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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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Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform 46

Classification of Emerging Neural Activity from Planning to ...

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

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

ISBN: (纸本)9798350371499

There have been different reports of developing Brain-computer Interface (BCI) platforms to investigate the noninvasive electroencephalography (EEG) signals associated with plan-to-grasp tasks in humans. However, these reports were unable to clearly show evidence of emerging neural activity from the planning (observation) phase - dominated by the vision cortices - to grasp execution - dominated by the motor cortices. In this study, we developed a novel vision-based-grasping BCI platform that distinguishes different grip types (power and precision) through the phases of plan-to-grasp tasks using EEG signals. Using our platform and extracting features from Filter Bank Common Spatial Patterns (FBCSP), we show that frequency-band specific EEG contains discriminative spatial patterns present in both the observation and movement phases. Support Vector Machine (SVM) classification (power vs precision) yielded high accuracy percentages of 74% and 68% for the observation and movement phases in the alpha band, respectively. © 2024 IEEE.

关键词： Filter banks

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Traffic Collision Detection Using DenseNet

Traffic Collision Detection Using DenseNet

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2023 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2023

作者： Kaluza, Daniel Seiler, Marco Kashef, Rasha Toronto Metropolitan University Electrical Computer and Biomedical Engineering Faculty of Engineering and Architectural Science Toronto Canada

ISBN: (纸本)9798350323153

There has been a sharp rise in the number of fatalities and injuries caused by traffic accidents in urban areas. Cities often have video and image resources that can be analyzed manually using operators to address this problem. This paper introduces an automated collision detection system that utilizes publicly available images captured by Toronto's traffic camera system. The system is based on a Deep Learning model, specifically a DenseNet-161, employed to classify accidents and non-accidents. The results of this classification are then displayed on a graphical user interface. The primary aim of this study is to reduce medical response time and ultimately save lives by issuing automatic alerts. The proposed system has the potential to minimize the severity of accidents and decrease the number of fatalities by notifying emergency services once an accident is detected. © 2023 IEEE.

关键词： CCTV Collision detection Deep learning Internet of Things (IoT) machine learning Smart Cities traffic obfuscation

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Optimal Control of a Large Population of Interacting Agents with Partial Observations of a Lead Agent 63

Optimal Control of a Large Population of Interacting Agents ...

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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 considers a linear Gaussian state-space model to describe the dynamics of a system of many interacting, identical agents, and the partial observations of their aggregate state via the measurements of the state of a single lead agent. These partial observations are exploited by an optimal control law that modifies the collective dynamics of the agents, aimed to minimize a quadratic performance measure. The structure of this optimal control law is studied for finite number of agents, and its convergence to a limit that is optimal for infinitely many agents is examined. This limit is obtained as the solution to an optimal control problem stated for a finite-dimensional reduced order model representing infinitely many agents. © 2024 IEEE.

关键词： State space methods

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Deep Learning-Based Visibility Region Classification for Extra-Large Aperture Arrays

Deep Learning-Based Visibility Region Classification for Ext...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Hameed, Muhammad Zia Gunasinghe, Dulaj Amarasuriya, Gayan Southern Illinois University School of Electrical Computer and Biomedical Engineering CarbondaleIL62901 United States

ISBN: (纸本)9798350351255

Spatial non wide-sense stationarities cause partial visibility regions (VRs), and it is a unique propagation characteristic of emerging extra-large aperture arrays (ELAAs). Thus, classification of VRs is a necessity for accurate estimation of channels and efficient design of VR-aware precoders for ELAAs. In this paper, a deep learning framework is proposed to classify VRs in ELAAs. Our objective is to boost the accuracy of classifying VRs based on the uplink pilots received at the ELAAs. Consequently, we focus on guaranteeing user-fairness in the presence of wholly/partial VRs and improving the achievable rates by adopting VR-aware channel estimation and precoding. We propose a hybrid deep learning architecture comprising one dimensional convolutional neural networks and long-short term memory to classify VRs of each user at the ELAA. To achieve a higher accuracy, we generate a diverse dataset through Monte-Carlo simulations that captures numerous combinations of VRs at the ELAA. A transmit power allocation algorithm is also proposed to achieve a common downlink rate for all users irrespective of the different VRs, and its computational complexity is discussed. A set of numerical results is presented to evaluate the performance of our proposed framework. It is efficient and accurate in classifying VRs. Thus, it can be used to enhance the estimation accuracy of ELAA channels with VRs and thereby to design VR-aware precoders to boost spectral/energy efficiency of the next-generation wireless systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Noncontact Manipulation of Magnetic Particles by a Hexagonal Array of Electromagnets 50

Noncontact Manipulation of Magnetic Particles by a Hexagonal...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Hasan, M.D. Nazmul Madhusankha, Janaka Ekanayake, Lahiru Komaee, Arash School of Electrical Computer and Biomedical Engineering Southern Illinois University CarbondaleIL62901 United States

ISBN: (纸本)9781665464543

This paper reports the development of a magnetic manipulator that utilizes a hexagonal array of electromagnets for noncontact control of magnetic particles inside a planar, circular workspace. Relying on 6 independent electromagnets, this array provides 6 degrees of freedom for feedback control, which can be leveraged for planar manipulation of up to 3 magnetic particles simultaneously. The design procedure of the developed magnetic manipulator is briefly presented, emphasizing the design of its electromagnets and their voltage drivers. The proper operation of this magnetic manipulator under feedback control is verified by experiments. © 2024 IEEE.

关键词： Feedback control

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Statistical CSI-Based IRS-Aided Massive MIMO SWIPT Systems

Statistical CSI-Based IRS-Aided Massive MIMO SWIPT Systems

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Dassanayake, Janith Kavindu Gunasinghe, Dulaj Baduge, Gayan Amarasuriya Aruma Southern Illinois University School of Electrical Computer and Biomedical Engineering CarbondaleIL62901 United States

ISBN: (纸本)9798350351255

The phase-shift control of intelligent reflective surface (IRS)-aided massive multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) systems requires pilot-intensive instantaneous estimation of cascaded channels. As a remedy, in this paper, statistical composite channel state information (CSI)-based techniques for SWIPT in IRS-aided massive MIMO are explored. Two phase-shift/power optimization strategies are proposed to guarantee system-wide user-fairness in terms of the average harvested energy and achievable rates, while keeping the pilot overhead and computational complexity significantly lower than the current state-of-the-art counterparts. By considering a hybrid time-switching/power-splitting protocol with a non-linear energy harvesting model, the average harvested energy and achievable rates are quantified. This analysis considers spatially correlated fading, imperfectly estimated composite channels, and optimization of IRS phase-shifts and transmit power control based on statistical composite CSI. The design insights and performance gains of the proposed techniques are presented through numerical results. Monte-Carlo simulations are used to validate our analysis. © 2024 IEEE.

关键词： Channel estimation

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A Cyclic Vernier Digital-to-Time Converter for Time-Mode Successive Approximation TDC 66

A Cyclic Vernier Digital-to-Time Converter for Time-Mode Suc...

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

作者： Lee, Daniel Junehee Yuan, Fei Zhou, Yushi Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering TorontoON Canada Lakehead University Department of Electrical and Computer Engineering Thunder BayON Canada

ISBN: (纸本)9798350302103

This paper presents an 8-bit cyclic Vernier digital-to-time converter (DTC) for time-mode successive approximation register time-to-digital converters (SAR TDCs). The DTC offers a low degree of mismatch-induced nonlinearity hence a small lower bound of the dynamic range, a virtually unlimited upper bound of the dynamic range, and a small silicon area. The principle, operation, design, and calibration of the DTC are presented. The DTC is designed in TSMC 130 nm 1.2 V technology and analyzed using Spectre with BSIM3v3 device models. Simulation results show that the DTC achieves a dynamic range of 2.31 591.4 ps, a DNL of 0.37 LSB, an INL of 0.66 LSB, and consumes 1.49 mW. © 2023 IEEE.

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Evasive attacks against autoencoder-based cyberattack detection systems in power systems

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Energy and AI 2024年第3期17卷 126-135页

作者： Yew Meng Khaw Amir Abiri Jahromi Mohammadreza F.M.Arani Deepa Kundur Department of Electrical and Computer Engineering University of TorontoTorontoON M5S 3G4Canada School of Electronic and Electrical Engineering University of LeedsLeedsLS29JTUnited Kingdom Department of Electrical Computer&Biomedical EngineeringToronto Metropolitan University(formerly Ryerson University)TorontoON M5B 2K3Canada

The digital transformation process of power systems towards smart grids is resulting in improved reliability, efficiency and situational awareness at the expense of increased cybersecurity vulnerabilities. Given the availability of large volumes of smart grid data, machine learning-based methods are considered an effective way to improve cybersecurity posture. Despite the unquestionable merits of machine learning approaches for cybersecurity enhancement, they represent a component of the cyberattack surface that is vulnerable, in particular, to adversarial attacks. In this paper, we examine the robustness of autoencoder-based cyberattack detection systems in smart grids to adversarial attacks. A novel iterative-based method is first proposed to craft adversarial attack samples. Then, it is demonstrated that an attacker with white-box access to the autoencoder-based cyberattack detection systems can successfully craft evasive samples using the proposed method. The results indicate that naive initial adversarial seeds cannot be employed to craft successful adversarial attacks shedding insight on the complexity of designing adversarial attacks against autoencoder-based cyberattack detection systems in smart grids.

关键词： Cybersecurity Adversarial attacks Anomaly detection Iterative-based methods Substati on automati on

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Noncontact Magnetic Manipulation Using Permanent Magnets: System Architecture and Experimental Validation 50

Noncontact Magnetic Manipulation Using Permanent Magnets: Sy...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

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

ISBN: (纸本)9781665464543

This paper reports experimental results as the proof of concept to a novel approach for noncontact manipulation of magnetic objects using a combination of permanent magnets and mechanical actuators. In this approach, magnetic fields inside the workspace of magnetic manipulators are generated by arrays of permanent magnets, and are controlled by physical movement of the magnets using mechanical actuators. By feedback control of these actuators, the magnetic fields can be leveraged to precisely control the position of magnetic objects inside the workspace. To verify the proposed approach experimentally, it was realized by two laboratory setups with diametrically magnetized permanent magnets and rotary servomotors, and with axially magnetized permanent magnets and linear servomotors. In the first setup, the magnetic field inside its circular workspace is controlled by the directions of the magnets, while in the second, magnetic field is controlled by the distances of the magnets from its workspace. For each setup, a feedback law was designed and implemented to steer a small magnetic bead along complex reference trajectories inside the workspace. The control performance was evaluated by several experiments on both laboratory setups. © 2024 IEEE.

关键词： Permanent magnets

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