Difference between revisions of "Aufgaben:Exercise 5.2: Inverse Discrete Fourier Transform"

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[[File:P_ID1138__Sig_A_5_2.png|250px|right|frame|Five different sets for the spectral coefficients  $D(\mu)$]]
 
[[File:P_ID1138__Sig_A_5_2.png|250px|right|frame|Five different sets for the spectral coefficients  $D(\mu)$]]
  
With the ''Discrete Fourier Transform''  (DFT), the following are obtained
+
With the  '''Discrete Fourier Transform'''  $\rm (DFT)$,
*from the  $N$  time coefficients  $d(\nu)$   ⇒    samples of the continuous-time signal  $x(t)$
+
*the  $N$  spectral range coefficients  $D(\mu)$   are calculated
*the  $N$  spectral range coefficients  $D(\mu)$  
 
  
 +
*from the  $N$  time coefficients  $d(\nu)$   ⇒    samples of the continuous-time signal  $x(t)$.
  
are calculated. With  $\nu = 0$, ... , $N – 1$  and  $\mu = 0$, ... , $N – 1$  holds:
+
 
 +
With  $\nu = 0$, ... , $N – 1$  and  $\mu = 0$, ... , $N – 1$  holds:
 
   
 
   
 
:$$D(\mu) = \frac{1}{N} \cdot \sum_{\nu = 0 }^{N-1}
 
:$$D(\mu) = \frac{1}{N} \cdot \sum_{\nu = 0 }^{N-1}
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  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
  
For the ''Inverse Discrete Fourier Transform''  (IDFT)    ⇒    „inverse function” of the DFT, the following applies accordingly:
+
For the  '''Inverse Discrete Fourier Transform'''  $\rm (DFT)$   ⇒    "inverse function" of the DFT, the following applies accordingly:
 
 
 
   
 
   
 
:$$d(\nu) =  \sum_{\mu = 0 }^{N-1}
 
:$$d(\nu) =  \sum_{\mu = 0 }^{N-1}
 
  D(\mu) \cdot  {w}^{-\nu \hspace{0.05cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$
 
  D(\mu) \cdot  {w}^{-\nu \hspace{0.05cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$
  
In this task, the time coefficients  $d(\nu)$   are to be determined for various example sequences (which are labelled  $\rm A$, ... ,  $\rm E$ in the table above)  $D(\mu)$  ermittelt werden. Thus,  $N = 8$ always applies.
+
In this task, the time coefficients  $d(\nu)$   are to be determined for various sequences  $D(\mu)$   (which are labelled  $\rm A$, ... ,  $\rm E$  in the table above).  Thus,  $N = 8$ always applies.
  
  
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*This task belongs to the chapter  [[Signal_Representation/Discrete_Fourier_Transform_(DFT)|Discrete Fourier Transformation (DFT)]].
 
*This task belongs to the chapter  [[Signal_Representation/Discrete_Fourier_Transform_(DFT)|Discrete Fourier Transformation (DFT)]].
 
   
 
   
*The topic dealt with here is also dealt with in the interactive applet  [[Applets:Diskrete_Fouriertransformation_und_Inverse|Discrete Fourier Transform and Inverse]].
+
*The topic dealt with here is also dealt with in the interactive applet  [[Applets:Discrete_Fouriertransform_and_Inverse|Discrete Fourier Transform and Inverse]].
  
  
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<quiz display=simple>
 
<quiz display=simple>
{What are the time coefficients&nbsp; $d(\nu)$&nbsp; for the&nbsp; $D(\mu)$–values of column&nbsp; $\rm A$?
+
{What are the time coefficients&nbsp; $d(\nu)$&nbsp; for the&nbsp; $D(\mu)$&nbsp; values of column&nbsp; $\rm A$?
 
|type="{}"}
 
|type="{}"}
 
$d(0)\ = \ $  { 1 3% }
 
$d(0)\ = \ $  { 1 3% }
 
$d(1)\ = \ $ { 1 3% }
 
$d(1)\ = \ $ { 1 3% }
  
{What are the time coefficients&nbsp; $d(ν)$&nbsp; for the&nbsp; $D(\mu)$–values of column&nbsp; $\rm B$?
+
{What are the time coefficients&nbsp; $d(ν)$&nbsp; for the&nbsp; $D(\mu)$&nbsp; values of column&nbsp; $\rm B$?
 
|type="{}"}
 
|type="{}"}
 
$d(0)\ = \ $ { 1 3% }
 
$d(0)\ = \ $ { 1 3% }
 
$d(1)\ = \ $ { 0.707 3% }
 
$d(1)\ = \ $ { 0.707 3% }
  
{What are the time coefficients&nbsp; $d(ν)$&nbsp; for the&nbsp; $D(\mu)$–values of column&nbsp; $\rm C$?
+
{What are the time coefficients&nbsp; $d(ν)$&nbsp; for the&nbsp; $D(\mu)$&nbsp; values of column&nbsp; $\rm C$?
 
|type="{}"}
 
|type="{}"}
 
$d(0)\ = \ $ { 1 3% }
 
$d(0)\ = \ $ { 1 3% }
 
$d(1)\ = \ $ { 0. }
 
$d(1)\ = \ $ { 0. }
  
{What are the time coefficients&nbsp; $d(ν)$&nbsp; for the&nbsp; $D(\mu)$–values of column&nbsp; $\rm D$?
+
{What are the time coefficients&nbsp; $d(ν)$&nbsp; for the&nbsp; $D(\mu)$&nbsp; values of column&nbsp; $\rm D$?
 
|type="{}"}
 
|type="{}"}
 
$d(0)\ = \ ${ 1 3% }
 
$d(0)\ = \ ${ 1 3% }
 
$d(1)\ = \ $ { -1.03--0.97 }
 
$d(1)\ = \ $ { -1.03--0.97 }
  
{What are the time coefficients&nbsp; $d(ν)$&nbsp; for the&nbsp; $D(\mu)$–values of column&nbsp; $\rm E$?
+
{What are the time coefficients&nbsp; $d(ν)$&nbsp; for the&nbsp; $D(\mu)$&nbsp; values of column&nbsp; $\rm E$?
 
|type="{}"}
 
|type="{}"}
 
$d(0)\ = \ $ { 2 3% }
 
$d(0)\ = \ $ { 2 3% }
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===Solution===
 
===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp;  From the IDFT equation, with&nbsp; $D(\mu) = 0$&nbsp; for&nbsp; $\mu \ne 0$:
+
'''(1)'''&nbsp;  From the IDFT equation,&nbsp; with&nbsp; $D(\mu) = 0$&nbsp; for&nbsp; $\mu \ne 0$:
 
    
 
    
 
:$$d(\nu) = D(0) \cdot w^0 = D(0) =1\hspace{0.5cm}(0 \le \nu \le 7)\ \hspace{0.5cm} \Rightarrow\hspace{0.5cm}\hspace{0.15 cm}\underline{d(0) = d(1) = 1}.$$
 
:$$d(\nu) = D(0) \cdot w^0 = D(0) =1\hspace{0.5cm}(0 \le \nu \le 7)\ \hspace{0.5cm} \Rightarrow\hspace{0.5cm}\hspace{0.15 cm}\underline{d(0) = d(1) = 1}.$$
  
*This set of parameters describes the discrete form of the Fourier correspondence of the DC signal:
+
*This parameter set describes the discrete form of the Fourier correspondence of the DC signal:
 
   
 
   
 
:$$x(t) = 1 \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm}
 
:$$x(t) = 1 \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm}
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'''(2)'''&nbsp;  All spectral coefficients are zero except&nbsp; $D_1 = D_7 = 0.5$. It follows for&nbsp; $0 ≤ ν ≤ 7$:
+
'''(2)'''&nbsp;  All spectral coefficients are zero except&nbsp; $D_1 = D_7 = 0.5$.&nbsp; It follows for&nbsp; $0 ≤ ν ≤ 7$:
 
   
 
   
 
:$$d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu}
 
:$$d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu}
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'''(3)'''&nbsp; Compared to subtask&nbsp; '''(2)'''&nbsp;, the oscillation frequency is now twice as large, namely&nbsp; $2 f_{\rm A}$&nbsp; instead of&nbsp; $f_{\rm A}$:
+
'''(3)'''&nbsp; Compared to subtask&nbsp; '''(2)''',&nbsp; the oscillation frequency is now twice as large, namely&nbsp; $2 f_{\rm A}$&nbsp; instead of&nbsp; $f_{\rm A}$:
 
   
 
   
 
:$$x(t) = \cos(2 \pi \cdot (2f_{\rm A}) \cdot t) \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm}
 
:$$x(t) = \cos(2 \pi \cdot (2f_{\rm A}) \cdot t) \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm}
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'''(5)'''&nbsp; The Discrete Fourier Transform is also linear. Therefore, the superposition principle is still applicable:  
 
'''(5)'''&nbsp; The Discrete Fourier Transform is also linear. Therefore, the superposition principle is still applicable:  
 
*The coefficients&nbsp; $D(\mu )$&nbsp; from column&nbsp; $\rm E$&nbsp; result as the sums of columns&nbsp; $\rm A$&nbsp; and&nbsp; $\rm D$.  
 
*The coefficients&nbsp; $D(\mu )$&nbsp; from column&nbsp; $\rm E$&nbsp; result as the sums of columns&nbsp; $\rm A$&nbsp; and&nbsp; $\rm D$.  
*Therefore, the alternating sequence&nbsp;  $\langle \hspace{0.1cm}d(ν) \hspace{0.1cm}\rangle $&nbsp;  becomes the sequence shifted up by&nbsp; $1$&nbsp; according to subtask&nbsp; '''(4)'''&nbsp;:
+
*Therefore, the alternating sequence&nbsp;  $\langle \hspace{0.1cm}d(ν) \hspace{0.1cm}\rangle $&nbsp;  becomes the sequence shifted up by&nbsp; $1$&nbsp; according to subtask&nbsp; '''(4)''':
 
   
 
   
 
:$$ \hspace{0.15 cm}\underline{d(0) =d(2) =d(4) =d(6)= 2}, \hspace{0.2cm}\hspace{0.15 cm}\underline{d(1) =d(3) =d(5) =d(7)  = 0}
 
:$$ \hspace{0.15 cm}\underline{d(0) =d(2) =d(4) =d(6)= 2}, \hspace{0.2cm}\hspace{0.15 cm}\underline{d(1) =d(3) =d(5) =d(7)  = 0}

Latest revision as of 15:38, 16 May 2021

Five different sets for the spectral coefficients  $D(\mu)$

With the  Discrete Fourier Transform  $\rm (DFT)$,

  • the  $N$  spectral range coefficients  $D(\mu)$  are calculated
  • from the  $N$  time coefficients  $d(\nu)$   ⇒   samples of the continuous-time signal  $x(t)$.


With  $\nu = 0$, ... , $N – 1$  and  $\mu = 0$, ... , $N – 1$  holds:

$$D(\mu) = \frac{1}{N} \cdot \sum_{\nu = 0 }^{N-1} d(\nu)\cdot {w}^{\hspace{0.05cm}\nu \hspace{0.05cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$

Here  $w$  denotes the complex rotation factor:

$$w = {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} 2 \pi /N} = \cos \left( {2 \pi}/{N}\right)-{\rm j} \cdot \sin \left( {2 \pi}/{N}\right) \hspace{0.05cm}.$$

For the  Inverse Discrete Fourier Transform  $\rm (DFT)$   ⇒   "inverse function" of the DFT, the following applies accordingly:

$$d(\nu) = \sum_{\mu = 0 }^{N-1} D(\mu) \cdot {w}^{-\nu \hspace{0.05cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$

In this task, the time coefficients  $d(\nu)$  are to be determined for various sequences  $D(\mu)$  (which are labelled  $\rm A$, ... ,  $\rm E$  in the table above).  Thus,  $N = 8$ always applies.





Hints:


Questions

1

What are the time coefficients  $d(\nu)$  for the  $D(\mu)$  values of column  $\rm A$?

$d(0)\ = \ $

$d(1)\ = \ $

2

What are the time coefficients  $d(ν)$  for the  $D(\mu)$  values of column  $\rm B$?

$d(0)\ = \ $

$d(1)\ = \ $

3

What are the time coefficients  $d(ν)$  for the  $D(\mu)$  values of column  $\rm C$?

$d(0)\ = \ $

$d(1)\ = \ $

4

What are the time coefficients  $d(ν)$  for the  $D(\mu)$  values of column  $\rm D$?

$d(0)\ = \ $

$d(1)\ = \ $

5

What are the time coefficients  $d(ν)$  for the  $D(\mu)$  values of column  $\rm E$?

$d(0)\ = \ $

$d(1)\ = \ $


Solution

(1)  From the IDFT equation,  with  $D(\mu) = 0$  for  $\mu \ne 0$:

$$d(\nu) = D(0) \cdot w^0 = D(0) =1\hspace{0.5cm}(0 \le \nu \le 7)\ \hspace{0.5cm} \Rightarrow\hspace{0.5cm}\hspace{0.15 cm}\underline{d(0) = d(1) = 1}.$$
  • This parameter set describes the discrete form of the Fourier correspondence of the DC signal:
$$x(t) = 1 \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm} X(f) = {\delta}(f) \hspace{0.05cm}.$$


(2)  All spectral coefficients are zero except  $D_1 = D_7 = 0.5$.  It follows for  $0 ≤ ν ≤ 7$:

$$d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} \hspace{0.05cm}.$$
  • However, due to periodicity, also holds:
$$d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left({\pi}/{4} \cdot \nu \right) \hspace{0.3cm} \Rightarrow \hspace{0.3cm}\hspace{0.15 cm}\underline{d(0) = 1}, \hspace{0.2cm}\hspace{0.15 cm}\underline{d(1) = {1}/{\sqrt{2}} \approx 0.707} \hspace{0.05cm}.$$
  • It is therefore the discrete-time equivalent of
$$x(t) = \cos(2 \pi \cdot f_{\rm A} \cdot t) \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm} X(f) = {1}/{2} \cdot {\delta}(f + f_{\rm A}) + {1}/{2} \cdot {\delta}(f - f_{\rm A}) \hspace{0.05cm},$$
where  $f_{\rm A}$  denotes the smallest frequency that can be represented in the DFT.


(3)  Compared to subtask  (2),  the oscillation frequency is now twice as large, namely  $2 f_{\rm A}$  instead of  $f_{\rm A}$:

$$x(t) = \cos(2 \pi \cdot (2f_{\rm A}) \cdot t) \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm} X(f) = {1}/{2} \cdot {\delta}(f + 2f_{\rm A}) + {1}/{2} \cdot {\delta}(f - 2f_{\rm A}) \hspace{0.05cm},$$
  • Thus the sequence  $\langle \hspace{0.1cm}d(ν)\hspace{0.1cm}\rangle $  describes two periods of the cosine oscillation, and it holds for  $0 ≤ ν ≤ 7$:
$$ d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /2) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /2) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left({\pi}/{2} \cdot \nu \right)\hspace{0.3cm} \Rightarrow \hspace{0.3cm}\hspace{0.15 cm}\underline{d(0) = 1, \hspace{0.2cm}d(1) = 0} \hspace{0.05cm}.$$


(4)  By further doubling the cosine frequency to  $4 f_{\rm A}$  one finally arrives at the continuous-time Fourier correspondence

$$d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} \pi \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} \pi \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left(\pi \cdot \nu \right) \hspace{0.05cm}$$
and thus to the time coefficients
$$d(0) =d(2) =d(4) =d(6) \hspace{0.15 cm}\underline{= +1}, \hspace{0.2cm}d(1) =d(3) =d(5) =d(7) \hspace{0.15 cm}\underline{= -1} \hspace{0.05cm}.$$
  • Note that here the two Dirac functions coincide in the discrete-time representation due to periodicity.
  • The coefficients  $D (+4) = 0.5$  and  $D (-4) = 0.5$  together give  $D (4) = 1$.


(5)  The Discrete Fourier Transform is also linear. Therefore, the superposition principle is still applicable:

  • The coefficients  $D(\mu )$  from column  $\rm E$  result as the sums of columns  $\rm A$  and  $\rm D$.
  • Therefore, the alternating sequence  $\langle \hspace{0.1cm}d(ν) \hspace{0.1cm}\rangle $  becomes the sequence shifted up by  $1$  according to subtask  (4):
$$ \hspace{0.15 cm}\underline{d(0) =d(2) =d(4) =d(6)= 2}, \hspace{0.2cm}\hspace{0.15 cm}\underline{d(1) =d(3) =d(5) =d(7) = 0} \hspace{0.05cm}.$$