Back to IMS Schedule
Tue 9 Jun | 08:00 - 09:40
Room 157AB
This session highlights the integration of machine learning and digital signal processing to solve challenges in the RF and mm-wave domains. The presentations highlight innovations such as spiking neural networks on FPGAs for high-speed modulation recognition, PointMLP architectures for sparse radar data classification, physics-informed state-space models for robust tracking in multipath environments, and multimodal IR-radar fusion to ensure privacy-preserving event recognition.
08:00 - 08:20
Tu1G-1 Real-Time, Over-the-Air Modulation Recognition Using a 320MS/s Spiking Neural Network on FPGA
08:20 - 08:40
Tu1G-2 Sparsity-Based Range-Velocity-Time PointMLP for FMCW Radar Human Activity Classification with Efficient Computation
08:40 - 09:00
Tu1G-3 Parallel Kalman Filtering with Physics-Informed Selective State-Space Models for Robust Radar Sensing
09:00 - 09:20
Tu1G-4 Learning to Track: Deep Association for Multipath-Resilient In-Air Writing with D-Band FMCW Radar
09:20 - 09:40
Tu1G-5 Millimeter-Wave 3D Radar and IR Multimodal Sensing System Enabling AI-Based Event Recognition with Enhanced Privacy