Syed Aziz Shah, Qammer Hussain Abbasi, Jawad Ahmad, Muhammad Ali Imran
State-of-the-art network architectures ensure fast and dependable real-time communication with abundant data and minimal delays [...]
Yuhao Zhang, Yuhao Feng, S L Zhang, Peifeng Liang, Wei Guan
Accurate network traffic prediction is important for the autonomy, resilience and resource orchestration of 6G and AI-native communication infrastructures, while also supporting green networking and digital twin network applications. However, existing data-driven prediction model…
Jung-Min Moon, Na-Eun Park, Il-Gu Lee
Herein, an uncertainty quantification-based framework is proposed for estimating the root mean square (RMS) delay spread σ as a probability distribution in IEEE 802.11be environments. The method selects a guard interval (GI) using a safety margin derived from the 90th-percentile…
Le Khuong Phan, Dinh Tuan Nguyen, Thi Thu Ha Vu, Tan Hung Vo, Anh Kiet Nguyen, Jaeyeop Choi, et al.
Metallic blocks and components are indispensable across the aerospace, energy, and heavy-engineering industries, where undetected internal flaws such as cracks, voids, and inclusions may precipitate catastrophic structural failure. Reliable nondestructive evaluation (NDE) is esse…
This study proposes a sequential Hybrid GP-ANN architecture based on the concept of autonomous feature engineering for multi-output performance prediction of single-phase induction motors, serving as a high-fidelity surrogate model ready for digital twin integration. The required…
Antonios-Periklis Michalopoulos, Efstratios N. Paliodimos, Grigoris Nikolaou, Demetrios Cantzos, Stelios Α. Mitilineos
Robotic systems for search-and-rescue operations require robust, non-line-of-sight victim detection in order to locate trapped individuals behind obstacles with high precision. This paper presents a robotic victim-localization system based on a convolutional neural network—vision…
Yuanjie Peng, Yali Chen, Bei Liu, Tongbo Zou, Junming Xiao, Shenghui Zhou, et al.
This study aimed to develop and evaluate a deep learning diagnostic model integrating a convolutional autoencoder (CAE) and ResNet18 for the early and accurate diagnosis of developmental dysplasia of the hip (DDH) in children, addressing the subjectivity of traditional methods. T…