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Capacity and Fairness Maximization-Based Resource Allocation for Downlink NOMA Networks

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Computers, Materials & Continua 2021年第10期69卷 521-537页

作者： Mohammed Abd-Elnaby Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Non-orthogonal multiple access(NOMA)is one of the leading technologies for 5G communication.User pairing(UP)and power allocation(PA)are the key controlling mechanisms for the optimization of the performance of NOMA systems.This paper presents a novel UP and PA(UPPA)technique for capacity and fairness maximization in NOMA called(CFM-UPPA).The impact of the power allocation coefficient and the ratio between the channel gains of the paired users on the sum-rate capacity and the fairness in NOMA is firstly investigated.Then,based on this investigation,the PA and UP algorithms of the CFM-UPPA technique are proposed.The power allocation coefficient of the proposed PA is formulated as an exponentially decaying function of the ratio between the channel gains of the paired users to maximize the capacity and the fairness,and its maximum value is adjusted to guarantee the successive interference cancellation(SIC)constraints.The proposed UP is based on selecting the user that has the highest channel gain per subcarrier as the strong user to maximize the capacity and selecting the user that has the closest lower channel gain to the strong user’s channel gain as the weak user to improve the fairness and capacity.The performance evaluation of the proposed CFM-UPPA technique in terms of capacity,fairness,and outage probability demonstrates that its performance significantly outperforms that of the orthogonal multiple access(OMA)system and that of the NOMA system with random UP.Also,the simulation results demonstrate the efficiency of the proposed PA in improving the performance of other UP algorithms,such as the random UP algorithm.

关键词： 5G non-orthogonal multiple access user pairing power allocation capacity fairness

Lightweight Transfer Learning Models for Ultrasound-Guided Classification of COVID-19 Patients

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Computers, Materials & Continua 2021年第11期69卷 2295-2312页

作者： Mohamed Esmail Karar Omar Reyad Mohammed Abd-Elnaby abdel-Haleem abdel-Aty Marwa Ahmed Shouman College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Mathematics and Computer Science Faculty of ScienceSohag UniversitySohag82524Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Physics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Physics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt

Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients.Recently,advantages of portable Ultrasound(US)imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 cases.In this paper,a new framework of lightweight deep learning classifiers,namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US images.Compared to traditional deep learning models,lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware resources.Four main lightweight deep learning models,namely MobileNets,ShuffleNets,MENet and MnasNet have been proposed to identify the health status of lungs using US images.Public image dataset(POCUS)was used to validate our proposed COVID-LWNet framework successfully.Three classes of infectious COVID-19,bacterial pneumonia,and the healthy lung were investigated in this study.The results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0%and 647.0 s,respectively.This paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobilebased radiological tool for clinical diagnosis of COVID-19 and other lung diseases.

关键词： Coronavirus medical image processing artificial intelligence ultrasound

Automatic PV Grid Fault Detection System with IoT and LabVIEW as Data Logger

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Computers, Materials & Continua 2021年第11期69卷 1709-1723页

作者： Rohit Samkria Mohammed Abd-Elnaby Rajesh Singh Anita Gehlot Mamoon Rashid Moustafa H.Aly Walid El-Shafai Project Engineer NWTFIndian Institute of TechnologyKanpurIndia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Electronics and Electrical Engineering Lovely Professional UniversityJalandharIndia School of Computer Science and Engineering Lovely Professional UniversityJalandharIndia Department of Electronics and Communications Engineering College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria 1029Egypt Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Fault detection of the photovoltaic(PV)grid is necessary to detect serious output power reduction to avoid PV modules’damage.To identify the fault of the PV arrays,there is a necessity to implement an automatic system.In this IoT and LabVIEW-based automatic fault detection of 3×3 solar array,a PV system is proposed to control and monitor Internet connectivity remotely.Hardware component to automatically reconfigure the solar PV array from the series-parallel(SP)to the complete cross-linked array underneath partial shading conditions(PSC)is centered on the Atmega328 system to achieve maximum power.In the LabVIEW environment,an automated monitoring system is developed.The automatic monitoring system assesses the voltage drop losses present in the DC side of the PV generator and generates a decimal weighted value depending on the defective solar panels and transmits this value to the remote station through an RF modem,and provides an indicator of the faulty solar panel over the built-in Interface LabVIEW.The managing of this GUI indicator helps the monitoring system to generate a panel alert for damaged panels in the PV system.Node MCU in the receiver section enables transmission of the fault status of PV arrays via Internet connectivity.The IoT-based Blynk app is employed for visualizing the fault status of the 3×3 PV array.The dashboard of Blynk visualizes every array with the status.

关键词： Blynk app IoT LabVIEW node MCU PV array RF modem WSN

Analysis of BrainMRI: AI-Assisted Healthcare Framework for the Smart Cities

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Intelligent Automation & Soft Computing 2023年第2期35卷 1843-1856页

作者： Walid El-Shafai Randa Ali Ahmed Sedik Taha El-Sayed Taha Mohammed Abd-Elnaby Fathi E.abd El-Samie Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of ROBOTICS and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The use of intelligent machines to work and react like humans is vital in emerging smart cities.Computer-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare applications.Among AI(Artiﬁcial Intelligence)driven healthcare applications,tumor detection is one of the contemporary researchﬁelds that have become attractive to research-ers.There are several modalities of imaging performed on the brain for the pur-pose of tumor detection.This paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)layers.The proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal states.The dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:30.The main contribution of this paper is introdu-cing an optimum deep learning structure of CNN layers.The simulation results are obtained for 50 epochs in the training phase.The simulation results reveal that the optimum CNN architecture consists of four layers.

关键词： Healthcare smart cities clinical automation CNN machine learning brain tumor medical diagnosis

Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

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Computer Systems Science & Engineering 2022年第10期43卷 87-102页

作者： Safaa El-Gazar Walid El-Shafai Ghada El-Banby Hesham F.A.Hamed Gerges M.Salama Mohammed Abd-Elnaby Fathi E.abd El-Samie Department of Electrical Engineering Faculty of EngineeringEgyptian-Russian UniversityCairoEgypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last decades.It achieves safe access to systems using biometrics instead of the traditional passwords.The utilization of a biometric in its original format makes it usable only once.Therefore,a cancelable biometric template should be used,so that it can be replaced when it is attacked.Cancelable biometrics aims to enhance the security and privacy of biometric authentication.Digital encryption is an efficient technique to be used in order to generate cancelable biometric templates.In this paper,a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification systems.The considered biometric in this paper is the speech signal.The speech signal is transformed into its spectrogram.Then,the spectrogram is encrypted using two cascaded optical encryption algorithms.The first algorithm is the Optical Scanning Holography(OSH)for its efficiency as an encryption tool.The OSH encrypted spectrogram is encrypted using Double Random Phase Encoding(DRPE)by implementing two Random Phase Masks(RPMs).After the two cascaded optical encryption algorithms,the cancelable template is obtained.The verification is implemented through correlation estimation between enrolled and test templates in their encrypted format.If the correlation value is larger than a threshold value,the user is authorized.The threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two curves.The implementation of optical encryption is adopted using its software rather than the optical setup.The efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation results.It can improve the biometric data security without deteriorating the recognition accuracy.Simulation results give close-to-zero This values for the Equal Error Rate(EER)and close-to-one values for the Area under Receiver Operator Characteristic(AROC)curve.

关键词： Cancelable biometrics spectrogram OSH DRPE EER AROC

Fair subcarrier-power allocation scheme for multiuser multicarrier systems

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Journal of Central South University 2015年第8期22卷 3033-3041页

作者： Mohammed Abd-Elnaby Germien G.Sedhom Nagy W.Messiha Xu Zhu Fathi E.abd El-Samie Faculty of Electronic Engineering Menoufia University Deptartment of Electrical Engineering and Electronics University of Liverpool

The main objective of multiuser orthogonal frequency division multiple access(MU-OFDM) is to maximize the total system capacity in wireless communication systems. Thus, the problem in MU-OFDM system is the adaptive allocation of the resources(subcarriers, bits and power) to different users subject to several restrictions to maximize the total system capacity. In this work, a proposed subcarrier allocation algorithm was presented to assign the subcarriers with highest channel gain to the users. After the subcarrier allocation, subcarrier gain-based power allocation(SGPA) was employed for power and bit loading. The simulation results show that the proposed subcarrier-power allocation scheme can achieve high total system capacity and good fairness in allocating the resources to the users with slightly high computational complexity compared to the existing subcarrier allocation algorithms.

关键词：多载波系统分配方案载波功率多用户子载波分配正交频分多址接入无线通信系统 OFDM系统

Efficient Deep CNN Model for COVID-19 Classification

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Computers, Materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Mohammed Abd-Elnaby Fathi E.abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in China.World Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death rate.Chest X-Ray(CXR)and Computerized Tomography(CT)screening of infected persons are essential in diagnosis applications.There are numerous ways to identify positive COVID-19 cases.One of the fundamental ways is radiology imaging through CXR,or CT images.The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality.Hence,automated classification techniques are required to facilitate the diagnosis process.Deep Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical images.The deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image datasets.In this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is offered.Three activation functions as well as three optimizers are tested and compared for this task.The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it.The performance is tested and investigated on the CT and CXR datasets.Three activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch sizes.Different optimizers are studied with different batch sizes and a constant learning rate.Finally,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is determined.Hence,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage.The proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR and CT datasets,respectively.The best results are

关键词： COVID-19 image classification CNN DL activation functions optimizers

Solid Particles Injection in Gas Turbulent Channel Flow

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Energy and Power Engineering 2016年第12期8卷 367-388页

作者： abd elnaby Kabeel Medhat Elkelawy Hagar Alm-Eldin Bastawissi Ahmed mohammed Elbanna Mechanical Power Engineering Department Faculty of Engineering Tanta University Tanta Egypt

This paper represents a review of the recent researches that investigate the behavior of the gas turbulent flow laden with solid particles. The significant parameters that influence the interactions between the both phases, such as particle size, loading ratio and the gas velocity, have been extensively reviewed. Those parameters are presented in dimensionless numbers in which the applicability of studying its effect in terms of all circumstances of the gas turbulent channel flow at different condition is possible. The represented results show that the turbulence degree is proportional to the particle size. It was found that at the most flow conditions even at low mass ratio, the particle shape, density and size significantly alter the turbulence characteristics. However, the results demonstrate that the particle Reynolds number is a vital sign: the turbulence field becomes weaker if particle Reynolds number is lower than the critical limit and vies verse. The gas velocity has a strong effect on the particles settling along the channel flow and as a result, the pressure drop will be affected.

关键词： Two-Phase Flow Particle-Laden Flow Gas-Solid Channel Flow Particle Size Effect

Agrobacterium-Mediated Transformation of Rice: Constraints and Possible Solutions

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Rice science 2019年第3期26卷 133-146页

作者： Sulaiman mohammed Azman abd SAMAD Zaidah RAHMAT Department of Biotechnology and Medical Engineering Faculty of Biosciences and Medical Engineering Universiti Teknologi Malaysia Department of Biological Sciences Faculty of Science Gombe State University

Genetic transformation of rice(Oryza sativa L.) by introducing beneficial traits is now a central research instrument in plant physiology and a practical tool for plant improvement. Many approaches are verified for stable introduction of foreign genes into the plant genome. The review examined the different constraints that limit the success of rice genetic transformation via Agrobacterium-mediated approach and suggested possible solutions. Explant identification, gene transfer technique and construct to tailor the integration, transgene expression without collateral to genetic damage and transformant selection are among the technical challenges affecting the rice transformation. Despite the contests, Agrobacteriummediated transformation system has been a better option for producing transgenic rice varieties because of its exact T-DNA processing and simple integration of low copy-number transgene. This information is necessary for improving the transformation system for recalcitrant rice varieties.

关键词： rice Agrobacterium-mediated transformation tissue culture gene transfer T-DNA integration