Continuous and Discrete Time Signals

• define and analyze linear systems
• classify periodic and aperiodic signals defined for continuous time and for discrete time
• determine the Fourier spectra of periodic and aperiodic continuous-time and discrete-time signals
• apply the Fourier transform theorems
• apply computationally efficient algorithms to transform between the time and frequency domains
• analyse aliasing and choose the sampling frequency accordingly
• apply fundamental types of signal processing, e.g. filtering, korrelation, and modulation
• utilize the relationship between the Fourier transforms in relation to sampling and signal repetition
• apply power spectrum estimation methods
• define stochastic signals and their spectra as well as complex signals
• define and utilize modulation forms for continuous-time and discrete-time signals
• apply Python for analysis of systems and signals, as well as for signal processing.

The course addresses linear systems and continuous and discrete time signals: linearity, impulse and frequency responses, Fourier Series, Fourier Transform, Discrete Fourier Transform, Discrete Time Fourier Transform, FFT, power spectra, Nyquist sampling frequency, aliasing, stochastic signals, complex signals, modulation, Python/Matlab.

01035/30015

Lectures and exercises.

Python will be substituted for Matlab in the sense that Python will be used for problems and exam, but the textbook will not be replaced, even though it uses Matlab for examples.