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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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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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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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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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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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Latency and Cost Minimization for Task Offloading with Energy Harvesting in UAV-MEC Network

Latency and Cost Minimization for Task Offloading with Energ...

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2023 IEEE Symposium on Wireless Technology and Applications, ISWTA 2023

作者： Basharat, Mehak Naeem, Muhammad Anpalagan, Alagan Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering TorontoON Canada Comsats University Islamabad Department of Electrical Engineering Wah Campus Pakistan

ISBN: (纸本)9798350310955

Unmanned aerial vehicles (UAVs) are recently considered for various Internet of things (IoT) applications. Furthermore, mobile edge computing offers low-latency data computation and improves network performance by bringing computing resources closer to IoT devices. UAVs equipped with MEC capabilities are expected to play a critical role in providing reliable and efficient communication in future wireless networks. The low-power IoT devices can harvest energy for sustainable operations in different applications such as public safety. Resource management and efficient task offloading are the major challenges to take full advantage of UAV-MEC networks. In this paper, we formulate an optimization problem to minimize latency and cost as well as maximize the number of IoT devices connected with UAVs. We consider constraints on computation, caching, energy harvested, latency, and the maximum number of connections a given UAV allows. A heuristic with learning placement (HLP) algorithm is proposed based on an unsupervised learning algorithm and an iterative rounding algorithm to solve the problem with less complexity. Simulation results show the effectiveness of the proposed HLP algorithm in UAV-MEC networks compared with the branch and bound algorithm (optimal results). © 2023 IEEE.

关键词： Energy harvesting

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Image-to-Image Translation with Deep Neural Networks for the Enhancement of Monostatic Synthetic-Aperture Ultrasound Images

Image-to-Image Translation with Deep Neural Networks for the...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Bosco, Edoardo Stellino, Chiara Cotogni, Marco Ramalli, Alessandro Cusano, Claudio Matrone, Giulia University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy University of Florence Department of Information Engineering Florence Italy

ISBN: (纸本)9798350346459

In synthetic aperture (SA) ultrasound imaging, whether monostatic or multistatic, all elements of the transducer array are used in turn one-by-one during the transmission phase. However, during reception, the monostatic technique activates only the same element used in transmission, whereas the multistatic one employs the entire transducer array. The monostatic approach thus represents a potential solution for the implementation of an ultra-portable device, given that it would require a single-channel system to work. However, a downside would be a significant loss in terms of image contrast and signal-to-noise ratio caused by single-element transmission/reception. In a previous investigation, we introduced a deep neural network (DNN) for reconstructing high-quality images from radio-frequency (RF) signals acquired using a monostatic SA approach. Differently, in the current study, we propose a similar DNN to perform contrast enhancement of the images obtained from the same RF signals but through delay-and-sum (DAS) beamforming, addressing an image-to-image translation task. The dataset used for training the network consisted of pairs of monostatic/multistatic SA images of elliptical targets simulated in Field II. We also evaluated the effects of training the DNN in an adversarial framework and we compared the results in terms of contrast on a test set comprising both simulated and experimental scans. The results show that the DNN effectively enhances the contrast between the targets and the surrounding background. Additionally, the adversarial training introduces an improvement in speckle pattern and fine-grained details reproduction within the output images. © 2023 IEEE.

关键词： Image enhancement

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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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Vibrational Nanoscopy of Intracellular Compartments via Visible Fourier Reweighted Stimulated Raman Scattering

Vibrational Nanoscopy of Intracellular Compartments via Visi...

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2023 Computational Optical Sensing and Imaging, COSI 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： Lin, Haonan Tan, Yuying Cheng, Ji-Xin Department of Biomedical Engineering United States Department of Electrical & Computer Engineering Boston University BostonMA02215 United States

We present visible Fourier-reweighted stimulated Raman scattering (VFR-SRS), a vibrational imaging scheme that achieves sub-100 nm resolution by computationally amplifying high spatial frequency components, enabling l... 详细信息

ISBN: (纸本)9781957171289

关键词： Stimulated Raman scattering

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