6 papers
NARRAS: Edge-Triggered Distributed Inference for CSI-Based Localization in Vehicular IoT Networks
Rodrigo Oliver, Ricardo Vazquez Alvarez, Alejandro Lancho +1
CSI-based localization with spatially distributed antenna arrays exposes a basic resource trade-off. Each array can provide a rich view of the channel, but forwarding observations…
Extremum-Based Joint Compression and Detection for Distributed Sensing
Amir Weiss, Alejandro Lancho
We study joint compression and detection in distributed sensing systems motivated by emerging applications such as IoT-based localization. Two spatially separated sensors observe n…
Learning to Separate RF Signals Under Uncertainty: Detect-Then-Separate vs. Unified Joint Models
Ariel Rodrigez, Alejandro Lancho, Amir Weiss
The increasingly crowded radio frequency (RF) spectrum forces communication signals to coexist, creating heterogeneous interferers whose structure often departs from Gaussian model…
Advancing AI Challenges for the United States Department of the Air Force
Christian Prothmann, Vijay Gadepally, Jeremy Kepner +35
The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers…
A Vector-Quantized Foundation Model for Patient Behavior Monitoring
Rodrigo Oliver, Josué Pérez-Sabater, Leire Paz-Arbaizar +4
Foundation models have achieved remarkable success across various domains, yet their adoption in healthcare remains limited. While significant advances have been made in medical im…
RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge
Alejandro Lancho, Amir Weiss, Gary C. F. Lee +4
We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods. A primary contribution…