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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Cao, Alvin Christofferson, Ken Ruth, Parker Rabbani, Naveed Shi, Yuanchun Mariakakis, Alex Wang, Yuntao Patel, Shwetak University of Washington Department of Electrical and Computer Engineering United States University of Toronto Department of Computer Science Canada Stanford University Computer Science Department United States Harvard Medical School Department of Pediatrics United States Tsinghua University Department of Computer Science and Technology China

ISBN: (纸本)9798350371499

Cardiac auscultation is often impractical in telehealth settings because it requires that physicians be co-located with patients in order to operate a stethoscope. We address this gap with EarSteth - a system that leverages consumer-grade active noise-cancelling earbuds to reconstruct cardiac auscultation audio signals. The system processes audio captured by the earbuds' inner microphone with a machine learning model that reconstructs audio similar to what would be produced by a digital stethoscope during cardiac auscultation. We evaluate two existing audio super-resolution CNNs and further adapt them for heart sound reconstruction, resulting in a proposed model called EarStethNet. EarSteth models were trained using synchronous audio collected from 15 healthy adult participants with an earbud and a digital stethoscope. We found that EarStethNet was able to estimate interbeat interval with a mean absolute error of 36.6 ± 51.1 ms and was able to reconstruct cardiac auscultation audio with a mean log spectral distance of 1.22 dB. © 2024 IEEE.

关键词： Image analysis

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Stable switching behavior of low-temperature ZrO_(2)RRAM devices realized by combustion synthesis-assisted photopatterning

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Journal of Materials science & Technology 2024年第22期189卷 68-76页

作者： Bongho Jang Junil Kim Jieun Lee Jaewon Jang Hyuk-Jun Kwon Department of Electrical Engineering and Computer Science DGISTDaegu 42988Korea School of Electronic and Electrical Engineering Kyungpook National UniversityDaegu 41566Korea School of Electronics Engineering Kyungpook National UniversityDaegu 41566Korea Convergence Research Advanced Centre for Olfaction DGISTDaegu 42988Korea

We have realized efficient photopatterning and high-quality ZrO_(2)films through combustion synthesis and manufactured resistive random access memory(RRAM)devices with excellent switching stability at low temperatures(250℃)using these *** synthesis reduces the energy required for oxide conversion,thus accelerating the decomposition of organic ligands in the UV-exposed area,and promoting the formation of metal-oxygen networks,contributing to *** analysis confirmed a reduction in the conversion temperature of combustion precursors,and the prepared combustion ZrO_(2)films exhibited a high proportion of metal-oxygen bonding that constitutes the oxide lattice,along with an amorphous ***,the synergistic effect of combustion synthesis and UV/O_(3)-assisted photochemical activation resulted in patterned ZrO_(2)films forming even more complete metal-oxygen *** devices fabricated with patterned ZrO_(2)films using combustion synthesis exhibited excellent switching characteristics,including a narrow resistance distribution,endurance of 103 cycles,and retention for 105 s at 85℃,despite low-temperature *** synthesis not only enables the formation of high-quality metal oxide films with low external energy but also facilitates improved photopatterning.

关键词： ZrO_(2) Combustion Sol-gel RRAM Patterning

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Improving Power Response of Single-Controllable VSG-Based Active Distribution Network

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CSEE Journal of Power and Energy Systems 2025年第1期11卷 102-114页

作者： Shah Fahad Arman Goudarzi Pierluigi Siano Ji Xiang Department of Electrical and Computer Engineering Missouri University of Science and TechnologyUSA University of Victoria Canada Department of Management&Innovation Systems University of SalernoItaly Department of Electrical and Electronic Engineering Science University of JohannesburgJohannesburg2006South Africa National Laboratory of Industrial Control Technology College of Electrical EngineeringZhejiang UniversityHangzhou310058China

Controlling an active distribution network(ADN)from a single PCC has been advantageous for improving the performance of coordinated Intermittent RESs(IRESs).Recent studies have proposed a constant PQ regulation approach at the PCC of ADNs using coordination of non-MPPT based ***,due to the intermittent nature of DGs coupled with PCC through uni-directional broadcast communication,the PCC becomes vulnerable to transient *** address this challenge,this study first presents a detailed mathematical model of an ADN from the perspective of PCC regulation to realize rigidness of PCC against ***,an H_(∞)controller is formulated and employed to achieve optimal performance against disturbances,consequently,ensuring the least oscillations during transients at ***,an eigenvalue analysis is presented to analyze convergence speed limitations of the newly derived system ***,simulation results show the proposed method offers superior performance as compared to the state-of-the-art methods.

关键词： Cooperative control of DGs hierarchical control system high penetration of intermittent RESs H_(∞)control optimal control in power systems single-controllable active distribution networks

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Improved Coronary Artery Disease (CAD) Prediction using Machine Learning Method with Ensemble Technique 4

Improved Coronary Artery Disease (CAD) Prediction using Mach...

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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Mehmood, Waqas Khaliq, Abdul Qamar, Zoha Qudsia, Mammona Raza, Syed Saqib Capital University of Science & Technology Computer Science Department Islamabad Pakistan Capital University of Science & Technology Electrical Engineering Department Islamabad Pakistan Institute of Space Technology Electrical Engineering Department Islamabad Pakistan National University of Science & Technology Computer Science Department Islamabad Pakistan

ISBN: (纸本)9798350391862

Predicting Coronary Artery Disease (CAD) presents a critical and intricate challenge within medical science. Late-stage detection of CAD can gravely affect cardiac and vascular health, often leading to obstructions in the veins and perilous conditions. The application of Machine Learning (ML) in assessing CAD risks is pivotal for the early identification and mitigation of these severe consequences. Our study introduces a voting classifier that integrates eight distinct classification algorithms, employing an ensemble method to refine the accuracy of CAD diagnosis. This approach incorporates supervised learning algorithms such as Logistic Regression, Naive Bayes Classifier, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Trees, Bagging, Boosting, with multi model approach to construct a more generalized and robust predictive framework. A comparative evaluation with existing methodologies was performed to ascertain effectiveness of our proposed model. The experimental findings reveals that our proposed technique markedly enhances diagnostic precision, achieving an accuracy rate of 99% by leveraging the benefits of multi model aggregation in ensemble learning. The high accuracy of our ensemble and more generalized model not only improves diagnostic precision but also holds potential for better patient outcomes by facilitating earlier intervention and treatment. © 2024 IEEE.

关键词： Risk assessment

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Handling Imbalanced Data Using a Cascade Model for Image-Based Human Action Recognition

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Journal of Computing science and engineering 2023年第4期17卷 207-215页

作者： Wahyono Suprapto Rezky, Adam Rokhman, Nur Jo, Kang-Hyun Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Department of Electrical Engineering University of Ulsan Ulsan Korea Republic of

Human action recognition plays a crucial role in intelligent monitoring systems, which are based on analyzing the possibility of anomalous events related to human behavior, such as theft, fights, and other incidents. However, by definition, anomalous events occur somewhat infrequently, thus leading to small and unbalanced data compared to data on other events. Such a data imbalance causes the human action recognition model to fail to produce optimal accuracy. To overcome the problem of imbalanced data, the typical methods used are oversampling and undersampling. However, these two methods are not considered to be very effective, because they cause the loss of a significant amount of information or deviations from reality. Therefore, the current paper proposes a cascade modeling strategy to address data imbalance problems, particularly in the case of human action recognition. The proposed strategy consists of several steps: preprocessing, feature extraction, modeling, and evaluation. The BAR dataset experiment found that the cascade model outperformed the other examined methods with an accuracy of 56.38%. However, there is still potential for further improvement through continued research. Copyright © 2023. The Korean Institute of Information Scientists and Engineers

关键词： Support vector machines

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Enhancing 5G NR Downlink Performance through Advanced Waveform Generation Techniques 3

Enhancing 5G NR Downlink Performance through Advanced Wavefo...

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3rd Odisha International Conference on electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Samanta, Indu Sekhar Rout, Pravat Kumar Joshi, Sukumar Mohanty, Sarthak Panda, Subhasis Department of Computer Science and Engineering Bhubaneswar India Department of Electrical and Electronics Engineering Bhubaneswar India Department of Electrical Engineering Bhubaneswar India Srinix College of Engineering Department of Electrical Engineering Balasore India

ISBN: (纸本)9798350354379

The 5G New Radio (NR) network rollout calls for innovative methods to satisfy the growing demand for low latency, large data speeds, and consistent connectivity. Focussing on integrating several channels and signals, numerology, and configuration objects inside MATLAB and Simulink environments, this work investigates creating and implementing complex downlink waveform-generating algorithms in 5G NR. The proposed approach optimizes the main components of waveform production and summarises them to improve general system performance. © 2024 IEEE.

关键词： MATLAB

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Differential Coaxial Coils for Frequency-Independent Deep Magnetic Focusing on Implantable Devices

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 3792-3801页

作者： Shah, Syed Ahson Ali Kim, Ye R. Kim, Yun-Su Jeong, Seog Y. Rim, Chun T. Department of Electrical Engineering and Computer Science Gwangju61005 Korea Republic of Samsung Electronics Company Ltd. Research and Development Campus Seoul06765 Korea Republic of

Magnetic field focusing in longitudinal direction has been a missing link for 3-D synthesized magnetic focusing (3D SMF). Deep magnetic focusing (DMF) by multiple coaxial coils, a sort of 3D SMF, is first proposed in this article, where magnetic field becomes maximum at the focal range in longitudinal direction. DMF is requisite for transcranial magnetic stimulation (TMS) and wireless power transfer (WPT). As the simplest DMF, a set of differential coils with opposite current direction is implemented. Optimum coil currents, coil radii, and coil distance are designed to provide with zero magnetic field at zero range and maximum magnetic field at a focal range. This DMF performance is found to be achieved with a penalty of peak magnetic field reduction. Experiments for the differential coils of diameters 10 and 5 cm with 3 cm gap showed 4 cm depth DMF within 6 cm diameter. Specific numbers of turns make the DMF be driven by only a single-voltage source, which is first adopted in SMF. The frequency versatile DMF characteristic is experimentally verified for 60 Hz and 40 kHz, where 27 mW is delivered to a focal point that is a demonstration for safer WPT of implantable devices. © 1963-2012 IEEE.

关键词： Inductive power transmission

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Comparative analysis of machine learning and ensemble approaches for hepatitis B prediction using data mining with synthetic minority oversampling technique

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Health and Technology 2024年第1期14卷 109-118页

作者： Alizargar, Azadeh Chang, Yang-Lang Tan, Tan-Hsu Liu, Tsung-Yu Department of Electrical Engineering College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei 10608 Taiwan Department of Multimedia and Game Science Lunghwa University of Science and Technology Taoyuan 333326 Taiwan

Purpose: Hepatitis B, caused by the Hepatitis B virus (HBV), can harm the liver without noticeable symptoms. Early detection is crucial to prevent transmission and enhance recovery. The main goal is to predict Hepatitis B through cost-effective lab test data, by utilizing machine learning. The primary focus is on evaluating the effectiveness of various algorithms in predicting the disease and their potential to enhance early diagnosis capabilities. Methods: Six distinct algorithms (Support Vector Machine, K-nearest Neighbors, Logistic Regression, decision tree, extreme gradient boosting, random forest) were employed alongside an ensemble model. Analysis involved two rounds: considering all features and key attributes. The Synthetic Minority Oversampling Technique (SMOTE) was employed for data imbalance. Various metrics, including the confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC) curve, area under the curve (AUC), and mean absolute error (MAE), were utilized to assess the efficacy of each predictive technique. The National Health and Nutrition Examination Survey (NHANES) dataset was employed. Results: The experimental results demonstrate that the ensemble model attained the highest accuracy (97%) and AUC (0.997) in comparison to existing models. The analysis revealed that specific crucial features possess substantial predictive significance within this model. Conclusion: The study underscores the potential of the ensemble model as a valuable tool for medical practitioners, leveraging cost-effective and readily obtainable laboratory test data to predict Hepatitis B with remarkable accuracy. By facilitating early diagnosis and intervention, this research presents a promising avenue to enhance patient outcomes in the context of Hepatitis B. © 2023, The Author(s) under exclusive licence to International Union for Physical and engineering sciences in Medicine (IUPESM).

关键词： Early detection Ensemble model Index terms— Hepatitis B Liver damage Machine learning SMOTE

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DSTL:A dual-step transfer learning-based prediction model for next-generation intelligent cellular networks

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Intelligent and Converged Networks 2025年第1期6卷 82-101页

作者： Waqar A.Aziz Iacovos I.Ioannou Marios Lestas Vasos Vassiliou Department of Computer Science University of CyprusNicosia 2109Cyprusand also with CYENS Centre of ExcellenceNicosia 1076Cyprus Department of Electrical Engineering Frederick UniversityNicosia 1036Cyprus

Traffic modeling and prediction are indispensable to future extensive data-driven automated intelligent cellular *** contributes to proactive and autonomic network control operations within cellular *** methodologies typically rely on established prediction models designed for univariate and multivariate time series ***,these approaches often demand a substantial volume of training data and extensive computational resources for prediction model *** this study,we introduce a dual-step transfer learning(DSTL)-based prediction model specifically designed for the prediction of multivariate spatio-temporal cellular *** technique involves the categorization of gNodeBs(gNBs)into distinct clusters based on their traffic pattern *** of training the prediction model individually on each gNB,a base model is trained on the aggregated dataset of all the gNBs within a base cluster using a combination of recurrent neural network(RNN)and bidirectional long-short term memory(RNN-BLSTM)*** the first-step transfer learning(TL),the base model is provided to the gNBs within the base cluster and to the other clusters,where it undergoes the process of fine-tuning the intra-cluster aggregated *** the model is trained on the aggregated dataset within each cluster,it is provided to the gNBs within the respective cluster in the second-step *** model received by each gNB through the proposed DSTL technique either necessitates minimal fine-tuning or,in some cases,requires no further *** conduct extensive experiments on a real-world Telecom Italia cellular traffic *** results demonstrate that the proposed DSTL-based prediction model achieves a mean absolute percentage error of 2.97%,9.85%,and 9.73%in predicting spatio-temporal Internet,calling,and messaging traffic,respectively,while utilizing less computational resources and requiring less training time than traditional model training and

关键词： dual-step transfer learning(DSTL) multivariate spatio-temporal cellular traffic prediction bidirectional long-short term memory(RNN-BLSTM)

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A Measurement Study of the Ethereum Underlying P2P Network

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computers, Materials & Continua 2024年第1期78卷 515-532页

作者： Mohammad ZMasoud Yousef Jaradat Ahmad Manasrah Mohammad Alia Khaled Suwais Sally Almanasra Electrical Engineering Department Al-Zaytoonah University of JordanAmman11733Jordan Mechanical Engineering Department Al-Zaytoonah University of JordanAmman11733Jordan Computer Science Department Al-Zaytoonah University of JordanAmman11733Jordan Faculty of Computer Studies Arab Open UniversityRiyadh11681Saudi Arabia

This work carried out a measurement study of the Ethereum Peer-to-Peer(P2P)network to gain a better understanding of the underlying *** was applied because it pioneered distributed applications,smart contracts,and ***,its application layer language“Solidity”is widely used in smart contracts across different public and private *** this end,we wrote a new Ethereum client based on Geth to collect Ethereum node ***,various web scrapers have been written to collect nodes’historical data fromthe Internet Archive and the Wayback Machine *** collected data has been compared with two other services that harvest the number of *** has collectedmore than 30% more than the other *** data trained a neural network model regarding time series to predict the number of online nodes in the *** findings show that there are less than 20% of the same nodes daily,indicating thatmost nodes in the network change *** poses a question of the stability of the ***,historical data shows that the top ten countries with Ethereum clients have not changed since *** popular operating system of the underlying nodes has shifted from Windows to Linux over time,increasing node *** results have also shown that the number of Middle East and North Africa(MENA)Ethereum nodes is neglected compared with nodes recorded from other *** opens the door for developing new mechanisms to encourage users from these regions to contribute to this ***,the model has been trained and demonstrated an accuracy of 92% in predicting the future number of nodes in the Ethereum network.

关键词： Ethereum measurement ethereum client neural network time series forecasting web-scarping wayback machine blockchain

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