Fix some typo.

git-svn-id: http://newslabx.csie.ntu.edu.tw/svn/Ginger@71 5747cdd2-2146-426f-b2b0-0570f90b98ed

trunk/RTCSA_SS/01Introduction.tex

@@ -5,7 +5,7 @@ The turn over frequency while sleeping is an important index to quantify the
 health of elderly. Many wearable devices can also achieve the same purpose, but
 many study show that the elderly feel uncomfortable with wearing such devices
 all days.\textcolor{red}{(source?)}. By the low resolution thermal camera, we
-can obtain the daily activities informations, but not reveal too much privacy
+can obtain the daily activities information, but not reveal too much privacy
 like the RGB camera.
 
 {\bf Contribution}
@@ -16,7 +16,7 @@ Super-resolution techniques, and our method, we can have 50\% recall rate and 83
 on turning over detection.
 
 
-The remaining of this paper is organized as follow. Section~\ref{sec:bk_related}
+The remaining of this paper is organized as follows. Section~\ref{sec:bk_related}
 presents background for developing the methods. Section~\ref{sec:design} presents
 the system architecture, and the developed mechanisms.  Section~\ref{sec:eval}
 presents the evaluation results of proposed mechanism and Section~\ref{sec:conclusion} summaries our works.

trunk/RTCSA_SS/02Background.tex

@@ -26,7 +26,7 @@ patches.
 %% \end{figure}
 
 \subsection{Thermal cameras}
-In this work, we use two different resolution thermal camera to play the role
+In this work, we use two different resolution thermal cameras to play the role
 of low-resolution and high-resolution camera. For low-resolution camera, we use
 Grid-EYE thermal camera. Grid-EYE is a thermal camera that can output
 $8 \times 8$ pixels thermal data with $2.5^\circ C$ accuracy and $0.25^\circ C$

trunk/RTCSA_SS/03Design.tex

@@ -5,7 +5,7 @@
 We designed a thermal-box to collect the data. It has four Grideye sensors on the
 corners of a 10 cm square and a Lepton 3 at the central. Figure~\ref{fig:method} shows
 the system of our method. It consists four parts. The first part is to fuse multiple
-Grideye image, since the resolution of singel Grideye sensor only has 64 pixels. The
+Grideye image, since the resolution of single Grideye sensor only has 64 pixels. The
 second part, we train the SRCNN model with fused Grideye image
 as low-resolution and downscaled Lepton 3 image as high-resolution image.
 The third part, we use the
@@ -29,7 +29,7 @@ On the thermal-box, there are four Grideye sensors. At the beginning, we let
 the thermal-box faces to an empty bed and records the background temperature.
 All the following frames will subtract this background temperature. After that,
 we resize four $8 \times 8$ Grideye images to $64 \times 64$ by bilinear
-interpolation and than merge them dependence on the distance between thermal-box and
+interpolation and then merge them dependence on the distance between thermal-box and
 bed, width of sensor square and the FOV of Grideye sensor.
 
 \begin{enumerate}
@@ -51,8 +51,8 @@ distinguish the residual heat on bed and the person as Figure~\ref{fig:residual_
 situation will slowly disappear after one or two minutes.
 
 To determination the pose, first we use a median filter with a window size of five
-to filter out the noise. Than, find the curve hull line of the upper bound and
-lower bound of the data. Finally calculate the middle line of upper bound and lower bound.
+to filter out the noise. Then, find the curve hull line of the upper bound and
+lower bound of the data. Finally, calculate the middle line of upper bound and lower bound.
 Figure~\ref{fig:trend} shows the data and these lines.
 
 We divide every data into 10 second time windows. If the middle line of the time window

trunk/RTCSA_SS/04Evaluation.tex

@@ -27,7 +27,7 @@ For training the pose recognition model, we collect 200 images of lay on back an
 of lay on right or left side. The result shows that the accuracy of single frame detection
 can be improved about 5\% by SRCNN.
 
-We let a person lay on bed and change his pose every minutes. The pose is repeating
-lay on back, lay on left, lay on back and lay on right. Our method will output the currect pose
+We let a person lay on bed and change his pose every minute. The pose is repeating
+lay on back, lay on left, lay on back and lay on right. Our method will output the current pose
 every 10 seconds and check if the pose changed. The accuracy of pose detection is 65\%, and the
 turning over detection has 50\% recall rate and 83\% precision.