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

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The oscillation parameters  $f_0$  and  $A$  can be taken from the graph.
 
The oscillation parameters  $f_0$  and  $A$  can be taken from the graph.
  
It is assumed that the signals are sampled equidistantly at the 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 analysed.
+
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 is carried out by 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:
+
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}$$
Here  $f_{\rm G}$  indicates the cut-off frequency of the rectangular low-pass filter. For this shall apply:
+
Here  $f_{\rm G}$  indicates the cut-off frequency of the rectangular low-pass filter.  For this shall apply:
 
:$$f_{\rm G} = \frac{1}{ 2 \cdot T_{\rm  A}}\hspace{0.05cm}.$$
 
:$$f_{\rm G} = \frac{1}{ 2 \cdot T_{\rm  A}}\hspace{0.05cm}.$$
 
The sampling theorem is fulfilled if  $y(t) = x(t)$  holds.
 
The sampling theorem is fulfilled if  $y(t) = x(t)$  holds.
Line 32: Line 32:
  
 
''Hints:''  
 
''Hints:''  
*This task belongs to the chapter  [[Signal_Representation/Time_Discrete_Signal_Representation|Time Discrete Signal Representation]].
+
*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: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]]
  
  
Line 41: Line 41:
  
 
<quiz display=simple>
 
<quiz display=simple>
{Wie groß sind Amplitude und Frequenz der Signale&nbsp; $x_1(t)$,&nbsp; $x_2(t)$&nbsp; und&nbsp; $x_3(t)$?
+
{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 \hspace{0.25cm} = \ $  { 2 3% } &nbsp;$\text{V}$
 
$A \hspace{0.25cm} = \ $  { 2 3% } &nbsp;$\text{V}$
Line 47: 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="[]"}
 
+ $x_1(t)$,
 
+ $x_1(t)$,
Line 54: 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;$\text{V}$
Line 85: Line 85:
  
  
[[File:P_ID1130__Sig_Z_5_1_c.png|right|frame|Spectrum&nbsp; $X_{\rm A}(f)$&nbsp; of the sampled signal - real part and imaginary part.]]
+
[[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; $f_{\rm A} = 2 \cdot f_0 = 10 \ \text{kHz}$.  
*Only in the special case of the cosine signal is the sampling theorem now satisfied and it holds:
+
*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:
 
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:
Line 100: 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 122: 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|Reconstruction of the sampled sinusoidal signal]]
+
[[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^]]
+
[[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.