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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Olowolaju, Joshua Thapa, Jitendra Hossain, Rakib Benidris, Mohammed Livani, Hanif University of Nevada Department of Electrical & Biomedical Engineering RenoNV89557 United States Michigan State University Department of Electrical and Computer Engineering East LansingMI48824 United States

ISBN: (纸本)9798350320169

Decarbonizing the grid by incorporating more renewable energy sources has made power system reliability more critical. Although Monte Carlo simulation is a traditional method for power system reliability assessment, the computational costs limit its utility as power grid complexity increases. As a result, this study introduces a Graphics Processing Unit (GPU) based Convolution Neural Network (CNN) model to a non-sequential Monte Carlo Simulation (MCS) model to improve computation efficiency while maintaining high evaluation accuracy during reliability evaluation. Furthermore, this study explores the viability of the proposed model for sensitivity analysis of power system components on the overall system reliability condition. The proposed model is tested using the IEEE Reliability Test System 79 and is compared to the baseline Monte Carlo simulation approach. Results show that the model provides more than 80% time savings during reliability analysis while maintaining good evaluation accuracy for sensitivity assessments. © 2023 IEEE.

关键词： Neural network models

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Statistical Device Simulation and Machine Learning of Process Variation Effects of Vertically Stacked Gate-All-Around Si Nanosheet CFETs

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IEEE Transactions on Nanotechnology 2024年 23卷 386-392页

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

In this study, we report the process variation effect (PVE) including the work function fluctuation (WKF) on the DC/AC characteristic fluctuation of stacked gate-all-around silicon complementary field-effect transistors (CFETs). The PVE affects characteristic fluctuation significantly;in particular, for the variability of off-state current. Owing to the bottom channel of a fin-type, the P-FET suffers from the worst off-state current fluctuation (more than 200% variation) compared to the N-FET. The device variability induced by the WKF is marginal because of amorphous-type metal grains. As input features to an artificial neural network (ANN) model, low and high work function values, as well as parameters of PVE that have prevalent effects on CEFT transfer characteristics are further considered and modeled. The estimated values of R2-score prove that the ANN model properly grasps information from the dataset successfully;thus, it can be used to model emerging CFETs for circuit simulation. © 2002-2012 IEEE.

关键词： Neural networks

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Polyvinyl Alcohol Cryogels (PVA-C): Preparation and Multimodal Imaging Applications

Polyvinyl Alcohol Cryogels (PVA-C): Preparation and Multimod...

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Medical Imaging 2025: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Qi, Olivia R.Y. Peters, Terry M. Moore, John Chen, Elvis C.S. Western University Canada Robarts Research Institute London Canada School of Biomedical Engineering Western University Canada Department of Medical Biophysics Western University Canada Department of Electrical and Computer Engineering Western University Canada

ISBN: (纸本)9781510685949

Polyvinyl alcohol cryogel (PVA-C) is a widely used tissue-mimicking material in medical imaging phantoms, essential for evaluating imaging techniques, training clinicians, and simulating anatomical tissue structures. Its tunable mechanical and imaging properties make it valuable for multimodal imaging. However, PVA-C phantom reproducibility remains challenging due to fabrication variations, including inconsistencies in freeze-thaw cycle (FTC) conditions, PVA concentration, and imaging additives. This study systematically examines how PVA concentration, FTC profiles, and contrast-enhancing additives affect the mechanical and imaging properties of PVA-C phantoms in ultrasound, magnetic resonance imaging (MRI), and computed tomography (CT). Using in-house experiments and a comprehensive literature review, we analyze FTC conditions' role in modulating stiffness, attenuation, and relaxation times. We also assess contrast agents such as barium sulfate, gadolinium, and silica-based scatterers on imaging visibility and result. Our findings identify optimal fabrication conditions that enhance PVA-C phantom reproducibility and performance across imaging modalities. Our findings offer a comprehensive framework for optimizing PVA-C phantoms, ensuring reliable multimodal imaging applications. © 2025 SPIE

关键词： computerized tomography

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Scar-Aware Late Mechanical Activation Detection Network for Optimal Cardiac Resynchronization Therapy Planning 22

Scar-Aware Late Mechanical Activation Detection Network for ...

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22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Xing, Jiarui Wang, Shuo Petal, Amit R. Bilchick, Kenneth C. Epstein, Frederick H. Zhang, Miaomiao School of Medicine University of Virginia Health System United States University of Virginia Health System Department of Biomedical Engineering United States University of Virginia Department of Electrical and Computer Engineering United States University of Virginia Department of Computer Science United States

ISBN: (纸本)9798331520526

Accurate identification of late mechanical activation (LMA) regions is crucial for optimal cardiac resynchronization therapy (CRT) lead implantation. However, existing approaches using cardiac magnetic resonance (CMR) imaging often over-look myocardial scar information, which may be mistakenly identified as delayed activation regions. To address this issue, we propose a scar-aware LMA detection network that simultaneously detects myocardial scar and prevents LMA localization in these scarred regions. More specifically, our model integrates a pre-trained scar segmentation network using late gadolinium enhancement (LGE) CMRs into a LMA detection network based on highly accurate strain derived from displacement encoding with stimulated echoes (DENSE) CMRs. We introduce a novel scar-aware loss function that utilizes the segmented scar information to discourage false-positive detections of late activated areas. Our model can be trained with or without paired LGE data. During inference, our model does not require the input of LGE images, leveraging learned patterns from strain data alone to mitigate false-positive LMA detection in potential scar regions. We evaluate our model on subjects with and without myocardial scar, demonstrating significantly improved LMA detection accuracy in both scenarios. Our work paves the way for improved CRT planning, potentially leading to better patient outcomes. © 2025 IEEE.

关键词： Cardiac resynchronization therapy

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A mathematical approach to mitigate the impact of compression waves in shear wave elastography 12

A mathematical approach to mitigate the impact of compressio...

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Optical Elastography and Tissue Biomechanics XII 2025

作者： Asemani, Hamidreza Rolland, Jannick P. Parker, Kevin J. The Institute of Optics University of Rochester RochesterNY United States Department of Electrical and Computer Engineering University of Rochester RochesterNY United States Department of Biomedical Engineering University of Rochester RochesterNY United States

ISBN: (纸本)9781510683907

Shear wave elastography (SWE) has emerged as a valuable imaging technique across several modalities such as ultrasound, magnetic resonance imaging, and optical coherence tomography. Its capacity to assess tissue stiffness and identify lesions positions it as a promising clinical tool for diagnosing a wide range of diseases. The primary aim of SWE is to measure shear wave speed (SWS);however, unwanted compression waves and bulk tissue motion can complicate this process. Conventional methods, in many cases, face challenges in separating shear and compression waves, resulting in inaccurate SWS measurements. This study introduces a novel estimator, called integrated difference autocorrelation (IDA), designed to specifically estimate reverberant shear wave speed in the presence of compression waves and noise. Unlike conventional methods, the IDA estimator computes the velocity differences between adjacent particles, effectively mitigating the impact of long-wavelength compression waves and other wide-area movements like those induced by respiration. The effectiveness of the IDA estimator was assessed through several approaches: elastography simulation of a y-shaped cylinder in a soft background, magnetic resonance elastography (MRE) on a brain phantom with two lesions, ultrasound elastography on a breast phantom with a lesion, and ultrasound elastography in the human liver-kidney region. Our findings indicate that the IDA estimator provides accurate SWS estimates (within 19% of reference) even in the presence of strong compression waves that can cause earlier estimators to completely fail. Moreover, the IDA estimator demonstrates consistent performance across different modalities and excitation scenarios, underscoring its robustness and potential clinical applicability. © 2025 SPIE.

关键词： Shear waves

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Errors Induced by Partial Pathlength Variations Due to Mammographic Compression in Dynamic Diffuse Optical Breast Imaging 16

Errors Induced by Partial Pathlength Variations Due to Mammo...

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Optical Tomography and Spectroscopy of Tissue XVI 2025

作者： Niknam, Kaiser Paul, Mannu Bardhan Donaldson, Anthony Das, Mini Department of Physics University of Houston HoustonTX United States Department of Biomedical Engineering University of Houston HoustonTX United States Department of Electrical and Computer Engineering University of Houston HoustonTX United States

ISBN: (纸本)9781510683761

Optical imaging methods have the potential to overcome many of the drawbacks posed by current breast imaging modalities. Previous studies have found that mammographic compression induces different hemodynamic effects in cancerous and healthy breast tissue. This effect could be exploited in continuous-wave near-infrared spectroscopic imaging (CW-NIRS) for fast and accurate breast cancer screening. The primary issue with this approach is that breast tissue (and the cancerous mass) is displaced during the compression process, potentially introducing a considerable amount of errors and noise into the NIRS measurements with the current simple models used in estimating blood volume (and/or oxy/deoxy Hb) concentrations. In this work, we examine how these errors change with signal depth with breast compression and investigate methods to correct these based on simulations and experiments. © 2025 SPIE.

关键词： Mammography

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KNX-ZigBee Gateway

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IFAC-PapersOnLine 2024年第9期58卷 85-90页

作者： Dominik Grzegorz David Vala VSB - Technical University of Ostrava Faculty of electrical engineering and computer science Department of cybernetics and biomedical engineering CZ

This work is about building automation. It deals with the implementation of the connection between KNX TP bus and ZigBee wireless technology. The connection focuses on the control of lighting end devices supporting th... 详细信息

关键词： ZigBee ZigBee Lighting IEEE 802.15.4 ISO/OSI KNX TP CC2652RB

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Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study

Evaluating Deep Learning Models for Breast Cancer Classifica...

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Medical Imaging 2025: Digital and Computational Pathology

作者： Eskandari, Sania Eslamian, Ali Cheng, Qiang Department of Electrical and Computer Engineering University of Kentucky Lexington United States Department of Computer Science University of Kentucky Lexington United States Institute for Biomedical Informatics University of Kentucky Lexington United States

ISBN: (纸本)9781510686045

The classification of histopathological images is crucial for the early and precise detection of breast cancer. This study investigates the efficiency of deep learning models in distinguishing between Invasive Ductal Carcinoma (IDC) and non-IDC in histopathology slides. We conducted a thorough comparison examination of eight sophisticated models: ResNet-50, DenseNet-121, ResNeXt-50, Vision Transformer (ViT), GoogLeNet (Inception v3), EfficientNet, MobileNet, and SqueezeNet. This analysis was carried out using a large dataset of 277,524 image patches. Our research makes a substantial contribution to the field by offering a comprehensive assessment of the performance of each model. We particularly highlight the exceptional efficacy of attention-based mechanisms in the ViT model, which achieved a remarkable validation accuracy of 94%, surpassing conventional convolutional networks. This study highlights the promise of advanced machine learning approaches in clinical settings, offering improved precision as well as efficiency in breast cancer diagnosis. © 2025 SPIE.

关键词： Breast cancer Deep learning Tumor growth modeling Medical imaging Pathology Alternate lighting of surfaces computer engineering computer science Health informatics

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Fiber-optic sensor based on Bragg grating and 3D printing technique for monitoring the heart rate of the human body

Fiber-optic sensor based on Bragg grating and 3D printing te...

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Optical Sensors 2023

作者： Krizan, Daniel Kostelansky, Michal Mohammed, Mazin Abed Martinek, Radek Nedoma, Jan Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Czech Republic Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Czech Republic College of Computer Science and Information Technology University of Anbar Anbar Iraq

ISBN: (数字)9781510662650

ISBN: (纸本)9781510662643

This paper discusses a proposed fiber-optic sensor that was based on the combination of the material called polyethylene terephthalate glycol (PETG) and a fiber Bragg grating (FBG). The practical experiments were conducted under laboratory conditions with a group of 6 volunteers. The data were compared with conventional electrocardiogram (ECG) and processed using a Bland-Altman method. The results described in this paper show that the 3D printing technique can be used for encapsulating the FBG and for the purpose of biomedical engineering. © 2023 SPIE.

关键词： Fiber Bragg gratings

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Using Sparse Autoencoders to Perform Blind Source Separation of High-Density Myoelectric Signal 33

Using Sparse Autoencoders to Perform Blind Source Separation...

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33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023

作者： Romano, Marcelo Ramos Duarte, Leonardo Tomazeli Elias, Leonardo Abdala University of Campinas School of Applied Sciences SP Limeira Brazil University of Campinas Neural Engineering Research Laboratory Center for Biomedical Engineering SP Campinas Brazil University of Campinas Department of Electronics and Biomedical Engineering School of Electrical and Computer Engineering SP Campinas Brazil

ISBN: (纸本)9798350324112

A recent approach to address the blind source separation (BSS) problem relies on autoencoders with a discriminator as a generative adversarial network (GAN). However, this model is difficult to train due to the lack of metrics that ensure the convergence of a GAN. In this study, we try to provide an alternative solution by replacing the discriminator with appropriate regularization functions based on prior information on the sources. We tested this solution in the high-density electromyogram (HD-EMG) separation problem for a scenario with synthetic but realistic data and compared it with a classic algorithm from the literature. An interesting feature of the proposal is that it can be easily adapted for other source separation problems. Moreover, although the performance is slightly worse than the state-of the-art HD-EMG algorithm, which considers very detailed information on the problem, our proposal provides good results with a mean rate of agreement of 91.88% between the true and estimated spikes. © 2023 IEEE.

关键词： Generative adversarial networks

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