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International Conference on Innovations in Science, engineering and technology (ICISET)

作者： Md Salman Khan Mithil Md Abu Talha Shameem Ahmad Khandoker Mainul Islam Department of Electrical Engineering and Computer Science Electrical Engineering (MS) South Dakota School of Mines & Technology Rapid City SD United States Department of Electrical and Electronic Engineering Faculty of Engineering American International University–Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

This paper details developing and implementing a comprehensive power monitoring system, designed for real-time data acquisition and fault detection. Utilizing a cRIO 9039 platform, the system measures voltage and current, calculates RMS values, frequency, and complex power, and identifies fault conditions in voltage levels, frequency, or power factor. A unique aspect of our approach integrates FPGA-based data processing, ensuring high-speed data handling and error detection without interrupting primary data flow. The system also features a user-friendly interface for real-time data display and fault management. If any errors have been detected, the user will be notified using visual and audio warning and the reading and timestamp corresponding to the fault will be recorded. The system accurately monitors power metrics and detects deviations, enhancing industrial power management systems. The architecture, workflow, and user interface design align with industrial standards, offering practical insights into power system monitoring.

关键词： Visualization Voltage measurement Power measurement Fault detection Power system management User interfaces Real-time systems Frequency measurement Monitoring Field programmable gate arrays

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Stacking Classifier with Random Forest functioning as a Meta Classifier for Diabetes Diseases Classification 26

Stacking Classifier with Random Forest functioning as a Meta...

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26th International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Ali, Maria Haider, Muhammad Nasim Lashari, Saima Anwar Sharif, Wareesa Khan, Abdullah Ramli, Dzati Athiar Institute of Computer Science and Information Technology The University of Agriculture Peshawar Pakistan College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Faculty of Computing The Islamia University of Bahawalpur Pakistan School of Electrical and Electronic Engineering University Sains Malaysia USM Engineering Campus Pulau Pinang Nibong Tebal14300 Malaysia

Diabetes has been an offensive condition in recent years, and it can lead to major health problems. If diabetes is not addressed, it can lead to a variety of health problems, including heart disease, stroke, blindness, and kidney failure. Diabetes condition must be addressed promptly to avoid a significant health risk. Machine learning algorithms can assist the doctor in identifying and diagnosing diabetes and other diseases. Different types of classifiers have been used to diagnose diabetes. To improve the performance of integrated flexible individual classifiers and lower the possibility of misclassifying a single instance, an ensemble approach named "Stacking Classifier" was developed. Several classifiers, such as Naïve Bayes, KNN, Linear regression, and decision tree (DT) were used but all these models have low accuracy. However, additional study is needed to detect diabetic condition due to a lack of major work and low accuracy. Therefore this study proposed an ensemble technique termed "Stacking Classifier" was designed to increase the performance of integrated flexible individual classifiers and reduce the probability of misclassifying a single instance. This study uses a variety of classifiers, including Naïve Bayes, KNN, Linear Discriminant Analysis, and Decision Tree, with Random Forest functioning as a Meta classifier. In terms of F-measure, Recall, Accuracy, and Precision, the proposed stacking classifier achieves a higher accuracy of 97.35 % when compared to current models such as Nave Naïve Bayes, KNN, Decision Tree, and Linear Discriminant Analysis, which are 74.60 %, 78.57 %, and 77.35 %, respectively. © 2022 The Authors. Published by Elsevier B.V.

关键词： Decision trees

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Optimal Design to Dual-Scale Channel Estimation for Sensing-Assisted Communication Systems

arXiv

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arXiv 2024年

作者： Bai, Zhiyue Hou, Fen Cai, Lin X. Shan, Hangguan State Key Laboratory of IOTSC Department of ECE Faculty of Science and Technology University of Macau China Department of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago United States

Sensing-assisted communication is critical to enhance the system efficiency in integrated sensing and communication (ISAC) systems. However, most existing literature focuses on large-scale channel sensing, without considering the impacts of small-scale channel aging. In this paper, we investigate a dual-scale channel estimation framework for sensing-assisted communication, where both large-scale channel sensing and small-scale channel aging are considered. By modeling the channel aging effect with block fading and incorporating CRB (Cramér-Rao bound)-based sensing errors, we optimize both the time duration of large-scale detection and the frequency of small-scale update within each subframe to maximize the achievable rate while satisfying sensing requirements. Since the formulated optimization problem is non-convex, we propose a two-dimensional search-based optimization algorithm to obtain the optimal solution. Simulation results demonstrate the superiority of our proposed optimal design over three counterparts. Copyright © 2024, The Authors. All rights reserved.

关键词： Cramer-Rao bounds

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An Iterative Clipping and Filtering Algorithm for PAPR Reduction in OFDM System

An Iterative Clipping and Filtering Algorithm for PAPR Reduc...

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Microelectronics (IICM), Iranian International Conference on

作者： Abbas Nasri Mostafa Katebi Sirous Toofan Mohammad Mostafavi Department of Electronic Faculty of Engineering University of Zanjan Zanjan Iran Department of Electronic Faculty of Engineering Iran University of Science and Technology Tehran Iran Faculty of Electrical and Computer Engineering University of Tariz Tabriz Iran

Peak-to-average power ratio (PAPR) one of the most important issues in orthogonal frequency division multiplexing (OFDM). Iterative clipping and filtering (CF) is a high efficiency technique to reduce PAPR in OFDM systems, but it needs numerous iterations to minimize the peak regrowth problem. This paper presents a new CF method which requires one iteration. The previous method needs 2K+1 IFFT/FFT blocks where K is iterations number. By using this algorithm, the number of FFT and IFFT operations have been decreased to three. The simulated results show that the QPSK modulated OFDM signal with 528 sub-carriers achieves a P APR reduction of 6.1 dB by passing the OFDM signal from white Gaussian noise channel with a good bit error rate performance at the receiver.

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Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

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computer Systems Science & engineering 2023年第12期47卷 2791-2814页

作者： Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering the Islamic UniversityNajafIraq

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and *** rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within *** the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information *** paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)***,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant *** by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for ***,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute *** propose a COA-based feature selection approach for medical data classification using machine learning *** approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature *** evaluate the performance of our approach on various medical datasets employing diverse machine learning *** results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification *** study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various *** provide further validation,we conduct detailed experiments on a benchmark

关键词： Association rule mining data classification healthcare data machine learning parameter tuning data mining feature selection MLARMC-HDMS COA stochastic gradient descent Apriori algorithm

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Energy management system based on wireless sensor networks and power line communications

Energy management system based on wireless sensor networks a...

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2022 International Conference Automatics and Informatics, ICAI 2022

作者： Omar, Azhar-Husain Malekian, Reza Bogatinoska, Dijana Capeska University of Pretoria Department of Electrical Electronic and Computer Engineering Pretoria082 South Africa Malmö University Department of Computer Science and Media Technology Malmö205 06 Sweden Mach. Intell. and Robotics University of Information Science and Technology St. Paul the Apostle Faculty of Applied IT Ohrid Macedonia

ISBN: (数字)9781665476256

ISBN: (纸本)9781665476256

In this paper, we developed a power line communication (PLC) system design, power measurement sensor design, light sensor design, temperature sensor design, and the integration of these components into an advanced sensor network to allow for energy metering and environment monitoring. A power measurement sensor was implemented through a current and voltage sensing circuitry was interfaced multi-plug power adapter to allow for non-invasive measurement of power usage of appliances. The sensors produce signals corresponding to the drawn voltage and current, which are then sampled and processed to estimate power usage. The PLC communications operated at an average accuracy of 95%. The power measurement sensor had an accuracy of 92%, which made it appropriate for home user estimations. The light sensor had an accuracy of between 91-97%, which was suitable for home lighting measurement. © 2022 IEEE.

关键词： Wireless sensor networks

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Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification

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computers, Materials & Continua 2023年第12期77卷 2813-2831页

作者： Sahar Arooj Muhammad Farhan Khan Tariq Shahzad Muhammad Adnan Khan Muhammad Umar Nasir Muhammad Zubair Atta-ur-Rahman Khmaies Ouahada Faculty of Computing Riphah International UniversityIslamabad45000Pakistan Department of Forensic Sciences University of Health SciencesLahore54000Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O.Box 524Johannesburg2006South Africa School of Computing Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Faculty of Artificial Intelligence&SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science Bahria UniversityLahore CampusLahore54000Pakistan Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health *** is the leading reason of death affecting females between the ages of 20 to 59 around the *** detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer *** cancer tumor develops when cells grow improperly and attack the healthy tissue in the human *** are classified as benign or malignant,and the absence of cancer in the breast is considered *** learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like *** research assists in the identification and classification of *** implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative *** used 3 datasets,A,B,and *** fuzzed these datasets and got 2 datasets,A2C and *** A2C is the fusion of A,B,and C with 2 classes categorized as benign and *** B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and *** used customized AlexNet according to our datasets and BCIC in our proposed *** achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning *** proposed fuzzed dataset model using transfer *** fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.

关键词： Breast cancer classification deep learning machine learning transfer learning learning rate

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Dynamic-Matrix-Encryption Based Secure Strong PUF for Device Authentication Protocols

Dynamic-Matrix-Encryption Based Secure Strong PUF for Device...

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International Conference on Solid-State and Integrated Circuit technology

作者： Liangxiao Zhao Gang Li Pengjun Wang Xuejiao Ma Ziyu Zhou The College of Electrical and Electronic Engineering Wenzhou University Wenzhou China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (数字)9798350361834

ISBN: (纸本)9798350361841

Strong physical unsolvable function (PUF) has a wide range of applications in internet of things (IoT) security. However, it is vulnerable to machine learning (ML) modeling attacks. This paper proposes a strong PUF anti-ML attack method based on dynamic-matrix encryption. It obfuscates the challenge by multiplying the dynamic encryption matrix (DEM) with the challenge matrix synchronizing initial values during the registration phase of the device security authentication protocol prevents our obfuscation from being invalidated when the algorithms and structures motivating the obfuscation are made public. Each round of DEM is updated with respect to each previous round of challenge which is similar to the properties of sequential logic circuits in that the current response does not depend only on the current challenge but is related to previous challenge. Experimental results show that even if an attacker collects 1 million challenge response pairs, the prediction accuracies of the several ML attacks we use are below 53%, with negligible effects on randomness, reliability, and uniqueness.

关键词： Support vector machines Protocols Accuracy Heuristic algorithms Authentication Encryption Sequential circuits Internet of Things Synchronization Integrated circuit modeling

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Slots Effect on CPW Pentagonal Patch Antenna for 3.5 GHz 5G and 5.9 GHz DSRC Applications

Slots Effect on CPW Pentagonal Patch Antenna for 3.5 GHz 5G ...

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International Symposium on Antennas and Propagation (ISAP)

作者： Amier Hafizun Ab Rashid Badrul Hisham Ahmad Nornikman Hassan Saidatul Norlyna Azemi Centre of Telecommunication Research and Innovation Faculty of Electronics and Computer Technology and Engineering Universiti Teknikal Malaysia Melaka (UTeM) Durian Tunggal Melaka Malaysia Kolej Komuniti Segamat Johor Segamat Johor Malaysia Advanced Communication Engineering Center of Excellence Faculty of Electronic Engineering & Technology Universiti Malaysia Perlis Arau Perlis Malaysia

ISBN: (数字)9798350364774

ISBN: (纸本)9798350364781

This research investigates the effects of two different slots on the CPW pentagonal patch antenna for 5G and Dedicated short-range communications (DSRC). Initially, first antenna design, Antenna A is pentagonal patch antenna with pentagonal slots while the second antenna design B is constructed by a pentagonal patch antenna with mini multiple slots structure. This Antenna A performs best at 3.32 GHz and 5.77 GHz with - 20.83 dB and - 49.56 dB, respectively. Besised that, the Antenna B perfoms at 3.50 GHz and 5.92 GHz with - 46.13 dB and - 20.09 dB, respectively.

关键词： Slot antennas 5G mobile communication Patch antennas Design methodology Coplanar waveguides Dedicated short range communication Gain Antenna radiation patterns

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Solving Transmission-Line (TL) Problems Using Simple Coupled Integral Equations and the Method of Moments (MM) for Undergraduate Education

Solving Transmission-Line (TL) Problems Using Simple Coupled...

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2021 Research, Invention, and Innovation Congress: Innovation Electricals and electronics, RI2C 2021

作者： Jongsuebchoke, Itthinat Torrungrueng, Danai Kittiwittayapong, Sangwon Akkaraekthalin, Prayoot Asian University Faculty of Engineering and Technology Department of Electrical and Electronic Engineering Thailand Department of Teacher Training in Electrical Engineering Faculty of Technical Education Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9781665403009

Integral equations (IEs) have been commonly used in solving electromagnetic problems. Thus, it is important to expose undergraduate students to IEs by illustrating with familiar, simple and intuitive examples. Using uncoupled IEs to solve transmission line (TL) problems as an example for undergraduate students was proposed in the literature, but a mathematical complexity still remains, which should be avoided for the introductory course on IEs. In this paper, solving TL problems using coupled IEs and the method of moments (MM) is proposed instead to further reduce the mathematical complexity and gain better understanding of the coupled nature of associated voltage and current distributed along TLs. Several numerical examples for different load terminations are given to illustrate that the coupled IEs with the MM can provide accurate results indeed. © 2021 IEEE.

关键词： Electric lines

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