FHWS/Fusion2018
Archived
4
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Small fixes

Browse Source
This commit is contained in:
MBulli
2018-02-26 10:13:43 +01:00
parent e8c1f453d0
commit 8f459b25b4
2 changed files with 3 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
tex/chapters/experiments.tex
View File

@@ -52,8 +52,8 @@ However, both cases do not give a deeper insight of the error behavior of our me
\subsection{Performance} \subsection{Performance}
In the following, we underpin the promising theoretical linear time complexity of our method with empirical time measurements compared to other methods. In the following, we underpin the promising theoretical linear time complexity of our method with empirical time measurements compared to other methods.
All tests are performed on a Intel Core \mbox{i5-7600K} CPU with a frequency of \SI{4.2}{\giga\hertz}, and \SI{16}{\giga\byte} main memory. All tests are performed on a Intel Core \mbox{i5-7600K} CPU with a frequency of \SI{4.2}{\giga\hertz}, and \SI{16}{\giga\byte} main memory.
We compare our C++ implementation of the extended box filter based KDE approximation to the KernSmooth R package and the \qq{FastKDE} Python implementation \cite{oBrien2016fast}. We compare our C++ implementation of the box filter based KDE approximation to the \texttt{ks} R package and the fastKDE Python implementation \cite{oBrien2016fast}.
The KernSmooth packages provides a FFT-based BKDE implementation based on optimized C functions at its core. The \texttt{ks} packages provides a FFT-based BKDE implementation based on optimized C functions at its core.
% Vergleich zu weighted average (in c++) um unseren großen Geschwindigkeitsvorteil zu zeigen. % Vergleich zu weighted average (in c++) um unseren großen Geschwindigkeitsvorteil zu zeigen.
With state estimation problems in mind, we additionally provide a C++ implementation of a weighted average estimator. With state estimation problems in mind, we additionally provide a C++ implementation of a weighted average estimator.
\commentByToni{Vielleicht sollten wir hier noch paar Worte über die Implementierung verlieren. Ist das alles std c++? nehmen wir iwas mega kraßes? usw. vielleicht im camera ready sogar nen link zum coder oder sowas.} \commentByToni{Vielleicht sollten wir hier noch paar Worte über die Implementierung verlieren. Ist das alles std c++? nehmen wir iwas mega kraßes? usw. vielleicht im camera ready sogar nen link zum coder oder sowas.}

1
tex/chapters/realworld.tex
View File

@@ -46,6 +46,7 @@ Additionally, in most real world scenarios many particles share the same weight
\end{figure} \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} MC 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. 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. 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. While the system's dynamics are moving the particles outside, the faulty Wi-Fi readings are holding back a majority by assigning corresponding weights.