\begin{figure}
  \centering
  \begin{subfigure}[b]{0.49\textwidth}
    \centering
    \begin{adjustbox}{width=\textwidth, height=0.25\textheight}
      \begin{tikzpicture}
        \begin{axis}[tick style = {draw = none}, xticklabel = \empty,
          yticklabel=\empty]
          \addplot [mark options={scale = 0.7}, mark = o] table
          [x=x_d,y=y_d, col sep = comma] {Plots/Data/sin_conv.csv}; 
          \addplot [red, mark=x] table [x=x_i, y=y_i, col sep=comma, color ='black'] {Plots/Data/sin_conv.csv};
        \end{axis}
      \end{tikzpicture}
    \end{adjustbox}
    \caption{True position (\textcolor{red}{red}), distorted position data (black)}
  \end{subfigure}
  \begin{subfigure}[b]{0.49\textwidth}
    \centering
    \begin{adjustbox}{width=\textwidth, height=0.25\textheight}
      \begin{tikzpicture}
        \begin{axis}[tick style = {draw = none}, xticklabel = \empty,
          yticklabel=\empty]
          \addplot [mark options={scale = 0.7}, mark = o] table [x=x,y=y, col
          sep = comma] {Plots/Data/sin_conv.csv};
          \addplot [red, mark=x] table [x=x_i, y=y_i, col sep=comma, color ='black'] {Plots/Data/sin_conv.csv};
        \end{axis}
      \end{tikzpicture}
    \end{adjustbox}
    \caption{True position (\textcolor{red}{red}), filtered position data (black)}
  \end{subfigure}
  \caption[Signal smoothing using convolution]{Example for noise reduction using convolution with simulated
    positional data. As filter
    $g(i)=\left(\nicefrac{1}{3},\nicefrac{1}{4},\nicefrac{1}{5},\nicefrac{1}{6},\nicefrac{1}{20}\right)_{(i-1)}$
      is chosen and applied to the $x$ and $y$ coordinate
      data seperately. The convolution of both signals with $g$
      improves the MSE of the positions from 0.196 to 0.170 and
      visibly smoothes the data.
  }
  \label{fig:sin_conv}
\end{figure}

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