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

作者： Zhao, Ruiyang Wang, Zepeng Lam, Fan University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL United States University of Illinois Urbana-Champaign Department of Bioengineering UrbanaIL United States University of Illinois Urbana-Champaign Beckman Institute for Advanced Science and Technology UrbanaIL United States

ISBN: (纸本)9798350371499

We proposed a new way to represent and reconstruct multidimensional MR images. Specifically, a representation capable of disentangling different types of features in high-dimensional images was learned via training an autoencoder with separated sets of latent spaces for image style transfer, e.g., contrast or geometry transfer. A latent diffusion model was introduced to capture the distributions of the disentangled latents for constrained reconstruction. A new formulation was developed to integrate the pre-learned representation with other complementary constraints for reconstruction from sparse data. We demonstrated the ability of our model to disentangle contrast and geometry features in multicontrast MR images, and its effectiveness in accelerated T1 and T2 mapping. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Sulistio, Bambang Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327472

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%. © 2023 IEEE.

关键词： Tweets

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Brain Tumor Detection and Segmentation Using Deep Learning Models 6

Brain Tumor Detection and Segmentation Using Deep Learning M...

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Abiodun, Moses Kazeem Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Aworinde, Halleluyah Tinuoye, George Imoize, Agbotiname Lucky Li, Chun-Ta Lee, Cheng-Chi Landmark University Department of Computer Science Kwara State Omu-Aran Nigeria Bowen University Department of Computer Science Osun State Iwo Nigeria University of Ilorin Department of Computer Science Kwara State Ilorin Nigeria University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Nigeria Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City Taiwan Fu Jen Catholic University Department of Library and Information Science New Taipei City Taiwan

ISBN: (纸本)9798350374162

This study focuses on brain tumor detection and segmentation using Convolutional Neural Networks (CNN) with architectures of Fully Convolutional Net-work (FCN) and VGG16. The dataset imported for this study consists of brain MRI images, which were preprocessed to improve the overall image resolution. Data augmentation techniques were applied to heighten the variability and volume of the training data. For feature extraction, CNN models were utilized, specifically the VGG16 architectures. This model was trained on a preprocessed dataset to learn the important features associated with the presence of brain tumor. Image segmentation was performed to precisely outline and identify the tumor region within the brain images. FCN architecture was employed for this task, which allowed pixel-level classification of tumor regions. Finally, image classification was carried out to group brain images based on the absence or presence of a tumor. The trained models were evaluated using accuracy, precision, recall and F1score as the performance metrics. The results of the study demonstrated promising outcomes, achieving an accuracy score of 93% and a precision, recall and f1score ranging from 75% to 100% based on the specific type of tumor enlisted. This indicates that the developed CNN models, with the FCN and VGG16 architectures, effectively detect and segment brain tumors from MRI images. The findings of this study contribute to the field of medical image analysis and gives a foundation for further research and development in brain tumor diagnosis and treatment. The study recommends the use of more deep learning algorithms for the process of detecting brain tumors. © 2024 IEEE.

关键词： Convolutional neural networks

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Bubble-pen lithography:Fundamentals and applications

Aggregate

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Aggregate 2022年第4期3卷 85-100页

作者： Pavana Siddhartha Kollipara Ritvik Mahendra Jingang Li Yuebing Zheng Walker Department of Mechanical Engineering The University of Texas at AustinAustinTexasUSA Department of Electrical and Computer Engineering The University of Texas at AustinAustinTexasUSA Material Science and Engineering Program Texas Materials InstituteThe University of Texas at AustinAustinTexasUSA

Developing on-chip functional devices requires reliable fabrication methods with high resolution for miniaturization,desired components for enhanced performance,and high throughput for fast prototyping and mass ***,laser-based bubble-pen lithography(BPL)has been developed to enable sub-micron linewidths,in situ synthesis of custom materials,and on-demand patterning for various functional components and *** exploits Marangoni convection induced by a laser-controlled microbubble to attract,accumulate,and immobilize particles,ions,and molecules onto different *** years have witnessed tremendous progress in theory,engineering,and application of BPL,which motivated us to write this ***,an overview of experimental demonstrations and theoretical understandings of BPL is ***,we discuss the advantages of BPL and its diverse applications in quantum dot displays,biological and chemical sensing,clinical diagnosis,nanoalloy synthesis,and *** conclude this review with our perspective on the challenges and future directions of BPL.

关键词： additive manufacturing capillary flow lithography marangoni convection microbubbles sensing

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Design Concept of a Wearable Device for Sleep Related Brain Wave Detection and Stimulation 4

Design Concept of a Wearable Device for Sleep Related Brain ...

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4th IEEE International Conference on Data science and computer Application, ICDSCA 2024

作者： Yin, Ariel R. Zhang, David Z. Mao, David D. Li, Sichuang Cheng, Haoliang Deng, Yangfan Guo, Yifan Mao, Helen X. Yin, Jijun Mao, Zhi-Hong Canyon Crest Academy San DiegoCA United States University High School IrvineCA United States North Allegheny Intermediate High School PittsburghPA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Computer Science University of Pittsburgh PittsburghPA United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN United States Yale College Yale University New HavenCT United States Departments of Electrical and Computer Engineering and Bioengineering University of Pittsburgh PittsburghPA United States

ISBN: (纸本)9798350368239

This paper presents a design concept of an innovative wearable device for brain wave detection and stimulation to aid sleep. Sleep’s importance for a healthy life emphasizes the value of studying the application of technology to improve sleep quality for individuals unable to receive a good night’s rest. Utilizing electroencephalography (EEG) for accurate sleep tracking, the device aims to be portable and efficient. It includes the EEG sensing and processing module, adaptive control and machine learning module, and electronic stimulation module, working together to guide the brain toward relaxation. Preliminary results demonstrate promising performances or necessities of the subsystems of the proposed design. ©2024 IEEE.

关键词： Sleep research

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Sliding Mode Control of the MMC-based Power System 48

Sliding Mode Control of the MMC-based Power System

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Aghahadi, Morteza Piegari, Luigi Lekic, Aleksandra Shetgaonkar, Ajay Politecnico di Milano Department of Electronics Information and Bioengineering Milan Italy Mathematics and Computer Science Tu Delft Intelligent Electrical Power Grids Faculty of Electrical Engineering Delft Netherlands

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

The modular multilevel converter has become a popular topology for many applications in medium and high-power conversion systems such as multi-terminal direct current power systems. In this paper, Modular multilevel converter structure and related equations are presented. Then, control methods for circulating current, output current, and energy balance among legs and upper and lower arms of each phase for conventional proportional-integral control and sliding mode control are described. Notably, this study concentrates on the multi-terminal direct current configuration link with masterslave control by presenting a π model for the high voltage direct current transmission line. Moreover, dq-frame is used in the control strategy with a modified first-order sliding mode control and a second-order sliding mode control for preventing chattering. The results show that applying the proposed method in a hybrid power system can provide fast transient responses, zero overshoot, and better stability. Finally, the results are verified by simulations in MATLAB/Simulink. © 2022 IEEE.

关键词： Sliding mode control

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An Integrated Computational Framework for the Neurobiology of Memory Based on the ACT-R Declarative Memory System

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Computational Brain and Behavior 2024年第1期7卷 129-149页

作者： Stocco, Andrea Rice, Patrick Thomson, Robert Smith, Briana Morrison, Don Lebiere, Christian Department of Psychology University of Washington Seattle WA United States Department of Psychology Rice University Houston TX United States Department of Electrical Engineering & Computer Science United States Military Academy West Point NY United States Department of Bioengineering University of Washington Seattle WA United States Department of Psychology Carnegie Mellon University Pittsburgh PA United States

Memory is a complex process that spans multiple time-scales and stages, and, as expected, involves multiple brain regions. Traditionally, computational models of memory are either too abstract (Shiffrin & Steyvers, 1997) to be meaningfully connected to a biological substrate, or, when explicitly connected, are narrowly focused on one specific region and process (Blum & Abbott, 1996;Weber et al., 2017). By contrast, a comprehensive model of memory with a plausible neural interpretation would be extremely valuable to drive further research in memory function and dysfunction. In this paper, we attempt to fill in this gap by providing a detailed biological analysis of ACT-R’s declarative memory system. This system, developed over four decades, has evolved into a consistent framework that describes how memories are formed, retrieved, forgotten, mistaken, and merged. Building on existing mappings between some components and their biological counterpart, as well as the existing literature, this paper provides a comprehensive view of how the framework’s various computations map onto different brain regions, their network dynamics and functional connectivity, and biological structure. We also show that these mappings provide further insights and explanations for puzzling findings in the memory disorders literature. Finally, we outline the remaining gaps (such as the transition from episodic to semantic memory) and how they could be addressed by future research and modeling efforts. © 2023, The Author(s).

关键词： ACT-R Computational model Episodic memory Forgetting Hippocampus Long-term memory Prefrontal cortex Retrieval Semantic memory

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Curriculum-based Reinforcement Learning for an Effective Multi-Agent Path Planning Algorithm in Warehouse Scenarios 15

Curriculum-based Reinforcement Learning for an Effective Mul...

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15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： de Oliveira, Iure Rosa L. de Carvalho, Kevin B. Brandão, Alexandre S. Graduate Program in Computer Science Universidade Federal de Viçosa Viçosa Brazil Electrical Engineering Department Universidade Federal de Viçosa Viçosa Brazil

ISBN: (纸本)9798350314182

Multi-robot systems can provide substantial increase in efficiency and/or flexibility in different scenarios. Applications in various settings have been studied in the literature, such as disaster management, surveillance, object transportation as well as search-and-rescue. A particular case that can highly benefit from the employment of multiple agents is the logistics in a warehouse scenario. This work proposes an multi-agent Q-learning based algorithm with curriculum learning and transfer learning to perform the path planning process. With progressively more complex stages of training as well as knowledge transfer from one stage to another, the algorithm is capable of achieve high success rates. In order to validate the proposed method, simulations were done to compare it to other combinations of the used techniques, as well as using Q-learning only. Scalability tests were also performed. The proposed method achieved up to 94% success rate after training. © 2023 IEEE.

关键词： Curriculum Learning Mobile Robotics Multi-Agent System Reinforcement Learning Transfer Learning

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Density Functional Theory Study of Surface Stability and Phase Diagram of Orthorhombic CsPbI3

arXiv

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arXiv 2024年

作者： Li, Kejia Wang, Mengen Department of Electrical and Computer Engineering and Materials Science and Engineering Program SUNY BinghamtonNY13902 United States

CsPbI3 has been recognized as a promising candidate for optoelectronic device applications. To further improve the efficiency of the devices, it is imperative to better understand the surface properties of CsPbI3, which affect charge carrier transport and defect formation properties. In this study, we perform density functional theory calculations to explore the stability of the (001), (110), and (100) surfaces of orthorhombic CsPbI3, considering different stoichiometries and surface reconstructions. Our results show that, under the chemical potentials confined by the thermodynamically stable region of bulk CsPbI3, the CsI-terminated surfaces of (001) and (110) and the stoichiometric surface of (100) are stable. Among these three surfaces, the CsI-terminated (110) surface has the lowest surface energy and no mid-gap states, which benefits the transport properties of the material. © 2024, CC BY-NC-SA.

关键词： Stoichiometry

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Single-shot quantitative phase gradient microscopy using physics-based untrained neural network phase retrieval 11

Single-shot quantitative phase gradient microscopy using phy...

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Quantitative Phase Imaging XI 2025

作者： Hur, Sun Woong Sengupta, Sourya Kwon, Minsung Manoharaan, Revathi Anastasio, Mark Bhargava, Rohit Department of Bioengineering University of Illinois Urbana-ChampaignIL United States Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignIL United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-ChampaignIL United States Department of Computer Science Carle-Illinois College of Medicine University of Illinois Urbana-ChampaignIL United States Department of Chemical and Biomolecular Engineering Mechanical Science and Engineering and Chemistry Cancer Center at Illinois University of Illinois Urbana-ChampaignIL United States CZ Biohub Chicago LLC IL United States

ISBN: (纸本)9781510684065

Single-shot quantitative phase gradient microscopy (ss-QPGM) enables quantitative phase imaging (QPI) based on differential interference contrast (DIC) microscopy. In ss-QPGM, the analyzer is replaced by a quarter-wave plate and a polarization camera, allowing for phase gradient extraction from a single intensity measurement. The quantitative phase image is reconstructed via numerical integration, but direct integration along a single axis introduces artifacts and phase errors due to missing frequency response along the axis of orthogonal to the shear axis and noise-induced streak lines. In this work, we propose a phase retrieval method for ss-QPGM using an untrained neural network (UNN), which integrates physical priors without requiring paired ground truth and input datasets, overcoming the limitations of traditional regularization methods such as Tikhonov regularization and the alternating direction method of multipliers (ADMM) with total variation (TV) regularization. We demonstrate that the UNN approach not only offers superior phase reconstruction but also alleviates artifacts induced by missing frequency response along the axis and noise-induced streak lines, as shown by phase images of a calibrated target and biological samples. © 2025 SPIE.

关键词： Image reconstruction

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