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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|Fünf verschiedene Sätze für die Spektralkoeffizienten  D(μ)]]
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[[File:P_ID1138__Sig_A_5_2.png|250px|right|frame|Five different sets for the spectral coefficients  D(μ)]]
  
Bei der ''Diskreten Fouriertransformation''  (DFT) werden
+
With the  '''Discrete Fourier Transform'''  $\rm (DFT)$,
*aus den  $N  Zeitkoeffizienten d(\nu)  ⇒     Abtastwerte des zeitkontinuierlichen Signals x(t)$
+
*the  N  spectral range coefficients  D(μ)   are calculated
*die  N  Spektralbereichskoeffizienten  D(μ)  
 
  
 +
*from the  N  time coefficients  d(ν)   ⇒    samples of the continuous-time signal  x(t).
  
berechnet. Mit  ν=0, ... , N – 1  und  \mu = 0, ... , N – 1  gilt:
+
 
 +
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}
 
   d(\nu)\cdot  {w}^{\hspace{0.05cm}\nu \hspace{0.05cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$
 
   d(\nu)\cdot  {w}^{\hspace{0.05cm}\nu \hspace{0.05cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$
  
Hierbei bezeichnet  w  den komplexen Drehfaktor:
+
Here  w  denotes the complex rotation factor:
 
   
 
   
 
:$$w  = {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} 2 \pi /N}
 
:$$w  = {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} 2 \pi /N}
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  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
  
Für die ''Inverse Diskrete Fouriertransformation''  (IDFT)    ⇒    „Umkehrfunktion” der DFT gilt entsprechend:
+
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 dieser Aufgabe sollen für verschiedene Beispielfolgen  D(\mu)  (die in der obigen Tabelle mit  \rm A, ... ,  \rm E bezeichnet sind) die Zeitkoeffizienten  $d(\nu)$  ermittelt werden. Es gilt somit stets  N = 8.
+
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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''Hinweise:''  
+
''Hints:''  
*Die Aufgabe gehört zum  Kapitel  [[Signal_Representation/Discrete_Fourier_Transform_(DFT)|Diskrete Fouriertransformation (DFT)]].
+
*This task belongs to the chapter  [[Signal_Representation/Discrete_Fourier_Transform_(DFT)|Discrete Fourier Transformation (DFT)]].
 
   
 
   
*Die hier behandelte Thematik wird auch im interaktiven Applet  [[Applets:Diskrete_Fouriertransformation_und_Inverse|Diskrete Fouriertransformation und Inverse]] behandelt.
+
*The topic dealt with here is also dealt with in the interactive applet  [[Applets:Discrete_Fouriertransform_and_Inverse|Discrete Fourier Transform and Inverse]].
  
  
  
===Fragebogen===
+
===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Wie lauten die Zeitkoeffizienten&nbsp; d(\nu)&nbsp; für die&nbsp; D(\mu)–Werte von Spalte&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% }
  
{Wie lauten die Zeitkoeffizienten&nbsp; d(ν)&nbsp; für die&nbsp; D(\mu)–Werte von Spalte&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% }
  
{Wie lauten die Zeitkoeffizienten&nbsp; d(ν)&nbsp; für die&nbsp; D(\mu)–Werte von Spalte&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. }
  
{Wie lauten die Zeitkoeffizienten&nbsp; d(ν)&nbsp; für die&nbsp; D(\mu)–Werte von Spalte&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 }
  
{Wie lauten die Zeitkoeffizienten&nbsp; d(ν)&nbsp; für die&nbsp; D(\mu)–Werte von Spalte&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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</quiz>
 
</quiz>
  
===Musterlösung===
+
===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp; Aus der IDFT–Gleichung wird mit&nbsp; D(\mu) = 0&nbsp; für&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}.
  
*Dieser Parametersatz beschreibt die diskrete Form der Fourierkorrespondenz des Gleichsignals:
+
*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; Alle Spektralkoeffizienten sind Null mit Ausnahme von&nbsp; D_1 = D_7 = 0.5. Daraus folgt für&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}
 
  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
  
*Aufgrund der Periodizität gilt aber auch:
+
*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}
 
:$$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}.$$
 
  \hspace{0.05cm}.$$
  
*Es handelt sich also um das zeitdiskrete Äquivalent zu
+
*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(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},$$
 
  X(f) =  {1}/{2} \cdot {\delta}(f + f_{\rm A}) +  {1}/{2} \cdot {\delta}(f - f_{\rm A}) \hspace{0.05cm},$$
  
:wobei&nbsp; f_{\rm A}&nbsp; die kleinste in der DFT darstellbare Frequenz bezeichnet.
+
:where&nbsp; f_{\rm A}&nbsp; denotes the smallest frequency that can be represented in the DFT.
  
  
'''(3)'''&nbsp; Gegenüber der Teilaufgabe&nbsp; '''(2)'''&nbsp; ist nun die Schwingungsfrequenz doppelt so groß, nämlich&nbsp; 2 f_{\rm A}&nbsp; anstelle von&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}
 
  X(f) = {1}/{2} \cdot {\delta}(f + 2f_{\rm A}) + {1}/{2} \cdot {\delta}(f - 2f_{\rm A}) \hspace{0.05cm},$$
 
  X(f) = {1}/{2} \cdot {\delta}(f + 2f_{\rm A}) + {1}/{2} \cdot {\delta}(f - 2f_{\rm A}) \hspace{0.05cm},$$
  
*Damit beschreibt die Folge&nbsp;  \langle \hspace{0.1cm}d(ν)\hspace{0.1cm}\rangle &nbsp; zwei Perioden der Cosinusschwingung, und es gilt für&nbsp; 0 ≤ ν ≤ 7:
+
*Thus the sequence&nbsp;  \langle \hspace{0.1cm}d(ν)\hspace{0.1cm}\rangle &nbsp; describes two periods of the cosine oscillation, and it holds for&nbsp; 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}
 
:$$ 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}
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'''(4)'''&nbsp; Durch eine weitere Verdoppelung der Cosinusfrequenz auf&nbsp; 4 f_{\rm A}&nbsp; kommt man schließlich zur zeitkontinuierlichen Fourierkorrespondenz
+
'''(4)'''&nbsp; By further doubling the cosine frequency to&nbsp; 4 f_{\rm A}&nbsp; 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)
 
:$$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}$$
 
  \hspace{0.05cm}$$
  
:und damit zu den Zeitkoeffizienten
+
: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}
 
:$$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}.$$
 
  \hspace{0.05cm}.$$
  
*Zu beachten ist, dass hier die beiden Diracfunktionen in der zeitdiskreten Darstellung aufgrund der Periodizität zusammenfallen.
+
*Note that here the two Dirac functions coincide in the discrete-time representation due to periodicity.
*Die Koeffizienten&nbsp; D (+4) = 0.5&nbsp; und&nbsp; D (-4) = 0.5&nbsp; ergeben zusammen&nbsp; D (4) = 1.
+
*The coefficients&nbsp; D (+4) = 0.5&nbsp; and&nbsp; D (-4) = 0.5&nbsp; together give&nbsp; D (4) = 1.
  
  
  
'''(5)'''&nbsp; Die Diskrete Fouriertransformation ist ebenfalls linear. Deshalb ist das Superpositionsprinzip weiterhin anwendbar:  
+
'''(5)'''&nbsp; The Discrete Fourier Transform is also linear. Therefore, the superposition principle is still applicable:  
*Die Koeffizienten&nbsp; D(\mu )&nbsp; aus Spalte&nbsp; \rm E&nbsp; ergeben sich als die Summen der Spalten&nbsp; \rm A&nbsp; und&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.  
*Deshalb wird aus der alternierenden Folge&nbsp;  \langle \hspace{0.1cm}d(ν) \hspace{0.1cm}\rangle &nbsp; entsprechend Teilaufgabe&nbsp; '''(4)'''&nbsp; die um&nbsp; 1&nbsp; nach oben verschobene Folge:
+
*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}
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[[Category:Exercises for Signal Representation|^5.2 Discrete Fourier Transform^]]
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[[Category:Signal Representation: Exercises|^5.2 Discrete Fourier Transform^]]

Latest revision as of 16:38, 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}.