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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yafis, Balqis Huang, Jane-Hwa Yu, Chih-Min Wang, Li-Chun National Yang Ming Chiao Tung University Eecs Int'l Graduate Program Hsinchu Taiwan National Chi Nan University Dept. of Electrical Engineering Nantou County Taiwan Chung Yuan Christian University Department of Information and Computer Engineering Taoyuan City Taiwan

ISBN: (纸本)9798350351255

Reconfigurable Intelligent Surface (RIS) is a transformative technology using the passive beamforming capabilities to create the programmable wireless environments. However, the control overhead is a challenging issue. The system needs to deliver the high-precision reflection coefficients to the RIS controller, for adjusting the phase shifts of the numerous RIS elements. Therefore, we propose a Spirograph-based discrete beamdirection strategy, which selects a set of non-uniformly distributed beam directions based on the generic patterns generated by the Spirograph (SG). We evaluate the achievable data rate and the number of controlling bits of SG-based method. We compare the SG-based method, the ideal continuous phase-shift method, and the SISO method. Simulation results show that with the properly-selected discrete beam directions, the SG-based method can achieve a comparable data rate as the ideal method, while having a lower control overhead. © 2024 IEEE.

关键词： Beamforming

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Numerical Inverse Kinematics Solution of 7-DOF Exoskeleton 14

Numerical Inverse Kinematics Solution of 7-DOF Exoskeleton

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14th IEEE International Conference on Power Electronics and Drive Systems, PEDS 2023

作者： Sanjuan De Caro, Javier Dario Ahmed, Tanvir Rahaman Khan, Md Mahafuzur Zarif, Md Ishrak Islam Ahamed, Sheikh Iqbal Saad, Maarouf Rahman, Mohammad H. University of Wisconsin-Milwaukee Department of Mechanical Engineering MilwaukeeWI53212 United States University of Wisconsin-Milwaukee Department of Biomedical Engineering MilwaukeeWI53212 United States Marquette University Department of Computer Science MilwaukeeWI53233 United States École de Technologie Supérieure Electrical Engineering Department MontrealQC Canada

This document is a model and instructions for LATEX. This and the *** file define the components of your paper [title, text, heads, etc.]. *CRITICAL: Do Not Use Symbols, Special Characters, Footnotes, or Math in Paper... 详细信息

ISBN: (纸本)9798350399967

关键词： Exoskeleton (Robotics)

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Feasibility of MRI-US Registration in Oropharynx for Transoral Robotic Surgery

Feasibility of MRI-US Registration in Oropharynx for Transor...

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Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Chen, Wanwen Zeng, Qi Milner, Thomas D. Bagherinasab, Razeyeh Sabiq, Farahna Prisman, Eitan Pang, Emily H.T. Salcudean, Septimiu E. Department of Electrical and Computer Engineering University of British Columbia VancouverBC Canada Department of Otolaryngology University of British Columbia VancouverBC Canada Department of Radiology University of British Columbia VancouverBC Canada School of Biomedical Engineering University of British Columbia VancouverBC Canada

ISBN: (纸本)9781510660373

Trans-oral robotic surgery (TORS) is an alternative surgery technique used to treat head-and-neck cancer. Compared with conventional surgery, robot assistance allows surgeons to operate within areas with restricted access, such as the oropharynx, reducing the operative morbidity, risk of reconstructive surgery and improving patient outcomes. TORS is a challenging procedure, and intra-operative ultrasound (US) has the potential to improve anatomy visualization to lessen the cognitive load on surgeons. To date, only intra-oral US has been used in exploratory studies, but intra-oral US can interfere with robot tools. In this study, we assess the feasibility of using transcervical 3D US with TORS: we propose to place the US probe on the patient's neck to evaluate oropharyngeal anatomy intra-operatively. We also perform the first feasibility study of image registration between transcervical 3D US and magnetic resonance imaging (MRI) for the oropharynx. We collected 3D US and MRI data from five healthy volunteers and four patients with oropharyngeal cancer, and we use a semi-automatic MRI-US registration algorithm to estimate an affine transformation between the two image spaces. The average target registration error (TRE) is 8.26 ± 7.41mm for healthy volunteers and 9.63 ± 5.91mm for patients, and our case studies show that image quality is the key factor for good registration. Our work shows that 3D transcervical US has the clinical potential to enable intraoperative oropharynx imaging and interventional MR guidance during TORS. © 2023 SPIE.

关键词： Magnetic resonance imaging

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PhageBox: An Open Source Digital Microfluidic Extension with Applications for Phage Discovery

TechRxiv

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TechRxiv 2023年

作者： Albin, Dreycey Buecherl, Lukas Kochavi, Eitan Niehaus, Elise Novack, Sasha Uragoda, Shenali Myers, Chris J. Alistar, Mirela Department of Computer Science CU Boulder BoulderCO United States Biomedical Engineering Program CU Boulder United States Department of Electrical Computer and Energy Engineering and Biomedical Engineering Program CU Boulder United States ATLAS Institute The Department of Computer Science CU Boulder BoulderCO United States

Objective: Recent advancements demonstrate the significant role of digital microfluidics in automating laboratory work with DNA and on-site viral testing. However, since commercially available instruments are limited to droplet manipulation, our work addresses the need for accelerated integration of other components, such as temperature control, that can expand the application domain. Methods: We developed PhageBox— an accessible device that can be used as a biochip extension. At hardware level, PhageBox integrates temperature and electromagnetic control modules. At software level, PhageBox is controlled by embedded software containing a unique model for bio-protocol programming, and a graphical user interface for visual device feedback and operation. Results: To evaluate PhageBox’s efficacy for biomedical applications, we performed functional testing. Similarly, we validated the temperature control using thermography, obtaining a range of ±0.2◦C. The electromagnets produced a magnetic force of 15 milliTesla, demonstrating precise immobilization of magnetic beads. We show the potential of PhageBox for bacteriophage research through three initial protocols: a universal framework for PCR, T7 bacteriophage restriction enzyme digestion, and concentrating X174 RF genomic DNA. Conclusion: Our work presents an open-source hardware and software extension for digital microfluidics devices. This extension integrates temperature and electromagnetic modules, demonstrating efficacy in biomedical applications and potential for bacteriophage research. Significance: We developed PhageBox to be accessible: the components are off-the-shelf at a low cost (≤$200), and the hardware designs and software code are open-source. With the long aim of ensuring reproducibility and accelerating collaboration, we also provide a DIY-build document. (GitHub: https://***/Dreycey/PhageBox) © 2023, CC BY-SA.

关键词： Digital microfluidics

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Cross Modal Global Local Representation Learning from Radiology Reports and X-Ray Chest Images

Cross Modal Global Local Representation Learning from Radiol...

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Medical Imaging 2023: computer-Aided Diagnosis

作者： Hadjiyski, Nathan Vosoughi, Ali Wismüller, Axel Department of Computer Science University of Rochester NY United States Department of Electrical and Computer Engineering University of Rochester NY United States Department of Imaging Sciences University of Rochester NY United States Department of Biomedical Engineering University of Rochester NY United States Faculty of Medicine Institute of Clinical Radiology Ludwig Maximilian University Munich Germany

ISBN: (纸本)9781510660359

Deep learning models can be applied successfully in real-work problems;however, training most of these models requires massive data. Recent methods use language and vision, but unfortunately, they rely on datasets that are not usually publicly available. Here we pave the way for further research in the multimodal language-vision domain for radiology. In this paper, we train a representation learning method that uses local and global representations of the language and vision through an attention mechanism and based on the publicly available Indiana University Radiology Report (IU-RR) dataset. Furthermore, we use the learned representations to diagnose five lung pathologies: atelectasis, cardiomegaly, edema, pleural effusion, and consolidation. Finally, we use both supervised and zero-shot classifications to extensively analyze the performance of the representation learning on the IU-RR dataset. Average Area Under the Curve (AUC) is used to evaluate the accuracy of the classifiers for classifying the five lung pathologies. The average AUC for classifying the five lung pathologies on the IU-RR test set ranged from 0.85 to 0.87 using the different training datasets, namely CheXpert and CheXphoto. These results compare favorably to other studies using UI-RR. Extensive experiments confirm consistent results for classifying lung pathologies using the multimodal global local representations of language and vision information. © 2023 SPIE.

关键词： computer vision

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Classification of Schizophrenia Using Large-Scale Kernelized Granger Causality (lsKGC) and Functional MR Imaging

Classification of Schizophrenia Using Large-Scale Kernelized...

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Medical Imaging 2023: computer-Aided Diagnosis

作者： Vosoughi, Ali Kasturi, Akhil Hadjiyski, Nathan Wismüller, Axel Department of Electrical and Computer Engineering University of Rochester NY United States Department of Imaging Sciences University of Rochester NY United States Department of Biomedical Engineering University of Rochester NY United States Faculty of Medicine Institute of Clinical Radiology Ludwig Maximilian University Munich Germany Department of Computer Science University of Rochester NY United States

ISBN: (纸本)9781510660359

Alterations in brain network connectivity play an important role in the pathogenesis of schizophrenia. We investigate whether large-scale Kernelized Granger Causality (lsKGC) can capture such alterations using resting-state fMRI data. Our method utilizes dimension reduction combined with the augmentation of source time-series in a predictive time-series model for estimating directed causal relationships among fMRI time-series. As a multivariate approach, lsKGC identifies the relationship of the underlying dynamic system in the presence of all other time-series. Here, we examine the ability of lsKGC to accurately identify schizophrenia patients from fMRI data using a subset of 31 subjects from the Centers of biomedical Research Excellence (COBRE) data repository. We use brain connections estimated by lsKGC as features for classification. After feature extraction, we perform feature selection by Kendall's tau rank correlation coefficient followed by classification using a support vector machine. For reference, we compare our results with cross-correlation, typically used in the literature as a standard measure of functional connectivity, and several other standard methods. Using 100 different training/test data splits with 10-fold cross-validation we obtain mean/std f1-scores of 84.87% ± 19.78% and mean Area Under the receiver operating characteristic Curve (AUC) values of 93.00% ± 16.61% across all tested numbers of features for lsKGC, which is significantly better than the results obtained with cross-correlation (AUC=53.25% ± 29.29%, f1-score=45.03% ± 30.82%), and multiple other competing methods, including partial correlation, tangent, precision, and covariance methods. Our results suggest the applicability of lsKGC as a potential imaging biomarker for schizophrenia. © 2023 SPIE.

关键词： Magnetic resonance imaging

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

Assessing Biological Responses of Saccharomyces Cerevisiae t...

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ELEKTRO

作者： Marek Bajtoš Roman Radil Ladislav Janoušek Nhat Dang Department of Electromagnetic and Biomedical Engineering Faculty of Electrical Engineering and Information Technology University of Zilina Zilina Slovak Republic Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder Colorado USA

ISBN: (数字)9798350372359

ISBN: (纸本)9798350372366

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.

关键词： Electric potential Biological system modeling Cells (biology) Organisms Electromagnetic fields Risk management Reliability

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Dynamic Environment-Aware Lifetime Prediction of SiC MOSFET Modules Through LSTM 14

Dynamic Environment-Aware Lifetime Prediction of SiC MOSFET ...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Hasan, Md Zakir Choi, Seungdeog Aider, Youssef Singh, Prashant Liu, Chun-Hung Mississippi State University Department of Electrical and Computer Engineering Starkville United States University of Tennessee Department of Mechanical Aerospace & Biomedical Engineering Knoxville United States

ISBN: (纸本)9798331516116

The state-of-the-art determines the remaining useful lifetime (RUL) through a steady-state, fixed power cycling tests (PCT) without considering the impact of dynamically changing environmental conditions. It has resulted in considerable RUL prediction errors in the real world. However, the dynamic changing conditions (e.g., large temperature swings) may affect the degradation evolution of SiC MOSFET, which could eventually result in RUL changes. Thus, it must be integrated to make accurate predictions. To precisely understand the RUL variation complexity, the junction temperature (Tj) has been measured with a Negative Thermal Coefficient (NTC) thermistor, Temperature Sensitive electrical Parameter (TSEP), and these profiles have been modeled through the thermal model RC foster network using Extended Kalman Filter (EKF). Then, the on-state resistance (Rds,on) variations and Degradation Acceleration Factor (DAF) under the dynamic environment conditions are integrated into a lifetime prediction model to accurately predict the RUL through the Long Short-Term Memory (LSTM) machine learning algorithm. © 2025 IEEE.

关键词： Silicon carbide

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A new three-dimensional elastography using phase based shifted Fourier transform

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Medicine in Novel Technology and Devices 2022年第4期16卷 323-334页

作者： Hadis Faraji Alireza Shirazinodeh Najmeh Meimani Hossein Ahmadi Noubari Bahador Makki Abadi Research Center for Molecular and Cellular Imaging Imam Khomeini HospitalTehranIran Department of Biomedical Engineering Science and Research BranchIslamic Azad UniversityTehranIran Department of Medical Physics and Biomedical Engineering Tehran University of Medical ScienceTehranIran Department of Electrical and Computer Engineering University of British ColumbiaCanada

Elastography is an imaging technique with the ability to determine low quantities of some of the mechanical properties of *** aim of our research is to design a new 3D algorithm using the Shifted Fourier Transform(SFT)to perform a quasi-static *** innovative idea is implementation of a 3D convolution instead of using three 2D *** first,we collected the raw data from Abaqus engineering software in the form of breast tissue with a coefficient of elasticity of healthy tissue and tumor tissue with a coefficient of elasticity of tumor *** primary raw data consists of a number of points with x,y and z specified for tumor and healthy breast *** this step,we simulated the displacements in directions of x,y and z at each point of the prescribed tissues for 15 mm displacement of probe in–Y direction then we collected 1831 points for tumor and 4186 points for breast before and after *** applying a novel reconstruction algorithm,we convolved all images with the 3D Gabor filters to obtain phases,represented displacements of the breast and tumor images for before and after *** reach this goal,we designed a Gabor filter bank based on the dimensions of the input images in different scales,directions,and *** the 3D SFT,we calculated the displacements of the breast and tumor tissues followed by 3D elastogram representation of the ***,we implemented a 2D analysis of SFT in order to investigate validation of the 3D *** 2D algorithm,we used three two-dimensional convulsions in XY,YZ and XZ *** results obtained from the small displacements marked by circles,confirmed the accuracy of the 3D SFT *** areas of interest are the tumor areas in the 2D analysis.

关键词： 3D elastography 3D shifted Fourier Transform 3D and 2D Gabor filters Breast and tumor tissues Reconstruction algorithm

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Optimal Operation of Networked Microgrids With Distributed Multi-Agent Reinforcement Learning

Optimal Operation of Networked Microgrids With Distributed M...

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IEEE General Meeting Power& Energy Society

作者： Hepeng Li Haibo He Department of Computer Science and Information Systems Bradley University USA Department of Electrical Computer and Biomedical Engineering University of Rhode Island USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

This paper presents distributed multi-agent deep reinforcement learning (MADRL) approach for optimizing power flow management in networked microgrids within distribution systems. In contrast to centralized training methods, our proposed approach leverages the multi-agent trust region policy optimization (MATRPO) algorithm to learn distributed policies that minimize operational costs while considering power flow constraints in distribution networks. We model the cooperation among networked MGs as a partially observable Markov game and learn the distributed policies via peer-to-peer communication to address the challenges associated with centralized training. This approach offers scalability and efficiency benefits. The proposed approach is evaluated on a 24.9 kV distributed network with interconnected 4.16 kV MGs based on modified IEEE-34 and IEEE-13 bus systems. By comparing our approach with state-of-the-art MADRL methods, we demonstrate its effectiveness in enabling cooperative optimization of networked MGs, showcasing its applicability for managing distributed energy systems.

关键词： Training Costs Scalability Microgrids Games Distribution networks Deep reinforcement learning Peer-to-peer computing Optimization Load flow

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