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International Youth Conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Hanima Kannan C H Deepa Gupta Parul Mathur Dhanesh G Kurup Tilesh Sanjay Chaudhari Priyanka Mathur Department of Electronics and Communication Engineering RF and Wireless Systems Laboratory Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Chemical Engineering Creative and Advanced Research Based on Nanomaterials Laboratory Indian Institute of Technology Hyderabad School of Engineering and Applied Science Bennett University Greater Noida U.P. India

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) sensor interacting with the material under test (MUT) generate a cross-dataset to validate the MLP regression model, utilizing the frequency-dependent complex dielectric constant for material characterization. This paper utilizes the frequency-dependent response of the OECP sensor (scattering parameters) as input features for the MLP model, while the complex dielectric constant of the MUT serves as the output feature. The optimized MLP model has an $R^{2}$ of 99.991% and MSE of $6.65 \times 10^{-5}$ , with predictions closely aligning with literature values for standard loads such as distilled water, acetone, and ethanol, obtained using a commercially available dielectric assessment software kit. This cost-effective model enables precise dielectric property estimation, with future work extending to unknown materials, mixtures, and electrolyte solutions.

关键词： Power engineering Dielectric constant Predictive models Mathematical models Software Scattering parameters Probes Permittivity Standards Load modeling

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Energy Efficiency Optimization for Full-Duplex D2D Communications Underlaying Distributed Antenna systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Liu, Zhan Liao, Zhiyuan Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. Hunan University of Humanities Science and Technology School of Information Loudi417000 China Nanchang University School of Information Engineering Nanchang330031 China Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang330031 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

In this paper, we investigate the total system energy efficiency (EE) of full-duplex (FD) device-to-device (D2D) communications underlaying distributed antenna systems (DAS), where remote access units (RAUs), D2D users (DUs), and cellular users (CUs) are all capable of FD operation. Specifically, we jointly optimize subcarrier assignment and power allocation under the quality of service (QoS) requirements of CUs and DUs and the maximum power constraints of RAUs, CUs, and DUs. In addition, we propose a novel spectrum sharing strategy that allows each subcarrier to be assigned to multiple CUs and/or multiple D2D pairs (DPs) for flexibility. To solve the formulated non-convex optimization problem, we first employ fractional programming to transform the objective function in the optimization problem from the fractional form into the equivalent subtractive form. Then, an efficient iterative resource allocation algorithm is proposed, which needs to solve an inner problem in each iteration. After relaxing the variables and introducing penalty factors, the non-convex inner problem is transformed into a convex problem through the successive convex approximation (SCA) method and solved by iterative algorithm. Simulation results demonstrate that the proposed algorithm can considerably improve the system EE compared to other benchmark schemes. Furthermore, the proposed spectrum sharing strategy is superior to the existing sharing strategies. © 2017 IEEE.

关键词： Convex optimization

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Tackling Noisy Labels with Network Parameter Additive Decomposition

arXiv

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arXiv 2024年

作者： Wang, Jingyi Xia, Xiaobo Lan, Long Wu, Xinghao Yu, Jun Yang, Wenjing Han, Bo Liu, Tongliang The Department of Intelligent Data Science College of Computer Science and Technology National University of Defense Technology Changsha410073 China The Trustworthy Machine Learning Lab School of Computer Science Faculty of Engineering University of Sydney DarlingtonNSW2008 Australia The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Department of Automation University of Science and Technology of China Hefei230026 China The Department of Computer Science Hong Kong Baptist University Hong Kong

Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization. The memorization effect of deep networks shows that although the networks have the ability to memorize all noisy data, they would first memorize clean training data, and then gradually memorize mislabeled training data. A simple and effective method that exploits the memorization effect to combat noisy labels is early stopping. However, early stopping cannot distinguish the memorization of clean data and mislabeled data, resulting in the network still inevitably overfitting mislabeled data in the early training stage. In this paper, to decouple the memorization of clean data and mislabeled data, and further reduce the side effect of mislabeled data, we perform additive decomposition on network parameters. Namely, all parameters are additively decomposed into two groups, i.e. , parameters w are decomposed as w = σ + γ. Afterward, the parameters σ are considered to memorize clean data, while the parameters γ are considered to memorize mislabeled data. Benefiting from the memorization effect, the updates of the parameters σ are encouraged to fully memorize clean data in early training, and then discouraged with the increase of training epochs to reduce interference of mislabeled data. The updates of the parameters γ are the opposite. In testing, only the parameters σ are employed to enhance generalization. Extensive experiments on both simulated and real-world benchmarks confirm the superior performance of our method. The implementation is available at: https://***/wangjingyi9924/TNLPAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Modular Multilevel DC-DC Converter with Inherent Bipolar Operation Capability for Resilient Bipolar MVDC Grids

Modular Multilevel DC-DC Converter with Inherent Bipolar Ope...

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作者： Zhao, Shanshan Chen, Yu Cui, Shenghui Hu, Jingxin State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology China Seoul National University Department of Electrical and Computer Engineering Korea Republic of Institute for Power Generation and Storage Systems E. on Energy Research Center RWTH Aachen University Germany

Bipolar medium-voltage dc (MVDC) distribution systems are of great interests nowadays due to its high availability and reliability. A bipolar MVDC grid can be established by the installation of power-balancers or directly by converters with inherent bipolar operation capability to avoid the extra cost and volume. Therefore, this paper proposes a modular multilevel dc-dc converter (MMDC) with inherent bipolar operation capability for the interconnection of bipolar MVDC grids and LVDC girds. Considering that the MMDC is normally only capable of operating with monopole MVDC distribution systems due to the symmetric structure and operation scheme, a center-Tapped high-frequency interface transformer is employed on the MV side of the MMDC. Based on the concept of the flux dc-bias cancellation, a dedicated operation method is proposed, whereby the power flows of the two MVDC poles can be regulated independently with a simple control scheme, and no penalty of increased current rating is imposed on the semiconductor devices or the interface transformer compared to the conventional MMDC with monopole operation. Additionally, the MMDC can realize single-pole operation in case one pole is faulty and deliver at least 50% of the rated power capacity, which significantly enhance the reliability and availability of the power delivery. The validity of the proposed bipolar operation scheme of the MMDC has been verified by both simulations and experiments with a down-scale prototype. © 2017 CPSS.

关键词： Poles

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Effective dimension in bandit problems under censorship 22

Effective dimension in bandit problems under censorship

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Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Gauthier Guinet Saurabh Amin Patrick Jaillet AWS AI Labs MIT and Dept. of Civil and Environmental Engineering & Laboratory for Information and Decision Systems MIT and Dept. of Electrical Engineering and Computer Science & Laboratory for Information and Decision Systems

ISBN: (纸本)9781713871088

In this paper, we study both multi-armed and contextual bandit problems in censored environments. Our goal is to estimate the performance loss due to censorship in the context of classical algorithms designed for uncensored environments. Our main contributions include the introduction of a broad class of censorship models and their analysis in terms of the effective dimension of the problem - a natural measure of its underlying statistical complexity and main driver of the regret bound. In particular, the effective dimension allows us to maintain the structure of the original problem at first order, while embedding it in a bigger space, and thus naturally leads to results analogous to uncensored settings. Our analysis involves a continuous generalization of the Elliptical Potential Inequality, which we believe is of independent interest. We also discover an interesting property of decision- making under censorship: a transient phase during which initial misspecification of censorship is self-corrected at an extra cost; followed by a stationary phase that reflects the inherent slowdown of learning governed by the effective dimension. Our results are useful for applications of sequential decision-making models where the feedback received depends on strategic uncertainty (e.g., agents' willingness to follow a recommendation) and/or random uncertainty (e.g., loss or delay in arrival of information).

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Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics

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IEEE Transactions on Communications 2025年

作者： Yang, Zonghui Gao, Shijian Cheng, Xiang Yang, Liuqing Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Internet of Things Thrust Guangzhou511400 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

Integrated sensing and communication (ISAC) technology is vital for vehicular networks, yet the time-varying communication channels and rapid movement of targets present significant challenges for real-time precoding design. Traditional optimization-based methods are computationally complex and strongly depend on perfect prior information, which is often unavailable in double-dynamic scenarios. In this paper, we propose a synesthesia of machine (SoM)-enhanced precoding paradigm that leverages modalities such as positioning and initial channel information to adapt to these dynamics. Utilizing a deep reinforcement learning (DRL) framework, our approach pushes ISAC performance boundaries. We also introduce a parameter-shared actor-critic architecture to accelerate training in complex state and action spaces. Extensive experiments validate the superiority of our method over existing approaches. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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Lightweight Security Authentication Scheme for Vehicle-Road Collaboration using Physical Unclonable Function

Lightweight Security Authentication Scheme for Vehicle-Road ...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zhuoqun Xia Chao Su Zisang Xu Kejun Long School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems Changsha China

The Internet of Vehicles (IoVs) is widely used to obtain information about vehicles and road conditions, which is transmitted in public channels. Hence, the most important requirement is the data security, which needs to keep in a strict delay. Authentication is a common method to solve it. Due to limited resources and delay sensitivity of IoVs, vehicles must complete authentication within appropriate resources cost and delay. However, existing schemes are prone to physical, forgery and collusion attacks, and moreover, they are computationally heavy. Therefore, we propose a lightweight security identity authentication scheme for vehicle-road collaboration, which utilizes lightweight Physical Unclonable Function (PUF) as the trust root of entities to resist physical and collusion attacks; Besides, most of computation is offloaded to Road Side Units (RSUs) certified by Trusted Authority (TA) through the vehicle-road collaboration architecture. In addition, we utilize challenge-response pairs (CRPs) to protect privacy and expose malicious vehicular identities in identity tracking phase. Furthermore, there are formal and informal security analyses to prove our scheme is secure. Finally, the simulation experiment shows our scheme is more secure and efficient than other schemes in real scenarios.

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Remote Sensing Image Object Detection by Fusing Multi-Scale Contextual Features and Channel Enhancement

Remote Sensing Image Object Detection by Fusing Multi-Scale ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xuesen Ma Jindian Dong Weixin Wei Biao Zheng Ji Ma Tianbao Zhou School of Computer Science and Information Engineering Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

Remote sensing technology is becoming more sophisticated and is extensively used for object tracking, urban planning, military reconnaissance and other fields. Complex backgrounds and diverse object scales are two important factors that affect the object detection effect of remote sensing images. To address this problem, this paper proposes a remote sensing object detection model that incorporates channel enhancement and multi-scale contextual features. Firstly, the multi-scale contextual feature enhancement module is constructed, which performs multi-order spatial interaction by cascading recursive convolution to obtain contextual information of feature maps at different scales, and introduces attention to reinforce unique features of objects and suppress background interference. Then, the spatial pyramid channel enhancement module combining sub-pixel convolution and adaptive sampling factor is designed to mitigate the semantic weakening of the depth feature maps caused by channel downscaling, thus enhancing the sampling effect between feature maps of different scales and reducing information loss. Finally, the effectiveness of the model is verified on the large-scale remote sensing image object detection dataset DIOR.

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Diffusion Model-based Metaverse Rendering in UAV-Enabled Edge Networks With Dual Connectivity

Diffusion Model-based Metaverse Rendering in UAV-Enabled Edg...

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IEEE Conference on Wireless Communications and Networking

作者： Guoquan Wu Jiangtian Nie Jianhang Tang Yuling Chen Yang Zhang Luchao Han Zehui Xiong School of Information Science and Engineering Yanshan University China School of Computer Science and Engineering Nanyang Technological University Singapore State Key Laboratory of Public Big Data Guizhou University China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China National Natural Science Foundation of China China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Metaverse is an immersive, seamless, interactive, comprehensive virtual world, as well as a replication, extension, and transcendence of the real world. Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is becoming a key technology for ubiquitous Metaverse services. To enhance network resource utilization, we introduce dual connectivity (DC) technologies in UAV-enabled MEC, which increases the time complexity associated with resource management. Considering the specific features of DC communication channels, we propose a UAV-assisted Metaverse rendering problem to enhance the Metaverse service experience and reduce the energy cost of edge devices. To solve the rendering problem with low complexity, we propose a diffusion model-based Metaverse rendering algorithm, where a novel diffusion model is used to generate integer rendering decisions with the aid of the gradient provided by the model-based Metaverse rendering problem. Moreover, with the given rendering decisions, the communication and computation resource allocation results are derived by the model-based optimization method. Finally, we conduct extensive simulation experiments based on real-world datasets. Comprehensive simulation results demonstrate that the diffusion model-based Metaverse rendering algorithm can reduce the Metaverse frame rendering time and improve user experience.

关键词： Costs Metaverse Computational modeling System performance Rendering (computer graphics) Autonomous aerial vehicles User experience

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A ROS-Based Autonomous Vehicle Testbed for the Internet of Vehicles

A ROS-Based Autonomous Vehicle Testbed for the Internet of V...

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International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Nikolaos Sarantinoudis George Tsinarakis Lefteris Doitsidis Savvas Chatzichristofis George Arampatzis School of Production Engineering and Management Technical University of Crete Chania Crete Greece Department of Computer Science Intelligent Systems Laboratory (ISLab) Neapolis University Pafos Cyprus

This paper describes the design and implementation procedure of a Robot Operating System (ROS) testbed for research on the concept of Internet of Vehicles and the integration of Digital Twins in autonomous vehicles. The proposed testbed integrates communication devices, sensors, and micro-controllers to collect real-time data from the physical environment, enabling the replication of such systems in a virtual environment using Digital Twins. The framework provides a hands-on approach for researchers and students to understand the operation and control of autonomous vehicles without requiring full-scale vehicles. The interactive and immersive learning experience simulates a real-sized vehicle, allowing users to explore the effects of different control algorithms and sensor configurations. Developed using open-source software such as ROS and low-cost microcontrollers and sensors, the platform is affordable for research and development, providing an engaging and practical learning experience.

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