diff --git a/ReadMe.md b/ReadMe.md
index 4582864..33cb8e2 100644
--- a/ReadMe.md
+++ b/ReadMe.md
@@ -25,19 +25,30 @@ Accelerometer "random walk" | `accelerometer_random_walk` | <img src="https://la
     * Use the included `bagconvert` ROS package to do this
     * Example: `rosrun bagconvert bagconvert imu.bag /imu0`
 3. Run the included matlab scripts to generate an allan deviation plot for the readings
-    * We have multiple versions of the script
-    * We do *not* recommend the ROS version as matlab parsing of raw ROS bags is slow
-    * If using ROS version, it uses the matlab robotics toolbox
     * If using the parallel version, it uses the matlab parallel toolbox
+    * Need to specify the mat file that the bagconverter made, and the rate of IMU messages
 4. Interpret the generated charts to find noise values
     * Run the process results script
     * Will fit a -1/2 line to the left side of the allan plot
     * White noise is at tau=1 (according to [kalibr wiki](https://github.com/ethz-asl/kalibr/wiki/IMU-Noise-Model#from-the-allan-standard-deviation-ad))
     * Will fit a 1/2 line to the right side of the allan plot
     * Random walk is at tau=3 (according to [kalibr wiki](https://github.com/ethz-asl/kalibr/wiki/IMU-Noise-Model#from-the-allan-standard-deviation-ad))
+5. Some example data can be found this **[this](https://drive.google.com/drive/folders/1a3Es85JDKl7tSpVWEUZryOwtsXB8793o?usp=sharing)**
+    * XSENS MTI-G-700
+    * Tango Yellowstone Tablet
+    * ASL-ETH VI-Sensor
 
+### Example Plot - XSENS MTI-G-700
+![allan chart acceleration](data/results_20170908T182715_accel.png)
 
-## Example Plots (xsens mti-G-700)
+![allan chart angular velocity](data/results_20170908T182715_gyro.png)
+
+### Example Plot - Tango Yellowstone Tablet
 ![allan chart acceleration](data/results_20171031T115123_accel.png)
 
 ![allan chart angular velocity](data/results_20171031T115123_gyro.png)
+
+### Example Plot - ASL-ETH VI-Sensor
+![allan chart acceleration](data/results_20180206T140217_accel.png)
+
+![allan chart angular velocity](data/results_20180206T140217_gyro.png)
diff --git a/data/results_20170908T182715_accel.png b/data/results_20170908T182715_accel.png
index e85c67a..2d9fe22 100644
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diff --git a/data/results_20170908T182715_gyro.png b/data/results_20170908T182715_gyro.png
index 64cfd82..cd9887e 100644
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diff --git a/data/results_20171031T115123_accel.png b/data/results_20171031T115123_accel.png
index 9845bc9..57b4bfe 100644
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diff --git a/data/results_20171031T115123_gyro.png b/data/results_20171031T115123_gyro.png
index 06ef5a3..46814c5 100644
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diff --git a/data/results_20180206T140217_accel.png b/data/results_20180206T140217_accel.png
new file mode 100644
index 0000000..b021d5d
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diff --git a/data/results_20180206T140217_gyro.png b/data/results_20180206T140217_gyro.png
new file mode 100644
index 0000000..cc63617
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diff --git a/matlab/SCRIPT_allan_matparallel.m b/matlab/SCRIPT_allan_matparallel.m
index 4ca80b1..b124afb 100644
--- a/matlab/SCRIPT_allan_matparallel.m
+++ b/matlab/SCRIPT_allan_matparallel.m
@@ -7,11 +7,13 @@
 
 % Our bag information
 %mat_path = '../data/imu_mtig700.mat';
-mat_path = '../data/imu_tango.mat';
+%mat_path = '../data/imu_tango.mat';
+mat_path = '../data/imu_visensor.mat';
 
 % IMU information (todo: move this to the yaml file)
 %update_rate = 400;
-update_rate = 100;
+%update_rate = 100;
+update_rate = 200;
 
 
 %% Data processing
@@ -99,7 +101,6 @@
 %% Save workspace
 filename = ['results_',datestr(now,30),'.mat'];
 fprintf('saving to: %s\n',filename);
-%save(['../data/',filename])
 save(['../data/',filename],'update_rate','ts_imua','ts_imuw','tau','taumax','results_ax','results_ay','results_az','results_wx','results_wy','results_wz')
 fprintf('done saving!\n');
 
diff --git a/matlab/SCRIPT_allan_matserial.m b/matlab/SCRIPT_allan_matserial.m
deleted file mode 100644
index 055f455..0000000
--- a/matlab/SCRIPT_allan_matserial.m
+++ /dev/null
@@ -1,101 +0,0 @@
-%% Initalization
-close all
-clear all
-
-% Read in our toolboxes
-addpath('functions/allan_v3')
-
-% Our bag information
-mat_path = '../data/imu.mat';
-
-
-% IMU information (todo: move this to the yaml file)
-update_rate = 400;
-
-
-%% Data processing
-% Load the mat file (should load "data_imu" matrix)
-fprintf('opening the mat file.\n')
-load(mat_path);
-
-% Load our time series information
-fprintf('loading timeseries.\n')
-ts_imua = timeseries(data_imu(:,2:4),data_imu(:,1));
-ts_imuw = timeseries(data_imu(:,5:7),data_imu(:,1));
-
-%% Process the timeseries data
-
-% Find the frequency of the imu unit
-% delta = mean(diff(ts_imua.Time(1:10)));
-% update_rate = 1/delta;
-delta = 1/update_rate;
-fprintf('imu frequency of %.2f.\n',freq);
-fprintf('sample period of %.5f.\n',delta);
-
-% Set what our frequency is for the system
-data.rate = update_rate;
-
-% Calculate our tau range (max is half of the total measurements)
-taumax = floor((length(ts_imua.Time)-1)/2);
-%tau = logspace(log10(delta),log10(taumax),taumax);
-tau = linspace(1,taumax,taumax);
-
-
-%% Calculate the acceleration allan deviation of the time series data!
-fprintf('calculating acceleration x allan deviation.\n');
-data.freq = ts_imua.data(:,1)';
-[results_ax] = allan(data, tau);
-
-fprintf('calculating acceleration y allan deviation.\n');
-data.freq = ts_imua.data(:,2)';
-[results_ay] = allan(data, tau);
-
-fprintf('calculating acceleration y allan deviation.\n');
-data.freq = ts_imua.data(:,3)';
-[results_az] = allan(data, tau);
-
-% Plot the results on a figure
-figure(1);
-loglog(results_ax.tau1, sqrt(results_ax.sig2)); hold on;
-loglog(results_ay.tau1, sqrt(results_ay.sig2)); hold on;
-loglog(results_az.tau1, sqrt(results_az.sig2)); hold on;
-grid on;
-xlabel('\tau [sec]');
-ylabel('Normal Allan Deviation [m/s^2]');
-legend('x-acceleration','y-acceleration','z-acceleration');
-
-pause(5)
-
-
-%% Calculate the angular allan deviation of the time series data!
-fprintf('calculating angular x allan deviation.\n');
-data.freq = ts_imuw.data(:,1)';
-[results_wx] = allan(data, tau);
-
-fprintf('calculating angular y allan deviation.\n');
-data.freq = ts_imuw.data(:,2)';
-[results_wy] = allan(data, tau);
-
-fprintf('calculating angular y allan deviation.\n');
-data.freq = ts_imuw.data(:,3)';
-[results_wz] = allan(data, tau);
-
-
-
-% Plot the results on a figure
-figure(2);
-loglog(results_wx.tau1, sqrt(results_wx.sig2)); hold on;
-loglog(results_wy.tau1, sqrt(results_wy.sig2)); hold on;
-loglog(results_wz.tau1, sqrt(results_wz.sig2)); hold on;
-grid on;
-xlabel('\tau [sec]');
-ylabel('Normal Allan Deviation [rad/s]');
-legend('x-angular','y-angular','z-angular');
-
-
-
-
-
-
-
-
diff --git a/matlab/SCRIPT_allan_rosbag.m b/matlab/SCRIPT_allan_rosbag.m
deleted file mode 100644
index d12c65d..0000000
--- a/matlab/SCRIPT_allan_rosbag.m
+++ /dev/null
@@ -1,87 +0,0 @@
-%% Initalization
-close all
-clear all
-
-% Read in our toolboxes
-addpath('functions/allan_v3')
-
-% Our bag information
-bag_path = '../data/imu.bag';
-imu_topic = '/sensor/xsens/sensor/imu_raw';
-
-% Bag start and end time
-bagstart = 0;
-bagend = 30;
-
-
-%% Data processing
-% Open ros bag, and select topics we want
-fprintf('opening the ros bag.\n')
-filepath = fullfile(bag_path);
-bag = rosbag(filepath);
-
-% Select the topics we should insert
-fprintf('selecting topics.\n')
-bagselect = select(bag, 'Time', [bag.StartTime+bagstart bag.StartTime+bagend], 'Topic', imu_topic);
-
-% Load our time series information
-fprintf('loading timeseries.\n')
-ts_imuw = timeseries(bagselect, 'AngularVelocity.X', 'AngularVelocity.Y', 'AngularVelocity.Z');
-ts_imua = timeseries(bagselect, 'LinearAcceleration.X', 'LinearAcceleration.Y', 'LinearAcceleration.Z');
-
-% Change the start time so that everything is relative to the first one
-%ts_imuw.TimeInfo.StartDate = datestr(datetime(ts_imuw.Time(1), 'ConvertFrom', 'posixtime'));
-%ts_imua.TimeInfo.StartDate = datestr(datetime(ts_imua.Time(1), 'ConvertFrom', 'posixtime'));
-
-
-%% Process the timeseries data
-
-% Find the frequency of the imu unit
-% delta = mean(diff(ts_imua.Time(1:10)));
-% freq = 1/delta;
-freq = 400;
-delta = 1/freq;
-fprintf('imu frequency of %.2f.\n',freq);
-fprintf('sample period of %.5f.\n',delta);
-
-% Set what our frequency is for the system
-data.rate = freq;
-
-% Calculate our tau range (max is half of the total measurements)
-taumax = (length(ts_imua.Time)-1)/2;
-tau = delta*linspace(1,taumax,5000);
-
-
-% Calculate the allan deviation of the time series data!
-fprintf('calculating acceleration x allan deviation.\n');
-data.freq = ts_imua.data(:,1)';
-[results_ax] = allan(data, tau);
-
-fprintf('calculating acceleration y allan deviation.\n');
-data.freq = ts_imua.data(:,2)';
-[results_ay] = allan(data, tau);
-
-fprintf('calculating acceleration y allan deviation.\n');
-data.freq = ts_imua.data(:,3)';
-[results_az] = allan(data, tau);
-
-
-%% Plot the results on a figure
-figure;
-loglog(results_ax.tau1, sqrt(results_ax.sig2)); hold on;
-loglog(results_ay.tau1, sqrt(results_ay.sig2)); hold on;
-loglog(results_az.tau1, sqrt(results_az.sig2)); hold on;
-
-grid on;
-xlabel('\tau [sec]');
-ylabel('Normal Allan Deviation [m/s^2]');
-legend('x-accel','y-accel','z-accel');
-
-
-
-
-
-
-
-
-
diff --git a/matlab/SCRIPT_process_results.m b/matlab/SCRIPT_process_results.m
index 2ddaca6..2f9d734 100644
--- a/matlab/SCRIPT_process_results.m
+++ b/matlab/SCRIPT_process_results.m
@@ -7,28 +7,30 @@
 addpath('functions/allan_v3')
 
 % Our bag information
-titlestr = 'XSENS MTi-G-710';
-mat_path = '../data/results_20170908T182715.mat';
+%titlestr = 'XSENS MTi-G-710';
+%mat_path = '../data/bags/results_20170908T182715.mat';
 
 %titlestr = 'Tango Yellowstone #1';
-%mat_path = '../data/results_20171031T115123.mat';
+%mat_path = '../data/bags/results_20171031T115123.mat';
 
+titlestr = 'ADIS16448 VI-Sensor';
+mat_path = '../data/bags/results_20180206T140217.mat';
 
 % Load the mat file (should load "data_imu" matrix)
-fprintf('opening the mat file.\n')
+fprintf('=> opening the mat file.\n')
 load(mat_path);
 
 
 %% Get the calculated sigmas
 
-fprintf('plotting accelerometer.\n')
+fprintf('=> plotting accelerometer.\n')
 [fh1,sigma_a,sigma_ba] = gen_chart(results_ax.tau1,results_ax.sig2,...
                                     results_ay.sig2,results_az.sig2,...
                                     titlestr,'acceleration','m/s^2',...
                                     'm/s^2sqrt(Hz)','m/s^3sqrt(Hz)');
 
 
-fprintf('plotting gyroscope.\n')
+fprintf('=> plotting gyroscope.\n')
 [fh2,sigma_g,sigma_ga] = gen_chart(results_wx.tau1,results_wx.sig2,...
                                     results_wy.sig2,results_wz.sig2,...
                                     titlestr,'gyroscope','rad/s',...
@@ -37,6 +39,7 @@
                                 
 
 %% Print out for easy copying
+fprintf('=> final results\n');
 % Accelerometer
 fprintf('accelerometer_noise_density = %.8f\n',sigma_a);
 fprintf('accelerometer_random_walk   = %.8f\n',sigma_ba);
diff --git a/matlab/functions/gen_chart.m b/matlab/functions/gen_chart.m
index 808c55f..557b596 100644
--- a/matlab/functions/gen_chart.m
+++ b/matlab/functions/gen_chart.m
@@ -6,10 +6,10 @@
 % =======================================================================
 % Plot the results on a figure
 fh1 = figure;
-loglog(tau, sqrt(sigx)); hold on;
-loglog(tau, sqrt(sigy)); hold on;
-loglog(tau, sqrt(sigz)); hold on;
-loglog(tau, sqrt(sigavg)); hold on;
+loglog(tau, sigx); hold on;
+loglog(tau, sigy); hold on;
+loglog(tau, sigz); hold on;
+loglog(tau, sigavg); hold on;
 grid on;
 title([titlestr,' ',name]);
 xlabel('\tau [sec]');
@@ -20,18 +20,18 @@
 % Find location of tau=1 by finding where difference is near zero
 tauref = 1;
 taudiff = abs(tau-tauref);
-tauid1 = find(taudiff == min(taudiff));
+tauid1 = find(taudiff == min(taudiff),1);
 fprintf('tau = %.2f | tauid1 = %d\n',tau(tauid1),tauid1);
 
 % We will fit our "white-noise" line where tau is 1
-%windowsize = 50;
+windowsize = 50;
 %window = tauid1-windowsize:tauid1+windowsize;
 minid = find(sigavg == min(sigavg)); % find where the min is
-window = 1:minid; % go from start to min
+window = 1:minid-windowsize; % go from start to min
 
 % Get our x and y values
 x = tau(window);
-y = sqrt(sigavg(window));
+y = sigavg(window);
 nanx = isfinite(y);
 
 % Fit to log-log scale (slope of -1/2)
@@ -39,7 +39,7 @@
 coeffs(1)= -0.5;
 intcs = log(y(nanx)./x(nanx).^coeffs(1));
 coeffs(2) = mean(intcs);
-%fprintf('h_fit1 slope = %.4f | y-intercept = %.4f\n',coeffs(1),coeffs(2));
+fprintf('h_fit1 slope = %.4f | y-intercept = %.4f\n',coeffs(1),coeffs(2));
 
 % Convert from logarithmic scale to linear scale and plot
 h_fit1 = tau.^coeffs(1).*exp(coeffs(2));
@@ -50,18 +50,18 @@
 % =======================================================================
 % Next we should fit a 1/2 line to the bias side of the allan plot
 minid = find(sigavg == min(sigavg)); % find where the min is
-window = minid:length(tau); % go from min to the end
+window = minid+windowsize:length(tau); % go from min to the end
 
 % Get our x and y values
 x = tau(window);
-y = sqrt(sigavg(window));
+y = sigavg(window);
 nanx = isfinite(y);
 
 % Calculate the intercept given the slope of +1/2
 coeffs(1)= 0.5;
 intcs = log(y(nanx)./x(nanx).^coeffs(1));
 coeffs(2) = mean(intcs);
-%fprintf('h_fit2 slope = %.4f | y-intercept = %.4f\n',0.5,mean(intcs));
+fprintf('h_fit2 slope = %.4f | y-intercept = %.4f\n',0.5,mean(intcs));
 
 % Convert from logarithmic scale to linear scale and plot
 h_fit2 = tau.^coeffs(1).*exp(coeffs(2));
@@ -72,7 +72,7 @@
 % Get what our bias should be (should be at a tau of 3)
 tauref = 3;
 taudiff = abs(tau-tauref);
-tauid2 = find(taudiff == min(taudiff));
+tauid2 = find(taudiff == min(taudiff),1);
 fprintf('tau = %.2f | tauid2 = %d\n',tau(tauid2),tauid2);