Student, who passed the course satisfactorily will be able to:
- use parametric and nonparametric spectrum estimation methods
- get familiar with properties of biological signals such as EEG, EMG and EKG
- decompose signals into phase, amplitude and frequency dimensions
- use signal processing toolboxes effectively in Matlab
- implement signal processing methods with appropriate parameters
- preprocess and denoise biological signals using methods like principal component analysis and independent component analysis
- estimate fractal dimension of biological signals
- synthesize self-similar signals
- be familiar with random signals and estimate their moments such as mean, variance, skewness and kurtosis
- implement biased / unbiased second-order statistical measures of autocorrelation and cross-correlation from a given signal
- design FIR and IIR filters
- apply downsampling, upsampling, decimation and interpolation operations optimally
- learn the basics of Wavelet theory and Hilbert Transform and apply these on biological signals