Aufgaben:Exercise 3.1: Causality Considerations: Difference between revisions
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{{quiz-Header|Buchseite= | {{quiz-Header|Buchseite=Linear_and_Time_Invariant_Systems/Conclusions_from_the_Allocation_Theorem | ||
}} | }} | ||
[[File:EN_LZI_A_3_1.png|right|frame| | [[File:EN_LZI_A_3_1.png|right|frame|Two two-port networks]] | ||
The graph shows above the two-port network with the transfer function | |||
:$$H_1(f) = \frac{{\rm j}\cdot f/f_{\rm G}}{1+{\rm j}\cdot f/f_{\rm G}} | :$$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} | :$$f_{\rm G} = \frac{R}{2 \pi \cdot L}\hspace{0.05cm}.$$ | ||
By cascading $n$ two-port networks $H_1(f)$ built in the same way, the following transfer function is obtained: | |||
:$$H_n(f) = \big [H_1(f)\big ]^n =\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^n}{\big [1+{\rm j}\cdot f/f_{\rm G}\big ]^n} | :$$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}.$$ | ||
* | *Here, a suitable resistor decoupling is presumed, but this is not important for solving this exercise. | ||
* | *The lower graph shows for example the realization of the transfer function $H_2(f)$. | ||
In | In this exercise, such a two-port network is considered with respect to its causality properties. | ||
For any causal system, the real and imaginary parts of the spectral function $H(f)$ satisfy the [[Linear_and_Time_Invariant_Systems/Conclusions_from_the_Allocation_Theorem#Hilbert transformation|Hilbert transformation]], which is expressed by the following abbreviation: | |||
:$${\rm Im} \left\{ H(f) \right \} \quad | :$${\rm Im} \left\{ H(f) \right \} \quad\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad{\rm Re} \left\{ H(f) \right \}\hspace{0.05cm}.$$ | ||
\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. | |||
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Please note: | |||
*The exercise belongs to the chapter [[Linear_and_Time_Invariant_Systems/Conclusions_from_the_Allocation_Theorem|Conclusions from the Allocation Theorem]]. | |||
*Reference is also made to the theory pages [[Linear_and_Time_Invariant_Systems/Conclusions_from_the_Allocation_Theorem#Hilbert_transform|Hilbert transformation]] and [[Linear_and_Time_Invariant_Systems/Inverse_Laplace_Transform#Partial fraction decomposition|Partial fraction decomposition]]. | |||
* | |||
* | |||
=== | ===Questions=== | ||
<quiz display=simple> | <quiz display=simple> | ||
{ | {How can $H_1(f)$ be characterized? | ||
|type="()"} | |type="()"} | ||
- $H_1(f)$ | - $H_1(f)$ describes a low-pass filter. | ||
+ $H_1(f)$ | + $H_1(f)$ describes a high-pass filter. | ||
{ | {Does $H_1(f)$ describe a causal network? | ||
|type="()"} | |type="()"} | ||
+ | + Yes. | ||
- | - No. | ||
{ | {Compute the transfer function $H_2(f)$. What is the complex value for $f = f_{\rm G}$? | ||
|type="{}"} | |type="{}"} | ||
${\rm Re}\big[H_2(f = f_{\rm G})\big] \ = \ $ { 0. } | ${\rm Re}\big[H_2(f = f_{\rm G})\big] \ = \ $ { 0. } | ||
| Line 63: | Line 55: | ||
{ | {Which of the following statements are true? | ||
|type="[]"} | |type="[]"} | ||
+ $H_2(f)$ | + $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$ . | ||
| Line 73: | Line 65: | ||
</quiz> | </quiz> | ||
=== | ===Solution=== | ||
{{ML-Kopf}} | {{ML-Kopf}} | ||
'''(1)''' | '''(1)''' <u>Proposed solution 2</u> 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$$ | :$$H_1(f = 0) = 0, \hspace{0.2cm}H_1(f \rightarrow \infty) = 1$$ | ||
* | *This is a high-pass filter. | ||
* | *For very low frequencies, the inductivity $L$ constitutes a short circuit. | ||
'''(2)''' | '''(2)''' <u>Yes</u> is correct: | ||
* | *Every real network is causal. The impulse response $h(t)$ is equal to the output signal $y(t)$ if at time $t= 0$ an extremely short impulse – a so-called Dirac delta impulse – is applied to the input. | ||
* | *Then, a signal cannot occur at the output already for times $t< 0$ for causality reasons: | ||
:$$y(t) = h(t) = 0 \hspace{0.2cm}{\rm{f\ddot{u}r}} \hspace{0.2cm} | :$$y(t) = h(t) = 0 \hspace{0.2cm}{\rm{f\ddot{u}r}} \hspace{0.2cm}t<0 \hspace{0.05cm}.$$ | ||
*Formally, this can be shown as follows: The high-pass transfer function $H_1(f)$ can be rearranged as follows: | |||
* | :$$H_1(f) = \frac{{\rm j}\cdot f/f_{\rm G}}{1+{\rm j}\cdot f/f_{\rm G}}= 1- \frac{1}{1+{\rm j}\cdot f/f_{\rm G}}\hspace{0.05cm}.$$ | ||
:$$H_1(f) = \frac{{\rm j}\cdot f/f_{\rm G}}{1+{\rm j}\cdot f/f_{\rm G}} | *The second transfer function describes the low-pass function equivalent to $H_1(f)$, which results in the exponential function in the time domain. | ||
*The "$1$" becomes a Dirac delta function. Considering $T = 2\pi \cdot f_{\rm G}$ the following thus holds for $t \ge 0$: | |||
* | |||
* | |||
:$$h_1(t) = \delta(t) - {1}/{T} \cdot {\rm e}^{-t/T} \hspace{0.05cm}.$$ | :$$h_1(t) = \delta(t) - {1}/{T} \cdot {\rm e}^{-t/T} \hspace{0.05cm}.$$ | ||
* | *In contrast, $h_1(t)= 0$ holds for $t< 0$, which would prove causality. | ||
'''(3)''' | '''(3)''' The series connection of two high-pass filters results in the following transfer function: | ||
:$$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} | :$$H_2(f) = \big [H_1(f)\big ]^2 =\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^2}{\big [1+{\rm j}\cdot f/f_{\rm G}\big ]^2}=\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^2 \cdot \big [(1-{\rm j}\cdot f/f_{\rm G})\big ]^2}{\big [(1+{\rm j}\cdot f/f_{\rm G}) \cdot (1-{\rm j}\cdot f/f_{\rm G})\big ]^2}= \frac{(f/f_{\rm G})^4 - (f/f_{\rm G})^2 +{\rm j}\cdot 2\cdot (f/f_{\rm G})^3)}{\big [1+(f/f_{\rm G})^2 \big ]^2}\hspace{0.05cm}.$$ | ||
\cdot (f/f_{\rm G})^3)} | |||
* | *With $f = f_{\rm G}$ from this it follows that: | ||
:$$H_2(f = f_{\rm G}) = \frac{1 - 1 +{\rm j}\cdot 2} | :$$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}.$$ | ||
'''(4)''' | '''(4)''' <u>The first two proposed solutions</u> are correct: | ||
* | *Since the impulse response is $h_1(t) = 0$ for $t < 0$, the convolution operation $h_2(t) = h_1(t) \star h_1(t)$ also satisfies the causality condition. | ||
*Similarly, the $n$–fold convolution yields a causal impulse response: $h_n(t) = 0 \hspace{0.2cm}{\rm{for}} \hspace{0.2cm} | |||
t<0 \hspace{0.05cm}.$ | t<0 \hspace{0.05cm}.$ | ||
* | *However, the real and imaginary parts of the spectral function $H_2(f)$ are related via the Hilbert transformation for a causal impulse response $h_2(t)$ . | ||
:$$\frac{x^4- x^2}{x^4+2 x^2+1} \quad | *Thus, considering the shortcut $x = f/f_{\rm G}$ and the result of the subtask '''(3)''' the following holds: | ||
\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad | :$$\frac{x^4- x^2}{x^4+2 x^2+1} \quad\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad\frac{2x^3}{x^4+2 x^2+1}\hspace{0.05cm}.$$ | ||
\frac{2x^3}{x^4+2 x^2+1}\hspace{0.05cm}.$$ | |||
{{ML-Fuß}} | {{ML-Fuß}} | ||
[[Category:Linear and Time-Invariant Systems: Exercises|^3.1 | [[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

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:
- The exercise belongs to the chapter Conclusions from the Allocation Theorem.
- Reference is also made to the theory pages Hilbert transformation and Partial fraction decomposition.
Questions
Solution
- 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:
- $$\frac{x^4- x^2}{x^4+2 x^2+1} \quad\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad\frac{2x^3}{x^4+2 x^2+1}\hspace{0.05cm}.$$