Add presentation

.gitignore

@@ -8,6 +8,9 @@ database/
 imgs/
 sounds/*
 
+presentations/*
+
+!presentations/Ocean.pdf
 !sounds/Cock.wav
 
 *.pdf

Makefile

@@ -17,7 +17,7 @@ depend:
 	mkdir sounds
 
 paper:
-	cd docs/paper; pdflatex paper.tex; cd ../../;
+	cd docs/paper; pdflatex paper.tex; pdflatex paper.tex; pdflatex paper.tex cd ../../;
 
 documentation:
 	cd docs; doxygen Doxyfile; cd ..

docs/paper/paper.aux

@@ -7,3 +7,5 @@
 \@writefile{loa}{\contentsline {algorithm}{\numberline {1}{\ignorespaces Determining the Standard Deviation}}{3}}
 \newlabel{algo:Std}{{1}{3}}
 \@writefile{toc}{\contentsline {subsubsection}{\numberline {1.2.2}Multiple Source}{3}}
+\@writefile{loa}{\contentsline {algorithm}{\numberline {2}{\ignorespaces Multiple Source Sound Localization}}{4}}
+\newlabel{algo:MS}{{2}{4}}

docs/paper/paper.log

@@ -1,8 +1,8 @@
-This is pdfTeX, Version 3.1415926-2.5-1.40.14 (TeX Live 2013) (format=pdflatex 2013.5.30)  10 MAR 2014 01:48
+This is pdfTeX, Version 3.1415926-2.5-1.40.14 (TeX Live 2013) (format=pdflatex 2013.5.30)  10 MAR 2014 14:47
 entering extended mode
  restricted \write18 enabled.
  %&-line parsing enabled.
-**paper.tex
+**paper.tex cd ../../
 (./paper.tex
 LaTeX2e <2011/06/27>
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+[4] (./paper.aux) ) 
 Here is how much of TeX's memory you used:
- 2494 strings out of 493315
- 33359 string characters out of 6137904
- 98191 words of memory out of 5000000
- 5914 multiletter control sequences out of 15000+600000
+ 2495 strings out of 493315
+ 33368 string characters out of 6137904
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  14254 words of font info for 50 fonts, out of 8000000 for 9000
  957 hyphenation exceptions out of 8191
- 38i,17n,23p,231b,471s stack positions out of 5000i,500n,10000p,200000b,80000s
+ 38i,17n,23p,241b,471s stack positions out of 5000i,500n,10000p,200000b,80000s
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-Output written on paper.pdf (3 pages, 176698 bytes).
+Output written on paper.pdf (4 pages, 182662 bytes).
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- 62 compressed objects within 1 object stream
+ 89 PDF objects out of 1000 (max. 8388607)
+ 64 compressed objects within 1 object stream
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docs/paper/paper.tex

@@ -115,9 +115,9 @@ \subsubsection{Single Source}
 \STATE $t_{max} \leftarrow \Function{GetMaxTime}(\Var{eventList})$
 \STATE $t_{min} \leftarrow \Function{GetMinTime}(\Var{eventList})$
 \STATE $t_{err} \leftarrow 1 - \mathlarger{\frac{t_{max} - \Var{event}.\Var{timestamp}}{t_{max} - t_{min} + \epsilon}}$
-\STATE $err \leftarrow \frac{c}{t_{err} + \Var{event}.\Var{confidence}}$
+\STATE $\Var{err} \leftarrow \frac{c}{t_{err} + \Var{event}.\Var{confidence}}$
 \COMMENT {$c$ is a scaling constant}
-\RETURN $err$
+\RETURN $\Var{err}$
 
 \end{algorithmic}
 \end{algorithm}
@@ -151,6 +151,37 @@ \subsubsection{Multiple Source}
 \grad{x, y}(\mathcal{-P}, x_g, y_g, r, spl, events) \end{align*}
 
 The variables, $r$, $spl$, $events$ are the same as in $\mathcal{L}$, however,
-$x_g$ and $y_g$ are the initial guesses for the gradient decent.
+$x_g$ and $y_g$ are the initial guesses for the gradient decent. The function
+returns the $x$ and $y$ values that minimizes the equation $-\mathcal{P}$ given
+constant arguments $r$, $spl$, and $events$.
+
+Now that we are able to bias the gradient decent, it is possible for us to find
+mutliple local maximas in the probability density function, $\mathcal{P}$. This
+is done by iterating through the list of events and performing the gradient
+decent function with an initial guess of the position of the event. Once we
+have a list of $x, y$ points, we are able to apply an affinity propogation
+algorithm to determine the point clusters and therefore distinguish between
+multiple sources of the same sound in our WASN. This is shown in
+\textbf{Algorithm} \ref{algo:MS}.
+
+\begin{algorithm}[ht] \caption{Multiple Source Sound Localization}
+\label{algo:MS} \begin{algorithmic}[1] \setcounter{ALC@line}{0}
+
+\vspace*{1mm}
+
+\STATE $\Function{GetLocations}(\Var{r}, \Var{spl}, \Var{eventList}) \rightarrow$
+\STATE $\Var{locations} \leftarrow \Function{InitLocationList}()$
+\FORALL {$e \in \Var{eventList}$}
+\STATE $\Var{locations}.\Var{add}(\mathcal{L}_m(\Var{r}, \Var{spl}, \Var{eventList}, e.\Var{x}, e.\Var{y}))$
+\ENDFOR
+\STATE $\Var{centers} \leftarrow \Function{AffinityPropagation}(\Var{locations})$
+\COMMENT {\Var{centers} is a list of $(x, y, c)$ points where $c$ is the confidence}
+\RETURN $\Var{centers}$
+
+\end{algorithmic}
+\end{algorithm}
+
+Now given reference data and a list of node events, we it is possible to
+determine the position of multiple instances of the same sound.
 
 \end{document}

tests/test_server.py

@@ -18,7 +18,7 @@
 server_port = 8000
 
 test_sound_class = "Chicken"
-test_sound_name = "Cock"
+test_sound_name = "Koel"
 
 loc = api.Locaudio(server_addr, server_port)