DSP Lecture 13: The Sampling Theorem скачать в хорошем качестве

DSP Lecture 13: The Sampling Theorem

ECSE-4530 Digital Signal Processing Rich Radke, Rensselaer Polytechnic Institute Lecture 13: The Sampling Theorem (10/16/14) 0:00:02 The sampling theorem 0:02:25 Periodic sampling of a continuous-time signal 0:04:22 Non-ideal effects 0:07:54 Ways of reconstructing a continuous signal from discrete samples 0:10:02 Nearest neighbor 0:10:39 Zero-order hold 0:11:26 First-order hold (linear interpolation) 0:12:41 Each reconstruction algorithm corresponds to filtering a set of impulses with a specific filter 0:18:25 What can go wrong with interpolating samples? 0:20:54 Matlab example of sampling and reconstruction of a sine wave 0:23:37 Bandlimited signals 0:25:10 Statement of the sampling theorem 0:26:29 The Nyquist rate 0:27:50 Impulse-train version of sampling 0:30:18 The FT of an impulse train is also an impulse train 0:32:38 The FT of the (continuous time) sampled signal 0:34:42 Sampling a bandlimited signal: copies in the frequency domain 0:37:19 Aliasing: overlapping copies in the frequency domain 0:39:36 The ideal reconstruction filter in the frequency domain: a pulse 0:41:06 The ideal reconstruction filter in the time domain: a sinc 0:42:43 Ideal reconstruction in the time domain 0:43:37 Sketch of how sinc functions add up between samples 0:45:45 Example: sampling a cosine 0:51:43 Why can't we sample exactly at the Nyquist rate? 0:54:49 Phase reversal (the "wagon-wheel" effect) 0:58:31 Matlab examples of sampling and reconstruction 0:58:41 The dial tone 1:03:43 Ringing tone 1:05:27 Music clip 1:10:25 Prefiltering to avoid aliasing 1:11:48 Conversions between continuous time and discrete time; what sample corresponds to what frequency? Follows Section 6.1 of the textbook (Proakis and Manolakis, 4th ed.).