Difference between revisions of "Aufgaben:Exercise 4.10Z: Correlation Duration"

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{{quiz-Header|Buchseite=Stochastische Signaltheorie/*Autokorrelationsfunktion (AKF)*
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{{quiz-Header|Buchseite=Theory_of_Stochastic_Signals/Auto-Correlation_Function
 
}}
 
}}
  
[[File:P_ID393__Sto_Z_4_10.png|right|Musterfunktionen ergodischer Prozesse]]
+
[[File:P_ID393__Sto_Z_4_10.png|right|frame|Pattern signals of ergodic processes]]
Das nebenstehende Bild zeigt Mustersignale zweier Zufallsprozesse $\{x_i(t)\}$ und $\{y_i(t)\}$ mit jeweils gleicher Leistung  $P_x = P_y = 5\hspace{0.05 cm} \rm mW$. Vorausgesetzt ist hierbei der Widerstand $R = 50\hspace{0.05 cm}\rm  \Omega$. Der Prozess $\{x_i(t)\}$
+
The graphic shows pattern signals of two random processes  $\{x_i(t)\}$  and  $\{y_i(t)\}$  with equal power 
* ist mittelwertfrei $(m_x = 0)$,
+
:$$P_x = P_y = 5\hspace{0.05 cm} \rm mW.$$  
* besitzt die gaußförmige AKF
+
Assuming here the resistance  $R = 50\hspace{0.05 cm}\rm \Omega$.  
:$$\varphi_x (\tau) = \varphi_x (\tau = 0) \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_x)^2},$$
 
* und weist eine äquivalente AKF-Dauer $\nabla \tau_x = 5\hspace{0.05 cm}\rm \mu s $ auf.
 
  
  
Wie aus dem unteren Bild zu erkennen ist, hat der Prozess $\{y_i(t)\}$ sehr viel stärkere innere statistische Bindungen als der Prozess $\{x_i(t)\}$.
+
The random process  $\{x_i(t)\}$
 +
* is zero mean  $(m_x = 0)$,
 +
* has the Gaussian ACF   $\varphi_x (\tau) = \varphi_x (\tau = 0) \cdot {\rm e}^{- \pi \hspace{0.03cm}  \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_x)^2},$  and
 +
* exhibits the equivalent ACF duration  $\nabla \tau_x = 5\hspace{0.05 cm}\rm µ s $ .
  
Oder anders ausgedrückt: Der Zufallsprozess $\{y_i(t)\}$  ist niederfrequenter als  $\{x_i(t)\}$. Die äquivalente AKF-Dauer ist $\nabla \tau_y = 10 \hspace{0.05 cm}\rm \mu s $.
 
  
Aus der Skizze ist auch zu erkennen, dass $\{y_i(t)\}$ im Gegensatz zu $\{x_i(t)\}$ nicht gleichsignalfrei ist. Der Gleichsignalanteil beträgt vielmehr $m_y = -0.3 \hspace{0.05 cm}\rm V$.
+
As can be seen from the diagram below,  the random process  $\{y_i(t)\}$  has much stronger internal statistical bindings than the random process  $\{x_i(t)\}$.
 +
Or,  to put it another way:
 +
*The random process  $\{y_i(t)\}$  is lower frequency than  $\{x_i(t)\}$.  
 +
*The equivalent ACF duration is  $\nabla \tau_y = 10 \hspace{0.05 cm}\rm µ s $.
  
  
''Hinweise:''
 
*Die Aufgabe gehört zum  Kapitel [[Stochastische_Signaltheorie/Autokorrelationsfunktion_(AKF)|Autokorrelationsfunktion]].
 
*Bezug genommen wird insbesondere auf die Seite [[Stochastische_Signaltheorie/Autokorrelationsfunktion_(AKF)#Interpretation_der_Autokorrelationsfunktion|Interpretation der Autokorrelationsfunktion]].
 
*Sollte die Eingabe des Zahlenwertes „0” erforderlich sein, so geben Sie bitte „0.” ein.
 
  
  
===Fragebogen===
+
From the sketch it can also be seen that  $\{y_i(t)\}$  in contrast to  $\{x_i(t)\}$  is not DC free.  The DC signal component is rather  $m_y = -0.3 \hspace{0.05 cm}\rm V$.
 +
 
 +
 
 +
 
 +
 
 +
 
 +
'''Hint''':
 +
*The exercise belongs to the chapter  [[Theory_of_Stochastic_Signals/Auto-Correlation_Function|Auto-Correlation Function]].
 +
*Reference is made in particular to the section  [[Theory_of_Stochastic_Signals/Auto-Correlation_Function#Interpretation_of_the_auto-correlation_function|Interpretation of the auto-correlation function]].
 +
 +
 
 +
 
 +
 
 +
 
 +
===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Welchen Effektivwert besitzen die Mustersignale des Prozesses {<i>x<sub>i</sub></i>(<i>t</i>)}?
+
{What is the standard deviation&nbsp; $(\sigma_x)$&nbsp; of the pattern signals of the process&nbsp; $\{x_i(t)\}$?
 
|type="{}"}
 
|type="{}"}
$\sigma_x$ = { 0.5 3% } V
+
$\sigma_x \ = \ $ { 0.5 3% } $\ \rm V$
  
  
{Welche AKF-Werte ergeben sich f&uuml;r <i>&tau;</i> = 2 &mu;s bzw. f&uuml;r <i>&tau;</i> = 5 &mu;s?
+
{What ACF values result for&nbsp; $\tau = 2\hspace{0.05 cm}\rm &micro;s$ &nbsp;resp.&nbsp; $\tau = 5\hspace{0.05 cm}\rm &micro; s$?
 
|type="{}"}
 
|type="{}"}
$\phi_x(\tau = 2 \mu s)$ = { 3.025 3% } $mW$
+
$\varphi_x(\tau = 2\hspace{0.05 cm}{\rm &micro; s}) \ = \ $ { 3.025 3% } $\ \rm mW$
$\phi_x(\tau = 5 \mu s)$ = { 0.216 3% } $mW$
+
$\varphi_x(\tau = 5\hspace{0.05 cm}{\rm &micro; s}) \ = \ $ { 0.216 3% } $\ \rm mW$
  
  
{Wie gro&szlig; ist die Korrelationsdauer <i>T</i><sub>K</sub>, also derjenige Zeitpunkt, bei dem die AKF auf die H&auml;lfte des Maximums abgefallen ist?
+
{What is the correlation time&nbsp; $T_{\rm K}$,&nbsp; i.e. the time at which the ACF has dropped to half of the maximum?
 
|type="{}"}
 
|type="{}"}
$T_K$ = { 2.35 3% } $\mu s$
+
$T_{\rm K}  \ = \ $ { 2.35 3% } $\ \rm &micro; s$
  
  
{Welchen Effektivwert besitzen die Mustersignale des Prozesses {<i>y<sub>i</sub></i>(<i>t</i>)}?
+
{What is the standard deviation&nbsp; $(\sigma_y)$&nbsp; of the pattern signals of the process $\{y_i(t)\}$?
 
|type="{}"}
 
|type="{}"}
$\sigma_y$ = { 0.4 3% } V
+
$\sigma_y \ = \ $ { 0.4 3% } $\ \rm V$
  
  
{Berechnen Sie die AKF <i>&phi;<sub>y</sub></i>(<i>&tau;</i>). Wie groß ist der AKF-Wert bei <i>&tau;</i> = 10 &mu;s? Welcher AKF-Verlauf ergäbe sich bei positivem Mittelwert (<i>m<sub>y</sub></i> = 0.3 V)?
+
{Calculate the ACF&nbsp; $\varphi_x(\tau)$.&nbsp; What is the ACF value at&nbsp; $\tau = 10\hspace{0.05 cm}\rm &micro; s$?&nbsp; What would be the ACF curve with positive mean&nbsp; $(m_y = +0.3 \hspace{0.05 cm}\rm V)$?
 
|type="{}"}
 
|type="{}"}
$\phi_y(\tau = 10 \mu s)$ = { 1.938 3% } $mW$
+
$\varphi_y(\tau = 10\hspace{0.05 cm}{\rm &micro; s}) \ = \ $ { 1.938 3% } $\ \rm mW$
  
  
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</quiz>
 
</quiz>
  
===Musterlösung===
+
===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
:<b>1.</b>&nbsp;&nbsp;Der quadratische Mittelwert ergibt sich zu <i>R</i> &middot; <i>P<sub>x</sub></i> = 50 &Omega; &middot; 5 mW = 0.25 V<sup>2</sup>. Daraus folgt der Effektivwert <i>&sigma;<sub>x</sub></i> <u>= 0.5V</u>.
+
'''(1)'''&nbsp; The second moment results to&nbsp; $m_{2x} = R \cdot P_x = 50 \hspace{0.05 cm}{\rm \Omega}\cdot 5 \hspace{0.05 cm}{\rm mW}= 0.25 \hspace{0.05 cm}{\rm V}^2.$
 +
*From this follows the standard deviation&nbsp; $\sigma_x\hspace{0.15 cm}\underline{= 0.5\hspace{0.05 cm}{\rm V}}$.
 +
 
 +
 
  
:<b>2.</b>&nbsp;&nbsp;Wegen <i>P<sub>x</sub></i> = <i>&phi;<sub>x</sub></i>(<i>&tau;</i> = 0) gilt f&uuml;r die AKF allgemein:
+
'''(2)'''&nbsp; Because of&nbsp; $P_x = \varphi_x (\tau = 0)$&nbsp; holds for the ACF in general:  
 
:$$\varphi_x (\tau) = 5 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_x)^2}.$$
 
:$$\varphi_x (\tau) = 5 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_x)^2}.$$
 +
*From this we obtain:
 +
:$$\varphi_x (\tau = {\rm 2\hspace{0.1cm} &micro; s}) = 5 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- {\rm 0.16 }\pi } \hspace{0.15cm}\underline{= 3.025 \hspace{0.1cm} \rm mW},$$
 +
:$$\varphi_x (\tau = {\rm 5\hspace{0.1cm} \rm &micro; s}) = 5 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- \pi } \hspace{0.15cm}\underline{= 0.216 \hspace{0.1cm} \rm mW}.$$
 +
 +
 +
 +
[[File:P_ID394__Sto_Z_4_10_e.png|right|frame|Two times Gaussian ACF]]
 +
'''(3)'''&nbsp; Here the following determination equation holds:
 +
:$${\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(T_{\rm K} / {\rm \nabla} \tau_x)^2} \stackrel{!}{=} {\rm 0.5} \hspace{0.5cm}\Rightarrow\hspace{0.5cm} (T_{\rm K} / {\rm \nabla} \tau_x)^2 = \sqrt{{ \ln(2)}/{\pi}}\hspace{0.05cm}.$$
 +
 +
*From this follows&nbsp; $T_{\rm K}\hspace{0.15 cm}\underline{= 2.35\hspace{0.05 cm}{\rm &micro; s}}$.
 +
*With another ACF form,&nbsp; a different ratio is obtained for&nbsp; $T_{\rm K} / {\rm \nabla} \tau_x$.
 +
  
:Daraus erh&auml;lt man:
 
:$$\varphi_x (\tau = {\rm 2\hspace{0.1cm} \mu s}) = 5 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- {\rm 0.16 }\pi } \hspace{0.15cm}\underline{= 3.025 \hspace{0.1cm} \rm mW},$$
 
:$$\varphi_x (\tau = {\rm 5\hspace{0.1cm} \rm \mu s}) = 5 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- \pi } \hspace{0.15cm}\underline{= 0.216 \hspace{0.1cm} \rm mW}.$$
 
  
:<b>3.</b>&nbsp;&nbsp;Hier gilt folgende Bestimmungsgleichung:
 
:$${\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(T_{\rm K} / {\rm \nabla} \tau_x)^2} \stackrel{!}{=} {\rm 0.5} \hspace{0.5cm}\Rightarrow\hspace{0.5cm} (T_{\rm K} / {\rm \nabla} \tau_x)^2 = \sqrt{{ ln(2)}/{\pi}}\hspace{0.05cm}.$$
 
  
:Daraus folgt <i>T</i><sub>K</sub> <u>= 2.35 &mu;s</u>. Bei anderer AKF-Form erhält man ein anderes Verhältnis für <i>T</i><sub>K</sub>/&#8711;<i>&tau;<sub>x</sub></i>.
+
'''(4)'''&nbsp; Because of&nbsp; $P_x = P_y$&nbsp; the second order moments of&nbsp; $x$&nbsp; and&nbsp; $y$&nbsp; are equal &nbsp; $0.25\hspace{0.05 cm}\rm V^2$.  
 +
*Taking into account the mean value&nbsp; $m_y = -0.3 \hspace{0.05 cm}\rm V$&nbsp; holds:
 +
:$$m_y^2 + \sigma_y^2 = \rm 0.25 \hspace{0.05 cm} V^2.$$
 +
*From this follows:
 +
:$$\sigma_y\hspace{0.15 cm}\underline{= 0.4\hspace{0.05 cm}{\rm V}}.$$
  
:<b>4.</b>&nbsp;Wegen <i>P<sub>x</sub></i> = <i>P<sub>y</sub></i> sind die quadratischen Mittelwerte von <i>x</i> und <i>y</i> gleich, und zwar jeweils 0.25 V<sup>2</sup>. Unter Ber&uuml;cksichtigung des Mittelwertes <i>m<sub>y</sub></i> = &ndash;0.3 V gilt:
 
:$$m_y^2 + \sigma_y^2 = \rm 0.25 V^2.$$
 
[[File:P_ID394__Sto_Z_4_10_e.png|right|]]
 
  
:Daraus folgt <i>&sigma;<sub>y</sub></i> <u>= 0.4 V</u>.
 
  
:<b>5.</b>&nbsp;&nbsp;Bezogen auf den Einheitswiderstand <i>R</i> = 1 &Omega; lautet die AKF des Prozesses {<i>y<sub>i</sub></i>(<i>t</i>)}:
+
'''(5)'''&nbsp; In terms of unit resistance&nbsp; $ R = 1 \hspace{0.05 cm}{\rm \Omega}$&nbsp; the ACF of the process&nbsp; $\{y_i(t)\}$ is:
 
:$$\varphi_y (\tau) = m_y^2 + \sigma_y^2 \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_y)^2}.$$
 
:$$\varphi_y (\tau) = m_y^2 + \sigma_y^2 \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_y)^2}.$$
  
:Rechts sehen Sie den Funktionsverlauf. Bezogen auf den Widerstand <i>R</i> = 50 &Omega; ergeben sich die nachfolgend angegebenen AKF-Werte:
+
*On the right you can see the ACF curve.&nbsp; Related to the resistor&nbsp; $ R = 50 \hspace{0.05 cm}{\rm \Omega}$&nbsp; results in the following ACF values:
:$$\varphi_y (\tau = 0) = 5 \hspace{0.1cm} {\rm mW} , \hspace{0.1cm} \atop \varphi_y (\tau \rightarrow \infty) = 1.8\hspace{0.1cm} {\rm mW} .$$
+
:$$\varphi_y (\tau = 0) = 5 \hspace{0.1cm} {\rm mW} , \hspace{0.5cm} \varphi_y (\tau \rightarrow \infty) = 1.8\hspace{0.1cm} {\rm mW} .$$
  
:Daraus folgt:
+
*From this follows:
:$$\varphi_y(\tau) = 1.8 \hspace{0.1cm} {\rm mW} + 3.2 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_y)^2}$$
+
:$$\varphi_y(\tau) = 1.8 \hspace{0.1cm} {\rm mW} + 3.2 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_y)^2} \hspace{0.3cm }\Rightarrow \hspace{0.3cm }\varphi_y(\tau = 10\hspace{0.05 cm}{\rm &micro; s})
 +
\hspace{0.15 cm}\underline{=1.938\hspace{0.05 cm}\rm mW}.$$
  
:mit dem <u>Zahlenwert 1.938 mW bei <i>&tau;</i> = 10 &mu;s</u>. Bei positivem Mittelwert <i>m<sub>y</sub></i> (mit gleichem Betrag) w&uuml;rde sich an der AKF nichts &auml;ndern, da <i>m<sub>y</sub></i> in die AKF-Gleichung quadratisch eingeht.
+
*With positive mean&nbsp; $m_y$&nbsp; $($having the same magnitude$)$,&nbsp; there would be no change in the ACF,&nbsp; since&nbsp; $m_y$&nbsp; is squared in the ACF equation.
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  
  
  
[[Category:Aufgaben zu Stochastische Signaltheorie|^4.4 Autokorrelationsfunktion (AKF)^]]
+
[[Category:Theory of Stochastic Signals: Exercises|^4.4 Auto-Correlation Function^]]

Latest revision as of 19:52, 20 March 2022

Pattern signals of ergodic processes

The graphic shows pattern signals of two random processes  $\{x_i(t)\}$  and  $\{y_i(t)\}$  with equal power 

$$P_x = P_y = 5\hspace{0.05 cm} \rm mW.$$

Assuming here the resistance  $R = 50\hspace{0.05 cm}\rm \Omega$.


The random process  $\{x_i(t)\}$

  • is zero mean  $(m_x = 0)$,
  • has the Gaussian ACF   $\varphi_x (\tau) = \varphi_x (\tau = 0) \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_x)^2},$  and
  • exhibits the equivalent ACF duration  $\nabla \tau_x = 5\hspace{0.05 cm}\rm µ s $ .


As can be seen from the diagram below,  the random process  $\{y_i(t)\}$  has much stronger internal statistical bindings than the random process  $\{x_i(t)\}$. Or,  to put it another way:

  • The random process  $\{y_i(t)\}$  is lower frequency than  $\{x_i(t)\}$.
  • The equivalent ACF duration is  $\nabla \tau_y = 10 \hspace{0.05 cm}\rm µ s $.



From the sketch it can also be seen that  $\{y_i(t)\}$  in contrast to  $\{x_i(t)\}$  is not DC free.  The DC signal component is rather  $m_y = -0.3 \hspace{0.05 cm}\rm V$.



Hint:



Questions

1

What is the standard deviation  $(\sigma_x)$  of the pattern signals of the process  $\{x_i(t)\}$?

$\sigma_x \ = \ $

$\ \rm V$

2

What ACF values result for  $\tau = 2\hspace{0.05 cm}\rm µs$  resp.  $\tau = 5\hspace{0.05 cm}\rm µ s$?

$\varphi_x(\tau = 2\hspace{0.05 cm}{\rm µ s}) \ = \ $

$\ \rm mW$
$\varphi_x(\tau = 5\hspace{0.05 cm}{\rm µ s}) \ = \ $

$\ \rm mW$

3

What is the correlation time  $T_{\rm K}$,  i.e. the time at which the ACF has dropped to half of the maximum?

$T_{\rm K} \ = \ $

$\ \rm µ s$

4

What is the standard deviation  $(\sigma_y)$  of the pattern signals of the process $\{y_i(t)\}$?

$\sigma_y \ = \ $

$\ \rm V$

5

Calculate the ACF  $\varphi_x(\tau)$.  What is the ACF value at  $\tau = 10\hspace{0.05 cm}\rm µ s$?  What would be the ACF curve with positive mean  $(m_y = +0.3 \hspace{0.05 cm}\rm V)$?

$\varphi_y(\tau = 10\hspace{0.05 cm}{\rm µ s}) \ = \ $

$\ \rm mW$


Solution

(1)  The second moment results to  $m_{2x} = R \cdot P_x = 50 \hspace{0.05 cm}{\rm \Omega}\cdot 5 \hspace{0.05 cm}{\rm mW}= 0.25 \hspace{0.05 cm}{\rm V}^2.$

  • From this follows the standard deviation  $\sigma_x\hspace{0.15 cm}\underline{= 0.5\hspace{0.05 cm}{\rm V}}$.


(2)  Because of  $P_x = \varphi_x (\tau = 0)$  holds for the ACF in general:

$$\varphi_x (\tau) = 5 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_x)^2}.$$
  • From this we obtain:
$$\varphi_x (\tau = {\rm 2\hspace{0.1cm} µ s}) = 5 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- {\rm 0.16 }\pi } \hspace{0.15cm}\underline{= 3.025 \hspace{0.1cm} \rm mW},$$
$$\varphi_x (\tau = {\rm 5\hspace{0.1cm} \rm µ s}) = 5 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- \pi } \hspace{0.15cm}\underline{= 0.216 \hspace{0.1cm} \rm mW}.$$


Two times Gaussian ACF

(3)  Here the following determination equation holds:

$${\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(T_{\rm K} / {\rm \nabla} \tau_x)^2} \stackrel{!}{=} {\rm 0.5} \hspace{0.5cm}\Rightarrow\hspace{0.5cm} (T_{\rm K} / {\rm \nabla} \tau_x)^2 = \sqrt{{ \ln(2)}/{\pi}}\hspace{0.05cm}.$$
  • From this follows  $T_{\rm K}\hspace{0.15 cm}\underline{= 2.35\hspace{0.05 cm}{\rm µ s}}$.
  • With another ACF form,  a different ratio is obtained for  $T_{\rm K} / {\rm \nabla} \tau_x$.



(4)  Because of  $P_x = P_y$  the second order moments of  $x$  and  $y$  are equal   $0.25\hspace{0.05 cm}\rm V^2$.

  • Taking into account the mean value  $m_y = -0.3 \hspace{0.05 cm}\rm V$  holds:
$$m_y^2 + \sigma_y^2 = \rm 0.25 \hspace{0.05 cm} V^2.$$
  • From this follows:
$$\sigma_y\hspace{0.15 cm}\underline{= 0.4\hspace{0.05 cm}{\rm V}}.$$


(5)  In terms of unit resistance  $ R = 1 \hspace{0.05 cm}{\rm \Omega}$  the ACF of the process  $\{y_i(t)\}$ is:

$$\varphi_y (\tau) = m_y^2 + \sigma_y^2 \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_y)^2}.$$
  • On the right you can see the ACF curve.  Related to the resistor  $ R = 50 \hspace{0.05 cm}{\rm \Omega}$  results in the following ACF values:
$$\varphi_y (\tau = 0) = 5 \hspace{0.1cm} {\rm mW} , \hspace{0.5cm} \varphi_y (\tau \rightarrow \infty) = 1.8\hspace{0.1cm} {\rm mW} .$$
  • From this follows:
$$\varphi_y(\tau) = 1.8 \hspace{0.1cm} {\rm mW} + 3.2 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_y)^2} \hspace{0.3cm }\Rightarrow \hspace{0.3cm }\varphi_y(\tau = 10\hspace{0.05 cm}{\rm µ s}) \hspace{0.15 cm}\underline{=1.938\hspace{0.05 cm}\rm mW}.$$
  • With positive mean  $m_y$  $($having the same magnitude$)$,  there would be no change in the ACF,  since  $m_y$  is squared in the ACF equation.