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Amplifying the Invisible: Enhancing Pulse Estimation with EVM and Transformers

In this project, I explored how video-based physiological monitoring could be significantly improved using a fusion of signal amplification and deep learning. I designed a hybrid system that combines Eulerian Video Magnification (EVM), 3D CNNs, and Transformer architectures to estimate pulse rates from facial videos. By amplifying subtle skin color variations and modeling long-range temporal dependencies, this method achieved a mean absolute error (MAE) of 12.47 bpm-outperforming existing approaches on Indian datasets. This work is part of a broader push to build inclusive, non-contact health monitoring tools tailored to real-world populations.

  • Computer Vision
  • Deep Learning
  • Healthcare AI
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00:02:31:99
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JamGuardian: Signal Recovery and Optimization in Adversarial Jamming Environments

This research tackles signal degradation in adversarial environments, particularly under jamming attacks in wireless networks. JamGuardian introduces a robust recovery framework that combines intelligent error correction, jamming detection, and secure data transmission strategies like Dirty Paper Coding. By emulating jamming scenarios in a 6-node wireless testbed, the system identifies and neutralizes interference, optimizing communication efficiency in high-noise and attack-prone environments.

  • Wireless Security
  • Signal Processing
  • Adversarial ML
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00:02:21:86