Mengke Tang¹, Yong Gan^2,*, Yifan Zhang¹, Xinxin Gan³

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2699-2716, 2025, DOI:10.32604/cmc.2025.064541 - 03 July 2025

Abstract Infrared imaging technology has been widely adopted in various fields, such as military reconnaissance, medical diagnosis, and security monitoring, due to its excellent ability to penetrate smoke and fog. However, the prevalent low resolution of infrared images severely limits the accurate interpretation of their contents. In addition, deploying super-resolution models on resource-constrained devices faces significant challenges. To address these issues, this study proposes a lightweight super-resolution network for infrared images based on an adaptive attention mechanism. The network’s dynamic weighting module automatically adjusts the weights of the attention and non-attention branch outputs based on the… More >

Kirubavathi G.^1,*, Arjun Pulliyasseri¹, Aswathi Rajesh¹, Amal Ajayan¹, Sultan Alfarhood^2,*, Mejdl Safran², Meshal Alfarhood², Jungpil Shin³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3005-3031, 2025, DOI:10.32604/cmes.2025.065698 - 30 June 2025

Abstract The exponential expansion of the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Transportation Management of Things (TMoT) produces vast amounts of real-time streaming data. Ensuring system dependability, operational efficiency, and security depends on the identification of anomalies in these dynamic and resource-constrained systems. Due to their high computational requirements and inability to efficiently process continuous data streams, traditional anomaly detection techniques often fail in IoT systems. This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems. Extensive experiments were carried out on multiple real-world datasets, achieving… More >

Tarek Muallim¹, Haluk Kucuk^2,*, Muhammet Bareket¹, Metin Kahraman¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3557-3581, 2025, DOI:10.32604/cmes.2025.064733 - 30 June 2025

Abstract This study introduces a lightweight deep learning model and a novel synthetic dataset designed to restore damaged one-dimensional (1D) barcodes and Quick Response (QR) codes, addressing critical challenges in logistics operations. The proposed solution leverages an efficient Pix2Pix-based framework, a type of conditional Generative Adversarial Network (GAN) optimized for image-to-image translation tasks, enabling the recovery of degraded barcodes and QR codes with minimal computational overhead. A core contribution of this work is the development of a synthetic dataset that simulates realistic damage scenarios frequently encountered in logistics environments, such as low contrast, misalignment, physical wear,… More >

Zhe Chen^*, Yinyang Zhang, Sihao Xing

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1787-1803, 2025, DOI:10.32604/cmc.2025.065238 - 09 June 2025

Abstract Unmanned aerial vehicle (UAV) imagery poses significant challenges for object detection due to extreme scale variations, high-density small targets (68% in VisDrone dataset), and complex backgrounds. While YOLO-series models achieve speed-accuracy trade-offs via fixed convolution kernels and manual feature fusion, their rigid architectures struggle with multi-scale adaptability, as exemplified by YOLOv8n’s 36.4% mAP and 13.9% small-object AP on VisDrone2019. This paper presents YOLO-LE, a lightweight framework addressing these limitations through three novel designs: (1) We introduce the C2f-Dy and LDown modules to enhance the backbone’s sensitivity to small-object features while reducing backbone parameters, thereby improving More >

Ya-Jie Sun¹, Li-Wei Qiao¹, Sai Ji^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1769-1785, 2025, DOI:10.32604/cmc.2025.062950 - 09 June 2025

Abstract Vehicle re-identification involves matching images of vehicles across varying camera views. The diversity of camera locations along different roadways leads to significant intra-class variation and only minimal inter-class similarity in the collected vehicle images, which increases the complexity of re-identification tasks. To tackle these challenges, this study proposes AG-GCN (Attention-Guided Graph Convolutional Network), a novel framework integrating several pivotal components. Initially, AG-GCN embeds a lightweight attention module within the ResNet-50 structure to learn feature weights automatically, thereby improving the representation of vehicle features globally by highlighting salient features and suppressing extraneous ones. Moreover, AG-GCN adopts More >

Mengqi Li, Xiaodong Huang^*, Lifeng Wu

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 975-995, 2025, DOI:10.32604/cmc.2025.062621 - 09 June 2025

Abstract Micro-expressions, fleeting involuntary facial cues lasting under half a second, reveal genuine emotions and are valuable in clinical diagnosis and psychotherapy. Real-time recognition on resource-constrained embedded devices remains challenging, as current methods struggle to balance performance and efficiency. This study introduces a semi-lightweight multifunctional network that enhances real-time deployment and accuracy. Unlike prior simplistic feature fusion techniques, our novel multi-feature fusion strategy leverages temporal, spatial, and differential features to better capture dynamic changes. Enhanced by Residual Network (ResNet) architecture with channel and spatial attention mechanisms, the model improves feature representation while maintaining a lightweight design. More >

Mohsen Ali Alawami¹, Dahyun Jung², Yewon Park², Yoonseo Ku², Gyeonghwan Choi², Ki-Woong Park^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2361-2389, 2025, DOI:10.32604/cmes.2025.063819 - 30 May 2025

Abstract To date, many previous studies have been proposed for driver authentication; however, these solutions have many shortcomings and are still far from practical for real-world applications. In this paper, we tackle the shortcomings of the existing solutions and reach toward proposing a lightweight and practical authentication system, dubbed DriveMe, for identifying drivers on cars. Our novelty aspects are ① Lightweight scheme that depends only on a single sensor data (i.e., pressure readings) attached to the driver’s seat and belt. ② Practical evaluation in which one-class authentication models are trained from only the owner users and tested using… More >

Daoming Wei¹, Fangyan Ge², Bopeng Zhang¹, Zhiqiang Zhao³, Dequan Li^3,*, Lizong Xi⁴, Jinrong Hu^5,*, Xin Wang⁶

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5729-5746, 2025, DOI:10.32604/cmc.2025.061402 - 19 May 2025

Abstract Accurate cloud classification plays a crucial role in aviation safety, climate monitoring, and localized weather forecasting. Current research has been focusing on machine learning techniques, particularly deep learning based model, for the types identification. However, traditional approaches such as convolutional neural networks (CNNs) encounter difficulties in capturing global contextual information. In addition, they are computationally expensive, which restricts their usability in resource-limited environments. To tackle these issues, we present the Cloud Vision Transformer (CloudViT), a lightweight model that integrates CNNs with Transformers. The integration enables an effective balance between local and global feature extraction. To… More >

Hongliang Tian, Meiruo Li^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5539-5559, 2025, DOI:10.32604/cmc.2025.060297 - 19 May 2025

Abstract The accelerated advancement of the Internet of Things (IoT) has generated substantial data, including sensitive and private information. Consequently, it is imperative to guarantee the security of data sharing. While facilitating fine-grained access control, Ciphertext Policy Attribute-Based Encryption (CP-ABE) can effectively ensure the confidentiality of shared data. Nevertheless, the conventional centralized CP-ABE scheme is plagued by the issues of key misuse, key escrow, and large computation, which will result in security risks. This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues. The integrity… More >

Xiaoying Qiu^1,*, Xiaoyu Ma¹, Guangxu Zhao¹, Jinwei Yu², Wenbao Jiang¹, Zhaozhong Guo¹, Maozhi Xu³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2025-2040, 2025, DOI:10.32604/cmc.2025.061869 - 16 April 2025

Abstract Physical layer authentication (PLA) in the context of the Internet of Things (IoT) has gained significant attention. Compared with traditional encryption and blockchain technologies, PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium itself. Some existing PLA solutions rely on static mechanisms, which are insufficient to address the authentication challenges in fifth generation (5G) and beyond wireless networks. Additionally, with the massive increase in mobile device access, the communication security of the IoT is vulnerable to spoofing attacks. To overcome the above challenges, this paper proposes a lightweight deep More >