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

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[[File:P_ID1129__Sig_Z_5_1.png|right|frame|Drei harmonische Schwingungen gleicher Frequenz  f0  und gleicher Amplitude  A]]
+
[[File:P_ID1129__Sig_Z_5_1.png|right|frame|Three harmonic oscillations of equal frequency  f0  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:
 
:x1(t)=Acos(2πf0t),
 
:x1(t)=Acos(2πf0t),
 
:x2(t)=Asin(2πf0t),
 
:x2(t)=Asin(2πf0t),
 
:x3(t)=Acos(2πf0t60).
 
:x3(t)=Acos(2πf0t60).
Die Schwingungsparameter  f0  und  A  können Sie der Grafik entnehnen.
+
The oscillation parameters  f0  and  A  can be taken from the graph.
  
Angenommen wird, dass die Signale äquidistant zu den Zeitpunkten  νTA  abgetastet werden, wobei die Parameterwerte  T_{\rm A} = 80 \ µ \text{s}  und  T_{\rm A} = 100 \ µ \text{s}  analysiert werden sollen.
+
It is assumed that the signals are sampled equidistantly at times  νTA  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  yA(t)=xA(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  yA(t)=xA(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  fG  die Grenzfrequenz des rechteckförmigen Tiefpassfilters an. Für diese soll gelten:
+
Here  fG  indicates the cut-off frequency of the rectangular low-pass filter.  For this shall apply:
 
:fG=12TA.
 
:fG=12TA.
Das Abtasttheorem ist erfüllt, wenn  y(t)=x(t)  gilt.
+
The sampling theorem is fulfilled if  y(t)=x(t)  holds.
  
  
Line 31: Line 31:
  
  
''Hinweise:''  
+
''Hints:''  
*Die Aufgabe gehört zum  Kapitel  [[Signal_Representation/Time_Discrete_Signal_Representation|Zeitdiskrete Signaldarstellung]].
+
*This task belongs to the chapter  [[Signal_Representation/Time_Discrete_Signal_Representation|Discrete-Time Signal Representation]].
 
   
 
   
*Zu der hier behandelten Thematik gibt es ein interaktives Applet:  [[Applets:Abtastung_periodischer_Signale_und_Signalrekonstruktion_(Applet)|Abtastung periodischer Signale & Signalrekonstruktion]]
+
*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]]
  
  
===Fragebogen===
+
 
 +
===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Wie groß sind Amplitude und Frequenz der Signale&nbsp; x1(t),&nbsp; x2(t)&nbsp; und&nbsp; x3(t)?
+
{What are the amplitude and frequency of the signals&nbsp; x1(t),&nbsp; x2(t)&nbsp; and&nbsp; x3(t)?
 
|type="{}"}
 
|type="{}"}
 
A=   { 2 3% } &nbsp;V
 
A=   { 2 3% } &nbsp;V
Line 46: Line 47:
  
  
{Bei welchen Eingangssignalen ist das Abtasttheorem erfüllt &nbsp; &rArr;  &nbsp;  y(t)=x(t), wenn&nbsp; \underline{T_{\rm A} = 80 \ {\rm &micro;} \text{s}}&nbsp; 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="[]"}
 
+ x1(t),
 
+ x1(t),
Line 53: Line 54:
  
  
{Wie lautet das rekonstruierte Signal&nbsp; y_1(t) = A_1 \cdot \cos (2\pi f_0 t – \varphi_1)&nbsp; mit dem Abtastabstand&nbsp; \underline{T_{\rm A} = 100 \ {\rm &micro;} \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\hspace{0.2cm} = \ { 2 3% } &nbsp;\text{V}
 
A_1\hspace{0.2cm} = \ { 2 3% } &nbsp;\text{V}
\varphi_1\hspace{0.2cm} = \ { 0. } &nbsp;$\text{Grad}$
+
\varphi_1\hspace{0.2cm} = \ { 0. } &nbsp;$\text{Deg}$
  
  
{Welche Amplitude&nbsp; A_2&nbsp; besitzt das rekonstruierte Signal&nbsp; y_2(t), wenn das Sinussignal&nbsp; x_2(t)&nbsp; anliegt? Es gelte weiterhin&nbsp; \underline{T_{\rm A} = 100 \ {\rm &micro;} \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\hspace{0.2cm} = \ { 0. } &nbsp;\text{V}
 
A_2\hspace{0.2cm} = \ { 0. } &nbsp;\text{V}
  
  
{Welche Amplitude&nbsp; A_3&nbsp; besitzt das rekonstruierte Signal&nbsp; y_3(t), wenn das Signal&nbsp; x_3(t)&nbsp; anliegt? Es gelte weiterhin&nbsp; \underline{T_{\rm A} = 100 \ {\rm &micro;} \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\hspace{0.2cm} = \ { 1 3% } &nbsp;\text{V}
 
A_3\hspace{0.2cm} = \ { 1 3% } &nbsp;\text{V}
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</quiz>
 
</quiz>
  
===Musterlösung===
+
===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp;  Aus der Grafik erkennt man die Amplitude&nbsp; \underline{A = 2\ \text{V}}&nbsp; sowie die Periodendauer&nbsp; T_0 = 0.2 \ \text{ms}.  
+
'''(1)'''&nbsp;  The graph shows the amplitude&nbsp; \underline{A = 2\ \text{V}}&nbsp; and the period&nbsp; T_0 = 0.2 \ \text{ms}.  
*Daraus ergibt sich die Signalfrequenz&nbsp; f_0 = 1/T_0 \; \underline{= 5 \ \text{kHz}}.
+
*This results in the signal frequency&nbsp; f_0 = 1/T_0 \; \underline{= 5 \ \text{kHz}}.
  
  
'''(2)'''&nbsp; Richtig sind <u>alle Löungsvorschläge</u>:
+
'''(2)'''&nbsp; <u>All proposed solutions</u> are correct:
*Die Abtastrate beträgt hier&nbsp; f_{\rm A} = 1/T_{\rm A} = 12.5 \ \text{kHz}.  
+
*The sampling rate here is&nbsp; f_{\rm A} = 1/T_{\rm A} = 12.5 \ \text{kHz}.  
*Dieser Wert ist größer als&nbsp; 2 \cdot f_0 = 10 \ \text{kHz}.  
+
*This value is greater than&nbsp; 2 \cdot f_0 = 10 \ \text{kHz}.  
*Damit ist das Abtasttheorem unabhängig von der Phase erfüllt, und es gilt stets&nbsp; y(t) = x(t).   
+
*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|right|frame|Spektrum&nbsp; X_{\rm A}(f)&nbsp; des abgetasteten Signals &ndash; Realteil und Imaginärteil]]
+
[[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; Die Abtastrate beträgt nun&nbsp; f_{\rm A} = 2 \cdot f_0 = 10 \ \text{kHz}.  
+
'''(3)'''&nbsp; The sampling rate is now&nbsp; f_{\rm A} = 2 \cdot f_0 = 10 \ \text{kHz}.  
*Nur im Sonderfall des Cosinussignals ist jetzt das Abtasttheorem erfüllt und es gilt:
+
*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}.
 
: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:
Dieses Ergebnis soll nun noch mathematisch hergeleitet werden, wobei im Hinblick auf die noch anstehenden Teilaufgaben bereits auch eine Phase&nbsp; \varphi&nbsp; im Eingangssignal berücksichtigt wird:
 
 
:$$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 99: 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&nbsp; $f_{\rm A} = 2f_0$&nbsp; abgetasteten Signals&nbsp; $x_{\rm A}(t)$&nbsp; 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&nbsp; X_{\rm A}(f)&nbsp; aus Diracfunktionen bei&nbsp; \pm f_0,&nbsp; \pm 3f_0,&nbsp; \pm 5f_0,&nbsp; usw. besteht.  
+
:*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.  
:*Alle Gewichte sind rein reell und gleich&nbsp; 2 \cdot R.  
+
:*All weights are purely real and equal to&nbsp; 2 \cdot R.  
:*Die Imaginärteile des periodisch fortgesetzten Spektrums heben sich auf.
+
:*The imaginary parts of the periodically continued spectrum cancel out.
  
*Berücksichtigt man weiter den rechteckförmigen Tiefpass, dessen Grenzfrequenz exakt bei&nbsp; f_{\rm G} = f_0&nbsp; liegt, sowie&nbsp; 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
Line 121: Line 121:
 
\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|Rekonstruktion des abgetasteten Sinussignals]]
+
[[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&nbsp; \varphi&nbsp; ein cosinusförmiger Verlauf.  
+
*Thus, a cosine-shaped progression results independent of the input phase&nbsp; \varphi&nbsp;.
*Ist&nbsp; \varphi = 0&nbsp; wie beim Signal&nbsp; x_1(t), so ist auch die Amplitude des Ausgangssignals gleich&nbsp; A.
+
*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; Das Sinussignal hat die Phase&nbsp; 90^\circ.  
+
'''(4)'''&nbsp; The sine signal has the phase&nbsp; 90^\circ.  
*Daraus folgt direkt&nbsp; y_2(t) = 0  &nbsp; &rArr; &nbsp;    Amplitude \underline{A_2 = 0}.  
+
*From this follows directly&nbsp; y_2(t) = 0  &nbsp; &rArr; &nbsp;    amplitude \underline{A_2 = 0}.  
  
*Dieses Ergebnis wird verständlich, wenn man sich die Abtastwerte in der Grafik betrachtet.  
+
*This result becomes understandable if you look at the samples in the graph.  
*Alle Abtastwerte (rote Kreise) sind Null, so dass auch nach dem Filter kein Signal vorhanden sein kann.
+
*All samples (red circles) are zero, so there can be no signal even after the filter.
  
  
[[File:P_ID1133__Sig_Z_5_1_e.png|right|frame|Rekonstruktion einer harmonischen Schwingung mit&nbsp; 60^\circ Phase]]
+
[[File:P_ID1133__Sig_Z_5_1_e.png|right|frame|Reconstruction of a harmonic oscillation with&nbsp; 60^\circ phase]]
'''(5)'''&nbsp; Trotz&nbsp; \varphi = 60^\circ gilt \varphi_3 = 0 &nbsp; &rArr; &nbsp; auch das rekonstruierte Signal&nbsp; y_3(t) ist cosinusförmig. Die Amplitude ist gleich
+
'''(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.
*Sie kennen ja den türkisfarbenen Verlauf nicht.
+
*Because, you don't know the turquoise curve.
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  
  
 
__NOEDITSECTION__
 
__NOEDITSECTION__
[[Category:Exercises for Signal Representation|^5.1 Time Discrete Signal Representation^]]
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[[Category:Signal Representation: Exercises|^5.1 Discrete-Time Signal Representation^]]

Latest revision as of 11:03, 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.