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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： Amjath Ali Jakul Basha K. Saravanan T Mangayarkarasi C Balarengadurai RCC - ENGG Department Lecturer Computer Engineering Electrical and Electronic Section Department of Engineering Sultanate of Oman University of Technology and Applied Sciences Ibra Department of Information Technology R.M.D Engineering College Chennai Tamil Nadu India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru Karnataka India

ISBN: (纸本)9781665462761

COVID-19 has lately infected a big number of people worldwide. Medical service frameworks are strained as a result of the infection. The emergency unit, which is part of the medical services area, has experienced several challenges as a result of the low data quality offered by existing ICU clinical equipment. The Internet of Things has enhanced the capability for essential information mobility in medical services in the twenty-first century. Nonetheless, many of today's ideal models use IoT innovation to assess patients' well-being. As a result, executives lack understanding regarding the most effective method to apply such innovation to ICU clinical equipment. The IoT Based Paradigm for Medical Equipment Management Systems, a breakthrough IoT-based paradigm for successfully administering clinical hardware in ICUs, is introduced in this study. During the COVID-19 episode, IoT technology is used to boost the data stream between clinical hardware, executive frameworks, and ICUs, enabling the maximum level of openness and reasonableness in clinical equipment redistribution. IoT MEMS conceptual and functional features were painstakingly drawn. Using IoT MEMS expands the capacity and limits of emergency clinics, effectively easing COVID-19. It will also have a substantial impact on the nature of the data and will improve the partners' trust and transparency.

关键词： COVID-19 Micromechanical devices Biomedical equipment Technological innovation Medical services Production Hardware

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Long-range photodetection of organic exciton-polaritons

Long-range photodetection of organic exciton-polaritons

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Bin Liu Xinjing Huang Stephen R. Forrest Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor USA Applied Physics Program University of Michigan Ann Arbor USA Department of Physics University of Michigan Ann Arbor USA Department of Material Science and Engineering University of Michigan Ann Arbor USA

In light-matter strong coupling regime, we observe long-range photodetection response at room temperature mediated by organic exciton-polaritons, which results from strong interactions between organic excitons and low... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Couplings Surface waves Excitons Lasers and electrooptics Laser excitation Electrooptical waveguides

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Application of Segment Anything Model for Civil Infrastructure Defect Assessment

arXiv

引用

arXiv 2023年

作者： Ahmadi, Mohsen Lonbar, Ahmad Gholizadeh Naeini, Hajar Kazemi Beris, Ali Tarlani Nouri, Mohammadsadegh Javidi, Amir Sharifzadeh Sharifi, Abbas Department of Electrical Engineering and Computer Science Florida Atlantic University FL United States Department of Civil Architectural and Environmental Engineering Missouri University of Science and Technology RollaMO United States School of Civil Engineering College of Engineering University of Tehran Iran Department of Civil Engineering University of Texas at Arlington ArlingtonTX United States Department of Mechanical Engineering College of Engineering Boston University BostonMA United States School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran Center for Applied Coastal Research Department of Civil and Environmental Engineering University of Delaware NewarkDE United States Department of Civil and Environmental Engineering Florida International University MiamiFL United States

This research assesses the performance of two deep learning models, SAM and U-Net, for detecting cracks in concrete structures. The results indicate that each model has its own strengths and limitations for detecting different types of cracks. Using the SAM's unique crack detection approach, the image is divided into various parts that identify the location of the crack, making it more effective at detecting longitudinal cracks. On the other hand, the U-Net model can identify positive label pixels to accurately detect the size and location of spalling cracks. By combining both models, more accurate and comprehensive crack detection results can be achieved. The importance of using advanced technologies for crack detection in ensuring the safety and longevity of concrete structures cannot be overstated. This research can have significant implications for civil engineering, as the SAM and U-Net model can be used for a variety of concrete structures, including bridges, buildings, and roads, improving the accuracy and efficiency of crack detection and saving time and resources in maintenance and repair. In conclusion, the SAM and U-Net model presented in this study offer promising solutions for detecting cracks in concrete structures and leveraging the strengths of both models that can lead to more accurate and comprehensive results. Copyright © 2023, The Authors. All rights reserved.

关键词： Concrete buildings

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Optimized Security Mechanism for publicly Secret Key Sharing over Cloud using Blockchain

Optimized Security Mechanism for publicly Secret Key Sharing...

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2024 International Symposium on Securing Next Generation Systems Using Future Artificial Intelligence Technologies, SNSFAIT 2024

作者： Byeon, Haewon Quraishi, Aadam Thuniki, Praveen Keshta, Ismail Soni, Mukesh Shabaz, Mohammad Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of Intervention Treatment Institute HoustonTX United States Sr Program Analyst Georgia CummingGA30040 United States Computer Science and Information Systems Department College of Applied Sciences Al Maarefa University Riyadh Saudi Arabia Dr. D. Y. Patii Vidyapeeth Pune Dr. D. Y. Patii School of Science & Technology Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

A publicly verifiable key sharing mechanism based on threshold key sharing is provided to explore the security of users' private keys on the blockchain. Participating nodes check the key fragment after receiving it, effectively preventing it from being abused. The crucial sections of the nodes that participated in the critical splicing are made public during the critical recovery stage to prevent them from performing harmful things during the critical recovery stage. Add IDs to the nodes that participated in the crucial splicing during the key distribution stage;a dynamic threshold system is intended to track and update the status of malicious nodes in real time. When the node that possesses the crucial component fails, the owner of the critical component and the main node relocate a key element to the new participating nodes. To safeguard sensitive information. The experimental results show that this system has a key recovery rate of 80% and threshold qualities such as traceability, enforceability, and recoverability. © 2024 Copyright for this paper by its authors.

关键词： Blockchain Encryption Key sharing mechanism Privacy Secret key Security

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On the Utilization of Unique Node Identifiers in Graph Neural Networks

arXiv

引用

arXiv 2024年

作者： Bechler-Speicher, Maya Eliasof, Moshe Schönlieb, Carola-Bibiane Gilad-Bachrach, Ran Globerson, Amir Blavatnik School of Computer Science Tel-Aviv University Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Bio-Medical Engineering Tel-Aviv University Edmond J. Safra Center for Bioinformatics Tel-Aviv University Meta United States Google Research United States

Graph neural networks have inherent representational limitations due to their message-passing structure. Recent work has suggested that these limitations can be overcome by using unique node identifiers (UIDs). Here we argue that despite the advantages of UIDs, one of their disadvantages is that they lose the desirable property of permutation-equivariance. We thus propose to focus on UID models that are permutation-equivariant, and present theoretical arguments for their advantages. Motivated by this, we propose a method to regularize UID models towards permutation equivariance, via a contrastive loss. We empirically demonstrate that our approach improves generalization and extrapolation abilities while providing faster training convergence. On the recent BREC expressiveness benchmark, our proposed method achieves state-of-the-art performance compared to other random-based approaches. © 2024, CC BY.

关键词： Graph neural networks

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Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants

arXiv

引用

arXiv 2022年

作者： Gao, L. Mars Kutz, J. Nathan Paul G. Allen School of Computer Science & Engineering University of Washington SeattleWA United States Department of Applied Mathematics and Electrical and Computer Engineering University of Washington SeattleWA United States

Recent progress in autoencoder-based sparse identification of nonlinear dynamics (SINDy) under `1 constraints allows joint discoveries of governing equations and latent coordinate systems from spatio-temporal data, including simulated video frames. However, it is challenging for `1-based sparse inference to perform correct identification for real data due to the noisy measurements and often limited sample sizes. To address the data-driven discovery of physics in the low-data and high-noise regimes, we propose Bayesian SINDy autoencoders, which incorporate a hierarchical Bayesian sparsifying prior: Spike-and-slab Gaussian Lasso. Bayesian SINDy autoencoder enables the joint discovery of governing equations and coordinate systems with a theoretically guaranteed uncertainty estimate. To resolve the challenging computational tractability of the Bayesian hierarchical setting, we adapt an adaptive empirical Bayesian method with Stochatic gradient Langevin dynamics (SGLD) which gives a computationally tractable way of Bayesian posterior sampling within our framework. Bayesian SINDy autoencoder achieves better physics discovery with lower data and fewer training epochs, along with valid uncertainty quantification suggested by the experimental studies. The Bayesian SINDy autoencoder can be applied to real video data, with accurate physics discovery which correctly identifies the governing equation and provides a close estimate for standard physics constants like gravity g, for example, in videos of a pendulum. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

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Topological superconductors from a materials perspective

arXiv

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arXiv 2023年

作者： Mandal, Manasi Drucker, Nathan C. Siriviboon, Phum Nguyen, Thanh Boonkird, Tiya Lamichhane, Tej Nath Okabe, Ryotaro Chotrattanapituk, Abhijatmedhi Li, Mingda Quantum Measurement Group MIT CambridgeMA02139 United States Department of Nuclear Science and Engineering MIT CambridgeMA02139 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Physics MIT CambridgeMA02139 United States Department of Chemistry MIT CambridgeMA02139 United States Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States

Topological superconductors (TSCs) have garnered significant research and industry attention in the past two decades. By hosting Majorana bound states which can be used as qubits that are robust against local perturbations, TSCs offer a promising platform toward (non-universal) topological quantum computation. However, there has been a scarcity of TSC candidates, and the experimental signatures that identify a TSC are often elusive. In this perspective, after a short review of the TSC basics and theories, we provide an overview of the TSC materials candidates, including natural compounds and synthetic material systems. We further introduce various experimental techniques to probe TSC, focusing on how a system is identified as a TSC candidate, and why a conclusive answer is often challenging to draw. We conclude by calling for new experimental signatures and stronger computational support to accelerate the search for new TSC candidates. © 2023, CC BY.

关键词： Quantum computers

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Personalized Federated Learning for Histopathological Prediction of Lung Cancer

Personalized Federated Learning for Histopathological Predic...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Browne Judith Ayekai Chen Wenyu Gyarteng Emmanuel Sarpong Addai Ativi Wisdom Xornam Klugah Stephanie Mawuena Cobbinah Bernard Mawuli Delanyo Kulevome Bless Lord Agbley Regina Esi Turkson Ewald Erubaar Kuupole School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science and Information Technology University of Cape Coast PMB University Post Office Cape Coast Ghana C.K. Tedam University of Tech. and Applied Sciences Navrongo Ghana

Lung cancer is a leading contributor to cancer-related fatalities worldwide, and histopathological image analysis plays a critical role in cancer detection by identifying morphological abnormalities in tissue samples. Artificial intelligence (AI) in medicine has evolved but still faces challenges, notably in maintaining data privacy and security. Federated Learning (FL) has emerged as a promising solution, enabling the training of robust models without jeopardizing data privacy. However, the effectiveness of existing FL approaches often falters in non-independent and identically distributed (non-IID) scenarios, where data distributions vary across clients. Addressing this challenge, our research presents a personalized federated learning (PFL) method specifically designed for lung cancer prediction using histopathological scans. This novel framework leverages client-specific autoencoders coupled with hierarchical clustering for personalized federated learning for lung cancer prediction. Our findings demonstrate the efficacy of the proposed method for collaborative lung cancer prediction in medical heterogeneous data environments while preserving data privacy.

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ENHANCING PRINTED CIRCUIT BOARD DEFECT DETECTION THROUGH ENSEMBLE LEARNING

arXiv

引用

arXiv 2024年

作者： Law, Ka Nam Canaan Yu, Mingshuo Zhang, Lianglei Zhang, Yiyi Xu, Peng Liu, Jun Gao, Jerry Department of Applied Data Science San José State University San José United States Dept. of ISE Virginia Tech Blacksburg United States Dept. of Computer Engineering San José State University San José United States

The quality control of printed circuit boards (PCBs) is paramount in advancing electronic device technology. While numerous machine learning methodologies have been utilized to augment defect detection efficiency and accuracy, previous studies have predominantly focused on optimizing individual models for specific defect types, often overlooking the potential synergies between different approaches. This paper introduces a comprehensive inspection framework leveraging an ensemble learning strategy to address this gap. Initially, we utilize four distinct PCB defect detection models utilizing state-of-the-art methods: EfficientDet, MobileNet SSDv2, Faster RCNN, and YOLOv5. Each method is capable of identifying PCB defects independently. Subsequently, we integrate these models into an ensemble learning framework to enhance detection performance. A comparative analysis reveals that our ensemble learning framework significantly outperforms individual methods, achieving a 95% accuracy in detecting diverse PCB defects. These findings underscore the efficacy of our proposed ensemble learning framework in enhancing PCB quality control processes. © 2024, CC BY.

关键词： Adversarial machine learning

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Inverted-circuit zero-noise extrapolation for quantum gate error mitigation

arXiv

引用

arXiv 2024年

作者： Koenig, Kathrin F. Reinecke, Finn Hahn, Walter Wellens, Thomas Fraunhofer Institute for Applied Solid State Physics Tullastr. 72 Freiburg79108 Germany Department of Sustainable Systems Engineering University of Freiburg Emmy-Noether-Str. 2 Freiburg79110 Germany Department of Computer Science University of Freiburg Georges-Köhler-Allee 51 Freiburg79110 Germany

A common approach to deal with gate errors in modern quantum-computing hardware is zero-noise extrapolation. By artificially amplifying errors and extrapolating the expectation values obtained with different error strengths towards the zero-error (zero-noise) limit, the technique aims at rectifying errors in noisy quantum computing systems. For accurate extrapolation, it is essential to know the exact factors of the noise amplification. In this article, we propose a simple method for estimating the strength of errors occurring in a quantum circuit and demonstrate improved extrapolation results. The method determines the error strength for a circuit by appending to it the inverted circuit and measuring the probability of the initial state. The estimation of error strengths is easy to implement for arbitrary circuits and does not require previous characterization of noise properties. We compare this method with the conventional zero-noise extrapolation method and show that our method leads to a more accurate calculation of expectation values on current quantum hardware, showcasing its suitability for near-term quantum computing applications. © 2024, CC BY.

关键词： Quantum computers

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