Difference between revisions of "Aufgaben:Exercise 5.5: Fast Fourier Transform"

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{{quiz-Header|Buchseite=Signaldarstellung/Fast-Fouriertransformation (FFT)
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{{quiz-Header|Buchseite=Signal_Representation/Fast_Fourier_Transform_(FFT)
 
}}
 
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[[File:P_ID1177__Sig_A_5_5_neu.png|250px|right|FFT-Algorithmus (Aufgabe A5.5)]]
+
[[File:EN_Sig_A_5_5.png|right|frame|FFT algorithm for  $N=8$]]
  
Die Grafik zeigt den Signalflussplan der DFT für $N$ = 8. Aus den Zeitkoeffizienten $d$(0), ... , $d$(7) werden die dazugehörigen Spektralkoeffizienten $D$(0), ... , $D$(7) ermittelt.
+
The graph shows the signal flow diagram of the Fast Fourier Transform  $\rm (FFT)$  for  $N = 8$.  
Für diese gilt mit 0 ≤ μ ≤ 7:
+
 
 +
The associated spectral coefficients  $D(0), \hspace{0.03cm}\text{...} \hspace{0.1cm} , D(7)$  are determined from the time coefficients  $d(0), \hspace{0.03cm}\text{...} \hspace{0.1cm}, d(7)$.  The following applies to these with  $0 ≤ μ ≤ 7$:
 
   
 
   
$$D(\mu) =  \frac{1}{N}\cdot \sum_{\nu = 0 }^{N-1}
+
:$$D(\mu) =  \frac{1}{N}\cdot \sum_{\nu = 0 }^{N-1}
  d(\nu) \cdot  {w}^{\nu \hspace{0.03cm} \cdot
+
  d(\nu) \cdot  {w}^{\hspace{0.03cm}\nu \hspace{0.05cm} \cdot
 
  \hspace{0.05cm}\mu}\hspace{0.05cm},$$
 
  \hspace{0.05cm}\mu}\hspace{0.05cm},$$
  
wobei der komplexe Drehfaktor $w = \text{exp}(–\text{j}2\pi /N)$ zu verwenden ist, also $\text{exp}(–\text{j}\pi /4)$ für $N$ = 8.
+
where the complex rotation factor  $w = \text{e}^{-\text{j}\hspace{0.05cm} \cdot
Am Eingang wird die alternierende ±1–Folge 〈 $d(ν)$ 〉 angelegt. Nach der Bitumkehroperation ergibt sich daraus die Folge 〈 $b(κ)$ .
+
\hspace{0.05cm}2\pi /N}$  is to be used, i.e.  $w = \text{e}^{-\text{j}\hspace{0.05cm} \cdot
Es gilt $b(κ) = d(ν)$, wenn man $ν$ als Dualzahl darstellt und die resultierenden 3 Bit als $κ$ in umgekehrter Reihenfolge geschrieben werden. Beispielsweise
+
\hspace{0.05cm}\pi /4}$  für  $N = 8$.
* folgt aus $ = 1 (binär 001) die Position $ = 4 (binär 100),
+
 
* verbleibt $d$(2) an der gleichen Position 2 (binär 010).
+
*The alternating $±1$ sequence  $\langle\hspace{0.05cm} d(ν)\hspace{0.05cm}\rangle$  is applied to the input.
 +
*After the bit reversal operation, this results in the sequence  $\langle \hspace{0.05cm}b(\kappa)\hspace{0.05cm}\rangle$.
 +
 
 +
 
 +
It holds that  $b(κ) = d(ν)$, if  $ν$  is represented as a dual number and the resulting three bits are written as  $κ$  in reverse order.  For example
 +
*   $ν = 1$  $($binary  $001)$  is followed by  $κ = 4$  $($binary  $100)$,
 +
*   $d(2)$  remains at the same position  $2$  $($binary  $010)$.
 +
 
 +
 
 +
The actual FFT algorithm happens for the example  $N = 8$  in  $\log_2 N = 3$  stages, denoted  $L = 1$,  $L =2$  and  $L = 3$.  Further:
 +
* In each stage, four basic operations - so-called  '''butterflies'''  - are to be performed.
 +
* The values at the output of the first stage are designated in this task as&nbsp; $X(0),\hspace{0.03cm}\text{...} \hspace{0.1cm} , X(7)$, <br>those of the second as&nbsp; $Y(0), \hspace{0.03cm}\text{...} \hspace{0.1cm}  , Y(7)$.
 +
* After the third and last stage, all values must be divided by&nbsp; $N$.&nbsp; <br>The final result&nbsp; $D(0), \hspace{0.03cm}\text{...} \hspace{0.1cm}  , D(7)$&nbsp; is available here.
  
  
Der eigentliche FFT–Algorithmus geschieht für das Beispiel $N$ = 8 in log2 $N$ = 3 Stufen, die mit L = 1, 2 und 3 bezeichnet werden. Weiter gilt:
 
* In jeder Stufe sind vier Basisoperationen – sog. Butterflies – durchzuführen sind.
 
* Die Werte am Ausgang der ersten Stufe werden in dieser Aufgabe mit $X$(0), ... , $X$(7) bezeichnet, die der zweiten mit $Y$(0), ... , $Y$(7).
 
* Nach der dritten und letzten Stufe sind alle Werte noch durch $N$ zu dividieren.
 
  
  
Hinweis: Die Aufgabe bezieht sich auf den Theorieteil zu Kapitel 5.5.
 
  
===Fragebogen===
+
 
 +
 
 +
''Hint:''
 +
*This task belongs to the chapter&nbsp; [[Signal_Representation/Fast_Fourier_Transform_(FFT)|Fast Fourier Transform (FFT)]].
 +
 +
 
 +
 
 +
 
 +
===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
  
{Berechnen Sie den DFT–Koeffizienten $D$(3).
+
{Calculate the DFT coefficient&nbsp; $D(\mu=3)$.
 
|type="{}"}
 
|type="{}"}
$D(3) =$ { 0 }
+
$D(\mu=3) \ = \ $ { 0. }
  
{Berechnen Sie den DFT–Koeffizienten $D$(4).
+
{Calculate the DFT coefficient&nbsp; $D(\mu=4)$.
 
|type="{}"}
 
|type="{}"}
$D(4) =$ { 1 }
+
$D(\mu=4) \ = \ $ { 1 3% }
  
{Ermitteln Sie die Ausgangswerte $X$(0), ... , $X$(7) der ersten Stufe. Welche der folgenden Aussagen sind zutreffend?
+
{Determine the initial values&nbsp; $X(0)$, ... , $X(7)$&nbsp; of the first stage.&nbsp; Which of the following statements are true?
 
|type="[]"}
 
|type="[]"}
- Alle Werte mit geradzahligen Indizes sind gleich 2.
+
- All&nbsp; $X$ values with even indices are equal to&nbsp; $2$.
+ Alle Werte mit ungeradzahligen Indizes sind gleich 0.
+
+ All&nbsp; $X$ values with odd indices are equal to&nbsp; $0$.
 +
 
 +
{Determine the initial values&nbsp; $Y(0)$, ... , $Y(7)$&nbsp; of the second stage.&nbsp; Enter the values&nbsp; $Y(0)$&nbsp; and&nbsp; $Y(4)$&nbsp; as a check.
  
{Ermitteln Sie die Ausgangswerte $Y$(0), ... , $Y$(7) der zweiten Stufe. Geben Sie zur Kontrolle die Werte $Y$(0) und $Y$(4) ein.
 
 
|type="{}"}
 
|type="{}"}
$Y(0) =$ { 4 }
+
$Y(0) \ = \ $ { 4 3% }
$Y(4) =$ { -4 }
+
$Y(4) \ = \ $ { -4.12--3.88 }
  
{Berechnen Sie alle $N$ Spektralwerte $D(\mu)$, insbesondere
+
{Calculate all&nbsp; $N$&nbsp; spectral values&nbsp; $D(\mu)$, in particular
 
|type="{}"}
 
|type="{}"}
$D(\mu = 4) =$ { 4 }
+
$D(\mu =3) \ = \ $ { 0. }
$D(\mu \neq 4) =$ { 0 }
+
$D(\mu = 4) \ = \ $ { 1 3% }
  
{Welche Spektralkoeffizienten würden sich für $d(ν = 4) = 1$ und $d(ν \neq 4) = 0$ ergeben? Geben Sie zur Kontrolle die Werte $D$(3) und $D$(4) ein:
+
{What would be the spectral coefficients for&nbsp; $d(ν = 4) = 1$&nbsp; and&nbsp; $d(ν \neq 4) = 0$&nbsp;? <br>Enter the values&nbsp; $D(\mu =3)$&nbsp; and&nbsp; $D(\mu =4)$&nbsp; as a check.
 
|type="{}"}
 
|type="{}"}
$D(\mu = 3) =$ { -1 }
+
$D(\mu = 3) \ = \ $ { -1.03--0.97 }
$D(\mu = 4) =$ { 1 }
+
$D(\mu = 4) \ = \ $ { 1 3% }
  
  
 
</quiz>
 
</quiz>
  
===Musterlösung===
+
===Solution===
  
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''1.''' Entsprechend der auf dem Angabenblatt gegebenen allgemeinen DFT–Gleichung gilt mit $w = \text{exp}(–\text{j}\pi /4)$ unter Berücksichtigung der alternierenden Zeitkoeffizienten:
+
'''(1)'''&nbsp; According to the general DFT equation given on the specification sheet, with&nbsp; $w = \text{e}^{-\text{j}\hspace{0.05cm} \cdot
 +
\hspace{0.05cm}\pi /4}$&nbsp; taking into account the alternating time coefficients:
 
   
 
   
$$\begin{align*}8 \cdot D(3) & =    w^0 - w^3 + w^6- w^9+ w^{12}- w^{15}+ w^{18}-
+
:$$8 \cdot D(3) =    w^0 - w^3 + w^6- w^9+ w^{12}- w^{15}+ w^{18}-
w^{21} \\ & =    w^0 - w^3 + w^2- w^1+ w^{4}- w^{7}+ w^{6}-
+
w^{21} =    w^0 - w^3 + w^2- w^1+ w^{4}- w^{7}+ w^{6}-
w^{5}\hspace{0.05cm}.\end{align*}$$
+
w^{5}\hspace{0.05cm}.$$
  
Hierbei ist berücksichtigt, dass aufgrund der Periodizität $w_9 = w_1$, $w_{12} = w_4$, $w_{15} = w_7$, $w_{18} = w_2$ und $w_{21} = w_5$ ist. Nach Umsortieren gilt in gleicher Weise
+
*Here it is taken into account that due to the periodicity&nbsp; $w_9 = w_1$,&nbsp; $w_{12} = w_4$,&nbsp; $w_{15} = w_7$,&nbsp; $w_{18} = w_2$&nbsp; und&nbsp; $w_{21} = w_5$&nbsp; ist.  
 +
*After re-sorting, the same applies:
 
   
 
   
$$\begin{align*} 8 \cdot D(3) & =  (w^0 + w^4) - (w^1 + w^5)+ (w^2 + w^6) - (w^3 + w^7) = \\
+
:$$8 \cdot D(3) =  (w^0 + w^4) - (w^1 + w^5)+ (w^2 + w^6) - (w^3 + w^7) =  (1 + w + w^2+ w^3) \cdot (w^0 + w^4)\hspace{0.05cm}.$$
& =  (1 + w + w^2+ w^3) \cdot (w^0 + w^4)\hspace{0.05cm}.\end{align*}$$
 
  
Wegen $w_0 = 1$ und $w_4 = \text{exp}(–\text{j} \cdot \pi) = –1$ erhält man somit $D$(3) = 0.
+
*Thus, because&nbsp; $w_0 = 1$&nbsp; and&nbsp; $w_4 = \text{e}^{-\text{j}\pi } = \hspace{0.08cm} - \hspace{-0.08cm}1$&nbsp;, we obtain&nbsp; $\underline {D(\mu=3) = 0}$.
  
'''2.''' In analoger Weise zu 1) ergibt sich nun:
+
 
 +
'''(2)'''&nbsp; In analogy to sub-taske&nbsp; '''(1)'''&nbsp;, we now get:
 
   
 
   
$$\begin{align*} 8 \cdot D(4) & =    w^0 - w^4 + w^8- w^{12}+ w^{16}- w^{20}+
+
:$$ 8 \cdot D(4) =    w^0 - w^4 + w^8- w^{12}+ w^{16}- w^{20}+
w^{24}- w^{28}= \\ & =  4 \cdot (w^0 - w^4)= 8 \hspace{0.3cm}
+
w^{24}- w^{28}=  4 \cdot (w^0 - w^4)= 8 \hspace{0.3cm}
\Rightarrow \hspace{0.3cm}\hspace{0.15 cm}\underline{D(4) = 1}\hspace{0.05cm}.\end{align*}$$
+
\Rightarrow \hspace{0.3cm}\hspace{0.15 cm}\underline{D(\mu=4) = 1}\hspace{0.05cm}.$$
  
[[File:P_ID1178__Sig_A_5_5c_neu.png|250px|right|Beispiel für den FFT-Algorithmus (ML zu Aufgabe A5.5)]]
 
  
'''3.''' Der Term $w_0$ = 1 muss nicht berücksichtigt werden. Alle Ausgangswerte mit ungeraden Indizes sind somit durch die Subtraktion zweier identischer Eingangswerte gleich 0.
+
[[File:P_ID1178__Sig_A_5_5c_neu.png|right|frame|Example for the FFT algorithm]]
Die erste Aussage trifft dagegen nicht zu: Es gilt $X$(0) = $X$(2) = +2 und $X$(4) = $X$(6) = –2  ⇒  Lösungsvorschlag 2.  
+
'''(3)'''&nbsp;  <u>Proposed solution 2</u> is correct:
 +
*The term&nbsp; $w^0 = 1$&nbsp; does not have to be taken into account.
 +
*All output values with odd indices are zero by subtracting two identical input values.
 +
*The first statement is not true: &nbsp; It holds&nbsp; $X(0) = X(2) = +2$&nbsp; and&nbsp; $X(4) = X(6) = - 2$.  
  
  
'''4.''' Auf die Multiplikation mit $w_2$ = –j kann verzichtet werden, da im Signalflussplan die entsprechenden Eingangsgrößen 0 sind.
 
Man erhält somit $Y$(0) = 4 und $Y$(4) = –4. Alle anderen Werte sind 0.
 
  
 +
'''(4)'''&nbsp; The multiplication with&nbsp; $w^{2} = -{\rm j}$&nbsp; can be dispensed with, since in the signal flow diagram the corresponding input values are zero.
 +
*One thus obtains&nbsp; $Y(0) \;\underline{= 4}$&nbsp; and&nbsp; $Y(4) \;\underline{=  - \hspace{-0.03cm}4}$.
 +
*All other values are zero.
  
'''5.''' Wegen $Y$(5) = $Y$(6) =$Y$(7) = 0 spielen auch in der dritten Stufe die Multiplikationen mit $w$, $w_2$ und $w_3$ keine Rolle. Alle Spektralkoeffizienten $D(\mu)$ ergeben sich zu 0 mit Ausnahme von
+
 
 +
 
 +
'''(5)'''&nbsp; Because of&nbsp; $Y(5) = Y(6) =Y(7) = 0$, the multiplications with&nbsp; $w$,&nbsp; $w^2$&nbsp; and&nbsp; $w^3$&nbsp; do not matter in the third stage either.&nbsp; All spectral coefficients&nbsp; $D(\mu)$&nbsp; therefore result in zero with the exception of
 
   
 
   
$$\hspace{0.15 cm}\underline{D(4)} =  {1}/{N}\cdot \left[Y(0) - Y(4) \right ] \hspace{0.15 cm}\underline{= 1}
+
:$$\hspace{0.15 cm}\underline{D(\mu=4)} =  {1}/{N}\cdot \left[Y(0) - Y(4) \right ] \hspace{0.15 cm}\underline{= 1}
 +
\hspace{0.05cm},$$
 +
:$$\hspace{0.15 cm}\underline{D(\mu=3)} = D(\mu\ne 4)  \hspace{0.15 cm}\underline{= 0}
 
\hspace{0.05cm}.$$
 
\hspace{0.05cm}.$$
  
Dieses Ergebnis stimmt mit den Ergebnissen aus 1) und 2) überein.  
+
This result agrees with the results from&nbsp; '''(1)'''&nbsp; und&nbsp; '''(2)'''.
 +
 
 +
 
 +
 
 +
'''(6)'''&nbsp; Since both the time coefficients&nbsp; $d(ν)$&nbsp; and all spectral coefficients&nbsp; $D(\mu)$&nbsp; are purely real, there is no difference between the FFT and the IFFT.
 +
*This means at the same time:&nbsp; The input and output values can be interchanged.
  
'''6.''' Nachdem sowohl die Zeitkoeffizienten $d(ν)$ als auch alle Spektralkoeffizienten $D(\mu)$ rein reell sind, besteht kein Unterschied zwischen der FFT und der IFFT. Das bedeutet gleichzeitig: die Eingangs– und Ausgangswerte können vertauscht werden.
+
*Subtask&nbsp; '''(5)'''&nbsp; gave the following result:
Die Teilaufgabe 5) hat das folgende Ergebnis geliefert:
 
 
   
 
   
$$d({\rm gerades}\hspace{0.15cm}\nu) =  +1, \hspace{0.2cm}d({\rm
+
:$$d({\rm even}\hspace{0.15cm}\nu) =  +1, \hspace{0.2cm}d({\rm
ungerades}\hspace{0.15cm}\nu)=  -1 \hspace{0.3cm} \Rightarrow
+
odd}\hspace{0.15cm}\nu)=  -1$$
 +
:$$\Rightarrow
 
\hspace{0.3cm}D(\mu = 4)= 1,\hspace{0.2cm}D(\mu \ne 4)= 0.$$
 
\hspace{0.3cm}D(\mu = 4)= 1,\hspace{0.2cm}D(\mu \ne 4)= 0.$$
  
Durch Vertauschen der Eingangs– und Ausgangswerte kommt man zur Aufgabenstellung von (6):
+
*By swapping the input and output values, we arrive at problem&nbsp; '''(6)''':
 
   
 
   
$$d(\nu = 4)= 1, \hspace{0.2cm}d(\nu \ne 4)= 0 \hspace{0.3cm}
+
:$$d(\nu = 4)= 1, \hspace{0.2cm}d(\nu \ne 4)= 0 \hspace{0.3cm}
 
\Rightarrow \hspace{0.3cm}D({\rm gerades}\hspace{0.15cm}\mu) = +1,
 
\Rightarrow \hspace{0.3cm}D({\rm gerades}\hspace{0.15cm}\mu) = +1,
 
\hspace{0.2cm}D({\rm ungerades}\hspace{0.15cm}\mu)=  -1
 
\hspace{0.2cm}D({\rm ungerades}\hspace{0.15cm}\mu)=  -1
 
\hspace{0.05cm}.$$
 
\hspace{0.05cm}.$$
  
Insbesondere würde sich $D$(3) = –1 und D(4) = +1 ergeben.
+
*In particular, this results in&nbsp; $D(\mu=3) \; \underline{= -1}$&nbsp; und&nbsp; $D(\mu=4) \; \underline{= +1}$.
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  
 
__NOEDITSECTION__
 
__NOEDITSECTION__
[[Category:Aufgaben zu Signaldarstellung|^5. Zeit- und frequenzdisktrete Signaldarstellung^]]
+
[[Category:Signal Representation: Exercises|^5.5 Fast Fourier Transform ^]]

Latest revision as of 17:08, 21 May 2021

FFT algorithm for  $N=8$

The graph shows the signal flow diagram of the Fast Fourier Transform  $\rm (FFT)$  for  $N = 8$.

The associated spectral coefficients  $D(0), \hspace{0.03cm}\text{...} \hspace{0.1cm} , D(7)$  are determined from the time coefficients  $d(0), \hspace{0.03cm}\text{...} \hspace{0.1cm}, d(7)$.  The following applies to these with  $0 ≤ μ ≤ 7$:

$$D(\mu) = \frac{1}{N}\cdot \sum_{\nu = 0 }^{N-1} d(\nu) \cdot {w}^{\hspace{0.03cm}\nu \hspace{0.05cm} \cdot \hspace{0.05cm}\mu}\hspace{0.05cm},$$

where the complex rotation factor  $w = \text{e}^{-\text{j}\hspace{0.05cm} \cdot \hspace{0.05cm}2\pi /N}$  is to be used, i.e.  $w = \text{e}^{-\text{j}\hspace{0.05cm} \cdot \hspace{0.05cm}\pi /4}$  für  $N = 8$.

  • The alternating $±1$ sequence  $\langle\hspace{0.05cm} d(ν)\hspace{0.05cm}\rangle$  is applied to the input.
  • After the bit reversal operation, this results in the sequence  $\langle \hspace{0.05cm}b(\kappa)\hspace{0.05cm}\rangle$.


It holds that  $b(κ) = d(ν)$, if  $ν$  is represented as a dual number and the resulting three bits are written as  $κ$  in reverse order.  For example

  •   $ν = 1$  $($binary  $001)$  is followed by  $κ = 4$  $($binary  $100)$,
  •   $d(2)$  remains at the same position  $2$  $($binary  $010)$.


The actual FFT algorithm happens for the example  $N = 8$  in  $\log_2 N = 3$  stages, denoted  $L = 1$,  $L =2$  and  $L = 3$.  Further:

  • In each stage, four basic operations - so-called  butterflies  - are to be performed.
  • The values at the output of the first stage are designated in this task as  $X(0),\hspace{0.03cm}\text{...} \hspace{0.1cm} , X(7)$,
    those of the second as  $Y(0), \hspace{0.03cm}\text{...} \hspace{0.1cm} , Y(7)$.
  • After the third and last stage, all values must be divided by  $N$. 
    The final result  $D(0), \hspace{0.03cm}\text{...} \hspace{0.1cm} , D(7)$  is available here.




Hint:



Questions

1

Calculate the DFT coefficient  $D(\mu=3)$.

$D(\mu=3) \ = \ $

2

Calculate the DFT coefficient  $D(\mu=4)$.

$D(\mu=4) \ = \ $

3

Determine the initial values  $X(0)$, ... , $X(7)$  of the first stage.  Which of the following statements are true?

All  $X$ values with even indices are equal to  $2$.
All  $X$ values with odd indices are equal to  $0$.

4

Determine the initial values  $Y(0)$, ... , $Y(7)$  of the second stage.  Enter the values  $Y(0)$  and  $Y(4)$  as a check.

$Y(0) \ = \ $

$Y(4) \ = \ $

5

Calculate all  $N$  spectral values  $D(\mu)$, in particular

$D(\mu =3) \ = \ $

$D(\mu = 4) \ = \ $

6

What would be the spectral coefficients for  $d(ν = 4) = 1$  and  $d(ν \neq 4) = 0$ ?
Enter the values  $D(\mu =3)$  and  $D(\mu =4)$  as a check.

$D(\mu = 3) \ = \ $

$D(\mu = 4) \ = \ $


Solution

(1)  According to the general DFT equation given on the specification sheet, with  $w = \text{e}^{-\text{j}\hspace{0.05cm} \cdot \hspace{0.05cm}\pi /4}$  taking into account the alternating time coefficients:

$$8 \cdot D(3) = w^0 - w^3 + w^6- w^9+ w^{12}- w^{15}+ w^{18}- w^{21} = w^0 - w^3 + w^2- w^1+ w^{4}- w^{7}+ w^{6}- w^{5}\hspace{0.05cm}.$$
  • Here it is taken into account that due to the periodicity  $w_9 = w_1$,  $w_{12} = w_4$,  $w_{15} = w_7$,  $w_{18} = w_2$  und  $w_{21} = w_5$  ist.
  • After re-sorting, the same applies:
$$8 \cdot D(3) = (w^0 + w^4) - (w^1 + w^5)+ (w^2 + w^6) - (w^3 + w^7) = (1 + w + w^2+ w^3) \cdot (w^0 + w^4)\hspace{0.05cm}.$$
  • Thus, because  $w_0 = 1$  and  $w_4 = \text{e}^{-\text{j}\pi } = \hspace{0.08cm} - \hspace{-0.08cm}1$ , we obtain  $\underline {D(\mu=3) = 0}$.


(2)  In analogy to sub-taske  (1) , we now get:

$$ 8 \cdot D(4) = w^0 - w^4 + w^8- w^{12}+ w^{16}- w^{20}+ w^{24}- w^{28}= 4 \cdot (w^0 - w^4)= 8 \hspace{0.3cm} \Rightarrow \hspace{0.3cm}\hspace{0.15 cm}\underline{D(\mu=4) = 1}\hspace{0.05cm}.$$


Example for the FFT algorithm

(3)  Proposed solution 2 is correct:

  • The term  $w^0 = 1$  does not have to be taken into account.
  • All output values with odd indices are zero by subtracting two identical input values.
  • The first statement is not true:   It holds  $X(0) = X(2) = +2$  and  $X(4) = X(6) = - 2$.


(4)  The multiplication with  $w^{2} = -{\rm j}$  can be dispensed with, since in the signal flow diagram the corresponding input values are zero.

  • One thus obtains  $Y(0) \;\underline{= 4}$  and  $Y(4) \;\underline{= - \hspace{-0.03cm}4}$.
  • All other values are zero.


(5)  Because of  $Y(5) = Y(6) =Y(7) = 0$, the multiplications with  $w$,  $w^2$  and  $w^3$  do not matter in the third stage either.  All spectral coefficients  $D(\mu)$  therefore result in zero with the exception of

$$\hspace{0.15 cm}\underline{D(\mu=4)} = {1}/{N}\cdot \left[Y(0) - Y(4) \right ] \hspace{0.15 cm}\underline{= 1} \hspace{0.05cm},$$
$$\hspace{0.15 cm}\underline{D(\mu=3)} = D(\mu\ne 4) \hspace{0.15 cm}\underline{= 0} \hspace{0.05cm}.$$

This result agrees with the results from  (1)  und  (2).


(6)  Since both the time coefficients  $d(ν)$  and all spectral coefficients  $D(\mu)$  are purely real, there is no difference between the FFT and the IFFT.

  • This means at the same time:  The input and output values can be interchanged.
  • Subtask  (5)  gave the following result:
$$d({\rm even}\hspace{0.15cm}\nu) = +1, \hspace{0.2cm}d({\rm odd}\hspace{0.15cm}\nu)= -1$$
$$\Rightarrow \hspace{0.3cm}D(\mu = 4)= 1,\hspace{0.2cm}D(\mu \ne 4)= 0.$$
  • By swapping the input and output values, we arrive at problem  (6):
$$d(\nu = 4)= 1, \hspace{0.2cm}d(\nu \ne 4)= 0 \hspace{0.3cm} \Rightarrow \hspace{0.3cm}D({\rm gerades}\hspace{0.15cm}\mu) = +1, \hspace{0.2cm}D({\rm ungerades}\hspace{0.15cm}\mu)= -1 \hspace{0.05cm}.$$
  • In particular, this results in  $D(\mu=3) \; \underline{= -1}$  und  $D(\mu=4) \; \underline{= +1}$.