Acoustic impulsive event recognition over telecom fiber cables

Fiber sensing detects physical parameters such as phase and polarization changes in optical signals. This optical data must be interpreted into human-understandable events and scene information for real-world applications.

We developed applied machine learning and signal processing algorithms that transform low-level sensory inputs into high-level information, providing real-time perception of the physical world.

The solution can be utilized for reconstructing crime scenes and continuously monitoring events such as car alarms triggered by theft, home break-ins, gunshots, and prohibited fireworks, to enhance public safety in smart and secure city applications.

To Know More

  1. Deep learning-based intrusion detection and impulsive event classification for distributed acoustic sensing across telecom networks.” Journal of Lightwave Technology, 2024.
  2. Field tests of impulsive acoustic event detection, localization, and classification over telecom fiber networks.” OECC Postdeadline Paper, 2022.
Shaobo Han
Shaobo Han
Senior Researcher

Machine learning