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TexSense: Comparative ECG Analysis of E-Textile and Sticky Electrodes During Motion Artifacts

TexSense: Comparative ECG Analysis of E-Textile and Sticky E...

2024 International Conference on the Challenges, Opportunities, Innovations and Applications in Electronic Textiles, E-Textiles 2024

作者： Al Rumon, Md Abdullah Cerullo, Alexander Berube, Leah Ravichandran, Vignesh Shahriari, Yalda Solanki, Dhaval Mankodiya, Kunal University of Rhode Island Dept. of Electrical Computer and Biomedical Engineering KingstonRI United States

ISBN: (纸本)9783000804298

The rapid advancement of wearable technology is transforming health monitoring and driving demand for more comfortable, long-term ECG solutions. Traditional sticky or gel electrodes (SE), while highly accurate in stationary medical environments, often cause discomfort and skin irritation with prolonged adhesive use. Textile-based dry electrodes (TE) eliminate the need for conductive gel, offering a promising alternative for continuous ECG monitoring in dynamic conditions. However, the performance of textile electrodes in real-world settings remains largely unexplored, highlighting a critical research gap in understanding how dry electrodes compare to sticky electrodes when motion artifacts are present. This study investigated a comparative evaluation of gel-based sticky and textile dry electrodes during exercise-induced motion artifact tasks. We performed the study with 10 adult healthy participants and designed 16 distinctive motion artifact tasks that included reclining, sitting, walking, and running. The qualitative, and quantitative analysis illustrated the comparison of ECG signal, Heart Rate (HR) detection accuracy and error%, signal quality, SNR, Short-Time Fourier Transform (STFT), Kurtosis, Entropy, Continuous Wavelet Transform (CWT), and Bland-Altman analysis. The analysis showed textile electrodes achieved an HR detection accuracy of approximately 99% and an average Signal-to-Noise Ratio (SNR) of 15.8 dB. This performance approaches the 17.3 dB average of sticky electrodes, demonstrating strong resilience to noise in low-to-moderate motion tasks. The STFT analysis indicated reliable capture of essential QRS frequency components (5-15 Hz) by textile electrodes, though high-motion tasks produced slight deviations that may limit detailed QRS morphology analysis. The CWT analysis revealed the TE showed an average magnitude difference of 0.15 in low-motion tasks to 0.5 in high-motion tasks compared to SE, indicating increased noise sensitivity above 30 Hz. The

关键词： Electrotherapeutics

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Bispectrum Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types 46

Bispectrum Analysis of Noninvasive EEG Signals Discriminates...

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

作者： Ghafoori, Sima Rabiee, Ali Cetera, Anna Shahriari, Yalda Abiri, Reza University of Rhode Island Dept. of Electrical Computer and Biomedical Engineering KingstonRI United States

ISBN: (纸本)9798350371499

The bispectrum stands out as a revolutionary tool in frequency domain analysis, leaping the usual power spectrum by capturing crucial phase information between frequency components. In our innovative study, we have utilized the bispectrum to analyze and decode complex grasping movements, gathering EEG data from five human subjects. We put this data through its paces with three classifiers, focusing on both magnitude and phase-related features. The results highlight the bispectrum's incredible ability to delve into neural activity and differentiate between various grasping motions with the Support Vector Machine (SVM) classifier emerging as a standout performer. In binary classification, it achieved a remarkable 97% accuracy in identifying power grasp, and in the more complex multiclass tasks, it maintained an impressive 94.93% accuracy. This finding not only underscores the bispectrum's analytical strength but also showcases the SVM's exceptional capability in classification, opening new doors in our understanding of movement and neural dynamics. © 2024 IEEE.

关键词： Frequency domain analysis

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Preventing Actuator Saturation in Linear Quadratic Regulators 63

Preventing Actuator Saturation in Linear Quadratic Regulator...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Komaee, Arash Southern Illinois University School of Electrical Computer and Biomedical Engineering CarbondaleIL62901 United States

ISBN: (纸本)9798350316339

This paper presents a nonlinear state feedback law for regulation of linear systems with control constraints. Such constraints mathematically describe the physical limitations on the actuators driving these systems, usually known as actuator saturation. The proposed state feedback law resembles a linear quadratic regulator (LQR) with an adaptable gain, dynamically adjusted to prevent actuator saturation. This feedback law is computationally inexpensive for real-time implementation, and offers a viable alternative to the typically more complex control laws derived from a constrained LQR formulation. Simulation results are presented that demonstrate the effectiveness of the presented feedback law. © 2024 IEEE.

关键词： State feedback

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Machine Learning Approach for Detecting Potential Anomalous Cosmic Rays Particles Tracks in Earth-Scale Cosmic Ray Extremely Distributed Observatory 1

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Tyc, Jan Zub, Marcin Hachaj, Tomasz Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Krakow Al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (数字)9783031857034

ISBN: (纸本)9783031857027

In this paper, we present a novel contribution to the problem of the detection of potential anomalous cosmic rays particle tracks acquired by Earth-scale complex distributed observatory infrastructure. Detection of such signals provides an opportunity to analyze the performance of the entire research infrastructure of a complex system (for example, potential failures) and a chance to record unknown physical phenomena that can be recorded simultaneously with space radiation observations. The observatory comprises a worldwide network of CMOS detectors cooperating in the citizen science paradigm. We propose, evaluate, and compare several data processing, feature extractions, and potential anomaly discovery pipelines based on machine learning approaches. Although, to date, there have already been published works performing detection and analysis of anomalies in the Cosmic Ray Extremely Distributed Observatory Project (CREDO) dataset, they applied single methods without comparing the results with other possible approaches to analyze potential anomalies. In this work, for the first time, we have used and discussed many anomalous discovery approaches not previously applied to this issue to perform analysis of this extensive dataset (nearly 600,000 images). For this reason, the results published here are of significant cognitive importance to the problem under consideration. We have published both our research’s source codes and dataset so our results can be reproduced. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cosmic rays

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PWM-Based Hardware Authentication for Smart Inverters: Detecting Counterfeit Inverter through Transient Response Analysis

PWM-Based Hardware Authentication for Smart Inverters: Detec...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Hingu, Chanakya Fu, Xingang Mishan, Ramkrishna Kim, Taesic University of Nevada Reno Department of Electrical and Biomedical Engineering Reno United States University of Missouri-Columbia Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9798350376067

Amidst the growing complexity of inverter-based power systems, the security of smart inverters is becoming increasingly critical, particularly for protecting power electronics components from counterfeit threats. Typical methods of PCB hardware authentication utilize a signal injection to the PCB through a hardware interface such as JTAG, which might be impractical in smart inverters. This paper presents a cost-effective hardware authentication method using Pulse Width Modulation (PWM) and transient response to detect counterfeit solar inverters. By conducting a detailed analysis and comparing the transient responses from both inverters, this work reveals significant differences that can serve as analog signatures for authentication. The effectiveness of this validation method is demonstrated through an extensive statistical analysis, providing a solid basis for implementing this strategy as a safeguard against counterfeit penetration in power systems. Additionally, this method shows a potential tool for industry practitioners and researchers to verify, detect, and distinguish between counterfeit and genuine printed circuit boards, enhancing the security posture of power systems infrastructure. © 2024 IEEE.

关键词： Transient analysis

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A Comparison of Selected Agent-Based Modelling Frameworks 1

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Gacek, Paulina Bocheńczyk, Dominika Krajewski, Maciej Kruczek, Bartosz Lazarz, Krzysztof Magnuszewski, Pawel Zięciak, Antoni Kamiński, Filip Wąs, Jaroslaw Department of Applied Computer Science Faculty of Electrical Engineering Automatics IT and Biomedical Engineering AGH University of Krakow Mickiewicza 30 Krakow30-059 Poland

ISBN: (数字)9783031857034

ISBN: (纸本)9783031857027

In recent years, a variety of new frameworks streamlining the process of agent-based modeling has emerged. These frameworks serve different purposes and each offers a unique set of features. In this practical comparative study, we evaluate the performance of various ABM frameworks through a series of benchmark simulations. By comparing the distinct functionalities offered by these tools, we aim to assist in the selection of an appropriate ABM toolkit for developing system models. This review presents a concise overview of seven popular agent-based modeling tools aiming to inspire further exploration and investigation into this subject. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Digital elevation model

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Decentralized Federated Deep Learning Image Recognition Models 4

Decentralized Federated Deep Learning Image Recognition Mode...

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4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Kugan, Sharun Islam, Md Quyyum Ul Kashef, Rasha Toronto Metropolitan University Department of Electrical Computer Biomedical Engineering Toronto Canada

ISBN: (纸本)9798350348248

In the era of IoT, numerous frameworks and cutting-edge models have been introduced to enhance user experience and privacy and reduce the risk of data breaches. Over time, IoT device usage has grown tremendously, and a flood of data has been sent to servers for processing. Federated learning has been deployed for efficient decentralization while preserving privacy. Federated learning has been applied in various IoT-related applications such as image classification, object segmentation, object detection, and sensor analytics. Existing centralized image recognition models fall short of providing accurate image classification with acceptable processing time for real-time deployment while preserving privacy. In this paper, we designed two decentralized deep learning models using federated learning, the CNN-TFF and the VGG16-TFF. With around 250 training iterations, we achieved a high accuracy rate of up to 90% with a decrease in the loss value for the CIFAR-100 dataset using the VGG16-TFF model while maintaining data privacy using federated learning. © 2023 IEEE.

关键词： Image classification

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Assessing Biological Responses of Saccharomyces Cerevisiae to Low-Frequency Electromagnetic Fields: Insights and Implications 15

Assessing Biological Responses of Saccharomyces Cerevisiae t...

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15th International Conference Elektro, ELEKTRO 2024

作者： Bajtos, Marek Radil, Roman Janousek, Ladislav Dang, Nhat University of Zilina Faculty of Electrical Engineering and Information Technology Department of Electromagnetic and Biomedical Engineering Zilina Slovakia University of Colorado Boulder Department of Electrical Computer and Energy Engineering BoulderCO United States

ISBN: (纸本)9798350372359

This study investigates the biological effects of Low-Frequency Electromagnetic Fields (LF-EMF) on Saccharomyces cerevisiae yeast cells, an essential model organism in biological research. To achieve controlled exposure, the exposure system ensuring uniform exposition while maintaining identical cultivation conditions for both exposed and control samples is used. To evaluate the impact of LF-EMF, yeast cell growth and proliferation were quantified using a Bürker chamber before and after exposure. Our findings reveal statistically significant differences between the exposed and control samples, suggesting a potential biological response to LF-EMF. The present research offers valuable insights into the effects of LF-EMF on biological cells, contributing to the broader understanding of electromagnetic field's impact on living organisms. Furthermore, the proposed exposure system provides a reliable platform for future bioelectromagnetic research, facilitating health risk assessments and investigations into the therapeutic potential of LF-EMF exposure. © 2024 IEEE.

关键词： Electromagnetic fields

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A 3.97 μW 11.2b 500 kS/s Hybrid SAR ADC via Time-Mode Signal Processing 66

A 3.97 μW 11.2b 500 kS/s Hybrid SAR ADC via Time-Mode Signa...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Perczak, Ian Yuan, Fei Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering TorontoON Canada

ISBN: (纸本)9798350302103

This paper presents a 12b hybrid ADC consisting of a 8b successive approximation register (SAR) analog-to-digital converter (ADC) with top-plate sampling, a variable-slope voltage-to-time converter with built-in absolute-value function, and a 3-stage cyclic gated Vernier TDC. The proposed ADC features the ability to accommodate a large input without sacrificing linearity, low power consumption, and technology compatibility. A number of techniques are proposed to improve performance and power efficiency of the ADC. These techniques include a sub-threshold delay-locked loop with pulsed control signals for clock generation, clock kickback reduction to minimize kickback and mismatch induced sensitivity loss of the comparator of the SAR ADC, a gated cyclic Vernier TDC to minimize the resolution loss caused by the improper injection of time inputs and a virtually unlimited dynamic range, a self-resetting arbiter with zero metastability window to improve the resolution of Vernier TDC, and a significantly simplified synchronous SAR for a better power/area efficiency. The ADC is designed in a TSMC 130 nm 1.2 V CMOS technology with a reduced supply voltage of 0.8 V and analyzed using Spectre with BSIM3.3 device models. Simulation results show that at 500 kS/s, the ADC offers a SNDR of 69.04 dB and ENOB of 11.17 while consuming 3.70 μW, yielding a Walden FOM of 3.21 fJ/conv. © 2023 IEEE.

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