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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Arnold, Kellen P. Slaby, Joel B. Dattilo, Hannah M. Kaylor, C. Alex Schrimpf, Ronald D. Fleetwood, Daniel M. Ralph, Stephen E. Reed, Robert Weiss, Sharon M. Interdisciplinary Materials Science Program Vanderbilt University 2201 West End Ave NashvilleTN37235 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Electrical and Computer Engineering Vanderbilt University 2201 West End Ave NashvilleTN37235 United States

We report a simple, vacuum-compatible fiber attach process for in situ study of grating-coupled photonic devices. The robustness of this technique is demonstrated on grating-coupled waveguides exposed to multiple X-ra... 详细信息

关键词： Photonic devices

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Machine Learning for Unmanned Aerial Vehicles Navigation: An Overview

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SN computer science 2024年第2期5卷 256页

作者： Fagundes-Junior, Leonardo A. de Carvalho, Kevin B. Ferreira, Ricardo S. Brandão, Alexandre S. Núcleo de Especialização em Robótica Department of Electrical Engineering Graduate Program in Computer Science Federal University of Viçosa-UFV Av. Petter Henry Rolfs MG Viçosa 36570-900 Brazil

Unmanned aerial vehicles (UAVs) are a valuable source of data for a wide range of real-time applications, due to their functionality, availability, adaptability, and maneuverability. Working as mobile sensors, they can provide a cost-effective solution for extremely complex tasks, such as inspection, air-to-ground communications, search and rescue, surveillance, among others. Nevertheless, the robots needs to navigate in quite distinct environments and in different dynamism levels, usually facing unpredicted situations, very often using limited sensing and computing capabilities. A large number of solutions to this problem has been featured by the scientific community in the last years, some of them based on machine-learning (ML) methods. Due to its great capability to deal with big data and complexity, as well as its speedy and high-accuracy processing, the ML framework has been used to improve existing technologies and control techniques. In this context, its adoption in several UAV navigation strategies is expected to provide solutions for various problems where UAVs are used in real-time applications. Thus, in order to contextualize the most recent advances, this work provides a detailed survey of relevant researches in which ML techniques have been used in UAV navigation to improve some functional aspects, such as energy-efficiency, communication, execution time, resource management, obstacle avoidance, and path planning. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Autonomous navigation Path planning UAV-based solutions Visual-based navigation

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SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARNING

SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARN...

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International Conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Liu, Linfeng Juwono, Filbert H. Wong, W.K. Purwanto, Erick Jin, Tingyan Zhen, Yuyang Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing China School of Instrument Science and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781837242689

In this study, we explore the classification and prediction capabilities of three models-Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)-on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction. © The Institution of engineering & Technology 2024.

关键词： Genetic programming

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Spectral DefocusCam: hyperspectral imaging using defocus and a spectral filter array

Spectral DefocusCam: hyperspectral imaging using defocus and...

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Computational Optical Sensing and Imaging, COSI 2022

作者： Foley, Christian Monakhova, Kristina Yanny, Kyrollos Waller, Laura Department of Electrical Engineering and Computer Sciences University of California Berkeley United States UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley United States

We propose a compact hyperspectral camera based on a focus-tunable lens and spectral filter array. The camera captures then fuses multiple defocused measurements to obtain a high-resolution hyperspectral volume. ©... 详细信息

ISBN: (纸本)9781557528209

关键词： Hyperspectral imaging

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Learning Disentangled Representation for Multidimensional MR Image Reconstruction 46

Learning Disentangled Representation for Multidimensional MR...

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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

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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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