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31st National and 9th International Iranian Conference on Biomedical engineering, ICBME 2024

作者： Ghoorchian, Mina Soltanian-Zadeh, Hamid Pedram, Mir Mohsen Sadeghi, Mohsen Mir Mohammad Institute for Cognitive Science Studies Department of Cognitive Science Tehran Iran University of Tehran School of Electrical and Computer Engineering Tehran Iran Kharazmi University Department of Electrical and Computer Engineering Faculty of Engineering Tehran Iran

ISBN: (纸本)9798331529710

IQ is one of the indicators that has always been of interest to psychiatrists, doctors and cognitive science researchers. Since this index plays a key role in people's lives and also in the occurrence of brain abnormalities, many studies have been conducted on it. In this paper, we used the resting state magnetic resonance imaging data available in the HCP database to classify people into three groups with low, medium, and high IQ and applied machine learning methods on the matrix of causal connections between related network regions. We applied brain highlights. We calculated the matrix of causal relationships in this network by the spDCM algorithm and then obtained the differences of the three groups using the ANOVA statistical test and 6 edges out of 169 edges with p-value © 2024 IEEE.

关键词： Support vector regression

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Modelling and Performance Analysis of Visible Light Communication System in Industrial Implementations

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computers, Materials & Continua 2023年第11期77卷 2189-2204页

作者： Mohammed S.M.Gismalla Asrul I.Azmi Mohd R.Salim Farabi Iqbal Mohammad F.L.Abdullah Mosab Hamdan Muzaffar Hamzah Abu Sahmah M.Supa’at Faculty of Electrical Engineering Faculty of EngineeringUniversiti TeknologiMalaysiaSkudai81310Malaysia Lightwave Communications Research Group(LCRG) Faculty of Electrical EngineeringUniversiti TeknologiSkudaiJohorMalaysia Department of Communication Engineering Faculty of Electrical and Electronic EngineeringUniversiti Tun Hussein Onn Malaysia(UTHM)Parit RajaBatu PahatJohor86400Malaysia Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Department of Computer Science University of São PauloSão PauloBrazil

Visible light communication(VLC)has a paramount role in industrial implementations,especially for better energy efficiency,high speed-data rates,and low susceptibility to ***,since studies on VLC for industrial implementations are in scarcity,areas concerning illumination optimisation and communication performances demand further *** such,this paper presents a new modelling of light fixture distribution for a warehouse model to provide acceptable illumination and communication *** proposed model was evaluated based on various semi-angles at half power(SAAHP)and different height levels for several parameters,including received power,signal to noise ratio(SNR),and bit error rate(BER).The results revealed improvement in terms of received power and SNR with 30 Mbps data *** modulations were studied to improve the link quality,whereby better average BER values of 5.55×10^(−15) and 1.06×10^(−10) had been achieved with 4 PAM and 8 PPM,*** simulation outcomes are indeed viable for the practical warehouse model.

关键词： Visible light communication(VLC) industrial applications warehouse model light fixtures bit error rate(BER)

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Ensemble based unsupervised anomaly detection with concept drift adaptation for time series data 10

Ensemble based unsupervised anomaly detection with concept d...

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10th IEEE Smart World Congress, SWC 2024

作者： Li, Danlei Nair, Nirmal Wang, Kevin I-Kai Sakurai, Kouichi University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Kyushu University Department of Informatics Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9798331520861

Anomaly detection is the identification of instances that substantially deviate from the majority of the data and do not conform to a well-defined normal behavior. Investigating time series anomalies has become increasingly popular in recent years due to widely deployed IoT devices and sensors. Although most existing methods apply image anomaly detection algorithms directly on time series and commonly assume the data is IID (independent and identically distributed). However, in most realworld applications, the underlying data distribution alters over time. Changes in data over time are referred to as concept drift and can significantly degrade the efficacy of an anomaly detection model. Existing research also indicates that the majority of anomaly detection frameworks ignored concept drift, resulting in poor long-term model performance. This paper presents an ensemble-based, unsupervised anomaly detection framework that is adaptive to the concept drift in time series data. Both distribution and performance-based techniques are used to detect concept drift and update the model to ensure the framework's longterm performance reliability. The proposed method is evaluated using a real-world univariate time series data that captures sea surface temperature data over a 2 -year period. By comparing our proposed framework to other state-of-the-art algorithms, we demonstrate our proposed method has obvious benefits in accuracy and long-term stability. © 2024 IEEE.

关键词： Anomaly detection Concept drift adaptation Ensemble method Time series Unsupervised method

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A comprehensive review of effective parameters to improve the performance of the Savonius turbine using a computational model and comparison with practical results

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Water-Energy Nexus 2024年第1期7卷 266-274页

作者： Jebelli, Ali Lotfi, Nafiseh Saeid Zare, Mohammad Yagoub, Mustapha C.E. Department of Engineering and Design Robotic Century Inc Alberta Canada Faculty of New Science and Technologies Tehran University Tehran Iran School of Electrical Engineering and Computer Science University of Ottawa ON Canada

Amidst growing global concerns over climate change and escalating greenhouse gas emissions from fossil fuels, the pursuit of renewable energy sources has become critical. This study focuses on harnessing hydropower using Savonius turbines, which are known for their efficiency in generating energy at lower flow rates. However, the intrinsic low efficiency of these turbines necessitates precise optimization tailored to specific river or channel conditions. In this research, we optimized the performance of Savonius turbines by analyzing key parameters such as the height-to-diameter ratio, blade twist, and the integration of multi-stage configurations with deflectors. Our findings reveal significant efficiency improvements through strategic modifications. Specifically, by reducing the height-to-diameter ratio from 1.2 to 0.8 and maintaining a Tip Speed Ratio (TSR) of 0.6, the power coefficient increased by 15%, from 0.33 to 0.38. Further optimization was achieved by adjusting the blade twist angle, with an increase in power coefficient up to an optimal angle of 45°, beyond which efficiency declined. Implementing a two-stage turbine setup with a 90-degree phase difference between stages further improved the power coefficient to 0.51 at the same TSR. Additionally, the use of deflectors, particularly at a 90° angle, significantly boosted the power coefficient, highlighting their effectiveness in optimizing water flow impact on the turbine. This comprehensive study not only advances the understanding of Savonius turbine optimization but also contributes to broader renewable energy applications. The research offers critical insights into sustainable hydroelectric power generation, providing practical solutions to enhance turbine performance for real-world applications. © 2024

关键词： Clean energy

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Electronic structure prediction of multimillion atom systems through uncertainty quantification enabled transfer learning

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npj Computational Materials 2024年第1期10卷 1423-1437页

作者： Shashank Pathrudkar Ponkrshnan Thiagarajan Shivang Agarwal Amartya S.Banerjee Susanta Ghosh Department of Mechanical Engineering-Engineering Mechanics Michigan Technological UniversityHoughtonMIUSA Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA90095USA Department of Materials Science and Engineering University of CaliforniaLos AngelesCA90095USA Faculty member of the Center for Data Sciences Michigan Technological UniversityHoughtonMIUSA

The ground state electron density—obtainable using Kohn-Sham Density Functional Theory(KSDFT)simulations—contains a wealth of material information,making its prediction via machine learning(ML)models ***,the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation,making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system ***,we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data,while comprehensively sampling systemconfigurations using *** ML models are less reliant on heuristics,and being based on Bayesian neural networks,enable uncertainty *** show that our models incur significantly lower data generation costs while allowing confident—and when verifiable,accurate—predictions for a wide variety of bulk systems well beyond training,including systems with defects,different alloy compositions,and at multi-million-atom ***,such predictions can be carried out using only modest computational resources.

关键词： alloy prediction transfer

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Blockchain-based color medical image cryptosystem for industrial Internet of Healthcare Things (IoHT)

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Multimedia Tools and Applications 2024年 1-55页

作者： Khallaf, Fatma El-Shafai, Walid El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Ahram Canadian University 6th October City Giza Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

In recent years, the proliferation of smart devices and associated technologies, such as the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Internet of Medical Things (IoMT), has witnessed a substantial growth. However, the limited processing power and storage capacity of smart devices make them vulnerable to cyberattacks, rendering traditional security and cryptography techniques inadequate. To address these challenges, blockchain (BC) technology has emerged as a promising solution. This study introduces an efficient framework for the Internet of Healthcare Things (IoHT), presenting a novel cryptosystem for color medical images using BC technology in conjunction with the IoT, Secure Hash Algorithm 256-bit (SHA256), shuffling, and bitwise XOR operations. The encryption scheme is specifically designed for an IIoT grid network computing system, relying on diffusion and confusion principles. In this paper, the proposed cryptosystem strength is evaluated against differential attacks with several comprehensive metrics. Simulation results and theoretical analysis demonstrate the cryptosystem effectiveness, showcasing its ability to provide high levels of security and immunity to data leakage. The proposed cryptosystem offers a versatile range of technical solutions and strategies that are adaptable to various scenarios. The evaluation metrics, with approximate values of 99.61% for Number of Pixels Change Rate (NPCR), 33.46% for Unified Average Changed Intensity (UACI), and 8 for information entropy, closely align with the desired ideal outcomes. Consequently, this paper contributes to the advancement of secure and private systems for medical image encryption based on BC technology, potentially mitigating the risks associated with cyberattacks on smart medical devices. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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A Comprehensive Two-Step Methodology for Automated Segmentation of Architectural Distortion in Mammograms 6

A Comprehensive Two-Step Methodology for Automated Segmentat...

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6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Ibrahim, Sameh E. Elnokrashy, Ahmed F. Mohamed, Wael A. Benha University Benha Faculty of Engineering Department of Electrical Engineering Benha Egypt Benha University Electrical Engineering Benha Egypt Nile University Computer Science Giza Egypt

ISBN: (纸本)9798350378511

Breast cancer is one of the most prevalent forms of cancer worldwide and a leading cause of mortality among women. Early detection of breast cancer is crucial for effective treatment. Architectural distortion (AD) is an early sign of breast cancer, characterized by a subtle contraction of breast tissue that often goes unnoticed. Traditional methods of detection heavily rely on the expertise of radiologists, making the process more difficult. To address this, we propose a deep learning approach to automate precise and efficient AD segmentation. Our approach involves a two-step process. In the first step, we utilize a Mask R-CNN Detectron2 model to perform AD segmentation across the entire set of mammography images. This initial segmentation provides a baseline for identifying AD regions. The ResNet-18 patch model is incorporated into the Mask R-CNN model's segmentation pipeline in the second stage. The purpose of this combination is to improve AD area localization and precision. By combining the strengths of both models, we achieve improved accuracy in AD segmentation. The evaluation of our fully automated method yielded remarkable outcomes on a diverse test set consisting of private datasets, including the Baheya dataset and the NCI dataset, as well as the publicly available Digital Database for Screening Mammography (DDSM). The results showed a Segmentation Accuracy of 0.852, Classification Accuracy of 0.915, and Mean Average Precision (mAP) of 0.894. These findings demonstrate potential to enhance the efficiency and accuracy of AD detection and segmentation in mammogram images, contributing to early diagnosis and treatment planning for patients at risk of breast cancer. © 2024 IEEE.

关键词： Mammography

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Isolation Enhancement in a Compact Four-Element MIMO Antenna for Ultra-Wideband Applications

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computers, Materials & Continua 2023年第4期75卷 911-925页

作者： Awais Khan Shahid Bashir Salman Ghafoor Hatem Rmili Jawad Mirza Ammar Ahmad Department of Electrical Engineering University of Engineering and TechnologyPeshawar25000Pakistan Department of Electrical Engineering University of Science and TechnologyBannu28100Pakistan School of Electrical Engineering and Computer Science(SEECS) National University of Sciences and Technology(NUST)Islamabad44000Pakistan Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah21589Saudi Arabia SEECS Photonic Research Group National University of Sciences and TechnologyIslamabad44000Pakistan

Mutual coupling reduction or isolation enhancement in antenna arrays is an important area of research as it severely affects the performance of an *** this paper,a new type of compact and highly isolated Multiple-Input-Multiple-Output(MIMO)antenna for ultra-wideband(UWB)applications is *** design consists of four radiators that are orthogonally positioned and confined to a compact 40×40×0.8 mm3 *** final antenna design uses an inverted L shape partial ground to produce an acceptable reflection coefficient(S11<−10 dB)in an entire UWB band(3.1–10.6)giga hertz(GHz).Moreover,the inter-element isolation has also been enhanced to>20 db for majority of the UWB *** antenna was fabricated and tested with the vector network analyzer(VNA)and in an anechoic chamber for scattering parameters and radiation ***,different MIMO diversity performance metrics are also measured to validate the proposed *** simulation results and the experimental results from the constructed model agree quite *** proposed antenna is compared with similar designs in recently published literature for various performance *** of its low envelope correlation coefficient(ECC<0.1),high diversity gain(DG>9.99 dB),peak gain of 4.6 dB,reduced channel capacity loss(CCL<0.4 b/s/Hz),and average radiation efficiency of over 85%,the proposed MIMO antenna is ideally suited for practical UWB applications.

关键词： Multiple input multiple output antennas envelope correlation coefficient defected ground structure ultra wide band diversity gain

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A Probabilistic Approach for Scheduling of Wind-Energy-Integrated Power Systems Incorporating Dynamic Lines and Transformers Rating 8

A Probabilistic Approach for Scheduling of Wind-Energy-Integ...

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8th International Conference on Technology and Energy Management, ICTEM 2023

作者： Akhlaghi, Mohammad Moravej, Zahra Bagheri, Amir Semnan University Faculty of Electrical and Computer Engineering Semnan Iran University of Zanjan Faculty of Engineeing Department of Electrical Engineering Zanjan Iran

ISBN: (纸本)9781665452854

In today's electric power systems, transmission congestion and integration of renewable-based power generations are challenging issues. To tackle with these problems, power system operators can make use of smart-grid technologies. Dynamic transformer rating (DTR) and dynamic line rating (DLR) are employed as two smart-grid technologies for stochastic day-Ahead optimal power flow (OPF) under uncertainty condition. The uncertainty scenarios are generated using Monte-Carlo simulation where the K-means clustering algorithm is employed for the scenario reduction purpose. A convexified version of AC power flow and dynamic rating relations are employed to formulate the optimization problem as a mixed-integer quadratically-constrained programming (MIQCP). The conducted approach is applied to the IEEE-24-bus network in GAMS, and the simulation results are investigated to validate efficiency of the proposed method. The simulations show that the wind power curtailment and load shedding becomes zero in the case of utilizing dynamic ratings. Also, a total cost reduction of 41% is observed by the aid of DTR and DLR. © 2023 IEEE.

关键词： Wind power

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A Miniaturized Slotted Patch Antenna for Medical Implant Applications 23

A Miniaturized Slotted Patch Antenna for Medical Implant App...

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13th International Conference on Biomedical engineering and Technology, ICBET 2023

作者： Leelatien, Pongphan Department of Electrical and Computer Engineering Faculty of Engineering Thammasat School of Engineering Thammasat University Thailand

ISBN: (纸本)9798400707438

This paper presents a novel miniaturized implantable antenna operating in the industrial, scientific, and medical band of 2.4-2.48 GHz for medical implant applications. A biocompatible material, Rogers 3210 with a dielectric constant (ϵr) = 10.2 and a loss tangent (tan δ) = 0.0023 and a thickness of 0.635 mm is utilized as both the substrate and superstrate. The antenna has a compact volume of 10 × 10 × 1.27 mm3. The miniaturization was realized by inserting slots of different shapes onto the radiating patch. The simulated results show an antenna bandwidth of 16.3 % (2.27-2.67 GHz) and a maximum peak gain of -22.8 dBi. Furthermore, the specific absorption rate (SAR) values are confirmed to comply with the health safety regulations and meet the IEEE safety standard. The results suggest that the presented antenna is a capable candidate for implant medical devices. © 2023 ACM.

关键词： Microstrip antennas

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