current code and TeX. code fine?!?!?!

tex/bare_conf.tex

@@ -81,6 +81,8 @@
 %\usepackage{ulem}
 
 
+%\setcounter{figure}{0}
+%\renewcommand{\thefigure}{A\arabic{section}.\arabic{figure}}
 
 
 % replacement for the SI package

tex/chapters/experiments.tex

@@ -41,13 +41,13 @@
 				\centering
 				\input{gfx/all_fingerprints.tex}
 			}
-			\label{fig:referenceMeasurements}
 			\caption{
 				Locations of the 121 reference measurements.
 				The size of each square denotes the number of permanently installed \docAPshort{}s
 				that are visible at this location,
 				and ranges between 2 and 22 with an average of 9.
 			}
+			\label{fig:referenceMeasurements}
 		\end{figure}
 	
 		% visible APs:
@@ -56,12 +56,12 @@
 		\begin{figure}[b]
 			\centering
 			\input{gfx/compare-wifi-in-out.tex}
-			\label{fig:wifiIndoorOutdoor}
 			\caption{
 				Measurable signal strengths of a testing \docAPshort{} (black dot).
 				While the signal diminishes slowly along the corridor (upper rectangle)
 				the metallised windows (dashed outline) attenuate the signal by over \SI{30}{\decibel} (lower rectangle).
 			}
+			\label{fig:wifiIndoorOutdoor}
 		\end{figure}
 		
 		Figure \ref{fig:wifiIndoorOutdoor} depicts the to-be-expected issues by examining the signal strength 
@@ -105,11 +105,11 @@
 		\begin{figure}
 			\input{gfx/wifi_model_error_0_95.tex}
 			%\input{gfx/wifi_model_error_95_100.tex}
-			\label{fig:wifiModelError}
 			\caption{
 				Comparison between different optimization strategies by examining the error (in \decibel) at each reference measurement.
 				The higher the number of variable parameters, the better the model resembles real world conditions.
 			}
+			\label{fig:wifiModelError}
 		\end{figure}
 		
 		% statds:
@@ -135,30 +135,77 @@
 	
 		As we try to minimize the system's setup time as much as possible, we need to determine
 		the amount of necessary reference measurements for the optimization to produce viable model parameters.
-		Depending on the number of to-be-optimized model parameters, more measurements are required.
-		This especially holds true for {\em \optPerRegion{}} where each region needs at least some measurements 
-		to determine transmitter positions and parameters. 
+		Depending on the chosen model and thus the number of to-be-optimized parameters, more measurements are required.
+		
+		While there was almost no difference between using 121 or 30 reference measurements for 
+		{\em \optParamsAllAP{}} and {\em \optParamsEachAP{}}
+		(average \SIrange{5.3}{5.4}{\decibel} and \SIrange{4.5}{5.0}{\decibel}), 
+		{\em \optPerRegion{}} is highly affected
+		(average \SIrange{2.0}{6.2}{\decibel}), as it needs at least a certain number of measurements for each
+		of its regions for the optimization to converge.
 		
 		\begin{figure}[b]
 			\input{gfx/wifi_model_error_num_fingerprints_method_5_0_90.tex}
 			\input{gfx/wifi_model_error_num_fingerprints_method_5_90_100.tex}
-			\label{fig:wifiNumFingerprints}%
 			\caption{%
-				number of fingerprints
+				Impact of reducing the number of reference measurements during optimization on {\em \optPerRegion{}}.
+				The model's cumulative error distribution is determined by comparing the its signal strength prediction against all 121 measurements.
+				While using only \SI{50}{\percent} of the 121 scans has barely an impact on the error,
+				30 measurements (\SI{25}{\percent}) are clearly insufficient. 
 			}%
+			\label{fig:wifiNumFingerprints}%
 		\end{figure}
 		
-		Figure \ref{fig:wifiNumFingerprints} depicts the impact of reducing the number of fingerprints 
-		for the {\em \optPerRegion{}} strategy. Only using 60 of the 121 fingerprints yields only a slightly 
-		increasing model error and still provides good results. While using only \SI{25}{\percent} of the reference 
-		measurements increases the error rapidly, \SI{75}{\percent} of all considered errors are still better
-		than using just empiric values without any reference measurements.  
+		Figure \ref{fig:wifiNumFingerprints} depicts the impact of reducing the number of reference measurements
+		during the optimization process for the {\em \optPerRegion{}} strategy.
+		The error is determined by using the (absolute) difference between expected signal strength and
+		the optimized model's corresponding prediction for all of the 121 reference measurements.		
+		%
+		Considering only 60 of the 121 scans (\SI{50}{\percent}) yields a slightly increasing model error and still provides good results.
+		While using only \SI{25}{\percent} of the reference  measurements increases the error rapidly,
+		for \SI{75}{\percent} of the 121 considered cases the estimation is still better than using just empiric values without optimization.
+		The extremely large outlier depicted in the lower half of figure \ref{fig:wifiNumFingerprints} (red line) relates to one
+		sub-model with only one assigned reference measurement, where the optimized result is unable to predict values
+		for the rest of the sub-model's region. \todo{versteht man das?}
 		
 		Additionally we examined the impact of skipping reference measurements for difficult locations
-		like aforementioned staircases, surrounded by concrete. While this slightly decreases the
-		estimation error for all other positions, the error within those locations is dramatically
+		like staircases, surrounded by steel-enforced concrete. While this slightly decreases the
+		estimation error for all other positions (hallway, etc) as expected, the error within the skipped locations is dramatically
 		increasing (see lower half of figure \ref{fig:wifiNumFingerprints}). It is thus highly recommended
-		to include such locations.
+		to also perform reference measurements for locations, that are expected to strongly deviate (signal strength)
+		from their surroundings.
+		
+
+
+		%leaving out fingerprints for model 1
+		%	 25%: cnt(1128)	min(0.007439)	max(27.804710)	range(27.797272)	med(4.404236)	avg(5.449720)	stdDev(4.470373)	
+		%	 50%: cnt(1128)	min(0.006027)	max(27.732193)	range(27.726166)	med(4.367859)	avg(5.437861)	stdDev(4.475426)	
+		%	 100%: cnt(1128)	min(0.000282)	max(27.705376)	range(27.705093)	med(4.272881)	avg(5.411202)	stdDev(4.493495)	
+		%	 noStair%: cnt(1128)	min(0.000801)	max(27.209221)	range(27.208420)	med(4.333328)	avg(5.459918)	stdDev(4.459484)	
+
+		%leaving out fingerprints for model 2
+		%	 25%: cnt(1128)	min(0.000320)	max(29.752560)	range(29.752239)	med(3.837357)	avg(5.027578)	stdDev(4.617191)	
+		%	 50%: cnt(1128)	min(0.015305)	max(34.152130)	range(34.136826)	med(3.627090)	avg(4.635868)	stdDev(4.135866)	
+		%	 100%: cnt(1128)	min(0.000488)	max(25.687740)	range(25.687252)	med(3.319756)	avg(4.441193)	stdDev(3.912525)	
+		%	 noStair%: cnt(1128)	min(0.017693)	max(25.687740)	range(25.670048)	med(3.304321)	avg(4.507620)	stdDev(3.957071)	
+
+		%leaving out fingerprints for model 3
+		%	 25%: cnt(1128)	min(0.003242)	max(39.470978)	range(39.467735)	med(3.371758)	avg(4.977330)	stdDev(5.213937)	
+		%	 50%: cnt(1128)	min(0.002808)	max(30.113415)	range(30.110607)	med(2.941238)	avg(4.015042)	stdDev(3.696969)	
+		%	 100%: cnt(1128)	min(0.000557)	max(16.813850)	range(16.813293)	med(3.056915)	avg(3.813013)	stdDev(3.062580)	
+		%	 noStair%: cnt(1128)	min(0.002518)	max(30.370636)	range(30.368118)	med(3.016884)	avg(3.983101)	stdDev(3.508327)	
+
+		%leaving out fingerprints for model 4
+		%	 25%: cnt(1128)	min(0.000000)	max(62.233345)	range(62.233345)	med(2.502831)	avg(5.432897)	stdDev(8.664582)	
+		%	 50%: cnt(1128)	min(0.000000)	max(56.843803)	range(56.843803)	med(1.543137)	avg(2.937506)	stdDev(4.417061)	
+		%	 100%: cnt(1128)	min(0.000046)	max(33.175812)	range(33.175766)	med(1.537933)	avg(2.441976)	stdDev(2.793499)	
+		%	 noStair%: cnt(1128)	min(0.000000)	max(62.233345)	range(62.233345)	med(1.493668)	avg(2.744918)	stdDev(4.428092)	
+
+		%leaving out fingerprints for model 5
+		%	 25%: cnt(1128)	min(0.000000)	max(62.620842)	range(62.620842)	med(2.140709)	avg(6.257105)	stdDev(11.638572)	
+		%	 50%: cnt(1128)	min(0.000000)	max(57.371948)	range(57.371948)	med(1.357452)	avg(2.982217)	stdDev(5.877471)	
+		%	 100%: cnt(1128)	min(0.000000)	max(14.837151)	range(14.837151)	med(1.251358)	avg(1.989277)	stdDev(2.189072)	
+		%	 noStair%: cnt(1128)	min(0.000000)	max(62.233345)	range(62.233345)	med(1.143669)	avg(2.316189)	stdDev(4.164822)	
 
 		
 		
@@ -198,8 +245,8 @@
 		at location $\mPosVec$ returned from the to-be-examined prediction model.
 		For all comparisons we use a constant uncertainty $\sigma = $\SI{8}{\decibel}.
 		
-		The quality of the estimated location is determined by comparing the estimation 
-		$\mPosVec^*$ with the pedestrian's ground truth position at the time the scan $\mRssiVec$
+		The quality of the estimated location is determined by using the Euclidean distance between estimation 
+		$\mPosVec^*$ and the pedestrian's ground truth position at the time the scan $\mRssiVec$
 		has been received.
 		
 		
@@ -226,18 +273,20 @@
 		
 		\begin{figure}[b]
 			\input{gfx/modelPerformance_meter.tex}
-			\label{fig:modelPerformance}
 			\caption{
 				Error between ground truth and estimation using \refeq{eq:bestWiFiPos} depending
-				on the underlying signal strength prediction model
+				on the underlying signal strength prediction model.
+				Extremely high errors between the \SIrange{90}{100}{\percent} quartile are related to bad \docWIFI{}
+				coverage within outdoor areas (see figure \ref{fig:wifiIndoorOutdoor}).
 			}
+			\label{fig:modelPerformance}
 		\end{figure}
 		
 		%To estimate the overall performance of the prediction models, we compare the position estimation
 		%for each \docWIFI{} measurement within the recorded paths (3756 \docAPshort{} scans in total)
 		%against the corresponding ground-truth, which indicates the absolute 3D error in meter. 
-		The position estimation for each \docWIFI{} measurement within the recorded walks (3756 \docAPshort{} scans in total)
-		is compared against its corresponding ground-truth, indicating the absolute 3D error.
+		The position estimation for each \docWIFI{} measurement within the recorded walks (3756 scans in total)
+		is compared against its corresponding ground-truth, indicating the 3D error.
 		The resulting cumulative error distribution can be seen in figure \ref{fig:modelPerformance}.
 		The quality of the location estimation directly scales with the quality of the signal strength prediction model.
 		However, as discussed earlier, the maximal estimation error might increase for some setups.
@@ -252,13 +301,13 @@
 		
 		\begin{figure}[t]
 			\input{gfx/wifiMultimodality.tex}
-			\label{fig:wifiMultimodality}
 			\caption{
 				Location probability \refeq{eq:bestWiFiPos} for three scans. Higher color intensities are more likely.
 				Ideally, places near the ground truth (black) are highly highly probable (green).
 				Often, other locations are just as likely as the ground truth (blue),
 				or the location with the highest probability does not match at all (red).
 			}
+			\label{fig:wifiMultimodality}
 		\end{figure}
 		
 		Figure \ref{fig:wifiMultimodality} depicts aforementioned issues of multimodal (blue) or wrong (red) location 
@@ -318,13 +367,13 @@
 		\begin{figure}
 			\input{gfx/wifiCompare_normalVsExp_cross.tex}
 			\input{gfx/wifiCompare_normalVsExp_meter.tex}
-			\label{fig:normalVsExponential}
 			\caption{
 				Comparison between normal- (black) and exponential-distribution (red) for \refeq{eq:wifiProb}.
 				While misclassifications are slightly reduced (upper chart),
 				the median error between ground-truth and estimation (lower chart) increases by
 				about \SI{1}{\meter}.		 		
 			}
+			\label{fig:normalVsExponential}
 		\end{figure}
 		
 

tex/chapters/work.tex

@@ -100,12 +100,12 @@
 	
 		\begin{figure}[t!]
 			\input{gfx/wifiop_show_optfunc_params}
-			\label{fig:wifiOptFuncTXPEXP}
 			\caption{
 				The average error (in \SI{}{\decibel}) between all reference measurements and corresponding model predictions
 				for one \docAPshort{} dependent on \docTXP{} \mTXP{} and \docEXP{} \mPLE{}
 				[known position $\mPosAPVec{}$, fixed \mWAF{}] denotes a convex function.
 			}
+			\label{fig:wifiOptFuncTXPEXP}
 		\end{figure}
 	
 		For systems that demand a higher accuracy, one can choose a compromise between fingerprinting and
@@ -138,12 +138,12 @@
 	
 		\begin{figure}[t!]
 			\input{gfx/wifiop_show_optfunc_pos_yz}
-			\label{fig:wifiOptFuncPosYZ}
 			\caption{
 				The average error (in \SI{}{\decibel}) between reference measurements and model predictions
 				for one \docAPshort{} dependent on $y$- and $z$-position [fixed $x$, \mTXP{}, \mPLE{} and \mWAF{}]
 				usually denotes a non-convex function with multiple [here: two] local minima.
 			}
+			\label{fig:wifiOptFuncPosYZ}
 		\end{figure}
 		
 		Such functions demand for optimization algorithms, that are able to deal with non-convex functions,
@@ -186,6 +186,9 @@
 		axis-aligned bounding box. This approach allows a distinction between in- and outdoor-regions
 		or locations that are expected to highly differ from their surroundings.
 		
+		\todo{AP wird in einer region nur dann beruecksichtigt, wenn mindestanzahl an messungen vorhanden ist!}
+		
+		\todo{das heißt aber, dass an unterschiedlichen stellen unterschiedlich viele APs verglichen werden. das geht ned. deshalb feste -100}
 	
 	\subsection{\docWIFI{} quality factor}
 	
@@ -243,8 +246,11 @@
 		When scanning for \docAPshort{}s one will thus receive several responses from the same hardware, all with
 		a very small delay (micro- to milliseconds). Such measurements may be grouped using some aggregate
 		function like average, median or maximum.
-
-
+		
+		Furthermore, VAP grouping can be used to suppress unlikely observations: If a physical hardware is known
+		to provide six virtual networks, it is unlikely to only see one of those networks. This is likely due to
+		temporal effects and/or multipath signal propagation and the received signal strength will often be far from
+		the normal average. It thus makes sense to just omit such unlikely observations, focusing on the remaining, stable ones.
 
 
 

tex/gfx/build.sh

@@ -1,4 +1,4 @@
 for file in *.gp 
 do
-	gnuplot $file;
+	gnuplot "$file";
 done