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How to Make Money From Fresh Data: Subscription Strategies in Age-Based Systems

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arXiv 2024年

作者： Kaswan, Priyanka Bastopcu, Melih Ulukus, Sennur Rasoul Etesami, S. Başar, Tamer Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Coordinated Science Laboratory University of Illinois Urbana Champaign UrbanaIL61801 United States

We consider a communication system consisting of a server that tracks and publishes updates about a time-varying data source or event, and a gossip network of users interested in closely tracking the event. The timeliness of the information is measured through the version age of information. The users wish to have their expected version ages remain below a threshold, and have the option to either rely on gossip from their neighbors or subscribe to the server directly to follow updates about the event if the former option does not meet the timeliness requirements. The server wishes to maximize its profit by increasing the number of subscribers and reducing costs associated with the frequent sampling of the event. We model the problem setup as a Stackelberg game between the server and the users, where the server commits to a frequency of sampling the event, and the users make decisions on whether to subscribe or not. As an initial work, we focus on directed networks with unidirectional flow of information and obtain the optimal equilibrium strategies for all the players. We provide simulation results to confirm the theoretical findings and provide additional insights. Copyright © 2024, The Authors. All rights reserved.

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Ultrafast Superconducting Qubit Readout with the Quarton Coupler

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arXiv 2024年

作者： Ye, Yufeng Kline, Jeremy B. Chen, Sean O'Brien, Kevin P. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States

Fast, high-fidelity, and quantum nondemolition (QND) qubit readout is an essential element of quantum information processing. For superconducting qubits, state-of-the-art readout is based on a dispersive cross-Kerr coupling between a qubit and its readout resonator. The resulting readout can be high-fidelity and QND, but readout times are currently limited to the order of 50 ns due to the dispersive cross-Kerr of magnitude 10 MHz. Here, we present a new readout scheme that uses the quarton coupler to facilitate a large (greater than 250 MHz) cross-Kerr between a transmon qubit and its readout resonator. Full master equation simulations show a 5 ns readout time with greater than 99% readout and QND fidelity. Unlike state-of-the-art dispersive readout, the proposed "quartonic readout" scheme relies on a transmon with linearized transitions as the readout resonator. Such operational points are found from a detailed theoretical treatment and parameter study of the coupled system. The quartonic readout circuit is also experimentally feasible and preserves the coherence properties of the qubit. Our work reveals a new path for order-of-magnitude improvements of superconducting qubit readout by engineering nonlinear light-matter couplings in parameter regimes unreachable by existing designs. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

Fixed-time Adaptive Neural Control for Physical Human-Robot Collaboration with Time-Varying Workspace Constraints

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arXiv 2023年

作者： Sun, Yuzhu Van, Mien McIlvanna, Stephen Nhat, Nguyen Minh McLoone, Seán Ceglarek, Dariusz Ge, Shuzhi Sam The Centre for Intelligent Autonomous Manufacturing Systems School of Electronics Electrical Engineering and Computer Science Queen’s University Northern Ireland Belfast United Kingdom The Warwick Manufacturing Group University of Warwick Coventry United Kingdom The Department of Electrical and Computer Engineering Social Robotics Laboratory National University of Singapore Singapore

Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-varying workspace constraints that occur in physical human-robot collaboration while also guaranteeing compliance during intended force interactions. The proposed methodology combines the benefits of compliance control, time-varying integral barrier Lyapunov function (TVIBLF) and fixed-time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time-varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed-time converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two-link robot manipulator. Simulation results show that the proposed controller is superior in the sense of both tracking error and convergence time compared with the existing barrier Lyapunov functions based controllers, while simultaneously guaranteeing compliance and safety. Copyright © 2023, The Authors. All rights reserved.

关键词： Lyapunov functions

Neurological Disorder Diagnosis through Deep Residual Network-based EEG Signal Analysis

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Neurological Disorder Diagnosis through Deep Residual Networ...

International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Afifa Jridi Ridha Djemal Kais Belwafi ATMS- Laboratory ENIS: National School of Engineering Rte de Soukra Sfax Tunisia Electrical Engineering Department ENIS: National School of Engineering Rte de Soukra Sfax Tunisia Computer Engineering Department College of Computing and Informatics University of Sarjah UAE

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Quantifying anomalies in brain signals can reveal various brain conditions and pathologies. Most recent studies on neurological disorders diagnosis such as epilepsy and autism spectrum disorder (ASD) based on electroencephalogram (EEG) rely on custom feature extraction techniques. Traditional methods of feature extraction approaches are time-consuming and provide limited accuracy. So, to balance accuracy and efficiency, more ability is required in the choice of such significant features. This study introduces a feature extraction model based on a deep residual network (ResNet) capable of automatically extracting representative features from EEG signal to address this issue. This proposed method consists of three steps: signal preprocessing using a static filtering method, hidden pattern extraction from EEG signals using the ResNet model, and classification using a SoftMax layer. EEG data sets from the UBonn University database and King Abdulaziz University (KAU) are used in this study. The ResNet model’s results are evaluated using accuracy, sensitivity, and specificity. In this study, the proposed diagnostic system achieves a classification for 3 classes accuracy of 100% for Epilepsy in offline diagnosis and an accuracy of 95.5% for Autism in a three-class classification.

关键词： Neurological diseases Autism Analytical models Accuracy Epilepsy Feature extraction Brain modeling

Energy efficient cutting parameter optimization

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Frontiers of Mechanical engineering 2021年第2期16卷 221-248页

作者： Xingzheng CHEN Congbo LI Ying TANG Li LI Hongcheng LI College of Engineering and Technology Southwest UniversityChongqing 400715China State Key Laboratory of Mechanical Transmission Chongqing UniversityChongqing 400044China Department of Electrical and Computer Engineering Rowan UniversityGlassboroNJ 08028USA College of Advanced Manufacturing Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China

Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented.

关键词： energy efficiency cutting parameter optimization machining process

Non-invasive pipeline pressure monitoring based on Intrinsic Fabry-Perot interferometers 27

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Non-invasive pipeline pressure monitoring based on Intrinsic...

27th International Conference on Optical Fiber Sensors, OFS 2022

作者： Li, Yuqi Wang, Qirui Wright, Ruishu Lalam, Nageswara Rao Diemler, Nathan Hawley, Adam G. Chen, Kevin P. Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States National Energy Technology Laboratory PittsburghPA15261 United States Southwest Research Institute San AntonioTX78238 United States

This paper demonstrates a non-invasive pipeline pressure monitoring using intrinsic Fabry-Perot interferometer (IFPI) fiber optic sensors. Fiber sensors installed outside the pipe can achieve 2% pressure measurement a... 详细信息

ISBN: (纸本)9781957171142

关键词： Fiber optic sensors

A Cyber-Physical Anomaly Detection for Wide-Area Protection Using Machine Learning

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A Cyber-Physical Anomaly Detection for Wide-Area Protection ...

IEEE General Meeting Power& Energy Society

作者： Vivek Kumar Singh Manimaran Govindarasu Security and Resiliency Group National Renewable Energy Laboratory Electrical &Computer Engineering Iowa State University

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408240

Wide-area protection scheme (WAPS) provides system-wide protection by detecting and mitigating small and large-scale disturbances that are difficult to resolve using local protection schemes. As this protection scheme is evolving from a substation-based distributed remedial action scheme (DRAS) to the control center-based centralized RAS (CRAS), it presents severe challenges to their cybersecurity because of its heavy reliance on an insecure grid communication, and its compromise would lead to system failure. This article presents an architecture and methodology for developing a cyber-physical anomaly detection system (CPADS) that utilizes synchrophasor measurements and properties of network packets to detect data integrity and communication failure attacks on measurement and control signals in CRAS. The proposed machine leaning-based methodology applies a rules-based approach to select relevant input features, utilizes variational mode decomposition (VMD) and decision tree (DT) algorithms to develop multiple classification models, and performs final event identification using a rules-based decision logic. We have evaluated the proposed methodology of CPADS using the IEEE 39 bus system for several performance measures (accuracy, recall, precision, and F-measure) in a cyber-physical testbed environment. Our experimental results reveal that the proposed algorithm (VMD-DT) of CPADS outperforms the existing machine learning classifiers during noisy and noise-free measurements while incurring an acceptable processing overhead.

关键词： Machine learning algorithms Data integrity Machine learning Phasor measurement units Classification algorithms Noise measurement Decision trees

Jointly Optimizing Terahertz based Sensing and Communications in Vehicular Networks: A Dynamic Graph Neural Network Approach

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arXiv 2024年

作者： Li, Xuefei Chen, Mingzhe Hu, Ye Zhang, Zhilong Liu, Danpu Mao, Shiwen The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States

In this paper, the problem of vehicle service mode selection (sensing, communication, or both) and vehicle connections within terahertz (THz) enabled joint sensing and communications over vehicular networks is studied. The considered network consists of several service provider vehicles (SPVs) that can provide: 1) only sensing service, 2) only communication service, and 3) both services, sensing service request vehicles, and communication service request vehicles. Based on the vehicle network topology and their service accessibility, SPVs strategically select service request vehicles to provide sensing, communication, or both services. This problem is formulated as an optimization problem, aiming to maximize the number of successfully served vehicles by jointly determining the service mode of each SPV and its associated vehicles. To solve this problem, we propose a dynamic graph neural network (GNN) model that selects appropriate graph information aggregation functions according to the vehicle network topology, thus extracting more vehicle network information compared to traditional static GNNs that use fixed aggregation functions for different vehicle network topologies. Using the extracted vehicle network information, the service mode of each SPV and its served service request vehicles will be determined. Simulation results show that the proposed dynamic GNN based method can improve the number of successfully served vehicles by up to 17% and 28% compared to a GNN based algorithm with a fixed neural network model and a conventional optimization algorithm without using GNNs. Copyright © 2024, The Authors. All rights reserved.

关键词： Vehicles

Optimizing Profitability in Timely Gossip Networks

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arXiv 2024年

作者： Kaswan, Priyanka Bastopcu, Melih Ulukus, Sennur Etesami, S. Rasoul Başar, Tamer Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Coordinated Science Laboratory University of Illinois Urbana Champaign UrbanaIL61801 United States

We consider a communication system where a group of users, interconnected in a bidirectional gossip network, wishes to follow a time-varying source, e.g., updates on an event, in real-time. The users wish to maintain their expected version ages below a threshold, and can either rely on gossip from their neighbors or directly subscribe to a server publishing about the event, if the former option does not meet the timeliness requirements. The server wishes to maximize its profit by increasing subscriptions from users and minimizing event sampling frequency to reduce costs. This leads to a Stackelberg game between the server and the users where the sender is the leader deciding its sampling frequency and the users are the followers deciding their subscription strategies. We investigate equilibrium strategies for low-connectivity and high-connectivity topologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Profitability