Advanced Signal Processing and Analysis

Course Description

This course introduces classical and contemporary methods in signal processing and analysis with particular emphasis on problems in biomedical research. Main topics include: stochastic signals and systems: random processes, white noise, stationarity, correlations, properties of linear time-invariant systems excited by white noise, entropy and information measures; time-frequency analysis: Fourier transform and spectral analysis, wavelets transform and multi-resolution analysis; optimal and adaptive filters: matched filters, Wiener filters and Kalman filters; multivariate signal decomposition and analysis: principal component analysis and independent component analysis, blind source separation; examples from biomedical signal processing and image processing, simulation and implementation with MATLAB?.


Probability and Statistics, Digital Signal Processing, MATLABĀ® Programming

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