2 citations · 2 across the 2 of their papers we have counts for
2 papers
eess.SP2026★ 2 cited
SMCNet: Supervised Surface Material Classification Using mmWave Radar IQ Signals and Complex-valued CNNs
Stefan Hägele, Fabian Seguel, Driton Salihu +2
Understanding surface material properties is crucial for enhancing indoor robot perception and indoor digital twinning. However, not all sensor modalities typically employed for th…
eess.SP2025
ACCOR: Attention-Enhanced Complex-Valued Contrastive Learning for Occluded Object Classification Using mmWave Radar IQ Signals
Stefan Hägele, Adam Misik, Constantin Patsch +1
Millimeter-wave (mmWave) radar provides robust sensing under adverse conditions and can penetrate thin materials for non-visual perception in industrial and robotic settings. Recen…