Aufgaben:Exercise 3.1: Causality Considerations: Difference between revisions

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{{quiz-Header|Buchseite=Linear_and_Time_Invariant_Systems/Conclusions_from_the_Allocation_Theorem
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[[File:P_ID1755__LZI_A_3_1.png|right|]]
[[File:EN_LZI_A_3_1.png|right|frame|Two two-port networks]]
:Die Grafik zeigt oben den Vierpol mit der Übertragungsfunktion
The graph shows above the two-port network with the transfer function
:$$H_1(f) = \frac{{\rm j}\cdot f/f_{\rm G}}{1+{\rm j}\cdot f/f_{\rm G}}
:$$H_1(f) = \frac{{\rm j}\cdot f/f_{\rm G}}{1+{\rm j}\cdot f/f_{\rm G}}\hspace{0.05cm},$$
\hspace{0.05cm},$$


:wobei <i>f</i><sub>G</sub> die 3dB&ndash;Grenzfrequenz angibt:
where&nbsp; $f_{\rm G}$&nbsp; represents the&nbsp; $\rm 3dB$&nbsp; cut-off frequency:
:$$f_{\rm G} = \frac{R}{2 \pi \cdot L}
:$$f_{\rm G} = \frac{R}{2 \pi \cdot L}\hspace{0.05cm}.$$
\hspace{0.05cm}.$$


:Durch Hintereinanderschalten <i>n</i> gleich aufgebauter Vierpole <i>H</i><sub>1</sub>(<i>f</i>) kommt man zu der Übertragungsfunktion
By cascading &nbsp;$n$&nbsp; two-port networks &nbsp;$H_1(f)$&nbsp; built in the same way, the following transfer function is obtained:
:$$H_n(f) = \left [H_1(f)\right ]^n =\frac{\left [{\rm j}\cdot f/f_{\rm G}\right ]^n}{\left [1+{\rm j}\cdot f/f_{\rm G}\right ]^n}
:$$H_n(f) = \big [H_1(f)\big ]^n =\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^n}{\big [1+{\rm j}\cdot f/f_{\rm G}\big ]^n}\hspace{0.05cm}.$$
\hspace{0.05cm}.$$


:Vorausgesetzt ist hierbei eine geeignete Widerstandsentkopplung, die aber zur Lösung dieser Aufgabe nicht von Bedeutung ist. Die untere Grafik zeigt zum Beispiel die Realisierung der Übertragungsfunktion <i>H</i><sub>2</sub>(<i>f</i>).
*Here, a suitable resistor decoupling is presumed,&nbsp; but this is not important for solving this exercise.  
*The lower graph shows for example the realization of the transfer function &nbsp;$H_2(f)$.


:In dieser Aufgabe wird ein solcher Vierpol im Hinblick auf seine Kausalitätseigenschaften betrachtet. Bei einem jeden kausalen System erfüllen der Real&ndash; und der Imaginärteil der Spektralfunktion <i>H</i>(<i>f</i>) die Hilbert&ndash;Transformation, was durch das folgende Kurzzeichen ausgedrückt wird:
:$${\rm Im} \left\{ H(f) \right \}  \quad
\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad
{\rm Re} \left\{ H(f) \right \}\hspace{0.05cm}.$$


:Da die Hilbert&ndash;Transformation nicht nur für Übertragungsfunktionen, sondern auch für Signale wichtige Aussagen liefert, wird die Korrespondenz häufig durch die allgemeine Variable <i>x</i> ausgedrückt, die je nach Anwendungsfall als normierte Frequenz oder als normierte Zeit zu interpretieren ist.
In this exercise,&nbsp; such a two-port network is considered with respect to its causality properties.  


:<b>Hinweis:</b> Die Aufgabe bezieht sich auf das Kapitel 3.1.
For any causal system,&nbsp; the real and imaginary parts of the spectral function &nbsp;$H(f)$&nbsp; satisfy the&nbsp; [[Linear_and_Time_Invariant_Systems/Conclusions_from_the_Allocation_Theorem#Hilbert transformation|Hilbert transformation]],&nbsp; which is expressed by the following abbreviation:
:$${\rm Im} \left\{ H(f) \right \}  \quad\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad{\rm Re} \left\{ H(f) \right \}\hspace{0.05cm}.$$


Since the Hilbert transformation provides important information not only for transfer functions but also for time signals,&nbsp; the correspondence is often expressed by the general variable &nbsp;$x$,&nbsp; which is to be interpreted - depending on the application - as normalized frequency or as normalized time.


===Fragebogen===
 
 
 
 
Please note:
*The exercise belongs to the chapter&nbsp;  [[Linear_and_Time_Invariant_Systems/Conclusions_from_the_Allocation_Theorem|Conclusions from the Allocation Theorem]].
*Reference is also made to the theory pages &nbsp;[[Linear_and_Time_Invariant_Systems/Conclusions_from_the_Allocation_Theorem#Hilbert_transform|Hilbert transformation]]&nbsp; and &nbsp;[[Linear_and_Time_Invariant_Systems/Inverse_Laplace_Transform#Partial fraction decomposition|Partial fraction decomposition]].
 
 
===Questions===


<quiz display=simple>
<quiz display=simple>
{Wie kann <i>H</i><sub>1</sub>(<i>f</i>) charakterisiert werden?
{How can &nbsp;$H_1(f)$&nbsp; be characterized?
|type="[]"}
|type="()"}
- <i>H</i><sub>1</sub>(<i>f</i>) beschreibt einen Tiefpass.
- $H_1(f)$&nbsp; describes a low-pass filter.
+ <i>H</i><sub>1</sub>(<i>f</i>) beschreibt einen Hochpass.
+ $H_1(f)$&nbsp; describes a high-pass filter.




{Beschreibt <i>H</i><sub>1</sub>(<i>f</i>) ein kausales Netzwerk?
{Does &nbsp;$H_1(f)$&nbsp; describe a causal network?
|type="[]"}
|type="()"}
+ Ja.
+ Yes.
- Nein.
- No.




{Berechnen Sie die Übertragungsfunktion <i>H</i><sub>2</sub>(<i>f</i>). Welcher komplexe Wert ergibt sich für <i>f</i> = <i>f</i><sub>G</sub>?
{Compute the transfer function &nbsp;$H_2(f)$. &nbsp; What is the complex value for &nbsp;$f = f_{\rm G}$?
|type="{}"}
|type="{}"}
$Re{H_2(f = f_G)}$ = { 0 3% }
${\rm Re}\big[H_2(f = f_{\rm G})\big] \ = \ $ { 0. }
$Im{H_2(f = f_G)}$ = { 0.5 3% }
${\rm Im}\big[H_2(f = f_{\rm G})\big] \ = \ $ { 0.5 3% }




{Welche der nachfolgenden Aussagen treffen zu?
{Which of the following statements are true?
|type="[]"}
|type="[]"}
+ <i>H</i><sub>2</sub>(<i>f</i>) beschreibt ein kausales System.
+ $H_2(f)$&nbsp; describes a causal system.
+ (<i>x</i><sup>4</sup>&ndash;<i>x</i><sup>2</sup>)/(<i>x</i><sup>4</sup>+2<i>x</i><sup>2</sup>+1) und 2<i>x</i><sup>3</sup>/(<i>x</i><sup>4</sup>+2<i>x</i><sup>2</sup>+1) sind ein Hilbert&ndash;Paar.
+ The expressions&nbsp; $(x^4 - x^2)/(x^4 +2 x^2 + 1)$&nbsp; and &nbsp;$2x^3/(x^4 +2 x^2 + 1)$&nbsp; are a Hilbert pair.
- Für <i>n</i> > 2 ist die Kausalitätsbedingung nicht erfüllt.
- The causality condition is not satisfied for &nbsp;$n > 2$&nbsp;.




Line 59: Line 65:
</quiz>
</quiz>


===Musterlösung===
===Solution===
{{ML-Kopf}}
{{ML-Kopf}}
:<b>1.</b>&nbsp;&nbsp;Mit der angegebenen Übertragungsfunktion kann man nach dem Spannungsteilerprinzip
'''(1)'''&nbsp; <u>Proposed solution 2</u>&nbsp; is correct:
*The given transfer function can be computed according to the voltage divider principle. &nbsp; The following holds:
:$$H_1(f = 0) = 0, \hspace{0.2cm}H_1(f \rightarrow \infty) = 1$$
:$$H_1(f = 0) = 0, \hspace{0.2cm}H_1(f \rightarrow \infty) = 1$$
*This is a high-pass filter.
*For very low frequencies, the inductivity&nbsp; $L$&nbsp; constitutes a short circuit.


:berechnen &#8658; Es handelt sich um einen <u>Hochpass</u>. Für sehr niedrige Frequenzen stellt die Induktivität <i>L</i> einen Kurzschluss dar.


:<b>2.</b>&nbsp;&nbsp;Jedes reale Netzwerk ist kausal. Die Impulsantwort <i>h</i>(<i>t</i>) ist gleich dem Ausgangssignal <i>y</i>(<i>t</i>), wenn zum Zeitpunkt <i>t</i> = 0 am Eingang ein extrem kurzfristiger Impuls &ndash; ein sog. Diracimpuls &ndash; angelegt wird. Aus Kausalitätsgründen kann dann natürlich am Ausgang nicht schon für Zeiten <i>t</i> < 0 ein Signal auftreten:
'''(2)'''&nbsp; <u>Yes</u>&nbsp; is correct:
:$$y(t) = h(t) = 0 \hspace{0.2cm}{\rm{f\ddot{u}r}} \hspace{0.2cm}
*Every real network is causal.&nbsp; The impulse response&nbsp; $h(t)$&nbsp; is equal to the output signal&nbsp; $y(t)$&nbsp; if at time&nbsp; $t= 0$&nbsp; an extremely short impulse &ndash; a so-called Dirac delta impulse &ndash; is applied to the input.  
t<0 \hspace{0.05cm}.$$
*Then,&nbsp; a signal cannot occur at the output already for times&nbsp; $t< 0$&nbsp; for causality reasons:
:$$y(t) = h(t) = 0 \hspace{0.2cm}{\rm{f\ddot{u}r}} \hspace{0.2cm}t<0 \hspace{0.05cm}.$$
*Formally, this can be shown as follows: &nbsp; The high-pass transfer function&nbsp; $H_1(f)$&nbsp; can be rearranged as follows:
:$$H_1(f) = \frac{{\rm j}\cdot f/f_{\rm G}}{1+{\rm j}\cdot f/f_{\rm G}}= 1- \frac{1}{1+{\rm j}\cdot f/f_{\rm G}}\hspace{0.05cm}.$$
*The second transfer function describes the low-pass function equivalent to&nbsp; $H_1(f)$,&nbsp; which results in the exponential function in the time domain.
*The&nbsp; "$1$"&nbsp; becomes a Dirac delta function.&nbsp; Considering&nbsp; $T = 2\pi \cdot f_{\rm G}$&nbsp; the following thus holds for&nbsp; $t \ge 0$:
:$$h_1(t) = \delta(t) - {1}/{T} \cdot {\rm e}^{-t/T} \hspace{0.05cm}.$$
*In contrast,&nbsp; $h_1(t)= 0$ holds for&nbsp; $t< 0$,&nbsp; which would prove causality.


:Formal lässt sich dies folgendermaßen zeigen: Die Hochpass&ndash;Übertragungsfunktion <i>H</i><sub>1</sub>(<i>f</i>) kann wie folgt umgeformt werden:
:$$H_1(f) = \frac{{\rm j}\cdot f/f_{\rm G}}{1+{\rm j}\cdot f/f_{\rm G}}
= 1- \frac{1}{1+{\rm j}\cdot f/f_{\rm G}}
\hspace{0.05cm}.$$


:Die zweite Übertragungsfunktion beschreibt die zu <i>H</i><sub>1</sub>(<i>f</i>) äquivalente Tiefpassfunktion, die im Zeitbereich zur Exponentialfunktion führt. Die &bdquo;1&rdquo; wird zu einer Diracfunktion. Mit <i>T</i> = 2&pi; &middot; <i>f</i><sub>G</sub> gilt somit für <i>t</i> &#8805; 0:
:$$h_1(t) = \delta(t) - \frac{1}{T} \cdot {\rm e}^{-t/T} \hspace{0.05cm}.$$


:Für <i>t</i> < 0 gilt dagegen <i>h</i><sub>1</sub>(<i>t</i>) = 0, womit die Kausalität nachgewiesen wäre &nbsp;&nbsp;&#8658;&nbsp;&nbsp; <u>Antwort Ja</u>.
'''(3)'''&nbsp; The series connection of two high-pass filters results in the following transfer function:
:$$H_2(f) = \big [H_1(f)\big ]^2  =\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^2}{\big [1+{\rm j}\cdot f/f_{\rm G}\big ]^2}=\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^2 \cdot \big [(1-{\rm j}\cdot f/f_{\rm G})\big ]^2}{\big [(1+{\rm j}\cdot f/f_{\rm G}) \cdot (1-{\rm j}\cdot f/f_{\rm G})\big ]^2}= \frac{(f/f_{\rm G})^4 - (f/f_{\rm G})^2 +{\rm j}\cdot 2\cdot  (f/f_{\rm G})^3)}{\big [1+(f/f_{\rm G})^2 \big ]^2}\hspace{0.05cm}.$$


:<b>3.</b>&nbsp;&nbsp;Die Hintereinanderschaltung zweier Hochpässe führt zu folgender Übertragungsfunktion:
*With&nbsp; $f = f_{\rm G}$&nbsp; from this it follows that:
:$$H_2(f) = \left [H_1(f)\right ]^2 =\frac{\left [{\rm j}\cdot f/f_{\rm G}\right ]^2}{\left [1+{\rm j}\cdot f/f_{\rm G}\right ]^2}
:$$H_2(f = f_{\rm G}) = \frac{1 - 1 +{\rm j}\cdot 2}{4}= {\rm j} /{2} \hspace{0.5cm}\Rightarrow \hspace{0.5cm}{\rm Re} \left\{ H_2(f = f_{\rm G}) \right \}  \hspace{0.15cm}\underline{ = 0}, \hspace{0.4cm}{\rm Im} \left\{ H_2(f = f_{\rm G}) \right \} \hspace{0.15cm}\underline{ = 0.5}\hspace{0.05cm}.$$
=\frac{\left [{\rm j}\cdot f/f_{\rm G}\right ]^2 \cdot \left [(1-{\rm j}\cdot f/f_{\rm G})\right ]^2}
  {\left [(1+{\rm j}\cdot f/f_{\rm G}) \cdot (1-{\rm j}\cdot f/f_{\rm G})\right ]^2}= \\  = \frac{(f/f_{\rm G})^4 - (f/f_{\rm G})^2 +{\rm j}\cdot 2
\cdot  (f/f_{\rm G})^3)}
{\left [1+(f/f_{\rm G})^2 \right ]^2}\hspace{0.05cm}.$$


:Mit <i>f</i> = <i>f</i><sub>G</sub> folgt daraus:
:$$H_2(f = f_{\rm G})  = \frac{1 - 1 +{\rm j}\cdot 2}
{4}= \frac{\rm j} {2}$$
:$$\Rightarrow \hspace{0.5cm}{\rm Re} \left\{ H_2(f = f_{\rm G}) \right \}  = 0, \hspace{0.4cm}
{\rm Im} \left\{ H_2(f = f_{\rm G}) \right \} \hspace{0.15cm}\underline{ = 0.5}\hspace{0.05cm}.$$


:<b>4.</b>&nbsp;&nbsp;Richtig sind hier <u>die beiden ersten Lösungsvorschläge</u>. Da <i>h</i><sub>1</sub>(<i>t</i>) = 0 für <i>t</i> < 0 ist, erfüllt auch die Faltungsoperation <i>h</i><sub>2</sub>(<i>t</i>) = <i>h</i><sub>1</sub>(<i>t</i>) &#8727; <i>h</i><sub>1</sub>(<i>t</i>) die Kausalitätsbedingung. Ebenso ergibt die <i>n</i>&ndash;fache Faltung eine kausale Impulsantwort:
:$$h_n(t) = 0 \hspace{0.2cm}{\rm{f\ddot{u}r}} \hspace{0.2cm}
t<0 \hspace{0.05cm}.$$


:Bei kausaler Impulsantwort <i>h</i><sub>2</sub>(<i>t</i>) hängen aber der Real&ndash; und der Imaginärteil der Spektralfunktion <i>H</i><sub>2</sub>(<i>f</i>) über die Hilbert&ndash;Transformation zusammen. Mit der Abkürzung <i>x</i> = <i>f</i>/<i>f</i><sub>G</sub> und dem Ergebnis aus der Teilaufgabe 3) gilt somit:
'''(4)'''&nbsp; <u>The first two proposed solutions</u>&nbsp; are correct:
:$$\frac{x^4- x^2}{x^4+2 x^2+1} \quad
*Since the impulse response is &nbsp;$h_1(t) = 0$&nbsp; for &nbsp;$t < 0$,&nbsp; the convolution operation &nbsp;$h_2(t) = h_1(t) \star h_1(t)$&nbsp; also satisfies the causality condition.&nbsp;
\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad
*Similarly, the&nbsp; $n$&ndash;fold convolution yields a causal impulse response: &nbsp;  $h_n(t) = 0 \hspace{0.2cm}{\rm{for}} \hspace{0.2cm}
\frac{2x^3}{x^4+2 x^2+1}\hspace{0.05cm}.$$
t<0 \hspace{0.05cm}.$
*However,&nbsp; the real and imaginary parts of the spectral function &nbsp;$H_2(f)$&nbsp; are related via the Hilbert transformation for a causal impulse response &nbsp;$h_2(t)$&nbsp;.&nbsp;
*Thus,&nbsp; considering the shortcut &nbsp;$x = f/f_{\rm G}$&nbsp; and the result of the subtask&nbsp; '''(3)'''&nbsp; the following holds:
:$$\frac{x^4- x^2}{x^4+2 x^2+1} \quad\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad\frac{2x^3}{x^4+2 x^2+1}\hspace{0.05cm}.$$
{{ML-Fuß}}
{{ML-Fuß}}






[[Category:Aufgaben zu Lineare zeitinvariante Systeme|^3.1 Folgerungen aus dem Zuordnungssatz^]]
[[Category:Linear and Time-Invariant Systems: Exercises|^3.1 Conclusions from the Assignment Theorem^]]
[[de:Aufgaben:Aufgabe 3.1: Kausalitätsbetrachtungen]]

Latest revision as of 17:55, 16 March 2026

Two two-port networks

The graph shows above the two-port network with the transfer function

$$H_1(f) = \frac{{\rm j}\cdot f/f_{\rm G}}{1+{\rm j}\cdot f/f_{\rm G}}\hspace{0.05cm},$$

where  $f_{\rm G}$  represents the  $\rm 3dB$  cut-off frequency:

$$f_{\rm G} = \frac{R}{2 \pi \cdot L}\hspace{0.05cm}.$$

By cascading  $n$  two-port networks  $H_1(f)$  built in the same way, the following transfer function is obtained:

$$H_n(f) = \big [H_1(f)\big ]^n =\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^n}{\big [1+{\rm j}\cdot f/f_{\rm G}\big ]^n}\hspace{0.05cm}.$$
  • Here, a suitable resistor decoupling is presumed,  but this is not important for solving this exercise.
  • The lower graph shows for example the realization of the transfer function  $H_2(f)$.


In this exercise,  such a two-port network is considered with respect to its causality properties.

For any causal system,  the real and imaginary parts of the spectral function  $H(f)$  satisfy the  Hilbert transformation,  which is expressed by the following abbreviation:

$${\rm Im} \left\{ H(f) \right \} \quad\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad{\rm Re} \left\{ H(f) \right \}\hspace{0.05cm}.$$

Since the Hilbert transformation provides important information not only for transfer functions but also for time signals,  the correspondence is often expressed by the general variable  $x$,  which is to be interpreted - depending on the application - as normalized frequency or as normalized time.



Please note:


Questions

1 How can  $H_1(f)$  be characterized?

$H_1(f)$  describes a low-pass filter.
$H_1(f)$  describes a high-pass filter.

2 Does  $H_1(f)$  describe a causal network?

Yes.
No.

3 Compute the transfer function  $H_2(f)$.   What is the complex value for  $f = f_{\rm G}$?

${\rm Re}\big[H_2(f = f_{\rm G})\big] \ = \ $
${\rm Im}\big[H_2(f = f_{\rm G})\big] \ = \ $

4 Which of the following statements are true?

$H_2(f)$  describes a causal system.
The expressions  $(x^4 - x^2)/(x^4 +2 x^2 + 1)$  and  $2x^3/(x^4 +2 x^2 + 1)$  are a Hilbert pair.
The causality condition is not satisfied for  $n > 2$ .


Solution

(1)  Proposed solution 2  is correct:

  • The given transfer function can be computed according to the voltage divider principle.   The following holds:
$$H_1(f = 0) = 0, \hspace{0.2cm}H_1(f \rightarrow \infty) = 1$$
  • This is a high-pass filter.
  • For very low frequencies, the inductivity  $L$  constitutes a short circuit.


(2)  Yes  is correct:

  • Every real network is causal.  The impulse response  $h(t)$  is equal to the output signal  $y(t)$  if at time  $t= 0$  an extremely short impulse – a so-called Dirac delta impulse – is applied to the input.
  • Then,  a signal cannot occur at the output already for times  $t< 0$  for causality reasons:
$$y(t) = h(t) = 0 \hspace{0.2cm}{\rm{f\ddot{u}r}} \hspace{0.2cm}t<0 \hspace{0.05cm}.$$
  • Formally, this can be shown as follows:   The high-pass transfer function  $H_1(f)$  can be rearranged as follows:
$$H_1(f) = \frac{{\rm j}\cdot f/f_{\rm G}}{1+{\rm j}\cdot f/f_{\rm G}}= 1- \frac{1}{1+{\rm j}\cdot f/f_{\rm G}}\hspace{0.05cm}.$$
  • The second transfer function describes the low-pass function equivalent to  $H_1(f)$,  which results in the exponential function in the time domain.
  • The  "$1$"  becomes a Dirac delta function.  Considering  $T = 2\pi \cdot f_{\rm G}$  the following thus holds for  $t \ge 0$:
$$h_1(t) = \delta(t) - {1}/{T} \cdot {\rm e}^{-t/T} \hspace{0.05cm}.$$
  • In contrast,  $h_1(t)= 0$ holds for  $t< 0$,  which would prove causality.


(3)  The series connection of two high-pass filters results in the following transfer function:

$$H_2(f) = \big [H_1(f)\big ]^2 =\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^2}{\big [1+{\rm j}\cdot f/f_{\rm G}\big ]^2}=\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^2 \cdot \big [(1-{\rm j}\cdot f/f_{\rm G})\big ]^2}{\big [(1+{\rm j}\cdot f/f_{\rm G}) \cdot (1-{\rm j}\cdot f/f_{\rm G})\big ]^2}= \frac{(f/f_{\rm G})^4 - (f/f_{\rm G})^2 +{\rm j}\cdot 2\cdot (f/f_{\rm G})^3)}{\big [1+(f/f_{\rm G})^2 \big ]^2}\hspace{0.05cm}.$$
  • With  $f = f_{\rm G}$  from this it follows that:
$$H_2(f = f_{\rm G}) = \frac{1 - 1 +{\rm j}\cdot 2}{4}= {\rm j} /{2} \hspace{0.5cm}\Rightarrow \hspace{0.5cm}{\rm Re} \left\{ H_2(f = f_{\rm G}) \right \} \hspace{0.15cm}\underline{ = 0}, \hspace{0.4cm}{\rm Im} \left\{ H_2(f = f_{\rm G}) \right \} \hspace{0.15cm}\underline{ = 0.5}\hspace{0.05cm}.$$


(4)  The first two proposed solutions  are correct:

  • Since the impulse response is  $h_1(t) = 0$  for  $t < 0$,  the convolution operation  $h_2(t) = h_1(t) \star h_1(t)$  also satisfies the causality condition. 
  • Similarly, the  $n$–fold convolution yields a causal impulse response:   $h_n(t) = 0 \hspace{0.2cm}{\rm{for}} \hspace{0.2cm}
t<0 \hspace{0.05cm}.$
  • However,  the real and imaginary parts of the spectral function  $H_2(f)$  are related via the Hilbert transformation for a causal impulse response  $h_2(t)$ . 
  • Thus,  considering the shortcut  $x = f/f_{\rm G}$  and the result of the subtask  (3)  the following holds:
$$\frac{x^4- x^2}{x^4+2 x^2+1} \quad\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad\frac{2x^3}{x^4+2 x^2+1}\hspace{0.05cm}.$$