Digital Signal Processing Applications

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