3 papers
eess.SP2026
DUSG-Tomo-Net: A Deep Unfolded Neural Network for Super-Resolving Gridless Spaceborne SAR Tomography via Learned Toeplitz-Structured Covariance Representation
Kun Qian, Zhuge Xia, Qian Ma +3
Synthetic aperture radar tomography (TomoSAR) enables 3-D imaging by exploiting multibaseline acquisitions and has become an important tool for urban mapping. To achieve super-reso…
eess.SP2025
DeltaDPD: Exploiting Dynamic Temporal Sparsity in Recurrent Neural Networks for Energy-Efficient Wideband Digital Predistortion
Yizhuo Wu, Yi Zhu, Kun Qian +5
Digital Predistortion (DPD) is a popular technique to enhance signal quality in wideband RF power amplifiers (PAs). With increasing bandwidth and data rates, DPD faces significant…
cs.LG2024
Investigating KAN-Based Physics-Informed Neural Networks for EMI/EMC Simulations
Kun Qian, Mohamed Kheir
The main objective of this paper is to investigate the feasibility of employing Physics-Informed Neural Networks (PINNs) techniques, in particular KolmogorovArnold Networks (KANs),…