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Joint Optimization of Communication Latency and Platoon Control Based on Uplink RSMA for Future V2X Networks

IEEE Transactions on Vehicular technology 2025年

作者： Liu, Gang Hu, Jiewen Ma, Zheng Fan, Pingzhi Yu, F. Richard Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

As an effective solution to address road congestion and improve traffic efficiency, vehicle platooning has received a lot of attention in recent years. However, vehicle-to-vehicle (V2V) communication latency can largely impact on the performance of vehicle platooning, which has not been well investigated. Rate-splitting multiple access (RSMA), as one of the promising technologies for 6 G, holds great potential for application in vehicle platooning to reduce the communication latency and thereby enhance platoon control performance. Motivated by the above considerations, platoon control and communication latency are jointly considered in this paper. We first introduce the communication latency into the vehicle dynamics model, and then derive a distributed model predictive control (DMPC)-based platoon control model. To reduce the communication latency, uplink RSMA is introduced for inter-platoon V2V communication. Since the latency minimization problem is non-convex and challenging to solve directly, we first employ a bisection method to give a latency upper bound and transform the initial problem into a non-convex feasibility problems in each iteration. Subsequently, through theoretical derivation, the bandwidth allocation factor is expressed in terms of transmission power, which reduces the number of optimization variables and transforms the problem into a convex problem. Then we can obtain the optimal solution by solving a convex problem in each iteration. Finally, the simulation results demonstrate that the proposed RSMA significantly outperforms traditional frequency division multiple access (FDMA) and non-orthogonal multiple access (NOMA), and can significantly reduce communication latency, enhance platoon control safety, improve driving comfort, and lower the performance requirement on vehicle maximum torque. © 2025 IEEE.

关键词： Traffic congestion

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Exploiting NOMA and RIS in Integrated Sensing and Communication

Exploiting NOMA and RIS in Integrated Sensing and Communicat...

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作者： Zuo, Jiakuo Liu, Yuanwei Zhu, Chenming Zou, Yixuan Zhang, Dengyin Al-Dhahir, Naofal China Information Consulting and Designing Institute Company Ltd. Institute of Communications and Information Technology Nanjing210003 China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210003 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

A novel integrated sensing and communication (ISAC) system is proposed, where a dual-functional base station is utilized to transmit the superimposed non-orthogonal multiple access (NOMA) communication signal for serving communication users and sensing targets simultaneously. Furthermore, a new reconfigurable intelligent surface (RIS)-aided-sensing structure, where a dedicated RIS is deployed to provide virtual line-of-sight (LoS) links for radar targets, is also proposed to address the significant path loss or blockage of LoS links for the sensing task. Based on this setup, the beampattern gain at the RIS for the radar target is derived and adopted as a sensing metric. The objective of this paper is to maximize the minimum beampattern gain by jointly optimizing active beamforming at the base station (BS), power allocation coefficients among NOMA users and passive beamforming at the RIS. To tackle the non-convexity of the formulated optimization problem, the beampattern gain and constraints are first transformed into more tractable forms. Then, an iterative block coordinate descent (IBCD) algorithm is proposed by employing successive convex approximation (SCA), Schur complement, semidefinite relaxation (SDR) and sequential rank-one constraint relaxation (SRCR) methods. To reduce the complexity of the proposed IBCD algorithm, a low-complexity iterative alternating optimization (IAO) algorithm is proposed. Particularly, the active beamforming is optimized by solving a semidefinite programming (SDP) problem and the closed-form solutions of the power allocation coefficients are derived. Numerical results show that: i) the proposed RIS-NOMA-ISAC system always outperforms the RIS-ISAC system without NOMA in beampattern gain and illumination power;ii) the low-complexity IAO algorithm achieves a comparable performance to that achieved by the IBCD algorithm. iii) high beampattern gain can be achieved by the proposed joint optimization algorithms in underloaded and overloaded

关键词： Optimization

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Advanced insulating materials contributing to“carbon neutrality”:Opportunities,issues and challenges

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High Voltage 2022年第4期7卷 607-609页

作者： Chuanyang Li Yunlong Zi Yang Cao Giovanni Mazzanti Davide Fabiani Di Zhang Yunqi Xing Jinliang He State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijing100084China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and TechnologyClear Water BayHong Kong SARChina Electrical and Computer Engineering University of Connecticut371 Fairfield WayStorrsCT 06269USA Department Electrical Electronic and Information EngineeringUniversity of BolognaBologna 40136Italy Electrical and Computer Engineering Department Naval Postgraduate School1 University CircleMontereyCA 93943USA State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of TechnologyTianjin300131China

The vision of 2050 worldwide carbon-neutral ignites a revo-lution in all *** innovations in both electrical power equipment and electrified transportation assets are shaping our future energy *** materials,as the core element of energy infrastructure,are facing unprece-dented challenges that stand in the pathways to global carbon ***,researchers from both academic in-stitutions and industrial sections are working to turn the challenges into opportunities.

关键词： structure. neutral carbon

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A Deep Learning and Transfer Learning-Based Application for Skin Cancer Classification

A Deep Learning and Transfer Learning-Based Application for ...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Sabrina Karboua Fouzi Harrag Sarrah Karboua Amal Booutadjine Mohamed Deriche Department of Computer Science Setif 1 University-Ferhat ABBAS Abdelhamid MEHRI - Constantine 2 University Algeria College of Engineering and Information Technology AIRC Ajman University UAE

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Skin cancer is the most prevalent type of cancer worldwide, accounting for approximately one-third of all cancer cases, with its incidence steadily rising in recent years. This is why early and precise identification of skin cancer is crucial for effective treatment and enhancing survival rates. Leveraging advancements in deep learning and transfer learning, we developed a comprehensive system to address this challenge. In this study, we employed the HAM10000 dataset, a benchmark resource for skin cancer research, to train and evaluate deep learning models. Two architectures, DenseNet201 and ResNet50, were fine- tuned and modified to enhance their accuracy in classifying skin lesions as benign or malignant. Rigorous preprocessing, data augmentation, and model fine-tuning were utilized to address challenges such as dataset imbalance and improve classification accuracy. Our experiments revealed that the modified fine-tuned DenseNet201 model achieved superior performance compared to fined-tuned ResNet50 and other leading models reviewed in the literature with an accuracy of 95.04%. This model was then integrated into a mobile application, “SkinSafe,” to provide an accessible and user-friendly tool for real-time skin cancer classification.

关键词： Deep learning Accuracy Transfer learning Skin Real-time systems Data models Mobile applications Lesions Skin cancer Residual neural networks

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Block cross-interactive residual smoothing for Lanczos-type solvers for linear systems with multiple right-hand sides

arXiv

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arXiv 2024年

作者： Aihara, Kensuke Imakura, Akira Morikuni, Keiichi Department of Computer Science Faculty of Information Technology Tokyo City University 1-28-1 Tamazutsumi Setagaya-ku Tokyo158-8557 Japan Institute of Systems and Information Engineering University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8573 Japan

Lanczos-type solvers for large sparse linear systems often exhibit large oscillations in the residual norms. In finite precision arithmetic, large oscillations increase the residual gap (the difference between the recursively updated residual and the explicitly computed residual) and a loss of attainable accuracy of the approximations. This issue is addressed using cross-interactive residual smoothing (CIRS). This approach improves convergence behavior and reduces the residual gap. Similar to how the standard Lanczos-type solvers have been extended to global and block versions for solving systems with multiple right-hand sides, CIRS can also be extended to these versions. While we have developed a global CIRS scheme (Gl-CIRS) in our previous study [K. Aihara, A. Imakura, and K. Morikuni, SIAM J. Matrix Anal. Appl., 43 (2022), pp.1308–1330], in this study, we propose a block version (Bl-CIRS). Subsequently, we demonstrate the effectiveness of Bl-CIRS from various perspectives, such as theoretical insights into the convergence behaviors of the residual and approximation norms, numerical experiments on model problems, and a detailed rounding error analysis for the residual gap. For Bl-CIRS, orthonormalizing the columns of direction matrices is crucial in effectively reducing the residual gap. This analysis also complements our previous study and evaluates the residual gap of the block Lanczos-type *** Codes 65F10, 65F25 Copyright © 2024, The Authors. All rights reserved.

关键词： Linear systems

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Emotion Recognition System via Facial Expressions and Speech Using Machine Learning and Deep Learning Techniques

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SN computer Science 2023年第4期4卷 363页

作者： Chaudhari, Aayushi Bhatt, Chintan Nguyen, Thanh Thi Patel, Nisarg Chavda, Kirtan Sarda, Kalind U & P U. Patel Department of Computer Engineering Chandubhai S. Patel Institute of Technology Charotar University of Science And Technology (CHARUSAT) Gujarat Changa 388421 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Knowledge Corridor Gujarat Gandhinagar 382007 India School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Victoria 3216 Australia Bishop University 2600 College St Sherbrooke J1M 1Z7 QC Canada Crest Data Systems Private Limited Gandhinagar Highway Makarba Gujarat Ahmedabad 380051 India Programmer Analyst Meditab Software Pvt Ltd Kalasagar Mall Ghatlodiya Gujarat Ahmedabad 380061 India

Patients in hospitals frequently exhibit psychological issues such as sadness, pessimism, eccentricity, and anxiety. However, hospitals normally lack tools and facilities to continuously monitor the psychological health of patients. It is desirable to identify depression in patients so that it can be managed by instantly providing better therapy. This can be possible by advances in machine learning for image processing with notable applications in the domain of emotion recognition using facial expressions. In this paper, we have proposed two different methods, i.e. facial expression detection and voice analysis, to predict emotions. For facial expression recognition, we have used two approaches, one is the use of Gabor filters for feature extraction with support vector machine for classification and another is using convolutional neural network (CNN). For voice analysis, we extracted mel-frequency cepstral coefficients from speech data and, based on those features, predicted the emotions of the speech using a CNN model. Experimental results show that our proposed emotion recognition methods obtained high accuracy and thus could be potentially deployed to real-world applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Expressions Facial emotion Machine learning Speech SVM

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Blending Optimal Control and Biologically Plausible Learning for Noise-Robust Physical Neural Networks

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Physical Review Letters 2025年第1期134卷 017301-017301页

作者： Satoshi Sunada Tomoaki Niiyama Kazutaka Kanno Rin Nogami André Röhm Takato Awano Atsushi Uchida Faculty of Mechanical Engineering Institute of Science and Engineering Kanazawa University Kakuma-machi Kanazawa Ishikawa 920-1192 Japan Department of Information and Computer Sciences Saitama University 255 Shimo-Okubo Sakura-ku Saitama City Saitama 338-8570 Japan Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan

The rapidly increasing computational demands for artificial intelligence (AI) have spurred the exploration of computing principles beyond conventional digital computers. Physical neural networks (PNNs) offer efficient neuromorphic information processing by harnessing the innate computational power of physical processes; however, training their weight parameters is computationally expensive. We propose a training approach for substantially reducing this training cost. Our training approach merges an optimal control method for continuous-time dynamical systems with a biologically plausible training method—direct feedback alignment. In addition to the reduction of training time, this approach achieves robust processing even under measurement errors and noise without requiring detailed system information. The effectiveness was numerically and experimentally verified in an optoelectronic delay system. Our approach significantly extends the range of physical systems practically usable as PNNs.

关键词： Continuous time systems

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Transition Waste Optimization for Coded Elastic Computing

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IEEE Transactions on information Theory 2023年第7期69卷 4442-4465页

作者： Dau, Son Hoang Gabrys, Ryan Huang, Yu-Chih Feng, Chen Luu, Quang-Hung Alzahrani, Eidah J. Tari, Zahir The School of Computing Technologies RMIT University MelbourneVIC3000 Australia The California Institute for Telecommunications and Information Technology University of California La Jolla San DiegoCA92093 United States The Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan KelownaBCV1V 1V7 Canada The Department of Computing Technologies School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia The Department of Civil Engineering Monash University ClaytonVIC3800 Australia The Department of Computer Science Al-Baha University Al Bahah65545 Saudi Arabia

Distributed computing, in which a resource-intensive task is divided into subtasks and distributed among different machines, plays a key role in solving large-scale problems. Coded computing is a recently emerging paradigm where redundancy for distributed computing is introduced to alleviate the impact of slow machines (stragglers) on the completion time. We investigate coded computing solutions over elastic resources, where the set of available machines may change in the middle of the computation. This is motivated by recently available services in the cloud computing industry (e.g., EC2 Spot, Azure Batch) where low-priority virtual machines are offered at a fraction of the price of the on-demand instances but can be preempted on short notice. Our contributions are three-fold. We first introduce a new concept called transition waste that quantifies the number of tasks existing machines must abandon or take over when a machine joins/leaves. We then develop an efficient method to minimize the transition waste for the cyclic task allocation scheme recently proposed in the literature (Yang et al. ISIT’19). Finally, we establish a novel solution based on finite geometry achieving zero transition wastes given that the number of active machines varies within a fixed range. © 2023 IEEE.

关键词： Toys

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Online Digital Twin-Empowered Content Resale Mechanism in Age of information-Aware Edge Caching Networks

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IEEE Transactions on Communications 2024年

作者： Yi, Yuhan Zhang, Guanglin Jiang, Hai Donghua University College of Information Science and Technology Shanghai China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

For users requesting popular contents from content providers, edge caching can alleviate backhaul pressure and enhance the quality of experience of users. Recently there is also a growing concern about content freshness that is quantified by age of information (AoI). Therefore, AoI-aware online caching algorithms are required, which is challenging because the content popularity is usually unknown in advance and may vary over time. In this paper, we propose an online digital twin (DT) empowered content resale mechanism in AoI-aware edge caching networks. We aim to design an optimal two-timescale caching strategy to maximize the utility of an edge network service provider (ENSP). The formulated optimization problem is non-convex and NP-hard. To tackle this intractable problem, we propose a DT-assisted Online Caching Algorithm (DT-OCA). In specific, we first decompose our formulated problem into a series of subproblems, each handling a cache period. For each cache period, we use a DT-based prediction method to effectively capture future content popularity, and develop an online caching strategy. Competitive ratio analysis and extensive experimental results demonstrate that our algorithm has promising performance, and outperforms other benchmark algorithms. Insightful observations are also found and discussed. © 1972-2012 IEEE.

关键词： Digital twin

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Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation

arXiv

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arXiv 2024年

作者： Zhao, Dong Wang, Shuang Zang, Qi Jiao, Licheng Sebe, Nicu Zhong, Zhun School of Artificial Intelligence Xidian University Shaanxi China Department of Information Engineering and Computer Science University of Trento Italy School of Computer Science and Information Engineering Hefei University of Technology China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

We study source-free unsupervised domain adaptation (SFUDA) for semantic segmentation, which aims to adapt a source-trained model to the target domain without accessing the source data. Many works have been proposed to address this challenging problem, among which uncertainty-based self-training is a predominant approach. However, without comprehensive denoising mechanisms, they still largely fall into biased estimates when dealing with different domains and confirmation bias. In this paper, we observe that pseudo-label noise is mainly contained in unstable samples in which the predictions of most pixels undergo significant variations during self-training. Inspired by this, we propose a novel mechanism to denoise unstable samples with stable ones. Specifically, we introduce the Stable Neighbor Denoising (SND) approach, which effectively discovers highly correlated stable and unstable samples by nearest neighbor retrieval and guides the reliable optimization of unstable samples by bi-level learning. Moreover, we compensate for the stable set by object-level object paste, which can further eliminate the bias caused by less learned classes. Our SND enjoys two advantages. First, SND does not require a specific segmentor structure, endowing its universality. Second, SND simultaneously addresses the issues of class, domain, and confirmation biases during adaptation, ensuring its effectiveness. Extensive experiments show that SND consistently outperforms state-of-the-art methods in various SFUDA semantic segmentation settings. In addition, SND can be easily integrated with other approaches, obtaining further improvements. The source code is available at https://***/DZhaoXd/SND. © 2024, CC BY.

关键词： Semantics

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