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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Kiana Pilevar Abrisham Khalil Alipour Bahram Tarvirdizadeh Mohammad Ghamari Department of Mechatronics Engineering School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology Advanced Service Robots (ASR) Laboratory University of Tehran Tehran Iran Department of Electrical and Computer Engineering Kettering University Flint Michigan USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Cardiovascular diseases (CVDs), a leading cause of global mortality, are intricately linked to arterial stiffness, a key factor in cardiovascular health. Non-invasive assessment of arterial stiffness, particularly through Carotid-to-femoral Pulse Wave Velocity (cf-PWV) – the gold standard in this field – is vital for early detection and management of CVDs. This study introduces a novel approach, utilizing photoplethysmogram (PPG) signal spectrograms as inputs for deep learning models to estimate cf-PWV, a significant advancement over traditional methods. Employing a modified ResNet-18 architecture, we analyze PPG signals from digital, radial, and brachial arteries of a simulated dataset of 4374 healthy adults. Our methodology's innovation lies in its direct use of finely tuned spectrogram images, bypassing the complex feature extraction processes. This approach achieved R 2 (correlation coefficient) values of up to 0.9902 for the digital artery, 0.9898 for the radial artery, and 0.9825 for the brachial artery, coupled with significantly lower Mean Absolute Percentage Errors (MAPE) of approximately 1.61% for the digital, 1.87% for the radial, and 2.08% for the brachial artery. These findings highlight the efficacy of PPG spectrograms, especially from the digital artery, in providing an accurate, user-friendly, and non-invasive method for cf-PWV estimation, thereby enhancing the capabilities of non-invasive cardiovascular diagnostics.

关键词： Accuracy Noise Estimation Predictive models Feature extraction Brachytherapy Data models Arteries Spectrogram Monitoring

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Optimal Bidding Strategy for a Wind Power Producer in the Energy and Spinning Reserve Markets

Optimal Bidding Strategy for a Wind Power Producer in the En...

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2021 North American Power Symposium, NAPS 2021

作者： Stratman, Anne Qiao, Wei Qu, Liyan University of Nebraska-Lincoln Power and Energy Systems Laboratory Department of Electrical and Computer Engineering LincolnNE68588-0511 United States

ISBN: (纸本)9781665420815

Wind power is one of the world's fastest-growing renewable energy resources. To achieve high levels of wind power penetration in the power supply, it will be necessary for wind power producers (WPPs) to contribute to system-wide reserves. This paper proposes a stochastic optimization model to determine the optimal offer strategy for a WPP that participates in the day-ahead and real-time energy and spinning reserve markets. The objective function maximizes the WPP's total expected profit while minimizing risk by allowing the WPP to split day-ahead offers between the energy and spinning reserve markets, which have lower penalties than the energy market for failing to deliver the cleared day-ahead offer. The model is solved using the simplex method, and hourly offer curves are generated for several levels of risk-aversion. Case study results show that the proposed offer strategy increases expected profit and significantly decreases risk compared to the case where the WPP only participates in the energy market. © 2021 IEEE.

关键词： Wind power

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Developing Adaptive, Personalised, Autonomous Social Robots Using Physiological Signals: System Development and a Pilot Study

Developing Adaptive, Personalised, Autonomous Social Robots ...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Shruti Chandra Isha Sharma Benjamin David Schnapp Mike J. Dixon Kerstin Dautenhahn Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada Department of Systems Design Engineering University of Waterloo Waterloo ON Canada Department of Mechanical and Mechatronics Engineering University of Waterloo ON Canada Department of Psychology University of Waterloo ON Canada

Maintaining physical, emotional and psychological health is vital for well-being. Social robots have been increasingly used in healthcare to support physical and mental health. Providing appropriate, adaptive and personalised feedback based on the user’s internal states is crucial for effective and engaging human-robot interaction, especially in one-to-one interaction scenarios. In this research, we developed an adaptive and autonomous system, integrating a social robot, a wearable non-intrusive Polar chest sensor and algorithms to guide people in three application scenarios to promote physical, emotional, and psychological well-being. The social robot senses users’ psychophysiological measures such as heart rate and heart-rate variability via the wearable sensor, monitors their stress responses, provides real-time feedback and guides them to perform activities. We detail the system development and a pilot study with fifteen participants to evaluate the system in the three scenarios. The findings suggest that the autonomous system could effectively guide participants through the activities by regulating their stress responses. Participants’ physiological data also support these results. Moreover, the system was well-accepted by its users.

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Performance Analysis of Distributed Filtering under Mismatched Noise Covariances

arXiv

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arXiv 2024年

作者： Lyu, Xiaoxu Wen, Guanghui Shi, Ling Duan, Peihu Duan, Zhisheng The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Laboratory of Security Operation and Control for Intelligent Autonomous Systems Department of Systems Science School of Mathematics Southeast University Nanjing211189 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China

This paper systematically investigates the performance of consensus-based distributed filtering under mismatched noise covariances. First, we introduce three performance evaluation indices for such filtering problems, namely the standard performance evaluation index, the nominal performance evaluation index, and the estimation error covariance. We derive difference expressions among these indices and establish one-step relations among them under various mismatched noise covariance scenarios. We particularly reveal the effect of the consensus fusion on these relations. Furthermore, the recursive relations are introduced by extending the results of the one-step relations. Subsequently, we demonstrate the convergence of these indices under the collective observability condition, and show this convergence condition of the nominal performance evaluation index can guarantee the convergence of the estimation error covariance. Additionally, we prove that the estimation error covariance of the consensus-based distributed filter under mismatched noise covariances can be bounded by the Frobenius norms of the noise covariance deviations and the trace of the nominal performance evaluation index. Finally, the effectiveness of the theoretical results is verified by numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Wiener filtering

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DDPS: Dynamic Differential Pricing-Based Edge Offloading System With Energy Harvesting Devices

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IEEE Transactions on Network Science and engineering 2025年

作者： Xue, Hai Xia, Yun Xiong, Neal N. Zhang, Di Pei, Songwen University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Sul Ross State University Department of Computer Science and Mathematics AlpineTX79830 United States Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Zhengzhou University Henan International Joint Laboratory of Intelligent Health Information System Zhengzhou450001 China Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China

Mobile edge computing (MEC) mitigates the energy and computation burdens on mobile users (MUs) by offloading tasks to the network edge. To optimize MEC server utilization through effective resource allocation, a well-designed pricing strategy is indispensable. In this paper, we propose a dynamic differential pricing scheme (DDPS) for an edge offloading scenario with energy harvesting devices, which determines prices based on computing resource usage to enhance edge server (ES) utilization. First, an offloading decision algorithm is proposed to balance harvested and consumed energy, determining whether and how much data to offload. Second, a Stackelberg game-based differential pricing algorithm is proposed to optimize computing resource allocation for MUs and reallocate surplus resources to delay-sensitive devices. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme. Specifically, in comparison to the existing best-performing pricing scheme, for different task arrival rates, DDPS can achieve a 5.3% decrease in average execution delay, a 1.7% increase in ES utility (Userver, which represents the payment from MUs minus penalties for discarded tasks), and a 2.1% increase in the average ratio of service for MUs. In addition, DDPS also improves 2.8% Userver on average with different ES computation capacities. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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Accurate Parameter Estimation for PMSMs using electrical and Mechanical Models Considering Temperature Effect and Inverter Distortion

Accurate Parameter Estimation for PMSMs using Electrical and...

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2022 Chinese Automation Congress, CAC 2022

作者： Tong, Zhe Meng, Bo Ding, Beichen Feng, Guodong Lai, Chunyan Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen China Sun Yat-sen University School of Intelligent Systems Engineering Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou510006 China Concordia University Department of Electrical and Computer Engineering Montreal Canada

ISBN: (纸本)9781665465335

This paper presents a decoupled parameter estimation method for PMSM considering temperature rise and inverter distortion. Both electrical and mechanical models are explored to derive the estimation model to achieve better parameter estimation accuracy. Specifically, the proposed approach firstly performs the initial estimation of the PM flux, winding resistance and friction coefficient by considering the temperature effect, and then derives the decoupled model for inductance estimation using both the electrical and mechanical measurements. Compared with other approaches, our decoupled approach employs the mechanical model and considers the temperature effect, which can make the parameter identification more accurate. The experiment is performed on a test PMSM to verify the estimation performance of the proposed approach. © 2022 IEEE.

关键词： Parameter estimation

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Intelligent Edge Caching for Metaverse Applications Based on Grover Search

Intelligent Edge Caching for Metaverse Applications Based on...

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Artificial Intelligence of Things (AIoT), IEEE Annual Congress on

作者： Guoquan Wu Jianhang Tang Yang Zhang Bo Wang Wenchao Jiang Zehui Xiong Yixiong Feng State Key Laboratory of Public Big Data Guizhou University China Department of Computer Science and Engineering Southern University of Science and Technology China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350392296

ISBN: (纸本)9798350392302

Metaverse is envisaged as an evolving Internet paradigm that allows people to play, work, and socialize in a shared and virtual ecosystem with immersive and seamless experiences. However, multiple users simultaneously request massive resources for avatar physics emulation and graphical rendering. How to provide high-quality and low-latency meta-verse services for massive concurrent users is a crucial problem. In this work, an intelligent Grover search-based edge caching (GEC) algorithm is proposed for metaverse applications, where quantum theory is used to generate caching solutions with low time complexity. Metaverse scenes can be divided into massive environment panoramic frames and dynamic objects. An edge server is deployed to render and cache the common environment panoramic frames, while the dynamic objects are rendered on head-mounted displays (HMDs). Based on the quantum theory, the panoramic frames are split into several tiles and represented by qubits. The optimal caching results are obtained by performing unitary transformations for the tiles’ features. Finally, we provide extensive simulation experiments by a real- world metaverse dataset. The numerical results reveal that the GEC algorithm can reduce 11.09% of the service time and increase 13.34% of the cache hit rate on average by comparing it with two benchmark caching algorithms.

关键词： Metaverse Heuristic algorithms Simulation Qubit Quantum mechanics Rendering (computer graphics) Internet of Things

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Robust Decentralized Learning with Local Updates and Gradient Tracking

arXiv

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arXiv 2024年

作者： Ghiasvand, Sajjad Reisizadeh, Amirhossein Alizadeh, Mahnoosh Pedarsani, Ramtin Electrical and Computer Engineering Department UC Santa Barbara Santa BarbaraCA United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA United States

As distributed learning applications such as Federated Learning, the Internet of Things (IoT), and Edge Computing grow, it is critical to address the shortcomings of such technologies from a theoretical perspective. As an abstraction, we consider decentralized learning over a network of communicating clients or nodes and tackle two major challenges: data heterogeneity and adversarial robustness. We propose a decentralized minimax optimization method that employs two important modules: local updates and gradient tracking. Minimax optimization is the key tool to enable adversarial training for ensuring robustness. Having local updates is essential in Federated Learning (FL) applications to mitigate the communication bottleneck, and utilizing gradient tracking is essential to proving convergence in the case of data heterogeneity. We analyze the performance of the proposed algorithm, Dec-FedTrack, in the case of nonconvex-strongly-concave minimax optimization, and prove that it converges a stationary point. We also conduct numerical experiments to support our theoretical findings. © 2024, CC BY.

关键词： Convex optimization

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Exploring Equilibrium Strategies in Network Games with Generative AI

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IEEE Network 2024年

作者： Yang, Yaoqi Du, Hongyang Sun, Geng Xiong, Zehui Niyato, Dusit Han, Zhu Army Engineering University of PLA College of Communications Engineering Nanjing210000 China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Jilin University College of Computer Science and Technology Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore College of Computing and Data Science NTU Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

Game theory offers a powerful framework for analyzing strategic interactions among decision-makers, providing tools to model, analyze, and predict their behavior. However, implementing game theory can be challenging due to difficulties in deriving solutions, understanding interactions, and ensuring optimal performance. Traditional non-AI and discriminative AI approaches have made valuable contributions but struggle with limitations in handling large-scale games and dynamic scenarios. In this context, generative AI emerges as a promising solution because of its superior data analysis and generation capabilities. This paper comprehensively summarizes the challenges, solutions, and outlooks of combining generative AI with game theory. We start with reviewing the limitations of traditional non-AI and discriminative AI approaches in employing game theory, and then highlight the necessity and advantages of integrating generative AI. Next, we explore the applications of generative AI in various stages of the game theory lifecycle, including model formulation, solution derivation, and strategy improvement. Additionally, from game theory viewpoint, we propose a generative AI-enabled framework for optimizing machine learning model performance against false data injection attacks, supported by a case study to demonstrate its effectiveness. Finally, we outline future research directions for generative AI-enabled game theory, paving the way for its further advancements and development. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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Unidirectional scattering with spatial homogeneity using photonic time disorder

arXiv

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arXiv 2022年

作者： Kim, Jungmin Lee, Dayeong Yu, Sunkyu Park, Namkyoo Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

The temporal degree of freedom in photonics has been a recent research hotspot due to its analogy with spatial axes, causality, and open-system characteristics. In particular, the temporal analogues of photonic crystals have stimulated the design of momentum gaps and their extension to topological and non-Hermitian photonics. Although recent studies have also revealed the effect of broken discrete time-translational symmetry in view of the temporal analogy of spatial Anderson localization, the broad intermediate regime between time order and time uncorrelated disorder has not been examined. Here we investigate the inverse design of photonic time disorder to achieve optical functionalities in spatially homogeneous platforms. By developing the structure factor and order metric using causal Green’s functions for the domain of time disorder, we demonstrate engineered time scatterer, which provides unidirectional scattering with controlled scattering amplitudes. We also reveal that the order-to-disorder transition in the time domain allows for the manipulation of scattering bandwidths, which inspires resonance-free temporal colour filtering. Our work will pave the way for advancing optical functionalities without spatial patterning. © 2022, CC BY.

关键词： Degrees of freedom (mechanics)

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