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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Xu, Le Fasolato, Simone Onori, Simona Stanford University Energy Science and Engineering Palo Alto United States University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9798350316339

The increasing adoption of Lithium Iron Phosphate (LFP) batteries in Electric Vehicles is driven by their affordability, abundant material supply, and safety advantages. However, challenges arise in controlling/estimating unmeasurable LFP states such as state of charge (SOC), due to its flat open circuit voltage, hysteresis, and path dependence dynamics during intercalation and de-intercalation processes. The Core-Shell Average Enhanced Single Particle Model (CSa-ESPM) effectively captures the electrochemical dynamics and phase transition behavior of LFP batteries by means of Partial Differential-Algebraic Equations (PDAEs). These governing PDAEs, including a moving boundary Ordinary Differential Equation (ODE), require a fine-grained spatial grid for accurate and stable solutions when employing the Finite Difference Method (FDM). This, in turn, leads to a computationally expensive system intractable for the design of real-time battery management system algorithms. In this study, we demonstrate that the Finite Volume Method (FVM) effectively discretizes the CSa-ESPM and provides accurate solutions with fewer than 4 control volumes while ensuring mass conservation across multiple operational cycles. The resulting control-oriented reduced-order FVM-based CSa-ESPM is experimentally validated using various C-rate load profiles and its observability is assessed through nonlinear observability analysis. Our results reveal that different current inputs and discrete equation numbers influence model observability, with non-observable regions identified where solid-phase concentration gradients are negligible. © 2024 IEEE.

关键词： Finite volume method

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computer Vision-Based Hydroponic Lettuce Contour and Area Recognition System

Computer Vision-Based Hydroponic Lettuce Contour and Area Re...

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2023 International Conference on electrical engineering and Informatics, ICEEI 2023

作者： Pranowo, Samuel Raymond Sukra, Zener Lie Asy'ari, Muhammad Zacky Paryasto, Marisa Binus Aso School of Engineering Bina Nusantara University Automotive and Robotics Program Computer Engineering Department Jakarta Indonesia Telkom University Computer Engineering Program Electrical Engineering Department Bandung Indonesia

ISBN: (纸本)9798350381290

Agriculture is a vital industry for the people of Indonesia, but there are several obstacles, including limited space in urban areas and inefficient conventional sorting and harvesting methods. Using computer vision to choose hydroponic lettuce that is ready to be harvested and a robotic arm to bring the selected lettuce yields, the produced system aims to solve this problem. The system's computer vision algorithms include color space conversion, intensity transformation, and an algorithm for following borders. This project's research methodology combines a quantitative approach with an experimental approach, which consists of conducting several trials on the built system. Several trials demonstrated that the computer vision software was able to accomplish the specified objectives. The average success rate of the developed computer vision system is 93%, while the average success rate of the robot arm is 85%. © 2023 IEEE.

关键词： Lettuce

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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Aerosol jet printing of surface acoustic wave microfluidic devices

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Microsystems & Nanoengineering 2024年第1期10卷 231-242页

作者： Joseph Rich Brian Cole Teng Li Brandon Lu Hanyu Fu Brittany N.Smith Jianping Xia Shujie Yang Ruoyu Zhong James L.Doherty Kanji Kaneko Hiroaki Suzuki Zhenhua Tian Aaron D.Franklin Tony Jun Huang Department of Biomedical Engineering Duke UniversityDurhamNC 27708USA Department of Electrical and Computer Engineering Duke UniversityDurhamNC 27708USA Department of Mechanical Engineering Virginia Polytechnic Institute and State UniversityBlacksburgVA 24061USA Thomas Lord Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA Deptartment of Precision Mechanics Faculty of Science and EngineeringChuo UniversityTokyo 112-8551Japan Department of Chemistry Duke UniversityDurhamNC 27708USA

The addition of surface acoustic wave(SAW)technologies to microfluidics has greatly advanced lab-on-a-chip applications due to their unique and powerful attributes,including high-precision manipulation,versatility,integrability,biocompatibility,contactless nature,and rapid ***,the development of SAW microfluidic devices is limited by complex and time-consuming micro/nanofabrication techniques and access to cleanroom facilities for multistep photolithography and vacuum-based *** simplify the fabrication of SAW microfluidic devices with customizable dimensions and functions,we utilized the additive manufacturing technique of aerosol jet *** successfully fabricated customized SAW microfluidic devices of varying materials,including silver nanowires,graphene,and poly(3,4-ethylenedioxythiophene)polystyrene sulfonate(PEDOT:PSS).To characterize and compare the acoustic actuation performance of these aerosol jet printed SAW microfluidic devices with their cleanroom-fabricated counterparts,the wave displacements and resonant frequencies of the different fabricated devices were directly measured through scanning laser Doppler ***,to exhibit the capability of the aerosol jet printed devices for lab-on-a-chip applications,we successfully conducted acoustic streaming and particle concentration ***,we demonstrated a novel solution-based,direct-write,single-step,cleanroom-free additive manufacturing technique to rapidly develop SAW microfluidic devices that shows viability for applications in the fields of biology,chemistry,engineering,and medicine.

关键词： printing wave fluid

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Investigating Deep Learning for Classification of Psychiatric Disorders on Brain MRI Data Using GAN-Based Augmentation

Investigating Deep Learning for Classification of Psychiatri...

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2023 International Conference on electrical and Information Technology, IEIT 2023

作者： Masengi, Gracia Angelina Jeniffer Sardjono, Tri Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Biomedical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Biomedical Engineering Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Department of Electrical Engineering Surabaya Indonesia

ISBN: (纸本)9798350327298

One of the difficulties in diagnosing patients in the field of psychiatry is the lack of objective biomarker measurements. In general, diagnosis is based on traditional methods relying on behavioral symptoms. Currently, diagnostic approaches predominantly rely on traditional methods grounded in behavioral symptoms. The evolution of neuroimaging techniques has prompted extensive investigations into biological indicators of psychiatric disorders. Among these endeavors, the application of deep learning for classification has demonstrated substantial promise. Through generative models like the generative adversarial network (GAN), deep learning is being investigated more in the field of medical image analysis to address the problems of small data samples and class imbalance. In this study, deep learning is implemented for automatic detection of psychiatric diseases from brain magnetic resonance imaging (MRI). Convolutional neural networks (CNN) are used for the diagnosis to categorize brain images into one of the classes: (1) normal, (2) schizophrenia, and (3) bipolar disorder. Image synthesis as data augmentation was performed with GAN. The suggested approach aims to advance the state-of-the-art in psychiatric diagnosis and promote additional investigation into the use of brain imaging in diagnostic plans. The findings show that the amount of data and subject variability significantly affect the efficacy of approaches for diagnosing psychiatric disorders. In this study, the data scarcity problem CNN training was not successfully resolved by GAN-based augmentation. It is determined that in order to develop a successful automatic psychiatric disease diagnostic system, a sufficient data sample must be collected while taking into account variables like subject variability. © 2023 IEEE.

关键词： Generative adversarial networks

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Field Uniformity Optimization for Integrated Capacitance Sensing of Tumor Treating Field-Treated Cancer Cell Cultures 66

Field Uniformity Optimization for Integrated Capacitance Sen...

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

作者： Gilpin, Yann Yankel, Joseph Dandin, Marc Department of Electrical and Computer Engineering United States Carnegie Mellon University Department of Biomedical Engineering Pittsburgh United States

ISBN: (纸本)9798350302103

An emerging modality in cancer treatment is tumor treating fields (TTF). It consists of electric fields of approximately 1 VRMS/cm in strength and of around 200 kHz in frequency. When these fields are aligned with a cancer cell's mitotic spindle, they can impede cell division. This method has improved overall patient survival in glioblastoma patients, remarkably with limited side effects. However there are still many unknowns associated with this therapy. Thus, to assist the discovery effort and facilitate further studies, we previously designed and successfully tested an in vitro integrated capacitance sensing TTF microsystem for autonomous and label-free detection of the effect TTFs on breast cancer cells. Herein, we propose an update to the microsystem's electrode geometry. Our novel approach eases the trade-offs between size, material usage, and consistency of treatment. Based on simulation studies and a physical model, we show that the new approach reduced an applied field's magnitude variance by 63% and angle variance by 54%. These gains should translate to improved repeatability, looser manufacturing tolerances, and smaller designs. © 2023 IEEE.

关键词： cancer cells CMOS electric field field optimization integrated capacitance sensing Tumor-treating fields

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Domain-Centroid-Guided Progressive Teacher-Based Knowledge Distillation for Source-Free Domain Adaptation of Histopathological Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1831-1843页

作者： Cheng, Kuo-Sheng Zhang, Qiong-Wen Tsai, Hung-Wen Li, Nien-Tsu Chung, Pau-Choo National Cheng Kung University Department of Biomedical Engineering Tainan70101 Taiwan National Cheng Kung University Hospital Department of Pathology Tainan70142 Taiwan National Cheng Kung University Department of Computer and Communication Engineering Tainan70101 Taiwan National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan

Deep neural networks are commonly used for histopathology image analysis. However, such data-driven models are sensitive to style variances across scanners and suffer a significant performance degradation as a result. Although the network performance can be improved by using domain adaptation methods, the source dataset required to perform the adaptation process is generally unavailable. This study shows that the performance degradation of deep neural networks when applied to histopathology images is the result partly of the wide distribution of the features generated when inferring the features of the target model using the feature centers of the source model. To address this problem, a teacher-student framework, designated as domain-centroid-guided progressive teacher-based knowledge distillation (DCGP-KD), is proposed which aims to learn compact target features in order to provide more accurate pseudo labels for the target model without the need for the original source dataset. In the proposed framework, the class-wise feature centers of the source data are progressively adapted to the distribution of the target data, and compact target features are then generated by gathering the features based on their class-wise centers. A strategy is additionally proposed to prevent catastrophic forgetting during the progressive adaption process. Finally, a prediction consistency loss function is introduced to improve the robustness of the target dataset. The feasibility of the proposed framework is demonstrated experimentally for the illustrative case of the tumor classification of histopathological images with staining variations. The results show that DCGP-KD provides a promising assistive tool for pathologists in various histopathological analysis tasks. © 2020 IEEE.

关键词： Distillation

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Impact of Environmental Conditions on the Remaining Useful Lifetime of SiC MOSFET

Impact of Environmental Conditions on the Remaining Useful L...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Hasan, Md Zakir Amin, Ashik Moniruzzaman, Md Choi, Seungdeog 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)9798350376067

Silicon carbide (SiC) power MOSFETs are widely applied to critical infrastructure in modern energy systems. Thus, accurately predicting its remaining useful lifetime (RUL) in real-world applications has become crucial. State-of-the-art explored its RUL through a power cycling test mostly considering constant environmental conditions (e.g., fixed temperature and humidity), which has resulted in considerable RUL prediction errors in real-world applications. This study directly integrates environmental factors into the RUL modeling to address this issue. Specifically, the junction temperature (Tj), on-state voltage (Vds,on), on-state resistance (Rds,on), and case temperature (TC) have been explored in various environmental conditions to understand their tight correlations with the RUL in the real world. Time series statistics models Autoregressive (AR) and Autoregressive Integrated Moving Average (ARIMA) have been used to predict SiC MOSFET RUL to gain new insights systematically. © 2024 IEEE.

关键词： Power MOSFET

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Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saad Arif Sara Rehman Muhammad Farrukh Qureshi Nagwan Abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

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Interpreting Multiclass Lung Cancer from CT Scans using Grad-CAM on Lightweight CNN Layers 6

Interpreting Multiclass Lung Cancer from CT Scans using Grad...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Muntasir, Fahim Datta, Ayon Mahmud, Shakib Brac University Department of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Bangladesh University of Engineering and Technology Department of Biomedical Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

Lung cancer is one of the most prevalent and deadly cancer kinds. CT scan imaging has made it easier to observe in-depth at different lung cancer conditions. Convolutional Neural Networks have already been utilized for detecting objects in images. This research implements a lightweight CNN architecture in classifying two types of cancerous images and distinguishing them from healthy ones. AlexNet, ResNet101 V2, DenseNet201, MobileNet V2, and other sizable deep learning and transfer learning models were tested against this unique architecture with global max pooling. The proposed CNN model overlaps in performance with much bigger models whilst using about eight times fewer parameters, thus being efficient. Apart from making the architecture smaller, Grad-CAM was used to explain the convolutional flow over different convolution layers. This integration of the Grad-CAM technique into the analysis adds a crucial layer of transparency to the deep-learning models. By vi-sualizing the regions within lung cancer images that significantly influence predictions, we aim to enhance the interpretability of our model, fostering trust and better understanding among healthcare professionals. © 2024 IEEE.

关键词： Convolutional neural networks

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