatures in digital transactions and remote document authentication is still challenging. The inherent variety in people’s signatures, which may occur due to factors such as mood, exhaustion, or even the writing tool used, contributes to the problem. Furthermore, the proliferation of sophisticated forgery methods, such as freehand mimicking and sophisticated picture manipulation, necessitates the development of reliable and precise tools for identifying authentic signatures from fake ones.The present paper suggests a method for identifying signatures based on integrating static (off-line) handwritten signature data. This is done by fusing three types of signature features: Linear Discriminant Analysis (LDA) as appearance-based features, Fast Fourier Transform (FFT) as frequency- features, and Gray-Level Co-occurrence Matrix (GLCM) as texture-features. Then, these fused features are inputted into four types of machine learning algorithms: Naive Bayes, K-Nearest Neighbor, Decision Tree, and AdaBoost classifiers, to identify each person and to find the most robust algorithm in terms of accuracy and precision and recall. For experiments, we have used two famous datasets: SigComp2011 and CEDAR. After training datasets, the highest accuracy achieved was 100% on the CEDAR dataset and 94.43% on the SigComp2011 dataset using a Naive Bayes classifier.
Computer
Sanaa Ali Jabber; Soukaena H. Hashem; Shatha H Jafer
Abstract
Finding an optimal solution to some problem, like minimizing andmaximizing the objective function, is the goal of Single-Objective Optimization (SOP).Real-world problems, on the other hand, are more complicated and involve a widerrange of objectives, several objectives should be maximized in such problems. ...
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Finding an optimal solution to some problem, like minimizing andmaximizing the objective function, is the goal of Single-Objective Optimization (SOP).Real-world problems, on the other hand, are more complicated and involve a widerrange of objectives, several objectives should be maximized in such problems. No singlesolution could be enhanced in all objectives without deteriorating at least one othergoal, which is the definition of Pareto-optimality. Understanding the idea of MultiObjective Optimization (MOP) is thus necessary to find the optimum solution. Multiobjective evolutionary algorithm (MOEA) are made to simultaneously assess manyobjectives and find Pareto-optimal solutions, MOEA can resolve multi-objective andsingle-objective optimization problems.This paper aims to introduce a survey study for optimization problem solutions bycomparing techniques, advantages, and disadvantages of SOP and MOP withmetaheuristics and evolutionary algorithms. From this study, we conduct that theefficiency of MOP lies in the present more than one SOP, but it takes a longer time toprocess and train and is not suitable for all applications, While SOP is faster and moreuseful in stock and profit maximization applications. And the posterior techniques areconsidered the dominant approach to solving multi-objective problems by the use of thefield of metaheuristics.
Essra A. Jaber; Ahmed S. Al-Araji; Hayder A. Dhahad
Abstract
This paper proposes a predictive nonlinear PID neural voltagetracking controller design for Proton Exchange Membrane Fuel Cell (PEMFC)Model with an on-line auto-tuning intelligent algorithm. The purpose of theproposed robust feedback nonlinear PID neural predictive voltage controller isto find the optimal ...
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This paper proposes a predictive nonlinear PID neural voltagetracking controller design for Proton Exchange Membrane Fuel Cell (PEMFC)Model with an on-line auto-tuning intelligent algorithm. The purpose of theproposed robust feedback nonlinear PID neural predictive voltage controller isto find the optimal value of the hydrogen partial pressure action in order tocontrol the stack terminal voltage of the (PEMFC) model for one-step-aheadprediction. The Chaotic Particle Swarm Optimization (CPSO) is utilized as astable and intelligent robust on-line auto-tuning algorithm to obtain the nearoptimal weights for the proposed controller so as to improve the performanceindex of the system as well as to minimize the energy consumption. TheSimulation results demonstrated the effectiveness of the proposed controllercompared with the linear PID neural controller
Suhad Ibrahim Mohammed
Abstract
in this paper, the system consists of many steps, the first step includes thehistogram equalization, detection, feature extraction, and classification. At first, the dataset of a face image is segmented into four segments, after that Local Binary Pattern (LBP)algorithm is performed to extract features ...
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in this paper, the system consists of many steps, the first step includes thehistogram equalization, detection, feature extraction, and classification. At first, the dataset of a face image is segmented into four segments, after that Local Binary Pattern (LBP)algorithm is performed to extract features for each segment. The best feature vectors forall persons are stored in a new dataset in the next stage in order to be used in the testingphase. Finally, the accuracy rate of performance is evaluated to prove its robustness.Experiments show satisfying results and more accuracy achieved by the paper.
Basma Ahmed; Salih Mahdi Al-Qaraawi
Abstract
This paper presents a system that is used in one of the most important fields inour daily life, shopping and entertainment. The system exploits a mobile application, QuickResponse (QR) code technique and network system. The QR tag is attached to each item inthe market, while the mobile application can ...
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This paper presents a system that is used in one of the most important fields inour daily life, shopping and entertainment. The system exploits a mobile application, QuickResponse (QR) code technique and network system. The QR tag is attached to each item inthe market, while the mobile application can be installed in the Smart Phone. The smartphone is used to scan the attached QR tag and send the result to a network system and thenetwork system will process the received data. A brief information will be brought from thelocal server database. However, if the customer is interested in more details, the networksystem brings these details from the main server and applies them on the smart phonescreen. When the customer decides to buy the item, s/he can add it to the shopping list, thenthe system updates the bill, sends it back to the customer and to the cashier at the sametime. The items will be prepared at the checkout point after payment. This system wasdesigned, implemented, and tested practically so that the performance of that system isachieved for two main points: quick attainment of information and network congestionavoidance
Computer
Afrah Salman Dawood
Abstract
Recently, the burgeoning disciplines of Machine Learning (ML) and Deep Learning (DL) have experienced considerable integration across diverse scientific domains. Of significant note is their integration into the medical sector, specifically in the intricate methodologies of pathological categorization. ...
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Recently, the burgeoning disciplines of Machine Learning (ML) and Deep Learning (DL) have experienced considerable integration across diverse scientific domains. Of significant note is their integration into the medical sector, specifically in the intricate methodologies of pathological categorization. Present-day innovations underscore the pivotal role of Deep Convolutional Neural Networks (DCNN) in mediating the tasks of image-based taxonomies and prognostications within this domain. In this research, a new DCNN with different modified intelligent architectures like CNN, modified VGG-16, VGG-19, ResNet50, and DenseNet121, besides the newly added classification layer, was implemented and tested for the detection and classification of Alzheimer’s disease. The evaluation and performance metrics are accuracy, loss, f1-score, precision, and recall. Experiments were made on Kaggle-based dataset and test results show that the CNN-based model is the most accurate model, with the highest accuracy of 96% and the lowest loss of 9.92%. Finally, the average performance percentage of the overall proposed model is as follows: accuracy is 91%, loss is 19.75%, precision is 89.4%, F1- score is 88.83%, and recall is 90%.
Computer
Asaad Raheem Kareem; Hasanen S. Abdullah
Abstract
The article provides an overview of two recent developments in technology: Business Intelligence (BI) and Deep Learning (DL). In order to support decision-making processes, BI entails gathering, integrating, and analyzing data from various sources, while DL uses artificial neural networks to learn and ...
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The article provides an overview of two recent developments in technology: Business Intelligence (BI) and Deep Learning (DL). In order to support decision-making processes, BI entails gathering, integrating, and analyzing data from various sources, while DL uses artificial neural networks to learn and generate predictions from complicated datasets. This paper introduces the concepts and principles and highlights recent developments and applications in different domains of research: education, organizations, stock market, forecasting, decision-making in real-time, and security. However, the fundamental problem with the business intelligence approach is that there is no learning involved. Other limitations and challenges include the capacity that affects the data analysis process, the variety of data in results, and the need for a complete presentation of results in the form of dashboards, scorecards, reports, and portals. The approach choice hinges on the problem's context and requirements and the nature and characteristics of the data. Although BI and DL are widespread, alternative methods may suit well too, such as machine learning, data mining, and statistical analysis. Justifying the selection based on precise needs and goals is crucial. Recurrent neural networks (RNN), convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent units (GRU), and Business intelligence tools are used in the research problem to address these limitations and explore the potential advantages and difficulties of integrating BI and DL to achieve an advantage in a given sector.
Hassan J. Hassan; Ammar Abdul-Amer R.; Hasnaa H. Abdulkareem
Abstract
Workshop contamination can lead to changes in the characteristics of the air.Welding process for example inside the workshop will generate different pollutants just likefume and gases. These gases may threat the environment. Also the direct exposure of thesegases by people inside the workshop may be ...
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Workshop contamination can lead to changes in the characteristics of the air.Welding process for example inside the workshop will generate different pollutants just likefume and gases. These gases may threat the environment. Also the direct exposure of thesegases by people inside the workshop may be considered as a risk on their health. For allreasons mentioned above air pollution monitoring system is important issue to decreasethe risk of low level of health. This paper introduces the implementation of wireless sensornetwork 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 Wirelesssensor Network. These sensors are calibrated using appropriate calibration technologies.They are controlled by ARDUINO based microcontroller. Also there is the main serverwhich Installed on raspberry pi 3 and contains the main database of the system whichsupports real time management strategies by using the web interface to monitor the airpollution in form of numbers and charts.Sensors which controlled by ARDUINO platform are connected to the server using thewireless technology (Wi-Fi) and the communication is done by using Message QueuingTelemetry Transport protocol (MQTT).When the level of Carbon Monoxide gas is abovethe 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 tomeasure the levels of harmful gases which may harm the people in the workshops.
Jabbar Kh. Mohammed
Abstract
Fiber Bragg gratings (FBGs) are one of the most effective technologies becauseof their suitability in many applications of the fiber techniques. Moreover, it can be utilizedin sensing elements and estimating the physical parameters of the optical fiber. In thecurrent research, various fiber bragg lengths ...
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Fiber Bragg gratings (FBGs) are one of the most effective technologies becauseof their suitability in many applications of the fiber techniques. Moreover, it can be utilizedin sensing elements and estimating the physical parameters of the optical fiber. In thecurrent research, various fiber bragg lengths are considered and a comparativeinvestagtion is made for the performance of the refractive index modulation at two differentapodization profiles namely: Gaussian index profiles and uniform index profiles. Also, acomparative study of the optical communication system performance has been carried outof refractive indexes with two apodization functions. A comprehensive comparison in termsof gain, noise figure, OSNR, quality factor, bit error rate and power has been performedwhere the performance of the optical system is studied under various optical channellengths (from 25 km to 100 km) with various FBG lengths, which are ranged from 4 mm to14 mm in both apodization profiles, i.e. uniform profile and Gaussian profile. In order tocarry out the simulation of the performance RZ, the optisystem version 9 is used for singlechannel based on over single mode fiber. The results showed that the maximum Q.F. as theperformance parameter is obtained at the FBG length of 8 mm at all optical lengths.Moreover, the varation effect of optical fiber channel length was greater than the varationeffect of refractive index for all the studied parameters (gain, noise figure, OSNR, qualityfactor, BER, and power) for two apodization functions.
Control
Ayad Q. Abdulkareem; Abdulrahim Th. Humod; Oday A. Ahmed
Abstract
To perform fault tolerance for Anti-lock Braking System (ABS), This paper proposes a hybrid Fault Detection and Fault Tolerant Control (FD-FTC) for ABS speed sensors. It utilizes a Fault Detection (FD) unit and a Data Construction (DC) unit. The first one, the FD unit, is based on a kNN classifier model ...
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To perform fault tolerance for Anti-lock Braking System (ABS), This paper proposes a hybrid Fault Detection and Fault Tolerant Control (FD-FTC) for ABS speed sensors. It utilizes a Fault Detection (FD) unit and a Data Construction (DC) unit. The first one, the FD unit, is based on a kNN classifier model with 99.9% fault detection accuracy to perform three tasks: early fault detection, fault location diagnosis, and excluding faulty signals from being utilized in further processes. On the other hand, the second one, the DC Unit, is based on two separate neural network models. These models have an MSE of 2.01139e-1 and a R2 of 999880 for the first model and an MSE of 1.12486e-0 and 0.999586 for the second model. They are employed to provide an estimated alternative signal for the ABS speed sensors. These estimated signals are employed to perform two tasks: confirming fault detection declared by the FD model and compensating for the excluded faulty signal to fulfill fault accommodation. Both methods are trained and tested with MATLAB and Simulink. Results demonstrate that the proposed hybrid method has the ability to accurately detect and tolerate sensor faults and fulfill its design purpose, especially during emergency braking.
