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5th IEEE Research and Applications of Photonics in Defense Conference, RAPID 2022

作者： Browning, Tyler Greenlee, Matt Joyce, Michael Landwehr, Aaron Kiamilev, Fouad Chip Design Systems NewarkDE19702 United States University of Delaware Department of Electrical and Computer Engineering NewarkDE19702 United States

Over the past year, CDS IRSP systems have seen many improvements, and the next generation of technology is under development. In preparation for the next-generation platform, several regressions were identified to be ... 详细信息

ISBN: (数字)9781665402231

ISBN: (纸本)9781665402231

关键词： FIR filters

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Impedance Estimation at Multi-Antenna Receivers 57

Impedance Estimation at Multi-Antenna Receivers

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57th Annual Conference on Information Sciences and systems, CISS 2023

作者： Wu, Shaohan Hughes, Brian L. MediaTek Usa Inc. Department of Communication Systems Design IrvineCA92618 United States North Carolina State University Department of Electrical and Computer Engineering RaleighNC27695-7911 United States

ISBN: (纸本)9781665451819

This paper considers antenna impedance matrix estimation using training sequences at multi-antenna receivers. We formulate this problem in a classical estimation framework, treating the unknown channel matrix as a nuisance parameter. The maximum-likelihood impedance estimator is derived as a function of the top block eigenvector of the sample covariance matrix in i.i.d. Rayleigh fading channels. Fundamental lower bounds, e.g., Cramér-Rao bounds and Miller-Chang bounds, on these estimators are derived, the latter of which better predicts the ML estimator's behavior using numerical simulations. Another finding is that transmit diversity may lead to diminishing returns if the total transmitted power is the constraint. © 2023 IEEE.

关键词： Covariance matrix

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Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xuefei Chen, Mingzhe Liu, Danpu Zhang, Zhilong Quek, Tony Q. S. Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Information Systems Technology and Design Pillar Singapore University of Technology and Design 487372 Singapore

ISBN: (纸本)9798350351255

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs). © 2024 IEEE.

关键词： Reinforcement learning

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Emulation Learning for Neuromimetic systems

arXiv

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arXiv 2023年

作者： Sun, Zexin Baillieul, John The Division of Systems Engineering Boston University United States The Departments of Mechanical Engineering Electrical and Computer Engineering The Division of Systems Engineering Boston University BostonMA02215 United States

Building on our recent research on neural heuristic quantization systems, results on learning quantized motions and resilience to channel dropouts are reported. We propose a general emulation problem consistent with the neuromimetic paradigm. This optimal quantization problem can be solved by model predictive control (MPC), but because the optimization step involves integer programming, the approach suffers from combinatorial complexity when the number of input channels becomes large. Even if we collect data points to train a neural network simultaneously, collection of training data and the training itself are still time-consuming. Therefore, we propose a general Deep Q Network (DQN) algorithm that can not only learn the trajectory but also exhibit the advantages of resilience to channel dropout. Furthermore, to transfer the model to other emulation problems, a mapping-based transfer learning approach can be used directly on the current model to obtain the optimal direction for the new emulation problems. © 2023, CC BY-NC-SA.

关键词： Integer programming

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An LLM-Based Digital Twin for Optimizing Human-in-the Loop systems

An LLM-Based Digital Twin for Optimizing Human-in-the Loop S...

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Foundation Models for Cyber-Physical systems & Internet of Things (FMSys), IEEE International Workshop on

作者： Hanqing Yang Marie Siew Carlee Joe-Wong Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9798350363456

ISBN: (纸本)9798350363463

The increasing prevalence of Cyber- Physical systems and the Internet of Things (CPS-loT) applications and Foundation Models are enabling new applications that leverage real-time control of the environment. For example, real-time control of Heating, Ventilation and Air-Conditioning (HVAC) systems can reduce its usage when not needed for the comfort of human occupants, hence reducing energy consumption. Collecting real-time feedback on human preferences in such human-in-the-Ioop (HITL) systems, however, is difficult in practice. We propose the use of large language models (LLMs) to deal with the challenges of dynamic environments and difficult-to-obtain data in CPS optimization. In this paper, we present a case study that employs LLM agents to mimic the behaviors and thermal preferences of various population groups (e.g. young families, the elderly) in a shopping mall. The aggregated thermal preferences are integrated into an agent-in-the-Ioop based reinforcement learning algorithm AitL-RL, which employs the LLM as a dynamic simulation of the physical environment to learn how to balance between energy savings and occupant comfort. Our results show that LLMs are capable of simulating complex population movements within large open spaces. Besides, AitL- RLdemonstrates superior performance compared to the popular existing policy of set point control, suggesting that adaptive and personalized decision-making is critical for efficient optimization in CPS-loT applications. Through this case study, we demonstrate the potential of integrating advanced Foundation Models like LLMs into CPS-loT to enhance system adaptability and efficiency. The project's code can be found on our GitHub repository.

关键词： Large language models Heuristic algorithms Sociology Reinforcement learning Ventilation Real-time systems Internet of Things

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Valuation of State-Of-Charge Management Risks of Energy Storage on Electricity Markets 5

Valuation of State-Of-Charge Management Risks of Energy Stor...

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5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022

作者： Ma, Jessie Venkatesh, Bala University of Waterloo Enterprise and Development Department of Systems Design Engineering and School of Environment Waterloo Canada Toronto Metropolitan University Centre for Urban Energy Department of Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

Independent System Operators (ISOs) are confronted with a challenge due to integration of large number of small sized energy storage (ES) units. The challenge stems from choosing: (a) the high cost of monitoring and controlling ES with knowledge of their state of charge (SOC), or (b) absorbing high costs arising from uncertainty and absence of knowing of the SOC values. In either case, these costs would eventually be reflected in electricity prices, and hence ISOs seek to know which of these is the lowest cost option. In this paper, we propose a tool that quantifies the risks associated with allowing private ES owners/operators to manage SOC. SOC can be unavailable at any given hour for any given amount. We solve the UC process for all these scenarios in order to compare impacts, in particular to total commitment costs and prices. We applied our algorithm to a system modelled on a practical transmission system in Ontario. ES units were placed in the system, and their impacts on total commitment costs and prices were observed. Factors that increase total commitments costs include time of day of unavailable ES, larger ES units, and lower availability factors, and vice versa. Using our method, ISOs can make sound policy choices around SOC management responsibility, risk management for unavailable ES, and the role for ES for their unique system. © 2022 IEEE.

关键词： Power markets

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RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent

arXiv

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arXiv 2025年

作者： Fang, Cheng Dixit, Rishabh Bajwa, Waheed U. Gürbüzbalaban, Mert Department of Electrical and Computer Engineering Rutgers University New BrunswickNJ United States Department of Mathematics University of California San DiegoCA United States Departments of Electrical and Computer Engineering and Statistics Rutgers University New BrunswickNJ United States Departments of Electrical and Computer Engineering Management Science and Information Systems and Statistics Rutgers University New BrunswickNJ United States

Empirical risk minimization (ERM) is a cornerstone of modern machine learning (ML), supported by advances in optimization theory that ensure efficient solutions with provable algorithmic convergence rates, which measure the speed at which optimization algorithms approach a solution, and statistical learning rates, which characterize how well the solution generalizes to unseen data. Privacy, memory, computational, and communications constraints increasingly necessitate data collection, processing, and storage across network-connected devices. In many applications, these networks operate in decentralized settings where a central server cannot be assumed, requiring decentralized ML algorithms that are both efficient and resilient. Decentralized learning, however, faces significant challenges, including an increased attack surface for adversarial interference during decentralized learning processes. This paper focuses on the man-in-the-middle (MITM) attack, wherein adversaries exploit communication vulnerabilities between devices to inject malicious updates during training, potentially causing models to deviate significantly from their intended ERM solutions. To address this challenge, we propose RESIST (Resilient dEcentralized learning using conSensus gradIent deScenT), an optimization algorithm designed to be robust against adversarially compromised communication links, where transmitted information may be arbitrarily altered before being received. Unlike existing adversarially robust decentralized learning methods, which often (i) guarantee convergence only to a neighborhood of the solution, (ii) lack guarantees of linear convergence for strongly convex problems, or (iii) fail to ensure statistical consistency as sample sizes grow, RESIST overcomes all three limitations. It achieves algorithmic and statistical convergence for strongly convex, Polyak–Lojasiewicz, and nonconvex ERM problems by employing a multistep consensus gradient descent framework and robust statis

关键词： Adversarial machine learning

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Progress Toward Production Grade Infrared LED Scene Projector systems

Progress Toward Production Grade Infrared LED Scene Projecto...

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IEEE Research and Applications of Photonics In Defense Conference (RAPID)

作者： F. Kiamilev H. Ahmed R. McGee A. Deputy J. Singh T. Browning B. Steenkamer T. Lassitter M. Greenlee M. Joyce L. Kiamilev Department of Electrical and Computer Engineering University of Delaware Newark DE USA Chip Design Systems Inc Newark DE USA

this paper describes progress being made toward production grade infrared LED projectors.

ISBN: (数字)9798350373097

ISBN: (纸本)9798350373103

this paper describes progress being made toward production grade infrared LED projectors.

关键词： Production Light emitting diodes Photonics

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Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

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IEEE Network 2025年

作者： Zhang, Ruichen He, Jiayi Luo, Xiaofeng Niyato, Dusit Kang, Jiawen Xiong, Zehui Li, Yonghui Sikdar, Biplab Nanyang Technological University College of Computing and Data Science Singapore Guangdong University of Technology School of Automation Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore University of Sydney School of Electrical and Information Engineering SydneyNSW2006 Australia National University of Singapore College of Design and Engineering Department of Electrical and Computer Engineering Singapore

The rapid development of generative artificial intelligence (AI) technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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An Efficient and Secure Post-Quantum Multi-Authority Ciphertext-Policy Attribute-Based Encryption Method Using Lattice

An Efficient and Secure Post-Quantum Multi-Authority Ciphert...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Bagchi, Prithwi Bera, Basudeb Maheshwari, Raj Das, Ashok Kumar Yau, David K. Y. Sikdar, Biplab International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Singapore University of Technology and Design Information Systems Technology and Design Singapore487372 Singapore National University of Singapore Department of Electrical and Computer Engineering Singapore117583 Singapore

ISBN: (纸本)9781665494274

This article deals with designing an efficient post-quantum lattice based encryption scheme that relies on the multi-authority Ciphertext-Policy Attribute-Based Encryption (CP-ABE). The security of the proposed scheme is based on the hardness of the ring learning with errors (RLWE) problem. The construction of the proposed scheme is done using the Shamir's threshold secret sharing along with the Lagrange interpolation during the key generation and decryption processes in order to achieve the segmentation and restoration of private keys. A comparative study with the existing state of art schemes has been performed to show the feasibility and efficiency of the proposed scheme. Furthermore, experiments on the proposed scheme have been conducted to illustrate the computational time required during the key generation, encryption and decryption processes. © 2023 IEEE.

关键词： Quantum cryptography

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