Sunil Kumar¹, Dilip Kumar¹, Ramraj Dangi², Gaurav Choudhary³, Nicola Dragoni⁴, Ilsun You^5,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 31-63, 2024, DOI:10.32604/cmc.2023.047084

Abstract The widespread and growing interest in the Internet of Things (IoT) may be attributed to its usefulness in many different fields. Physical settings are probed for data, which is then transferred via linked networks. There are several hurdles to overcome when putting IoT into practice, from managing server infrastructure to coordinating the use of tiny sensors. When it comes to deploying IoT, everyone agrees that security is the biggest issue. This is due to the fact that a large number of IoT devices exist in the physical world and that many of them have constrained resources such as electricity, memory,… More >

Jiarui Deng^1,2, Wuqiang Shen^1,3, Yihua Feng⁴, Guosheng Lu⁵, Guiquan Shen^1,3, Lei Cui^1,3, Shanxiang Lyu^1,2,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1345-1361, 2024, DOI:10.32604/cmc.2023.047079

Abstract The blockchain-empowered Internet of Vehicles (IoV) enables various services and achieves data security and privacy, significantly advancing modern vehicle systems. However, the increased frequency of data transmission and complex network connections among nodes also make them more susceptible to adversarial attacks. As a result, an efficient intrusion detection system (IDS) becomes crucial for securing the IoV environment. Existing IDSs based on convolutional neural networks (CNN) often suffer from high training time and storage requirements. In this paper, we propose a lightweight IDS solution to protect IoV against both intra-vehicle and external threats. Our approach achieves superior performance, as demonstrated by… More >

Li Li^*, Youran Kong, Qing Zhang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 551-567, 2024, DOI:10.32604/cmc.2023.045512

Abstract With the growth of the Internet, more and more business is being done online, for example, online offices, online education and so on. While this makes people’s lives more convenient, it also increases the risk of the network being attacked by malicious code. Therefore, it is important to identify malicious codes on computer systems efficiently. However, most of the existing malicious code detection methods have two problems: (1) The ability of the model to extract features is weak, resulting in poor model performance. (2) The large scale of model data leads to difficulties deploying on devices with limited resources. Therefore,… More >

Sangeeta Yadav¹, Preeti Gulia^1,*, Nasib Singh Gill^1,*, Piyush Kumar Shukla², Arfat Ahmad Khan³, Sultan Alharby⁴, Ahmed Alhussen⁴, Mohd Anul Haq⁵

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1253-1274, 2024, DOI:10.32604/cmc.2023.042869

Abstract Inpatient falls from beds in hospitals are a common problem. Such falls may result in severe injuries. This problem can be addressed by continuous monitoring of patients using cameras. Recent advancements in deep learning-based video analytics have made this task of fall detection more effective and efficient. Along with fall detection, monitoring of different activities of the patients is also of significant concern to assess the improvement in their health. High computation-intensive models are required to monitor every action of the patient precisely. This requirement limits the applicability of such networks. Hence, to keep the model lightweight, the already designed… More >

Yanhui Zhang¹, Lianhua Ma¹, Hailiang Su^1,2,3,*, Jirong Qin², Zhining Chen², Kaibiao Deng¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1961-1980, 2024, DOI:10.32604/cmes.2023.045570

Abstract In this paper, to present a lightweight-developed front underrun protection device (FUPD) for heavy-duty trucks, plain weave carbon fiber reinforced plastic (CFRP) is used instead of the original high-strength steel. First, the mechanical and structural properties of plain carbon fiber composite anti-collision beams are comparatively analyzed from a multi-scale perspective. For studying the design capability of carbon fiber composite materials, we investigate the effects of TC-33 carbon fiber diameter (D), fiber yarn width (W) and height (H), and fiber yarn density (N) on the front underrun protective beam of carbon fiber composite materials. Based on the investigation, a material-structure matching… More >

Xiaodan Wang^*, Peng Wang, Yafei Song, Qian Xiang, Jingtai Li

Computer Systems Science and Engineering, Vol.48, No.1, pp. 217-246, 2024, DOI:10.32604/csse.2023.039846

Abstract High-resolution range profile (HRRP) automatic recognition has been widely applied to military and civilian domains. Present HRRP recognition methods have difficulty extracting deep and global information about the HRRP sequence, which performs poorly in real scenes due to the ambient noise, variant targets, and limited data. Moreover, most existing methods improve the recognition performance by stacking a large number of modules, but ignore the lightweight of methods, resulting in over-parameterization and complex computational effort, which will be challenging to meet the deployment and application on edge devices. To tackle the above problems, this paper proposes an HRRP sequence recognition method… More >

Naeem Ullah¹, Javed Ali Khan^2,*, Sultan Almakdi³, Mohammed S. Alshehri³, Mimonah Al Qathrady⁴, Eman Abdullah Aldakheel^5,*, Doaa Sami Khafaga⁵

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3969-3992, 2023, DOI:10.32604/cmc.2023.041819

Abstract Tomato leaf diseases significantly impact crop production, necessitating early detection for sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying and classifying tomato leaf diseases. However, current DL methods often require substantial computational resources, hindering their application on resource-constrained devices. We propose the Deep Tomato Detection Network (DTomatoDNet), a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this. The Convn kernels used in the proposed (DTomatoDNet) framework is 1 × 1, which reduces the number of parameters and helps in more detailed and descriptive feature extraction for classification. The proposed DTomatoDNet model… More >

Xiaoqiao Yang, Jialiang Sun^*, Dongping Jin

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2665-2689, 2024, DOI:10.32604/cmes.2023.031482

Abstract Large cavity structures are widely employed in aerospace engineering, such as thin-walled cylinders, blades and wings. Enhancing performance of aerial vehicles while reducing manufacturing costs and fuel consumption has become a focal point for contemporary researchers. Therefore, this paper aims to investigate the topology optimization of large cavity structures as a means to enhance their performance, safety, and efficiency. By using the variable density method, lightweight design is achieved without compromising structural strength. The optimization model considers both concentrated and distributed loads, and utilizes techniques like sensitivity filtering and projection to obtain a robust optimized configuration. The mechanical properties are… More >

Pengxin Li¹, Rong Wang^1,2,*, Wenjing Zhang¹, Yinuo Liu¹, Chenyue Xu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2239-2255, 2024, DOI:10.32604/cmes.2023.030677

Abstract Human pose estimation aims to localize the body joints from image or video data. With the development of deep learning, pose estimation has become a hot research topic in the field of computer vision. In recent years, human pose estimation has achieved great success in multiple fields such as animation and sports. However, to obtain accurate positioning results, existing methods may suffer from large model sizes, a high number of parameters, and increased complexity, leading to high computing costs. In this paper, we propose a new lightweight feature encoder to construct a high-resolution network that reduces the number of parameters… More >

Jiansheng Peng^1,2,*, Qing Yang¹, Yaru Hou¹

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1929-1948, 2023, DOI:10.32604/cmc.2023.043524

Abstract In recent years, with the continuous deepening of smart city construction, there have been significant changes and improvements in the field of intelligent transportation. The semantic segmentation of road scenes has important practical significance in the fields of automatic driving, transportation planning, and intelligent transportation systems. However, the current mainstream lightweight semantic segmentation models in road scene segmentation face problems such as poor segmentation performance of small targets and insufficient refinement of segmentation edges. Therefore, this article proposes a lightweight semantic segmentation model based on the LiteSeg model improvement to address these issues. The model uses the lightweight backbone network… More >