Hong Zhao, Hongbin Chen^*, Zhihui Guo, Ling Zhan, Shichao Li

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-25, 2026, DOI:10.32604/cmc.2025.072776 - 09 December 2025

Abstract UAV-mounted intelligent reflecting surface (IRS) helps address the line-of-sight (LoS) blockage between sensor nodes (SNs) and the fusion center (FC) in Internet of Things (IoT). This paper considers an IoT assisted by multiple UAVs-mounted IRS (U-IRS), where the data from ground SNs are transmitted to the FC. In practice, energy efficiency (EE) and mission completion time are crucial metrics for evaluating system performance and operational costs. Recognizing their importance during data collection, we formulate a multi-objective optimization problem to maximize EE and minimize total mission completion time simultaneously. To characterize this tradeoff while considering optimization… More >

Shih-Lin Lin^*, Cheng-Wei Li

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071915 - 09 December 2025

Abstract This study focuses on developing a deep learning model capable of recognizing vehicle brands and models, integrated with a law enforcement intelligence platform to overcome the limitations of existing license plate recognition techniques—particularly in handling counterfeit, obscured, or absent plates. The research first entailed collecting, annotating, and classifying images of various vehicle models, leveraging image processing and feature extraction methodologies to train the model on Microsoft Custom Vision. Experimental results indicate that, for most brands and models, the system achieves stable and relatively high performance in Precision, Recall, and Average Precision (AP). Furthermore, simulated tests… More >

Lei Wang^1,2, Menghan Wei², Ziwei Huangfu³, Shunjie Zhu², Xuejian Ge^1,*, Zhengquan Li⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-32, 2026, DOI:10.32604/cmc.2025.071295 - 09 December 2025

Abstract Iterative Learning Control (ILC) provides an effective framework for optimizing repetitive tasks, making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems (ITS). This paper presents a systematic review of ILC’s developmental progress, current methodologies, and practical implementations across these two critical domains. The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows. Through case studies, it evaluates demonstrated improvements in positioning accuracy, surface finish quality, and production throughput. Furthermore, the study examines ILC’s applications in ITS, with particular focus on vehicular motion control More >

Baixuan Han¹, Yueping Peng^1,*, Zecong Ye², Hexiang Hao¹, Xuekai Zhang¹, Wei Tang¹, Wenchao Kang¹, Qilong Li¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-31, 2026, DOI:10.32604/cmc.2025.071071 - 09 December 2025

Abstract Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm, a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed. By integrating multi-head grouped self-attention mechanism and Partial-Conv, a two-way feature grouping fusion module (DFPF) was designed, which carried out effective channel segmentation and fusion strategies to reduce redundant calculations and memory access. C3K2 module was improved, and then unstructured pruning and feature distillation technology were used. The algorithm model is lightweight, and the feature extraction ability for airborne visual abnormal behavior… More >

Lino Gonzalez-Garcia^*, Miguel-Angel Sicilia, Elena García-Barriocanal

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070813 - 09 December 2025

Abstract Classifying job offers into occupational categories is a fundamental task in human resource information systems, as it improves and streamlines indexing, search, and matching between openings and job seekers. Comprehensive occupational databases such as NET or ESCO provide detailed taxonomies of interrelated positions that can be leveraged to align the textual content of postings with occupational categories, thereby facilitating standardization, cross-system interoperability, and access to metadata for each occupation (e.g., tasks, knowledge, skills, and abilities). In this work, we explore the effectiveness of fine-tuning existing language models (LMs) to classify job offers with occupational descriptors… More >

Xiaoyi Duan, Tianqi Zou, Chenyang Wang, Yu Gu, Xiuying Li^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-24, 2026, DOI:10.32604/cmc.2025.070683 - 09 December 2025

Abstract Person recognition in photo collections is a critical yet challenging task in computer vision. Previous studies have used social relationships within photo collections to address this issue. However, these methods often fail when performing single-person-in-photos recognition in photo collections, as they cannot rely on social connections for recognition. In this work, we discard social relationships and instead measure the relationships between photos to solve this problem. We designed a new model that includes a multi-parameter attention network for adaptively fusing visual features and a unified formula for measuring photo intimacy. This model effectively recognizes individuals More >

Zhongyun Tang^1,2,3, Hanyi Xu², Haiyang Hu^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-29, 2026, DOI:10.32604/cmc.2025.070616 - 09 December 2025

Abstract With the deep integration of smart manufacturing and IoT technologies, higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection. For industrial fans, base bolt loosening faults are difficult to identify through conventional spectrum analysis, and the extreme scarcity of fault data leads to limited training datasets, making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity. This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution. This More >

Sanghyuk Lee¹, Eunmi Lee^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.070504 - 09 December 2025

Abstract Hesitation analysis plays a crucial role in decision-making processes by capturing the intermediary position between supportive and opposing information. This study introduces a refined approach to addressing uncertainty in decision-making, employing existing measures used in decision problems. Building on information theory, the Kullback–Leibler (KL) divergence is extended to incorporate additional insights, specifically by applying temporal data, as illustrated by time series data from two datasets (e.g., affirmative and dissent information). Cumulative hesitation provides quantifiable insights into the decision-making process. Accordingly, a modified KL divergence, which incorporates historical trends, is proposed, enabling dynamic updates using conditional More >

Haibo Lei^1,2, Xu An Wang^1,*, Wenhao Liu¹, Lingling Wu¹, Chao Zhang¹, Weiwei Jiang³, Xiao Zou⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-31, 2026, DOI:10.32604/cmc.2025.070030 - 09 December 2025

Abstract With the rapid expansion of the Internet of Things (IoT), user data has experienced exponential growth, leading to increasing concerns about the security and integrity of data stored in the cloud. Traditional schemes relying on untrusted third-party auditors suffer from both security and efficiency issues, while existing decentralized blockchain-based auditing solutions still face shortcomings in correctness and security. This paper proposes an improved blockchain-based cloud auditing scheme, with the following core contributions: Identifying critical logical contradictions in the original scheme, thereby establishing the foundation for the correctness of cloud auditing; Designing an enhanced mechanism that… More >

Dong Hyun Lee¹, Na Kyung Lee², Young Seo Lee^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.069976 - 09 December 2025

Abstract There has been an increasing emphasis on performing deep neural network (DNN) inference locally on edge devices due to challenges such as network congestion and security concerns. However, as DRAM process technology continues to scale down, the bit-flip errors in the memory of edge devices become more frequent, thereby leading to substantial DNN inference accuracy loss. Though several techniques have been proposed to alleviate the accuracy loss in edge environments, they require complex computations and additional parity bits for error correction, thus resulting in significant performance and storage overheads. In this paper, we propose FeatherGuard,… More >