Cite fixes

tex/chapters/experiments.tex

@@ -42,7 +42,7 @@ However, both cases do not give a deeper insight of the error behavior of our me
 \begin{figure}[t]
 	%\includegraphics[width=\textwidth,height=6cm]{gfx/tmpPerformance.png}
 	\input{gfx/perf.tex}
-    \caption{Logarithmic plot of the runtime performance with increasing grid size $G$ and bivariate data. The weighted average estimate (blue) performs fastest followed by the boxKDE (orange) approximation. Both the BKDE (red), and the fastKDE (green) are magnitudes slow, especially for $G<10^4$.}\label{fig:performance}
+    \caption{Logarithmic plot of the runtime performance with increasing grid size $G$ and bivariate data. The weighted average estimate (blue) performs fastest followed by the boxKDE (orange) approximation. Both the BKDE (red), and the fastKDE (green) are magnitudes slower, especially for $G<10^4$.}\label{fig:performance}
 \end{figure}
 
 % kde, box filter, exbox in abhänigkeit von h (bild)
@@ -94,8 +94,5 @@ In addition, modern CPUs do benefit from the recursive computation scheme of the
 Furthermore, the computation is easily parallelized, as there is no data dependency between the one-dimensional filter passes in algorithm~\ref{alg:boxKDE}.
 Hence, the inner loops can be parallelized using threads or SIMD instructions, but the overall speedup depends on the particular architecture and the size of the input.
 
-\commentByFrank{Fig4 (error over time) checken ob die beiden farbigen linien jetzt richtig rum sind. NIEMALS GENERIERTE TEX GRAFIKEN DIREKT EDITIEREN}
-
-
 
 \input{chapters/realworld}

tex/chapters/realworld.tex

@@ -45,8 +45,7 @@ Additionally, in most real world scenarios many particles share the same weight
 	\label{fig:realWorldTime}
 \end{figure}
 
-Further investigating fig. \ref{fig:realWorldTime}, the boxKDE performs slightly better then the weighted-average, however after deploying \SI{100} MC runs, the difference becomes insignificant.
+Further investigating fig. \ref{fig:realWorldTime}, the boxKDE performs slightly better then the weighted-average, however after deploying \SI{100} Monte Carlo runs, the difference becomes insignificant.
-\commentByMarkus{Was sind MC Runs? Die Abkürzung kommt das erste mal vor.} 
 The main reason for this are again multimodalities caused by faulty or delayed measurements, especially when entering or leaving rooms. 
 Within our experiments the problem occurred due to slow and attenuated Wi-Fi signals inside thick-walled rooms. 
 While the system's dynamics are moving the particles outside, the faulty Wi-Fi readings are holding back a majority by assigning corresponding weights. 

tex/egbib.bib

@@ -2864,7 +2864,7 @@ year = {2003}
 @inproceedings{Fetzer2016OMC,
    author    = {T. Fetzer and F. Ebner and L. K{\"o}ping and M. Grzegorzek and F. Deinzer},
    title     = {{On Monte Carlo Smoothing in Multi Sensor Indoor Localisation}},
-   booktitle = {Indoor Positioning and Indoor Navigation (IPIN), Int. Conf. on},
+   booktitle = {Int. Conf. on Indoor Positioning and Indoor Navigation (IPIN)},
    editor    = {},
    year      = {2016},
    publisher = {IEEE},
@@ -2876,7 +2876,7 @@ year = {2003}
 @inproceedings{Verma2003,
 author = {Verma, Vandi and Thrun, Sebastian and Simmons, Reid},
 doi = {10.1.1.68.4380},
-booktitle={Proc. of the International Joint Conference on Artificial Intelligence (IJCAI)}, 
+booktitle={Proc. of the Int. Joint Conf. on Artificial Intelligence (IJCAI)}, 
 pages = {976--984},
 title = {{Variable resolution particle filter}},
 year = {2003}
@@ -2886,7 +2886,7 @@ year = {2003}
 @inproceedings{kovesi2010fast,
     title={Fast almost-gaussian filtering},
     author={Kovesi, Peter},
-    booktitle={Proceedings of the 2010 International Conference on Digital Image Computing: Techniques and Applications},
+    booktitle={Proceedings of the 2010 Int. Conf. on Digital Image Computing: Techniques and Applications},
     pages={121--125},
     year={2010},
     publisher={IEEE}
@@ -2895,7 +2895,7 @@ year = {2003}
 @inproceedings{gwosdek2011theoretical,
     title={Theoretical foundations of gaussian convolution by extended box filtering},
     author={Gwosdek, Pascal and Grewenig, Sven and Bruhn, Andr{\'e}s and Weickert, Joachim},
-    booktitle={International Conference on Scale Space and Variational Methods in Computer Vision},
+    booktitle={Int. Conf. on Scale Space and Variational Methods in Computer Vision},
     pages={447--458},
     year={2011},
     publisher={Springer}
@@ -2911,7 +2911,7 @@ year = {2003}
 @inproceedings{gray2003nonparametric,
   title={Nonparametric density estimation: Toward computational tractability},
   author={Gray, Alexander G and Moore, Andrew W},
-  booktitle={Proceedings of the 2003 SIAM International Conference on Data Mining},
+  booktitle={Proceedings of the 2003 SIAM Int. Conf. on Data Mining},
   pages={203--211},
   year={2003},
   organization={SIAM}
@@ -3075,14 +3075,13 @@ JOURNAL = {ISPRS International Journal of Geo-Information},
 VOLUME = {6},
 YEAR = {2017},
 NUMBER = {8},
-URL = {http://www.mdpi.com/2220-9964/6/8/233},
 ISSN = {2220-9964},
 DOI = {10.3390/ijgi6080233}
 }
 
 @INPROCEEDINGS{Fetzer17,
 author={T. Fetzer and F. Ebner and F. Deinzer and M. Grzegorzek},
-booktitle={2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
+booktitle={2017 Int. Conf. on Indoor Positioning and Indoor Navigation (IPIN)},
 title={Recovering from sample impoverishment in context of indoor localisation},
 year={2017},
 pages={1-8},