Merge remote-tracking branch 'upstream/master'

14 files changed, 645 insertions, 665 deletions

diff --git a/buch/papers/ellfilter/einleitung.tex b/buch/papers/ellfilter/einleitung.tex
index 581d452..05061d1 100644
--- a/buch/papers/ellfilter/einleitung.tex
+++ b/buch/papers/ellfilter/einleitung.tex
@@ -2,10 +2,10 @@
 
 Filter sind womöglich eines der wichtigsten Elemente in der Signalverarbeitung und finden Anwendungen in der digitalen und analogen Elektrotechnik.
 Besonders hilfreich ist die Untergruppe der linearen Filter.
-Elektronische Schaltungen mit linearen Bauelementen wie Kondensatoren, Spulen und Widerständen führen immer zu linearen zeitinvarianten Systemen (LTI-System von englich \textit{time-invariant system}).
+Elektronische Schaltungen mit linearen Bauelementen wie Kondensatoren, Spulen und Widerständen führen immer zu linearen zeitinvarianten Systemen (LTI-System von englisch \textit{time-invariant system}).
 Durch die Linearität werden beim Filtern keine neuen Frequenzanteile erzeugt, was es erlaubt, einen Frequenzanteil eines Signals verzerrungsfrei herauszufiltern.
 Diese Eigenschaft macht es sinnvoll, lineare Filter im Frequenzbereich zu beschreiben.
-Die Übertragungsfunktion eines linearen Filters im Frequenzbereich $H(\Omega)$ ist dabei immer eine rationale Funktion, also ein Quotient von zwei Polynomen.
+Die Übertragungsfunktion $H(\Omega)$ eines linearen Filters im Frequenzbereich ist dabei immer eine rationale Funktion, also ein Quotient von zwei Polynomen.
 Dabei ist $\Omega = 2 \pi f$ die Frequenzeinheit.
 Die Polynome haben dabei immer reelle oder komplexkonjugierte Nullstellen.
 
@@ -40,8 +40,8 @@ Des weiteren müssen alle Nullstellen und Pole von $F_N$ auf der linken Halbeben
 $w$ ist die normalisierte Frequenz, die es erlaubt ein Filter unabhängig von der Grenzfrequenz zu beschrieben.
 Bei $w=1$ hat das Filter eine Dämpfung von $1/(1+\varepsilon^2)$.
 $N \in \mathbb{N} $ gibt die Ordnung des Filters vor, also die maximale Anzahl Pole oder Nullstellen.
-Je höher $N$ gewählt wird, desto steiler ist der Übergang in denn Sperrbereich.
-Grössere $N$ sind erfordern jedoch aufwendigere Implementierungen und haben mehr Phasenverschiebung.
+Je höher $N$ gewählt wird, desto steiler ist der Übergang im Sperrbereich.
+Grössere $N$ erfordern jedoch aufwendigere Implementierungen und haben mehr Phasenverschiebung.
 
 Eine einfache Funktion, die für $F_N$ eingesetzt werden kann, ist das Polynom $w^N$.
 Tatsächlich erhalten wir damit das Butterworth Filter, wie in Abbildung \ref{ellfilter:fig:butterworth} ersichtlich.
@@ -63,12 +63,12 @@ Eine Reihe von rationalen Funktionen können für $F_N$ eingesetzt werden, um Ti
 \end{align}
 Mit der Ausnahme vom Butterworth-Filter sind alle Filter nach speziellen Funktionen benannt.
 Alle diese Filter sind optimal hinsichtlich einer Eigenschaft.
-Es scheint so als sind gewisse Eigenschaften dieser speziellen Funktionen verantwortlich für die Optimalität dieser Filter.
+Es scheint so, als sind gewisse Eigenschaften dieser speziellen Funktionen verantwortlich für die Optimalität dieser Filter.
 Das Butterworth-Filter, zum Beispiel, ist maximal flach im Durchlassbereich.
 In vielen Anwendung sind Filter mit einem steilen Übergang gewünscht.
 Da es technisch nicht möglich ist, mit einer rationalen Funktion mit begrenzter Anzahl Pole eine steile Flanke zu erreichen, während der Durchlass- und Sperrbereich flach und monoton sind, gibt es Filtertypen, die absichtlich Welligkeiten in der Frequenzantwort aufweisen.
 Besonders effizient sind Filter mit Equiripple-Verhalten, wessen Welligkeit optimal definiert wird für eine maximal steile Flanke, während die maximale Abweichung zum idealen Filter begrenzt ist.
-Die Welligkeit beansprucht dabei einen begrenzen Verstärkungsintervall und nützt diesen Vollständig aus, indem sie periodisch die Grenzen des Intervalls berührt.
+Die Welligkeit beansprucht dabei einen begrenzen Verstärkungsintervall und nützt diesen vollständig aus, indem sie periodisch die Grenzen des Intervalls berührt.
 Das Tschebyscheff-1 Filter, zum Beispiel, hat Equiripple-Verhalten im Durchlassbereich, währendem es im Sperrbereich monoton abfallend ist.
 Beim Tschebyscheff-2 Filter ist es umgekehrt.
 
diff --git a/buch/papers/ellfilter/elliptic.tex b/buch/papers/ellfilter/elliptic.tex
index 81821c1..651d6bc 100644
--- a/buch/papers/ellfilter/elliptic.tex
+++ b/buch/papers/ellfilter/elliptic.tex
@@ -2,16 +2,16 @@
 
 Kommen wir nun zum eigentlichen Teil dieses Papers, den rationalen elliptischen Funktionen \cite{ellfilter:bib:orfanidis}
 \begin{align}
-    R_N(\xi, w) &= \cd \left(N~f_1(\xi)~\cd^{-1}(w, 1/\xi), f_2(\xi)\right) \label{ellfilter:eq:elliptic}\\
+    R_N(w, \xi) &= \cd \left(N~f_1(\xi)~\cd^{-1}(w, 1/\xi), f_2(\xi)\right) \label{ellfilter:eq:elliptic}\\
                 &= \cd \left(N~\frac{K_1}{K}~\cd^{-1}(w, k), k_1\right) , \quad k= 1/\xi, k_1 = 1/f(\xi) \\
                 &= \cd \left(N~K_1~z , k_1 \right), \quad w= \cd(z K, k)
 \end{align}
 Beim Betrachten dieser Definition, fällt die Ähnlichkeit zur trigonometrische Darstellung der Tsche\-byschef-Polynome \eqref{ellfilter:eq:chebychef_polynomials} auf.
 Wie bei den Tschebyscheff-Polynomen ist die Formel mit speziellen Funktionen geschrieben.
 Es kann jedoch gezeigt werden, dass es sich tatsächlich um rationale Funktionen handelt, wie es für ein lineares Filter vorausgesetzt wird.
-Die elliptischen Funktionen werden also genau so eingesetzt, dass die resultierenden Nullstellen und Pole eine rationale Funktion ergeben. 
+Die elliptischen Funktionen werden also genau so eingesetzt, dass die resultierenden Nullstellen und Pole eine rationale Funktion ergeben.
 Anstelle des Kosinus bei den Tschebyscheff-Polynomen kommt hier die $\cd$-Funktion zum Einsatz.
-Die Ordnungszahl $N$ kommt auch als Faktor for.
+Die Ordnungszahl $N$ kommt auch als Faktor vor.
 Zusätzlich werden noch zwei verschiedene elliptische Moduli $k$ und $k_1$ gebraucht.
 Bei $k = k_1 = 0$ wird der $\cd$ zum Kosinus und wir erhalten in diesem Spezialfall die Tschebyschef-Polynome.
 
@@ -52,18 +52,21 @@ Abbildung \ref{ellfilter:fig:elliptic_freq} zeigt eine rationale elliptische Fun
 \end{figure}
 
 Da sich die Funktion im Übergangsbereich nur zur nächsten Reihe von Polstellen bewegt, ist der Übergangsbereich monoton steigend.
-Theoretisch könnte eine gleiches Durchlass- und Sperrbereichsverhalten erreicht werden, wenn die Funktion auf eine andere Reihe ansteigen würde.
+Theoretisch könnte eine gleiches Durchlass- und Sperrbereichsverhalten erreicht werden, wenn die Funktion auf eine andere Reihe ansteigen würde, z.B. $\mathrm{Im(z) = 3K^\prime}$.
 Dies würde jedoch zu Oszillationen zwischen $1$ und $1/k$ im Übergangsbereich führen.
 
 \subsection{Gradgleichung}
 
 Damit die Pol- und Nullstellen genau in dieser Konstellation durchfahren werden, müssen die elliptischen Moduli des inneren und äusseren $\cd$ aufeinander abgestimmt werden.
-In der reellen Richtung müssen sich die Periodizitäten $K$ und $K_1$ um den Faktor $N$ unterscheiden, während die imagiäre Periodizitäten $K^\prime$ und $K^\prime_1$ gleich bleiben müssen.
+In der reellen Richtung müssen sich die Periodizitäten $K$ und $K_1$ um den Faktor $N$ unterscheiden, während die imaginäre Periodizitäten $K^\prime$ und $K^\prime_1$ gleich bleiben müssen.
 Zur Erinnerung, $K$ und $K^\prime$ sind durch elliptische Integrale definiert und vom Modul $k$ abhängig wie ersichtlich in Abbildung \ref{ellfilter:fig:kprime}.
 \begin{figure}
     \centering
     \input{papers/ellfilter/python/k.pgf}
-    \caption{Die Periodizitäten in realer und imaginärer Richtung in Abhängigkeit vom elliptischen Modul $k$.}
+    \caption{
+        Die Periodizitäten in realer und imaginärer Richtung in Abhängigkeit vom elliptischen Modul $k$.
+        In der rechten Grafik sind $K$ und $K^\prime$ gegenübergestellt, wobei alle möglichen Kombinationen auf der eingezeichneten Ortskurve liegen.
+    }
     \label{ellfilter:fig:kprime}
 \end{figure}
 $K$ und $K^\prime$ sind durch die Ortskurve $K + jK^\prime$ aneinander gebunden und benötigen den Zusatzfaktor $K_1/K$ in \eqref{ellfilter:eq:elliptic}, um die genanten Forderungen einzuhalten.
@@ -84,7 +87,7 @@ k_1 = k^N \prod_{i=1}^L \sn^4 \Bigg( \frac{2i - 1}{N} K, k \Bigg),
 \quad \text{wobei} \quad
 N = 2L+r.
 \end{equation}
-Die Herleitung ist sehr umfassend und wird in \cite{ellfilter:bib:orfanidis} im Detail angeschaut.
+Die Herleitung ist sehr umfangreich und wird in \cite{ellfilter:bib:orfanidis} im Detail angeschaut.
 
 \subsection{Berechnung der rationalen Funktion}
 
@@ -102,7 +105,7 @@ Wenn $k$ und $N$ bekannt sind, können die Position der Pol- und Nullstellen $p_
     }
     \label{ellfilter:fig:pn}
 \end{figure}
-Dabei muss aufgepasst werden, dass insgesamt nur $N$ Nullstellen und $N$ Pole gesetzt werden, da bei der transformation mit dem $\cd$ mehrere Werte auf einen abgebildet werden und mehrfache Pole und Nullstellen nicht erwünscht sind.
+Dabei muss aufgepasst werden, dass insgesamt nur $N$ Nullstellen und $N$ Pole gesetzt werden, da bei der Transformation mit dem $\cd$ mehrere Werte auf einen abgebildet werden und mehrfache Pole und Nullstellen nicht erwünscht sind.
 Wegen der Periodizität sind diese in der komplexen $z$-Ebene linear angeordnet:
 \begin{align}
     n_i(k) &= K\frac{2i+1}{N} \\
@@ -116,7 +119,7 @@ wobei $r_0$ so gewählt werden muss, dass $R_N(w, k) = 1$.
 
 \section{Elliptisches Filter}
 
-Um ein elliptisches Filter auszulegen werden aber nicht die Pol- und Nullstellen der rationalen Funktion gebraucht, sondern diejenigen der Übertragungsfunktion $H(s)$ der komplexen Frequenz $s = j\Omega + \sigma$.
+Um ein elliptisches Filter auszulegen, werden aber nicht die Pol- und Nullstellen der rationalen Funktion gebraucht, sondern diejenigen der Übertragungsfunktion $H(s)$ der komplexen Frequenz $s = j\Omega + \sigma$.
 Der Bezug zum quadratischen Amplitudengang \eqref{ellfilter:eq:quadratic_transfer} ist dabei
 \begin{equation}
     |H(\Omega)|^2 = H(s) H(s^*),
@@ -124,9 +127,3 @@ Der Bezug zum quadratischen Amplitudengang \eqref{ellfilter:eq:quadratic_transfe
 wobei $*$ die komplexe Konjugation kennzeichnet.
 Die genaue Berechnung geht einiges tiefer in die Filtertheorie, und verlässt das Gebiet der speziellen Funktionen.
 Der interessierte Leser wird auf \cite[Kapitel~5]{ellfilter:bib:orfanidis} verwiesen.
-
-% \subsection{Schlussfolgerung}
-
-% Die elliptischen Filter können als direkte Erweiterung der Tschebyscheff-Filter verstanden werden.
-% Bei den Tschebyscheff-Polynomen haben wir gesehen, dass die Trigonometrische Formel zu einfachen Polynomen umgewandelt werden kann.
-% Im elliptischen Fall entstehen so rationale Funktionen mit Nullstellen und auch Pole.
diff --git a/buch/papers/ellfilter/jacobi.tex b/buch/papers/ellfilter/jacobi.tex
index 841cd7d..06548a5 100644
--- a/buch/papers/ellfilter/jacobi.tex
+++ b/buch/papers/ellfilter/jacobi.tex
@@ -1,6 +1,6 @@
 \section{Jacobische elliptische Funktionen}
 
-Für das elliptische Filter werden, wie es der Name bereits deutet, elliptische Funktionen gebraucht.
+Für das elliptische Filter werden, wie es der Name bereits andeutet, elliptische Funktionen gebraucht.
 Wie die trigonometrischen Funktionen Zusammenhänge eines Kreises darlegen, beschreiben die elliptischen Funktionen Ellipsen.
 Es ist daher naheliegend, dass der Kosinus des Tschebyscheff-Filters gegen ein elliptisches Pendant ausgetauscht werden könnte.
 Der Begriff elliptische Funktion wird für sehr viele Funktionen gebraucht, daher ist es hier wichtig zu erwähnen, dass es hier ausschliesslich um die Jacobischen elliptischen Funktionen geht.
@@ -32,7 +32,7 @@ Das Winkelargument $z$ kann durch das elliptische Integral erster Art
 \end{equation}
 mit dem Winkel $\phi$ in Verbindung gebracht werden.
 
-Dabei wird das vollständige und unvollständige elliptische integral unterschieden.
+Dabei wird das vollständige und unvollständige elliptische Integral unterschieden.
 Beim vollständigen Integral
 \begin{equation}
     K(k)
@@ -46,7 +46,7 @@ Beim vollständigen Integral
         }
     }
 \end{equation}
-wird über ein viertel Ellipsenbogen integriert, also bis $\phi=\pi/2$ und liefert das Winkelargument für eine Vierteldrehung.
+wird über ein Viertelellipsenbogen integriert, also bis $\phi=\pi/2$ und liefert das Winkelargument für eine Vierteldrehung.
 Die Zahl wird oft auch abgekürzt mit $K = K(k)$ und ist für das elliptische Filter sehr relevant.
 Alle elliptischen Funktionen sind somit $4K$-periodisch.
 
@@ -89,38 +89,10 @@ Mithilfe von $F^{-1}$ kann zum Beispiel $sn^{-1}$ mit dem elliptischen Integral
     w.
 \end{equation}
 
-% \begin{equation} %TODO remove unnecessary equations
-%     \phi
-%     =
-%      F^{-1}(z, k)
-%      =
-%      \sin^{-1} \big( \sn (z, k ) \big)
-%      =
-%     \sin^{-1} ( w )
-% \end{equation}
-
-% \begin{equation}
-%     F(\phi, k)
-%     =
-%     z
-%     =
-%     F( \sin^{-1} \big( \sn (z, k ) \big) , k)
-%     =
-%     F( \sin^{-1} ( w ), k)
-% \end{equation}
-
-% \begin{equation}
-%     \sn^{-1}(w, k)
-%     =
-%     F(\phi, k),
-%     \quad
-%     \phi = \sin^{-1}(w)
-% \end{equation}
-
 \subsection{Die Funktion $\sn^{-1}$}
 
 Beim Tschebyscheff-Filter konnten wir mit Betrachten des Arcuscosinus die Funktionalität erklären.
-Für das Elliptische Filter machen wir die gleiche Betrachtung mit der $\sn^{-1}$-Funktion.
+Für das elliptische Filter machen wir die gleiche Betrachtung mit der $\sn^{-1}$-Funktion.
 Der $\sn^{-1}$ ist durch das elliptische Integral
 \begin{align}
     \sn^{-1}(w, k)
diff --git a/buch/papers/ellfilter/tschebyscheff.tex b/buch/papers/ellfilter/tschebyscheff.tex
index 0a48949..84095a7 100644
--- a/buch/papers/ellfilter/tschebyscheff.tex
+++ b/buch/papers/ellfilter/tschebyscheff.tex
@@ -3,17 +3,17 @@
 Als Einstieg betrachten wir das Tschebyscheff-Filter, welches sehr verwandt ist mit dem elliptischen Filter.
 Genauer ausgedrückt erhält man die Tschebyscheff-1 und -2 Filter bei Grenzwerten von Parametern beim elliptischen Filter.
 Der Name des Filters deutet schon an, dass die Tschebyscheff-Polynome $T_N$ (siehe auch Kapitel \ref{buch:polynome:section:tschebyscheff}) für das Filter relevant sind:
-\begin{align}
+\begin{align*}
     T_{0}(x)&=1\\
     T_{1}(x)&=x\\
     T_{2}(x)&=2x^{2}-1\\
     T_{3}(x)&=4x^{3}-3x\\
     T_{n+1}(x)&=2x~T_{n}(x)-T_{n-1}(x).
-\end{align}
+\end{align*}
 Bemerkenswert ist, dass die Polynome im Intervall $[-1, 1]$ mit der trigonometrischen Funktion
 \begin{align} \label{ellfilter:eq:chebychef_polynomials}
     T_N(w) &= \cos \left( N \cos^{-1}(w) \right) \\
-           &= \cos \left(N~z \right), \quad w= \cos(z)
+           &= \cos \left(N~z \right), \quad w= \cos(z) \label{ellfilter:eq:chebychef_polynomials2}
 \end{align}
 übereinstimmen.
 Der Zusammenhang lässt sich mit den Doppel- und Mehrfachwinkelfunktionen der trigonometrischen Funktionen erklären.
@@ -36,7 +36,7 @@ Wenn wir die Tschebyscheff-Polynome quadrieren, passen sie perfekt in die Forder
 \end{figure}
 
 Die analytische Fortsetzung von \eqref{ellfilter:eq:chebychef_polynomials} über das Intervall $[-1,1]$ hinaus stimmt mit den Polynomen überein, wie es zu erwarten ist.
-Die genauere Betrachtung wird uns helfen die elliptischen Filter besser zu verstehen.
+Die genauere Betrachtung wird uns helfen, die elliptischen Filter besser zu verstehen.
 Starten wir mit der Funktion, die in \eqref{ellfilter:eq:chebychef_polynomials} als erstes auf $w$ angewendet wird, dem Arcuscosinus.
 Die invertierte Funktion des Kosinus kann als bestimmtes Integral dargestellt werden:
 \begin{align}
@@ -73,7 +73,7 @@ Der Integrand oder auch die Ableitung von $\cos^{-1}(x)$,
     },
 \end{equation}
 bestimmt dabei die Richtung, in welche die Funktion verläuft.
-Der reelle Arcuscosinus is bekanntlich nur für $|z| \leq 1$ definiert.
+Der reelle Arcuscosinus ist bekanntlich nur für $|z| \leq 1$ definiert.
 Hier bleibt der Wert unter der Wurzel positiv und das Integral liefert reelle Werte.
 Doch wenn $|z|$ über 1 hinausgeht, wird der Term unter der Wurzel negativ.
 Durch die Quadratwurzel entstehen für den Integranden zwei rein komplexe Lösungen.
@@ -82,14 +82,15 @@ Abbildung \ref{ellfilter:fig:arccos} zeigt den Arcuscosinus in der komplexen Ebe
 \begin{figure}
     \centering
     \input{papers/ellfilter/tikz/arccos.tikz.tex}
-    \caption{Die Funktion $z = \cos^{-1}(w)$ dargestellt in der komplexen ebene.}
+    \caption{Die Funktion $z = \cos^{-1}(w)$ dargestellt in der komplexen Ebene.}
     \label{ellfilter:fig:arccos}
 \end{figure}
-Wegen der Periodizität des Kosinus ist auch der Arcuscosinus $2\pi$-periodisch.
+Wegen der Periodizität des Kosinus werden periodisch Werte in der $z$-Ebene auf den gleichen Wert in $w$ abgebildet.
+Das gleiche Muster kommt daher periodisch vor.
 Das Einzeichnen von Pol- und Nullstellen ist hilfreich für die Betrachtung der Funktion.
 
 
-In \eqref{ellfilter:eq:chebychef_polynomials} wird $z$ mit dem Ordnungsfaktor $N$ multipliziert und durch die Kosinusfunktion zurück transformiert.
+In \eqref{ellfilter:eq:chebychef_polynomials2} wird $z$ mit dem Ordnungsfaktor $N$ multipliziert und durch die Kosinusfunktion zurück transformiert.
 Die Skalierung hat zur Folge, dass bei der Rücktransformation durch den Kosinus mehrere Nullstellen durchlaufen werden.
 Somit passiert $\cos \big( N~\cos^{-1}(w) \big)$ im Intervall $[-1, 1]$ $N$ Nullstellen, wie dargestellt in Abbildung \ref{ellfilter:fig:arccos2}.
 \begin{figure}
@@ -105,4 +106,4 @@ Somit passiert $\cos \big( N~\cos^{-1}(w) \big)$ im Intervall $[-1, 1]$ $N$ Null
     \label{ellfilter:fig:arccos2}
 \end{figure}
 Durch die spezielle Anordnung der Nullstellen hat die Funktion auf der reellen Achse Equiripple-Verhalten und ist dennoch ein Polynom, was sich perfekt für lineare Filter eignet.
-Für $|w| <= 1$ ist die Funktion begrenzt zwischen $-1$ und $1$.
+Für $|w| \le 1$ ist die Funktion begrenzt zwischen $-1$ und $1$.
diff --git a/buch/papers/fm/01_AM.tex b/buch/papers/fm/01_AM.tex
index 714b9a0..7c7107e 100644
--- a/buch/papers/fm/01_AM.tex
+++ b/buch/papers/fm/01_AM.tex
@@ -6,11 +6,19 @@
 \section{Amplitudenmodulation\label{fm:section:teil0}}
 \rhead{AM}
 
-Das Ziel ist FM zu verstehen doch dazu wird zuerst AM erklärt welches einwenig einfacher zu verstehen ist und erst dann übertragen wir die Ideen in FM.
+Das Ziel ist FM zu verstehen doch dazu wird zuerst AM erklärt, welches einwenig einfacher zu verstehen ist und erst dann übertragen wir die Ideen in FM.
 Nun zur Amplitudenmodulation verwenden wir das bevorzugte Trägersignal
 \[
     x_c(t) = A_c \cdot \cos(\omega_ct).
 \]
+Dabei entseht wine Umhüllende kurve die unserem ursprünglichen signal \(m(t)\) entspricht.
+\[
+    x_c(t) = m(t) \cdot \cos(\omega_ct).
+\]
+
+TODO: Bild Umhüllende
+
+
 Dies bringt den grossen Vorteil das, dass modulierend Signal sämtliche Anteile im Frequenzspektrum in Anspruch nimmt 
 und das Trägersignal nur zwei komplexe Schwingungen besitzt. 
 Dies sieht man besonders in der Eulerischen Formel
@@ -21,10 +29,20 @@ Dies sieht man besonders in der Eulerischen Formel
 Dabei ist die negative Frequenz der zweiten komplexen Schwingung zwingend erforderlich, damit in der Summe immer ein reellwertiges Trägersignal ergibt.
 Nun wird der Parameter \(A_c\) durch das  Modulierende Signal \(m(t)\) ersetzt, wobei so \(m(t) \leqslant |1|\) normiert wurde.
 
-Dabei entseht wine Umhüllende kurve die unserem ursprünglichen signal \(m(t)\) entspricht.
-\[
-    x_c(t) = m(t) \cdot \cos(\omega_ct).
-\]
+\subsection{Frequenzspektrum}
+Das Frequenzspektrum ist eine Darstellung von einem Signal im Frequenzbereich, das heisst man erkennt welche Frequenzen in einem Signal vorhanden sind.
+
+Das Frequenzspektrum zeigt uns wo welche Frequenzen sich befinden, dies ist gerade wichtig fürs Frequenzmultiplexen.
+Frequenzmultiplexen braucht man um mehrere Nachrichten verteilt zu übertragen.
+Dafür müsse wir \(x_c(t)\) Fourietransformieren da es nur zwei Summanden, wie \eqref{fm:eq:AM:euler} mit \(e^{+-j\omega_ct}\)gezeigt, verschiebt sich das Signal \(m(t)\) um die träger Frequenz \(\omega_ct\).
+Ist zudem \(m(t) 0 \sin(\omega_m t)\) so vereinfacht sich die Darstellung auf Dirac Impulse die jeweils rechts und links vom Träger signal sind.
+TODO Bild 
+Fürs Frequenzmultiplexen wird dann das Signal \(m(t)\) in die verschiedenen Frequenzen unterteilt um mehr Nachrichten zu übertragen.
+Doch aufs Frequenzmultiplexen und weitere formen wie SSB und DSB möchte ich nicht weiter eingehen.
+Diese können gerne im Nat Skript nachgelesen werden.
+
+Das sich unser Signal \(m(t)\)  so mit dem Träger Signal verhält findet es man wieder gut beim empfangen, mit Hilfe der bandbreite weiss man was alles dazugehört.
+Bei der Frequenzmodulation ist dies einwenig anders, dies sieht man auch (ref modulationsarten)
 
 \begin{figure}
 	\centering
@@ -35,13 +53,10 @@ Dabei entseht wine Umhüllende kurve die unserem ursprünglichen signal \(m(t)\)
 %
 TODO:
 Bilder 
-Hier beschrieib ich was AmplitudenModulation ist und mache dan den link zu Frequenzmodulation inkl Formel \[\cos( \cos x)\]
-so wird beschrieben das daraus eigentlich \(x_c(t) = A_c \cdot \cos(\omega_i)\) wird und somit \(x_c(t) = A_c \cdot \cos(\omega_c + \frac{d \varphi(t)}{dt})\).
-Da \(\sin \) abgeleitet \(\cos \) ergibt, so wird aus dem \(m(t)\) ein \( \frac{d \varphi(t)}{dt}\)  in der momentan frequenz. \[ \Rightarrow \cos( \cos x) \]
-\subsection{Frequenzspektrum}
-Das Frequenzspektrum ist eine Darstellung von einem Signal im Frequenzbereich, das heisst man erkennt welche Frequenzen in einem Signal vorhanden sind.
-Dafür muss man eine Fouriertransformation vornehmen. 
-Wird aus dieser Gleichung \eqref{fm:eq:AM:euler}die Fouriertransformation vorggenommen, so erhält man
+%Hier beschrieib ich was AmplitudenModulation ist und mache dan den link zu Frequenzmodulation inkl Formel \[\cos( \cos x)\]
+%so wird beschrieben das daraus eigentlich \(x_c(t) = A_c \cdot \cos(\omega_i)\) wird und somit \(x_c(t) = A_c \cdot \cos(\omega_c + \frac{d \varphi(t)}{dt})\).
+%Da \(\sin \) abgeleitet \(\cos \) ergibt, so wird aus dem \(m(t)\) ein \( \frac{d \varphi(t)}{dt}\)  in der momentan frequenz. \[ \Rightarrow \cos( \cos x) \]
+
 
 %
 %Ein Ziel der Modulation besteht darin, mehrere Nachrichtensignale von verschiedenen Sendern gleichzeitig
diff --git a/buch/papers/fm/02_FM.tex b/buch/papers/fm/02_FM.tex
index a01fb69..0413643 100644
--- a/buch/papers/fm/02_FM.tex
+++ b/buch/papers/fm/02_FM.tex
@@ -3,65 +3,84 @@
 %
 % (c) 2020 Prof Dr Andreas Müller, Hochschule Rapperswil
 %
-\section{FM
+\section{FM- Frequenzmodulation
 \label{fm:section:teil1}}
 \rhead{FM}
-\subsection{Frequenzmodulation}
 (skript Nat ab Seite 60)
 Als weiterer Parameter, um ein sinusförmiges Trägersignal \(x_c = A_c \cdot \cos(\omega_c t + \varphi)\) zu modulieren,
 bietet sich neben der Amplitude \(A_c\) auch der Phasenwinkel \(\varphi\) oder die momentane Frequenzabweichung \(\frac{d\varphi}{dt}\) an.
-Bei der Phasenmodulation (Englisch: phase modulation, PM) erzeugt das Nachrichtensignal \(m(t)\) eine Phasenabweichung \(\varphi(t)\) des modulierten Trägersignals im Vergleich zum nicht-modulierten Träger. Sie ist pro-
-%portional zum Nachrichtensignal \(m(t)\) durch eine Skalierung mit der Phasenhubkonstanten (Englisch: phase deviation constant) 
-%k p [rad],
-%welche die Amplitude des Nachrichtensignals auf die Phasenabweichung des
-%modulierten Trägersignals abbildet: φ(t) = k p · m(t). Damit ergibt sich für das phasenmodulierte Trägersi-
-%gnal:
-%x PM (t) = A c · cos (ω c t + k p · m(t))
-%(5.16)
-%Die modulierte Phase φ(t) verändert dabei auch die Momentanfrequenz (Englisch: instantaneous frequency)
-%ω i
-%, welche wie folgt berechnet wird:
-%f i = 2π
-%ω i (t) = ω c +
-%d φ(t)
-%dt
-%(5.17)
-%Bei der Frequenzmodulation (Englisch: frequency modulation, FM) ist die Abweichung der momentanen
-%Kreisfrequenz ω i von der Trägerkreisfrequenz ω c proportional zum Nachrichtensignal m(t). Sie ergibt sich,
-%indem m(t) mit der (Kreis-)Frequenzhubkonstanten (Englisch: frequency deviation constant) k f [rad/s] ska-
-%liert wird: ω i (t) = ω c + k f · m(t). Diese sich zeitlich verändernde Abweichung von der Kreisfrequenz ω c
-%verursacht gleichzeitig auch Schwankungen der Phase φ(t), welche wie folgt berechnet wird:
-%φ(t) =
-%Z t
-%−∞
-%ω i (τ ) − ω c dτ =
-%Somit ergibt sich für das frequenzmodulierte Trägersignal:
-%
-%Z t
-%−∞
-%x FM (t) = A c · cos  ω c t + k f
-%k f · m(t) dτ
-%Z t
-%−∞
-%
-%m(τ ) dτ 
-%(5.18)
-%(5.19)
-%Die Phase φ(t) hat dabei einen kontinuierlichen Verlauf, d.h. das FM-modulierte Signal x FM (t) weist keine
-%Stellen auf, wo sich die Phase sprunghaft ändert. Aus diesem Grund spricht man bei frequenzmodulierten
-%Signalen – speziell auch bei digitalen FM-Signalen – von einer Modulation mit kontinuierlicher Phase (Eng-
-%lisch: continuous phase modulation).
-%Wie aus diesen Ausführungen hervorgeht, sind Phasenmodulation und Frequenzmodulation äquivalente Mo-
-%dulationsverfahren. Beide variieren sowohl die Phase φ wie auch die Momentanfrequenz ω i . Dadurch kann
-%man leider nicht – wie vielleicht erhofft – je mit einem eigenen Nachrichtensignal ein gemeinsames Trägersi-
-%gnal unabhängig PM- und FM-modulieren, ohne dass sich diese Modulationen für den Empfänger untrennbar
-%vermischen würden.
-%
-%Um die mathematische Behandlung der nicht-linearen Winkelmodulation etwas zu verkürzen, ist es aufgrund
-%dieser Äquivalenzen gerechtfertigt, dass PM und FM gemeinsam behandelt werden. Jeweils vor der Modu-
-%lation bzw. nach der Demodulation kann dann noch eine Differentiation oder Integration durchgeführt wird,
-%um von der einen Modulationsart zur anderen zu gelangen.
-%\subsection{Frequenzbereich}
+Bei der Phasenmodulation (Englisch: phase modulation, PM) erzeugt das Nachrichtensignal \(m(t)\) eine Phasenabweichung \(\varphi(t)\)
+des modulierten Trägersignals im Vergleich zum nicht-modulierten Träger.
+Sie ist proportional zum Nachrichtensignal \(m(t)\) durch eine Skalierung mit der Phasenhubkonstanten (Englisch: phase deviation constant) 
+\[
+    k_p [rad],
+\]
+welche die Amplitude des Nachrichtensignals auf die Phasenabweichung des
+modulierten Trägersignals abbildet: \(\varphi(t) = k_p \cdot m(t)\).
+Damit ergibt sich für das phasenmodulierte Trägersignal:
+\[
+    x_{PM} (t) = A_c \cdot \cos (\omega_c t + k_p \cdot m(t))
+\]
+Die modulierte Phase \(\varphi(t)\) verändert dabei auch die Momentanfrequenz (Englisch: instantaneous frequency) \(\omega_i\)
+, welche wie folgt berechnet wird:
+\[
+    f_i = 2\pi \omega_i (t) = \omega_c + \frac{d\varphi(t)}{dt}
+\]
+Bei der Frequenzmodulation (Englisch: frequency modulation, FM) ist die Abweichung der momentanen
+Kreisfrequenz \(\omega_i\) von der Trägerkreisfrequenz \(\omega_c\) proportional zum Nachrichtensignal \(m(t)\).
+Sie ergibt sich, indem \(m(t)\) mit der (Kreis-)Frequenzhubkonstanten (Englisch: frequency deviation constant) \(k_f [rad/s] \)skaliert wird: 
+\[
+    \omega_i (t) = \omega_c + k_f \cdot m(t).
+\]
+Diese sich zeitlich verändernde Abweichung von der Kreisfrequenz \(\omega_c\)
+verursacht gleichzeitig auch Schwankungen der Phase \(\varphi(t)\),
+welche wie folgt berechnet wird:
+\[
+    \varphi (t) =
+    \int_{-\infty}^t \omega_i (\tau ) - \omega_c\, d\tau =
+    \int_{-\infty}^t k_f \cdot m(t)\,d\tau
+\]
+%\intertext{Somit ergibt sich für das frequenzmodulierte Trägersignal: }
+\[
+    x_{FM} (t) = A_c \cdot \cos \left( \omega_c t +  \int_{-\infty}^t k_f  \cdot m ( \tau) \,d\tau \right) 
+\]
+Die Phase \(\varphi(t)\) hat dabei einen kontinuierlichen Verlauf, d.h. das FM-modulierte Signal \(x_{FM}(t)\) weist keine Stellen auf,
+ wo sich die Phase sprunghaft ändert. Aus diesem Grund spricht man bei frequenzmodulierten
+ Signalen - speziell auch bei digitalen FM-Signalen - von einer Modulation mit kontinuierlicher Phase (Englisch: continuous phase modulation).
+Wie aus diesen Ausführungen hervorgeht, sind Phasenmodulation und Frequenzmodulation äquivalente Modulationsverfahren.
+Beide variieren sowohl die Phase \(\varphi\) wie auch die Momentanfrequenz \(\omega_i.\)
+Dadurch kannman leider nicht - wie vielleicht erhofft - je mit einem eigenen Nachrichtensignal ein gemeinsames Trägersignal unabhängig PM- und FM-modulieren,
+ ohne dass sich diese Modulationen für den Empfänger untrennbar vermischen würden.
+Um die mathematische Behandlung der nicht-linearen Winkelmodulation etwas zu verkürzen, ist es aufgrund dieser Äquivalenzen gerechtfertigt,
+dass PM und FM gemeinsam behandelt werden. 
+Da beide nur durch die Operation differenzieren getrennt wird, sind diese zwei Modulationen so miteinenader Verwandt das ich nur auf die Frequenzmodulation eingehe.
+Jeweils vor der Modulation bzw. nach der Demodulation kann dann noch eine Differentiation oder 
+Integration durchgeführt wird, um von der einen Modulationsart zur anderen zu gelangen.
+\citeauthor{fm:NAT}
+
+\subsection{Frequenzspektrum}
+
+Im die Foriertransformation zu berechnen muss man dieses Integral lösen,
+\[
+    \int
+\]
+(sollte ich wirklich diese Fouriertransformation zeigen?)
+jedoch einfacher ist es wenn man mit Hilfe der Besselfunktion den Term \( \cos \cos()\) wandelt,  erhält man
+\[
+    \sum
+\]
+Dieses zu transformien ist einfacher da es wieder Summen sind.
+Damit ist die Fouriertransformation
+\[
+    Fourier
+    \label{fm:FM:fourie}
+\]
+
+Nun sieht ein einfaches Frequenzmodulirtes Sigbnal mit \(m(t) = \sin(t)\) im Frequenzspektrum so aus.
+TODO Bild.
+Wie man auf diese Umformt von \(cos (cos())\) in die Summe zeige ich im nächsten Kapittel, auch was die eigentliche Bessselfunktion aussieht.
+
+\
 %Nun
 %TODO
 %Hier Beschreiben ich FM und FM im Frequenzspektrum.
diff --git a/buch/papers/fm/03_bessel.tex b/buch/papers/fm/03_bessel.tex
index 3c2cb71..37d99dd 100644
--- a/buch/papers/fm/03_bessel.tex
+++ b/buch/papers/fm/03_bessel.tex
@@ -3,30 +3,41 @@
 %
 % (c) 2020 Prof Dr Andreas Müller, Hochschule Rapperswil
 %
-\section{FM und Bessel-Funktion 
+\section{Frequenzmodulation und Bessel-Funktionen
 \label{fm:section:proof}}
 \rhead{Herleitung}
 Die momentane Trägerkreisfrequenz \(\omega_i\), wie schon in (ref) beschrieben ist, bringt die Ableitung \(\frac{d \varphi(t)}{dt}\) mit sich.
 Diese wiederum kann durch \(\beta\sin(\omega_mt)\) ausgedrückt werden, wobei es das modulierende Signal \(m(t)\) ist.
-Somit haben wir unser \(x_c\) welches 
+Somit haben wir unser Signal
 \[
-\cos(\omega_c t+\beta\sin(\omega_mt))
+x_c(t)
+=
+\cos(\omega_c t+\beta\sin(\omega_mt)),
 \]
-ist.
+welches nun als Superposition von harmonischen Schwingungen
+geschrieben werden soll, damit man das Frequenzspektrum ablesen 
+kann.
 
-\subsection{Herleitung}
-Das Ziel ist, unser moduliertes Signal mit der Bessel-Funktion so auszudrücken:
+\begin{satz}
+\label{fm:satz:spektrum}
+Das frequenzmodulierte Signal $x_c(t)$ lässt sich schreiben als
 \begin{align}
     x_c(t)
     = 
     \cos(\omega_ct+\beta\sin(\omega_mt))
     &=
-    \sum_{k= -\infty}^\infty J_{k}(\beta) \cos((\omega_c+k\omega_m)t)
+    \sum_{k= -\infty}^\infty J_{k}(\beta) \cos((\omega_c+k\omega_m)t).
     \label{fm:eq:proof}
 \end{align}
+\end{satz}
+
+\subsection{Herleitung}
+Zum Beweise von Satz~\ref{fm:satz:spektrum} werden zunächst einige
+Hilfsmittel gebraucht, deren Anwendung später die Darstellung
+\eqref{fm:eq:proof} liefert.
 
 \subsubsection{Hilfsmittel}
-Doch dazu brauchen wir die Hilfe der Additionsthoerme 
+Wir brauchen die Hilfe der Additionstheoreme 
 \begin{align}
     \cos(A + B) 
     &= 
@@ -43,7 +54,7 @@ Doch dazu brauchen wir die Hilfe der Additionsthoerme
     \cos(A-B)-\cos(A+B)
     \label{fm:eq:addth3}
 \end{align}
-und die drei Bessel-Funktionsindentitäten,
+und die drei Bessel-Funktionsidentitäten,
 \begin{align}
     \cos(\beta\sin\phi)
     &=
@@ -58,7 +69,11 @@ und die drei Bessel-Funktionsindentitäten,
     J_{-n}(\beta) &= (-1)^n J_n(\beta)
     \label{fm:eq:besselid3}
 \end{align}
-welche man im Kapitel \eqref{buch:fourier:eqn:expinphireal}, \eqref{buch:fourier:eqn:expinphiimaginary}, \eqref{buch:fourier:eqn:symetrie} findet.
+welche man im Kapitel~\ref{buch:chapter:fourier}
+als Gleichungen
+\eqref{buch:fourier:eqn:expinphireal},
+\eqref{buch:fourier:eqn:expinphiimaginary},
+und \eqref{buch:fourier:eqn:symetrie} findet.
 
 \subsubsection{Anwenden des Additionstheorem}
 Mit dem \eqref{fm:eq:addth1} wird aus dem modulierten Signal
@@ -67,145 +82,250 @@ Mit dem \eqref{fm:eq:addth1} wird aus dem modulierten Signal
     =
     \cos(\omega_c t + \beta\sin(\omega_mt))
     =
-    \cos(\omega_c t)\cos(\beta\sin(\omega_m t)) - \sin(\omega_ct)\sin(\beta\sin(\omega_m t)).
+    \underbrace{
+	\cos(\omega_c t)\cos(\beta\sin(\omega_m t))
+    }_{\displaystyle=c(t)}
+     -
+    \underbrace{
+	\sin(\omega_ct)\sin(\beta\sin(\omega_m t))
+    }_{\displaystyle=s(t)}.
     \label{fm:eq:start}
 \]
-%-----------------------------------------------------------------------------------------------------------
-\subsubsection{Cos-Teil}
-Zu beginn wird der Cos-Teil
-\begin{align*}
+Die beiden Terme auf der rechten Seite werden jetzt unabhängig 
+voneinander analysiert.
+
+\subsubsection{Cosinus-Teil}
+Zu Beginn wird der Cosinus-Teil
+\begin{align}
     c(t) 
     &=
     \cos(\omega_c t)\cdot\cos(\beta\sin(\omega_mt))  
-\end{align*}
-mit hilfe der Besselindentität \eqref{fm:eq:besselid1} zum
-\begin{align*}
-    c(t)
+\notag
+\intertext{mit Hilfe der Bessel-Indentität \eqref{fm:eq:besselid1} zum}
     &=
-    \cos(\omega_c t) \cdot \bigg[ J_0(\beta) + 2\sum_{k=1}^\infty J_{2k}(\beta) \cos( 2k \omega_m t)\, \bigg]
+    \cos(\omega_c t) \cdot \bigg[ J_0(\beta)
+    +
+    2\sum_{k=1}^\infty J_{2k}(\beta) \cos( 2k \omega_m t)\, \bigg]
+\notag
     \\
     &=
-    J_0(\beta) \cdot \cos(\omega_c t) + \sum_{k=1}^\infty J_{2k}(\beta) \underbrace{2\cos(\omega_c t)\cos(2k\omega_m t)}_{\text{Additionstheorem \eqref{fm:eq:addth2}}}
-\end{align*}
-%intertext{}   Funktioniert nicht.
-wobei mit dem Additionstheorem \eqref{fm:eq:addth2} \(A = \omega_c t\) und \(B = 2k\omega_m t \) ersetzt wurden.
-Nun kann die Summe in zwei Summen 
-\begin{align*}
-    c(t)
+    J_0(\beta) \cdot \cos(\omega_c t)
+    +
+    \sum_{k=1}^\infty J_{2k}(\beta)
+	\underbrace{
+		2\cos(\omega_c t)\cos(2k\omega_m t)
+	}_{\displaystyle\text{Additionstheorem \eqref{fm:eq:addth2}}},
+\notag
+\intertext{wobei mit dem Additionstheorem \eqref{fm:eq:addth2}
+\(A = \omega_c t\) und \(B = 2k\omega_m t \) ersetzt wurden.
+Nun kann die Summe in zwei Summen }
     &=
-    J_0(\beta) \cdot \cos(\omega_c t) + \sum_{k=1}^\infty J_{2k}(\beta) \cos((\omega_c - 2k \omega_m) t) \,+\, \cos((\omega_c + 2k \omega_m) t) \}
+    J_0(\beta) \cdot \cos(\omega_c t)
+    +
+    \sum_{k=1}^\infty J_{2k}(\beta) \cos((\omega_c - 2k \omega_m) t)
+    +
+    \cos((\omega_c + 2k \omega_m) t) \}
     \\
     &=
-    \sum_{k=\infty}^{1} J_{2k}(\beta)  \underbrace{\cos((\omega_c - 2k \omega_m) t)} 
-    \,+\,J_0(\beta)\cdot \cos(\omega_c t) 
-    \,+\, \sum_{k=1}^\infty J_{2k}(\beta)\cos((\omega_c + 2k \omega_m) t) 
-\end{align*}
-aufgeteilt werden.
-Wenn bei der ersten Summe noch \(k\) von \(-\infty \to -1\) läuft, wird diese summe zu \(\sum_{k=-1}^{-\infty} J_{-2k}(\beta)  {\cos((\omega_c + 2k \omega_m) t)} \)
-Zudem kann die Besselindentität \eqref{fm:eq:besselid3} gebraucht werden.  \(n \) wird  mit \(2k\) ersetzt, da dies immer gerade ist so gilt: \(J_{-n}(\beta) = J_n(\beta)\)
-Somit bekommt man zwei gleiche Summen
-\begin{align*}
-    c(t)
+    \sum_{k=1}^{\infty} J_{2k}(\beta) \cos((\omega_c - 2k \omega_m) t) 
+    +
+    J_0(\beta)\cdot \cos(\omega_c t) 
+    +
+    \sum_{k=1}^\infty J_{2k}(\beta)\cos((\omega_c + 2k \omega_m) t) 
+\notag
+\intertext{aufgeteilt werden.
+Damit man die beiden Summen in eine zusammenfassen kann, müssen
+die Kosinus-Terme in die gleiche Form gebracht werden.
+Dazu kann man $k$ durch $-k$ ersetzen, dann muss in der ersten Summe
+aber von $-1$ bis $-\infty$ summiert werden.
+Ausserdem ist nach Bessel-Indentität \eqref{fm:eq:besselid3}
+für gerade Ordnung der Besselfunktion $J_{-2k}(\beta)=J_{2k}(\beta)$,
+so dass die Summe
+}
     &=
-    \sum_{k=-\infty}^{-1} J_{2k}(\beta) \cos((\omega_c + 2k \omega_m) t)
-    \,+\,J_0(\beta)\cdot \cos(\omega_c t+ 2 \cdot 0 \omega_m) 
-    \,+\, \sum_{k=1}^\infty J_{2k}(\beta)\cos((\omega_c + 2k \omega_m) t) 
-\end{align*}
-Diese können wir vereinfachter schreiben,
-\begin{align*}
-    \sum_{n\, \text{gerade}} J_{n}(\beta) \cos((\omega_c + n \omega_m) t),
+    \sum_{k=-\infty}^{-1}
+	J_{2k}(\beta)
+	\cos\bigl((\omega_c + 2k \omega_m) t\bigr) 
+    +
+    J_0(\beta)\cdot \cos\bigl((\omega_c + 0\omega_m) t\bigr)
+    +
+    \sum_{k=1}^\infty
+	J_{2k}(\beta)
+	\cos\bigl((\omega_c + 2k \omega_m) t\bigr) 
+\notag
+\\
+    &=
+    \sum_{k=-\infty}^\infty
+	J_{2k}(\beta)
+	\bigl(\cos(\omega_c + 2k\omega_m)t\bigr).
+\notag
+\intertext{Dies kann vereinfacht als}
+    &=
+    \sum_{\text{$n$ gerade}}
+	J_{n}(\beta)
+	\cos\bigl((\omega_c + n \omega_m) t\bigr)
     \label{fm:eq:gerade}
-\end{align*}
-da \(2k\) für alle negativen, wie positiven geraden Zahlen zählt.
-%----------------------------------------------------------------------------------------------------------------
-\subsubsection{Sin-Teil}
-Nun zum zweiten Teil des Term \eqref{fm:eq:start}, den Sin-Teil
-\begin{align*}
+\end{align}
+geschrieben werden.
+
+\subsubsection{Sinus-Teil}
+Nun zum zweiten Teil der Summe \eqref{fm:eq:start}, den Sinus-Teil
+\begin{align}
     s(t) 
     &=
-    -\sin(\omega_c t)\cdot\sin(\beta\sin(\omega_m t)).
-\end{align*}
-Dieser wird mit der \eqref{fm:eq:besselid2} Besselindentität zu
-\begin{align*}
-    s(t) 
+    -\sin(\omega_c t)\cdot\sin\bigl(\beta\sin(\omega_m t)\bigr).
+\notag
+\intertext{Dieser wird mit der \eqref{fm:eq:besselid2} Bessel-Indentität zu}
     &=
-    -\sin(\omega_c t) \cdot \bigg[ 2 \sum_{k=0}^\infty J_{ 2k + 1}(\beta) \cos(( 2k + 1) \omega_m t) \bigg]
+    -\sin(\omega_c t) \cdot \bigg[
+	2 \sum_{k=0}^\infty
+	J_{ 2k + 1}(\beta)
+	\cos\bigl(( 2k + 1) \omega_m t\bigr)
+    \bigg]
     \\
     &=
-    \sum_{k=0}^\infty -1 \cdot J_{2k+1}(\beta) 2\sin(\omega_c t)\cos((2k+1)\omega_m t).
-\end{align*}
-Da \(2k + 1\) alle ungeraden positiven Ganzzahlen entspricht wird es durch \(n\) ersetzt. 
-Wird die Besselindentität \eqref{fm:eq:besselid3} gebraucht, so ersetzten wird \(J_{-n}(\beta) = -1\cdot J_n(\beta)\) ersetzt:
-\begin{align*}
-    s(t)
+    \sum_{k=0}^\infty
+	-1 \cdot J_{2k+1}(\beta)
+	2\sin(\omega_c t)
+	\cos\bigl((2k+1)\omega_m t\bigr).
+\notag
+\intertext{\(2k + 1\) durchläuft alle ungeraden positiven Ganzzahlen.
+Nach der Bessel-Identität \eqref{fm:eq:besselid3} ist
+\(J_{-(2k+1)}(\beta) = -1\cdot J_{2k+1}(\beta)\).
+Damit kann die Summe schreiben als eine Summe über die ungeraden Zahlen $n$:
+}
     &=
-    \sum_{n=0}^\infty J_{-n}(\beta) \underbrace{2\sin(\omega_c t)\cos(n \omega_m t)}_{\text{Additionstheorem \eqref{fm:eq:addth3}}}.
-\end{align*}
-Auch hier wird ein Additionstheorem \eqref{fm:eq:addth3} gebraucht, dabei ist \(A = \omega_c t\) und \(B = n \omega_m t \), 
-somit wird daraus:
-\begin{align*}
-    s(t)
+    \sum_{\text{$n>0$ ungerade}}^\infty
+	J_{-n}(\beta)
+	\underbrace{
+		2\sin(\omega_c t)\cos(n \omega_m t)
+	}_{\displaystyle\text{Additionstheorem \eqref{fm:eq:addth3}}}.
+\notag
+\intertext{Auch hier wird ein Additionstheorem \eqref{fm:eq:addth3}
+gebraucht, dabei ist \(A = \omega_c t\) und \(B = n \omega_m t \), 
+und es entsteht:}
     &=
-    \sum_{n=0}^\infty J_{-n}(\beta) \{ \underbrace{\cos((\omega_c - n\omega_m) t)} \,-\, \cos((\omega_c + n\omega_m) t) \}
-    \\
+    \sum_{\text{$n>0$ ungerade}}
+	J_{-n}(\beta)
+	\{
+	\cos\bigl((\omega_c - n\omega_m) t\bigr)
+	-
+	\cos\bigl((\omega_c + n\omega_m) t\bigr)
+	\}.
+\notag
+\intertext{Wieder ersetzen wir $n$ durch $-n$ in der ersten Summe,
+um die gleichen Kosinus-Terme zu erhalten:}
     &=
-   \sum_{n=- \infty}^{0} J_{n}(\beta)  \overbrace{\cos((\omega_c + n \omega_m) t)}
-   \,-\, \sum_{n=0}^\infty J_{-n}(\beta) \cos((\omega_c + n\omega_m) t)
-\end{align*}
-Auch hier wurde wieder eine zweite Summe \(\sum_{-\infty}^{-1}\) gebraucht um das Minus zu einem Plus zu wandeln.
-Wenn \(n = 0 \) ist der Minuend gleich dem Subtrahend und somit dieser Teil \(=0\), das bedeutet \(n\) ended bei \(-1\) und started bei \(1\).
-\begin{align*}
-    s(t)
+    \sum_{\text{$n<0$ ungerade}}
+	J_{n}(\beta)
+	\cos\bigl((\omega_c + n \omega_m) t\bigr)
+    -
+    \sum_{\text{$n>0$ ungerade}}
+	J_{-n}(\beta)
+	\cos\bigl((\omega_c + n\omega_m) t\bigr)
+\notag
+\\
     &=
-    \sum_{n=- \infty}^{-1} J_{n}(\beta)  \cos((\omega_c + n \omega_m) t)
-    \underbrace{\,-\, \sum_{n=1}^\infty J_{-n}(\beta)} \cos((\omega_c + n\omega_m) t)
-\end{align*}
-Um aus diesem Subtrahend eine Addition zu kreiernen, wird die Besselindentität \eqref{fm:eq:besselid3} gebraucht, 
-jedoch so \(-1 \cdot J_{-n}(\beta) = J_n(\beta)\) und daraus wird dann:
-\begin{align*}
-    s(t)
+    \sum_{\text{$n<0$ ungerade}}
+	J_{n}(\beta)
+	\cos\bigl((\omega_c + n \omega_m) t\bigr)
+    +
+    \sum_{\text{$n>0$ ungerade}}
+	J_{n}(\beta)
+	\cos\bigl((\omega_c + n\omega_m) t\bigr)
+\notag
+\intertext{
+In der zweiten Gleichung haben wir wieder die
+Bessel-Identität $J_{-n}(\beta)=-J_{n}(\beta)$
+verwendet, die für $n$ ungerade gilt.
+Jetzt kann man die beiden Summen wieder zusammenfassen in
+eine einzige, die über alle positiven und negativen ungeraden
+Zahl läuft:}
     &=
-    \sum_{n=- \infty}^{-1} J_{n}(\beta)  \cos((\omega_c + n \omega_m) t)
-    \,+\, \sum_{n=1}^\infty J_{n}(\beta) \cos((\omega_c + n\omega_m) t)
-\end{align*}
-Da \(n\) immer ungerade ist und \(0\) nicht zu den ungeraden Zahlen zählt, kann man dies so vereinfacht 
-\[
-    s(t)
-    =
-    \sum_{n\, \text{ungerade}} J_{n}(\beta) \cos((\omega_c + n\omega_m) t).
-    \label{fm:eq:ungerade}
-\]
-, mit allen positiven und negativen Ganzzahlen schreiben.
-%------------------------------------------------------------------------------------------
+    \sum_{\text{$n$ ungerade}}
+	J_{n}(\beta)
+	\cos\bigl((\omega_c + n \omega_m) t\bigr).
+\label{fm:eq:ungerade}
+\end{align}
+
 \subsubsection{Summe Zusammenführen}
-Beide Teile \eqref{fm:eq:gerade} Gerade 
-\[
-    \sum_{n\, \text{gerade}} J_{n}(\beta) \cos((\omega_c + n\omega_m) t)
-\]
-und \eqref{fm:eq:ungerade} Ungerade 
+Beide Teile \eqref{fm:eq:gerade} für die geraden Terme 
+\begin{align*}
+c(t)
+&=
+    \sum_{n\, \text{gerade}}
+	J_{n}(\beta)
+	\cos\bigl((\omega_c + n\omega_m) t\bigr)
+\intertext{und \eqref{fm:eq:ungerade} für die ungeraden Terme }
+s(t)
+&=
+    \sum_{n\, \text{ungerade}}
+	J_{n}(\beta)
+	\cos\bigl((\omega_c + n\omega_m) t\bigr)
+\intertext{ergeben zusammen}
+x_c(t)
+&=c(t)+s(t)
+\\
+&=
+    \cos\bigl(\omega_ct+\beta\sin(\omega_mt)\bigr)
+    =
+    \sum_{k= -\infty}^\infty
+	J_{n}(\beta)
+	\cos\bigl((\omega_c+ n\omega_m)t\bigr).
+\end{align*}
+Somit ist \eqref{fm:eq:proof} bewiesen.
+
+\subsection{Bessel und Frequenzspektrum}
+Unser FM-signal Fourientransformiert \eqref{fm:FM:fourie} wird zusammengestzt aus den einzelen Reihenteilen mit der gewichtung der Besselfunktion.
+
+
+Nochmals zur erinnerung sah unser Träger Signal anfangs so aus:
+\(x_c(t) = A_c \cdot \cos(\omega_c(t)+\varphi)\), dabei modulierten wir den parameter \( \varphi = \beta\sin(\omega_mt) \).
+Davon sahen wir das sich ursprünglich unser Signal\(m_{FM}(t) = \cos(\omega_m t)\) war. 
+Wie das Beat zusammenhängt sieht man im Abschnitt ( ref). 
+Wird es weiter transformiert zur Summe, so erhält man ein
 \[
-    \sum_{n\, \text{ungerade}} J_{n}(\beta) \cos((\omega_c + n\omega_m) t)
+    x_{FM} = 
+    \sum_{k= -\infty}^\infty J_{n}(\beta) \cos((\omega_c+ n\omega_m)t).
 \]
-ergeben zusammen
+Diese Summe nun Fourier Transformiert ergibt
 \[
-    \cos(\omega_ct+\beta\sin(\omega_mt))
-    =
-    \sum_{k= -\infty}^\infty J_{n}(\beta) \cos((\omega_c+ n\omega_m)t).
+    x_{FM} = 
+    \sum_{k= -\infty}^\infty J_{n}(\beta) \cdot \frac{1}{2} \biggl(  e^{j(\omega_c+ n\omega_m)t}\;+\; e^{-j(\omega_c+ n\omega_m)t}\biggr).
 \]
-Somit ist \eqref{fm:eq:proof} bewiesen.
-\newpage
-%-----------------------------------------------------------------------------------------
-\subsection{Bessel und Frequenzspektrum}
-Um sich das ganze noch einwenig Bildlicher vorzustellenhier einmal die Bessel-Funktion \(J_{k}(\beta)\) in geplottet.
+Dies ergibt wiederum zwei dirac impulse im Frequenzbereich, aber pro Summand mit der Gewichtung der Besselfunktion indices und deren \(\beta\). 
+Gegeneüber \textit{AM} hat sich \textit{FM} zu einer Summe mit gewichtungen der Besselfunktion verändert.
+Überall wo die Besselfunktion gegn 0 tendiert können wir diese Summand vernachlässigen.
+Um dies zu sehn plotten wir einmal \(J_{n}(\beta)\):
 \begin{figure}
 	\centering
 	\input{papers/fm/Python animation/bessel.pgf}
-	\caption{Bessle Funktion \(J_{k}(\beta)\)}
+	\caption{Bessle Funktion \(J_{n}(\beta)\)}
+	\label{fig:bessel}
+\end{figure}
+Hier sieht man gut das für kleine \( \beta \lessgtr \) nur die ersten Summanden \( n\) zuständig sind.
+So kann man mit dem \(\beta\) gut bestimmen bis wo die Summe berchnet werden soll. 
+
+Für ein Beispiel nehmen wir  \(\beta = ... \omega_m = ... \)
+Dann sieht unser \(x_{FM}\) so aus:
+\begin{figure}
+	\centering
+	
+	\caption{Beispiel eines FM Übertragenen Signal}
 	\label{fig:bessel}
 \end{figure}
-TODO Grafik einfügen,
-\newline
-Nun einmal das Modulierte FM signal im Frequenzspektrum mit den einzelen Summen dargestellt
+Nun verändern wir die drei Parameter \(\beta \omega_c \omega_m \) und sehen was sich verändern wird
+\subsubsection{Beta}
+Da \(\beta\) in keiner abhängigkeit zo den anderen parameter steht, und jegliglich die anzahl der nötigen Summanden bestimmt.
+So wird es auch diese Anzahl bestimmen was man hier sehen kann.
+\subsubsection{omega c}
+Dieser ist unser Trägerfrequenz auf die unser Signal aufmoduliert wurde, diese bestimmt die Frequenz in welchem sich das signal befindet
+\subsubsection{omega m}
+Dieser parametr hat unsere bandbreite auf welchem unser Moduliertes Signal \(x_{AM}\) befindet bestimmt, auch im FM wird es wieder die Bandbreite bestimmen, was wir hier sehen.
+
+Nun einmal das Modulierte FM signal mit dem \(\beta =  \omega_m =  \) berechnet:
+
+
 
 TODO
 Hier wird beschrieben wie die Bessel Funktion der FM im Frequenzspektrum hilft, wieso diese gebrauch wird und ihre Vorteile.
@@ -214,6 +334,7 @@ Hier wird beschrieben wie die Bessel Funktion der FM im Frequenzspektrum hilft,
     \item Im Frequenzspektrum darstellen mit Farben, ersichtlich machen. 
     \item Parameter tuing der Trägerfrequenz, Modulierende frequenz und Beta. 
 \end{itemize}
+\newpage
 
 
 %\subsection{De finibus bonorum et malorum
diff --git a/buch/papers/fm/04_fazit.tex b/buch/papers/fm/04_fazit.tex
index 8d5eca4..26f541d 100644
--- a/buch/papers/fm/04_fazit.tex
+++ b/buch/papers/fm/04_fazit.tex
@@ -6,7 +6,7 @@
 \section{Fazit
 \label{fm:section:fazit}}
 \rhead{Zusamenfassend}
+Ohne die Besselfunktion könnte man die Einzelen Peaks der Fm nichicht sounabängig von einander berchenen und herausfinden.
+Da die Besselfunktion schnell abklingt, brauchte es auch wenige Besselkoeffizente um das Nachrichten Signal wieder zurückzugewinnen.
 
-TODO  Anwendungen erklären und Sinn des Ganzen.
-
-
+TODO  Anwendungen erklären und Sinn des Ganzen.
\ No newline at end of file
diff --git a/buch/papers/fm/Python animation/Bessel-FM.ipynb b/buch/papers/fm/Python animation/Bessel-FM.ipynb
index 4074765..f475f45 100644
--- a/buch/papers/fm/Python animation/Bessel-FM.ipynb
+++ b/buch/papers/fm/Python animation/Bessel-FM.ipynb
@@ -2,10 +2,11 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
+    "\n",
     "import numpy as np\n",
     "from scipy import signal\n",
     "from scipy.fft import fft, ifft, fftfreq\n",
@@ -15,7 +16,15 @@
     "import matplotlib as mpl\n",
     "# Use the pgf backend (must be set before pyplot imported)\n",
     "# mpl.use('pgf')\n",
-    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "\n",
     "def fm(beta):\n",
     "    # Number of samplepoints\n",
@@ -43,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -90,19 +99,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.7651976865579666\n"
+      "0.44605905843961724\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -115,12 +124,12 @@
    ],
    "source": [
     "\n",
-    "for n in range (-2,4):\n",
-    "    x = np.linspace(-11,11,1000)\n",
+    "for n in range (-1,7):\n",
+    "    x = np.linspace(-11,16,1000)\n",
     "    y = sc.jv(n,x)\n",
     "    plt.plot(x, y, '-',label='n='+str(n))\n",
     "#plt.plot([1,1],[sc.jv(0,1),sc.jv(-1,1)],)\n",
-    "plt.xlim(-10,10)\n",
+    "plt.xlim(-5,15)\n",
     "plt.grid(True)\n",
     "plt.ylabel('Bessel $J_n(\\\\beta)$')\n",
     "plt.xlabel(' $ \\\\beta $ ')\n",
@@ -128,17 +137,17 @@
     "plt.legend()\n",
     "#plt.show()\n",
     "plt.savefig('bessel.pgf', format='pgf')\n",
-    "print(sc.jv(0,1))"
+    "print(sc.jv(2,2.5))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -150,21 +159,35 @@
     }
    ],
    "source": [
-    "\n",
     "x = np.linspace(0,0.1,2000)\n",
-    "y = np.sin(100 * 2.0*np.pi*x+1.5*np.sin(30 * 2.0*np.pi*x))\n",
-    "plt.plot(x, y, '-')\n",
-    "plt.show()"
+    "ratio = 2\n",
+    "first = 1\n",
+    "(length,)  = x.shape\n",
+    "slop = int(length/6)\n",
+    "second = ratio-first\n",
+    "odd = ratio % 2\n",
+    "\n",
+    "first = int(first * length/ratio) \n",
+    "second = int( second * length/ratio) + odd\n",
+    "slop = np.array(np.append(np.zeros(first-slop) , (np.arange(slop))/slop))\n",
+    "#steep = np.ones(int(first * length/ratio)+ odd) - np.exp(-np.arange(int(first * length/ratio) + odd)/200)\n",
+    "steep = (np.ones(first) + slop)*0.5\n",
+    "\n",
+    "step = np.append(steep,  np.ones(second))\n",
+    "m = np.sin(5 * 2.0 * np.pi * x) * step \n",
+    "plt.plot(x, step, '-')\n",
+    "plt.plot(x, m, '-')\n",
+    "plt.savefig('m_t.pgf', format='pgf')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -176,24 +199,71 @@
     }
    ],
    "source": [
-    "ratio = 2\n",
-    "first = 1\n",
-    "(length,)  = x.shape\n",
-    "slop = int(length/6)\n",
-    "second = ratio-first\n",
-    "odd = ratio % 2\n",
     "\n",
-    "first = int(first * length/ratio) \n",
-    "second = int( second * length/ratio) + odd\n",
-    "slop = np.array(np.append(np.zeros(first-slop) , (np.arange(slop))/slop))\n",
-    "#steep = np.ones(int(first * length/ratio)+ odd) - np.exp(-np.arange(int(first * length/ratio) + odd)/200)\n",
-    "steep = (np.ones(first) + slop)*0.5\n",
+    "y = (np.sin(20 * 2.0*np.pi*x)+np.sin(5* 2.0*np.pi*x)+np.sin(2* 2.0*np.pi*x))/3\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nat Modulationsarten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "t = np.linspace(0,1,5000)\n",
     "\n",
-    "step = np.append(steep,  np.ones(second))\n",
-    "m = np.sin(5 * 2.0 * np.pi * x) * step \n",
-    "plt.plot(x, step, '-')\n",
-    "plt.plot(x, m, '-')\n",
-    "plt.savefig('m_t.pgf', format='pgf')"
+    "# m(t)\n",
+    "k = 0.25\n",
+    "dc = 0.25\n",
+    "f1 = 1\n",
+    "f2 = 1.4\n",
+    "\n",
+    "# x_c(t)\n",
+    "fc = 10\n",
+    "kp = 1.5 * 2 * np.pi\n",
+    "kf = 15\n",
+    "\n",
+    "m = dc + k* np.cos(2*np.pi*f1*t) + k*np.sin(2*np.pi*f2*t)\n",
+    "\n",
+    "AM = 2* m* np.cos(2* np.pi *fc*t)\n",
+    "\n",
+    "PM = 1* np.cos(2* np.pi * fc*t+kp*m)\n",
+    "\n",
+    "dt= t[1]-t[0]\n",
+    "phi = kp * m + np.gradient(phi,dt)\n",
+    "phidt =  np.append(0, kf*2*np.pi*m[:-1])\n",
+    "FM = 1* (np.cos(2 *np.pi *fc *t + phidt))\n",
+    "plt.plot(t, m, '-')\n",
+    "#plt.plot(t, AM,'b')\n",
+    "plt.plot(t,PM)\n",
+    "plt.plot(t,FM)\n",
+    "plt.show()"
    ]
   }
  ],
diff --git a/buch/papers/fm/Python animation/m_t.pgf b/buch/papers/fm/Python animation/m_t.pgf
index edcfb33..8011e31 100644
--- a/buch/papers/fm/Python animation/m_t.pgf
+++ b/buch/papers/fm/Python animation/m_t.pgf
@@ -78,7 +78,7 @@
 \pgfusepath{stroke,fill}%
 }%
 \begin{pgfscope}%
-\pgfsys@transformshift{0.918664in}{0.500000in}%
+\pgfsys@transformshift{0.961364in}{0.500000in}%
 \pgfsys@useobject{currentmarker}{}%
 \end{pgfscope}%
 \end{pgfscope}%
@@ -86,7 +86,7 @@
 \definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}%
 \pgfsetstrokecolor{textcolor}%
 \pgfsetfillcolor{textcolor}%
-\pgftext[x=0.918664in,y=0.402778in,,top]{\color{textcolor}\sffamily\fontsize{10.000000}{12.000000}\selectfont 0.0}%
+\pgftext[x=0.961364in,y=0.402778in,,top]{\color{textcolor}\sffamily\fontsize{10.000000}{12.000000}\selectfont 0.00}%
 \end{pgfscope}%
 \begin{pgfscope}%
 \pgfsetbuttcap%
@@ -103,7 +103,7 @@
 \pgfusepath{stroke,fill}%
 }%
 \begin{pgfscope}%
-\pgfsys@transformshift{1.772658in}{0.500000in}%
+\pgfsys@transformshift{1.806818in}{0.500000in}%
 \pgfsys@useobject{currentmarker}{}%
 \end{pgfscope}%
 \end{pgfscope}%
@@ -111,7 +111,7 @@
 \definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}%
 \pgfsetstrokecolor{textcolor}%
 \pgfsetfillcolor{textcolor}%
-\pgftext[x=1.772658in,y=0.402778in,,top]{\color{textcolor}\sffamily\fontsize{10.000000}{12.000000}\selectfont 0.2}%
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 \begin{pgfscope}%
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@@ -128,7 +128,7 @@
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 }%
 \begin{pgfscope}%
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+\pgfsys@transformshift{2.652273in}{0.500000in}%
 \pgfsys@useobject{currentmarker}{}%
 \end{pgfscope}%
 \end{pgfscope}%
@@ -136,7 +136,7 @@
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 \begin{pgfscope}%
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@@ -153,7 +153,7 @@
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 \begin{pgfscope}%
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+\pgfsys@transformshift{3.497727in}{0.500000in}%
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@@ -161,7 +161,7 @@
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-\pgftext[x=3.480647in,y=0.402778in,,top]{\color{textcolor}\sffamily\fontsize{10.000000}{12.000000}\selectfont 0.6}%
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 \begin{pgfscope}%
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@@ -178,7 +178,7 @@
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 }%
 \begin{pgfscope}%
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+\pgfsys@transformshift{4.343182in}{0.500000in}%
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@@ -186,7 +186,7 @@
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@@ -211,7 +211,7 @@
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@@ -228,7 +228,7 @@
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 }%
 \begin{pgfscope}%
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+\pgfsys@transformshift{0.750000in}{0.637273in}%
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@@ -236,7 +236,7 @@
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 \begin{pgfscope}%
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@@ -253,7 +253,7 @@
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 }%
 \begin{pgfscope}%
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@@ -261,7 +261,7 @@
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 \begin{pgfscope}%
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@@ -278,7 +278,7 @@
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 }%
 \begin{pgfscope}%
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@@ -286,7 +286,7 @@
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 \begin{pgfscope}%
 \pgfsetbuttcap%
@@ -303,7 +303,7 @@
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 }%
 \begin{pgfscope}%
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@@ -311,7 +311,7 @@
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@@ -336,82 +336,7 @@
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diff --git a/buch/papers/kra/anwendung.tex b/buch/papers/kra/anwendung.tex
index 704de43..dbe1171 100644
--- a/buch/papers/kra/anwendung.tex
+++ b/buch/papers/kra/anwendung.tex
@@ -1,6 +1,5 @@
 \section{Anwendung \label{kra:section:anwendung}}
 \rhead{Anwendung}
-\newcommand{\dt}[0]{\frac{d}{dt}}
 
 Die Matrix-Riccati Differentialgleichung findet unter anderem Anwendung in der Regelungstechnik beim RQ- und RQG-Regler oder aber auch beim Kalman-Filter.
 Im folgenden Abschnitt möchten wir uns an einem Beispiel anschauen wie wir mit Hilfe der Matrix-Riccati-Differentialgleichung (\ref{kra:equation:matrixriccati}) ein Feder-Masse-System untersuchen können \cite{kra:riccati}.
@@ -163,12 +162,12 @@ In Matrixschreibweise erhalten wir
 
 \subsection{Phasenraum}
 \subsubsection{Motivation}
-Die Beschreibung eines klassischen physikalischen Systems führt in der Newtonschen-Mechanik, wie wir in \ref{kra:subsection:feder-masse-system} gesehen haben, auf eine DGL 2. Ordung der Dimension $n$.
+Die Beschreibung eines klassischen physikalischen Systems führt in der Newtonschen-Mechanik, wie wir in \ref{kra:subsection:feder-masse-system} gesehen haben, auf eine Differentialgleichung 2. Ordung der Dimension $n$.
 Zur Betrachung des Systems verwenden wir dabei den Konfigurationsraum, ein Raum $\mathbb{R}^n$, bei dem ein einziger Punkt die Position aller $n$ Teilchen festlegt.
 Der Nachteil des Konfigurationsraums ist dabei, dass dieser nur die Positionen der Teilchen widerspiegelt.
 Um den Zustand eines Systems vollständig zu beschreiben, muss man aber nicht nur wissen wo sich die Teilchen zu einem bestimmten Zeitpunkt befinden, sondern auch wie sie sich bewegen.
 
-Im Gegensatz dazu führt die Beschreibung des Systems mit Hilfe der Hamilton-Mechanik \ref{kra:subsection:hamilton-funktion}, auf eine DGL 1. Ordnung der Dimension $2n$.
+Im Gegensatz dazu führt die Beschreibung des Systems mit Hilfe der Hamilton-Mechanik \ref{kra:subsection:hamilton-funktion}, auf eine Differentialgleichung 1. Ordnung der Dimension $2n$.
 Die Betrachtung erfolgt im einem Raum $\mathbb{R}^{2n}$, bei dem ein einzelner Punkt den Bewegungszustand vollständig beschreibt, dem sogennanten Phasenraum.
 Die Phasenraumdarstellung eignet sich somit sehr gut für die systematische Untersuchung der Feder-Masse-Systeme.
 
@@ -187,45 +186,23 @@ Abbildung~\ref{kra:fig:phasenraum} zeigt Phasenraumtrajektorien mit den Energien
 \end{figure}
 
 \subsubsection{Erweitertes Feder-Masse-System}
-Wir interessieren uns nun dafür, wie der Phasenwinkel $U = PQ^{-1}$ von der Zeit abhängt,
-wir suchen also die Grösse $\Theta = \dt U$.
-Ersetzten wir in der Gleichung \eqref{kra:equation:hamilton-multispringmass} die Matrix $G$ mit $\tilde{G}$ so erhalten wir
+Die Lösung der Gleichung \eqref{kra:equation:hamilton-multispringmass} beschreibt sowohl die zeitliche Entwicklung der Position als auch der Impulse.
+Um das System im Phasenraum zu untersuchen, reicht uns aber auch die zeitliche Entwicklung des Phasenwinkels $U(t) = P(t)Q^{-1}(t)$.
+Nach Satz~\ref{kra:satz:riccati-matrix-dgl} erhalten wir für Ableitung von $U$
 \begin{equation}
-    \dt
-    \begin{pmatrix}
-        Q \\
-        P
-    \end{pmatrix}
-    =
-    \underbrace{
-        \begin{pmatrix}
-            A & B \\
-            C & D
-        \end{pmatrix}
-    }_{\displaystyle{\tilde{G}}}
-    \begin{pmatrix}
-        Q \\
-        P
-    \end{pmatrix}.
-\end{equation}
-Ausgeschrieben folgt
-\begin{align*}
-    \dot{Q} = AQ + BP \\
-    \dot{P} = CQ + DP
-\end{align*}
-\begin{equation}
-    \label{kra:equation:feder-masse-riccati-matrix}
     \begin{split}
-        \dt U   &= \dot{P} Q^{-1} + P \dt Q^{-1} \\
-        &= (CQ + DP) Q^{-1} - P (Q^{-1} \dot{Q} Q^{-1}) \\
-        &= C\underbrace{QQ^{-1}}_\text{$I$} + D\underbrace{PQ^{-1}}_\text{$U$} - P(Q^{-1} (AQ + BP) Q^{-1}) \\
-        &= C + DU - \underbrace{PQ^{-1}}_\text{$U$}(A\underbrace{QQ^{-1}}_\text{$I$} + B\underbrace{PQ^{-1}}_\text{$U$}) \\
-        &= C  + DU - UA - UBU
+        \dt U   &= K  + 0U(t) - U(t)0 - U(t)MU(t) \\
+        &= K + U(t)MU(t),
     \end{split}
 \end{equation}
-was uns direkt auf die Matrix-Riccati Gleichung \eqref{kra:equation:matrixriccati} führt.
-Wir sehen das sich die Dimension der DGL reduziert, dabei aber gleichzeitig der Grad erhöht.
+eine Riccati-Matrix-Differentialgleichung.
+Die Matrix $U(t)$ beschreibt, wie man die Impulse $P$ zur Zeit $t$ aus den Positionen $Q$ berechnen kann.
+Die Berechnung der Position $Q$ zur Zeit $t$ aus den Anfangsbedingungen ermöglicht die Matrix $Q$.
+Die Inverse $Q^{-1}$ rechnet dann von den aktuellen Auslenkungen zurück auf Auslenkungen zur Zeit $t=0$.
+Die Matrix-Riccati-Differentialgleichung löst also das Problem die Impulse aus den Positionen zu berechnen, wenn man die Anfangsinpulsverteilung kennt.
+
+Durch die Beschränkung auf den Phasenwinkel wird die Dimension der Differentialgleichung \eqref{kra:equation:hamilton-multispringmass} reduziert, dabei aber gleichzeitig deren Grad erhöht.
 
 \subsection{Fazit}
-Wir haben gezeigt wie wir ein Federmassesystem mit Hilfe der Hamilton-Funktion Beschreiben und im Phasenraum untersuchen können.
-Ausserdem haben wir gesehen, dass sich bei der Entstehung der Riccati-Gleichung \eqref{kra:equation:feder-masse-riccati-matrix} die Dimension auf Kosten des Grades reduziert wird.
\ No newline at end of file
+Wir haben gezeigt wie wir ein Federmassesystem mit Hilfe der Hamilton-Funktion Beschreiben und im Phasenraum untersuchen können und wie dabei die Matrix-Riccati-Differentialgleichung in Erscheinung tritt.
+Ausserdem haben wir gesehen, dass dabei die Dimension auf Kosten des Grades reduziert wird.
\ No newline at end of file
diff --git a/buch/papers/kra/einleitung.tex b/buch/papers/kra/einleitung.tex
index 0503742..b5b76a8 100644
--- a/buch/papers/kra/einleitung.tex
+++ b/buch/papers/kra/einleitung.tex
@@ -3,7 +3,7 @@
 Die riccatische Differentialgleichung ist eine nicht lineare gewöhnliche Differentialgleichung erster Ordnung der Form
 \begin{equation}
     \label{kra:equation:riccati}
-    y' = f(x)y + g(x)y^2 + h(x)
+    y' = f(x)y + g(x)y^2 + h(x).
 \end{equation}
 Sie ist benannt nach dem italienischen Grafen Jacopo Francesco Riccati (1676–1754) der sich mit der Klassifizierung von Differentialgleichungen befasste.
 Als Riccati Gleichung werden auch Matrixgleichungen der Form
diff --git a/buch/papers/kra/loesung.tex b/buch/papers/kra/loesung.tex
index 18ac853..604a5ec 100644
--- a/buch/papers/kra/loesung.tex
+++ b/buch/papers/kra/loesung.tex
@@ -15,13 +15,13 @@ Durch Ausschreiben des Differentialquotienten
 \begin{equation}
     \frac{dy}{dx} = fy^2 + gy + h
 \end{equation}
-erkennt man, dass die DGL separierbar ist. Die Lösung findet man nun durch die Berechnung des Integrals
+erkennt man, dass die Differentialgleichung separierbar ist. Die Lösung findet man nun durch die Berechnung des Integrals
 \begin{equation} \label{kra:equation:case1_int}
     \int \frac{dy}{fy^2 + gy + h} = \int dx.
 \end{equation}
 
 \subsubsection{Fall 2: Bekannte spezielle Lösung}
-Kennt man eine spezielle Lösung $y_p$, so kann die riccatische DGL mit Hilfe einer Substitution auf eine lineare Gleichung reduziert werden.
+Kennt man eine spezielle Lösung $y_p$, so kann die riccatische Differentialgleichung mit Hilfe einer Substitution auf eine lineare Gleichung reduziert werden.
 Wir wählen als Substitution
 \begin{equation} \label{kra:equation:substitution}
     z = \frac{1}{y - y_p},
@@ -33,7 +33,7 @@ durch Umstellen von \eqref{kra:equation:substitution} folgt
 \begin{equation}
     y' = y_p' - \frac{1}{z^2}z',
 \end{equation}
-mit Einsetzten in die DGL \eqref{kra:equation:riccati} resultiert
+mit Einsetzten in die Differentialgleichung \eqref{kra:equation:riccati} resultiert
 \begin{equation}
     y_p' - \frac{1}{z^2}z' = f(x)(y_p + \frac{1}{z}) + g(x)(y_p + \frac{1}{z})^2 + h(x)
 \end{equation}
@@ -49,7 +49,9 @@ Diese kann nun mit den Methoden zur Lösung von linearen Differentialgleichungen
 Durch die Rücksubstitution \eqref{kra:equation:backsubstitution} erhält man dann die Lösung von \eqref{kra:equation:riccati}.
 
 \subsection{Matrix-Riccati-Differentialgleichung} \label{kra:loesung:riccati}
-Im Folgenden wollen wir uns anschauen wie die Matrix-Riccati-DGL entsteht und wie sie gelöst werden kann.
+Im Folgenden wollen wir uns anschauen wie die Matrix-Riccati-Differentialgleichung entsteht und wie sie gelöst werden kann.
+
+\subsubsection{Entstehung}
 Der Ausgangspunkt bildet die Matrix-Differentialgleichung
 \begin{equation}
     \label{kra:equation:matrix-dgl}
@@ -63,19 +65,77 @@ Der Ausgangspunkt bildet die Matrix-Differentialgleichung
             A & B \\
             C & D
         \end{pmatrix}
-    }_{\displaystyle{H}},
+    }_{\displaystyle{H}}
+    \begin{pmatrix}
+        X(t) \\
+        Y(t)
+    \end{pmatrix}
 \end{equation}
-mit den allgemeinen quadratischen Matrizen $A, B, C$ und $D$ welche zusammen die sogennante Hamilonsche-Matrix bilden.
-Betrachten wir das Verhältniss von $Y$ zu $X$
+mit den allgemeinen quadratischen Matrizen $A, B, C$ und $D$, welche in der sogenannten Hamiltonschen-Matrix $H$ zusammengefasst werden können.
+Wir führen eine neue Grösse
 \[
-    P(t) = Y(t)X^{-1}
+    U(t) = Y(t)X(t)^{-1}
 \]
-und deren Ableitung $\dot{P}(t)$, so erhalten wir die Riccati-Matrix-DGL
+ein, für dessen Ableitung $\dt U(t)$ wir mit
 \[
-    \dot{P}(t) = C  + DU - UA - UBU.
+    \dot{X}(t) = AX(t) + BY(t) \quad \text{und} \quad \dot{Y}(t) = CX(t) + DY(t)
 \]
+folgendes Ergebnis erhalten
+\begin{equation}
+    \label{kra:equation:feder-masse-riccati-matrix}
+    \begin{split}
+        \dt U(t)   &= \dot{Y}(t) X(t)^{-1} + Y(t) \dt X(t)^{-1} \\
+        &= (CX(t) + DY(t)) X(t)^{-1} - Y(t) (X(t)^{-1} \dot{X}(t) X(t)^{-1}) \\
+        &= C\underbrace{X(t)X(t)^{-1}}_\text{$I$} + D\underbrace{Y(t)X(t)^{-1}}_\text{$U(t)$} - Y(t)(X(t)^{-1} (AX(t) + BY(t)) X(t)^{-1}) \\
+        &= C + DU(t) - \underbrace{Y(t)X(t)^{-1}}_\text{$U(t)$}(A\underbrace{X(t)X(t)^{-1}}_\text{$I$} + B\underbrace{Y(t)X(t)^{-1}}_\text{$U(t)$}) \\
+        &= C  + DU(t) - U(t)A - U(t)BU(t).
+    \end{split}
+\end{equation}
+\begin{satz}
+    \label{kra:satz:riccati-matrix-dgl}
+    Die Ableitung $\dt U(t) = \dt (Y(t)X(t)^{-1})$ ist eine Matrix-Riccati-Differentialgleichung.
+\end{satz}
 
-Die Lösung erhalten wir dann mit
+\subsubsection{Lösung}
+Sei
+\[
+    V(t)
+    =
+    \begin{pmatrix}
+        X(t) \\
+        Y(t)
+    \end{pmatrix},
+    \quad
+    \dot{V}(t) = HV(t)
+\]
+eine Matrix-Differentialgleichung 1. Ordnung, dann ist
+\[
+    V(t) = e^{H(t)} V(0)
+\]
+eine Lösung.
+Die Berechnung des Matrixexpontentials $e^{H(t)}$ kann mittels Diagonalisierung
+\[
+    H = Q \Lambda Q^{-1}
+\]
+effizient berechnet werden.
+Es folgt dann, dass
+\[
+    e^{Ht}
+    =
+    Q
+    e^{\Lambda t}
+    Q^{-1}
+    =
+    Q
+    \begin{pmatrix}
+        e^{\lambda_1 t} & 0               & \dots  & 0               \\
+        0               & e^{\lambda_2 t} & \ddots & \vdots          \\
+        \vdots          & \ddots          & \ddots & 0               \\
+        0               & \dots           & 0      & e^{\lambda_n t}
+    \end{pmatrix}
+    Q^{-1}
+\]
+ist. Die Lösung der Matrix-Riccati-Differentialgleichung erhalten wir analog mit
 \begin{equation}
     \label{kra:matrixriccati-solution}
     \begin{pmatrix}
@@ -108,4 +168,5 @@ Die Lösung erhalten wir dann mit
     \end{pmatrix}
     ^{-1}
 \end{equation}
-wobei $\Phi(t_0, t) = e^{H(t - t_0)}$ die sogenannte Zustandsübergangsmatrix von \eqref{kra:equation:matrix-dgl} ist \cite{kra:kalmanisae}.
+wobei $\Phi(t_0, t) = e^{H(t - t_0)}$ die sogenannte Zustandsübergangsmatrix von \eqref{kra:equation:matrix-dgl} ist,
+welche die Zeitentwicklung der einzelnen Lösungen beschreibt \cite{kra:kalmanisae}.
diff --git a/buch/papers/kra/main.tex b/buch/papers/kra/main.tex
index a84ebaf..9e41039 100644
--- a/buch/papers/kra/main.tex
+++ b/buch/papers/kra/main.tex
@@ -3,6 +3,8 @@
 %
 % (c) 2020 Hochschule Rapperswil
 %
+\newcommand{\dt}[0]{\frac{d}{dt}}
+
 \chapter{Riccati Differentialgleichung\label{chapter:kra}}
 \lhead{Riccati Differentialgleichung}
 \begin{refsection}