Advanced Digital Signal Processing

Denoising fMRI Time-Series – An application of adaptive signal processing

 

Course Code:             EE6101
Credit Hours:             3

Pre-requisite:           
Undergraduate course in Signal Processing or consent of the instructor.

Target Audience      
MS/PhD students wishing to pursue research in the areas of signal processing, image processing, or digital communication.

 

A Course to provide a coherent and structured presentation of the fundamentals of adaptive signal processing and noise reduction methods
Synopsis:
The course starts with an introduction to signal processing, and provides a brief review of signal processing methodologies and applications. An introduction to noise and distortion is then provided and several frequently used noise models are discussed. An introduction to the theory and applications of probability models and stochastic signal processing is then provided followed by designing of Wiener filters in time and frequency domains.

 

Adaptive signal processing is then introduced with emphasis on LMS algorithms. This is followed by discussion on the role of the power spectrum in identification of patterns and structures in a signal process. The Course considers nonparametric spectral estimation as well as model-based spectral estimation. Linear prediction is the last topic to be covered before the discussion of several adaptive filtering applications.

Instructor:
The course will be taught by Dr. S. M. Monir (monir@pafkiet.edu.pk). Dr. Monir holds a PhD degree with specialization in Signal Processing from Nanyang Technological University, Singapore. His research interest includes time-series analysis and image processing.

Recommended Books:

  • S. V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction third ed.: Wiley, 2006.
  • Simon Haykin, Adaptive Filter Theory 4th ed.
  • A. Papoulis and S.U. Pillai, Probability, Random Variables and Stochastic Processes
    Fourth Edition, 2002