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IRS-Enhanced V2X Communication and Computation Systems: Resource Allocation and Performance Optimization

IEEE INTERNET OF THINGS JOURNAL 2025年第7期12卷 9180-9194页

作者： Cao, Xueyan Wang, Shubin Ren, Xiaolei Inner Mongolia Univ Sch Elect Informat Engn Hohhot 010031 Peoples R China Beijing Inst Technol Sch Mech Engn Beijing 100811 Peoples R China

Vehicle-to-Everything (V2X) communication and computation encounter challenges in achieving ultrareliable, low-latency communication, and optimizing energy consumption in dynamic vehicular environments. To overcome these issues, intelligent reflecting surfaces (IRSs) are introduced to boost communication efficiency and reliability while lowering latency and energy use. This article presents an IRS-enhanced V2X system, employing multiple IRSs to improve vehicle communication and computation offloading through spectrum reuse principles. An effective utility function is developed to quantify total latency and energy consumption, facilitating precise system evaluation and optimization. The complex optimization problem is divided into four subproblems: 1) vehicle operation mode selection;2) spectrum reuse allocation;3) computation offloading decision;and 4) beamforming design. A swap-matching-based tabu-search method solves the mode selection subproblem, while semi-definite relaxation and penalty functions address the other issues integrated through an alternating optimization algorithm. The optimized system achieves efficient and reliable communication with reduced latency and energy consumption. Simulations reveal that effective utility function minimization significantly enhances system efficiency compared to benchmarks. Strategic IRS deployment reduces channel losses, resulting in substantial performance improvements and supporting intelligent, sustainable transportation network advancement.

关键词： Vehicle-to-everything Resource management Optimization Energy consumption Interference Delays computational modeling Vehicle dynamics Transportation Servers Intelligent reflecting surface (IRS) mobile edge computing (MEC) resource allocation vehicular communication

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A Redundant Parallel Continuum Manipulator With Stiffness-Varying and Force-Sensing Capability

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 3635-3647页

作者： Tang, Shujie Liang, Zhenkun Zhang, Zhuang Duan, Xuyang Wang, Hao Chen, Genliang Shanghai Jiao Tong Univ Meta Robot Inst Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Digital Mfg Thin Walled Struct Shanghai 200240 Peoples R China Westlake Univ Sch Engn Hangzhou 310030 Zhejiang Peoples R China

This paper presents the design, analysis, and validation of a novel redundant planar parallel continuum manipulator (PCM) consisting of four flexible links coupled at the rigid mid-and end-platform. To address complex geometry/static hybrid constraints at the rigid platforms, a general framework is developed for the kinetostatics modeling and analysis. Benefiting from the redundant actuation design, the Cartesian stiffness of the studied PCM can be further adjusted as a secondary task to positioning. Utilizing the proposed deflection-based force sensing method, the external load exerted on the end-effector can also be identified by measuring the pose of rigid platforms. To validate the proposed design, a prototype is built, on which a series of experiments have been conducted. The results show that, with the proposed kinetostatics models, the prototype exhibits a mean position and orientation error of 0.52 mm and 0.41(degrees). Finally, the capability of stiffness-varying and force-sensing is demonstrated to verify the feasibility and potential applications of the designed redundant PCM. Note to Practitioners-This paper is motivated by the need for more adaptable robotic systems in industrial and interactive settings. In this work, a novel redundant parallel continuum manipulator with variable passive compliance and force-sensing capability is presented. This planar manipulator comprises four flexible limbs, each driven by independent linear modules, thus the redundancy in the actuation can be leveraged for varying Cartesian stiffness. By means of measuring the pose of the end-effector, the interaction force exerted on the end-effector can be identified according to the force sensing model. The structural compliance of the manipulator enhances safety in human-robot collaboration, while variable stiffness accommodates the specific requirements of different interaction tasks. Furthermore, the force-sensing capability allows for precise feedback control, significantly

关键词： Robot sensing systems Force Task analysis Sensors Robots Phase change materials computational modeling Parallel continuum manipulator kinetostatics analysis actuation redundancy variable stiffness force sensing

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Chattering-Avoidance Discrete-Time Fuzzy Control With Finite-Time Preselected Qualities

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2025年第3期33卷 997-1008页

作者： Bu, Xiangwei Luo, Ruining Lei, Humin Air Force Engn Univ Air Def & Antimissile Sch Xian 710051 Peoples R China

This article is devoted to the exploration of a novel design process for synthesizing fuzzy prescribed performance control (PPC), with a specific emphasis on achieving preselected behaviors in discrete-time. To overcome the limitations of existing studies that solely rely on a fixed and inflexible sliding mode control framework, we put forward an innovative indirect stabilization method that offers a practical and readily implementable design process in discrete-time. Different from current approaches, our method ensures enhanced prescribed performance by guaranteeing finite-time convergence and avoiding high frequency chattering. To achieve this, we directly develop discrete-time PPC protocols using a low-computational fuzzy approximation strategy based on completely unknown system dynamics, thus eliminating the need for complex model equivalent reconstruction as demanded by previous studies. Finally, we verify the effectiveness and superiority of our proposed design.

关键词： Protocols Steady-state Convergence Vectors Transient analysis computational modeling System dynamics Fuzzy control Feedback control Uncertainty Chattering-avoidance finite-time convergence fuzzy approximation indirect stabilization preselected quality

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Co-Designing Transformer Architectures for Distributed Inference With Low Communication

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第4期36卷 717-730页

作者： Du, Jiangsu Wei, Yuanxin Ye, Shengyuan Jiang, Jiazhi Chen, Xu Huang, Dan Lu, Yutong Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China

Transformer models have shown significant success in a wide range of tasks. However, the massive resources required for its inference prevent deployment on a single device with relatively constrainted resources, thus leaving a high threshold of integrating their advancements. Observing scenarios such as smart home applications on edge devices and cloud deployment on commodity hardware, it is promising to distribute Transformer inference across multiple devices. Unfortunately, due to the tightly-coupled feature of Transformer model, existing model parallelism approaches necessitate frequent communication to resolve data dependencies, making them unacceptable for distributed inference, especially under relatively weak interconnection. In this paper, we propose DeTransformer, a communication-efficient distributed Transformer inference system. The key idea of DeTransformer involves the co-design of Transformer architecture to reduce the communication during distributed inference. In detail, DeTransformer is based on a novel block parallelism approach, which restructures the original Transformer layer with a single block to the decoupled layer with multiple sub-blocks. Thus, it can exploit model parallelism between sub-blocks. Next, DeTransformer contains an adaptive execution approach that strikes a trade-off among communication capability, computing power and memory budget over multiple devices. It incorporates a two-phase planning for execution, namely static planning and runtime planning. The static planning runs offline, containing a profiling procedure and a weight placement strategy before execution. The runtime planning dynamically determines the optimal parallel computing strategy from an expertly crafted search space based on real-time requests. Notably, this execution approach can adapt to heterogeneous devices by distributing workload based on devices' computing capabilities. We conduct experiments for both auto-regressive and auto-encoder tasks of Transforme

关键词： Parallel processing Transformers computational modeling Data models Planning Distributed databases Runtime Hardware Adaptation models Throughput Model parallelism distributed transformer inference deep learning system

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Superpixel-Based Bipartite Graph Clustering Enriched With Spatial Information for Hyperspectral and LiDAR Data

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Cao, Zhe Lu, Yihang Xin, Haonan Wang, Rong Nie, Feiping Sebilo, Mathieu Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Shaanxi Peoples R China Sorbonne Univ Inst Ecol & Environm Sci Paris IEES F-75005 Paris France

The surge in remote sensing (RS) data underscores the need for improved data diversity and processing. While integrating hyperspectral (HS) and light detection and ranging (LiDAR) data enhances analysis and addresses spectral variability, the high dimensionality, noise, and outliers inherent in hyperspectral images present significant challenges. In addition, the precise labeling required for HS makes supervised classification labor-intensive, professional-focused, and time-consuming, further motivating the development of advanced HS clustering algorithms to address these issues. Unsupervised clustering addresses the above issues but still struggles due to the underutilization of auxiliary spatial and structural information, high data dimensionality with redundant hyperspectral bands, and information divergence from heterogeneity among multimodal data. These challenges impede the effective extraction of consistent structures, undermining clustering stability and overall model performance. To address these challenges, we propose a superpixel-based bipartite graph clustering (SBGC) enriched with spatial information for hyperspectral and LiDAR data models. Our proposed method fully utilizes spatial information to construct meaningful bipartite graphs for the efficient processing of multimodal RS data. By adopting a projected clustering paradigm, our approach simultaneously clusters and reduces dimensionality, effectively eliminating redundant bands. In addition, it innovatively stacks multimodal data into tensors, thoroughly exploring the consistent structures in the low-rank space among different modalities. This reduces the heterogeneity-induced information divergence and significantly enhances clustering performance. Extensive experiments on real datasets confirm the method's effectiveness and advanced capabilities.

关键词： Hyperspectral imaging Tensors Laser radar Discrete Fourier transforms Data models Feature extraction Land surface computational modeling Clustering algorithms Bipartite graph Bipartite graphs dimensionality reduction (DR) remote sensing (RS) scalable method spatial information tensor-based clustering unsupervised learning

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A Zeroing Neural Network Approach for Calculating Time-Varying G-Outer Inverse of Arbitrary Matrix

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第5期36卷 8843-8852页

作者： Stanimirovic, Predrag S. Mourtas, Spyridon D. Mosic, Dijana Katsikis, Vasilios N. Cao, Xinwei Li, Shuai Univ Nis Fac Sci & Math Nish 18000 Serbia Siberian Fed Univ Lab Hybrid Methods Modeling & Optimizat Complex S Krasnoyarsk 660041 Russia Natl & Kapodistrian Univ Athens Dept Econ Div Math Informat & Stat Econometr Athens 10559 Greece Jiangnan Univ Sch Business Wuxi 214122 Peoples R China Univ Oulu Fac Informat Technol Oulu 90570 Finland

Calculation of the time-varying (TV) matrix generalized inverse has grown into an essential tool in many fields, such as computer science, physics, engineering, and mathematics, in order to tackle TV challenges. This work investigates the challenge of finding a TV extension of a subclass of inner inverses on real matrices, known as generalized-outer (G-outer) inverses. More precisely, our goal is to construct TV G-outer inverses (TV-GOIs) by utilizing the zeroing neural network (ZNN) process, which is presently thought to be a state-of-the-art solution to tackling TV matrix challenges. Using known advantages of ZNN dynamic systems, a novel ZNN model, called ZNNGOI, is presented in the literature for the first time in order to compute TV-GOIs. The ZNNGOI performs excellently in performed numerical simulations and an application on addressing localization problems. In terms of solving linear TV matrix equations, its performance is comparable to that of the standard ZNN model for computing the pseudoinverse.

关键词： Mathematical models TV Dynamical systems computational modeling Nickel Location awareness Numerical models Dynamic system generalized inverse generalized-outer (G-outer) inverse zeroing neural network (ZNN)

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New Statistic Detector for Structural Image Similarity

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 1168-1183页

作者： Diaw, Moustapha Retraint, Florent Morain-Nicolier, Frederic Delahaies, Agnes Landre, Jerome Univ Reims CReST EA 3804 IUT Troyes F-51097 Reims France Univ Technol Troyes LIST3N UTT F-10300 Troyes France Jonkoping Univ Comp Sci & Informat Dept S-55318 Jonkoping Sweden

Social networks like LinkedIn, Facebook, and Instagram contribute significantly to the rise of image prevalence in daily life, with numerous images posted in everyday. Detecting image similarity is crucial for many applications. While deep learning methods like Learned Perceptual Image Patch Similarity (LPIPS) are popular, they often overlook image structure. An alternative method involves using pre-trained models ($e.g.$, LeNet-$5$ and VGG-$16$) to extract features and employing classifiers. However, deep learning methods demand substantial computational resources and they also suffer from uncontrolled false alarms. This paper proposes a novel Generalized Likelihood Ratio Test (GLRT) detector based on a hypothesis testing framework to identify the similarity of structural image pairs. The proposed approach minimizes the need for extensive computational resources, and false alarms can be regulated by employing a threshold. The detector is applied to Local Dissimilarity Maps (LDM), with gray-level values modeled by a statistical distribution. Experimental results on simulated and real data confirm its effectiveness for structural similarity detection. Additionally, a Simple Likelihood Ratio Test (SLRT) is tested on simulated data. Comparisons with deep learning and classical measures like Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) show the proposed detector performs comparably or better in terms of Area Under the Curve (AUC) with less computing time, especially for structural similarity.

关键词： Feature extraction Detectors Convolutional neural networks Transformers Image quality Noise computational modeling Indexes Image classification Training Classification local dissimilarity map weibull distribution GLRT similarity measures pre-trained CNN

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Multitenancy in Single Instance of Data Persistence Service for Supporting Low-Code Platforms

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IEEE ACCESS 2025年 13卷 13427-13439页

作者： Costa, Julio Gustavo Goncalves, Luiz Marcos Garcia Xavier-de-Souza, Samuel Univ Fed Rio Grande do Norte Programa Posgrad Engn Elect & Comp BR-59078970 Natal RN Brazil Univ Fed Rio Grande do Norte Dept Engn Comp & Automacao BR-59078970 Natal RN Brazil

Corporate digitalization, especially among Small and Medium Enterprises, has led to a significant increase in the demand for scarce professionals with expertise in the IT area, especially in Web Information Systems area. On the other hand, concerns about high levels of resource idleness in the cloud are constant. In this scenario, Low-Code Platforms have gained traction in the software industry, whose most commonly found service is the automation of code generation to perform data persistence tasks driven by data models. This approach, however, implies a mode of operation that is not the most mature among cloud computing providers, implying for each model a service instance running on the provider side. Here, we propose a data persistence service for these platforms that avoids code generation, since it interprets data models at runtime and operates in multi-tenant mode with a single service instance. This approach improves resource sharing, mitigating resource idleness within the platform. In addition, we present experiments to support the technical feasibility of the proposed approach. The proposed solution offers an alternative to code generation methods, with the potential to optimize resource utilization while preserving the flexibility to adapt to changes in data models as business needs evolve.

关键词： Data models Unified modeling language Load modeling Databases Business Software systems Runtime computational modeling Codes Transformers Model driven engineering low code development model interpretation multi-tenancy single instance separation of concerns

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Generative AI for Analog Integrated Circuit Design: Methodologies and Applications

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IEEE ACCESS 2025年 13卷 58043-58059页

作者： Noori Zadeh, Danial Elamien, Mohamed B. McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada

Electronic Design Automation (EDA) in analog Integrated Circuits (ICs) has been the focus of extensive research;however, unlike its digital counterpart, it has not achieved widespread adoption. In this systematic review, we discuss recent contributions in the last five years, highlighting methods that address data scarcity, topology exploration, process-voltage-temperature (PVT) variations, and layout parasitics. Our goal is to support researchers new to this domain by creating a comprehensive collection of references and practical application guidelines. We provide a methodological review of state-of-the-art machine learning (ML) approaches, including graph neural networks (GNNs), large language models (LLMs), and variational autoencoders (VAEs), which have been successfully applied to analog circuit sizing tasks. To the best of authors' knowledge, this is the first review to comprehensively explore the application of generative AI models in analog IC circuit design. We conclude that future research could focus on few-shot learning with domain-adaptation training of generative AI methods to simplify the design tasks such as human-tool interaction or guided design space exploration.

关键词： Integrated circuit modeling Automation Reviews Layout Topology computational modeling Data models Generative AI Voltage Training Analog integrated circuits (ICs) electronic design automation (EDA) layout-aware sizing machine learning (ML) large language models (LLM) graph neural networks (GNN) variational auto-encoders (VAE) artificial intelligence (AI)

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A Weighted Deep Learning-Based Predictive Control for Multimode Nonlinear System With Industrial Applications

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 10814-10826页

作者： Huang, Keke Cao, Wenpu Liu, Yishun Wu, Dehao Yang, Chunhua Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China

In response to the challenge of strongly nonlinear and multimode systems control, this paper introduces a weighted deep learning based adaptive predictive control method. This approach integrates LSTM networks for different operating modes using a set of weighting coefficients. These coefficients are dynamically updated during online control via an error-guided scheduling strategy to adapt to changing operation modes. Compared to offline identification based methods, the proposed method eliminates the need for mode recognition or model switching strategies and can adapt to drifted operation modes. In contrast to online methods, it achieves rapid model convergence and reduced computational cost, requiring only minimal data to update the weighting coefficients without necessitating the retraining of the LSTM networks. Theoretical convergence and stability analysis ensure the reliability of the proposed method. Numerical simulations and industrial control experiments demonstrate that the proposed approach exhibits favorable control performance across both known and drifted operation modes. Note to Practitioners-Considering the changing operation modes in complex industrial processes and the detrimental effect of slow or unstable control during system operation, this paper proposes a weighted LSTM based predictive control method for strongly nonlinear and multimode systems. Extensive experiments demonstrate that compared to other state-of-the-art methods, this method can rapidly adapt to changes in operating modes with a small amount of data, meeting both real-time and stability requirements for online control.

关键词： Predictive models Long short term memory Adaptation models Mathematical models Convergence computational modeling Predictive control Optimization Computer architecture Switches Multimode control weighted networks deep learning long short-term memory network model predictive control

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