(add introduction)
 
(10 intermediate revisions by 2 users not shown)
Line 14: Line 14:
 
<font size = 3>
 
<font size = 3>
 
==Outline==
 
==Outline==
 +
#Background
 
#Introduction
 
#Introduction
 
#Derivation
 
#Derivation
 
#Example
 
#Example
 
#Conclusion
 
#Conclusion
 +
----
  
 
----
 
----
 +
== Background ==
 +
<font size = 2>
 +
 +
<math>{f}_{s}</math> = sampling frequency (number of samples/second) Hz
 +
<br>
 +
<math>{T}_{s}</math> = sampling period (number of seconds/sample) seconds
 +
<br>
 +
<math> {f}_{s} =  {\frac{1}{{T}_{s}}} </math>
 +
<br><br>
 +
Sampling above Nyquist frequency guarantees a bandlimited sampled CT signal's reconstruction. **add source**
 +
<br>
 +
Define Nyquist Sampling rate as <math> {f}_{Nyquist} = 2{f}_{M} </math>
 +
<br>
 +
<math>{f}_{M} </math> is max frequency of CT signal
 +
<br>
 +
 +
----
 +
  
 
----
 
----
Line 25: Line 45:
  
 
== Introduction ==
 
== Introduction ==
For real world application, higher sampling rates are preferred because reconstruction doesn't require filter to be as close to the ideal (sharper cutoff).
+
Sampling at frequencies much larger than Nyquist requires a filter for reconstruction with a less sharp cutoff. A digital LPF can be used to then obtain the reconstructed signal. *add source*
 +
<br><br>
 +
Assume <math> {x}_{c}(t) </math> is a bandlimited CT signal,
 +
<math> {x}_{1}[n] </math> is a DT sampled signal of <math> {x}_{c}(t) </math> with sampling period <math> {T}_{1} </math>
 +
<br><br>
 
This leads to the question, can you use  
 
This leads to the question, can you use  
  
<math> {x}_{1}[n] = x_{c}(n{T}_{1})</math>  
+
<math> {x}_{1}[n] = x_{c}(n{T}_{1})</math>
  
 
to obtain  
 
to obtain  
  
<math>{x}_{u}[n] = {x}_{c}(n{T}_{u})</math>
+
<math>{x}_{u}[n] = {x}_{c}(n{T}_{u})</math>, a signal sampled at a HIGHER sampling frequency than <math> {x}_{1}[n]</math>,
 
without having to fully reconstruct <math> {x}_{c}(t) </math>  
 
without having to fully reconstruct <math> {x}_{c}(t) </math>  
  
assume <math> {x}_{c}(t) </math> is a bandlimited CT signal,
 
<math> {x}_{1}[n] </math> is a DT sampled signal of <math> {x}_{c}(t) </math> with sampling period <math> {T}_{1} </math>
 
  
 
----
 
----
 
----
 
----
 
 
== Derivation ==
 
== Derivation ==
 +
We want <math> {f}_{u} > {f}_{Nyquist} </math>. In this situation, this means <math> {f}_{u} > {f}_{1} </math>.
 +
<br>
 +
Therefore, we want <math>{T}_{u} < {T}_{1}</math>. (i.e. <math> {x}_{u}[n]</math> is sampled at a higher frequency than <math> {x}_{1}[n]</math>)
 +
<br> In other words, <br>
 +
<math> {T}_{u} =  {\frac{{T}_{1}}{D}} </math> for some integer D.
 +
<br>
 +
<br>
 +
<math> {x}_{1}[n] = x_{c}(n{T}_{1})</math>
 +
<br>
 +
<math>{x}_{u}[n] = {x}_{c}(n{T}_{u})</math>
 +
<br>
  
 +
<math>{x}_{u}[n] ={x}_{1}[n/D] = {x}_{c}(n{T}_{1}({T}_{u}/{T}_{1})) = {x}_{c}(n{T}_{u}) </math>if n/D is an integer
 +
<br>
 +
<math>{x}_{u}[n] = 0 </math> else.
 +
<br><br>
 +
In frequency domain: <br>
 +
<math>{X}_{u}({\omega}) = {\sum_{n = -{\infty}}^{\infty} {x}_{u}[n]e^{-j{\omega}n}}</math>
 +
<br>
 +
<math>{X}_{u}({\omega}) = {\sum_{n = -{\infty}}^{\infty} {x}_{1}[n/D]e^{-j{\omega}n}}</math>
 +
<br> let n=mD <br>
 +
<math>{X}_{u}({\omega}) = {\sum_{m = -{\infty}}^{\infty} {x}_{1}[m]e^{-j{\omega}mD}}</math>
 +
<br>
 +
<math>{X}_{u}({\omega}) ={X}_{1}(D{\omega}) </math> notice this is a rescaled version of <math> {X}_{1} </math>
  
 +
<br><br>
 +
In order to get <math>{x}_{int}(n) </math>, the reconstructed signal, we need to LPF <math> {X}_{u}({\omega}) </math>.
 +
<br>
 +
<math> {x}_{int}(n) = {x}_{u} * h(n) </math>
 +
<br>
 +
<math> h(n) = sinc(n/D) </math>
 +
----
 +
 +
 +
----
 +
 +
== Example ==
 +
 +
[[Image:X1w.jpg]]
 +
<br>
 +
[[Image:Xuw.jpg]]
 +
<br>
 +
[[Image:Hlpf.jpg]]
 +
<br>
 +
[[Image:Xint.jpg]]
 +
<br>
 +
----
 +
 +
 +
----
 +
Source: Prof. Mireille Boutin
 +
----
 +
----
 +
 +
== Questions/Comments ==
 +
[[QuestionPage]]
 +
 +
----
 
----
 
----
 +
[[2014_Fall_ECE_438_Boutin_digital_signal_processing_slectures|Back to ECE438 slectures, Fall 2014]]

Latest revision as of 09:00, 14 March 2015

Frequency Domain View of Upsampling

Why Interpolator needs a LPF after Upsampling


A slecture by ECE student Chloe Kauffman

Partly based on the ECE438 Fall 2014 lecture material of Prof. Mireille Boutin.


Outline

  1. Background
  2. Introduction
  3. Derivation
  4. Example
  5. Conclusion


Background

$ {f}_{s} $ = sampling frequency (number of samples/second) Hz
$ {T}_{s} $ = sampling period (number of seconds/sample) seconds
$ {f}_{s} = {\frac{1}{{T}_{s}}} $

Sampling above Nyquist frequency guarantees a bandlimited sampled CT signal's reconstruction. **add source**
Define Nyquist Sampling rate as $ {f}_{Nyquist} = 2{f}_{M} $
$ {f}_{M} $ is max frequency of CT signal




Introduction

Sampling at frequencies much larger than Nyquist requires a filter for reconstruction with a less sharp cutoff. A digital LPF can be used to then obtain the reconstructed signal. *add source*

Assume $ {x}_{c}(t) $ is a bandlimited CT signal, $ {x}_{1}[n] $ is a DT sampled signal of $ {x}_{c}(t) $ with sampling period $ {T}_{1} $

This leads to the question, can you use

$ {x}_{1}[n] = x_{c}(n{T}_{1}) $

to obtain

$ {x}_{u}[n] = {x}_{c}(n{T}_{u}) $, a signal sampled at a HIGHER sampling frequency than $ {x}_{1}[n] $, without having to fully reconstruct $ {x}_{c}(t) $




Derivation

We want $ {f}_{u} > {f}_{Nyquist} $. In this situation, this means $ {f}_{u} > {f}_{1} $.
Therefore, we want $ {T}_{u} < {T}_{1} $. (i.e. $ {x}_{u}[n] $ is sampled at a higher frequency than $ {x}_{1}[n] $)
In other words,
$ {T}_{u} = {\frac{{T}_{1}}{D}} $ for some integer D.

$ {x}_{1}[n] = x_{c}(n{T}_{1}) $
$ {x}_{u}[n] = {x}_{c}(n{T}_{u}) $

$ {x}_{u}[n] ={x}_{1}[n/D] = {x}_{c}(n{T}_{1}({T}_{u}/{T}_{1})) = {x}_{c}(n{T}_{u}) $if n/D is an integer
$ {x}_{u}[n] = 0 $ else.

In frequency domain:
$ {X}_{u}({\omega}) = {\sum_{n = -{\infty}}^{\infty} {x}_{u}[n]e^{-j{\omega}n}} $
$ {X}_{u}({\omega}) = {\sum_{n = -{\infty}}^{\infty} {x}_{1}[n/D]e^{-j{\omega}n}} $
let n=mD
$ {X}_{u}({\omega}) = {\sum_{m = -{\infty}}^{\infty} {x}_{1}[m]e^{-j{\omega}mD}} $
$ {X}_{u}({\omega}) ={X}_{1}(D{\omega}) $ notice this is a rescaled version of $ {X}_{1} $



In order to get $ {x}_{int}(n) $, the reconstructed signal, we need to LPF $ {X}_{u}({\omega}) $.
$ {x}_{int}(n) = {x}_{u} * h(n) $
$ h(n) = sinc(n/D) $




Example

X1w.jpg
Xuw.jpg
Hlpf.jpg
Xint.jpg




Source: Prof. Mireille Boutin



Questions/Comments

QuestionPage



Back to ECE438 slectures, Fall 2014

Alumni Liaison

Have a piece of advice for Purdue students? Share it through Rhea!

Alumni Liaison