Zhen Wu^1,2, Hao Liu³, Linlin Zhang⁴, Zehui Zhang^5,*, Jie Wu¹, Haibin He¹, Bin Zhou⁶

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075156 - 10 February 2026

Abstract Recently, Internet of Things (IoT) has been increasingly integrated into the automotive sector, enabling the development of diverse applications such as the Internet of Vehicles (IoV) and intelligent connected vehicles. Leveraging IoV technologies, operational data from core vehicle components can be collected and analyzed to construct fault diagnosis models, thereby enhancing vehicle safety. However, automakers often struggle to acquire sufficient fault data to support effective model training. To address this challenge, a robust and efficient federated learning method (REFL) is constructed for machinery fault diagnosis in collaborative IoV, which can organize multiple companies to collaboratively More >

Yu-Xuan Zhou¹, Kai-Qing Zhou^1,*, Wei-Lin Chen¹, Zhou-Hua Liao¹, Di-Wen Kang^1,2

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075099 - 10 February 2026

Abstract The Pigeon-Inspired Optimization (PIO) algorithm constitutes a metaheuristic method derived from the homing behaviour of pigeons. Initially formulated for three-dimensional path planning in unmanned aerial vehicles (UAVs), the algorithm has attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation, coupled with advantages in real-time performance and robustness. Nevertheless, as applications have diversified, limitations in convergence precision and a tendency toward premature convergence have become increasingly evident, highlighting a need for improvement. This review systematically outlines the developmental trajectory of the PIO algorithm, with a particular focus on its core… More >

Argho Dey¹, Yunfei Yin^1,2,*, Zheng Yuan¹, Zhiwen Zeng¹, Xianjian Bao³, Md Minhazul Islam¹

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074122 - 10 February 2026

Abstract Autonomous vehicles rely heavily on accurate and efficient scene segmentation for safe navigation and efficient operations. Traditional Bird’s Eye View (BEV) methods on semantic scene segmentation, which leverage multimodal sensor fusion, often struggle with noisy data and demand high-performance GPUs, leading to sensor misalignment and performance degradation. This paper introduces an Enhanced Channel Attention BEV (ECABEV), a novel approach designed to address the challenges under insufficient GPU memory conditions. ECABEV integrates camera and radar data through a de-noise enhanced channel attention mechanism, which utilizes global average and max pooling to effectively filter out noise while… More >

Beike Yu, Dafang Wang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.076439 - 29 January 2026

Abstract Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving, owing to its efficiency and applicability in both training and evaluating algorithms. Consequently, there has been increasing attention on generating highly realistic and consistent driving videos, particularly those involving viewpoint changes guided by the control commands or trajectories of ego vehicles. However, current reconstruction approaches, such as Neural Radiance Fields and 3D Gaussian Splatting, frequently suffer from limited generalization and depend on substantial input data. Meanwhile, 2D generative models, though capable of producing unknown scenes, still have room for improvement in terms… More >

Tai Liu^1,2, Mao Ye^2,*, Feng Wu³, Chao Zhu², Bo Chen², Guoyan Zhang^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072692 - 12 January 2026

Abstract With the continuous advancement of unmanned technology in various application domains, the development and deployment of blind-spot-free panoramic video systems have gained increasing importance. Such systems are particularly critical in battlefield environments, where advanced panoramic video processing and wireless communication technologies are essential to enable remote control and autonomous operation of unmanned ground vehicles (UGVs). However, conventional video surveillance systems suffer from several limitations, including limited field of view, high processing latency, low reliability, excessive resource consumption, and significant transmission delays. These shortcomings impede the widespread adoption of UGVs in battlefield settings. To overcome these… More >

Mohammed Alnusayri¹, Ghulam Mujtaba², Nouf Abdullah Almujally³, Shuoa S. Aitarbi⁴, Asaad Algarni⁵, Ahmad Jalal^2,6, Jeongmin Park^7,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071804 - 12 January 2026

Abstract This paper presents a unified Unmanned Aerial Vehicle-based (UAV-based) traffic monitoring framework that integrates vehicle detection, tracking, counting, motion prediction, and classification in a modular and co-optimized pipeline. Unlike prior works that address these tasks in isolation, our approach combines You Only Look Once (YOLO) v10 detection, ByteTrack tracking, optical-flow density estimation, Long Short-Term Memory-based (LSTM-based) trajectory forecasting, and hybrid Speeded-Up Robust Feature (SURF) + Gray-Level Co-occurrence Matrix (GLCM) feature engineering with VGG16 classification. Upon the validation across datasets (UAVDT and UAVID) our framework achieved a detection accuracy of 94.2%, and 92.3% detection accuracy when More >

Tsu-Yang Wu¹, Chengyuan Yu¹, Yanan Zhao², Saru Kumari³, Chien-Ming Chen^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069139 - 12 January 2026

Abstract With the increasing complexity of logistics operations, traditional static vehicle routing models are no longer sufficient. In practice, customer demands often arise dynamically, and multi-depot systems are commonly used to improve efficiency. This paper first introduces a vehicle routing problem with the goal of minimizing operating costs in a multi-depot environment with dynamic demand. New customers appear in the delivery process at any time and are periodically optimized according to time slices. Then, we propose a scheduling system TS-DPU based on an improved ant colony algorithm TS-ACO to solve this problem. The classical ant colony More >

Jie Li¹, Nan An^2,3, Youliang Ding^2,3,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.072361 - 08 January 2026

Abstract Vehicle-induced response separation is a crucial issue in structural health monitoring (SHM). This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data. To extend the separation target from a fixed dataset to a continuously updating data stream, a block-wise sliding framework is first developed. This framework is further optimized considering the characteristics of real-time data streams, and its advantage in computational efficiency is theoretically demonstrated. During the decomposition and reconstruction processes, information from neighboring data blocks is fully utilized to reduce algorithmic complexity. In addition, a… More >

Supriya Wadekar^1,*, Shailendra Mittal¹, Ganesh Wakte², Rajshree Shinde²

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.071761 - 27 December 2025

Abstract Rapid evolutions of the Internet of Electric Vehicles (IoEVs) are reshaping and modernizing transport systems, yet challenges remain in energy efficiency, better battery aging, and grid stability. Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand, thus increasing energy costs and battery aging. This study proposes a smart charging station with an AI-powered Battery Management System (BMS), developed and simulated in MATLAB/Simulink, to increase optimality in energy flow, battery health, and impractical scheduling within the IoEV environment. The system operates through… More >

Tiankui Sun^*, Shuyi Zhuang, Yongling Lu, Wenqiang Xie, Ning Guo, Sudi Xu

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.070213 - 27 December 2025

Abstract To ensure an uninterrupted power supply, mobile power sources (MPS) are widely deployed in power grids during emergencies. Comprising mobile emergency generators (MEGs) and mobile energy storage systems (MESS), MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies, offering advantages such as flexibility and high resilience through electricity delivery via transportation networks. This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator (DWIG) intended for MEG applications, employing an improved particle swarm optimization (PSO) algorithm based on a back-propagation neural network (BPNN). A… More >