4 papers
RadarPLM: Adapting Pre-trained Language Models for Marine Radar Target Detection by Selective Fine-tuning
Qiying Hu, Yaowen Li, Shengyi Zhang +3
Recent advances in pre-trained language models (PLMs) have demonstrated their capabilities in capturing universal knowledge, making them promising for radar signal processing appli…
FaithSteer-BENCH: A Deployment-Aligned Stress-Testing Benchmark for Inference-Time Steering
Zikang Ding, Qiying Hu, Yi Zhang +4
Inference-time steering is widely regarded as a lightweight and parameter-free mechanism for controlling large language model (LLM) behavior, and prior work has often suggested tha…
SignalLLM: A General-Purpose LLM Agent Framework for Automated Signal Processing
Junlong Ke, Qiying Hu, Shenghai Yuan +2
Modern signal processing (SP) pipelines, whether model-based or data-driven, often constrained by complex and fragmented workflow, rely heavily on expert knowledge and manual engin…
When marine radar target detection meets pretrained large language models
Qiying Hu, Linping Zhang, Xueqian Wang +3
Deep learning (DL) methods are widely used to extract high-dimensional patterns from the sequence features of radar echo signals. However, conventional DL algorithms face challenge…