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SSRN 2022年

作者： Abiyev, Rahib H. Kaynak, Okyay Aliev, Rafiq Near East University Department of Computer Engineering Mersin-10 North Cyprus Lefkosa Turkey Bogazici University Department of Electrical and Electronics Engineering Bebek Istanbul34342 Turkey Joint MBA Program Georgia State University United States Azerbaijan State Oil and Industry University 20 Azadliq ave. BakuAZ1010 Azerbaijan

In this paper, a novel Z-number based Fuzzy Neural Network (Z-FNN) is proposed for dynamic system identification. The architecture and learning algorithm of the proposed Z-FNN are designed. The inference mechanism of Z-FNN is based on distance measures and interpolative reasoning. The structure of the Z-FNN is accordingly designed using an interpolative reasoning scheme. Genetic Algorithms (GA) is applied to update the unknown parameters of the network. The proposed Z-FNN system is applied for dynamic plant identification. Its performance is compared with those obtained by the use of other approaches from the literature and it is proved that the proposed approach is a suitable alternative for practical applications that are characterized by uncertain and imprecise information. © 2022, The Authors. All rights reserved.

关键词： Fuzzy neural networks

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Characterization of Fluorinated Polyimide Films

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MRS Online Proceedings Library 2020年第1期264卷 263-268页

作者： Pradnya V. Nagarkar Jiong Ping Lu David Volfson Klavs F. Jensen Stephen D. Senturia Electronics Packaging Program and Department of Materials Science & Engineering Massachusetts Institute of Technology Cambridge Electronics Packaging Program and Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Electronics Packaging Program and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge

X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR) have been used to characterize the polyimide film based on 4,4′ hexafluoro-isopropylidene -bis pthalic anhydride (HFDA) and 4,4′ -bis (4-aminophenoxy) biphenyl (APBP). Films of varying thicknesses made from diluted precursors were studied by IR and XPS. An elemental analysis and a tentative peak assignment for C 1s in XPS is presented. The HFDA-APBP thick films are stoichiometric in composition and binding energies are in good agreement with data on hexafluorodianhydride-oxydianiline (HFDA-ODA). For thinner films, certain chemical modifications were observed at high cure temperatures.

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Stability, metallicity, and magnetism in niobium silicide nanofilms

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Physical Review Materials 2022年第6期6卷 064402-064402页

作者： Xuezeng Lu Dominic P. Goronzy Carlos G. Torres-Castanedo Paul Masih Das Maryam Kazemzadeh-Atoufi Anthony McFadden Corey Rae H. McRae Peter W. Voorhees Vinayak P. Dravid Michael J. Bedzyk Mark C. Hersam James M. Rondinelli Department of Materials Science and Engineering Northwestern University Evanston Illinois 60208 USA International Institute for Nanotechnology Northwestern University Evanston Illinois 60208 USA National Institute of Standards and Technology Boulder Colorado 80305 USA Department of Electrical Computer & Energy Engineering University of Colorado Boulder Boulder Colorado 80309 USA Department of Physics University of Colorado Boulder Colorado 80309 USA Northwestern University Atomic and Nanoscale Characterization Experimental Center (NUANCE) Northwestern University Evanston Illinois 60208 USA Department of Physics and Astronomy Northwestern University Evanston Illinois 60208 USA Applied Physics Program Northwestern University Evanston Illinois 60208 USA Department of Chemistry Northwestern University Evanston Illinois 60208 USA Department of Electrical and Computer Engineering Northwestern University Evanston Illinois 60208 USA

Modern superconducting qubits based on two-dimensional (2D) transmons typically involve the growth of Nb thin films on high-resistivity Si substrates. Since imperfections at the Nb-Si heterointerface have been implicated as a source of two-level systems that limit quantum coherence times, detailed characterization and understanding of niobium silicide interfacial layers are critical to improving superconducting qubit technology. While bulk binary intermetallic niobium silicide phases are well understood, the thermodynamic phase stability and properties of ultrathin niobium silicides, such as those found at the Nb-Si heterointerface in 2D transmons, have not yet been explored. Here, we report finite-sized effects for ultrathin niobium silicide films using density functional theory calculations and predict nanoscale stabilization of Nb6Si5 over the bulk α−Nb5Si3 phase. This result is consistent with our experimental observations of a niobium silicide interfacial layer between a sputtered Nb thin film and the underlying Si substrate. Furthermore, our calculations show that Nb6Si5 nanofilms are nonmagnetic, making them superior to nanofilms of α−Nb5Si3 that exhibit antiferromagnetic correlations detrimental to long coherence times in superconducting qubits. By providing atomic-scale insight into niobium silicide nanofilms, this paper can help guide ongoing efforts to optimize Nb-Si heterointerfaces for long coherence times in superconducting qubits.

关键词： Interface & surface thermodynamics Interfaces Superconducting qubits Thin films Density functional theory

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Web Interactive Animation and Information for Electrodialysis to Desalinate Water

Web Interactive Animation and Information for Electrodialysi...

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2020 Annual Conference Northeast Section, ASEE-NE 2020

作者： Lehoe, James A. Whitlow, Joseph T. Alborz, Nakisa Dow, Douglas E. Program in Computer Eng. Wentworth Institute of Technology BostonMA United States Dept. of Iterdisciplinary Eng. Wentworth Institute of Technology BostonMA United States Dept. of Electrical and Computer Engineering Wentworth Institute of Technology BostonMA United States

ISBN: (纸本)9781665405256

Access to safe drinking water has progressively declined. Methods of purifying saltwater include reverse osmosis and distillation, but require cost and energy that may not be accessible to many. Another method for desalination is electrodialysis that imposes a voltage field to induce net movement of chlorine and sodium ions through two semipermeable membranes, resulting in water having a lower salt concentration. Electrodialysis has lower financial cost and energy requirements, showing promise as a potential method that may be more accessible in developing regions. To encourage education and exploration of this method for desalination, this project is developing an educational web-based informational site and interactive graphical program. Users can interact and visualize net movement of charged ions within voltage fields, and in the presence of semipermeable membranes. As these educational webbased tools would be available globally to anyone with a web assessible device, many people may be able to access and become knowledgeable about and interested in the potential of electrodialysis. © 2020 IEEE.

关键词： Desalination

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Model Technology Service E-Participation - Voting (E-PV) for Political Communication between the Council of Regional Representatives (DPRD) and the Citizens Using the Framework Information Technology Infrastructure Library (ITIL V.3) Transition Domain Service 7

Model Technology Service E-Participation - Voting (E-PV) for...

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Hilabi, Shofa Shofiah Harso Supangat, Suhono Hendric Spits Warnars, Harco Leslie Prabowo, Harjanto Mursitama, Tirta Nugraha Universitas Buana Perjuangan Faculty of Engineering and Computer Science Karawang Indonesia Institute of Technology Bandung School of Electrical Engineering and Informatics Bandung Indonesia Nusantara University Doctor of Computer Science Bina Computer Science Department BINUS Graduate Program Jakarta11530 Indonesia Bina Nusantara University Doctor of Computer Science Jakarta11530 Indonesia Bina Nusantara University Faculty of Humanities International Relations Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

E-voting is an electronic voting information technology service, while E-participation is an information technology service for citizens of the public to provide reports, polls, or suggestions on government policies electronically. The Technology Services E-Participation-VOT (EPV) model is a combined E-Participation and E-Voting service model. The concept of this service model is that citizens transform from E-participation and E-voting services into an EPV model. The purpose of this model is to provide information to participants (citizens) about online-based voting services. This method of developing the EP-Voting Anticipation (EPV) technology model uses agile methods that are effective and flexible in various cases of designing Information Technology models for Information Technology (IT) services that apply a Framework Information Technology Infrastructure Library (ITIL). The results obtained from this study are an Information Technology service system model that provides recommendations for participation in conducting electronic elections (E-Voting). © 2020 IEEE.

关键词： Surveys

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Giant Deep-Ultraviolet Circular Dichroism in Synthetic Architectures of Ordered Carbon Nanotubes

Giant Deep-Ultraviolet Circular Dichroism in Synthetic Archi...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Jacques Doumani Minhan Lou Oliver Dewey Nina Hong Jichao Fan Andrey Baydin Matteo Pasquali Yohei Yomogida Kazuhiro Yanagi Riichiro Saito Junichiro Kono Weilu Gao Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Electrical and Computer Engineering The University of Utah Salt Lake City UT USA Carbon Hub Rice University Houston TX USA Department of Chemical and Biomolecular Engineering Rice University Houston TX USA J.A. Woollam Co. Lincoln NE USA The Smalley-Curl Institute Rice University Houston TX USA Department of Chemistry Rice University Houston TX USA Department of Materials Science and NanoEngineering Rice University Houston TX USA Department of Physics and Astronomy Tokyo Metropolitan University Tokyo Japan Department Physics Tohoku University Sendai Japan Department Physics and Astronomy Rice University Houston TX USA

We demonstrate tunable, giant, and structure-induced deep-ultraviolet circular dichroism in macroscopically chiral assemblies of racemic carbon nanotubes prepared using two approaches: mechanical-rotation-assisted vac... 详细信息

ISBN: (纸本)9781957171258

关键词： Carbon nanotubes Chiral media Dichroism Graphene Optical activity Quantum information

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Reinforcement Learning-Based Edge Caching for IoT in Compute-First Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Asmat, Hamid Din, Ikram Ud Almogren, Ahmad Rodrigues, Joel J. P. C. Department of Information Technology University of Haripur Haripur22620 Pakistan King Saud University College of Computer and Information Sciences Chair of Cyber Security Department of Computer Science Riyadh11633 Saudi Arabia Post-graduation Program in Electrical Engineering Teresina Brazil

6G networks require ultra-low latency and intelligent resource management in IoT environments. Compute-First Networking (CFN), integrating distributed computing and edge caching, addresses backhaul congestion. This paper proposes CaRL, a DRL-based caching framework using TD3 algorithm. CaRL optimizes cache placement at edge nodes by incorporating real-time network state, content popularity, and resource utilization. Simulations show CaRL improves cache hit ratio, reduces latency, and enhances bandwidth efficiency, and ensures secure caching decisions under dynamic network conditions. Unlike existing methods, CaRL adapts to dynamic network conditions by integrating latency, CPU, and memory metrics, demonstrating scalability and adaptability in CFN-driven 6G environments. © 1975-2011 IEEE.

关键词： Edge computing

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Detecting QT prolongation From a Single-lead ECG With Deep Learning

arXiv

引用

arXiv 2023年

作者： Alam, Ridwan Aguirre, Aaron D. Stultz, Collin M. Research Laboratory of Electronics MIT CambridgeMA United States Computer Science & Artificial Intelligence Laboratory MIT CambridgeMA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Wellman Center for Photomedicine Massachusetts General Hospital BostonMA United States Division of Cardiology Massachusetts General Hospital BostonMA United States Harvard Medical School BostonMA United States Harvard-MIT Program in Health Sciences and Technology CambridgeMA United States Institute for Medical Engineering and Science MIT CambridgeMA United States

Background and Aims For a number of antiarrhythmics, drug loading requires a 3-day hospitalization with monitoring for QT-prolongation. Automated QT monitoring with wearable ECG monitors would facilitate out-of-hospital care. We aim to develop a deep learning model that infers QT intervals from ECG lead-I - the lead most often acquired from ambulatory ECG monitors - and we use this model to detect clinically meaningful QT-prolongation episodes during Dofetilide drug loading. Methods Using 4.22 million 12-lead ECG recordings from 903.6 thousand patients at the Massachusetts General Hospital, we develop a deep learning model, QTNet, that infers QT intervals from lead-I. Over 3 million ECGs from 653 thousand patients are used to train the model and an internal-test set containing 633 thousand ECGs from 135 thousand patients was used for testing. QTNet is further evaluated on an external-validation set containing 3.1 million ECGs from 667 thousand patients at another institution. QTNet was used to detect Dofetilide-induced QT prolongation in a publicly available database (ECGRDVQ-dataset) containing ECGs from subjects enrolled in a clinical trial evaluating the effects of antiarrhythmic drugs. Results QTNet achieves mean absolute errors of 12.63ms (internal-test) and 12.30ms (external-validation) for estimating absolute QT intervals. The associated Pearson correlation coefficients are 0.91 (internal-test) and 0.92 (external-validation). For the ECGRDVQ-dataset, QTNet detects Dofetilide-induced QTc prolongation with 87% sensitivity and 77% specificity. The negative predictive value of the model is greater than 95% when the pre-test probability of drug-induced QTc prolongation is below 25%. Conclusions Drug-induced QT prolongation risk can be tracked from ECG lead-I using deep learning. This research leads the path toward out-of-hospital care using wearable ECG devices for antiarrhythmic therapies. © 2023, CC BY-NC-ND.

关键词： Electrocardiograms

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Leveraging Computational Pathology AI for Noninvasive Optical Imaging Analysis Without Retraining

arXiv

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arXiv 2024年

作者： Barash, Danny Manning, Emilie Van Vleck, Aidan Hirsch, Omri Aye, Kyi Lei Li, Jingxi Scumpia, Philip O. Ozcan, Aydogan Aasi, Sumaira Rieger, Kerri E. Sarin, Kavita Y. Freifeld, Oren Winetraub, Yonatan Department of Computer Science Ben Gurion University Be'er Sheva8499000 Israel Department of Structural Biology Stanford University StanfordCA94305 United States Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Division of Dermatology University of California Los AngelesCA90095 United States Department of Dermatology Stanford University School of Medicine StanfordCA94305 United States Department of Pathology Stanford University School of Medicine Stanford Cancer Institute StanfordCA94305 United States The Bio-X Program StanfordCA94305 United States

Noninvasive optical imaging modalities can probe patient's tissue in 3D and over time generate gigabytes of clinically relevant data per sample. There is a need for AI models to analyze this data and assist clinical workflow. The lack of expert labelers and the large dataset required (>100,000 images) for model training and tuning are the main hurdles in creating foundation models. In this paper we introduce FoundationShift, a method to apply any AI model from computational pathology without retraining. We show our method is more accurate than state of the art models (SAM, MedSAM, SAM-Med2D, CellProfiler, Hover-Net, PLIP, UNI and ChatGPT), with multiple imaging modalities (OCT and RCM). This is achieved without the need for model retraining or fine-tuning. Applying our method to noninvasive in vivo images could enable physicians to readily incorporate optical imaging modalities into their clinical practice, providing real time tissue analysis and improving patient care. © 2024, CC BY-NC-ND.

关键词： Medical imaging

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Weight vector tuning and asymptotic analysis of binary linear classifiers

arXiv

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arXiv 2021年

作者： Niyazi, Lama B. Kammoun, Abla Dahrouj, Hayssam Alouini, Mohamed-Slim Al-Naffouri, Tareq Y. Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia Center of Excellence for NEOM Research at KAUST King Abdullah University of Science and Technology Thuwal Saudi Arabia

Unlike its intercept, a linear classifier’s weight vector cannot be tuned by a simple grid search. Hence, this paper proposes weight vector tuning of a generic binary linear classifier through the parameterization of a decomposition of the discriminant by a scalar which controls the trade-off between conflicting informative and noisy terms. By varying this parameter, the original weight vector is modified in a meaningful way. Applying this method to a number of linear classifiers under a variety of data dimensionality and sample size settings reveals that the classification performance loss due to non-optimal native hyperparameters can be compensated for by weight vector tuning. This yields computational savings as the proposed tuning method reduces to tuning a scalar compared to tuning the native hyperparameter, which may involve repeated weight vector generation along with its burden of optimization, dimensionality reduction, etc., depending on the classifier. It is also found that weight vector tuning significantly improves the performance of Linear Discriminant Analysis (LDA) under high estimation noise. Proceeding from this second finding, an asymptotic study of the misclassification probability of the parameterized LDA classifier in the growth regime where the data dimensionality and sample size are comparable is conducted. Using random matrix theory, the misclassification probability is shown to converge to a quantity that is a function of the true statistics of the data. Additionally, an estimator of the misclassification probability is derived. Finally, computationally efficient tuning of the parameter using this estimator is demonstrated on real data. Copyright © 2021, The Authors. All rights reserved.

关键词： Random variables

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