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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： T. Selvin Retna Raj J. Relin Francis Raj R. Santhana Krishnan G. Balamuralikrishnan D. Vikkiramapandian J. Nirmal Jothi Department of Electronics and Communication Engineering DMI College of Engineering Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering of Engineering and Technology Cheranmahadevi India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Information Technology PSNA College of Engineering and Technology Dindigul India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Wildfires pose significant threats to ecological systems, human safety, and economic stability, especially in vulnerable forest regions. To address this critical issue, a sustainable AI-driven framework, WiSEFire (Wildfire Surveillance and Estimation), is proposed for real-time monitoring and prediction of wildfires. The framework integrates a hybrid approach, utilizing Random Forest for feature analysis and Gated Recurrent Units (GRU) for temporal fire prediction. The system processes multi-source data, including satellite imagery, IoT sensors, and meteorological information, to enhance detection accuracy and prediction reliability. Simulation experiments were conducted using benchmark datasets to evaluate WiSEFire against state-of-the-art algorithms, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Performance metrics such as accuracy, latency, and detection efficiency were analyzed. The results demonstrated that WiSE Fire outperformed existing methods, achieving superior detection precision and reduced latency while maintaining energy efficiency. This research highlights the potential of sustainable AI frameworks for mitigating wildfire risks and improving emergency response capabilities

关键词： Wildfires Accuracy Surveillance Clustering algorithms Forestry Prediction algorithms Real-time systems Energy efficiency Sensor systems Satellite images

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Generalized Polarization-Spatial Modulation With Multi-Mode Transmission

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IEEE Internet of Things Journal 2025年

作者： Li, Jun Li, Shuangyuan Dang, Shuping Chen, Xuan Chen, Chuanxi Peng, Yuyang Yu, Ling Guangzhou University Research Center of Intelligent Communication Engineering School of Electronics and Communication Engineering Guangzhou510006 China Guangdong Provincial Hospital of Chinese Medicine Guangzhou510120 China University of Bristol School of Electrical Electronic and Mechanical Engineering BristolBS8 1UB United Kingdom Fujian Normal University College of Computer and Cyber Security Fuzhou350007 China Macau University of Science and Technology School of Computer Science and Engineering Taipa999078 China State Key Laboratory of Traditional Chinese Medicine Syndrome Guangzhou510120 China

Polarization-spatial modulation (PSM) has been recently proposed to improve the system performance and energy efficiency of the conventional polarization modulation (PM) by activating only a single dual-polarized (DP) antenna for signal transmission. However, the spectral efficiency (SE) of PSM is significantly degraded due to the single DP antenna transmission. To tackle this problem, we propose a generalized PSM (GPSM) scheme to enhance the SE of PSM through transmission using multiple DP antennas. In the GPSM scheme, the information bits are mapped to antenna activation patterns (AAPs), polarization matrix activation patterns (PAPs), and modulated symbols. To further enhance the SE of GPSM, we propose a more practical scheme termed multi-mode GPSM (MM-GPSM), which transmits information bits not only through the AAPs, PAPs, and modulated symbols but also via the constellation activation patterns (CAPs). To reduce the computational burden of the optimal maximum likelihood (ML) detector, we propose a low-complexity ML detector and a log-likelihood ratio (LLR) detector for both GPSM and MM-GPSM, with the negligible performance degradation. A closed-form upper bound on the bit error rate (BER) is derived to evaluate the performance of both GPSM and MM-GPSM. Simulation results demonstrate that GPSM and MM-GPSM outperform conventional PM and PSM schemes, particularly in the high signal-to-noise ratio (SNR) region, and validate the accuracy of the theoretical BER analysis. © 2014 IEEE.

关键词： Global positioning system

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Cognitive Disentanglement for Referring Multi-Object Tracking

arXiv

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arXiv 2025年

作者： Liang, Shaofeng Guan, Runwei Lian, Wangwang Liu, Daizong Sun, Xiaolou Wu, Dongming Yue, Yutao Ding, Weiping Xiong, Hui Qingdao China Guangzhou China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao China Wangxuan Institute of Computer Technology Peking University Beijing China School of Automation Southeast University Nanjing China School of Computer Science Beijing Institute of Technology Beijing China School of Artificial Intelligence and Computer Science Nantong University Nantong China Guangzhou China Department of Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom

As a significant application of multi-source information fusion in intelligent transportation perception systems, Referring Multi-Object Tracking (RMOT) involves localizing and tracking specific objects in video sequences based on language references. However, existing RMOT approaches often treat language descriptions as holistic embeddings and struggle to effectively integrate the rich semantic information contained in language expressions with visual features. This limitation is especially apparent in complex scenes requiring comprehensive understanding of both static object attributes and spatial motion information. In this paper, we propose a Cognitive Disentanglement for Referring Multi-Object Tracking (CDRMT) framework that addresses these challenges. It adapts the "what" and "where" pathways from human visual processing system to RMOT tasks. Specifically, our framework comprises three collaborative components: (1)The Bidirectional Interactive Fusion module first establishes cross-modal connections while preserving modality-specific characteristics;(2) Building upon this foundation, the Progressive Semantic-Decoupled Query Learning mechanism hierarchically injects complementary information into object queries, progressively refining object understanding from coarse to fine-grained semantic levels;(3) Finally, the Structural Consensus Constraint enforces bidirectional semantic consistency between visual features and language descriptions, ensuring that tracked objects faithfully reflect the referring expression. Extensive experiments on different benchmark datasets demonstrate that CDRMT achieves substantial improvements over state-of-the-art methods, with average gains of 6.0% in HOTA score on Refer-KITTI and 3.2% on Refer-KITTI-V2. Our approach advances the state-of-the-art in RMOT while simultaneously providing new insights into multi-source information fusion. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Dielectric Mechanisms and Health State Estimation for High-Voltage XLPE Cable Insulation Under Nonuniform Thermal Aging

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IEEE Transactions on Dielectrics and Electrical Insulation 2025年

作者： Dai, Xize Hao, Jian Moursi, Mohamed El Chen, Rongxin Liao, Ruijin Bak, Claus Leth Chongqing University State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing400044 China Aalborg University Department of Energy Aalborg9220 Denmark Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates

High-voltage (HV) crosslinked polyethylene (XLPE) cables are subjected to electrothermal stress gradients during service, which often leads to nonuniform degradation within insulation systems, significantly affecting their endurance, resilience, and overall lifetime. This article presents a comprehensive study of dielectric mechanisms and health state estimation of XLPE insulation under nonuniform thermal aging, with a particular emphasis on 500 kV XLPE cables. First, the distinct effects of uniform versus nonuniform thermal aging mechanisms on XLPE systems are compared through selected five combinations in two groups. Furthermore, the mechanisms by which nonuniform thermal aging influences XLPE insulation systems are revealed using three dielectric analysis techniques. Additionally, this work innovatively introduces a quantitative framework for the health state estimation of XLPE cable insulation under nonuniform thermal aging, utilizing the previously developed equivalent circuit model that incorporates a fractional-order circuit module (FOCM). The aging features extracted from the FOCM are utilized to develop health state estimation models for XLPE insulation under different nonuniform thermal aging conditions. The performance and limitations of health estimation models are discussed using a stacked XLPE system. This study deepens the understanding of nonuniform thermal aging mechanisms and provides insights to support condition-based maintenance of HV cable insulation under complex aging conditions. © 1994-2012 IEEE.

关键词： Cable sheathing

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Real-Time Environmental Monitoring and Control Using Cyber-Physical Systems: A Case Study in Agriculture 3

Real-Time Environmental Monitoring and Control Using Cyber-P...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Yadav, Dhananjay Kumar Kumar, Amit Devi, Suprava Mishra, Abhinav Venkadeshwaran, K. Reddy, V. Venkata Ramesh Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Sponsored Research Karnataka Bangalore India

ISBN: (纸本)9798331542375

Due to its nature, real-time environmental monitoring and control is an indispensable part of modern agriculture. At its core, it is the ongoing collection, analysis, and application of ecological data to improve crop yields while conserving resources and minimizing expenditures. This article proposes that cyber-physical systems are an effective organization that provides a building structure where real-time monitoring and control can be provided across different processes within agriculture. Here, we report on a detailed case study of the application of CPS as discussed for environmental monitoring and control in agriculture. This paper reviews the dominant components of CPS sensors, actuators, and communication systems and data analytics. Together, these elements help us to gather real-time data on critical environmental variables like temperature, humidity, soil moisture and nutrient levels. The algorithm analyzes the data and provides farmers with insights. With the help of CPS, farmers can automate and remotely monitor essential processes like watering, fertilization, pest control, etc., increasing efficiency while decreasing labor costs. Another benefit of CPS is that it makes accurate decisions in a timely manner using real-time data, which can yield better crops and be more effective in resource utilization. © 2025 IEEE.

关键词： Crops

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AgentPolyp: Accurate Polyp Segmentation via Image Enhancement Agent

arXiv

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arXiv 2025年

作者： Wang, Pu Zhang, Zhihua Lu, Dianjie Zhang, Guijuan Zhang, Youshan Zheng, Zhuoran School of Mathematics Shandong University Jinan250100 China School of Information Science and Engineering Shandong Normal University Jinan250100 China Department of Artificial Intelligence and Computer Science Yeshiva University New YorkNY United States School of cyber science and technology Sun Yat-sen University Shenzhen518000 China

Since human and environmental factors interfere, captured polyp images usually suffer from issues such as dim lighting, blur, and overexposure, which pose challenges for downstream polyp segmentation tasks. To address the challenges of noise-induced degradation in polyp images, we present AgentPolyp, a novel framework integrating CLIP-based semantic guidance and dynamic image enhancement with a lightweight neural network for segmentation. The agent first evaluates image quality using CLIP-driven semantic analysis (e.g., identifying "low-contrast polyps with vascular textures") and adapts reinforcement learning strategies to dynamically apply multi-modal enhancement operations (e.g., denoising, contrast adjustment). A quality assessment feedback loop optimizes pixel-level enhancement and segmentation focus in a collaborative manner, ensuring robust preprocessing before neural network segmentation. This modular architecture supports plug-and-play extensions for various enhancement algorithms and segmentation networks, meeting deployment requirements for endoscopic devices. © 2025, CC BY.

关键词： Semantic Segmentation

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An Optimization-Aware Pre-Routing Timing Prediction Framework Based on Multi-Modal Learning

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

作者： Cao, Peng Qin, Yusen He, Guoqing Ding, Wenjie Cheng, Xu Zhang, Zhanhua Ye, Yuyang Southeast University School of Integrated Circuits Nanjing210096 China National Center of Technology Innovation for Eda Nanjing210031 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong

Accurate and efficient pre-routing timing estimation is particularly crucial during placement to alleviate time-consuming design iterations. Machine learning (ML) based methods have been introduced recently to predict the post-routing timing results at placement stage, but most of them neglect the impact of timing optimization during physical design, suffering from accuracy loss due to inconsistent circuit netlist. In this work, an optimization-aware pre-routing timing prediction framework based on multi-modal learning is proposed to calibrate the timing changes between placement and routing stages, where the local netlist and layout information are extracted by graph neural network (GNN) and convolutional neural network (CNN) respectively while the global information along the path is further extracted by Transformer network. Based on the predicted post-routing timing results by the proposed framework, timing optimization guidance is generated to enhance traditional design flow with better physical implementation quality. Experimental results demonstrate that for the OpenCores benchmark circuits under TSMC 22nm process, the proposed framework achieves significant correlation and accuracy improvement with an average of 0.9219 in terms of R score and 2.12% of mean absolute percentage error (MAPE) as well as an average runtime acceleration of 645× compared with traditional design flow on testing designs. With the timing optimization guidance, significant worst negative slack (WNS) and total negative slack (TNS) improvement are achieved compared with traditional flow after placement and routing respectively without noticeable area, power, wire length, and the number of design rule check (DRC) violations increase. © 2025 IEEE.

关键词： Convolutional neural networks

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Efficient fragmentation-aware algorithms for multicast traffic in elastic optical networks with a novel demand size based fragmentation metric

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Optical Fiber technology 2025年 93卷

作者： Asadzadeh, Leila Khonsari, Ahmad Ghaffarpour Rahbar, Akbar School of Electrical and Computer Engineering University of Tehran Tehran14155-6619 Iran School of Computer Science Institute for Research in Fundamental Sciences Institute for Research in Fundamental Sciences Tehran19538-33511 Iran Computer Networks Research Lab Faculty of Electrical Engineering Tabriz University of Technology Tabriz Iran

With flexible allocation of spectrum resources and on-demand bandwidth provisioning, Elastic Optical Networks (EONs) have made it possible to meet the high volumes of traffic demands. Multicast Routing, Modulation level and Spectrum Assignment (MRMSA) algorithms are important in efficient multicast transmissions in EONs. The dynamic nature of allocating and releasing spectral resources causes the spectrum to be divided into small empty blocks that are not suitable to be allocated anymore, so the resource utilization reduces greatly. Many metrics have been introduced in the literature to measure the amount of fragmented spectrum. In this paper, we propose a novel demand size based fragmentation metric (DemFRAG) to evaluate the suitability of the optical links or paths to establish the requests. We also propose two fragmentation-aware algorithms based on this novel metric to efficiently solve MRMSA problem in EONs. The performance of the proposed fragmentation-aware MRMSA algorithms is evaluated by considering the novel DemFRAG metric and also other fragmentation metrics in terms of Blocking Probability (BP) and Bandwidth Blocking Probability (BBP) under different network topologies. Simulation results demonstrate that the proposed MRMSA algorithms with the DemFRAG metric allow for significant reduction in BP and BBP compared to other algorithms. © 2025

关键词： Blocking probability

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Joint Task Offloading and Resource Allocation for LEO Satellite-Based Mobile Edge Computing Systems With Heterogeneous Task Demands

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IEEE Transactions on Vehicular technology 2025年

作者： Zhong, Liang Li, Youyuan Ge, Ming-Feng Feng, Mingjie Mao, Shiwen China University of Geosciences School of Mechanical Engineering and Electronic Information Wuhan430072 China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan430074 China Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States

To satisfy the growing demand for supporting intelligent Internet of Things (IoT) applications in remote areas, satellite mobile edge computing (SMEC) systems are expected to be widely deployed. Meanwhile, as IoT applications are increasingly diversified, the tasks received by SMEC systems present heterogeneous demands for various resources and quality of service (QoS) metrics, necessitating customized design to accommodate such heterogeneity. In this paper, we investigate the problem of joint task offloading and resource allocation in SMEC systems with heterogeneous task demands. We first propose a customized task utility model that captures the diversified demands, which is an extension of traditional unified task utility models. Based on the model, we then formulate a mixed integer non-linear programming (MINLP) problem for joint optimization of task offloading, computing resource allocation, transmission power control, and user association, aiming to maximize the sum utility of all tasks. To solve the MINLP, a multi-layer iterative framework is proposed that decomposes the original problem into two subproblems, which are solved by successive convex approximation (SCA) and deep reinforcement learning (DRL) algorithms, respectively. Simulation results show that, by applying the proposed task utility model, the average utility performance of UEs can be improved by 50% and 100% compared to applying a single metric-based utility model and a typical classification-based utility model, respectively;the proposed task offloading and resource allocation scheme achieves a 30%-80% performance gain compared to benchmark schemes. © 1967-2012 IEEE.

关键词： Quality of service

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Fuzzy Logic-Based Deep Learning for Human-Machine Interaction and Gesture Recognition in Uncertain and Noisy Environments

Fuzzy Logic-Based Deep Learning for Human-Machine Interactio...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： John Anand V. Thamilarasi Ashish Rayal Himanshu Kumar Gupta Kamboji Jyothi Pramatma Vishwakarma Department of Computer Science & Engineering (AI) IIMT College of Engineering Greater Noida India Department of Computer Science & Engineering Sri Sarada College for Women(Autonomous) Salem-16 Tamilnadu India Department of Mathematics School of Applied and Life Sciences Uttaranchal University Dehradun India Department of Electrical Engineering Bipin Tripathi Kumaon Institute of Technology Dwarahat Almora India Department of Mathematics Chaitanya Bharathi Institute of Technology Proddatur AP India Department of Computer Aplication Lovely Professional University Punjab India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Conventional deep learning methods have considerable hurdles when it comes to Human-Machine Interaction (HMI) and gesture identification in noisy and uncertain situations. A Fuzzy Logic-Based Deep Learning (Fuzzy DL) method is presented in this work to improve the resilience, accuracy, and adaptability of human gesture recognition in a variety of scenarios. We test several algorithms on various datasets, lighting situations, and noise levels, such as CNN, LSTM, Transformer, and Fuzzy DL. The outcomes show that Fuzzy DL functions better than other systems, retaining 91.0% accuracy at high noise levels and reaching 97.5% accuracy under typical circumstances. In comparison to traditional structures, it also shows faster inference times and a lower mean squared error. Its superiority is further demonstrated by comparative analysis on precision, recall, and F1-score. This study presents a robust and noise-resilient solution for real-world HMI applications, including as assistive systems and virtual interfaces. The outcomes indicate that incorporating fuzzy logic with DL substantially enhances gesture recognition in difficult environments.

关键词： Deep learning Human computer interaction Fuzzy logic Accuracy Gesture recognition Transformers Noise measurement Long short term memory Noise level Resilience

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