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5th International IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2023

作者： Mohammed, Shaimaa H. Mohra, Ashraf S. Hassan, Ashraf Y. Elnokrashy, Ahmed F. Benha University Faculty of Engineering Department of Electrical Engineering Benha Benha Egypt Benha University Benha Faculty of Engineering Benha Egypt Nile University School of Information Technology and Computer Science Giza Egypt

ISBN: (纸本)9798350381030

Radio Frequency (RF) energy harvesting has been employed to power wireless devices. Nevertheless, RF energy harvesting encounters restrictions regarding the quantity of power it can harvest depending on signal accessibility. As a result, accurately predicting energy levels becomes crucial for enhancing the performance of energy harvesting circuits. Most research efforts have concentrated on enhancing power harvesting policies or theoretically estimating the energy obtained through RF energy harvesting. Moreover, the existing literature has primarily focused on single-band prediction approaches. This paper presents a multi-band RF energy prediction approach for RF energy harvesting systems. We collect real-time RF energy using software-defined radio technology. The proposed approach leverages Long Short-Term Memory (LSTM) neural networks to accurately predict the mean RF energy in different frequency bands for the next 100 samples, which corresponds to approximately one hour and a half. The research explores the research gap in modeling the radio frequency signal and the need for multi-band prediction techniques. The results demonstrate the effectiveness of the proposed approach in predicting RF energy across different frequency bands, with average accuracies above 98%. © 2023 IEEE.

关键词： Energy harvesting

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Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

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Intelligent Automation & Soft Computing 2024年第1期39卷 33-55页

作者： M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Arfat Ahmad Khan Department of Computer Engineering Government Polytechnic CollegeSevvapetTiruvallurTamil Nadu602025India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science and Engineering Annamalai UniversityChidambaramTamil Nadu608002India Department of Computer Science and Engineering Aarupadai Veedu Institute of TechnologyVinayaka Mission Research FoundationPaiyanurTamil Nadu603104India Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

关键词： Brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

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Coherence Awareness in Diffractive Neural Networks

Coherence Awareness in Diffractive Neural Networks

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Kleiner, Matan Michaeli, Lior Michaeli, Tomer Faculty of Electrical and Computer Engineering Technion Haifa32000 Israel Division of Engineering and Applied Science California Institute of Technology 1200 E. California Avenue PasadenaCA91125 United States

We demonstrate the significant i nfluence of th e il lumination co herence on diffractive networks, and propose a framework for network optimization with any prescribed degree of spatial and temporal coherence. We ana... 详细信息

ISBN: (纸本)9781957171395

关键词： Diffractive optics

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All-optically linearized silicon modulator with ultrahigh SFDR of 131 dB·Hz^(6/7)

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Photonics Research 2025年第2期13卷 433-441页

作者： QIANG ZHANG QIKAI HUANG PENGHUI XIA YAN LI XINGYI JIANG SHUYUE ZHANG SHENGYU FANG JIANYI YANG HUI YU Zhejiang Lab Hangzhou 311100China Institute of Integrated Microelectronic Systems College of Information Science and Electronic EngineeringZhejiang UniversityHangzhou 310027China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Integrated high-linearity modulators are crucial for high dynamic-range microwave photonic(MWP)*** linearization schemes usually involve the fine tuning of radio-frequency(RF)power distribution,which is rather inconvenient for practical applications and can hardly be implemented on the integrated photonics *** this paper,we propose an elegant scheme to linearize a silicon-based modulator in which the active tuning of RF power is *** device consists of two carrier-depletion-based Mach-Zehnder modulators(MZMs),which are connected in series by a 1×2 thermal optical switch(OS).The OS is used to adjust the ratio between the modulation depths of the two *** a proper ratio,the complementary third-order intermodulation distortion(IMD3)of the two sub-MZMs can effectively cancel each other *** measured spurious-free dynamic ranges for IMD3 are 131,127,118,110,and 109 d B·Hz^(6∕7)at frequencies of 1,10,20,30,and 40 GHz,respectively,which represent the highest linearities ever reached by the integrated modulator chips on all available material platforms.

关键词： microwave photonic systems ultrahigh SFDR all optical linearization silicon modulator active tuning rf power high linearity modulators third order intermodulation distortion integrated photonics

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Assessing a BERT-based model for analyzing subjectivity and classifying academic articles

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Multimedia Tools and Applications 2024年第42期83卷 90511-90532页

作者： Mehmood, Atif Shahid, Farah Khan, Rizwan Ahmed, Shahzad Ibrahim, Mostafa M. Zheng, Zhonglong School of Computer Science and Technology Zhejiang Normal University Jinhua321002 China Zhejiang Institute of Photoelectronics & Zhejiang Institute for Advanced Light Source Zhejiang Normal University Zhejiang Jinhua321004 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt

The metaverse concept extends beyond virtual worlds and can be applied to collaborative analysis environments. Data analysts worldwide may read academic article extracts in real-time in a shared digital workplace to mine and analyze data using the metaverse. Furthermore, a semantic metaverse in natural language processing might also involve creating a digital environment with linguistic and semantic connections. Many dedicated researchers navigating the complexities of natural language processing in the metaverse era have spent considerable time searching for relevant papers. However, online reviews and evaluations of articles are helpful for their assistance and may save the researcher time. In this work, human specialists manually produced a dataset from four conferences and evaluated subjectively using rule-based techniques. Subsequently, we aim to evaluate the effectiveness of pre-trained word embeddings and pre-trained BERT models seamlessly integrated with convolutional neural networks. This endeavor focuses on the subjective analysis and classification of contributions and previous work sentences extracted from academic literature. For comparison, various deep learning architectures were systematically employed, including long short-term memory-GloVe and bi-directional long short-term memory-GloVe, alongside classical machine learning methods. Our findings show that the proposed BERT model achieved state-of-the-art performance in classification and subjective analysis tasks with an accuracy of 91.50% and F1 score of 91.00%. Finally, we plan to utilize sentence similarity to identify contributions within abstracts, thus outlining potential avenues for future research. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Natural language processing systems

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Automatic Detection of Outliers in Multi-Channel EMG Signals Using MFCC and SVM

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Intelligent Automation & Soft Computing 2023年第4期36卷 169-181页

作者： Muhammad Irfan Khalil Ullah Fazal Muhammad Salman Khan Faisal Althobiani Muhammad Usman Mohammed Alshareef Shadi Alghaffari Saifur Rahman Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Software Engineering University of MalakandDir LowerPakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan

The automatic detection of noisy channels in surface Electromyogram(sEMG)signals,at the time of recording,is very critical in making a noise-free EMG *** an EMG signal contaminated by high-level noise is recorded,then it will be useless and can’t be used for any healthcare *** this research work,a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals.A modiﬁed version of mel fre-quency cepstral coefﬁcients(mMFCC)is proposed for the extraction of features from sEMG channels along with other statistical parameters i-e complexity coef-ﬁcient,hurst exponent,and root mean *** state-of-the-art classiﬁers such as Support Vector Machine(SVM),Ensemble Bagged Trees,Ensemble Sub-space Discriminant,and Logistic Regression are used to automatically identify an EMG channel either bad or good based on these extracted ***-based analyses of these classiﬁers have also been considered based on total classi-ﬁcation accuracy,prediction speed(observations/sec),and processing *** proposed method is tested on 320 simulated EMG channels as well as 640 experi-mental EMG *** is used as our main classiﬁer for the detection of noisy channels which gives a total classiﬁcation accuracy of 99.4%for simulated EMG channels whereas accuracy of 98.9%is achieved for experimental EMG channels.

关键词： Machine learning surface electromyography support vector machine classiﬁcation features

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A Joint Resource Allocation Algorithm for D2D Communication

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computers, Materials & Continua 2022年第2期70卷 3751-3762页

作者： Abdul Kadir Hamid Lamia Osman Widaa Fahd N.Al-Wesabi Imran Khan Anwer Mustafa Hilal Manar Ahmed Hamza Abu Sarwar Zaman Mohammed Rizwanullah Department of Electrical&Computer Engineering University of SharjahSharjahUnited Arab Emirates Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerKSA&Faculty of Computer and ITSana’a UniversityYemen Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The emergence of multimedia services has meant a substantial increase in the number of devices in mobile networks and driving the demand for higher data transmission *** result is that,cellular networks must technically evolve to support such higher rates,to be equipped with greater capacity,and to increase the spectral and energy *** with 4G technology,the 5G networks are being designed to transmit up to 100 times more data volume with devices whose battery life is 10 times ***,this new generation of networks has adopted a heterogeneous and ultra-dense architecture,where different technological advances are combined such as device-to-device(D2D)communication,which is one of the key elements of 5G *** has immediate applications such as the distribution of traffic load(data offloading),communications for emergency services,and the extension of cellular coverage,*** this communication model,two devices can communicate directly if they are close to each other without using a base station or a remote access ***,eliminating the interference between theD2Dand cellular communication in the *** interference management has become a hot issue in current *** order to address this problem,this paper proposes a joint resource allocation algorithm based on the idea of mode selection and resource *** results showthat the proposed algorithm effectively improves the systemperformance and reduces the interference as compared with existing algorithms.

关键词： D2D communication resource allocation wireless networks mobile communication

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Developing Engagement in the Learning Management System Supported by Learning Analytics

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computer Systems science & engineering 2022年第7期42卷 335-350页

作者： Suraya Hamid Shahrul Nizam Ismail Muzaffar Hamzah Asad W.Malik Department of Information System Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

Learning analytics is an emerging technique of analysing student par-ticipation and *** recent COVID-19 pandemic has signiﬁcantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to *** date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and ***,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notﬁrst identiﬁ***,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an *** required data was collected from an LMS that is currently utilised by a local Malaysian *** model was validated by a panel of experts as well as discussions with *** is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

关键词： Engagement analysis learning analytics learning management system student engagement

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Stereotype-Based QOS Awareness for Learning to Use Multimedia in Rural Networks 9

Stereotype-Based QOS Awareness for Learning to Use Multimedi...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Sucipto, Adi Jupriyadi Budiman, Arief Ahdan, Syaiful Zaki Hamidi, Eki Ahmad Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia Uin Sunan Gunung Djati Department of Electrical Engineering Bandung40132 Indonesia

ISBN: (纸本)9798350305029

The use of technology in supporting the education sector can improve the quality of education and human resources. The dissemination of learning content and the use of video conferencing technology in the distance learning process via the network will automatically make the learning process run well. A significant amount of network resources, such as a solid infrastructure and available bandwidth, are required to provide multimedia communication-based services. Most of Indonesia is rural, there are some constraints on network infrastructure in rural areas. Limited resources, difficult access, and high costs to access are the main problems for sending content. In this research, research is conducted on how to build rural network infrastructure, and how to deliver multimedia-based learning content. The results from this study can also be applied anywhere, not just in rural networks. On networks with constrained bandwidth, this is accomplished by paying attention to Quality of Service (QoS), which involves minimizing delays and lost packets. In this study, multimedia streams are categorized and resource allocation in a network with a limited capacity is done using a stereotype-based model. After testing, the test bed shows that using the recommended method on a network with limited bandwidth produces better results than not using the mechanism, as well as from previous studies. The video delay time can be reduced by 8-59%, and the audio delay time can be reduced by around 32-653%. Loss of audio and video packets is decreased by 0-0.05% and 0-0.06%, respectively. Although delays and lost packages are tolerable, but Vanet, medical and other technologies are very concerned about this. © 2023 IEEE.

关键词： Video conferencing

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A Sparse Optimization Approach for Beyond 5G mmWave Massive MIMO Networks

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computers, Materials & Continua 2022年第8期72卷 2797-2810页

作者： Waleed Shahjehan Abid Ullah Syed Waqar Shah Imran Khan Nor Samsiah Sani Ki-Il Kim Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Center for Artificial Intelligence Technology Faculty of Information Science&TechnologyUniversiti KebangsaanKajang43000Malaysia Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Korea

Millimeter-Wave(mmWave)Massive MIMO is one of the most effective technology for the fifth-generation(5G)wireless *** improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wave bandwidth and a large number of antennas at the base ***,increasing the number of antennas requires a large number of radio frequency(RF)chains which results in high power *** order to reduce the RF chain’s energy,cost and provide desirable quality-ofservice(QoS)to the subscribers,this paper proposes an energy-efficient hybrid precoding algorithm formm Wave massive MIMO networks based on the idea of RF chains *** sparse digital precoding problem is generated by utilizing the analog precoding ***,it is jointly solved through iterative fractional programming and successive convex optimization(SCA)*** results show that the proposed scheme outperforms the existing schemes and effectively improves the system performance under different operating conditions.

关键词： 5G mmwave precoding massive mimo complexity

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