Digital Signal Processing Applications

Course Code: 
Winter Semester
Credit Points: 

Course outline

A. Theory

  • Multirate signal processing, Sampling subsampling, multistage systems implementation, multiphase analysis, filterbanks, perfect reconstruction filters
  • Stochastic signals and their linear estimators, Wiener filters, adaptive filters, applications of adaptive filters, filter Kalman  
  • Eigen filters and eigen analysis
  • Face recognition based on low rank modeling
  • Image classification techniques based on generative neural networks
  • Sparse representations and signal processing 

B. Laboratory Exercises

  • Six (6) cutting-edge techniques, which refer to applications belonging to the above-mentioned topics and analyzed in detail in the course, are implemented 

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