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

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{{quiz-Header|Buchseite=Signaldarstellung/Diskrete Fouriertransformation (DFT)
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{{quiz-Header|Buchseite=Signal_Representation/Discrete_Fourier_Transform_(DFT)
 
}}
 
}}
  
[[File:P_ID1138__Sig_A_5_2.png|250px|right|Verwendete Spektralkoeffizienten]]
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[[File:P_ID1138__Sig_A_5_2.png|250px|right|frame|Five different sets for the spectral coefficients  $D(\mu)$]]
  
Bei der ''Diskreten Fouriertransformation'' (DFT) werden aus den $N$ Zeitkoeffizienten $d(\nu)$   ⇒    Abtastwerte des zeitkontinuierlichen Signals $x(t)$ – die $N$ Spektralbereichskoeffizienten $D(\mu)$ berechnet. Mit $\nu = 0, ... , N – 1$ und $\mu = 0, ... , N – 1$ gilt:
+
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(\mu) = \frac{1}{N} \cdot \sum_{\nu = 0 }^{N-1}
   d(\nu)\cdot  {w}^{\hspace{0.05cm}\nu \hspace{0.03cm} \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}
 
  = \cos \left(  {2 \pi}/{N}\right)-{\rm j} \cdot \sin \left(  {2 \pi}/{N}\right)
 
  = \cos \left(  {2 \pi}/{N}\right)-{\rm j} \cdot \sin \left(  {2 \pi}/{N}\right)
 
  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
  
Für die ''Inverse Diskrete Fouriertransformation'' (IDFT) gilt entsprechend   ⇒    „Umkehrfunktion” der DFT:
+
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{0.03cm} \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 obiger Tabelle mit „A”, ... , „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.
  
''Hinweise:''
 
*Die Aufgabe gehört zum  Kapitel [[Signaldarstellung/Zeitdiskrete_Signaldarstellung|Zeitdiskrete Signaldarstellung]].
 
*Sollte die Eingabe des Zahlenwertes „0” erforderlich sein, so geben Sie bitte „0.” ein.
 
*Zu der hier behandelten Thematik gibt es auch ein Interaktionsmodul:
 
:[[Abtastung periodischer Signale und Signalrekonstruktion]]
 
  
Hinweis: Die Aufgabe bezieht sich auf die theoretischen Grundlagen von Kapitel 5.2. Diese können Sie sich auch mit folgendem Interaktionsmodul verdeutlichen:
 
Diskrete Fouriertransformation
 
  
  
===Fragebogen===
+
 
 +
 
 +
 
 +
 
 +
''Hints:''
 +
*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:Discrete_Fouriertransform_and_Inverse|Discrete Fourier Transform and Inverse]].
 +
 
 +
 
 +
 
 +
===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Wie lauten die Zeitkoeffizienten $d(ν)$ für die $D(\mu)$–Werte von Spalte A?
+
{What are the time coefficients&nbsp; $d(\nu)$&nbsp; for the&nbsp; $D(\mu)$&nbsp; values of column&nbsp; $\rm A$?
 
|type="{}"}
 
|type="{}"}
$D(\mu )$ gemäß A: $d(0) =$ { 1 }
+
$d(0)\ = \ $ { 1 3% }
$D(\mu )$ gemäß A: $d(1) =$ { 1 }
+
$d(1)\ = \ $ { 1 3% }
  
{Wie lauten die Zeitkoeffizienten $d(ν)$ für die $D(\mu)$–Werte von Spalte B?
+
{What are the time coefficients&nbsp; $d(ν)$&nbsp; for the&nbsp; $D(\mu)$&nbsp; values of column&nbsp; $\rm B$?
 
|type="{}"}
 
|type="{}"}
$D(\mu )$ gemäß B: $d(0) =$ { 1 }
+
$d(0)\ = \ $ { 1 3% }
$D(\mu )$ gemäß B: $d(1) =$ { 0.707 3% }
+
$d(1)\ = \ $ { 0.707 3% }
  
{Wie lauten die Zeitkoeffizienten $d(ν)$ für die $D(\mu)$ –Werte von Spalte C?
+
{What are the time coefficients&nbsp; $d(ν)$&nbsp; for the&nbsp; $D(\mu)$&nbsp; values of column&nbsp; $\rm C$?
 
|type="{}"}
 
|type="{}"}
$D(\mu )$ gemäß C: $d(0) =$ { 1 }
+
$d(0)\ = \ $ { 1 3% }
$D(\mu )$ gemäß C: $d(1) =$ { 0 }
+
$d(1)\ = \ $ { 0. }
  
{Wie lauten die Zeitkoeffizienten $d(ν)$ für die $D(\mu)$–Werte von Spalte D?
+
{What are the time coefficients&nbsp; $d(ν)$&nbsp; for the&nbsp; $D(\mu)$&nbsp; values of column&nbsp; $\rm D$?
 
|type="{}"}
 
|type="{}"}
$D(\mu )$ gemäß D: $d(0) =$ { 1 }
+
$d(0)\ = \ ${ 1 3% }
$D(\mu )$ gemäß D: $d(1) =$ { -1 }
+
$d(1)\ = \ $ { -1.03--0.97 }
  
{Wie lauten die Zeitkoeffizienten $d(ν)$ für die $D(\mu)$–Wertevon Spalte E?
+
{What are the time coefficients&nbsp; $d(ν)$&nbsp; for the&nbsp; $D(\mu)$&nbsp; values of column&nbsp; $\rm E$?
 
|type="{}"}
 
|type="{}"}
$D(\mu )$ gemäß A: $d(0) =$ { 2 }
+
$d(0)\ = \ $ { 2 3% }
$D(\mu )$ gemäß A: $d(1) =$ { 0 }
+
$d(1)\ = \ $ { 0. }
  
  
 
</quiz>
 
</quiz>
  
===Musterlösung===
+
===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''1.''' Aus der IDFT–Gleichung wird mit $D(\mu)$ = 0 für $\mu \approx$ 0:
+
'''(1)'''&nbsp;  From the IDFT equation,&nbsp; with&nbsp; $D(\mu) = 0$&nbsp; for&nbsp; $\mu \ne 0$:
 
    
 
    
$$d(\nu) = D(0) \cdot w^0 = D(0) =1\hspace{0.5cm}(0 \le \nu \le 7)$$
+
:$$d(\nu) = D(0) \cdot w^0 = D(0) =1\hspace{0.5cm}(0 \le \nu \le 7)\ \hspace{0.5cm} \Rightarrow\hspace{0.5cm}\hspace{0.15 cm}\underline{d(0) = d(1) = 1}.$$
 
 
$$\Rightarrow\hspace{0.5cm}\hspace{0.15 cm}\underline{d(0) = d(1) = 1}.$$
 
  
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}
 
  X(f) = {\delta}(f) \hspace{0.05cm}.$$
 
  X(f) = {\delta}(f) \hspace{0.05cm}.$$
  
'''2.'''  Alle Spektralkoeffizienten sind 0 mit Ausnahme von $D_1$ = $D_7$ = 0.5. Daraus folgt für 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:
 
   
 
   
$$d(\nu)  =  0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left({\pi}/{4} \cdot \nu \right)$$ $$\Rightarrow  \hspace{0.3cm}\hspace{0.15 cm}\underline{d(0) = 1}, \hspace{0.2cm}\hspace{0.15 cm}\underline{d(1) = {1}/{\sqrt{2}} \approx 0.707}
+
:$$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) = \frac {1}{2} \cdot {\delta}(f + f_{\rm A}) + \frac {1}{2} \cdot {\delta}(f - f_{\rm A}) \hspace{0.05cm},$$
+
  X(f) = {1}/{2} \cdot {\delta}(f + f_{\rm A}) + {1}/{2} \cdot {\delta}(f - f_{\rm A}) \hspace{0.05cm},$$
 +
 
 +
:where&nbsp; $f_{\rm A}$&nbsp; denotes the smallest frequency that can be represented in the DFT.
  
wobei $f_A$ die kleinste in der DFT darstellbare Frequenz bezeichnet.
 
  
'''3.''' Gegenüber Aufgabe 2) ist nun die Frequenz doppelt so groß, nämlich 2 · $f_A$ anstelle von $f_A$:
+
'''(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) = \frac {1}{2} \cdot {\delta}(f + 2f_{\rm A}) + \frac {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 〈 $d(ν)$〉 zwei Perioden der Cosinusschwingung, und es gilt für 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; $0 ≤ ν ≤ 7$:
 
   
 
   
$$ d(\nu)  =  0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /2) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /2) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left({\pi}/{2} \cdot \nu \right)$$ $$\Rightarrow  \hspace{0.3cm}\hspace{0.15 cm}\underline{d(0) = 1, \hspace{0.2cm}d(1) = 0}
+
:$$ 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}
 
  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
  
'''4.'''   Durch eine weitere Verdoppelung der Cosinusfrequenz auf 4 $f_A$ 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 die beiden Diracfunktionen in der zeitdiskreten Darstellung aufgrund der Periodizität zusammenfallen. Das heißt: Die Koeffizienten $D$ (4) = 0.5 und $D$ (-4) = 0.5 ergeben zusammen $D$ (4) = 1.
+
*Note that here the two Dirac functions coincide in the discrete-time representation due to periodicity.
 +
*The coefficients&nbsp; $D (+4) = 0.5$&nbsp; and&nbsp; $D (-4) = 0.5$&nbsp; together give&nbsp; $D (4) = 1$.
 +
 
 +
 
  
'''5.''' Die Diskrete Fouriertransformation ist ebenfalls linear. Deshalb ist das Superpositionsprinzip weiterhin anwendbar. Die Koeffizienten $D(\mu )$ aus Spalte E ergeben sich als die Summen der Spalten A und D. Deshalb wird aus der alternierenden Folge 〈 $d(ν)$〉 entsprechend Teilaufgabe 4) die um 1 nach oben verschobene Folge:
+
'''(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; $\rm D$.  
 +
*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}
 
  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  
 
__NOEDITSECTION__
 
__NOEDITSECTION__
[[Category:Aufgaben zu Signaldarstellung|^5. Zeit- und frequenzdisktrete Signaldarstellung^]]
+
[[Category:Signal Representation: Exercises|^5.2 Discrete Fourier Transform^]]

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):
$$ \hspace{0.15 cm}\underline{d(0) =d(2) =d(4) =d(6)= 2}, \hspace{0.2cm}\hspace{0.15 cm}\underline{d(1) =d(3) =d(5) =d(7) = 0} \hspace{0.05cm}.$$