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| author | Philipp Le <philipp-le-prviat@freenet.de> | 2020-06-07 23:55:33 +0200 |
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| committer | Philipp Le <philipp-le-prviat@freenet.de> | 2021-03-04 22:44:39 +0100 |
| commit | afed1ed6b62aa9e0c347e608d57d3ae9118e80ca (patch) | |
| tree | 861ef72430545c30c9346eef1dfaaaeeb82b04fd /chapter06/content_ch06.tex | |
| parent | 19df42209c535cabed352432d8c2965d28935643 (diff) | |
| download | dcs-lecture-notes-afed1ed6b62aa9e0c347e608d57d3ae9118e80ca.zip dcs-lecture-notes-afed1ed6b62aa9e0c347e608d57d3ae9118e80ca.tar.gz dcs-lecture-notes-afed1ed6b62aa9e0c347e608d57d3ae9118e80ca.tar.bz2 | |
Added tasks for Exercise 5
Diffstat (limited to 'chapter06/content_ch06.tex')
| -rw-r--r-- | chapter06/content_ch06.tex | 45 |
1 files changed, 39 insertions, 6 deletions
diff --git a/chapter06/content_ch06.tex b/chapter06/content_ch06.tex index 23ad171..e50260c 100644 --- a/chapter06/content_ch06.tex +++ b/chapter06/content_ch06.tex @@ -186,8 +186,8 @@ However, the filter is implemented in the time-domain. \caption{Block diagram of an example \acs{IIR} filter} \label{fig:ch06:iir_filt} \end{figure}% -\nomenclature[Bm]{\begin{circuitikz}[baseline={(current bounding box.center)}]\draw (0,0) to[twoport,t=$z^{-1}$,>] (2,0);\end{circuitikz}}{Delay element}% -\nomenclature[Bm]{\begin{circuitikz}[baseline={(current bounding box.center)}]\node[adder](){};\end{circuitikz}}{Adder} +\nomenclature[Bd]{\begin{circuitikz}[baseline={(current bounding box.center)}]\draw (0,0) to[twoport,t=$z^{-1}$,>] (2,0);\end{circuitikz}}{Delay element}% +\nomenclature[Ba]{\begin{circuitikz}[baseline={(current bounding box.center)}]\node[adder](){};\end{circuitikz}}{Adder} Figure \ref{fig:ch06:iir_filt} shows an example filter. The block diagram has following digital components: \begin{itemize} @@ -251,7 +251,7 @@ A stable filter has always a value-limited impulse response (\ac{BIBO} stable). \end{equation} \item The conditions for \ac{BIBO} stability is that all poles are located \underline{within the unit circle}. \begin{equation} - \left|\underline{z}_{\infty,l}\right| < 1 \qquad \forall \; 0 \leq l \leq Q + \left|\underline{z}_{\infty,l}\right| < 1 \qquad \forall \; 0 \leq l \leq P \end{equation} \end{itemize} @@ -270,7 +270,7 @@ A digital filter without the feed-back path will not have any problems with stab \underline{H}(\underline{z}) = \sum\limits_{i=0}^{P} \underline{b}_i \underline{z}^{-i} \end{equation} \item The number of feed-back filter taps is $Q = 0$. - \item The filter does not have any poles. \textbf{The filter will always be \ac{BIBO} stable.} + \item All poles of the filter are $\underline{z}_{\infty,l} = 0 \quad \forall \; 0 \leq l \leq P$. \textbf{The filter will always be \ac{BIBO} stable.} \end{itemize} \begin{figure}[H] @@ -324,6 +324,8 @@ There is another simple explanation for the \ac{BIBO} stability. Digital filters without a feed-back branch will always have a finite-length impulse response. They are called \index{finite impulse response filter} \textbf{\acf{FIR} filters}. \ac{FIR} filters are always \ac{BIBO} stable. \end{definition} +As a drawback, \ac{FIR} filters require higher orders than an equivalent \ac{IIR} filter. This increases the complexity of its implementation. + \begin{example}{Gliding average filter} The formula of the average of a series of $N$ values is: \begin{equation} @@ -360,8 +362,41 @@ There is another simple explanation for the \ac{BIBO} stability. Both \ac{IIR} and \ac{FIR} are causal. Their impulse response is $\underline{h}[n] = 0 \quad \forall \; n < 0$. +\section{Digital Mixer} + \section{Resampling} +\begin{figure}[H] + \centering + \begin{tikzpicture} + \node[block,draw,align=center](High){High sampling rate}; + \node[block,draw,align=center,right=3cm of High](Low){Low sampling rate}; + + \draw[-latex] ([xshift=5mm] High.north east) -- node[midway,above,align=center]{Down-sampling\\ (Decimation)} ([xshift=-5mm] Low.north west); + \draw[-latex] ([xshift=-5mm] Low.south west) -- node[midway,below,align=center]{Up-sampling\\ (Interpolation)} ([xshift=5mm] High.south east); + \end{tikzpicture} + \caption{Relation between down-sampling (decimation) and up-sampling (interpolation).} +\end{figure} + +\begin{figure}[H] + \centering + \begin{circuitikz} + \node[block,draw,minimum height=3cm](Data){Data\\ Processing}; + + \draw ([shift={(-4cm,1cm)}] Data.west) node[left,align=right]{Input} to[adc,>,o-] ++(2cm,0) to[twoport,t=$\downarrow N$,>] ([yshift=1cm] Data.west) node[inputarrow]{}; + \draw ([yshift=-1cm] Data.west) to[twoport,t=$\uparrow M$,>] ++(-2cm,0) to[dac,>] ++(-2cm,0) node[inputarrow,rotate=180]{} node[left,align=right]{Output}; + \end{circuitikz} + \caption{A system with a down-sampler (decimation factor $N$) and up-sampler (interpolation factor $M$)} +\end{figure}% +\nomenclature[Bd]{\begin{circuitikz}[baseline={(current bounding box.center)}]\draw (0,0) to[twoport,t=$\downarrow N$,>] (2,0);\end{circuitikz}}{Down-sampler (decimation factor $N$)}% +\nomenclature[Bu]{\begin{circuitikz}[baseline={(current bounding box.center)}]\draw (0,0) to[twoport,t=$\uparrow M$,>] (2,0);\end{circuitikz}}{Up-sampler (interpolation factor $M$)}% + +\textbf{Why resampling?} +\begin{itemize} + \item Signals at lower sampling rates require less computation time and memory (software), or lower hardware complexity (less logic gates). The power consumption is reduced. + \item The \ac{ADC} can be operated at maximum sampling rate. The signal is oversampled. Down-sampling provides processing gain and enhances the receiver performance. +\end{itemize} + \todo{Downsampling, Decimation} \todo{Upsampling, Interpolation} @@ -370,8 +405,6 @@ Both \ac{IIR} and \ac{FIR} are causal. Their impulse response is $\underline{h}[ \todo{CIC filter} -\section{Digital Mixer} - \section{Fast Fourier Transform} \todo{FFT} |
