SeizNet: An AI-enabled Implantable Sensor Network System for Seizure Prediction

no code implementations • 12 Jan 2024 • Ali Saeizadeh, Douglas Schonholtz, Daniel Uvaydov, Raffaele Guida, Emrecan Demirors, Pedram Johari, Jorge M. Jimenez, Joseph S. Neimat, Tommaso Melodia

In this paper, we introduce SeizNet, a closed-loop system for predicting epileptic seizures through the use of Deep Learning (DL) method and implantable sensor networks.

Seizure prediction Specificity

SignCRF: Scalable Channel-agnostic Data-driven Radio Authentication System

no code implementations • 21 Mar 2023 • Amani Al-shawabka, Philip Pietraski, Sudhir B Pattar, Pedram Johari, Tommaso Melodia

Radio Frequency Fingerprinting through Deep Learning (RFFDL) is a data-driven IoT authentication technique that leverages the unique hardware-level manufacturing imperfections associated with a particular device to recognize (fingerprint) the device based on variations introduced in the transmitted waveform.

Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation

no code implementations • 20 Oct 2021 • Leonardo Bonati, Pedram Johari, Michele Polese, Salvatore D'Oro, Subhramoy Mohanti, Miead Tehrani-Moayyed, Davide Villa, Shweta Shrivastava, Chinenye Tassie, Kurt Yoder, Ajeet Bagga, Paresh Patel, Ventz Petkov, Michael Seltser, Francesco Restuccia, Abhimanyu Gosain, Kaushik R. Chowdhury, Stefano Basagni, Tommaso Melodia

In this paper, we introduce Colosseum as a testbed that is for the first time open to the research community.