@article{Lv2027, 
author = {Xiaohong Lv and Jing Wang and Shalli Rani and Ayush Dogra and Adam Slowik and Daohua Pan and Huamao Jiang and Yanhong Feng},
title = {Deep Neural Networks for Real-Time Medical Streaming in 6G Cross-Modal Semantic Communication Systems},
year = {2027},
journal = {Tsinghua Science and Technology},
volume = {32},
number = {1},
pages = {527-548},
keywords = {Six-Generation (6G), cross-modal semantic communication, deep neural networks, real-time medical streaming data},
url = {https://www.sciopen.com/article/10.26599/TST.2024.9010223},
doi = {10.26599/TST.2024.9010223},
abstract = {In the world of Six-Generation 6G network, real-time medical streaming plays an important part in providing fast and accurate services. A new method known as cross-modal semantic communication helps in sending meaningful information to different types of data. This paper presents a deep neural network based system that uses 6G cross-modal semantic communication systems to handle medical streaming in real time. The proposed system includes a semantic encoder, a semantic decoder, and a method capable of measuring similarity meaning across various data types. The semantic encoder extracts important features like text, sound, and pictures from different types of medical data, and combines them to create an integrated information. After this, the semantic decoder redesigns the data as per the required format. Using Siamese and pseudo-Siamese networks, the cross-modal semantic similarity evaluation technique compares the meaning of the original and redesigned data across different types of data, resulting in improved encoding and decoding processes. Experimental results show that the proposed framework excels in semantic similarity and real-time performance compared to traditional communication systems. This deep neural networks based encoding and decoding framework enables efficient and effective real-time medical streaming in 6G cross-modal semantic communication systems.}
}