Difference between revisions of "Aufgaben:Exercise 5.1Z: Sampling of Harmonic Oscillations"

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{{quiz-Header|Buchseite=Signaldarstellung/Zeitdiskrete Signaldarstellung
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{{quiz-Header|Buchseite=Signal_Representation/Time_Discrete_Signal_Representation
 
}}
 
}}
  
[[File:P_ID1129__Sig_Z_5_1.png|right|Zeitdiskrete Harmonische]]
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[[File:P_ID1129__Sig_Z_5_1.png|right|frame|Three harmonic oscillations of equal frequency  $f_0$  and equal amplitude  $A$]]
Wir betrachten drei harmonische Schwingungen mit gleicher Frequenz und gleicher Amplitude:
+
We consider three harmonic oscillations with the same frequency and the same amplitude:
 
:$$x_1(t)  =  A \cdot \cos (2 \pi \cdot f_0 \cdot t) \hspace{0.05cm}, $$
 
:$$x_1(t)  =  A \cdot \cos (2 \pi \cdot f_0 \cdot t) \hspace{0.05cm}, $$
 
:$$ x_2(t)  =  A \cdot \sin (2 \pi \cdot f_0 \cdot t) \hspace{0.05cm}, $$
 
:$$ x_2(t)  =  A \cdot \sin (2 \pi \cdot f_0 \cdot t) \hspace{0.05cm}, $$
 
:$$ x_3(t)  =  A \cdot \cos (2 \pi \cdot f_0 \cdot t - 60^{\circ}) \hspace{0.05cm}.$$
 
:$$ x_3(t)  =  A \cdot \cos (2 \pi \cdot f_0 \cdot t - 60^{\circ}) \hspace{0.05cm}.$$
Die Schwingungsparameter $f_0$ und $A$ können Sie der Grafik entnehnen.
+
The oscillation parameters  $f_0$  and  $A$  can be taken from the graph.
  
Angenommen wird, dass die Signale äquidistant zu den Zeitpunkten $\nu \cdot T_{\rm A}$ abgetastet werden, wobei die Parameterwerte $T_{\rm A} = 80 \ \mu \text{s}$ und $T_{\rm A} = 100 \ \mu \text{s}$ analysiert werden sollen.
+
It is assumed that the signals are sampled equidistantly at times  $\nu \cdot T_{\rm A}$  whereby the parameter values  $T_{\rm A} = 80 \ µ \text{s}$  and  $T_{\rm A} = 100 \ µ \text{s}$  are to be analyzed.
  
Die Signalrekonstruktion beim Empfänger erfolgt durch einen Tiefpass $H(f)$, der aus dem abgetasteten Signal $y_{\rm A}(t) = x_{\rm A}(t)$ das Signal $y(t)$ formt. Es gelte:
+
The signal reconstruction at the receiver takes place through a low-pass filter  $H(f)$,  which forms the signal  $y_{\rm A}(t) = x_{\rm A}(t)$  from the sampled signal  $y(t)$ . It applies:
 
:$$H(f)  = \left\{ \begin{array}{c} 1  \\ 0.5 \\
 
:$$H(f)  = \left\{ \begin{array}{c} 1  \\ 0.5 \\
 
  0  \\  \end{array} \right.\quad
 
  0  \\  \end{array} \right.\quad
Line 22: Line 22:
 
|f| > f_{\rm G}  \hspace{0.05cm}, \\
 
|f| > f_{\rm G}  \hspace{0.05cm}, \\
 
\end{array}$$
 
\end{array}$$
Hierbei gibt $f_{\rm G}$ die Grenzfrequenz des Tiefpassfilters an. Für diese soll gelten:
+
Here  $f_{\rm G}$  indicates the cut-off frequency of the rectangular low-pass filter.  For this shall apply:
 
:$$f_{\rm G} = \frac{1}{ 2 \cdot T_{\rm  A}}\hspace{0.05cm}.$$
 
:$$f_{\rm G} = \frac{1}{ 2 \cdot T_{\rm  A}}\hspace{0.05cm}.$$
Das Abtasttheorem ist erfüllt, wenn $y(t) = x(t)$ gilt.
+
The sampling theorem is fulfilled if  $y(t) = x(t)$  holds.
  
''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]]
 
  
  
===Fragebogen===
+
 
 +
 
 +
 
 +
''Hints:''
 +
*This task belongs to the chapter  [[Signal_Representation/Time_Discrete_Signal_Representation|Discrete-Time Signal Representation]].
 +
 +
*There is an interactive applet for the topic dealt with here:  [[Applets:Sampling_of_Analog_Signals_and_Signal_Reconstruction|Sampling of Analog Signals and Signal Reconstruction]]
 +
 
 +
 
 +
 
 +
===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Wie groß sind Amplitude und Frequenz der Signale $x_1(t)$, $x_2(t)$ und $x_3(t)$?
+
{What are the amplitude and frequency of the signals&nbsp; $x_1(t)$,&nbsp; $x_2(t)$&nbsp; and&nbsp; $x_3(t)$?
 
|type="{}"}
 
|type="{}"}
$A$ &nbsp;= { 2 3% } &nbsp;$\text{V}$
+
$A \hspace{0.25cm} = \ $ { 2 3% } &nbsp;$\text{V}$
$f_0$ &nbsp;= { 5 3% } &nbsp;$\text{kHz}$
+
$f_0\hspace{0.2cm} = \ $ { 5 3% } &nbsp;$\text{kHz}$
  
  
{Bei welchen Eingangssignalen ist das Abtasttheorem erfüllt &nbsp; &rArr;  &nbsp;  $y(t) = x(t)$, wenn $T_{\rm A} = 80 \ \mu \text{s}$ beträgt?
+
{For which input signals is the sampling theorem satisfied &nbsp; &rArr;  &nbsp;  $y(t) = x(t)$, when&nbsp; $\underline{T_{\rm A} = 80 \ {\rm &micro;} \text{s}}$&nbsp;?
 
|type="[]"}
 
|type="[]"}
 
+ $x_1(t)$,
 
+ $x_1(t)$,
Line 49: Line 54:
  
  
{Wie lautet das rekonstruierte Signal $y_1(t) = A_1 \cdot \cos (2\pi f_0 t – \varphi_1)$ mit dem Abtastabstand $T_{\rm A} = 100 \ \mu \text{s}$? Interpretieren Sie das Ergebnis.
+
{What is the reconstructed signal&nbsp; $y_1(t) = A_1 \cdot \cos (2\pi f_0 t – \varphi_1)$&nbsp; with the sampling distance&nbsp; $\underline{T_{\rm A} = 100 \ {\rm &micro;} \text{s}}$? Interpret the result.
 
|type="{}"}
 
|type="{}"}
$A_1$ &nbsp;= { 2 3% } &nbsp;$\text{V}$
+
$A_1\hspace{0.2cm} = \ ${ 2 3% } &nbsp;$\text{V}$
$\varphi_1$ &nbsp;= { 0. } &nbsp;$\text{Grad}$
+
$\varphi_1\hspace{0.2cm} = \ $ { 0. } &nbsp;$\text{Deg}$
  
  
{Welche Amplitude $A_2$ besitzt das rekonstruierte Signal $y_2(t)$, wenn das Sinussignal $x_2(t)$ anliegt? Es gelte weiterhin $T_{\rm A} = 100 \ \mu \text{s}$.
+
{What is the amplitude&nbsp; $A_2$&nbsp; of the reconstructed signal&nbsp; $y_2(t)$, when the sinusoidal signal&nbsp; $x_2(t)$&nbsp; is present?&nbsp; Let&nbsp; $\underline{T_{\rm A} = 100 \ {\rm &micro;} \text{s}}$ still apply.
 
|type="{}"}
 
|type="{}"}
$A_2$ &nbsp; = { 0. } &nbsp;$\text{V}$
+
$A_2\hspace{0.2cm} = \ $ { 0. } &nbsp;$\text{V}$
  
  
{Welche Amplitude $A_3$ besitzt das rekonstruierte Signal $y_3(t)$, wenn das Sinussignal $x_3(t)$ anliegt? Es gelte weiterhin $T_{\rm A} = 100 \ \mu \text{s}$.
+
{What is the amplitude&nbsp; $A_3$&nbsp; of the reconstructed signal&nbsp; $y_3(t)$, when the signal&nbsp; $x_3(t)$&nbsp; is present? &nbsp; $\underline{T_{\rm A} = 100 \ {\rm &micro;} \text{s}}$ is still valid.
 
|type="{}"}
 
|type="{}"}
$A_3$ &nbsp;= { 1 3% } &nbsp;$\text{V}$
+
$A_3\hspace{0.2cm} = \ $ { 1 3% } &nbsp;$\text{V}$
  
  
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</quiz>
 
</quiz>
  
===Musterlösung===
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===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''1.''' Aus der Grafik erkennt man die Amplitude $\underline{A = 2\ \text{V}}$ sowie die Periodendauer $T_0 = 0.2 \ \text{ms}$. Daraus ergibt sich die Signalfrequenz $f_0 = 1/T_0 \; \underline{= 5 \ \text{kHz}}$.
+
'''(1)'''&nbsp;  The graph shows the amplitude&nbsp; $\underline{A = 2\ \text{V}}$&nbsp; and the period&nbsp; $T_0 = 0.2 \ \text{ms}$.  
 +
*This results in the signal frequency&nbsp; $f_0 = 1/T_0 \; \underline{= 5 \ \text{kHz}}$.
  
'''2.'''  Die Abtastrate ist hier $f_{\rm A} = 1/T_{\rm A} = 12.5 \ \text{kHz}$. Dieser Wert ist größer als $2 \cdot f_0 = 10 \ \text{kHz}$. Damit ist das Abtasttheorem unabhängig von der Phase erfüllt, und es gilt stets $y(t) = x(t)$  &nbsp; &rArr; &nbsp;  Richtig sind somit <u>alle Löungsvorschläge</u>.
 
  
'''3.''' Die Abtastrate beträgt nun $f_{\rm A} = 2 \cdot f_0 = 10 \ \text{kHz}$. Nur im Sonderfall des Cosinussignals ist das Abtasttheorem erfüllt und es gilt $y_1(t) = x_1(t)$ &nbsp; &rArr; &nbsp; $A_1 \; \underline{2 \ \text{V}}$ und $\varphi_1 \; \underline{= 0}$.
+
'''(2)'''&nbsp; <u>All proposed solutions</u> are correct:
 +
*The sampling rate here is&nbsp; $f_{\rm A} = 1/T_{\rm A} = 12.5 \ \text{kHz}$.  
 +
*This value is greater than&nbsp; $2 \cdot f_0 = 10 \ \text{kHz}$.
 +
*Thus the sampling theorem is fulfilled independently of the phase and&nbsp; $y(t) = x(t)$ always applies.
  
[[File:P_ID1130__Sig_Z_5_1_c.png|Spektrum des abgetasteten Signals]]
 
  
Dieses Ergebnis soll nun noch mathematisch hergeleitet werden, wobei im Hinblick auf die noch anstehenden Teilaufgaben auch eine Phase $\varphi$ im Eingangssignal berücksichtigt wird:
+
[[File:P_ID1130__Sig_Z_5_1_c.png|right|frame|Spectrum&nbsp; $X_{\rm A}(f)$&nbsp; of the sampled signal &ndash; real part and imaginary part]]
 +
'''(3)'''&nbsp; The sampling rate is now&nbsp; $f_{\rm A} = 2 \cdot f_0 = 10 \ \text{kHz}$.
 +
*Only in the special case of the cosine signal the sampling theorem is  satisfied, and it holds:
 +
:$$y_1(t) = x_1(t) &nbsp; &rArr; &nbsp; A_1 \; \underline{=2 \ \text{V}} \text{ und }\varphi_1 \; \underline{= 0}.$$
 +
 
 +
This result is now to be derived mathematically, whereby a phase&nbsp; $\varphi$&nbsp; in the input signal is already taken into account with regard to the remaining subtasks:
 
:$$x(t) =  A \cdot \cos (2 \pi \cdot f_0 \cdot t - \varphi)
 
:$$x(t) =  A \cdot \cos (2 \pi \cdot f_0 \cdot t - \varphi)
 
  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
Dann gilt für die Spektralfunktion, die in der oberen Grafik skizziert ist:
+
*Then, for the spectral function sketched in the graph above:
 
:$$X(f) = {A}/{2} \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm e}^{{\rm j} \hspace{0.05cm}
 
:$$X(f) = {A}/{2} \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm e}^{{\rm j} \hspace{0.05cm}
 
  \cdot \hspace{0.05cm} \varphi} \cdot  \delta
 
  \cdot \hspace{0.05cm} \varphi} \cdot  \delta
Line 87: Line 99:
 
  \cdot \hspace{0.05cm} \varphi} \cdot  \delta
 
  \cdot \hspace{0.05cm} \varphi} \cdot  \delta
 
  (f- f_{\rm 0} )\hspace{0.05cm}.$$
 
  (f- f_{\rm 0} )\hspace{0.05cm}.$$
Mit den Abkürzungen
+
*With the abbreviations
 
:$$R = {A}/{2} \hspace{0.05cm} \cdot \hspace{0.05cm}
 
:$$R = {A}/{2} \hspace{0.05cm} \cdot \hspace{0.05cm}
\cos(\varphi) \hspace{0.5cm}{\rm und}  \hspace{0.5cm}I ={A}/{2} \hspace{0.05cm} \cdot
+
\cos(\varphi) \hspace{0.5cm}{\rm and}  \hspace{0.5cm}I ={A}/{2} \hspace{0.05cm} \cdot
 
\hspace{0.05cm} \sin(\varphi)$$
 
\hspace{0.05cm} \sin(\varphi)$$
kann hierfür auch geschrieben werden:
+
:can also be written for this:
 
:$$X(f) = (R + {\rm j} \cdot I) \cdot  \delta
 
:$$X(f) = (R + {\rm j} \cdot I) \cdot  \delta
 
  (f+ f_{\rm 0} ) + (R - {\rm j} \cdot I) \cdot  \delta
 
  (f+ f_{\rm 0} ) + (R - {\rm j} \cdot I) \cdot  \delta
 
  (f- f_{\rm 0} )\hspace{0.05cm}.$$
 
  (f- f_{\rm 0} )\hspace{0.05cm}.$$
Das Spektrum des mit $f_A = 2f_0$ abgetasteten Signals $x_A(t)$ lautet somit:
+
*The spectrum of the signal&nbsp; $x_{\rm A}(t)$&nbsp; sampled with&nbsp; $f_{\rm A} = 2f_0$&nbsp; is thus:
 
:$$X_{\rm A}(f) = \sum_{\mu = - \infty }^{+\infty} X (f- \mu \cdot f_{\rm A}
 
:$$X_{\rm A}(f) = \sum_{\mu = - \infty }^{+\infty} X (f- \mu \cdot f_{\rm A}
 
  )= \sum_{\mu = - \infty }^{+\infty} X (f- 2\mu \cdot f_{\rm 0}
 
  )= \sum_{\mu = - \infty }^{+\infty} X (f- 2\mu \cdot f_{\rm 0}
 
  )\hspace{0.05cm}.$$
 
  )\hspace{0.05cm}.$$
Die untere Grafik zeigt, dass $X_{\rm A}(f)$ aus Diracfunktionen bei $\pm f_0$, $\pm 3f_0$, $\pm 5f_0$, usw. besteht. Alle Gewichte sind rein reell und gleich $2 \cdot R$. Die Imaginärteile des periodisch fortgesetzten Spektrums heben sich auf.
+
:*The bottom graph shows that&nbsp; $X_{\rm A}(f)$&nbsp; consists of Dirac functions at&nbsp; $\pm f_0$,&nbsp; $\pm 3f_0$,&nbsp; $\pm 5f_0$,&nbsp; and so on.  
 +
:*All weights are purely real and equal to&nbsp; $2 \cdot R$.  
 +
:*The imaginary parts of the periodically continued spectrum cancel out.
  
Berücksichtigt man weiter den rechteckförmigen Tiefpass, dessen Grenzfrequenz exakt bei $f_{\rm G} = f_0$ liegt, sowie $H(f_{\rm G}) = 0.5$, so erhält man für das Spektrum nach der Signalrekonstruktion:
+
*If one further takes into account the rectangular low-pass filter whose cut-off frequency lies exactly at&nbsp; $f_{\rm G} = f_0$,&nbsp; as well as &nbsp; $H(f_{\rm G}) = 0.5$, one obtains for the spectrum after signal reconstruction:
 
:$$Y(f) = R \cdot  \delta
 
:$$Y(f) = R \cdot  \delta
 
  (f+ f_{\rm 0} ) + R  \cdot  \delta
 
  (f+ f_{\rm 0} ) + R  \cdot  \delta
 
  (f- f_{\rm 0} )\hspace{0.05cm}, \hspace{0.5cm} R = {A}/{2} \hspace{0.05cm} \cdot \hspace{0.05cm}
 
  (f- f_{\rm 0} )\hspace{0.05cm}, \hspace{0.5cm} R = {A}/{2} \hspace{0.05cm} \cdot \hspace{0.05cm}
 
\cos(\varphi)\hspace{0.05cm}.$$
 
\cos(\varphi)\hspace{0.05cm}.$$
Die Fourierrücktransformation führt auf
+
 
 +
*The inverse Fourier transformation leads to
 +
[[File:P_ID1131__Sig_Z_5_1_d.png|right|frame|Reconstruction of the sampled sine signal]]
 
:$$y(t) =  A \cdot \cos (\varphi)\cdot \cos (2 \pi \cdot f_0 \cdot t )
 
:$$y(t) =  A \cdot \cos (\varphi)\cdot \cos (2 \pi \cdot f_0 \cdot t )
 
  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
Es ergibt sich also unabhängig von der Eingangsphase $\varphi$ ein cosinusförmiger Verlauf. Ist $\varphi = 0$ wie beim Signal $x_1(t)$, so ist auch die Amplitude des Ausgangssignals gleich $A$.
 
[[File:P_ID1131__Sig_Z_5_1_d.png|right|Rekonstruktion eines abgetasteten Sinussignals]]
 
  
 +
*Thus, a cosine-shaped progression results independent of the input phase&nbsp; $\varphi$&nbsp;.
 +
*If&nbsp; $\varphi = 0$&nbsp; as with the signal&nbsp; $x_1(t)$, the amplitude of the output signal is also equal to&nbsp; $A$.
 +
 +
 +
 +
'''(4)'''&nbsp; The sine signal has the phase&nbsp; $90^\circ$.
 +
*From this follows directly&nbsp; $y_2(t) = 0$  &nbsp; &rArr; &nbsp;    amplitude $\underline{A_2 = 0}$.
  
'''4.'''  Das Sinussignal hat die Phase $90°$. Daraus folgt direkt $y_2(t) = 0  \Rightarrow$  Amplitude $\underline{A_2 = 0}$.  
+
*This result becomes understandable if you look at the samples in the graph.  
 +
*All samples (red circles) are zero, so there can be no signal even after the filter.
  
Dieses Ergebnis wird verständlich, wenn man sich die Abtastwerte in der Grafik betrachtet. Alle Abtastwerte (rote Kreise) sind $0$, so dass auch nach dem Filter kein Signal vorhanden sein kann.
 
[[File:P_ID1133__Sig_Z_5_1_e.png|right|Rekonstruktion einer abgetasteten Schwingung]]
 
  
'''5.''' Das rekonstruierte Signal $y_3(t)$ ist ebenfalls cosinusförmig  $\Rightarrow$  trotz $\varphi = 60°$ gilt $\varphi_3 = 0$. Die Amplitude ist gleich
+
[[File:P_ID1133__Sig_Z_5_1_e.png|right|frame|Reconstruction of a harmonic oscillation with&nbsp; $60^\circ$ phase]]
 +
'''(5)'''&nbsp; Despite &nbsp; &rArr; &nbsp; $\varphi = 60^\circ$ gilt $\varphi_3 = 0$ &nbsp; &rArr; &nbsp; the reconstructed signal&nbsp; $y_3(t)$&nbsp;  is cosine-shaped, too.&nbsp;
 +
*The amplitude is equal to
 
:$$A_3 =  A \cdot \cos (60^{\circ})= {A}/{2} \hspace{0.15 cm}\underline{= 1\,{\rm V}}
 
:$$A_3 =  A \cdot \cos (60^{\circ})= {A}/{2} \hspace{0.15 cm}\underline{= 1\,{\rm V}}
 
  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
Wenn Sie die rot eingezeichneten Abtastwerte in der Grafik betrachten, so werden Sie zugeben, dass Sie als „Signalrekonstrukteur” keine andere Entscheidung treffen würden als der Tiefpass.
+
*If you look at the samples drawn in red in the graph, you will admit that you as a "signal reconstructor" would not make any other decision than the low-pass.
 +
*Because, you don't know the turquoise curve.
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  
  
 
__NOEDITSECTION__
 
__NOEDITSECTION__
[[Category:Aufgaben zu Signaldarstellung|^5. Zeit- und frequenzdisktrete Signaldarstellung^]]
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[[Category:Signal Representation: Exercises|^5.1 Discrete-Time Signal Representation^]]

Latest revision as of 10:03, 11 October 2021

Three harmonic oscillations of equal frequency  $f_0$  and equal amplitude  $A$

We consider three harmonic oscillations with the same frequency and the same amplitude:

$$x_1(t) = A \cdot \cos (2 \pi \cdot f_0 \cdot t) \hspace{0.05cm}, $$
$$ x_2(t) = A \cdot \sin (2 \pi \cdot f_0 \cdot t) \hspace{0.05cm}, $$
$$ x_3(t) = A \cdot \cos (2 \pi \cdot f_0 \cdot t - 60^{\circ}) \hspace{0.05cm}.$$

The oscillation parameters  $f_0$  and  $A$  can be taken from the graph.

It is assumed that the signals are sampled equidistantly at times  $\nu \cdot T_{\rm A}$  whereby the parameter values  $T_{\rm A} = 80 \ µ \text{s}$  and  $T_{\rm A} = 100 \ µ \text{s}$  are to be analyzed.

The signal reconstruction at the receiver takes place through a low-pass filter  $H(f)$,  which forms the signal  $y_{\rm A}(t) = x_{\rm A}(t)$  from the sampled signal  $y(t)$ . It applies:

$$H(f) = \left\{ \begin{array}{c} 1 \\ 0.5 \\ 0 \\ \end{array} \right.\quad \begin{array}{*{5}c} {\rm{{\rm{f\ddot{u}r}}}} \\ {\rm{{\rm{f\ddot{u}r}}}} \\ {\rm{{\rm{f\ddot{u}r}}}} \\ \end{array}\begin{array}{*{5}c} |f| < f_{\rm G} \hspace{0.05cm}, \\ |f| = f_{\rm G} \hspace{0.05cm}, \\ |f| > f_{\rm G} \hspace{0.05cm}, \\ \end{array}$$

Here  $f_{\rm G}$  indicates the cut-off frequency of the rectangular low-pass filter.  For this shall apply:

$$f_{\rm G} = \frac{1}{ 2 \cdot T_{\rm A}}\hspace{0.05cm}.$$

The sampling theorem is fulfilled if  $y(t) = x(t)$  holds.




Hints:


Questions

1

What are the amplitude and frequency of the signals  $x_1(t)$,  $x_2(t)$  and  $x_3(t)$?

$A \hspace{0.25cm} = \ $

 $\text{V}$
$f_0\hspace{0.2cm} = \ $

 $\text{kHz}$

2

For which input signals is the sampling theorem satisfied   ⇒   $y(t) = x(t)$, when  $\underline{T_{\rm A} = 80 \ {\rm µ} \text{s}}$ ?

$x_1(t)$,
$x_2(t)$,
$x_3(t)$.

3

What is the reconstructed signal  $y_1(t) = A_1 \cdot \cos (2\pi f_0 t – \varphi_1)$  with the sampling distance  $\underline{T_{\rm A} = 100 \ {\rm µ} \text{s}}$? Interpret the result.

$A_1\hspace{0.2cm} = \ $

 $\text{V}$
$\varphi_1\hspace{0.2cm} = \ $

 $\text{Deg}$

4

What is the amplitude  $A_2$  of the reconstructed signal  $y_2(t)$, when the sinusoidal signal  $x_2(t)$  is present?  Let  $\underline{T_{\rm A} = 100 \ {\rm µ} \text{s}}$ still apply.

$A_2\hspace{0.2cm} = \ $

 $\text{V}$

5

What is the amplitude  $A_3$  of the reconstructed signal  $y_3(t)$, when the signal  $x_3(t)$  is present?   $\underline{T_{\rm A} = 100 \ {\rm µ} \text{s}}$ is still valid.

$A_3\hspace{0.2cm} = \ $

 $\text{V}$


Solution

(1)  The graph shows the amplitude  $\underline{A = 2\ \text{V}}$  and the period  $T_0 = 0.2 \ \text{ms}$.

  • This results in the signal frequency  $f_0 = 1/T_0 \; \underline{= 5 \ \text{kHz}}$.


(2)  All proposed solutions are correct:

  • The sampling rate here is  $f_{\rm A} = 1/T_{\rm A} = 12.5 \ \text{kHz}$.
  • This value is greater than  $2 \cdot f_0 = 10 \ \text{kHz}$.
  • Thus the sampling theorem is fulfilled independently of the phase and  $y(t) = x(t)$ always applies.


Spectrum  $X_{\rm A}(f)$  of the sampled signal – real part and imaginary part

(3)  The sampling rate is now  $f_{\rm A} = 2 \cdot f_0 = 10 \ \text{kHz}$.

  • Only in the special case of the cosine signal the sampling theorem is satisfied, and it holds:
$$y_1(t) = x_1(t)   ⇒   A_1 \; \underline{=2 \ \text{V}} \text{ und }\varphi_1 \; \underline{= 0}.$$

This result is now to be derived mathematically, whereby a phase  $\varphi$  in the input signal is already taken into account with regard to the remaining subtasks:

$$x(t) = A \cdot \cos (2 \pi \cdot f_0 \cdot t - \varphi) \hspace{0.05cm}.$$
  • Then, for the spectral function sketched in the graph above:
$$X(f) = {A}/{2} \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm e}^{{\rm j} \hspace{0.05cm} \cdot \hspace{0.05cm} \varphi} \cdot \delta (f+ f_{\rm 0} ) + {A}/{2} \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm e}^{-{\rm j} \hspace{0.05cm} \cdot \hspace{0.05cm} \varphi} \cdot \delta (f- f_{\rm 0} )\hspace{0.05cm}.$$
  • With the abbreviations
$$R = {A}/{2} \hspace{0.05cm} \cdot \hspace{0.05cm} \cos(\varphi) \hspace{0.5cm}{\rm and} \hspace{0.5cm}I ={A}/{2} \hspace{0.05cm} \cdot \hspace{0.05cm} \sin(\varphi)$$
can also be written for this:
$$X(f) = (R + {\rm j} \cdot I) \cdot \delta (f+ f_{\rm 0} ) + (R - {\rm j} \cdot I) \cdot \delta (f- f_{\rm 0} )\hspace{0.05cm}.$$
  • The spectrum of the signal  $x_{\rm A}(t)$  sampled with  $f_{\rm A} = 2f_0$  is thus:
$$X_{\rm A}(f) = \sum_{\mu = - \infty }^{+\infty} X (f- \mu \cdot f_{\rm A} )= \sum_{\mu = - \infty }^{+\infty} X (f- 2\mu \cdot f_{\rm 0} )\hspace{0.05cm}.$$
  • The bottom graph shows that  $X_{\rm A}(f)$  consists of Dirac functions at  $\pm f_0$,  $\pm 3f_0$,  $\pm 5f_0$,  and so on.
  • All weights are purely real and equal to  $2 \cdot R$.
  • The imaginary parts of the periodically continued spectrum cancel out.
  • If one further takes into account the rectangular low-pass filter whose cut-off frequency lies exactly at  $f_{\rm G} = f_0$,  as well as   $H(f_{\rm G}) = 0.5$, one obtains for the spectrum after signal reconstruction:
$$Y(f) = R \cdot \delta (f+ f_{\rm 0} ) + R \cdot \delta (f- f_{\rm 0} )\hspace{0.05cm}, \hspace{0.5cm} R = {A}/{2} \hspace{0.05cm} \cdot \hspace{0.05cm} \cos(\varphi)\hspace{0.05cm}.$$
  • The inverse Fourier transformation leads to
Reconstruction of the sampled sine signal
$$y(t) = A \cdot \cos (\varphi)\cdot \cos (2 \pi \cdot f_0 \cdot t ) \hspace{0.05cm}.$$
  • Thus, a cosine-shaped progression results independent of the input phase  $\varphi$ .
  • If  $\varphi = 0$  as with the signal  $x_1(t)$, the amplitude of the output signal is also equal to  $A$.


(4)  The sine signal has the phase  $90^\circ$.

  • From this follows directly  $y_2(t) = 0$   ⇒   amplitude $\underline{A_2 = 0}$.
  • This result becomes understandable if you look at the samples in the graph.
  • All samples (red circles) are zero, so there can be no signal even after the filter.


Reconstruction of a harmonic oscillation with  $60^\circ$ phase

(5)  Despite   ⇒   $\varphi = 60^\circ$ gilt $\varphi_3 = 0$   ⇒   the reconstructed signal  $y_3(t)$  is cosine-shaped, too. 

  • The amplitude is equal to
$$A_3 = A \cdot \cos (60^{\circ})= {A}/{2} \hspace{0.15 cm}\underline{= 1\,{\rm V}} \hspace{0.05cm}.$$
  • If you look at the samples drawn in red in the graph, you will admit that you as a "signal reconstructor" would not make any other decision than the low-pass.
  • Because, you don't know the turquoise curve.