(New page: = Lecture 13 Blog, ECE438 Fall 2010, Prof. Boutin = Wednesday September 22, 2010. ---- In Lecture #13, we continued considering the sampling <math>x_1[n]=x(T_1 n)</...) |
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| + | *I think this may be a good time to pass some advice to current/future ECE301 students on [[Peer_Legacy_ECE301|the peer legacy page]]. | ||
*Here is a [[Student_summary_sampling_part1_ECE438F09|Rhea page on sampling contributed by a student]]. | *Here is a [[Student_summary_sampling_part1_ECE438F09|Rhea page on sampling contributed by a student]]. | ||
*[[Hw5ECE438F10|HW5]] is now posted. It is due next Wednesday. | *[[Hw5ECE438F10|HW5]] is now posted. It is due next Wednesday. | ||
Latest revision as of 15:07, 22 September 2010
Lecture 13 Blog, ECE438 Fall 2010, Prof. Boutin
Wednesday September 22, 2010.
In Lecture #13, we continued considering the sampling
$ x_1[n]=x(T_1 n) $
of a continuous-time signal x(t). We obtained and discussed the relationship between the DT Fourier transform of $ x_1[n] $ and that of a downsampling $ y[n]=x_1[Dn] $, for some integer D>1. (Yes, I know it was a lot of math, but this is good for you, trust me!) We then obtained the relationship between the DT Fourier transform of $ x_1[n] $ and that of an upsampling of x[n] by a factor D. From this relationship, we concluded that, under certain circumstances, a low-pass filter could be applied to this upsampling so to obtain the signal
$ x_2[n]=x\left( n \frac{T_1}{D} \right) $.
Side notes:
- I think this may be a good time to pass some advice to current/future ECE301 students on the peer legacy page.
- Here is a Rhea page on sampling contributed by a student.
- HW5 is now posted. It is due next Wednesday.
Previous: Lecture 12; Next: Lecture 14
