IC CAE Team

IC CAE Team
IC CAE logo

Goals

The purpose of the IC CAE Team is to investigate the application of state-of-the-art image recognition / image segmentation machine learning algorithms with Hume’s work in radio frequency machine learning (RFML) applications. The team will implement state-of-the-art approaches (such as the well-known YOLO algorithm) and determine their strengths and weaknesses for wireless spectrum sensing applications. Through this investigation, a novel hybrid approach leveraging the individual strengths of the approaches, will mitigating their independent weaknesses, will be developed and its performance evaluated.

Issues Involved or Addressed

  • Applying state-of-the-art image recognition and image segmentation machine learning techniques to radio frequency applications
  • Investigating the tradeoffs between image-based machine learning (image inputs) and radio frequency machine learning (rf data inputs)
  • Develop collaborative image and rf based machine learning techniques that leverage the strengths of both approaches and mitigate their weaknesses

Methods and Technologies

  • Python (code development and dataset generation)
  • GNU Radio Companion (over-the-air testing)
  • PyTorch (deep learning training, validation, and testing)

Academic Majors of Interest

  • Computer Science
  • Computer Engineering
  • Computational Modeling and Data Analytics
  • Electrical and Computer Engineering
  • Other students with similar experience seeking future developer roles

Preferred Interests and Preparation

  • Wireless Communications
  • Dataset Creation
  • Machine Learning
  • Image Processing/Recognition

Team Advisors

William (Chris) Headley
Associate Director and Research Associate Professor, Electronic Systems Lab
Amos Johnson
Associate Professor, Morehouse College
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