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arXiv 2024年

作者： Doostmohammadian, Mohammadreza Pequito, Sérgio Mechatronics Department Faculty of Mechanical Engineering Semnan University Semnan Iran Department of Electrical and Computer Engineering Institute for Systems and Robotics Instituto Superior Técnico University of Lisbon Portugal

Distributed optimization finds many applications in machine learning, signal processing, and control systems. In these real-world applications, the constraints of communication networks, particularly limited bandwidth, necessitate implementing quantization techniques. In this paper, we propose distributed optimization dynamics over multi-agent networks subject to logarithmically quantized data transmission. Under this condition, data exchange benefits from representing smaller values with more bits and larger values with fewer bits. As compared to uniform quantization, this allows for higher precision in representing near-optimal values and more accuracy of the distributed optimization algorithm. The proposed optimization dynamics comprise a primary state variable converging to the optimizer and an auxiliary variable tracking the objective function's gradient. Our setting accommodates dynamic network topologies, resulting in a hybrid system requiring convergence analysis using matrix perturbation theory and eigenspectrumanalysis. © 2024, CC BY.

关键词： Support vector machines

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Toward Value-oriented Renewable Energy Forecasting: An Iterative Learning Approach

arXiv

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arXiv 2023年

作者： Zhang, Yufan Jia, Mengshuo Wen, Honglin Bian, Yuexin Shi, Yuanyuan Department of Electrical and Computer Engineering University of California San Diego United States Power Systems Laboratory ETH Zürich Zürich8092 Switzerland Department of Electrical Engineering Shanghai Jiao Tong University China

Energy forecasting is an essential task in power system operations. Operators usually issue forecasts and use them to schedule energy dispatch in advance. However, forecasting models are typically developed in a way that overlooks the decision value of forecasts. To bridge the gap, we design a value-oriented point forecasting approach for sequential energy dispatch problems with renewable energy sources. At the training phase, we align the training objective with the decision value, i.e., minimizing the overall operating cost. The forecasting model parameter estimation is formulated as a bilevel program. Under mild assumptions, we convert the upper-level objective into an equivalent form using the dual solutions obtained from the lower-level operation problems. In addition, a novel iterative solution strategy is proposed for the newly formulated bilevel program. Under such an iterative scheme, we show that the upper-level objective is locally linear with respect to the forecasting model output and can act as the loss function. Numerical experiments demonstrate that, compared to commonly used forecasts predicting expected realization, forecasts obtained by the proposed approach result in lower operating costs. Meanwhile, the proposed approach achieves performance comparable to that of two-stage stochastic programs, but is more computationally efficient. © 2023, CC BY.

关键词： Cost functions

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I Can See Your Secrets, A Way to Recognize User Pin Codes by Surveillance Camera 10

I Can See Your Secrets, A Way to Recognize User Pin Codes by...

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10th IEEE Smart World Congress, SWC 2024

作者： Guo, Zhenyang Cao, Jin Zan, Xiaoyong Yang, Jiawei Li, Ang Niu, Ben Sun, Zhe Yin, Lihua Li, Hui Xidian University School of Cyber Engineering Xi'an China The State Key Laboratory of Integrated Services Networks Xi'an China ByteDance Ltd Beijing China University of Maryland Department of Electrical & Computer Engineering College Park United States Institute of Information Engineering Chinese Academy of Sciences Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China

ISBN: (纸本)9798331520861

Surveillance cameras are currently widely used in public and private places to enhance area security and reduce crime rates. However, the extensive use of surveillance cameras also poses significant threats, particularly concerning the leakage of users' private information. Surveillance cameras are often installed in the corners of areas such as hotel front desks, supermarket checkout areas, or ticket offices to observe user behavior, which can lead to shoulder-surfing attacks. In these public or private settings, when users input their personal identification number (PIN) for unlocking or payment, this process may be intentionally or unintentionally exposed and recorded. If the PIN is compromised, it can result in severe privacy breaches and financial losses for users. Existing work primarily focuses on dedicated handheld recording devices such as cameras and smartphones, lacking discussion on public surveillance cameras. In this paper, we first present an attack model for such scenarios. Based on this model, we design and implement an automated attack system for PIN recognition. Experimental results demonstrate that our proposed attack model is both feasible and effective, achieving 90 % accuracy with our system. Finally, we urge users, smartphone manufacturers, and surveillance camera manufacturers to be aware of this attack vector to prevent further harm. © 2024 IEEE.

关键词： Smartphones

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General and Offset-Resistant Physical-Layer Acknowledgement Approach for Cross-Technology Communication

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IEEE Transactions on Communications 2025年

作者： Yao, Shumin Zhao, Qinglin Zhou, MengChu Feng, Li Zhang, Peiyun Albeshri, Aiiad Macau University of Science and Technology School of Computer Science and Engineering Avenida Wei Long Taipa China Pengcheng Laboratory Department of Broadband Communication Shenzhen518066 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Nanjing University of Information Science & Technology Engineering Research Center of Digital Forensics of Ministry of Education School of Computer Science Nanjing210044 China King Abdulaziz University Department of Computer Science Jeddah21481 Saudi Arabia

Cross-technology communication (CTC) enables direct communications among devices with heterogeneous wireless technologies (e.g., Bluetooth, WiFi, ZigBee, etc.), thereby reducing the cost and complexity of their interconnections. However, CTC is essentially unreliable due to the technology heterogeneity, and most existing CTC designs do not provide acknowledgment (ACK) feedback to ensure reliable data transmission. Few ACK designs are only applicable to feedback for ZigBee-WiFi pair and are vulnerable to sampling offsets that inherently exist in CTC. In this work, we propose a General and Offset-resistant Physical-layer ACK approach, called GOP-ACK, to support reliable feedback with practical considerations. Its core idea lies in encoding ACK messages with offset-resistant signal that has two benefits: 1) it can be adapted to a wide range of CTC scenarios with minimal adjustment, and 2) it can be effortlessly and robustly detected even in the presence of sampling offsets. We offer practical guidelines to tackle key deployment challenges related to signal construction, efficient and robust transmission, and effective firmware module reuse, enabling the application of GOP-ACK to specific CTC scenarios. Based on these guidelines, we implement two design cases: ZigBee-to-BLE and ZigBee-to-WiFi feedback, and propose a theoretical model to analyze their performance. We then conduct real-world experiments and extensive simulations to verify its feasibility and superiority over the state of the art, thereby enhancing the practicality of CTC greatly. © 1972-2012 IEEE.

关键词： Firmware

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Data-Driven Control of Distributed Energy Resources Using Smart Meters Data

Data-Driven Control of Distributed Energy Resources Using Sm...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Yasin Zabihinia Gerdroodbari Abu Bakr Pengwah Reza Razzaghi Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia

This paper proposes a novel data-driven model-free control framework for improving voltage regulation in distribution networks with distributed energy resources (DERs). In the proposed approach, first, a least squares method that estimates the active and reactive power sensitivity matrices from smart meter data is presented. Then, these matrices serve as an input for the proposed optimal power flow, which also takes into consideration the network constraints and fairness of prosumers in terms of generation curtailment. The performance of the proposed method is evaluated on an unbalanced 49 bus three-phase four-wire network simulated in OpenDSS. The simulation results exhibit that the proposed control strategy can significantly resolve the over-voltage problems associated with DERs. Furthermore, compared to a case where the complete network model is available, the errors in voltage levels are negligible, with 1.1% being the worst recorded error.

关键词： Meters Reactive power Sensitivity Simulation Energy resolution Smart meters Distributed power generation

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Simultaneous Perfect Anomalous Reflection and Angle-of-Arrival Sensing Using Patch Arrays

Simultaneous Perfect Anomalous Reflection and Angle-of-Arriv...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Mostafa Movahediqomi Sravan K. R. Vuyyuru Grigory Ptitcyn Risto Valkonen Do-Hoon Kwon Sergei A. Tretyakov Department of Electronics and Nanoengineering School of Electrical Engineering Aalto University Espoo Finland Nokia Bell Labs Espoo Finland Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA Department of Electrical and Computer Engineering University of Massachusetts Amherst Amherst MA USA

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

A novel optimization-based method tailored for the evolution of communication networks is presented, aimed at achieving a perfect power efficiency while accurately determining the angle of arrival of user-generated waves. Our approach involves optimizing aperiodic loaded structures, eliminating the constraints of electromagnetic periodicity while maintaining a fixed half-wavelength spacing. The proposed configuration employs practical square patches, each loaded with passive tunable elements. These load values can be dynamically adjusted, enabling a continuous scanning capability to capture user signals arriving from unknown angles.

关键词： Conferences Receivers Harmonic analysis Linear programming Current distribution Reflection Sensors

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LIGHTWEIGHT DATASET FOR DECOY DEVELOPMENT TO IMPROVE IOT SECURITY

arXiv

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arXiv 2024年

作者： Weissman, David Jayasumana, Anura P. Department of Systems Engineering Colorado State University Fort CollinsCO United States Department of Electrical and Computer Engineering Colorado State University Fort CollinsCO United States

In this paper, the authors introduce a lightweight dataset to interpret IoT (Internet of Things) activity in preparation to create decoys by replicating known data traffic patterns. The dataset comprises different scenarios in a real network setting. This paper also surveys information related to other IoT datasets along with the characteristics that make our data valuable. Many of the datasets available are synthesized (simulated) or often address industrial applications, while the IoT dataset we present is based on likely smart home scenarios. Further, there are only a limited number of IoT datasets that contain both normal operation and attack scenarios. A discussion of the network configuration and the steps taken to prepare this dataset are presented as we prepare to create replicative patterns for decoy purposes. The dataset, which we refer to as IoT Flex Data, consists of four categories, namely, IoT benign idle, IoT benign active, IoT setup, and malicious (attack) traffic associating the IoT devices with the scenarios under consideration. Copyright © 2024, The Authors. All rights reserved.

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On Optimizing Inter-and Intra-Chiplet Interconnection Topologies for Robust Multi-Chiplet systems

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Wang, Xiaohang Xu, Miao Singh, Amit Kumar Jiang, Yingtao Yang, Mei Zhejiang University State Key Laboratory of Blockchain and Data Security China Hangzhou China South China University of Technology School of Software Engineering China University of Essex School of Computer Science and Electronic Engineering United Kingdom University of Nevada Department of Electrical and Computer Engineering Las Vegas United States

Inter-and intra-chiplet interconnection networks play a vital role in the operation of many core systems made of multiple chiplets. However, these networks are susceptible to faults caused by manufacturing defects and attacks resulting from the malicious insertion of hardware Trojans and backdoors. Unlike conventional fault-tolerant or countermeasure methods, this paper focuses on optimizing network robustness to withstand both faults and attacks, while considering the constraints of chiplet area and power budget. To achieve this, this paper first defines network robustness as a quantifiable measure based on various network parameters, after which an optimization problem is formulated to optimize the robustness of the network topology. To efficiently solve this problem, a reinforcement learning algorithm is proposed. Experimental results demonstrate that the proposed method is capable of generating inter-and intra-chiplet interconnection networks that are significantly more robust than existing topology generation methods. Specifically, the proposed method improves robustness over ButterDonut and Kite, respectively by an average of 10.88% and 14.06% under random faults and by 9.37% and 7.81% under targeted attacks. These experimental results confirm that the proposed method is capable of generating robust inter-and intra-chiplet interconnection networks that can withstand both faults and attacks. By optimizing the network topology’s robustness, it provides a valuable contribution to the design and security of chiplet-based core systems. © 2025 IEEE. All rights reserved

关键词： Budget control

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NEURAL STOCHASTIC DIFFERENTIAL EQUATIONS FOR ROBUST AND EXPLAINABLE ANALYSIS OF ELECTROMAGNETIC UNINTENDED RADIATED EMISSIONS

arXiv

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arXiv 2023年

作者： Jha, Sumit Kumar Jha, Susmit Ewetz, Rickard Velasquez, Alvaro Computer Science Laboratory SRI International Electrical and Computer Engineering University of Central Florida United States Computer Science Department Florida International University United States Department of Computer Science University of Colorado Boulder United States

We present a comprehensive evaluation of the robustness and explainability of ResNet-like models in the context of Unintended Radiated Emission (URE) classification and suggest a new approach leveraging Neural Stochastic Differential Equations (SDEs) to address identified limitations. We provide an empirical demonstration of the fragility of ResNet-like models to Gaussian noise perturbations, where the model performance deteriorates sharply and its F1-score drops to near insignificance at 0.008 with a Gaussian noise of only 0.5 standard deviation. We also highlight a concerning discrepancy where the explanations provided by ResNet-like models do not reflect the inherent periodicity in the input data, a crucial attribute in URE detection from stable devices. In response to these findings, we propose a novel application of Neural SDEs to build models for URE classification that are not only robust to noise but also provide more meaningful and intuitive explanations. Neural SDE models maintain a high F1-score of 0.93 even when exposed to Gaussian noise with a standard deviation of 0.5, demonstrating superior resilience to ResNet models. Neural SDE models successfully recover the time-invariant or periodic horizontal bands from the input data, a feature that was conspicuously missing in the explanations generated by ResNet-like models. This advancement presents a small but significant step in the development of robust and interpretable models for real-world URE applications where data is inherently noisy and assurance arguments demand interpretable machine learning predictions. Copyright © 2023, The Authors. All rights reserved.

关键词： Gaussian distribution

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Point Cloud Compression with Implicit Neural Representations: A Unified Framework

Point Cloud Compression with Implicit Neural Representations...

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IEEE International Conference on Communications in China (ICCC)

作者： Hongning Ruan Yulin Shao Qianqian Yang Liang Zhao Dusit Niyato Department of Information Science and Electronic Engineering Zhejiang University Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City University of Macau S.A.R Macau School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Point clouds have become increasingly vital across various applications thanks to their ability to realistically depict 3D objects and scenes. Nevertheless, effectively compressing unstructured, high-precision point cloud data remains a significant challenge. In this paper, we present a pioneering point cloud compression framework capable of handling both geometry and attribute components. Unlike traditional approaches and existing learning-based methods, our framework utilizes two coordinatebased neural networks to implicitly represent a voxelized point cloud. The first network generates the occupancy status of a voxel, while the second network determines the attributes of an occupied voxel. To tackle an immense number of voxels within the volumetric space, we partition the space into smaller cubes and focus solely on voxels within non-empty cubes. By feeding the coordinates of these voxels into the respective networks, we reconstruct the geometry and attribute components of the original point cloud. The neural network parameters are further quantized and compressed. Experimental results underscore the superior performance of our proposed method compared to the octree-based approach employed in the latest G-PCC standards. Moreover, our method exhibits high universality when contrasted with existing learning-based techniques.

关键词： Point cloud compression Geometry Learning systems Three-dimensional displays Quantization (signal) Neural networks Rate-distortion

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