Fourier representation for discrete-time signals And Sampling Theorem Lecture #05 Fourier representation for discrete-time signals And Sampling Theorem meiling chen signals & systems
Fourier series for periodic discrete signals Fourier Transform for nonperiodic discrete signals meiling chen signals & systems
Example 3.2 meiling chen signals & systems
meiling chen signals & systems
Example 3.5 Inverse DTFS meiling chen signals & systems
Example 3.17 Find DTFT of the sequence meiling chen signals & systems
meiling chen signals & systems
Example 3.18 Find DTFT of the sequence meiling chen signals & systems
meiling chen signals & systems
Example 3.17 Find DTFT of a unit impulse spectrum Example 3.18 Find Inverse DTFT of a unit impulse spectrum meiling chen signals & systems
meiling chen signals & systems
Sampling Sampling is a process of converting a signal into a numeric sequence (a function of discrete time or space). The sampling theorem states that exact reconstruction of a continuous time baseband signal from its samples is possible if the signal is bandlimited and the sample frequency is greater than twice the signal bandwidth. meiling chen signals & systems
meiling chen signals & systems
Take Fourier transform For example meiling chen signals & systems
Fourier transform of p(t) Example 4.2 Fourier series of p(t) Fourier transform of p(t) meiling chen signals & systems
Fourier series Fourier Transform (v) Frequency shifting modulation meiling chen signals & systems
Case I: Case II: Aliasing meiling chen signals & systems
Sampling theorem : Let represent a band-limited signal, so that for . If , where Is the sampling frequency, then is uniquely determined by its samples The minimum sampling frequency, Nyquist sampling rate. meiling chen signals & systems
meiling chen signals & systems