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| | + | [[File:P_ID1129__Sig_Z_5_1.png|right|frame|Three harmonic oscillations of equal frequency f0 and equal amplitude A]] |
− | + | We consider three harmonic oscillations with the same frequency and the same amplitude: | |
:x1(t)=A⋅cos(2π⋅f0⋅t), | :x1(t)=A⋅cos(2π⋅f0⋅t), | ||
:x2(t)=A⋅sin(2π⋅f0⋅t), | :x2(t)=A⋅sin(2π⋅f0⋅t), | ||
:x3(t)=A⋅cos(2π⋅f0⋅t−60∘). | :x3(t)=A⋅cos(2π⋅f0⋅t−60∘). | ||
− | + | The oscillation parameters f0 and A can be taken from the graph. | |
− | + | It is assumed that the signals are sampled equidistantly at times ν⋅TA 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 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}$$ | \end{array}$$ | ||
− | + | Here fG indicates the cut-off frequency of the rectangular low-pass filter. For this shall apply: | |
:fG=12⋅TA. | :fG=12⋅TA. | ||
− | + | The sampling theorem is fulfilled if y(t)=x(t) holds. | |
Line 31: | Line 31: | ||
− | '' | + | ''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> | ||
− | { | + | {What are the amplitude and frequency of the signals x1(t), x2(t) and x3(t)? |
|type="{}"} | |type="{}"} | ||
A= { 2 3% } V | A= { 2 3% } V | ||
Line 46: | Line 47: | ||
− | { | + | {For which input signals is the sampling theorem satisfied ⇒ y(t)=x(t), when \underline{T_{\rm A} = 80 \ {\rm µ} \text{s}} ? |
|type="[]"} | |type="[]"} | ||
+ x1(t), | + x1(t), | ||
Line 53: | Line 54: | ||
− | { | + | {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. |
|type="{}"} | |type="{}"} | ||
A_1\hspace{0.2cm} = \ { 2 3% } \text{V} | A_1\hspace{0.2cm} = \ { 2 3% } \text{V} | ||
− | \varphi_1\hspace{0.2cm} = \ { 0. } $\text{ | + | \varphi_1\hspace{0.2cm} = \ { 0. } $\text{Deg}$ |
− | { | + | {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. |
|type="{}"} | |type="{}"} | ||
A_2\hspace{0.2cm} = \ { 0. } \text{V} | A_2\hspace{0.2cm} = \ { 0. } \text{V} | ||
− | { | + | {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. |
|type="{}"} | |type="{}"} | ||
A_3\hspace{0.2cm} = \ { 1 3% } \text{V} | A_3\hspace{0.2cm} = \ { 1 3% } \text{V} | ||
Line 72: | Line 73: | ||
</quiz> | </quiz> | ||
− | === | + | ===Solution=== |
{{ML-Kopf}} | {{ML-Kopf}} | ||
− | '''(1)''' | + | '''(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)''' | + | '''(2)''' <u>All proposed solutions</u> 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. |
− | [[File:P_ID1130__Sig_Z_5_1_c.png|right|frame| | + | [[File:P_ID1130__Sig_Z_5_1_c.png|right|frame|Spectrum X_{\rm A}(f) of the sampled signal – real part and imaginary part]] |
− | '''(3)''' | + | '''(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}. | :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: | |
− | |||
:$$x(t) = A \cdot \cos (2 \pi \cdot f_0 \cdot t - \varphi) | :$$x(t) = A \cdot \cos (2 \pi \cdot f_0 \cdot t - \varphi) | ||
\hspace{0.05cm}.$$ | \hspace{0.05cm}.$$ | ||
− | * | + | *Then, for the spectral function sketched in the graph above: |
:$$X(f) = {A}/{2} \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm e}^{{\rm j} \hspace{0.05cm} | :$$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}.$$ | (f- f_{\rm 0} )\hspace{0.05cm}.$$ | ||
− | * | + | *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 | + | \cos(\varphi) \hspace{0.5cm}{\rm and} \hspace{0.5cm}I ={A}/{2} \hspace{0.05cm} \cdot |
\hspace{0.05cm} \sin(\varphi)$$ | \hspace{0.05cm} \sin(\varphi)$$ | ||
− | : | + | :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}.$$ | (f- f_{\rm 0} )\hspace{0.05cm}.$$ | ||
− | * | + | *The spectrum of the signal $x_{\rm A}(t)$ sampled with $f_{\rm A} = 2f_0$ is thus: |
:$$X_{\rm A}(f) = \sum_{\mu = - \infty }^{+\infty} X (f- \mu \cdot f_{\rm A} | :$$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}.$$ | )\hspace{0.05cm}.$$ | ||
− | :* | + | :*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: |
:$$Y(f) = R \cdot \delta | :$$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}.$$ | \cos(\varphi)\hspace{0.05cm}.$$ | ||
− | * | + | *The inverse Fourier transformation leads to |
− | [[File:P_ID1131__Sig_Z_5_1_d.png|right|frame| | + | [[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}.$$ | \hspace{0.05cm}.$$ | ||
− | * | + | *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)''' | + | '''(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. |
− | [[File:P_ID1133__Sig_Z_5_1_e.png|right|frame| | + | [[File:P_ID1133__Sig_Z_5_1_e.png|right|frame|Reconstruction of a harmonic oscillation with 60^\circ phase]] |
− | '''(5)''' | + | '''(5)''' Despite ⇒ \varphi = 60^\circ gilt \varphi_3 = 0 ⇒ the reconstructed signal y_3(t) is cosine-shaped, too. |
+ | *The amplitude is equal to | ||
:$$A_3 = A \cdot \cos (60^{\circ})= {A}/{2} \hspace{0.15 cm}\underline{= 1\,{\rm V}} | :$$A_3 = A \cdot \cos (60^{\circ})= {A}/{2} \hspace{0.15 cm}\underline{= 1\,{\rm V}} | ||
\hspace{0.05cm}.$$ | \hspace{0.05cm}.$$ | ||
− | * | + | *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. |
{{ML-Fuß}} | {{ML-Fuß}} | ||
__NOEDITSECTION__ | __NOEDITSECTION__ | ||
− | [[Category: | + | [[Category:Signal Representation: Exercises|^5.1 Discrete-Time Signal Representation^]] |
Latest revision as of 11:03, 11 October 2021
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:
- This task belongs to the chapter Discrete-Time Signal Representation.
- There is an interactive applet for the topic dealt with here: Sampling of Analog Signals and Signal Reconstruction
Questions
Solution
- 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.
(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
- 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.
(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.