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Commit 939d9e13 authored by Iris Heisterklaus's avatar Iris Heisterklaus
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- first comments to reale Abtastung

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GDET3 Reale Abtastung.ipynb
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......@@ -45,19 +45,20 @@
"\n",
"# Reale Abtastung\n",
"\n",
"Im Gegensatz zur [idealen Abtastung](GDET3%20Ideale%20Abtastung.ipynb) werden hier zwei Verfahren zur realen Abtastung betrachtet.\n",
"Im Gegensatz zur [idealen Abtastung](GDET3%20Ideale%20Abtastung.ipynb) werden hier zwei Verfahren zur realen Abtastung betrachtet. Tatsächlich kann nicht mit einem idealen Dirac an einem definierten Zeitpunkt abgetastet werden. Im Folgenden werden zwei Verfahren der Abtastung, die *Shape-top Abtastung* und die *Flat-top Abtastung* beschrieben.\n",
"\n",
"## Shape-top Abtastung\n",
"\n",
"Abtastung in Intervallen endlicher Dauer $T_0$ mit Abstand $T=\\frac{1}{r}$\n",
"Bei der Shape-top Abtastung wird das kontinuierliche Signal $s(t)$ mit Abstand $T=\\frac{1}{r}$ abgetastet. \n",
"Anstatt einer Diracfolge wird eine Folge schmaler Rechteckimpulse mit endlicher Dauer $T_0$ verwendet. Das abgetastete Signal $s_0(t)$ ist also\n",
"\n",
"$$\n",
"s_0(t) \n",
"= s(t) \\cdot \\sum\\limits_{n=-\\infty}^\\infty \\mathrm{rect}\\left(\\frac{t-nT}{T_0}\\right) \n",
"= s(t) \\cdot \\left[\\mathrm{rect}\\left(\\frac{t}{T_0}\\right) \\ast \\sum\\limits_{n=-\\infty}^\\infty \\delta(t-nT) \\right]\n",
"= s(t) \\cdot \\left[\\mathrm{rect}\\left(\\frac{t}{T_0}\\right) \\ast \\sum\\limits_{n=-\\infty}^\\infty \\delta(t-nT) \\right].\n",
"$$\n",
"\n",
"Im Frequenzbereich\n",
"Im Frequenzbereich folgt durch Transformation\n",
"$$\n",
"S_0(f) \n",
"= S(f) \\ast \\left[T_0 \\mathrm{si}\\left(\\pi T_0 f\\right) \\cdot \\frac{1}{T}\\sum_{k=-\\infty}^\\infty \\delta(f-kr)\\right]\n",
......
%% Cell type:code id: tags:
``` python
# Copyright 2019 Institut für Nachrichtentechnik, RWTH Aachen University
%matplotlib widget
from ipywidgets import interact, interactive, fixed, HBox, VBox
import ipywidgets as widgets
from IPython.display import clear_output, display, HTML
from ient_nb.ient_plots import *
from ient_nb.ient_transforms import *
from ient_nb.ient_signals import *
signals_t = {'cos-Funktion': lambda t: np.cos(2 * np.pi * t),
'sin-Funktion': lambda t: np.sin(2 * np.pi * t),
'si-Funktion': lambda t: si(2 * np.pi * t),
'Rechteckimpuls': lambda t: rect(t / 1.05),
'Dreieckimpuls': tri}
signals_f = {'cos-Funktion': lambda f, F: np.isin(f/F, f[findIndOfLeastDiff(f/F, [-1, 1])]/F) * 0.5,
'sin-Funktion': lambda f, F: np.isin(f/F, f[findIndOfLeastDiff(f/F, [1])]/F) / 2j - np.isin(f/F, f[findIndOfLeastDiff(f/F, [-1])]/F) / 2j,
'si-Funktion': lambda f, F: 1/(2*np.abs(F))*rect(f / (2*F)),
'Rechteckimpuls': lambda f, F: 1/np.abs(F)*si(np.pi * f / F),
'Dreieckimpuls': lambda f, F: 1/np.abs(F)*si(np.pi * f/F) ** 2}
```
%% Cell type:markdown id: tags:
<div>
<img src="ient_nb/figures/rwth_ient_logo@2x.png" style="float: right;height: 5em;">
</div>
# Reale Abtastung
Im Gegensatz zur [idealen Abtastung](GDET3%20Ideale%20Abtastung.ipynb) werden hier zwei Verfahren zur realen Abtastung betrachtet.
Im Gegensatz zur [idealen Abtastung](GDET3%20Ideale%20Abtastung.ipynb) werden hier zwei Verfahren zur realen Abtastung betrachtet. Tatsächlich kann nicht mit einem idealen Dirac an einem definierten Zeitpunkt abgetastet werden. Im Folgenden werden zwei Verfahren der Abtastung, die *Shape-top Abtastung* und die *Flat-top Abtastung* beschrieben.
## Shape-top Abtastung
Abtastung in Intervallen endlicher Dauer $T_0$ mit Abstand $T=\frac{1}{r}$
Bei der Shape-top Abtastung wird das kontinuierliche Signal $s(t)$ mit Abstand $T=\frac{1}{r}$ abgetastet.
Anstatt einer Diracfolge wird eine Folge schmaler Rechteckimpulse mit endlicher Dauer $T_0$ verwendet. Das abgetastete Signal $s_0(t)$ ist also
$$
s_0(t)
= s(t) \cdot \sum\limits_{n=-\infty}^\infty \mathrm{rect}\left(\frac{t-nT}{T_0}\right)
= s(t) \cdot \left[\mathrm{rect}\left(\frac{t}{T_0}\right) \ast \sum\limits_{n=-\infty}^\infty \delta(t-nT) \right]
= s(t) \cdot \left[\mathrm{rect}\left(\frac{t}{T_0}\right) \ast \sum\limits_{n=-\infty}^\infty \delta(t-nT) \right].
$$
Im Frequenzbereich
Im Frequenzbereich folgt durch Transformation
$$
S_0(f)
= S(f) \ast \left[T_0 \mathrm{si}\left(\pi T_0 f\right) \cdot \frac{1}{T}\sum_{k=-\infty}^\infty \delta(f-kr)\right]
=
S(f) \ast \left[\frac{T_0}{T} \sum_{k=-\infty}^\infty \delta\left(f-\frac{k}{T}\right) \mathrm{si} \left(\pi T_0 \frac{k}{T}\right) \right]
$$
Jede spektrale Kopie, zentriert bei $\frac{k}{T}$, wird skaliert mit von $f$ unabhängigen Faktor $\mathrm{si} \left(\pi T_0 \frac{k}{T}\right)$.
Grenzübergang liefert ideale Abtastung: $\lim\limits_{T_0\rightarrow 0}\left(\frac{1}{T_0}s_0(t)\right) = s_\mathrm{a}(t)$.
%% Cell type:markdown id: tags:
## Flat-top Abtastung
Abtastung in Intervallen endlicher Dauer und um $T=\frac{1}{r}$ gehaltene Signalwerte
$$
s_0(t)
= \sum\limits_{n=-\infty}^\infty s(nT) \mathrm{rect}\left(\frac{t}{T}-\frac{1}{2}-nT\right)
= s_\mathrm{a}(t) \ast \mathrm{rect}\left(\frac{t}{T}-\frac{1}{2}\right)
= \left[s(t) \cdot \sum\limits_{n=-\infty}^\infty \delta(t-nT)\right] \ast \mathrm{rect}\left(\frac{t}{T}\right)\ast \delta\left(t-\frac{T}{2}\right)
$$
Im Frequenzbereich
$$
S_0(f)
= S_\mathrm{a}(f) \cdot T \cdot \mathrm{si}(\pi f T) \cdot \mathrm{e}^{-\mathrm{j}\pi f T}
= \left[S(f) \ast \sum\limits_{k=-\infty}^\infty \delta(f-kr)\right] \cdot \mathrm{si}(\pi f T) \cdot \mathrm{e}^{-\mathrm{j}\pi f T}
$$
Dieses Verfahren wird häufig in Analog-Digital-Wandlern eingesetzt.
%% Cell type:markdown id: tags:
### Abtastung
Zunächst wird [ideale Abtastung](GDET3%20Ideale%20Abtastung.ipynb) wiederholt mit
$s_\mathrm{a}(t) = \sum\limits_{n=-\infty}^\infty s(nT)\cdot\delta(t-nT)$ und Spektrum
$S_\mathrm{a}(f) = \frac{1}{T} \sum\limits_{k=-\infty}^\infty S(f-kr)$.
%% Cell type:code id: tags:
``` python
# Construction of s(t) and corresponding spectrum S(f)
t,deltat = np.linspace(-10,10,50001, retstep=True) # t-axis
f,deltaf = np.linspace(-50,50,len(t), retstep=True) # f-axis
F = 0.9 # frequency of the signal
s = lambda t: signals_t['cos-Funktion'](t*F);
S = lambda f: signals_f['cos-Funktion'](f, F);
# Ideal sampling: Construction of sa(t) and Sa(f)
r = 2; T = 1/r; # sampling rate
## Time domain
nT, snT = ient_sample(t, s(t), T)
## Frequency domain
Sa = np.zeros_like(S(f))
kMax = 16 # number of k values in sum for Sa(f), should be infinity :)
for k in np.arange(-kMax, kMax+1): # evaluate infinite sum only for 2*kMax+1 elements
Sa += S(f-k/T)
Sa = Sa/T
fSadirac = f[np.where(Sa)]; Sadirac = Sa[np.where(Sa)]
# Plot
## Time domain
fig, axs = plt.subplots(2,1)
ax = axs[0]; ax.set_title('Zeitbereich');
ax.plot(t, s(t), color='rwth', linestyle='--', label=r'$s(t)$');
ient_plot_dirac(ax, nT, snT, 'rot', label=r'$s_\mathrm{a}(t)$')
ax.set_xlabel(r'$\rightarrow t$'); ax.set_xlim([-7.5,7.5]); ax.legend(loc=2); ient_grid(ax); ient_axis(ax);
## Frequency domain
ax = axs[1]; ax.set_title('Frequenzbereich');
ient_plot_dirac(ax, fSadirac, Sadirac, 'rot', label=r'$S_\mathrm{a}(f)$');
ient_plot_dirac(ax, f[np.where(S(f))], S(f)[np.where(S(f))], 'rwth', label=r'$S(f)$');
ax.set_xlim([-7.5,7.5]); ax.legend(loc=2); ax.set_xlabel(r'$\rightarrow f$'); ient_grid(ax); ient_axis(ax);
txt,_=ient_annotate_xtick(ax, r'$r=2$', r, -.15, 'black'); txt.get_bbox_patch().set_alpha(1);
```
%% Cell type:markdown id: tags:
Nun wird das abgetastete Signal $s_0(t)$ im Zeitbereich betrachtet, welches mittels Flat-top Abtastung erzeugt wurde.
$$
s_0(t)
= s_\mathrm{a}(t) \ast \mathrm{rect}\left(\frac{t}{T}-\frac{1}{2}\right)
= \sum\limits_{n=-\infty}^\infty s(nT) \mathrm{rect}\left(\frac{t}{T}-\frac{1}{2}-nT\right)
$$
%% Cell type:code id: tags:
``` python
# Time-Domain
s0 = np.zeros_like(t) # sum of rects
Nmax = np.ceil(t[-1]/T) # Parts of the infinite rect sum, should be infinity :)
for n in np.arange(-Nmax,Nmax+1):
s0 = s0 + rect((t-n*T)/T-0.5) * s(n*T)
# Plot
fig, ax = plt.subplots(); ax.set_title('Zeitbereich')
ax.plot(t, s(t), 'rwth', linestyle='--', label=r'$s(t)$'); ax.plot(t, s0, 'rot', label=r'$s_0(t)$')
ax.set_xlabel(r'$\rightarrow t$'); ax.set_xlim([-2.9, 2.9]); ax.legend(loc=2); ient_grid(ax); ient_axis(ax);
```
%% Cell type:markdown id: tags:
Nun $S_0(f)$ im Frequenzbereich
$$
S_0(f)
= S_\mathrm{a}(f) \cdot T \mathrm{si}(\pi f T) \cdot \mathrm{e}^{-j\pi f T}
$$
%% Cell type:code id: tags:
``` python
# Frequency domain
S_si = T*si(np.pi*T*f)
S_exp = np.exp(-1j*np.pi*f*T)
S0 = Sa * S_si * S_exp
# Plot
## Magnitude
fig, axs = plt.subplots(2,1);
ax = axs[0]; ax.set_title('Betrag')
ax.plot(f, np.abs(S_si*S_exp), 'k--', label=r'$|T\mathrm{si}(\pi f T)e^{-\mathrm{j}\pi f T}|$')
fS0dirac = f[np.where(S0)]; S0dirac = S0[np.where(S0)] # sample S0 and f
ient_plot_dirac(ax, fS0dirac, np.abs(S0dirac), 'rot', label=r'$|S_0(f)$|');
ax.set_xlim([-7.5,7.5]); ax.legend(loc=2); ax.set_xlabel(r'$\rightarrow f$'); ient_grid(ax); ient_axis(ax);
## Phase
ax = axs[1]; ax.set_title('Phase')
ax.plot(f, np.angle(S_si*S_exp), 'k--', label=r'$\angle T\mathrm{si}(\pi f T)e^{-\mathrm{j}\pi f T}$')
ient_stem(ax, fS0dirac, np.angle(S0dirac), 'rot', label=r'$\angle S_0(f)$')
ax.set_xlim([-7.5,7.5]); ax.legend(loc=2); ax.set_xlabel(r'$\rightarrow f$'); ient_grid(ax); ient_axis(ax);
```
%% Cell type:markdown id: tags:
### Rekonstruktion
Durch die Multiplikation von $S_\mathrm{a}(f)$ mit $T \cdot \mathrm{si}(\pi f T) \cdot \mathrm{e}^{-j\pi f T}$ wird das Spektrum $S_0(f)$ im Basisband verzerrt. So ist es nicht möglich, $S(f)$ mittels eines einfachen idealen Tiefpasses zu rekonstruieren. Zusätzlich ist ein Filter zum Ausgleich nötig
$$
H_\mathrm{eq}(f) = \frac{1}{T \cdot \mathrm{si}(\pi f T) \cdot \mathrm{e}^{-j\pi f T}}
$$
%% Cell type:code id: tags:
``` python
# Reconstruction filters #plt.close('all')
## Ideal Low pass to crop the base band
H_lp = rect(f/(r+0.001)) # ideal low pass between -r/2 and r/2
## Equalizing filter to compensate the influence of si(...) and exp(...) terms in S_0(f)
H_eq = 1/(T*si(np.pi*T*f) * np.exp(-1j*np.pi*f*T))
## Overall reconstruction filter
H = H_lp * H_eq
# Plot
fig,axs = plt.subplots(2,1)
ax = axs[0]
ax.plot(f, np.abs(H_eq), 'k--', linewidth=1, label=r'$|H_\mathrm{eq}(f)|$')
ax.plot(f, np.abs(H), 'k', label=r'$|H(f)|$')
ax.set_xlim([-7.5,7.5]); ax.legend(loc=2); ax.set_ylim([-.1,21]);
ax.set_xlabel(r'$\rightarrow f$'); ient_grid(ax); ient_axis(ax);
ax = axs[1]
ax.plot(f, np.angle(H_eq), 'k--', linewidth=1, label=r'$\angle H_\mathrm{eq}(f)$')
ax.plot(f, np.angle(H)*H_lp, 'k', label=r'$\angle H(f)$')
ax.set_xlim([-7.5,7.5]); ax.legend(loc=2); #ax.set_ylim([-.1,11]);
ax.set_xlabel(r'$\rightarrow f$'); ient_grid(ax); ient_axis(ax);
```
%% Cell type:markdown id: tags:
Dieses Filter wird nun zur Rekonstruktion angewendet:
%% Cell type:code id: tags:
``` python
G = S0 * H*T;
g = ient_idft(G); g = np.fft.ifftshift(np.real(g)); # IDFT
G = np.real(G); # discards small imaginary numbers close to zero
# Plot
fig, axs = plt.subplots(2,1)
ax = axs[0]; fGdirac = f[np.where(G)]; Gdirac = G[np.where(G)]
ient_plot_dirac(ax, fGdirac, Gdirac, 'grun');
ax.set_xlabel(r'$\rightarrow f$'); ax.set_ylabel(r'$\uparrow G(f)$', bbox=ient_wbbox);
ax.set_xlim([-7.5,7.5]); ient_grid(ax); ient_axis(ax);
ax = axs[1]; ax.plot(t, s(t), color='rwth', linestyle='--', label=r'$s(t)$');
ax.plot(t/deltat/(f[-1]*2), g, color='grun', linestyle='-', label=r'$g(t)$');
ax.set_xlabel(r'$\rightarrow t$'); ax.set_xlim([-7.5,7.5]); ax.legend(loc=2); ient_grid(ax); ient_axis(ax);
```
%% Cell type:markdown id: tags:
## Demo
%% Cell type:code id: tags:
``` python
T0 = T/4 # width of rects for shape-top sampling
plt.close(); fig, axs = plt.subplots(4, 1); fig.canvas.layout.height = '800px'; plt.tight_layout();
@widgets.interact(sampling_type=widgets.Dropdown(options=['Shape-top', 'Flat-top'], description=r'Art der Abtastung:', style=ient_wdgtl_style),
s_type=widgets.Dropdown(options=list(signals_t.keys()), description=r'Wähle $s(t)$:'),
F=widgets.FloatSlider(min=0.1, max=2, value=0.9, step=.1, description=r'$F$', style=ient_wdgtl_style, continuous_update=False))
def update_plots(sampling_type, s_type, F):
s = lambda t: signals_t[s_type](t*F);
S = lambda f: signals_f[s_type](f, F);
nT, snT = ient_sample(t, s(t), T)
# Construct sampled signal
if sampling_type == 'Shape-top':
u = np.zeros_like(t) # sum of rects
for n in np.arange(-Nmax,Nmax+1):
u = u + rect((t-n*T)/T0)
s0 = s(t) * u
elif sampling_type == 'Flat-top':
s0 = np.zeros_like(t) # sum of rects
for n in np.arange(-Nmax,Nmax+1):
s0 = s0 + rect((t-n*T)/T-0.5) * s(n*T)
# Construct sampled spectrum
if sampling_type == 'Shape-top':
S0 = np.zeros_like(S(f))
for k in np.arange(-kMax, kMax+1): # evaluate infinite sum only for 2*kMax+1 elements
S0 += S(f-k/T) * si(np.pi*T0*k/T)
S0 = S0*T0/T
elif sampling_type == 'Flat-top':
Sa = np.zeros_like(S(f))
for k in np.arange(-kMax, kMax+1): # evaluate infinite sum only for 2*kMax+1 elements
Sa += S(f-k/T)
S0 = Sa * si(np.pi*T*f) * np.exp(-1j*np.pi*f*T)
# Reconstruct g(t)
if sampling_type == 'Shape-top':
H = H_lp
G = S0 * H * T / T0;
elif sampling_type == 'Flat-top':
H = H_lp * H_eq * T
G = S0 * H
g = ient_idft(G);
g = np.fft.ifftshift(np.real(g)); # IDFT
# Sample for plot
if s_type == 'cos-Funktion' or s_type == 'sin-Funktion':
fS0dirac = f[np.where(S0)]; S0dirac = S0[np.where(S0)]
fSdirac = f[np.where(S(f))]; Sdirac = S(f)[np.where(S(f))]
fGdirac = f[np.where(G)]; Gdirac = G[np.where(G)]
if s_type == 'sin-Funktion':
Sdirac = np.imag(Sdirac); S = lambda f: np.imag(signals_f[s_type](f, F));
G = np.imag(G); Gdirac = np.imag(Gdirac)
else:
Sdirac = np.real(Sdirac); S0 = np.real(S0); G = np.real(G); Gdirac = np.real(Gdirac)
else:
g /= (len(f)/(2*f[-1])) # Parseval :)
S0dirac = np.zeros_like(f)
G = np.real(G)
if sampling_type == 'Shape-top': # normalize to T0 for plotting reasons
S0 = S0/T0
S0dirac = S0dirac/T0
# Plot
if not axs[0].lines: # Call plot() and decorate axes. Usually, these functions take some processing time
ax = axs[0]; ax.set_title('Zeitbereich');
ax.plot(t, s(t), 'rwth', linestyle='--', label=r'$s(t)$');
ax.plot(t, s0, 'rot', label=r'$s_0(t)$')
ax.set_xlabel(r'$\rightarrow t$');
ax.set_xlim([-2.9, 2.9]); ax.legend(loc=2); ient_grid(ax); ient_axis(ax);
ax = axs[1]; ax.set_title('Frequenzbereich');
ax.plot(f, np.abs(H), '-', color='black', label=r'$|H(f)|$')
if s_type == 'cos-Funktion' or s_type == 'sin-Funktion':
ax.plot(f, np.ones_like(f)*np.nan, '-', color='rot', label=r'$|S_0(f)|$');
ient_plot_dirac(ax, fS0dirac, np.abs(S0dirac), 'rot');
else:
ax.plot(f, np.abs(S0), '-', color='rot', label=r'$|S_0(f)|$');
ient_plot_dirac(ax, [], [], 'rot');
ax.plot(f, S(f), color='rwth', linestyle='--', linewidth=1, label=r'$S(f)$')
ax.set_xlim([-7.5,7.5]); ax.set_ylim([-1,2]); ax.legend(loc=2);
ax.set_xlabel(r'$\rightarrow f$'); ient_grid(ax); ient_axis(ax);
txt,_=ient_annotate_xtick(ax, r'$r=2$', r, -.15, 'black'); txt.get_bbox_patch().set_alpha(1);
txt,_=ient_annotate_xtick(ax, r'$f_\mathrm{g}$', r/2, -.15, 'black'); txt.get_bbox_patch().set_alpha(1);
ax = axs[2];
if s_type == 'cos-Funktion' or s_type == 'sin-Funktion':
ax.plot(f, np.ones_like(f)*np.nan, '-', color='grun');
ient_plot_dirac(ax, fGdirac, Gdirac, 'grun');
else:
ax.plot(f, G, '-', color='grun');
ient_plot_dirac(ax, [], [], 'rot');
ax.set_xlabel(r'$\rightarrow f$'); ax.set_ylabel(r'$\uparrow G(f)$', bbox=ient_wbbox);
ax.set_xlim([-7.5,7.5]); ient_grid(ax); ient_axis(ax);
ax = axs[3]; ax.set_title('Zeitbereich (nach Rekonstruktion)');
ax.plot(t, s(t), color='rwth', linestyle='--', label=r'$s(t)$');
ax.plot(t/deltat/(f[-1]*2), g, 'grun', label=r'$g(t)$');
ax.set_xlabel(r'$\rightarrow t$'); ax.set_xlim([-7.5,7.5]); ax.legend(loc=2); ient_grid(ax); ient_axis(ax);
plt.tight_layout()
else:
axs[0].lines[0].set_ydata(s(t)); axs[0].lines[1].set_ydata(s0); axs[1].lines[-3].set_ydata(S(f));
axs[1].lines[0].set_ydata(np.abs(H))
if s_type == 'cos-Funktion' or s_type == 'sin-Funktion': # dirac plot
ient_dirac_set_data(axs[1].containers, fS0dirac, np.abs(S0dirac));
axs[1].lines[1].set_ydata(np.ones_like(f)*np.nan);
ient_dirac_set_data(axs[2].containers, fGdirac, Gdirac); axs[2].lines[0].set_ydata(np.ones_like(f)*np.nan);
else:
axs[1].lines[1].set_ydata(np.abs(S0)); ient_dirac_set_data(axs[1].containers, [], []);
axs[2].lines[0].set_ydata(G); ient_dirac_set_data(axs[2].containers, [], []);
axs[3].lines[0].set_ydata(s(t)); axs[3].lines[1].set_ydata(g);
if s_type == 'sin-Funktion': # Adapt labels
axs[1].lines[-3].set_label(r'$\mathrm{Im}\{S(f)\}$');
axs[2].yaxis.label.set_text(r'$\uparrow \mathrm{Im}\{G(f)\}$');
else:
axs[1].lines[-3].set_label(r'$S(f)$')
axs[2].yaxis.label.set_text(r'$\uparrow G(f)$');
axs[1].legend(loc=2)
tmp = np.concatenate((np.abs(S0),np.abs(S0dirac))); ient_update_ylim(axs[1], tmp, 0.19, np.max(np.abs(tmp))); ient_update_ylim(axs[2], G, 0.19, np.max(G));
ient_update_ylim(axs[3], np.concatenate((s(t),g)), 0.19, np.max(np.concatenate((s(t),g))));
```
%% Cell type:markdown id: tags:
This notebook is provided as [Open Educational Resource](https://en.wikipedia.org/wiki/Open_educational_resources) (OER). Feel free to use the notebook for your own purposes. The code is licensed under the [MIT license](https://opensource.org/licenses/MIT).
Please attribute the work as follows:
*Christian Rohlfing, Übungsbeispiele zur Vorlesung "Grundgebiete der Elektrotechnik 3 - Signale und Systeme"*, gehalten von Jens-Rainer Ohm, 2019, Institut für Nachrichtentechnik, RWTH Aachen University.
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