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Statistical Analysis of Intrinsic High-Frequency Characteristic Fluctuation of Emerging Silicon Gate-All-Around Nanosheet (NS) MOSFETs at Sub-3-nm Nodes

Statistical Analysis of Intrinsic High-Frequency Characteris...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sekhar Reddy Kola Yiming Li Min-Hui Chuang Kazuhiko Endo Seiji Samukawa Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Institute of Communications Engineering Department of Electronics and Electrical Engineering Institute of Biomedical Engineering Center for mm Wave Smart Radar System and Technologies National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Fluid Science Tohoku University 2-1-1 Katahira Aoba-ku Sendai Japan National Institute of Advanced Industrial Science and Technology Tsukuba Japan

In recent years, the GAA NS Si MOSFET has been explored as a leading technology. However, the intrinsic parameters of GAA NS Si MOSFETs are affected to varying degrees by various fluctuation sources, Statistically independent and identically distributed $(iid)$ assumptions on the aforementioned random variables overestimate the variability of high-frequency characteristics, compared with considering all fluctuation factors simultaneously. Notably, the random nanosized metal grains dominates the variations of voltage gain, cut-off frequency, and 3dB frequency because the random work functions strongly alter the channel surface potential.

关键词： MOSFET Fluctuations Statistical analysis Gallium arsenide Cutoff frequency Metals Logic gates

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The effects of mobile marketing collaborator on consumers' impulse buying behavior in online marketplace during pandemic 6

The effects of mobile marketing collaborator on consumers' i...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Kusuma, Alvita Vania Daniel, Juan Hasri, Raja Alan Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665449373

This study analyzes the influence of the COVID-19 pandemic on impulse buying on the marketplace platform in Jakarta. This research is motivated because the COVID-19 pandemic period lasted quite a long time. Therefore, this research was conducted so that it can be useful for entrepreneurs or marketplace sellers during the COVID-19 pandemic. The research method used is descriptive quantitative method. Data collection techniques using questionnaires and literature study. The techniques used in statistical testing include validity testing, reliability testing, and hypothesis testing. When the research was conducted from September 2020 to January 2021. The results obtained were that there was a relationship between variables, such as mobile marketing, discounts, and lifestyle that could affect impulse buying on the marketplace even though they were moderately related. © 2021 IEEE.

关键词： COVID-19

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The Impact of Online Learning System Collaborator on Students' Ability 8

The Impact of Online Learning System Collaborator on Student...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Adrian, Steven Tandy, Jovan Ardiyansyah, Sultan Adhitya Evangeline, Gabriella Dendy Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Bina Nusantara University School of Information System Information Systems Department Jakarta Barat Indonesia Bina Nusantara University Binus Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665416979

Pandemic Covid-19 has change learning process to use online learning platform to limit interaction between participant of learning and reduce the Covid-19. This shifting has a major impact on education. Even though, many things have been done by education institution, lecturers, and students in adapting to online learning. In this study focus to check the effect of online learning on students' ability to understand the learning process. In this research, we use quantitative approach. To collect the data, this study uses a questionnaire method and in data analysis this study uses descriptive methods. In the results of this research can conclude that there is a positive influence from online learning on student's ability to understand the material. Therefore, higher education institution have to consider online learning to be good so that in the future it can continue to be developed or upgrade so that it can make it easier for students to understand the material and learn effectively and efficiently. © 2021 IEEE.

关键词： Students

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Spin-orbit-locked hyperbolic polariton vortices carrying reconfigurable topological charges

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eLight 2022年第1期2卷 143-153页

作者： Mingsong Wang Guangwei Hu Saroj Chand Michele Cotrufo Yohannes Abate Kenji Watanabe Takashi Taniguchi Gabriele Grosso Cheng‑Wei Qiu Andrea Alù Photonics Initiative Advanced Science Research CenterCity University of New YorkNew York CityNY 10031USA Department of Electrical and Computer Engineering National University of SingaporeSingapore 116083Singapore Department of Physics and Astronomy The University of GeorgiaAthensGA 30602USA Research Center for Functional Materials National Institute for Materials ScienceTsukubaIbaraki 305‑0044Japan International Center for Materials Nanoarchitectonics National Institute for Materials ScienceTsukubaIbaraki 305‑0044Japan Physics Program Graduate CenterCity University of New YorkNew YorkNY 10016USA

The topological features of optical vortices have been opening opportunities for free-space and on-chip photonic technologies,e.g.,for multiplexed optical communications and robust information *** a parallel but disjoint effort,polar anisotropic van der Waals nanomaterials supporting hyperbolic phonon polaritons(HP2s)have been leveraged to drastically boost light-matter *** far HP2 studies have been mainly focusing on the control of their amplitude and scale *** we report the generation and observation of mid-infrared hyperbolic polariton vortices(HP2Vs)associated with reconfigurable topological ***-shaped gold disks coated with a flake of hexagonal boron nitride are exploited to tailor spin-orbit interactions and realise deeply subwavelength *** complex interplay between excitation spin,spiral geometry and HP2 dispersion enables robust reconfigurability of the associated topological *** results reveal unique opportunities to extend the application of HP2s into topological photonics,quantum information processing by integrating these phenomena with single-photon emitters,robust on-chip optical applications,sensing and nanoparticle manipulation.

关键词： Hyperbolic polariton vortex Light-matter interactions Spin-orbit interaction Topological charge Reconfigurability

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Chest X-Ray Pneumonia Detection by Dense-Net 31

Chest X-Ray Pneumonia Detection by Dense-Net

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31st International Conference on computer Theory and Applications, ICCTA 2021

作者： Jassam, Israa F. Elkaffas, Saleh Mesbah El-Zoghabi, Adel A. Institute of Graduate Studies and Research Alexandria University Alexandria Egypt University of Anbar College of Computer Sciences and Information Technology Iraq College of Computing and It Arab Academy for Science Technology and Maritime Transport Alexandria Egypt

ISBN: (纸本)9781665478540

Chest diseases include various types with varying levels of riskiness. Pneumonia is among the most fatal infectious chest diseases and kills more children than any other infectious disease. In 2016, the United Nations International Children's Emergency Fund (UNICEF) reported that pneumonia had infected the lives of over 800,000 children under the age of five. This paper presents a deep learning model architecture that aims to detect pneumonia disease in the chest using X-ray images. The proposed model is based on the Dense-Net model which consists of a 121-layer convolutional neural network. This research use the published dataset from Guangzhou Women and Children's Medical Center. The dataset include chest X-ray images for patients age ranges from one to five years old labeled by physicians containing the required binary classes of Normal and Pneumonia classes labels. The model consists of the Dense-Net Model, three convolution neural network (CNN) layers, and a global average pooling (GAP) layer. The overlapping pooling helps in reducing the overfitting. The Decoupled Weight Decay Regularization is used as the optimizer for the proposed model with a small learning rate. The proposed model could detect pneumonia with an accuracy of 90%. Such a model could be relied on by radiologists for providing early detection of pneumonia to speed up the process of reprioritization of hundreds of chest X-rays needed to be diagnosed. © 2021 IEEE.

关键词： Diseases

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Gallium Oxide as Photonic Integrated Platforms in the UV-Visible Spectra

Gallium Oxide as Photonic Integrated Platforms in the UV-Vis...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Zhou, Jingan Zhao, Xuan Xu, Rui Zhang, Xiang Fu, Kai Li, Tao Xu, Mingfei Luo, Shisong He, Ziyi Robinson, Jacob T. Zhu, Hanyu Ajayan, Pulickel M. Zhang, Xiaodong Zhao, Yuji Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Nanofabrication facility Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences Jiangsu Suzhou215123 China

We report the Ga2O3 as photonic integrated platforms and its nonlinear optical effects in the UV-visible spectra. A low propagation loss of 3.7 dB/cm and second-order susceptibility of x(2)=4.89xl03pm/V were obtained.... 详细信息

ISBN: (纸本)9781957171258

关键词： Gallium compounds

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Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic Regression

arXiv

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arXiv 2024年

作者： Lou, Siyu Liu, Chengchun Chen, Yuntian Mo, Fanyang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315200 China School of Materials Science and Engineering Peking University Beijing100871 China -Preferred Program Peking University Shenzhen Graduate School Shenzhen518500 China

Thin-layer chromatography (TLC) is a crucial technique in molecular polarity analysis. Despite its importance, the interpretability of predictive models for TLC, especially those driven by artificial intelligence, remains a challenge. Current approaches, utilizing either high-dimensional molecular fingerprints or domain-knowledge-driven feature engineering, often face a dilemma between expressiveness and interpretability. To bridge this gap, we introduce Unsupervised Hierarchical Symbolic Regression (UHiSR), combining hierarchical neural networks and symbolic regression. UHiSR automatically distills chemical-intuitive polarity indices, and discovers interpretable equations that link molecular structure to chromatographic behavior. Copyright © 2024, The Authors. All rights reserved.

关键词： Thin layer chromatography

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Detection of retinal microlesions through YOLOR-CSP architecture and image slicing with the SAHI algorithm

Detection of retinal microlesions through YOLOR-CSP architec...

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International Joint Conference on Neural Networks (IJCNN)

作者： Alejandro Pereira Carlos Santos Marilton Aguiar Daniel Welfer Marcelo Dias Marcelo Ribeiro Reza Ahmadi Computer Science Federal University of Pelotas - UFPel Pelotas Brazil Federal Institute of Education Science and Technology Farroupilha - IFFar Alegrete Brazil Postgraduate Program in Computing Federal University of Pelotas - UFPel Pelotas Brazil Department of Applied Computing Federal University of Santa Maria - UFSM Santa Maria Brazil Geobotica Brisbane Australia

Diabetic retinopathy affects millions of working-age people worldwide. Of the countries in Latin America, Brazil has the highest incidence of cases. Diabetic retinopathy is detected through images of the fundus that contain lesions such as hard exudates, soft exudates, microaneurysms, and hemorrhages. Early identification of these lesions prevents the progression of the disease, which leads to a decrease in visual capacity. In addition, the early identification of these lesions allows the screening of patients who need priority care. The detection of these lesions occurs through the processing and analysis of fundus images using deep learning models. In this work, we present a new method that uses the You Only Learn One Representation with Cross Stage Partial Network (YOLOR-CSP) architecture combined with the Slicing Aided Hyper Inference (SAHI) framework to detect lesions. The proposed method was trained, adjusted, and evaluated using the Dataset for Diabetic Retinopathy (DDR) and the Indian Diabetic Retinopathy Image Dataset (IDRiD). The proposed method obtained in the data set DDR mAP equal to 38.08%, in the validation set, and 22.25% in the test set with SGD optimizer. The presented results were superior in the detection of eye fundus lesions in comparison with similar works found in the state-of-the-art literature.

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AD-PU: A Novel Approach for Automated Identification of the Outliers in User Interface Testing (UAT)

AD-PU: A Novel Approach for Automated Identification of the ...

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International Automatic Testing Conference, AUTOTESTCON

作者： S.M.N.S.K Seneviratne Victoria Penenco Dharshana Kasthurirathna Faculty of Graduate Studies and Research Sri Lanka Institute of Information Technology Sri Lanka UAT London Stock Exchange Group London Dept. of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

User acceptance testing (UAT) is the process that validates the system under test (SUT) behaves according to the business requirements. A User would use the graphical user interface to interact with the underlying code. With the improvement of various devices, end-user testing has become even more complex and time consuming. Every user has a unique interaction pattern. Due to this expected test coverage would not be met by focusing only on the functionality itself. This research is to address the issue of UAT test automation coverage and reduce defect leakage in production environments. The proposed automation solution is to identify more defects in the UAT static testing phase and make the system under test more user friendly before handing the product to the consumers. The proposed solution use Machine learning approach to identify user behaviors that does not match the proposed new functionality. The proposed AD-PU model would evaluate non-prominent user behaviours as per perceived usefulness which would effect the decision to accept the functionality as user acceptable or not.

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