5 papers
PReD: An LLM-based Foundation Multimodal Model for Electromagnetic Perception, Recognition, and Decision
Zehua Han, Jing Xiao, Yiqi Duan +13
Multimodal Large Language Models have demonstrated powerful cross-modal understanding and reasoning capabilities in general domains. However, in the electromagnetic (EM) domain, th…
MERLIN: Building Low-SNR Robust Multimodal LLMs for Electromagnetic Signals
Junyu Shen, Zhendong She, Chenghanyu Zhang +13
The paradigm of Multimodal Large Language Models (MLLMs) offers a promising blueprint for advancing the electromagnetic (EM) domain. However, prevailing approaches often deviate fr…
EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding
Luqing Luo, Wenjin Gui, Yunfei Liu +11
Deep understanding of electromagnetic signals is fundamental to dynamic spectrum management, intelligent transportation, autonomous driving and unmanned vehicle perception. The fie…
RIS-MAE: A Self-Supervised Modulation Classification Method Based on Raw IQ Signals and Masked Autoencoder
Yunfei Liu, Mingxuan Liu, Wupeng Xie +6
Automatic modulation classification (AMC) is a basic technology in intelligent wireless communication systems. It is important for tasks such as spectrum monitoring, cognitive radi…
RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing
Fengxiang Wang, Yulin Wang, Mingshuo Chen +8
Recent advances in self-supervised learning for Vision Transformers (ViTs) have fueled breakthroughs in remote sensing (RS) foundation models. However, the quadratic complexity of…