Jiajia Liu^1,2, Shuchen Pang³, Peng Xie³, Haitao Zhou³, Chenxi Du³, Haoran Hu³, Bo Tang³, Jianhua Liu³, Fei Jia¹, Huibing Zhang^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077824 - 09 April 2026

Abstract In the unmanned aerial vehicle (UAV) assisted edge computing system, the broadcast characteristics of the UAV signal, the high mobility of the UAV, and the limited airborne energy make the task offloading strategy face challenges such as increased risk of information disclosure, limited computing resources, and the trade-off between energy consumption and flight time. To address these issues, we propose a K-means in-depth reinforcement learning algorithm based on Soft Actor-Critic (SAC). The proposed method first leverages the K-means clustering algorithm to determine the optimal deployment of ground jammers based on the final distribution of mobile… More >

Sihan Wang¹, Luyao Liu², Xingjun Wang^3,*, Yifan Liu^3,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077791 - 09 April 2026

Abstract This paper presents a novel dual-stage approach for efficient gait phase recognition and trajectory prediction, tailored for the operation of wearable devices such as exoskeletons. By leveraging dynamic template matching techniques and addressing their computational challenges, we propose an innovative algorithm that significantly enhances both prediction accuracy and computational efficiency. The approach integrates Dynamic Time Warping-KMeans (DTW-KM) template selection in the offline phase and a Soft Constraint Weighted (SCW) template matching technique in the online phase. In the offline stage, the DTW-KM method extracts diverse and generalizable gait patterns from a database, establishing a robust More >

Gangtao Han¹, Yurui Chen¹, Song Wang^1,*, Enqing Chen¹, Lingling Li², Gaofeng Pan³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077768 - 09 April 2026

Abstract The integrity of perception data transmitted over in-vehicle networks is important for the safety of autonomous driving. However, legacy protocols like the Controller Area Network (CAN) bus which lacks essential security features make In-Vehicle Networks (IVNs) vulnerable to data tampering attacks. Current research typically focuses on detecting the attack itself but ignores the information recovery from the missing data, leading to an unsafe autonomous driving system. To address the issue, we propose a 3D object recovery framework to recover the missing data caused by the tampering attack that occurred in in-vehicle networks. The proposed framework… More >

Bofan Yang, Bingbing Li, Chuanping Hu^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077697 - 09 April 2026

Abstract The rapid evolution of malware obfuscation and packing techniques significantly undermines the effectiveness of traditional static detection approaches. Transforming malware binaries into grayscale or RGB images enables learning-based classification, yet existing CNN- and ViT-based models depend heavily on fixed-resolution inputs and exhibit poor robustness under cross-resolution distortions. This study proposes a lightweight and sample-adaptive Multi-Scale Vision Transformer (MSA-ViT) for efficient and robust malware image classification. MSA-ViT leverages a fixed set of input scales and integrates them using a Scale-Attention Fusion (SAF) module, where the largest-scale CLS token serves as the query to dynamically aggregate cross-scale More >

Jaeseung Lee¹, Jehyeok Rew^2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077691 - 09 April 2026

Abstract The accelerating transition toward electrified mobility has positioned electric vehicles (EVs) as a primary technology in modern transportation systems. In this context, ensuring the reliability of EV drive motors (EVDMs) becomes increasingly critical, given their central role in propulsion performance and operational safety. Accurate and interpretable fault diagnosis of EVDMs is therefore essential for enabling effective maintenance and supporting the broader sustainability and resilience of EVs. This study presents a novel framework that combines decision tree-based fault classification with a multi-agent large language model (LLM) interpretation architecture to deliver transparent and human-readable diagnostic explanations. The… More >

Kai Li¹, Guolei Wang², Dunmin Lu^1,*, Yanbin Yao³, Zhiyong Li⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077651 - 09 April 2026

Abstract To address the need for improving the efficiency of spray painting large and complex curved surfaces, this study investigates the arm–rail coordinated spray painting operation method and proposes a robot workspace calculation method for efficient spray area partitioning. The steps for calculating the workspace under the constraints of the principal normal vector and the conical pose domain are introduced, along with an analysis of the robot’s forward and inverse kinematics. Simulation validation was conducted using a wind turbine blade as the target object. The results show that the workspace based on conical pose domain constraints More >

Anandkumar Balasubramaniam, Seung Yeob Nam^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077582 - 09 April 2026

Abstract The increasing connectivity of modern vehicles exposes the in-vehicle controller area network (CAN) bus to various cyberattacks, including denial-of-service, fuzzy injection, and spoofing attacks. Existing machine learning and deep learning intrusion detection systems (IDS) often rely on labeled data, struggle with class imbalance, lack interpretability, and fail to generalize well across different datasets. This paper proposes a lightweight and interpretable IDS framework based on non-negative matrix factorization (NMF) to address these limitations. Our contributions include: (i) evaluating NMF as both a standalone unsupervised detector and an interpretable feature extractor (NMF-W) for classical, unsupervised, and deep… More >

Seohyun Yoo, Joonseo Hyeon, Jaehyuk Cho^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077579 - 09 April 2026

Abstract Despite remarkable advances in medical large language models (LLMs), their deployment in real clinical settings remains impractical due to prohibitive computational requirements and privacy regulations that restrict cloud-based solutions. Small LLMs (sLLMs) offer a promising alternative for on-premise deployment, yet they require domain-specific fine-tuning that still exceeds the hardware capacity of most healthcare institutions. Furthermore, the impact of multilingual data composition on medical sLLM performance remains poorly understood. We present a resource-efficient fine-tuning pipeline that integrates Quantized Low-Rank Adaptation (QLoRA), Fully Sharded Data Parallelism (FSDP), and Sequence Packing, validated across two model scales: MedGemma 4B… More >

Xu Chen, Li Yang^*, Guohao Qiu

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077513 - 09 April 2026

Abstract Traffic prediction plays a crucial role in the efficient operation of satellite networks. However, due to resource consumption arising from redundant training of multiple individual prediction models, the dynamic and coupled spatial-temporal relationship of traffic, and maintenance of accurate traffic proportions, this problem is non-trivial to solve. Therefore, we consider this problem and makes the following contributions. First, a multi-granularity traffic prediction framework based on a shared feature extraction is designed to jointly predict total network traffic and service-specific traffic of satellite networks. This design ensures that both global and per service predictions benefit from… More >

Taimoor Hassan¹, Ibrar Hussain^1,*, Hafiz Mahfooz Ul Haque², Hamid Turab Mirza³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*, Changheun Oh⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077510 - 09 April 2026

Abstract The expeditious proliferation of the smart computing paradigm has a remarkable upsurge towards Artificial Intelligence (AI) assistive reasoning with the incorporation of context-awareness. Context-awareness plays a significant role in fulfilling users’ needs whenever and wherever needed. Context-aware systems acquire contextual information from sensors/embedded sensors using smart gadgets and/or systems, perform reasoning using reinforcement learning (RL) or other reasoning techniques, and then adapt behavior. The core intention of using an RL-based reasoning strategy is to train agents to take the right actions at the right time and in the right place. Generally, agents are rewarded for… More >