Radio library for deep learning experiments.
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datasets/Breathing
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README.md
radio.py

README.md

Deep Learning Radio Library

Radio signals are all around us, yet we lack standard datasets to uncover interesting patterns in the data. This project is here to change that. Following datasets (along with relevant documentations and notebooks) are under preparation:

  • Passive WiFi signals from human respiration
  • Radio signals from vibrating objects
  • Modulated signals in Rayleigh and Rician environments
  • Alphabet drawing recongition

In addition, the goal is to build standard tools for effective analysis of radio data. For any suggestions or contributions, please open an issue.

Papers

U. Khan., Z. Kabir., S. A. Hassan., & S. H. Ahmed. A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring. Submitted to Globecom, 2017.