Physical and physiological signals carry hidden information about the outside world around us and the inside world within us respectively. These signals can reflect the status of the various entities of these worlds giving more insight into them. For example, the signals emerging out of a heart can be measured to evaluate its health status. 

Voice can be recorded to recognize words and sentences and even the identity of language or speaker in a bio-metric sense. A small camera pill can capture video of the gut to detect the early stages of intestinal tumours.  Signals, images and video inherently carry information about everything from digital information in communication to medical applications.

The Signal Processing group at NIT Goa is home to 2 Faculty members and some 5 research staff and research students. There is a strong emphasis on healthcare technologies and applications related to computer vision and image processing. The group develops and applies signal processing and machine learning based algorithms for use within multimedia, communication, speech technology, instrumentation, medicine. The algorithms are typically intended for automation covering basic integral aspects like signal modelling, visualization, classification, detection, identification, learning and estimation, inference and regression. Coding and data compression are also prominent research themes. However, the research activities of the group are not limited to the above-mentioned areas.

If you are interested in engaging with us for collaborative research or joining the group as a student or visitor, you may wish to contact a member of staff whose interests aligns to yours.

Expertise (but not limited to)

  1. Underwater video surveillance
  2. Multiple object tracking in videos
  3. Microscopic image processing
  4. Digital image enhancement
  5. Biomedical signal processing
    1. Sleep quality measurement
    2. Seizure detection
    3. Alcoholism detection
    4. Cardiovascular disease detection and identification
    5. Measurement of quality of experience
    6. Speech technology
  6. Time-Frequency Analysis
  7. Multiresolution Analysis
  8. Soft computing techniques
  9. Pattern recognition and machine learning
  10. Detection and estimation
  11. Multivariate signal processing