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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Cui, Liangqing Wu, You Wu, Yuanpeng Mi, Zetian Kira, Mackillo Norris, Theodore B. Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

The energy difference between the 1s and 2p exciton states of bilayer GaN/AlN is measured and computed by comparing excitonic resonances in photoluminescence excitation spectra following single- versus two-photon abso... 详细信息

ISBN: (纸本)9781957171395

关键词： Photoluminescence

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Implementation of AI-Powered Robotic Chatbot for Intelligent Customer Interaction

Implementation of AI-Powered Robotic Chatbot for Intelligent...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Duraisamy, Premkumar Gokul, C. Jeevanandhan, A. Shirthik Ram, S. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Electrical Communication and Engineering Coimbatore India

ISBN: (纸本)9798331527495

This work presents the implementation of an AI-powered robotic chatbot designed to transform customer service in the online shopping sector. The chatbot is engineered to manage key customer interactions, including order placement, shipment tracking, order modifications, and cancellations, through a seamless conversational interface. Utilizing advanced natural language processing (NLP) models from Hugging Face Transformers and intent recognition via Dialogflow, the system ensures context-aware and precise responses. The integration reduces response times and enhances customer satisfaction. Experimental evaluation demonstrates high accuracy in intent recognition and response generation, validating the chatbot's efficacy as a reliable solution for automating customer service tasks. The paper details the system's architecture, implementation, and performance, highlighting its capability to optimize customer support and boost e-commerce operational efficiency. © 2025 IEEE.

关键词： Chatbots

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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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Full-Duplex Millimeter Wave MIMO Channel Estimation: A Neural Network Approach

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 1093-1108页

作者： Sattari, Mehdi Guo, Hao Gündüz, Deniz Panahi, Ashkan Svensson, Tommy Chalmers University of Technology Department of Electrical Engineering Gothenburg412 96 Sweden New York University Tandon School of Engineering Electrical and Computer Engineering Department BrooklynNY11201 United States Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2BX United Kingdom Chalmers University of Technology Department of Computer Science Gothenburg412 96 Sweden

Millimeter wave (mmWave) multiple-input-multi-output (MIMO) is now a reality with great potential for further improvement. We study full-duplex transmissions as an effective way to improve mmWave MIMO systems. Compared to half-duplex systems, full-duplex transmissions may offer higher data rates and lower latency. However, full-duplex transmission is hindered by self-interference (SI) at the receive antennas, and SI channel estimation becomes a crucial step to make the full-duplex systems feasible. In this paper, we address the problem of channel estimation in full-duplex mmWave MIMO systems using neural networks (NNs). Our approach involves sharing pilot resources between user equipments (UEs) and transmit antennas at the base station (BS), aiming to reduce the pilot overhead in full-duplex systems and to achieve a comparable level to that of a half-duplex system. Additionally, in the case of separate antenna configurations in a full-duplex BS, providing channel estimates of transmit antenna (TX) arrays to the downlink UEs poses another challenge, as the TX arrays are not capable of receiving pilot signals. To address this, we employ an NN to map the channel from the downlink UEs to the receive antenna (RX) arrays to the channel from the TX arrays to the downlink UEs. We further elaborate on how NNs perform the estimation with different architectures, (e.g., different numbers of hidden layers), the introduction of non-linear distortion (e.g., with a 1-bit analog-to-digital converter (ADC)), and different channel conditions (e.g., low-correlated and high-correlated channels). Our work provides novel insights into NN-based channel estimators. © 2023 CCBY.

关键词： Millimeter waves

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Federated Multi-Agent Reinforcement Learning for AoI Minimization in UAV-Enabled IoV-MEC Systems 22

Federated Multi-Agent Reinforcement Learning for AoI Minimiz...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Xu, Shoukun Zhang, Yu Wang, Xueyuan Gursoy, M. Cenk Changzhou University Academy of Computer Science and Artificial Intelligence Changzhou China Syracuse University Department of Electrical Engineering and Computer Science SyracuseNY13244 United States

ISBN: (纸本)9798331509712

With the advancement of Mobile Edge Computing (MEC), effective solutions for communication scenarios, including the industrial Internet of Things (IoT) and the Internet of Vehicles (IoV), are becoming increasingly feasible. Unmanned aerial vehicles (UAVs) can further enhance flexibility in delivering computational services within MEC contexts. Addressing the urgent need for information freshness, we propose a three-tier IoV-MEC system, supported by multiple UAVs and a cloud center, aiming to minimize the system's average age of information (AoI). We propose a heterogeneous multi-agent reinforcement learning algorithm based on the actor-critic framework, where vehicles act as data sources, UAVs serve as edge devices, and the cloud acts as a control center. All three tiers learn interaction strategies cooperatively based on the observations. To further enhance system performance, we implement an efficient federated learning method, allowing same-tier agents to share learning parameters, thus improving system performance and convergence speed. Extensive simulation results demonstrate that the proposed algorithm outperforms baseline algorithms in terms of average AoI and convergence speed. © 2024 IEEE.

关键词： Federated learning

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Graph Retrieval-Augmented Generation for Large Language Models: A Survey

Graph Retrieval-Augmented Generation for Large Language Mode...

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2024 IEEE International Conferences of AI, science, engineering, and Technology, AIxSET 2024

作者： Procko, Tyler Thomas Ochoa, Omar Embry-Riddle Aeronautical University Department of Electrical Engineering and Computer Science Daytona Beach United States

ISBN: (纸本)9798350390995

Large Language Models (LLMs) demonstrate general knowledge, but they suffer when specifically needed knowledge is not present in their training set. Two approaches to ameliorating this, without retraining, are 1) prompt engineering and 2) Retrieval-Augmented Generation (RAG). RAG is a form of prompt engineering, insofar as relevant lexical snippets retrieved from RAG corpora are vectorized and aggregated with prompts. However, RAG documents are often noisy, i.e., while relevant to a given prompt, they can contain much other information that obfuscates the desired snippet. If the purpose of pretraining a LLM on massive and general corpora is to engender a generally applicable model, RAG is not: it is a means of LLM optimization, and as such, RAG document selection must be precise, not general. For expert tasks, it is imperative that a RAG corpus be as noise-free as possible, in much the same way a good prompt should be free of irrelevant text. Knowledge Graphs (KGs) provide a concise means of representing domain knowledge free of noisy information. This paper surveys work incorporating KGs with LLM RAG, intending to equip scientists with a better understanding of this novel research area for future work. © 2024 IEEE.

关键词： Knowledge graph

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Dependence Induced Representations 60

Dependence Induced Representations

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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Xu, Xiangxiang Zheng, Lizhong Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States

ISBN: (纸本)9798331541033

We study the problem of learning feature repre-sentations from a pair of random variables, where we focus on the representations that are induced by their dependence. We provide sufficient and necessary conditions for such dependence induced representations, and illustrate their connections to Hirschfeld-Cebelein-Renyi (HGR) maximal correlation functions and minimal sufficient statistics. We characterize a large family of loss functions that can learn dependence induced representations, including cross entropy, hinge loss, and their regularized variants. In particular, we show that the features learned from this family can be expressed as the composition of a loss-dependent function and the maximal correlation function, which reveals a key connection between representations learned from different losses. Our development also gives a statistical interpretation of the neural collapse phenomenon observed in deep classifiers. Finally, we present the learning design based on the feature separation, which allows hyperparameter tuning during inference. © 2024 IEEE.

关键词： Contrastive Learning

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Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

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computer Systems science & engineering 2023年第6期45卷 2351-2362页

作者： S.Aanjanadevi S.Aanjankumar K.R.Ramela V.Palanisamy Department of Computer Applications Alagappa UniversityKaraikudi630003TamilnaduIndia Department of Computer Science and Engineering Sri Raaja Raajan College of Engineering and TechnologyAmaravathipudur630301TamilnaduIndia Department of Electrical and Electronics Engineering Ultra College of Engineering and TechnologyMadurai625020TamilnaduIndia

Due to the enormous usage of the internet for transmission of data over a network,security and authenticity become major *** challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database *** describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical(Face,fingerprint,Ear etc.)and behavioural(Gesture,Voice,tying etc.)by means of matching and verification *** this work,biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential *** proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary *** Face Attribute Convolutional Neural Network(FACNN)is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further *** Artificial Intelligence(AI)into the proposed system,automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while *** codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and *** main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify *** system enhances the system performance by 8%matching and verification and minimizes the False Acceptance Rate(FAR),False Rejection Rate(FRR)and Equal Error Rate(EER)by 6 times and increases the data privacy through the biometric cryptosystem by 98.2%while compared to other work.

关键词： FACNN biometric cryptography AI FAR FRR EER

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Magnetic tunnel junctions featuring the topological Weyl semimetal Co2MnGa

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Physical Review Applied 2024年第4期22卷 044024页

作者： Zhiping He Chung-Tao Chou Eugene Park Alexandre C. Foucher Brooke C. McGoldrick Qiuyuan Wang Justin T. Hou Luqiao Liu Department of Electrical Engineering and Computer Science Department of Physics Department of Materials Science and Engineering

Magnetic Weyl semimetals are promising materials for spintronic applications due to their unique properties in bulk and surface topological states and the rich interplay between band topology and magnetism. While various nontraditional magneto-electrical effects have been studied in magnetic Weyl semimetals, transport properties related to spin-polarized tunneling from these materials remain less explored. In this work, we developed fully epitaxial magnetic tunnel junctions (MTJs) based on a ferromagnetic Weyl semimetal Co2MnGa. By growing Co2MnGa films under different conditions, we fabricated a series of MTJs possessing different degrees of order in the semimetal electrodes and compared their tunneling magnetoresistance (TMR). We find that the TMR becomes enhanced with the improvement of the chemical ordering of Co2MnGa. Our results reveal the relationship between the spin tunneling in MTJs and the chemical order of the Co2MnGa electrode and provide insights on further enhancing TMR via semimetal engineering.

关键词： Spintronics Tunneling magnetoresistance Ferromagnets Heusler alloy Magnetic tunnel junctions Single crystal materials Weyl semimetal Epitaxy

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Enhanced Locational FDIA Detection in Smart Grids: A Scalable Distributed Framework 4

Enhanced Locational FDIA Detection in Smart Grids: A Scalabl...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Eddin, Maymouna Ez Massaoudi, Mohamed Shadmand, Mohammad Abdallah, Mohamed Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States University of Illinois at Chicago Department of Electrical and Computer Engineering Chicago United States Hamad Bin Khalifa University Information and Computing Technology Division Doha Qatar

ISBN: (纸本)9798350306262

Locational detection of the false data injection attack (FDIA) is essential for smart grid cyber-security. However, the FDIA detection techniques often falter in scalability as power network complexity increases. To address the research gap, this paper introduces an innovative distributed framework for locational FDIA detection that optimizes both performance and scalability. The proposed framework initially partitions the power grid using the improved Louvain community detection algorithm. The proposed solution utilizes the electrical Functional Strength (EFS) matrix and power supply modularity. Subsequently, a dedicated multi-label one-dimensional convolutional neural network model (1D CNN) locational detector is designed for each derived cluster. The proposed methodology is designed to increase detection accuracy and enhance the scalability of the model. This is achieved by reducing training and detection times, as well as lowering memory requirements, compared to traditional centralized approaches. The effectiveness of the proposed framework is validated through simulations on the IEEE 39-bus system. These simulations demonstrate the framework's capability to enhance detection accuracy by simplifying the locational FDIA detection challenge, achieved through strategic grid partitioning. © 2024 IEEE.

关键词： Cybersecurity

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