IEMI Overview

Our Goal

The Electromagnetic Interference (EMI) Detection group aims to develop a software based methodology to detect the presence of EMI and study the effects it has on software. Modern electronics are becoming more susceptible to EMI due to their rising clock speeds and smaller overall size which makes it ever so important for critical systems to understand how EMI affects them. Having the ability to understand when and how a device is being affected by EMI will allow critical systems to further harden against the effects of EMI and provide more reliable services. Furthermore, a software based analysis method allows for a better understanding of hardware that is infeasible to equip with laboratory sensors such as large-scale systems or consumer hardware. Our technique uses a lightweight watchdog to log changes in key registers and uses these registers values to construct states. When the device is active these states are recorded and used to give insight via categorical time series statistics and classification algorithms. Below you can find information about the device we use and specific detection methods.


For our experiments we examine the operation of a USB 2.0 host controller on a Rock Pro 64. We chose the Rock Pro 64 as it is a well documented and affordable system that has a built-in USB 2.0 controller. For further information on the Rock Pro 64 system please visit the link below.


  • Time Series Statistics
    • Dispersion
    • Serial Dependance
  • Classification
    • Hidden Markov Model
    • Recurrent Neural Network Long/Short Term Memory Classifier
    • Artificial Neural Network
    • Support Vector Machine
    • Random Forest Classifier
    • Gradient Boosted Classifier


  • Joel Schott - Doctorate Student
  • Evan Hite - Undergraduate Scholar
  • Connor Jones - Accelerated Master's Student
  • Austin Potter - Undergraduate Scholar

Additional Information

Device Info
This website contains additional information about the Rock Pro 64 system.