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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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Evolving granular classification neural networks

Evolving granular classification neural networks

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2009 International Joint Conference on Neural Networks, IJCNN 2009

作者： Leite, Daniel F. Costa Jr., Pyramo Gomide, Fernando Faculty of Electrical and Computer Engineering University of Campinas Brazil Graduate Program in Electrical Engineering Catholic University of Minas Gerais Brazil

ISBN: (纸本)9781424435531

The objective of this study is to introduce the concept of evolving granular neural networks (eGNN) and to develop a framework of information granulation and its role in the online design of neural networks. The suggested eGNN are neural models supported by granule-based learning algorithms whose aim is to tackle classification problems in continuously changing environments. eGNN are constructed from streams of data using fast incremental learning algorithms. eGNN models require a relatively small amount of memory to perform classification tasks. Basically, they try to find information occurring in the incoming data using the concept of granules and T-S neurons as basic processing elements. The main characteristics of eGNN models are continuous learning, self-organization, and adaptation to unknown environments. Association rules and parameters can be easily extracted from its structure at any step during the evolving process. The rule base gives a granular description of the behavior of the system in the input space together with the associated classes. To illustrate the effectiveness of the approach, the paper considers the Iris and Wine benchmark problems. © 2009 IEEE.

关键词： Learning algorithms

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OPRLM: A Web Tool and a Database for Positioning and Simulations of Proteins in Realistic Lipid Membranes

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Journal of Molecular Biology 2025年 168966页

作者： Park, Sang-Jun Schnitzer, Kyle A. Kovalenko, Alexey Cherepanov, Stanislav Patro, L. Ponoop Prasad Song, Zigang Pogozheva, Irina D. Lomize, Andrei L. Im, Wonpil Department of Computer Science and Engineering Lehigh University 111 Research Drive Bethlehem 18015 PA United States Department of Electrical Engineering and Computer Science College of Engineering University of Michigan 1221 Beal Ave Ann Arbor 48109-2102 MI United States Department of Computer Science College of Literature Science and the Arts University of Michigan 2260 Hayward Street Ann Arbor 48109-2121 MI United States Biophysics Program University of Michigan 930 N. University Avenue Ann Arbor 48109-1065 MI United States Department of Biological Sciences Lehigh University 111 Research Drive Bethlehem 18015 PA United States Department of Medicinal Chemistry College of Pharmacy University of Michigan 428 Church St. Ann Arbor 48109-1065 MI United States

Molecular dynamics (MD) simulations in explicit lipid bilayers enable modeling of protein-lipid interactions essential for membrane protein functions and regulation. The newly developed computational web tool, OPRLM (Orientations of Proteins in Realistic Lipid Membranes), automates the assembly of membrane protein structures with explicit lipids corresponding to 18 biological membrane types with symmetric or asymmetric lipid distributions, as well as 5 types of two-component lipid bilayers with varying cholesterol content. Built upon the CHARMM-GUI toolset and the PPM method, OPRLM simplifies the setup of complex simulation system involving integral and/or peripheral membrane proteins with explicit lipid mixtures and generates all necessary files for subsequent all-atom MD simulations. OPRLM has successfully generated protein-membrane systems for 286 tested protein structures in various biomembranes, including 138 structures containing ligands. The OPRLM database, an advanced successor of the OPM database, includes explicit protein-lipid systems for tested proteins in their native biomembranes. It provides coordinates of integral and peripheral membrane proteins from the Protein Data Bank embedded in planar or curved implicit lipid bilayers. Additionally, it includes the classification of proteins into types, superfamilies, and families, along with the information on intracellular localizations and membrane topology and visualization options. The OPRLM web tool and the database are publicly accessible at https://***. © 2025 The Authors

关键词： biomembrane explicit lipid lipid bilayer peripheral membrane protein transmembrane

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Emergent dimer-model topological order and quasi-particle excitations in liquid crystals: combinatorial vortex lattices

arXiv

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arXiv 2025年

作者： Meng, Cuiling Wu, Jin-Sheng Kos, Žiga Dunkel, Jörn Nisoli, Cristiano Smalyukh, Ivan I. Department of Physics and Chemical Physics Program University of Colorado BoulderCO80309 United States Hiroshima University Hiroshima Higashihiroshima Japan Department of Mathematics Massachusetts Institute of Technology CambridgeMA02139 United States Faculty of Mathematics and Physics University of Ljubljana Ljubljana Slovenia Department of Condensed Matter Physics Jožef Stefan Institute Ljubljana Slovenia Theoretical Division Physics of Condensed Matter and Complex Systems Center For Nonlinear Studies Los Alamos National Laboratory Los AlamosNM87545 United States Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program University of Colorado BoulderCO80309 United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado BoulderCO80309 United States

Liquid crystals have proven to provide a versatile experimental and theoretical platform for studying topological objects such as vortices, skyrmions, and hopfions. In parallel, in hard condensed matter physics, the concept of topological phases and topological order has been introduced in the context of spin liquids to investigate emergent phenomena like quantum Hall effects and high-temperature superconductivity. Here, we bridge these two seemingly disparate perspectives on topology in physics. Combining experiments and simulations, we show how topological defects in liquid crystals can be used as versatile building blocks to create complex, highly degenerate topological phases, which we refer to as ‘Combinatorial Vortex Lattices’ (CVLs). CVLs exhibit extensive residual entropy and support locally stable quasi-particle excitations in the form of charge-conserving topological monopoles, which can act as mobile information carriers and be linked via Dirac strings. CLVs can be rewritten and reconfigured on demand, endowed with various symmetries, and modified through laser-induced topological surgery — an essential capability for information storage and retrieval. We demonstrate experimentally the realization, stability, and precise optical manipulation of CVLs, thus opening new avenues for understanding and technologically exploiting higher-hierarchy topology in liquid crystals and other ordered media. Copyright © 2025, The Authors. All rights reserved.

关键词： Laser surgery

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Rapid design of testable, high-performance/capacity associative memories 2

Rapid design of testable, high-performance/capacity associat...

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2nd IEEE Symposium on Parallel and Distributed Processing, SPDP 1990

作者： Miller, P.M. Hurson, A.R. Computer Engineering Program Department of Electrical and Computer Engineering Pennsylvania State University University ParkPA16802 United States

ISBN: (纸本)0818620870

VLSI technology has created the opportunity for designers to develop special-purpose application-specific architectures based on associative processing techniques. However the diversity of these applications creates a need for a generalized methodology to quickly produce high-capacity associative chips with different functionalities. The paper introduces such a scheme for designing special-purpose content-addressable memories (CAMs). An overall CAM organization suitable for most special-purpose associative chips is presented and the design, layout, and performance of its modular components is discussed. An example CAM is examined to show the performance and area efficiency of CAMs designed using the proposed scheme. The paper also introduces an improved, highly compact theta -search associative cell. Finally, the testing of CAMs using this organization is discussed. © 1990 IEEE.

关键词： Associative storage

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Observability of a Specially Structured Model

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Journal of Vibration and Control 1995年第4期1卷 493-499页

作者： Ansary, Omid Electrical and Computer Engineering Technology Program Pennsylvania State University Middletown PA 17057 United States

The theorems associated with the observability matrices and gramians play a crucial role in both analysis and design of control systems. This paper presents the development of these theorems and their proofs in the context of a computationally attractive system model. This is accomplished by incorpo rating the structural properties of the model into the theorems that are found in the literature. The developed theorems lead to a generalized form of the Liapunov equation. An analytical solution for this equation is also presented. Finally, an example is given to illustrate the application of the proposed theorems. © 1995, Sage Publications. All rights reserved.

关键词： analysis descriptor-like model generalized Liapunov equation generalized observability gramian Generalized observability matrix

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Adaptive Neuro-Fuzzy Inference System As New Real-Time Approach for Parkinson Seizures Prediction 4

Adaptive Neuro-Fuzzy Inference System As New Real-Time Appro...

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4th International Conference on Bio-engineering for Smart Technologies, BioSMART 2021

作者： Daher, Alaa Yassin, Sally Alsamra, Hadi Ali, Hassan Abou Beirut Arab University Biomedical Engineering Program Faculty of Engineering Electrical and Computer Engineering Department Debbieh Lebanon

ISBN: (纸本)9781665408103

Parkinson's disease, as a definition, is a neurological condition that affects the brain and causes tremors, stiffness, and difficulties walking, balancing, and coordinating. Symptoms of Parkinson's disease normally appear gradually and worsen with time. People with Parkinson's disease may have difficulties walking and speaking as the condition develops. Numerous recent studies have shown a direct association between Parkinson's disease and the incident of having epileptic seizures, which is defined to be a burst of the uncontrollable electrical activity of the brain cells, that is associated with an increased risk of sudden unexplained deaths. This project aims to obtain a real-time seizure prediction system for Parkinson's disease patients based on the electroencephalogram (EEG) signals, enabling the detection of a seizure before it happens. This will hopefully save them from risky situations or sudden death, as they will be alerted and have the time enabling them to be prepared and take the needed precautions and steps to prevent the seizure from happening. For this project, we've used the Neural Network and the ANFIS ('udaptive neuro-fuzzy inference system '') to process and analyze the electroencephalogram (EEG) data signals of the Parkinson patients to detect seizures starting point. © 2021 IEEE.

关键词： Brain

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The Viterbi Algorithm as an Aid in Text Recognition

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IEEE Transactions on Information Theory 1975年第2期21卷 222-226页

作者： Neuhoff, David L. Electrical Engineering Department Stanford University Department of Electrical and Computer Engineering Computer Information and Control Engineering Program University of Michigan Stanford Calif. Ann Arbor Mich. United States

The results of an experiment are described in which contextual information is used to improve the performance of an optical character reader when reading English text. Specifically, English is modeled as a Markov source and the Viterbi algorithm is used to do maximum a posteriori sequence estimation on the output of an optical character reader (OCR). © 1975 IEEE. All rights reserved.

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Early Diagnosis of Osteoporosis: An Artificial Intelligence-Based Framework 7

Early Diagnosis of Osteoporosis: An Artificial Intelligence-...

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7th International Conference on Advances in Biomedical engineering, ICABME 2023

作者： Zaylaa, Amira J. Daher, Alaa Ayache, Mohammad Omeirat, Niveen Mahmoud, Razane Hawili, Bader Abou Beirut Arab University Biomedical Engineering Laboratory Biomedical Engineering Program Electrical and Computer Engineering Faculty of Engineering Lebanon

ISBN: (纸本)9798350325850

Osteoporosis is a common disease characterized by low bone density and structural deterioration of bone tissue. For a successful course of treatment and fracture avoidance, early diagnosis of this disease is essential. The aim was to provide a novel method for osteoporosis prediction using Artificial Intelligence (AI)-based framework. The purpose was to predict the likelihood of osteoporosis based on the Bone Mineral Density (BMD), along with other characteristics such as age, weight, height, gender, and Body Mass Index (BMI) extracted from medical reports and images collected from a comprehensive medical center in Lebanon. Three machine-learning algorithms were implemented and tested, Logistic Regression (LR), Support Vector Machines (SVM), and Decision Trees (DT). Variety of quantitative statistical metrics were used to evaluate the performance of our framework, upon training and testing our algorithms. The metrics that were employed to evaluate our results included accuracy, precision, sensitivity, and F-score, in addition to the Receiver Operating Characteristic (ROC) Curve and the Area Under the Curve (AUC). Experimental Results demonstrated that both the SVM and LR algorithms achieved the highest accuracy of detection of osteoporosis as compared to existing algorithms applied in this field, with an accuracy of 89%. The sensitivity of diagnosis obtained was 98% by LR and 97% by SVM and surpassed the sensitivity obtained by DT. As such LR showed the best performance. The output of the algorithms could help medical doctors assess patients automatically. These findings demonstrated the potential of AI in osteoporosis prediction and thus prevention, highlighting the significance of early diagnosis. Thereby as a future prospect, choosing carefully the framework is crucial and additional algorithms have to be considered and tested. © 2023 IEEE.

关键词： Decision trees

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Basic electrical parameters measurement laboratory: A K-12 outreach project 125

Basic electrical parameters measurement laboratory: A K-12 o...

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125th ASEE Annual Conference and Exposition

作者： Dua, Rohit Department of Electrical and Computer Engineering Missouri University of Science and Technology Cooperative Engineering Program United States

A basic laboratory exercise was designed and implemented, for K-12 students, which delves into the measurement of basics of electrical circuit parameters, such as resistance, current and voltage. This 2-3 hour lab exercise gives students a glimpse into basic electrical engineering concepts, which are covered in an undergraduate Sophomore level introductory Circuits-1 course that all electrical engineering majors are required to take. Laboratory participants cover the same material, in a small amount of time that the undergraduate students cover in 2-3 weeks. The lab exercise also includes an interactive exercise that helps students understand decimal to binary conversion for unsigned and signed decimal numbers via a tool already being used as part of interactive digital logic laboratory experience. In addition, students try their skill in generating power, under varying load conditions, using a bike-power generator as a fun-filled competitive activity. This paper outlines the laboratory exercise learning outcomes, implemented laboratory exercises, observations made, and results of a conducted exit survey. © American Society for engineering Education, 2018.

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