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and /dev/null differ diff --git a/BachelorThesis/Software/PyPrecisionGUI/calculated_positions/off_center_sf=0.001755_k=99.npy b/BachelorThesis/Software/PyPrecisionGUI/calculated_positions/off_center_sf=0.001755_k=99.npy deleted file mode 100644 index 4246919..0000000 Binary files a/BachelorThesis/Software/PyPrecisionGUI/calculated_positions/off_center_sf=0.001755_k=99.npy and /dev/null differ diff --git a/BachelorThesis/Software/PyPrecisionGUI/localization.py b/BachelorThesis/Software/PyPrecisionGUI/localization.py index 4da46a1..ac4e62e 100644 --- a/BachelorThesis/Software/PyPrecisionGUI/localization.py +++ b/BachelorThesis/Software/PyPrecisionGUI/localization.py @@ -1,6 +1,5 @@ import numpy as np import os -from copy import deepcopy class Localization: @@ -11,10 +10,13 @@ class Localization: #self.table = np.load('fingerprinting_tables/SmallManualFingerprint_Ferrite.npy') # manual fingerprinting table (Ferrite) [A bit off] #self.table = np.load('fingerprinting_tables/Julian_BThesis_table2_switchedAnt5&6 and 7&8.npy') # Switched Ant5<->6 and 7<->8 in Excel (No Ferrite) [Does not work!] self.table = np.load('fingerprinting_tables/Julian_THIS_ONE_IS_IT.npy') # 2cm precision this definetly worked for (No ferrite) - self.normalized_table = deepcopy(self.table) self.data = np.load('recorded_data/current_recording.npy') +<<<<<<< HEAD def localize(self, fv, exclude_frames=False): +======= + def localize(self, fv): +>>>>>>> parent of f2b8eb4 (Script that can analyze (Horizontal+Diagonal, Center, Off_center) quickly) """ Input: - fv [type = numpy array (mb list) [15, 1]]: A feature vector contains the antenna voltages of one sample in following order: frame1, frame2, ... frame 8, main1, main2, ... main8 @@ -31,26 +33,22 @@ class Localization: feature_vector = fv * self.scale_factor - if exclude_frames: - table_copy = deepcopy(self.table) # make a copy of the fingerprinting table - table_copy[:, 17:25] = 0 # set all mains in the copy to 0 + # table_copy = self.table # make a copy of the fingerprinting table + # table_copy[:, 17:25] = 0 # set all mains in the copy to 0 + # + # max_idx = np.argmax(abs(fv[:8])) # find highest Frame + # + # table_copy[:, max_idx+9] = 0 # set the row of highest Frame in fingerprinting to 0 + # + # print("fv =", fv) + # print("max_idx=", max_idx) + # print("tablecopy= ", table_copy[0, :]) - max_idx = np.argmax(abs(feature_vector[:8])) # find highest Frame - print("Feature_vector BEFORE taking highest frame out:\n", feature_vector) - print("Highest Frame:", max_idx+1) - feature_vector[max_idx] = 0 - print("Feature_vector AFTER taking highest frame out:\n", feature_vector) - table_copy[:, max_idx+9] = 0 # set the row of highest Frame in fingerprinting to 0 - print("First row of table", table_copy[0]) - # print("feature vector =", feature_vector) - # print("max_idx=", max_idx) - # print("tablecopy= ", table_copy[0, :]) - repeated_feature_vector = np.square(table_copy[:, 9:] - feature_vector) - else: - repeated_feature_vector = np.square(self.table[:, 9:] - feature_vector) # Before changing anything on this function + repeated_feature_vector = np.square(self.table[:, 9:] - feature_vector) # Before changing anything on this function + #repeated_feature_vector = np.square(table_copy[:, 9:] - feature_vector) euclidean_distances = np.sum(repeated_feature_vector, 1) order = np.argsort(euclidean_distances) diff --git a/BachelorThesis/Software/PyPrecisionGUI/plot_precision_tests.py b/BachelorThesis/Software/PyPrecisionGUI/plot_precision_tests.py index 96189a5..011cbee 100644 --- a/BachelorThesis/Software/PyPrecisionGUI/plot_precision_tests.py +++ b/BachelorThesis/Software/PyPrecisionGUI/plot_precision_tests.py @@ -430,6 +430,7 @@ def load_entire_ART(recording_names): # z_list.append(pos[2]) + # Input here ----------------------------------------------------------------------------------------------------------- recording_selection = ["z_axis"] sf_list = [0.0021] # np.arange(0.0015, 0.0025, 0.0001) @@ -453,18 +454,32 @@ if len(recording_selection) == 2: # if we want to look at multiple recordings rec2 = recording_dict_fs[recording_selection[1]] # Load IndLoc2 IndLoc_data = np.append(rec1, rec2, axis=0) # Append both IndLoc arrays correctly +======= +# Make the plot look nice +plot_surroundings() +# Load ART positions +ART_pos = load_entire_ART(["double_current_horizontal", "double_current_diagonal"]) +>>>>>>> parent of f2b8eb4 (Script that can analyze (Horizontal+Diagonal, Center, Off_center) quickly) + +# Load IndLoc data +horizontal = recordings_dict["double_current_horizontal"] +diagonal = recordings_dict["double_current_diagonal"] +IndLoc_data = np.append(horizontal, diagonal, axis=0) # shape (42, 2000, 19) = (P1-P42, 2k samples, fv) # Average IndLoc data -#IndLoc_data = np.average(IndLoc_data, axis=1) # shape (42, 19) = (P1-P42, fv_avg) +IndLoc_data = np.average(IndLoc_data, axis=1) # shape (42, 19) = (P1-P42, fv_avg) # Localize IndLoc data for each scaling factor in sf_list and each k in k_list +sf_list = [0.001] # [0.002] +k_list = [3] IndLoc_pos = calc_multiple_positions(data=IndLoc_data, - output_filename=str(recording_selection), + output_filename="horizontal_diagonal", only_fs=True, sf_list=sf_list, k_list=k_list) +<<<<<<< HEAD @@ -503,20 +518,74 @@ IndLoc_pos = calc_multiple_positions(data=IndLoc_data, plot_multiple_positions(positions_filename=str(recording_selection), sf_list=[0.001755], k_list=[19], +======= +# Find the best localization +best_mean = 10000 +best_std_dev = 10000 +best_both = 10000 +for i in range((len(sf_list)*len(k_list))): + distances, curr_mean, curr_std_dev = eval_loc(ART_pos=ART_pos, IndLoc_pos=IndLoc_pos[i]) + + curr_both = curr_mean + curr_std_dev + + if curr_mean <= best_mean: + best_both = curr_both + best_mean = curr_mean + best_std_dev = curr_std_dev + index = i + +best_sf = sf_list[index] + +print("\n----Best localization parameters were found!-----\n scaling_factor = ", best_sf, "\n mean_error = ", best_mean, + "\n standard_deviation = ", best_std_dev) + + +# Plot IndLoc pos +plot_multiple_positions(positions_filename="horizontal_diagonal", + sf_list=[best_sf], + k_list=k_list, +>>>>>>> parent of f2b8eb4 (Script that can analyze (Horizontal+Diagonal, Center, Off_center) quickly) only_label_once=True, - xy_title=plot_title, - yz_title=plot_title, - annotate_points=annotate_points, + xy_title="Statistical optimization of the scaling factor for the entire localization area", + yz_title="Statistical optimization of the scaling factor for the entire localization area", + annotate_points=False, annotationsize=15, - labels=iter([label]), # labels=iter(["k = " + str(best_k)]), + labels=iter(["scaling factor = " + str("{:.6f}".format(best_sf))]), color_all_black=True, markersize=10,) # Plot ART positions +<<<<<<< HEAD plot_art_positions(ART_pos, annotate_flag=True, markersize=100, annotation_size=15) +======= +plot_art_positions(ART_pos, annotate_flag=False, markersize=100, annotation_size=15) + + +# print(IndLoc_pos) +#recording_selection = "horizontal_and_diagonal" + + + +# scale_factor_list = [0.001755] +# +# calc_multiple_positions(recording_name=recording_selection, +# only_fs=True, +# sf_list=[0.001755], +# k_list=[3]) +# +# plot_multiple_positions(recording_name=recording_selection, +# sf_list=[0.001755], +# k_list=[3], +# only_label_once=True, +# xy_title="Statistical optimization of the scaling factor for an offcenter set of points", +# yz_title="Statistical optimization of the scaling factor for an offcenter set of points", +# annotate_points=False, +# labels=iter(["scaling factor = " + str("{:.6f}".format(0.001755))]), +# color_all_black=True) +>>>>>>> parent of f2b8eb4 (Script that can analyze (Horizontal+Diagonal, Center, Off_center) quickly) xy_subplot.legend(bbox_to_anchor=(1.01, 1.00), loc=2, borderaxespad=0., fontsize=10) plt.show() diff --git a/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.aux b/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.aux index 7fac254..0383df2 100644 --- a/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.aux +++ b/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.aux @@ -94,7 +94,6 @@ \@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces The magnetic flux in the infinite wire loop 2($\Phi _{21}$) and it's dependency on the distances to wire loop 1.\relax }}{7}\protected@file@percent } \@writefile{lof}{\contentsline {subfigure}{\numberline{(a)}{\ignorespaces {Magnetic flux in 2 due to the left wire of 1}}}{7}\protected@file@percent } \@writefile{lof}{\contentsline {subfigure}{\numberline{(b)}{\ignorespaces {Magnetic flux in 2 due to the right wire of 1}}}{7}\protected@file@percent } -\newlabel{eq:localization_function}{{6}{7}} \@writefile{toc}{\contentsline {section}{\numberline {3}The IndLoc system}{8}\protected@file@percent } \newlabel{sec:The IndLoc system}{{3}{8}} \@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces An overview of the main components of the IndLoc system and how they are connected to each other.\relax }}{8}\protected@file@percent } @@ -176,6 +175,7 @@ \@writefile{lof}{\contentsline {subfigure}{\numberline{(a)}{\ignorespaces {Heating up}}}{20}\protected@file@percent } \@writefile{lof}{\contentsline {subfigure}{\numberline{(b)}{\ignorespaces {setup is warm}}}{20}\protected@file@percent } \@writefile{tdo}{\contentsline {todo}{maybe cite the quickguide manual here?}{20}\protected@file@percent } +<<<<<<< HEAD \pgfsyspdfmark {pgfid42}{14468604}{15957387} \pgfsyspdfmark {pgfid45}{39101366}{15970843} \pgfsyspdfmark {pgfid46}{41018294}{15725657} @@ -228,6 +228,28 @@ \@writefile{toc}{\contentsline {section}{\numberline {6}RESULTS}{33}\protected@file@percent } \@writefile{toc}{\contentsline {section}{\numberline {7}DISCUSSION}{34}\protected@file@percent } \@writefile{toc}{\contentsline {section}{\numberline {8}SUMMARY, OUTLOOK INTO THE FUTURE}{35}\protected@file@percent } +======= +\pgfsyspdfmark {pgfid48}{14468604}{25488963} +\pgfsyspdfmark {pgfid51}{39101366}{25502419} +\pgfsyspdfmark {pgfid52}{41018294}{25257233} +\@writefile{toc}{\contentsline {subsection}{\numberline {5.2}Precision measurements}{20}\protected@file@percent } +\@writefile{toc}{\contentsline {subsubsection}{\numberline {5.2.1}Scaling factor test}{21}\protected@file@percent } +\@writefile{lof}{\contentsline {figure}{\numberline {14}{\ignorespaces Before being able to localize precisely the correct scaling factor, which multiplies each receiving coil voltages, has to be determined. For this the scaling factor from $10^{2}$ to $ 10^{-4}$ were tested.\relax }}{21}\protected@file@percent } +\newlabel{fig:estimation_scaling_factor_1}{{14}{21}} +\@writefile{lof}{\contentsline {figure}{\numberline {15}{\ignorespaces A zoomed in view of Figure \ref {fig:estimation_scaling_factor_1} shows which scaling factors result in a localization closest to the correct positions.\relax }}{22}\protected@file@percent } +\newlabel{fig:estimation_scaling_factor_2}{{15}{22}} +\@writefile{lof}{\contentsline {figure}{\numberline {16}{\ignorespaces Placing the object in 25 positions near the centre of the localization area with a scaling factor of 0.001755, resulting in mean error $\mu = 3.76\textit {m}m$ and standard deviation $\sigma = 3.28\textit {m}m$.\relax }}{23}\protected@file@percent } +\newlabel{fig:estimation_scaling_factor_3}{{16}{23}} +\@writefile{lof}{\contentsline {figure}{\numberline {17}{\ignorespaces Placing the object in 25 positions near the corner of the localization area with a scaling factor of 0.001755, , resulting in mean error $\mu = 6.18\textit {m}m$ and standard deviation $\sigma = 7.71\textit {m}m$.\relax }}{24}\protected@file@percent } +\newlabel{fig:estimation_scaling_factor_4}{{17}{24}} +\@writefile{lof}{\contentsline {figure}{\numberline {18}{\ignorespaces Placing the object in 42 positions running from the centre to the edges of the localization area with a scaling factor of 0.00204, , resulting in mean error $\mu = 7.42\textit {m}m$ and standard deviation $\sigma = 6.48\textit {m}m$.\relax }}{24}\protected@file@percent } +\newlabel{fig:estimation_scaling_factor_5}{{18}{24}} +\@writefile{lof}{\contentsline {figure}{\numberline {19}{\ignorespaces With averaged IndLoc feature vectors 0.00204, , resulting in mean error $\mu = 7.34\textit {m}m$ and standard deviation $\sigma = 6.49\textit {m}m$.\relax }}{25}\protected@file@percent } +\newlabel{fig:estimation_scaling_factor_6}{{19}{25}} +\@writefile{toc}{\contentsline {section}{\numberline {6}RESULTS}{26}\protected@file@percent } +\@writefile{toc}{\contentsline {section}{\numberline {7}DISCUSSION}{27}\protected@file@percent } +\@writefile{toc}{\contentsline {section}{\numberline {8}SUMMARY, OUTLOOK INTO THE FUTURE}{28}\protected@file@percent } +>>>>>>> parent of f2b8eb4 (Script that can analyze (Horizontal+Diagonal, Center, Off_center) quickly) \bibdata{Literatur_BA_2} \bibcite{HARRIEHAUSEN.2020b}{1} \bibcite{Albach.2014}{2} diff --git a/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.dvi b/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.dvi index fd92d2e..ddf8583 100644 Binary files a/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.dvi and b/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.dvi differ diff --git a/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.log b/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.log index 3180127..6029060 100644 --- a/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.log +++ b/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.log @@ -1,4 +1,8 @@ +<<<<<<< HEAD This is pdfTeX, Version 3.14159265-2.6-1.40.20 (MiKTeX 2.9.7250 64-bit) (preloaded format=pdflatex 2020.4.5) 15 FEB 2021 10:28 +======= +This is pdfTeX, Version 3.14159265-2.6-1.40.20 (MiKTeX 2.9.7250 64-bit) (preloaded format=pdflatex 2020.4.5) 9 FEB 2021 13:48 +>>>>>>> parent of f2b8eb4 (Script that can analyze (Horizontal+Diagonal, Center, Off_center) quickly) entering extended mode **"./2021_Seyffer_Investigating the precision of an induction-based localizatio n system for medical applications.tex" @@ -712,6 +716,7 @@ Package: verbatim 2019/11/10 v1.5r LaTeX2e package for verbatim enhancements \verbatim@in@stream=\read3 ) ("2021_Seyffer_Investigating the precision of an induction-based localization s +<<<<<<< HEAD ystem for medical applications.aux" LaTeX Warning: Label `fig:estimation_k_nearest_2' multiply defined. @@ -723,6 +728,9 @@ LaTeX Warning: Label `fig:estimation_k_nearest_3' multiply defined. 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/pgfcolorsamplecmyk 4 string def -/pgfcolorsamplergb 3 string def /pgfcolorsamplegray 3 string def /pgfrangecmyk -[0 1 0 1 0 1 0 1] def /pgfrangergb [0 1 0 1 0 1] def /pgfrangegray -[0 1] def +def /.setopacityalpha where {pop} {/.setopacityalpha{pop}def} ifelse +/.pgfsetfillopacityalpha{/pgffoa exch def /pgffill{gsave pgffoa .setopacityalpha +fill 1 .setopacityalpha newpath fill grestore newpath}bind def /pgfeofill{gsave +pgffoa .setopacityalpha eofill 1 .setopacityalpha newpath eofill grestore +newpath}bind def}bind def /.pgfsetstrokeopacityalpha{/pgfsoa exch def +/pgfstr{gsave pgfsoa .setopacityalpha stroke grestore newpath}bind +def}bind def /pgffoa 1 def /pgfsoa 1 def end /pgf1{gsave exec 1.0 pgfw 2.00002 0.0 moveto -6.00006 4.00005 lineto -3.00003 0.0 lineto -6.00006 -4.00005 lineto pgffill grestore} bind @@ -433,230 +817,233 @@ gsave pgffc pgffill grestore gsave pgfsc pgfstr grestore newpath grestore} bind def @fedspecial end -%%BeginFont: CMSY8 -%!PS-AdobeFont-1.0: CMSY8 003.002 -%%Title: CMSY8 +%%BeginFont: CMR5 +%!PS-AdobeFont-1.0: CMR5 003.002 +%%Title: CMR5 %Version: 003.002 %%CreationDate: Mon Jul 13 16:17:00 2009 %%Creator: David M. Jones %Copyright: Copyright (c) 1997, 2009 American Mathematical Society -%Copyright: (), with Reserved Font Name CMSY8. +%Copyright: (), with Reserved Font Name CMR5. % This Font Software is licensed under the SIL Open Font License, Version 1.1. % This license is in the accompanying file OFL.txt, and is also % available with a FAQ at: http://scripts.sil.org/OFL. %%EndComments -FontDirectory/CMSY8 known{/CMSY8 findfont dup/UniqueID known{dup -/UniqueID get 5096649 eq exch/FontType get 1 eq and}{pop false}ifelse +FontDirectory/CMR5 known{/CMR5 findfont dup/UniqueID known{dup +/UniqueID get 5000788 eq exch/FontType get 1 eq and}{pop false}ifelse {save true}{false}ifelse}{false}ifelse 11 dict begin /FontType 1 def /FontMatrix [0.001 0 0 0.001 0 0 ]readonly def -/FontName /CMSY8 def -/FontBBox {-30 -955 1185 779 }readonly def +/FontName /CMR5 def +/FontBBox {-10 -250 1304 750 }readonly def /PaintType 0 def /FontInfo 9 dict dup begin /version (003.002) readonly def -/Notice (Copyright 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Jones %Copyright: Copyright (c) 1997, 2009 American Mathematical Society -%Copyright: (), with Reserved Font Name CMTI10. +%Copyright: (), with Reserved Font Name CMSY6. % This Font Software is licensed under the SIL Open Font License, Version 1.1. % This license is in the accompanying file OFL.txt, and is also % available with a FAQ at: http://scripts.sil.org/OFL. %%EndComments -FontDirectory/CMTI10 known{/CMTI10 findfont dup/UniqueID known{dup -/UniqueID get 5000828 eq exch/FontType get 1 eq and}{pop false}ifelse +FontDirectory/CMSY6 known{/CMSY6 findfont dup/UniqueID known{dup +/UniqueID get 5096647 eq exch/FontType get 1 eq and}{pop false}ifelse {save true}{false}ifelse}{false}ifelse 11 dict begin /FontType 1 def /FontMatrix [0.001 0 0 0.001 0 0 ]readonly def -/FontName /CMTI10 def -/FontBBox {-35 -250 1124 750 }readonly def +/FontName /CMSY6 def +/FontBBox {-4 -948 1329 786 }readonly def /PaintType 0 def /FontInfo 9 dict dup begin /version (003.002) readonly def -/Notice 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Jones +%Copyright: Copyright (c) 1997, 2009 American Mathematical Society +%Copyright: (), with Reserved Font Name CMR6. +% This Font Software is licensed under the SIL Open Font License, Version 1.1. +% This license is in the accompanying file OFL.txt, and is also +% available with a FAQ at: http://scripts.sil.org/OFL. +%%EndComments +FontDirectory/CMR6 known{/CMR6 findfont dup/UniqueID known{dup +/UniqueID get 5000789 eq exch/FontType get 1 eq and}{pop false}ifelse +{save true}{false}ifelse}{false}ifelse +11 dict begin +/FontType 1 def +/FontMatrix [0.001 0 0 0.001 0 0 ]readonly def +/FontName /CMR6 def +/FontBBox {-20 -250 1193 750 }readonly def +/PaintType 0 def +/FontInfo 9 dict dup begin +/version (003.002) readonly def +/Notice (Copyright \050c\051 1997, 2009 American Mathematical Society \050\051, with Reserved Font Name CMR6.) readonly def +/FullName (CMR6) readonly def +/FamilyName (Computer Modern) readonly def +/Weight (Medium) readonly def +/ItalicAngle 0 def +/isFixedPitch false def +/UnderlinePosition -100 def +/UnderlineThickness 50 def +end readonly def +/Encoding 256 array +0 1 255 {1 index exch /.notdef put} for +dup 12 /fi put dup 40 /parenleft put dup 41 /parenright put dup 44 /comma put @@ -714,23 +1699,29 @@ dup 48 /zero put dup 49 /one put dup 50 /two put dup 51 /three put +dup 52 /four put +dup 53 /five put +dup 54 /six put +dup 55 /seven put +dup 56 /eight put dup 57 /nine put dup 58 /colon put +dup 63 /question put dup 65 /A put dup 66 /B put dup 67 /C put -dup 69 /E put -dup 70 /F put -dup 71 /G put +dup 68 /D put dup 73 /I put -dup 74 /J put -dup 77 /M put dup 78 /N put dup 79 /O put dup 80 /P put dup 82 /R put -dup 83 /S put dup 84 /T put +dup 87 /W put +dup 88 /X put +dup 90 /Z put +dup 91 /bracketleft put +dup 93 /bracketright put dup 97 /a put dup 98 /b put dup 99 /c put @@ -740,7 +1731,6 @@ dup 102 /f put dup 103 /g put dup 104 /h put dup 105 /i put -dup 106 /j put dup 107 /k put dup 108 /l put dup 109 /m put @@ 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61 /slash put dup 65 /A put -dup 66 /B put -dup 67 /C put -dup 69 /E put dup 72 /H put -dup 73 /I put -dup 74 /J put -dup 76 /L put dup 77 /M put -dup 78 /N put -dup 80 /P put dup 83 /S put -dup 97 /a put -dup 98 /b put +dup 84 /T put dup 99 /c put -dup 100 /d put -dup 101 /e put -dup 102 /f put dup 105 /i put dup 107 /k put -dup 108 /l put dup 109 /m put dup 110 /n put -dup 111 /o put dup 115 /s put -dup 116 /t put -dup 117 /u put -dup 120 /x put -dup 121 /y put dup 122 /z put -dup 126 /vector put readonly def currentdict end currentfile eexec @@ -1851,248 +2491,355 @@ A142F746367888AA119535F0CCAF7EAA461B790EB089D2D6962E28A398439BB7 E0D474E69BB90B0D130A32C4130974A1C961CF064A5BE8EC1056858553FFCF7C 0D78627BDE04E5D0B9F6F72F92B61A915DFF7AFE7DC3807891D039AECEAC3F50 0A09579E661D3DC3022206061E0250B09A05CF198DF82D4F28FCD2A85F717630 -90740F78F242019C349B65C2A455597DEE127E914C372C6EA0E250402117B903 -1D7D6FC7153D77458569B2E0E6FB978B06AAD616BBEB70B58E0BCAE1013D4405 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/equal put dup 63 /question put dup 65 /A put dup 66 /B put @@ -2183,7 +2926,6 @@ dup 77 /M put dup 78 /N put dup 79 /O put dup 80 /P put -dup 81 /Q put dup 82 /R put dup 83 /S put dup 84 /T put @@ -2194,8 +2936,9 @@ dup 88 /X put dup 89 /Y put dup 90 /Z put dup 91 /bracketleft put +dup 92 /quotedblleft put dup 93 /bracketright put -dup 95 /dotaccent put +dup 96 /quoteleft put dup 97 /a put dup 98 /b put dup 99 /c put @@ -2418,563 +3161,542 @@ D20A952BA5CC35B646ACD0A287C956B98C450051AF6AAF79DF37F8954473F8F6 6744856E689A479CB3A8BC081F33CB06755926204981DC0A45B3ACC18F6865BB EE2C50DB43B62E3630FC1D9B1FFB3BFFAA6D0A20C0381ADF48E4D916BEE85BA2 BB40F538F55C11D50F882B73913840B45161262BC8B0012694C3EF26452F9B77 -2CD7C7AD6BFEEAFE31C8A721C2D46AA00C10681BA9970D09F1E10DDC250E2AC3 -9A160EC8C9654FCEB36AC2B586E978D54744FC8A0E963D8EF6E228ADD22D093B -B889CF6B19B051C243541591BA42B0738C360E479BF8D58E5318CD72CC4BB057 -439C03CB5C9E7751731A4A86351C8B082E81EE08E8D3D565E42987DDAD250347 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+dup 39 /quoteright put +dup 40 /parenleft put +dup 41 /parenright put dup 44 /comma put dup 45 /hyphen put dup 46 /period put @@ -6135,15 +4943,12 @@ dup 76 /L put dup 77 /M put dup 78 /N put dup 79 /O put -dup 80 /P put dup 82 /R put dup 83 /S put dup 84 /T put dup 85 /U put dup 87 /W put dup 89 /Y put -dup 91 /bracketleft put -dup 93 /bracketright put dup 97 /a put dup 98 /b put dup 99 /c put @@ -6169,6 +4974,7 @@ dup 119 /w put dup 120 /x put dup 121 /y put dup 122 /z put +dup 127 /dieresis put readonly def currentdict end currentfile eexec @@ -6357,252 +5163,900 @@ BD82FA1638B4583C8E173DE8610AEA154A2956C5F3ED912C94C5301ED3B89477 1E0E4ED37681E287F5A5CB57BAC7192533D0EF6CB09A0A16D02CF3159869AA1E BF87F75FD4FACE6AB8A46C5A7A31F4D96AB5F0B7A97D1CA940090DB58980E412 B1C9B9AB7FAAF124A1A908681486B4BF99F59B10EF2F6989E36466DA59306861 -633038245FC75181A42E65C3E1CF5269B00E31FB47B4F77FDEA2FFA247A7399B -354B0EDEB8272D75E4EE205D1A3C52C6D95F6343E2E21E4F3393E99005BEFFFA 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a(20)p 0 TeXcolorgray eop end %%Trailer userdict /end-hook known{end-hook}if diff --git a/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.synctex.gz b/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.synctex.gz new file mode 100644 index 0000000..e852b39 Binary files /dev/null and b/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.synctex.gz differ diff --git a/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.tex b/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.tex index c184298..b218395 100644 --- a/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.tex +++ b/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.tex @@ -288,7 +288,7 @@ The magnetic flux in loop 2, that results from the current of wire loop 1 that r The total magnetic flux in wire loop 2 resulting from the currents in wire loop 1, can be described with the following formula. -\begin{eqnarray}\label{eq:localization_function} +\begin{eqnarray} \Phi_{21} = \Phi_{21_{r}} + \Phi_{21_{l}} = \frac{\mu_0 l}{2\pi}i_{1}(ln\frac{b}{a}-ln\frac{d}{c}) = \frac{\mu_0 l}{2\pi}i_{1}ln\frac{bc}{ad} \end{eqnarray} @@ -465,18 +465,12 @@ The receiving coils without ferrit core were self-made wooden cubes with an edge \label{fig:infrared_camera_1} \end{figure} -In order to verify the objects position, a reference measurement was performed. For this an infrared based localization system of the company ART (Advanced Realtime Tracking GmbH, Am Oeferl 6, 82362, Weilheim i.OB, Germany) was used. This system was chosen because of its sub millimetre precise localization and its adequate localization area of around $20\metre^2$. An alternative available system would have been the Qualisys system located in the LINK testing hall of the Fraunhofer IIS. During this project the Qualisys system was not adequate as its localization area of around $1400\metre^2$ is too large for this project. Also its availability is lower, since the LINK testing hall gets closed on several occasions for other Fraunhofer projects. The ART system was always available during this project. Because of these reasons the decision fell on the ART system. \\ -The following components of the ART system were used. Six infrared cameras of the series ARTTRACK5 (Figure \ref{fig:infrared_camera_1}) were used. These cameras act as both infrared light emitter and receiver. The ARTTRACK5 have a tracking range of up to 7.5\metre, a field of view of around 80 degrees and offer up to 1.3 \Mega pix at up to 300\hertz frame rate. The cameras were connected to the ART Controller (ATC), which then again was connected to the host PC. The host PC ran the software (DTrack2). The object was mounted onto the target CT11 (Figure \ref{fig:loc_object_on_art_frame}) with four infrared marker spheres. The spheres reflect the infrared light back to the cameras enabling them to track the object. After calibration (Chapter \ref{sec:Setting up the localization systems}) the system had an accuracy of 0.33\milli\metre , according to the DTrack2 software. +In order to verify the objects position, a reference measurement was performed. For this an infrared based localization system of the company ART (Advanced Realtime Tracking GmbH, Am Oeferl 6, 82362, Weilheim i.OB, Germany) was used. Six infrared cameras of the series ARTTRACK5 (Figure \ref{fig:infrared_camera_1}) were used. These cameras act as both infrared light emitter and receiver. The ARTTRACK5 have a tracking range of up to 7.5\metre, a field of view of around 80 degrees and offer up to 1.3 \Mega pix at up to 300\hertz. The cameras were connected to the ART Controller (ATC), which then again was connected to the host PC. The host PC ran the software (DTrack2). The object was mounted onto the target CT11 (Figure \ref{fig:loc_object_on_art_frame}) with four infrared marker spheres. The spheres reflect the infrared light back to the cameras enabling them to track the object. After calibration (Chapter \ref{sec:Setting up the localization systems}) the system had an accuracy of 0.33\milli\metre , according to the DTrack2 software. \newpage \section{Methods} -The following chapter is structured as follows. The first section (Chapter \ref{sec:Setting up the localization systems}) describes the necessary steps to set up the testing systems (Inductive localization system and infrared reference localization system). -\\ -The second section (Chapter \ref{sec:precision_measurements}) describes how the accuracy measurement data was created. The following subsections (Chapter \ref{sec:scaling_factor_test}, \ref{sec:K-nearest interpolation test}, \ref{sec:remove_highest_frame_receiving_coil_from_the_localization_test}) explain how the data was then interpreted using different software paramters. These parameters were then altered in an attempt to optimize the systems localization accuracy. - - \subsection{Setting up the localization systems}\label{sec:Setting up the localization systems} In order to determine the precision of the IndLoc setup a series of tests were performed. The localization object was placed in different positions and the position was then measured by the IndLoc system. These measured positions are then compared to an infrared based camera system. @@ -511,6 +505,7 @@ As the system is heating up the ART system can be calibrated. How to place the c \\ Now that both localization systems are calibrated correctly a few test measurements should be performed. In order to do this place the localization object on the localization plane and compare the position which both systems display. If they are reasonably close to each other (around 1-2\centi\metre) the systems are working correctly. Confirm this by repeating this process for two or three positions which are at least 10\centi\metre $\:$ apart. +<<<<<<< HEAD \subsection{Precision measurements}\label{sec:precision_measurements} All of the following precision measurements were performed by placing the localization object on specific positions in the localization area and measuring the resulting receiving coil voltages. It was positioned by eye using the millimetre sheet as a first rough positioning assistance. The exact position was then determined by recording three seconds with the ART system. As the frame rate of the ART system can differentiate, at least three seconds were recorded to make sure enough samples for further analysis were available. As the ART system is sub millimetre (0.33\milli\metre) precise this position was used as the correct reference position. \\ @@ -519,17 +514,24 @@ Afterwards the position was measured with the IndLoc system by recording 2000 sa 2000 samples were recorded for each point so later on the possibility of averaging and analyzing the signal to noise ratio was available. At a sampling rate of 1MHz recording 2000 samples took 2\milli s. These recordings contain the receiving coils voltages. Thus saving a 2D array of 2000 by 16. The file names of each measurement were documented. Then the object was placed 1 \centi\metre $\:$ to the side on the mm sheet and the process was repeated until a measurement set of points came together. The data of the precision measurements were then evaluated with different hard- or software parameters in order to optimize them. \subsubsection{Scaling factor test}\label{sec:scaling_factor_test} +======= +\subsection{Precision measurements} +All of the following precision measurements were performed by placing the localization object on specific positions in the localization area. It was positioned by eye using the millimetre sheet as a first rough positioning assistance. The exact position was then determined by recording three seconds with the ART system. Then the position was measured with the IndLoc system by recording 2000 samples with it. These recordings contain the receiving coils voltages. Thus saving a 2D array of 2000 by 16. The file names of each measurement were documented. Then the object was placed 1 \centi\metre +$\:$ to the side on the mm sheet and the process was repeated until a measurement set of points came together. The data of the precision measurements were then evaluated with different hard- or software parameters in order to optimize them. + +\subsubsection{Scaling factor test} +>>>>>>> parent of f2b8eb4 (Script that can analyze (Horizontal+Diagonal, Center, Off_center) quickly) The scaling factor is a factor that is multiplied onto each receiving coil voltage. Its purpose is to diminish the difference between the calculated receiving coil voltages in the fingerprinting table and the real measured voltages. As the fingerprinting table is created certain parameters are required as input, such as for example: Exciter wire resistance, Inductance of the receiving coils, Resistance of the receiving coils, Windings of the localization object, etc. As measuring all of these parameters would take too much effort the scaling factor has been implemented. Usually the scaling factor is tested empirically for each IndLoc setup or prototype that is built. \begin{figure}[h] \includegraphics[scale=0.4]{estimation_scaling_factor_1} \centering - \caption{Before being able to localize precisely the correct scaling factor, which multiplies each receiving coil voltages, has to be determined. For this the scaling factors $10^{2}$ to $ 10^{-4}$ were tested in logarithmic steps.} + \caption{Before being able to localize precisely the correct scaling factor, which multiplies each receiving coil voltages, has to be determined. For this the scaling factor from $10^{2}$ to $ 10^{-4}$ were tested.} \label{fig:estimation_scaling_factor_1} \end{figure} -A first rough estimation of the scaling factor was done by testing $10^{2}$ to $ 10^{-3}$ in logarithmic steps (Figure \ref{fig:estimation_scaling_factor_1}, \ref{fig:estimation_scaling_factor_2}). Figure \ref{fig:estimation_scaling_factor_1} shows the entire localization area. The blue outside rectangle represents the Exciter, which creates the primary magnetic field (Chapter \ref{sec:The_exciter}). The black rectangles along the exciter represent the receiving coils, measuring the secondary magnetic field of the localization object (Chapter \ref{sec:The_receiving_coils}). The axis span the localization area (Figure \ref{fig:Testing_setup}). The green cross markers in the middle of plot are the positions determined by the ART system. As the precision of the ART system is 0.33\milli\metre $\:$ the green markers are used as the correct reference position of the localization object. The different shaped black markers are the position the IndLoc system calculated with the different scaling factor. The scaling factor for each marker shape can be seen in the legend. It is to be noted that only the first sample of the ART and the IndLoc recordings were used during this test. How the noise affects the localization during the 2000 samples of one recording will be investigated later (Chapter \ref{sec:Noise_investigation}). +A first rough estimation of the scaling factor was done by testing $10^{2}$ to $ 10^{-3}$ in logarithmic steps (Figure \ref{fig:estimation_scaling_factor_1}, \ref{fig:estimation_scaling_factor_2}). Figure \ref{fig:estimation_scaling_factor_1} shows the entire localization area. The blue outside rectangle is the Exciter, which creates the primary magnetic field (Chapter \ref{sec:The_exciter}). The black rectangles along the exciter are the receiving coils, measuring the secondary magnetic field of the localization object (Chapter \ref{sec:The_receiving_coils}). The axis represent the localization area (Figure \ref{fig:Testing_setup}). The green cross markers in the middle of plot are the positions estimated by the ART system. As the precision of the ART system is 0.33\milli\metre $\:$ the green markers are used as the position the localization object is really in. The differently shaped black markers are the position the IndLoc system calculated with the different scaling factor, which can be looked up in the legend. It is to be noted that only the first sample of the ART and the IndLoc recordings were used during this test. \begin{figure}[h] @@ -543,15 +545,27 @@ Figure \ref{fig:estimation_scaling_factor_2} shows a magnified view of Figure \r As the evaluation of the differences in localization precision by eye became impossible a statistical method was attempted. For this the distance between each pair of points was calculated with the following formula. -\begin{equation}\label{eq:distances} -d_{Pi} = \sqrt{(x_{ART_{Pi}} - x_{IndLoc_{Pi}})^2 + (y_{ART_{Pi}} - y_{IndLoc_{Pi}})^2 } +\begin{equation} +d_{Pi} = \sqrt{(x_{ART_{Pi}} - x_{IndLoc_{Pi}})^2 + y_{ART_{Pi}} - y_{IndLoc_{Pi}})^2 } \end{equation} +<<<<<<< HEAD The mean distance for each scaling factor was calculated with: \begin{equation}\label{eq:mean} +======= + +Afterwards the mean distance for each scaling factor was calculated with. + +\begin{equation} +>>>>>>> parent of f2b8eb4 (Script that can analyze (Horizontal+Diagonal, Center, Off_center) quickly) \mu ={\frac {1}{n}}\sum _{i=1}^{n}d_{i}={\frac {d_{1}+d_{2}+\cdots +d_{n}}{n}} \end{equation} The standard deviation for each scaling factor was calculated with the formula: +<<<<<<< HEAD \begin{equation}\label{eq:std_dev} +======= + +\begin{equation} +>>>>>>> parent of f2b8eb4 (Script that can analyze (Horizontal+Diagonal, Center, Off_center) quickly) \sigma = \sqrt{\frac{\sum |P_{i} - \mu|^2}{N}} \end{equation} @@ -563,6 +577,9 @@ The standard deviation for each scaling factor was calculated with the formula: \label{fig:estimation_scaling_factor_3} \end{figure} +\newpage +Each equation was applied to a total of 500 different scaling factors ((0.0015, 0.002, 0.000001 - start, stop, step)). An optimum was found at a scaling factor of 0.001755 with $\mu = 3.76\milli\metre \:$ and $\sigma = 3.28\milli\metre \:$ resulting in the localization which can be seen in Figure \ref{fig:estimation_scaling_factor_3}. In order to verify these results another set of positions was used, which was positioned more to the edge of the localization area. + \begin{comment} avg distance 3.757373465139104 median distance 2.6566228652376584 @@ -570,31 +587,19 @@ mean distance 3.757373465139104 std dev distance 3.2792325092288714 \end{comment} -\clearpage -Each equation was applied to a total of 500 different scaling factors ((0.0015, 0.002, 0.000001 - start, stop, step)). An optimum was found at a scaling factor of 0.001755 with $\mu = 3.76\milli\metre \:$ and $\sigma = 3.28\milli\metre \:$ resulting in the localization which can be seen in Figure \ref{fig:estimation_scaling_factor_3}. In order to verify these results another set of positions was used, which was positioned near the corner of the localization area (Figure \ref{fig:estimation_scaling_factor_4}). - - - \begin{figure}[h] \includegraphics[scale=0.4]{estimation_scaling_factor_4} \centering - \caption{Placing the object in 25 positions near the corner of the localization area with a scaling factor of 0.002100, resulting in mean error $\mu = 10.39\milli\metre$ and standard deviation $\sigma = 7.52\milli\metre$.} + \caption{Placing the object in 25 positions near the corner of the localization area with a scaling factor of 0.001755, , resulting in mean error $\mu = 6.18\milli\metre$ and standard deviation $\sigma = 7.71\milli\metre$.} \label{fig:estimation_scaling_factor_4} \end{figure} -\begin{comment} - scaling_factor = 0.0021000000000000016 - mean_error = 10.39 - standard_deviation = 7.52 -Plotting positions for: sf= 0.0021000000000000016 , k= 3 -\end{comment} - -Statistically optimizing the scaling factor for the off centre set of points resulted in a scaling factor of 0.002105. Resulting in a mean error $\mu = 6.18\milli\metre \:$ and a standard deviation $\sigma = 7.71\milli\metre$. +Statistically optimizing the scaling factor for the off center set of points resulted in a scaling factor of 0.002105. Resulting in a mean error $\mu = 6.18\milli\metre \:$ and a standard deviation $\sigma = 7.71\milli\metre$. \begin{comment} Best scaling factor = mean = 6.180273611932338 std_dev = 7.715220033729418 ; min_both = 13.895493645661755 \end{comment} -As the localization accuracy declined it suggested that the optimal scaling factor is positional dependant. For this reason the whole localization system had to be taken into account. +As the localization accuracy declined it suggested that the optimal scaling factor is positional dependant. For this reason the whole localization system had to be taken into account (Figure \ref{fig:estimation_scaling_factor_5}). \begin{figure}[h] \includegraphics[scale=0.4]{estimation_scaling_factor_5} @@ -603,80 +608,19 @@ As the localization accuracy declined it suggested that the optimal scaling fact \label{fig:estimation_scaling_factor_5} \end{figure} -In order to look at the entire localization area the object was placed 42 positions running in a horizontal and a diagonal line from the centre to the edge of the localization area (Figure \ref{fig:estimation_scaling_factor_5}). Due to the symmetric setup these lines can be mirrored and as such give a rough estimation of the entire localization area. - \begin{comment} best sf = 0.00204 mean = 7.421424662824991 std dev = 6.481103235160666 ; min both = 13.902527897985657 \end{comment} -\clearpage -\subsubsection{K-nearest interpolation test}\label{sec:K-nearest interpolation test} -During localization a k-nearest neighbour interpolation is performed. The 16 measured receiving coil voltages are compared to the fingerprinting table (Chapter \ref{sec:The_host system}). The k best matching entries of the fingerprinting table are selected. The X-,Y-,Z-positions of the selected entries are then used for the k-nearest neighbour interpolation resulting in a final X-,Y-,Z-position. -\\ -If the k-nearest neighbour interpolation would not be performed the final position could only have \centi\metre $\:$ values without decimal places. The reason being that the fingerprinting table is created using a 1\centi\metre $\:$ X-,Y-,Z-step width (Chapter \ref{sec:The_host system}). That is why the k-nearest neighbour interpolation is a crucial step to achieve sub \centi\metre$\:$ precision. During all previous tests k=2 was used, as this is the lowest possible value. If k=1 would have been used the previously problem of achieving sub \centi\metre $\:$ precision would have occurred and may have distorted previous results. - -During this chapter multiple k values will be tested and the resulting localization precision will be determined. As previously mentioned (Chapter \ref{sec:precision_measurements}) the data, which was analysed during the scaling factor test (Chapter \ref{sec:scaling_factor_test}) will be analysed during this test as well. Namely the set of points in the centre (Figure \ref{fig:estimation_scaling_factor_3}), in the corner (Figure \ref{fig:estimation_scaling_factor_4}) and the set of points which cover the entirety of the localization area(Figure \ref{fig:estimation_scaling_factor_5}). In order to receive comparable results, the individual optimized scaling factor for each set of points were used (Chapter \ref{sec:scaling_factor_test}). \begin{figure}[h] - \includegraphics[scale=0.4]{estimation_k_nearest_1} + \includegraphics[scale=0.4]{estimation_scaling_factor_6} \centering - \caption{placing the localization object in 25 positions near the centre of the localization are with a k-nearest value of 19, resulting in mean error $\mu = 3.21\milli\metre$ and standard deviation $\sigma = 1.84\milli\metre$.} - \label{fig:estimation_k_nearest_1} -\end{figure} - -\begin{comment} - -[BEST MEAN]: k_nearest: 9 --> mean= 3.17 , std_dev= 2.04 -[BEST MEAN+STD]: current k_nearest: 19 --> mean= 3.21 , std_dev= 1.84 - -current k_nearest: 2 --> mean= 7.44 , std_dev= 4.87 -current k_nearest: 100 --> mean= 5.29 , std_dev= 2.19 - -Trend: mean and std start high, both decrease as k rises. Lowest mean at k=9. Lowest both at k=19 (statistical optimum). Then after that it gets worse until k=100 the mean rises until 5 and std_dev until 2. -\end{comment} - -A total of 100 k values were tested (start:1, stop:100, step:1) and were each evaluated using the formulas (\ref{eq:distances}), (\ref{eq:mean}) and (\ref{eq:std_dev}). An optimum was determined at k=19 with $\mu = 3.21\milli\metre$ and $\sigma = 1.84\milli\metre$ (Figure \ref{fig:estimation_k_nearest_1}). - - -\begin{figure}[h] - \includegraphics[scale=0.4]{estimation_k_nearest_2} - \centering - \caption{placing the localization object in 25 positions near the corner of the localization are with a k-nearest value of 12, resulting in mean error $\mu = 10.39\milli\metre$ and standard deviation $\sigma = 7.52\milli\metre$.} - \label{fig:estimation_k_nearest_2} -\end{figure} - -\begin{comment} - scaling_factor = 0.0021 - mean_error = 10.39 - standard_deviation = 7.52 -Plotting positions for: sf= 0.0021 , k= 3 -\end{comment} - -\begin{figure}[h] - \includegraphics[scale=0.4]{estimation_k_nearest_3} - \centering - \caption{Placing the object in 42 positions running from the centre to the edges of the localization area with a k-nearest value of 4, resulting in mean error $\mu = 7.57\milli\metre$ and standard deviation $\sigma = 5.34\milli\metre$.} - \label{fig:estimation_k_nearest_3} -\end{figure} - -\begin{comment} -scaling_factor = 0.00204 - mean_error = 7.57 - standard_deviation = 5.34 -Plotting positions for: sf= 0.00204 , k= 4 -\end{comment} - -\clearpage -\subsubsection{Remove highest frame receiving coil from the localization test}\label{sec:remove_highest_frame_receiving_coil_from_the_localization_test} -It is known in the team that the localization near the receiving coils declines. For this reason the frame receiving coil which shows the highest voltage, is removed from the localization calculations. - -\begin{figure}[h] - \includegraphics[scale=0.4]{estimation_removing_highest_frame_1} - \centering - \caption{(sf= 0.001755 , k= 19) Placing the localization object in 25 positions near the centre of the localization area with removing the highest frame receiving coil, resulting in mean error $\mu = 5.56\milli\metre$ and standard deviation $\sigma = 3.2\milli\metre$.} - \label{fig:estimation_removing_highest_frame_1} + \caption{With averaged IndLoc feature vectors 0.00204, , resulting in mean error $\mu = 7.34\milli\metre$ and standard deviation $\sigma = 6.49\milli\metre$.} + \label{fig:estimation_scaling_factor_6} \end{figure} +<<<<<<< HEAD \begin{comment} scaling_factor = 0.001755 mean_error = 5.56 @@ -743,6 +687,9 @@ The test was repeated with the localization object placed near a corner of the l \caption{(sf= 0.00204 , k= 4) Placing the object in 42 positions running from the centre to the edges of the localization area with a k-nearest value of 4, resulting in mean error $\mu = 15.21\milli\metre$ and standard deviation $\sigma = 14.33 \milli\metre$.} \label{fig:estimation_k_nearest_3} \end{figure} +======= +Averaging the 2000 samples before localizing Figure \ref{fig:estimation_scaling_factor_6}. +>>>>>>> parent of f2b8eb4 (Script that can analyze (Horizontal+Diagonal, Center, Off_center) quickly) \subsection{Movement measurements}\label{sec:movement_measurements} \clearpage @@ -757,7 +704,6 @@ The test was repeated with the localization object placed near a corner of the l \clearpage - \bibliography{Literatur_BA_2} \end{document} diff --git a/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.toc b/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.toc index 1b61a71..48c7fd6 100644 --- a/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.toc +++ b/BachelorThesis/Writing/2021_Seyffer_Investigating the precision of an induction-based localization system for medical applications.toc @@ -24,6 +24,7 @@ \contentsline {subsection}{\numberline {4.2}ART system}{17}% \contentsline {section}{\numberline {5}Methods}{19}% \contentsline {subsection}{\numberline {5.1}Setting up the localization systems}{19}% +<<<<<<< HEAD \contentsline {subsection}{\numberline {5.2}Precision measurements}{21}% \contentsline {subsubsection}{\numberline {5.2.1}Scaling factor test}{21}% \contentsline {subsubsection}{\numberline {5.2.2}K-nearest interpolation test}{26}% @@ -33,3 +34,10 @@ \contentsline {section}{\numberline {6}RESULTS}{33}% \contentsline {section}{\numberline {7}DISCUSSION}{34}% \contentsline {section}{\numberline {8}SUMMARY, OUTLOOK INTO THE FUTURE}{35}% +======= +\contentsline {subsection}{\numberline {5.2}Precision measurements}{20}% +\contentsline {subsubsection}{\numberline {5.2.1}Scaling factor test}{21}% +\contentsline {section}{\numberline {6}RESULTS}{26}% +\contentsline {section}{\numberline {7}DISCUSSION}{27}% +\contentsline {section}{\numberline {8}SUMMARY, OUTLOOK INTO THE FUTURE}{28}% +>>>>>>> parent of f2b8eb4 (Script that can analyze (Horizontal+Diagonal, Center, Off_center) quickly) diff --git a/BachelorThesis/Writing/Images/estimation_k_nearest_1.PNG b/BachelorThesis/Writing/Images/estimation_k_nearest_1.PNG deleted file mode 100644 index 3ea617c..0000000 Binary files a/BachelorThesis/Writing/Images/estimation_k_nearest_1.PNG and /dev/null differ diff --git a/BachelorThesis/Writing/Images/estimation_k_nearest_2.PNG b/BachelorThesis/Writing/Images/estimation_k_nearest_2.PNG deleted file mode 100644 index eee737f..0000000 Binary files a/BachelorThesis/Writing/Images/estimation_k_nearest_2.PNG and /dev/null differ diff --git a/BachelorThesis/Writing/Images/estimation_k_nearest_3.PNG b/BachelorThesis/Writing/Images/estimation_k_nearest_3.PNG deleted file mode 100644 index abb6f9d..0000000 Binary files a/BachelorThesis/Writing/Images/estimation_k_nearest_3.PNG and /dev/null differ diff --git a/BachelorThesis/Writing/Images/estimation_removing_highest_frame_1.PNG b/BachelorThesis/Writing/Images/estimation_removing_highest_frame_1.PNG deleted file mode 100644 index bf07e98..0000000 Binary files a/BachelorThesis/Writing/Images/estimation_removing_highest_frame_1.PNG and /dev/null differ diff --git a/BachelorThesis/Writing/Images/estimation_removing_highest_frame_2.PNG b/BachelorThesis/Writing/Images/estimation_removing_highest_frame_2.PNG deleted file mode 100644 index cf18292..0000000 Binary files a/BachelorThesis/Writing/Images/estimation_removing_highest_frame_2.PNG and /dev/null differ diff --git a/BachelorThesis/Writing/Images/estimation_removing_highest_frame_3.PNG b/BachelorThesis/Writing/Images/estimation_removing_highest_frame_3.PNG deleted file mode 100644 index 7af2c2a..0000000 Binary files a/BachelorThesis/Writing/Images/estimation_removing_highest_frame_3.PNG and /dev/null differ diff --git a/BachelorThesis/Writing/Images/estimation_scaling_factor_4.PNG b/BachelorThesis/Writing/Images/estimation_scaling_factor_4.PNG index 0405849..349a3ed 100644 Binary files a/BachelorThesis/Writing/Images/estimation_scaling_factor_4.PNG and b/BachelorThesis/Writing/Images/estimation_scaling_factor_4.PNG differ diff --git a/BachelorThesis/Writing/Images/estimation_scaling_factor_5.PNG b/BachelorThesis/Writing/Images/estimation_scaling_factor_5.PNG index b5388a4..15fe8e8 100644 Binary files a/BachelorThesis/Writing/Images/estimation_scaling_factor_5.PNG and b/BachelorThesis/Writing/Images/estimation_scaling_factor_5.PNG differ diff --git a/BachelorThesis/Writing/Notizen_ToDo.docx b/BachelorThesis/Writing/Notizen_ToDo.docx deleted file mode 100644 index 08952af..0000000 Binary files a/BachelorThesis/Writing/Notizen_ToDo.docx and /dev/null differ