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IEEE Electron Device Letters 2025年第6期46卷 999-1002页

作者： Mao, Linna Ke, Yizhen Su, Hengjie Zhang, Xinyang Sun, Biao Ma, Kaixue Li, Ting Institute of Biomedical Engineering Chinese Academy of Medical Sciences Peking Union Medical College Tianjin300192 China University of Houston Department of Electrical and Computer Engineering HoustonTX77005 United States Tianjin University School of Electrical and Information Engineering Tianjin300072 China Tianjin University School of Microelectronics Tianjin300072 China

Flexible neuromorphic computing systems have emerged as a promising candidate for next-generation edge computing systems, especially for the development of lightweight, low-power spiking neural network (SNN) systems. However, the mechanical coupling noise caused by flexible components poses challenges in uncertainty handling during the inference process in SNNs. Herein, we developed a stretchable leaky integrate-and-fire neuron device with enhanced uncertainty handling capabilities for stochastic SNN hardware implementation. These neuron devices utilize strain-induced uncertainty to model neuronal function as a conditional random variable. In addition, the SNN was incorporated in a Bayesian approximation model when the firing patterns exhibit mean stationarity over time steps. Simulations on classification and regression tasks on various datasets performed improved confidence and high generalization capability. The results demonstrate that the stretchable LIF neurons have significant potential for constructing robust and accurate SNNs for flexible neuromorphic computing. © 1980-2012 IEEE.

关键词： Edge computing

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Tracking Blood Glucose Concentration of Type 1 Diabetic Patients Using Reinforcement Learning 9

Tracking Blood Glucose Concentration of Type 1 Diabetic Pati...

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9th International Conference on Control, Instrumentation and Automation, ICCIA 2023

作者： Sabzevar, Peyman Vafadoost Sadrian, Hamid Hajipour, Ahmad Hakim Sabzevari University Electrical and Computer Faculty Biomedical Engineering Department Sabzevar Iran

ISBN: (纸本)9798350366013

Blood glucose control in diabetic patients has become one of the main concerns of researchers due to its widespread growth in society in recent years. Additionally, during the COVID-19 pandemic, research indicates that individuals who have diabetes have a higher susceptibility to contracting COVID-19 and experiencing more severe complications. This is attributed to a compromised immune system, resulting in a greater number of fatalities compared to individuals without diabetes. Therefore, the desired controller for blood glucose regulation should be very stable against hypoglycemia and perturbations such as meals, noise, and uncertainty, such as the pancreas of a healthy person. The metabolic disorders that cause diabetes in people are covered first in this article. Then Reinforcement Learning will be introduced as one of the machine learning methods to control the blood glucose concentration, and its robustness in the presence of disturbance will be investigated. © 2023 IEEE.

关键词： Reinforcement learning

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Analysis of Social Media Data Using Deep Learning and NLP Method for potential use as Natural Disaster Management in Indonesia 2

Analysis of Social Media Data Using Deep Learning and NLP Me...

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2nd International Conference on computer System, Information Technology, and electrical engineering, COSITE 2023

作者： Nurdin, Nadhila Kluza, Krzysztof Fitria, Maya Saddami, Khairun Utami, Rika Sri Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Krakow Krakow Poland

ISBN: (纸本)9798350343069

During the last decade, the use of social media has exploded. One of the internet’s most popular and accessible social media sites is Twitter in which people can have micro-blog to post their views on any subject, called a tweet. Indonesia becomes one of country which has the most active user of social media. On the other side, Indonesia has experienced a great deal of natural disasters because it is located in a pacific ring of fire. It is needed effective disaster management used in Indonesia, especially with the help of social media data. Big data analysis obtained during turbulent and unorganized emergency situations is the perfect fit for effective management. During a disaster, it is vital to manage the proper decision to help people affected with their needs. This research investigated some methods used in social media analysis and then apply deep learning model for supervised tasks which classify the related and unrelated disasters on tweet datasets and Latent Dirichlet Allocation (LDA) for unsupervised learning which extracts the details category on the disaster Twitter data. The LSTM model architecture obtained a higher score accuracy than CNN. Moreover, the LDA topic modeling obtained potential results on the details topic from the datasets which could describe useful information to help disaster management. © 2023 IEEE.

关键词： Disasters

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Photoacoustic microscopy using four-wave mixing in a large mode-area fiber

Photoacoustic microscopy using four-wave mixing in a large m...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Buma, Takashi Union College Department of Electrical Computer and Biomedical Engineering SchenectadyNY United States

ISBN: (纸本)9798350346459

Many applications in photoacoustic microscopy (PAM) require laser pulses at multiple wavelengths. High repetition rate multi-wavelength lasers have been demonstrated using nonlinear fiber optics, which produce a wide wavelength range but tend to suffer from low pulse energy (e.g. 100 nJ). This paper presents our preliminary investigation into multimode fibers as a path to achieve higher output laser pulse energy for multi-spectral PAM. Laser pulses from a 532 nm pulsed Nd:YAG laser are coupled into a large mode area (LMA) fiber that supports six spatial modes at 532 nm. Phase-matched four-wave mixing (FWM) between fiber modes produces a broad continuum extending past 800 nm. The pulse energy at 550, 650, and 750 nm spectral bands is 0.84 uJ, 1.31 uJ, and 0.73 uJ, respectively. Multi-spectral imaging experiments successfully differentiate vessel phantoms containing Allura Red, methylene blue, and IR-820 dyes. These results show the potential of using intermodal FWM in a few-mode multimode fiber to achieve uJ-level pulse energies for multi-spectral PAM applications. © 2023 IEEE.

关键词： Four wave mixing

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Customizable Graphite-On-Paper-Based Keypads: Toward Disposable and Recyclable Wireless Human-Machine Interfaces

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2023年第1期2卷 25-33页

作者： Zulfiqar, Muhammad Hamza Hassan, Mahmood Ul Maqbool, Khawaja Qasim Zubair, Muhammad Mehmood, Muhammad Qasim Riaz, Kashif Massoud, Yehia MicroNano Laboratory Department of Electrical Engineering Lahore54600 Pakistan University of Engineering and Technology Lahore Department of Biomedical Engineering Lahore54890 Pakistan Bahria University Department of Computer Science Lahore Campus Islamabad44000 Pakistan Thuwal23955 Saudi Arabia

There is a rapid increase in the use of affordable electronic devices and human-machine interfaces (HMIs) with short serviceable life in almost every aspect of our lives. It is estimated that the waste electrical and electronic equipment (WEEE) and electronic waste (e-waste) generated in year 2021 was 57.5 million metric tons (Mt) and it is expected that the production of e-waste will increase to 110 Mt by the end of 2050. To mitigate these wastes, green HMIs are required which can be customized for multiple applications and can be recycled or disposed of with minimal environmental impact. This work presents customizable graphite-on-paper (GOP)-based keypad consists of interdigitated capacitive (IDC) touch sensors is demonstrated as HMIs to interact with different electronic and media applications wirelessly. The GOP keypads are fabricated through facile and green fabrication process by direct writing of graphite on flexible paper substrate. The GOP keypads can be fabricated in a home setting as the required materials are readily available, i.e., paper, pencils, and Arduino. The GOP keypads can be easily disposed of or recycled at the end of its life or requirement due to the employment of biodegradable materials like paper and graphite. The IDC touch sensors are optimized by analyzing the number of electrode fingers, finger's width, finger's overlap length, and spacing between the electrode fingers. The same GOP keypad is customized to interact with different electronic and media applications wirelessly, i.e., laptop cursor navigation, calculator app on mobile, numeric keypad, etc. The customizable GOP keypads have potential to be used as green wireless HMIs to enforce a circular economy by mitigating electronic and plastic waste, which leads to the vision of a sustainable and green world. © 2022 IEEE.

关键词： Substrates

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Three-dimensional ultrasound subwavelength arbitrary focusing with broadband sparse metalens

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Science China(Physics,Mechanics & Astronomy) 2022年第2期65卷 50-56页

作者： Xue Jiang JiaJie He ChuanXin Zhang HuaLiang Zhao WeiQi Wang DeAn Ta Cheng-Wei Qiu Center for Biomedical Engineering School of Information Science and TechnologyFudan UniversityShanghai 200433China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

Ultrasound focusing in three-dimensional(3 D)space is of crucial and enduring significance in a variety of biomedical and industrial *** ultrasound focusing based on active phase array or passive geometry of bulky size is unable to realize the 3 D arbitrary focusing with subwavelength *** metamaterial of complex deep-subwavelength microstructure has facilitated the advanced airborne-sound-focusing but is inevitably not applicable for underwater ultrasound,restricted by the law between the multi-modes coupling/thermal viscosity and the feature size of the ***,we aim to circumvent the restriction by increasing the feature size of the metamaterial while keeping the compact overall geometry,and realize the robust subwavelength ultrasound focusing with the sparse metalens of the wavelength-scale *** theoretically propose and demonstrate numerically and experimentally the broadband arbitrary ultrasound focusing in 3 D *** axial and off-axis ultrasound focusing with the subwavelength resolution(FWHM<0.58λ)are achieved by the spatially sparse and compact metalens within one-octave *** advantages of 3 D freewheeling focusing,subwavelength resolution,spatial sparsity,geometric simplicity,and broadband,the sparse metalens would offer more initiatives to advanced researches in ultrasound focusing and empower applications such as precise biomedical imaging and therapy,nondestructive evaluation,integrated and multiplexed ultrasound devices.

关键词： broadband ultrasound focusing sparse metalens subwavelength resolution three-dimensional focusing

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Fractal Dimension Analysis Demonstrates Overestimation and Underestimation of Time in EEG Signal 1

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13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Azari, Maryam Mollazadeh Sarbaz, Yashar Koohestani, Behrooz Farzamnia, Ali Modeling Biological System’s Laboratory Department of Biomedical Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Faculty of Engineering Universiti Malaysia Sabah Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Keeping track of time is regarded as an essential human behavior. The question of how the brain deals with temporal information remains a subject of scholarly debate. The current investigation aims to explore the mechanism underlying time perception by extracting fractal dimension from an electroencephalogram (EEG) signal and its frequency bands. To accomplish this, Higuchi’s fractal dimension was calculated for 42 healthy subjects’ electroencephalogram (EEG) signal and its sub-bands during the time perception task. The EEG signal was recorded from 19 channels. Subsequently, a statistical analysis was conducted to compare participants who underestimated versus those who overestimated the elapsed time and significantly different channels were presented. The findings suggest an elevated level of fractal dimensionality in persons who displayed a tendency to overestimate time. The EEG signal and Gamma rhythm emerged as the most distinguishing signals between the two cohorts. The contrast in fractal dimension between the two groups was predominantly apparent in the parietal and central channels. To summarize, an increased level of complexity is discernible in the EEG signal and high-frequency rhythms when there is an overestimation of temporal duration. It can be asserted that the employment of FD yields presents an exceptional approach to comprehending cerebral functionalities, notably temporal perception. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electroencephalography

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Brain Fingerprinting-Based Community Structure 22

Brain Fingerprinting-Based Community Structure

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22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Athamnah, Sema Aviyente, Selin Michigan State University Department of Biomedical Engineering Department of Electrical and Computer Engineering East LansingMI United States

ISBN: (纸本)9798331520526

The ability to uniquely characterize individual subjects based on their functional connectome (FC), i.e., fingerprinting, is key for progress toward precision medicine. Over the past few years, a variety of methods have been proposed for fingerprinting. While early approaches focused on using either the whole brain connec-tivity or pre-defined subnetwork connectivity to discriminate between subjects, more recently different feature extraction methods such as principal component analysis (PCA) have been used to ex-tract discriminative features. However, these methods do not iden-tify functionally interpretable network components, i.e., commu-nities, that contribute most to discrimination across subjects. In this paper, we introduce a fingerprinting approach based on community structure. In particular, we introduce a signed multilayer modularity optimization approach to identify the community structure across a group of subjects from resting state fMRI FCs. The optimal community structure for identifying subjects and discrimi-nating between different tasks is learned through a community in-dicator vector which quantifies the relevance of each community for a subject. The proposed framework is tested on the Midnight Scan Club (MSC) data across different tasks. High identification rates between resting state and different tasks indicate that an indi-vidual's community indicator vector is intrinsic to that individual independent of the task. The proposed framework also provides interpretability to fingerprinting by determining the most discrimi-native brain regions between task pairs. © 2025 IEEE.

关键词： community detection fingerprinting Functional connectivity multilayer graphs signed graph

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Performance Analysis of Heart Disease Detection Using Different Machine Learning Approaches 6

Performance Analysis of Heart Disease Detection Using Differ...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Tousif, Md. Noumil Rabbi Ushan, Md. Nahian Akhter, Rumana Mahmud, Sadia Rashid, Sakera Mohammad, Sakib University of Dhaka Department of Biomedical Physics and Technology Dhaka Bangladesh Ahsanullah University of Science and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh European University of Bangladesh Department of Electrical and Electronic Engineering Dhaka Bangladesh Southern Illinois University Carbondale Department of Electrical Computer and Biomedical Engineering CarbondaleIL United States

ISBN: (纸本)9798350385779

The leading cause of illness and mortality globally, heart disease is a serious global health problem. For successful prevention and intervention, timely and precise cardiac disease prediction is essential. Using the well-known Cleveland dataset from the UCI repository, we underwent an extensive performance examination of several ML approaches in the context of heart disease prediction in this study. The 303 cases and 14 features in this collection include essential clinical factors. The KNN, Random Forest, Naive Bayes, AdaBoost, Decision Tree, and Logistic Regression machine learning algorithms are among those being examined. Each is being assessed for its predictive efficacy. Our work reveals fascinating insights into algorithm performance through substantial data preparation, robust model training, and a thorough assessment utilizing measures like accuracy, sensitivity, specificity, and PPV, NPV. Notably, our results show that the KNN algorithm is the most effective at predicting heart disease by 93%. This finding has implications for improving clinical decision support systems and early heart disease detection, thereby lowering the prevalence of the condition and its negative effects on public health. This research highlights the crucial contribution of ML to the development of cardiovascular medicine and the significance of algorithm choice for predicting accuracy. © 2024 IEEE.

关键词： Decision trees

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A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 181-194页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering the Institute of Biomedical Engineering the Department of Electronics and Electrical Engineering the Institute of Pioneer Semiconductor Innovation and the Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capture complex relationships and patterns in large datasets. Therefore, in this paper a novel design scheme based on dynamically adaptive neural network (DANN) is proposed to develop fast and accurate compact model (CM). This framework constitutes a powerful yet computationally efficient methodology and exhibits emergent dynamic behaviors. This paper demonstrates that the compact model based on ML can be designed to replicate the performance of conventional compact model for nanodevices. For this work, gate-all-around (GAA) nanosheet (NS) device characteristics are comprehensively analyzed for process variability sources using the proposed model. The device geometry parameters such as channel length, nanosheet width and nanosheet thickness are fed as input features to the DANN model. The adaptive neural network learns dynamically by updating weights of the model in accordance with the input features and achieves accurate neural weight convergence. The proposed model predicted the electrical characteristics of NS devices with less than 1% error rate. The model is also implemented and validated for the simulations of digital circuit designs such as inverter, and logic gates. ©;2023 The Authors.

关键词： Circuit simulation

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