Ryerson University Department of Electrical and Computer Engineering

 SAR Projectsline








Sparse representation and compressed sensing

Signal decomposition techniques

Time-frequency analysis

Big data analytics and machine learning

Audio and speech processing

Biomedical signal and image analysis


Above techniques are applied in:

Audio scene analytics

Atrial fibrillation and ventricular fibrillation

T-wave alternans

Gait analysis

Sleep signal processing


Technical Accomplishments


  • Defining new ways of robust signal feature extraction that incorporates signal non-stationarity, and classifying using simple pattern recognizers. This has numerous applications in various fields including the emerging areas of bioinformatics and biometrics.
  • Developing a new paradigm for data hiding using true time-frequency tools. Including the competency of the chirp detectors to perform as forward error correction codes. There is a compelling anxiety among the peers to see how the chirp detectors perform against the well established FEC schemes used in digital transmission.
  • Investigating non-conventional data compression schemes (time-frequency, wavelet-based, and grammar codes) for images and audio files. Based on the experiences gained in this field SAR members will be embarking on network-centric coding schemes with inbuilt information integrity and security.
  • Fine tuning established signal enhancement schemes for ground vehicle tracking, improved listening experience for hearing impaired people, and in determining better statistical parameters for lightening protection applications.
  • Working with real world data samples: knee sounds, pathological voice, EEG from alcoholics, EMG data from children's hospital, digital mammograms, GPS signals acquired in downtown Toronto, computer keystroke data, ultrasound signals from cells cultivated in lab, pulse (blood) volume signals,  lightening data from CN Tower, and commonly used speech/audio/image/sports video data.