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Artificial Intelligence (WSAI), World Symposium on

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

ISBN: (数字)9798350374162

ISBN: (纸本)9798350374179

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.

关键词： Deep learning Image segmentation Accuracy Magnetic resonance imaging Computational modeling Brain tumors Brain modeling Distance measurement Classification algorithms Convolutional neural networks

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(Invited) Radiation Damage in the Ultra Wide Bandgap Semiconductor Ga2O3 242

(Invited) Radiation Damage in the Ultra Wide Bandgap Semicon...

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242nd ECS Meeting

作者： Xia, Xinyi Li, Jian-Sian Sharma, Ribhu Ren, Fan Rasel, Md Abu Jafar Stepanoff, Sergei Al-Mamun, Nahid Haque, Amanul Wolfe, Douglas Modak, Sushrut Chernyak, Leonid Law, Mark Khachatrian, Ani Pearton, Stephen J. Department of Chemical Engineering University of Florida GainesvilleFL32606 United States Department of Materials Science and Engineering University of Florida GainesvilleFL32606 United States Department of Mechanical Engineering Penn State University University ParkPA16802 United States Department of Materials Science and Engineering Penn State University University ParkPA16802 United States Department of Physics University of Central Florida OrlandoFL32816 United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States US Naval Research Laboratory 4555 Overlook Ave WashingtonDC20375 United States

ISBN: (纸本)9781607685395

Ga2O3 is expected to show similar radiation resistance as GaN and SiC, considering their average bond strengths. However, this is not enough to explain the orders of magnitude difference of the relative resistance to radiation damage of these materials compared to GaAs and dynamic annealing of defects is much more effective in Ga2O3. Octahedral gallium monovacancies are the main defects produced under most radiation conditions because of the larger cross-section for interaction compared to oxygen vacancies. Proton irradiation introduces two main paramagnetic defects in Ga2O3, which are stable at room temperature. Charge carrier removal can be explained by Fermi-level pinning far from the conduction band minimum due to gallium interstitials (Ga i ), vacancies (VGa), and antisites (GaO). With few experimental or simulation studies on single event effects (SEE) in Ga2O3 ,it is apparent that while other wide bandgap semiconductors like SiC and GaN are robust against displacement damage and total ionizing dose, they display significant vulnerability to single event effects at high Linear Energy Transfer (LET) and at much lower biases than expected. We have analyzed the transient response of β-Ga2O3 rectifiers to heavy-ion strikes via TCAD simulations. Using field metal rings improves the breakdown voltage and biasing those rings can help control the breakdown voltage. Such biased rings help in the removal of the charge deposited by the ion strike. © 2022 ECS-The Electrochemical Society.

关键词： Wide band gap semiconductors

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Sentiment Analysis of Hashtag Activism on Social Media Twitter

Sentiment Analysis of Hashtag Activism on Social Media Twitt...

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computer science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Sidharta Sidharta Yaya Heryadi Lukas Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduated Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Currently, the performance of the police in Indonesia is often in the spotlight of the public with cases that occur, both on a national and regional scale, including personal experiences who also feel disappointed with the performance of the police. The cases, such as the rape of three children by a biological father in East Luwu, and the barrage of other cases that have occurred, in their handling have left the public disappointed, giving rise to the hashtag percumlaporpolisi on social media, especially on twitter. This hashtag became trending and was widely responded to by the public. Using free hashtag keywords, tweet data is collected using Twitter’s API for sentiment analysis. The datasets obtained from the crawling results is the latest data with a total of 100 datasets. This study applies the naïve bayes algorithm using textblob which aims to determine the value of polarity and subjectivity. From the results of the analysis carried out, the words that appear most often are useless police, police chief, and policeman. While the results of the sentiment analysis that has been carried out are 40 percent positive sentiment, 40 percent are neutral sentiment and 10 percent are negative sentiment from the hashtags used. From these results, according to the keywords used, the percentage of negative sentiment should be greater than positive and neutral sentiment. It is necessary to conduct a more in-depth study related to the model used in the next research.

关键词： computer science Sentiment analysis Pediatrics Law enforcement Social networking (online) Blogs Biology

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The First Switch Effect in Ferroelectric Field-Effect Transistors

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IEEE Transactions on Device and materials Reliability 2025年

作者： Ravikumar, Priyankka Venkatesan, Prasanna Park, Chinsung Afroze, Nashrah Tian, Mengkun Chern, Winston Datta, Suman Yu, Shimeng Mahapatra, Souvik Khan, Asif Georgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology GA United States Institute of Materials GA United States Georgia Institute of Technology School of Material Science and Engineering GA United States Indian Institute of Technology Department of Electrical Engineering Mumbai India

In this work, a ferroelectric field-effect transistor (FEFET) is systematically characterized and compared with an equivalent standard MOSFET with an equivalent oxide thickness. We show that these two devices, with a silicon channel, exhibit similar pristine state transfer characteristics but starkly different endurance characteristics. In contrast to the MOSFET, the FEFET shows a significant increase in sub-threshold swing in the first write pulse. Based on this, we reveal that this first write pulse (cycle 1) generates more than half of the total traps generated during the fatigue cycling in FEFETs. We call this the “First Switch Effect". Further, by polarizing a pristine FEFET step by step, we demonstrate a direct correlation between the switched polarization and interface trap density during the first switch. Through charge pumping measurements, we also observe that continued cycling generates traps more towards the bulk of the stack, away from the Si/SiO₂ interface in FEFETs. We establish that: (1) the first switch effect leads to approximately 50% of the total trap density (N_it) near the Si/SiO₂ interface until memory window closure;and (2) further bipolar cycling leads to trap generation both at and away from Si/SiO₂ interface in FEFETs. © 2001-2011 IEEE.

关键词： Charge pumping Endurance FEFET HfO2-based Ferroelectrics

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Constrained Optimization with Decision-Dependent Distributions

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IEEE Transactions on Automatic Control 2025年

作者： Wang, Zifan Liu, Changxin Parisini, Thomas Zavlanos, Michael M. Johansson, Karl H. KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures StockholmSE-10044 Sweden East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy Duke University Department of Mechanical Engineering and Materials Science DurhamNC United States

In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems with decision-dependent distributions has assumed either the absence of constraints or fixed constraints. This work considers a more general setting where the constraints can also dynamically adjust in response to changes in the decision variables. Specifically, we consider linear constraints and analyze the effect of decision-dependent distributions in both the objective function and constraints. Firstly, we establish a sufficient condition for the existence of a constrained equilibrium point, at which the distributions remain invariant under retraining. Moreover, we propose and analyze two algorithms: repeated constrained optimization and repeated dual ascent. For each algorithm, we provide sufficient conditions for convergence to the constrained equilibrium point. Furthermore, we explore the relationship between the equilibrium point and the optimal point for the constrained decision-dependent optimization problem. Notably, our results encompass previous findings as special cases when the constraints remain fixed. To show the effectiveness of our theoretical analysis, we provide numerical experiments on both a market problem and a dynamic pricing problem for parking based on real-world data. © 1963-2012 IEEE.

关键词： Optimization algorithms

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Real-time,in situ probing of gamma radiation damage with packaged integrated photonic chips

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Photonics Research 2020年第2期8卷 186-193页

作者： QINGYANG DU JEROME MICHON BINGZHAO LI DEREK KITA DANHAO MA HAIJIE ZUO SHAOLIANG YU TIAN GU ANURADHA AGARWAL MO LI JUEJUN HU Department of Materials Science&Engineering Massachusetts Institute of TechnologyCambridegMassachusetts 02139USA Department of Electrical and Computer Engineering University of WashingtonSeattleWashington 98195USA Department of Physics University of WashingtonSeattleWashington 98195USA

Integrated photonics is poised to become a mainstream solution for high-speed data communications and sensing in harsh radiation environments,such as outer space,high-energy physics facilities,nuclear power plants,and test fusion *** the impact of radiation damage in optical materials and devices is thus a prerequisite to building radiation-hard photonic systems for these *** this paper,we report real-time,in situ analysis of radiation damage in integrated photonic *** devices,integrated with an optical fiber array package and a baseline-correction temperature sensor,can be remotely interrogated while exposed to ionizing radiation over a long period without compromising their structural and optical *** also introduce a method to deconvolve the radiation damage responses from different constituent materials in a *** approach was implemented to quantify gamma radiation damage and post-radiation relaxation behavior of SiO2-cladded SiC photonic *** findings suggest that densification induced by Compton scattering displacement defects is the primary mechanism for the observed index change in ***,post-radiation relaxation in amorphous SiC does not restore the original pre-irradiated structural state of the *** results further point to the potential of realizing radiation-hard photonic device designs taking advantage of the opposite signs of radiation-induced index changes in SiC and SiO2.

关键词： scattering damage relaxation

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Bulk-Switching Memristor-based Compute-In-Memory Module for Deep Neural Network Training

arXiv

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arXiv 2023年

作者： Wu, Yuting Wang, Qiwen Wang, Ziyu Wang, Xinxin Ayyagari, Buvna Krishnan, Siddarth Chudzik, Michael Lu, Wei D. Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Applied Materials Inc. Santa ClaraCA95054 United States

The need for deep neural network (DNN) models with higher performance and better functionality leads to the proliferation of very large models. Model training, however, requires intensive computation time and energy. Memristor-based compute-in-memory (CIM) modules can perform vector-matrix multiplication (VMM) in situ and in parallel, and have shown great promises in DNN inference applications. However, CIM-based model training faces challenges due non-linear weight updates, device variations, and low-precision in analog computing circuits. In this work, we experimentally implement a mixed-precision training scheme to mitigate these effects using a bulk-switching memristor CIM module. Low-precision CIM modules are used to accelerate the expensive VMM operations, with high precision weight updates accumulated in digital units. Memristor devices are only changed when the accumulated weight update value exceeds a pre-defined threshold. The proposed scheme is implemented with a system on chip (SoC) of fully integrated analog CIM modules and digital sub-systems, showing fast convergence of LeNet training to 97.73%. The efficacy of training larger models is evaluated using realistic hardware parameters and shows that that analog CIM modules can enable efficient mix-precision DNN training with accuracy comparable to full-precision software trained models. Additionally, models trained on chip are inherently robust to hardware variations, allowing direct mapping to CIM inference chips without additional re-training. © 2023, CC BY.

关键词： Deep neural networks

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The Deep Learning Approach For American Sign Language Detection

The Deep Learning Approach For American Sign Language Detect...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Gusti Pangestu Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising.

关键词： Deep learning computer science Image recognition Gesture recognition computer architecture Auditory system Assistive technologies

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Influence of defects on the femtosecond laser damage resistance of multi-layer dielectric grating

Influence of defects on the femtosecond laser damage resista...

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CLEO: science and Innovations, S and I 2022

作者： Zhang, Simin Su, Ziyao Menoni, Carmen S. Chowdhury, Enam A. The Ohio State University Department of Materials Science and Engineering ColumbusOH43210 United States The Ohio State University Department of Physics ColumbusOH43210 United States Colorado State University Electrical and Computer Engineering Department Fort CollinsCO80523 United States XUV Lasers Inc. Fort CollinsCO80525 United States

The influence of various defects on the femtosecond laser damage resistance of a HfO2/SiO2/Ta2O5-based 48-layer grating designed for 800 nm is studied using the finite-difference time-domain method incorporated with t... 详细信息

ISBN: (纸本)9781557528209

关键词： Femtosecond lasers

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Interfacial strong coupling and negative dispersion of propagating polaritons in freestanding oxide membranes

arXiv

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arXiv 2025年

作者： Lukaskawcez, Brayden Varshney, Shivasheesh Choo, Sooho Park, Sang Hyun Seo, Dongjea Thompson, Liam Uram, Devon Binger, Hayden Koester, Steve Oh, Sang-Hyun Low, Tony Jalan, Bharat McLeod, Alexander School of Physics and Astronomy University of Minnesota Twin Cities MinneapolisMN55455 United States Department of Chemical Engineering and Materials Science University of Minnesota Twin Cities MinneapolisMN55455 United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Membranes of complex oxides like perovskite SrTiO3 extend the multi-functional promise of oxide electronics into the nanoscale regime of two-dimensional materials. Here we demonstrate that free-standing oxide membranes supply a reconfigurable platform for nano-photonics based on propagating surface phonon polaritons. We apply infrared near-field imaging and - spectroscopy enabled by a tunable ultrafast laser to study pristine nano-thick SrTiO3 membranes prepared by hybrid molecular beam epitaxy. As predicted by coupled mode theory, we find that strong coupling of interfacial polaritons realizes symmetric and antisymmetric hybridized modes with simultaneously tunable negative and positive group velocities. By resolving reflection of these propagating modes from membrane edges, defects, and substrate structures, we quantify their dispersion with position-resolved nano-spectroscopy. Remarkably, we find polariton negative dispersion is both robust and tunable through choice of membrane dielectric environment and thickness and propose a novel design for in-plane Veselago lensing harnessing this control. Our work lays the foundation for tunable transformation optics at the nanoscale using polaritons in a wide range of freestanding complex oxide membranes. © 2025, CC BY.

关键词： Perovskite

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