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 >

Lijuan Zhang^1,2, Xiaoyu Wang^1,2, Songtao Zhang³, Yutong Jiang^4,*, Dongming Li¹, Weichen Sun⁴

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1219-1237, 2025, DOI:10.32604/cmc.2025.059856 - 26 March 2025

Abstract In this paper, a reasoning enhancement method based on RGCN (Relational Graph Convolutional Network) is proposed to improve the detection capability of UAV (Unmanned Aerial Vehicle) on fast-moving military targets in urban battlefield environments. By combining military images with the publicly available VisDrone2019 dataset, a new dataset called VisMilitary was built and multiple YOLO (You Only Look Once) models were tested on it. Due to the low confidence problem caused by fuzzy targets, the performance of traditional YOLO models on real battlefield images decreases significantly. Therefore, we propose an improved RGCN inference model, which improves More >

Omar Said^1,3, Amr Tolba^2,3,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3887-3906, 2021, DOI:10.32604/cmc.2021.016076 - 01 March 2021

Abstract Recently, Internet of Things (IoT) technology has provided logistics services to many disciplines such as agriculture, industry, and medicine. Thus, it has become one of the most important scientific research fields. Applying IoT to military domain has many challenges such as fault tolerance and QoS. In this paper, IoT technology is applied on the military field to create an Internet of Military Things (IoMT) system. Here, the architecture of the aforementioned IoMT system is proposed. This architecture consists of four main layers: Communication, information, application, and decision support. These layers provided a fault tolerant coverage More >

Chunhua Zhou^*, Jianjing Shen, Xiaofeng Guo, Zhenyu Zhou

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 127-141, 2021, DOI:10.32604/iasc.2021.012532 - 07 January 2021

Abstract In the process of interaction between users and battlefield situation information, combat tasks are the key factors that affect users’ information selection. In order to solve the problems of battlefield situation information recommendation (BSIR) for combat tasks, we propose a task-oriented battlefield situation information hybrid recommendation model (TBSI-HRM) based on tensor factorization and deep learning. In the model, in order to achieve high-precision personalized recommendations, we use Tensor Factorization (TF) to extract correlation relations and features from historical interaction data, and use Deep Neural Network (DNN) to learn hidden feature vectors of users, battlefield situation More >

Chunhua Zhou^*, Jianjing Shen, Yuncheng Wang, Xiaofeng Guo

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1429-1440, 2020, DOI:10.32604/iasc.2020.011757 - 24 December 2020

Abstract With the rapid development of information technology, battlefield situation data presents the characteristics of “4V” such as Volume, Variety, Value and Velocity. While enhancing situational awareness, it also brings many challenges to battlefield situation information recommendation (BSIR), such as big data volume, high timeliness, implicit feedback and no negative feedback. Focusing on the challenges faced by BSIR, we propose a two-stage BSIR model based on deep neural network (DNN). The model utilizes DNN to extract the nonlinear relationship between the data features effectively, mine the potential content features, and then improves the accuracy of recommendation.… More >