Move the table and figure to top or buttom of page

git-svn-id: http://newslabx.csie.ntu.edu.tw/svn/Ginger@79 5747cdd2-2146-426f-b2b0-0570f90b98ed

trunk/RTCSA_SS/02Background.tex

@@ -34,7 +34,7 @@ resolution at $10$ fps. For the high-resolution one, we use Lepton 3. The
 specification of them are shown in Table~\ref{table:specificationofdevices}.
 
 
-\begin{table}[hb]
+\begin{table}[tbp]
   \centering
   \footnotesize
   \begin{tabular}{|p{3cm}<{\centering}|p{2cm}<{\centering}|p{2.5cm}<{\centering}|}

trunk/RTCSA_SS/03Design.tex

@@ -15,7 +15,7 @@ the residual heat on bed after turning over. We
 remove the noise by median filter, and determine the current pose according to
 the trend of the possibility from recognition network.
 
-\begin{figure}[htb]
+\begin{figure}[tbp]
 	\begin{center}
 		\includegraphics[width=1\linewidth]{figures/method.pdf}
 		\caption{Illustration of Proposed Method.}
@@ -63,7 +63,7 @@ bottom one fifth, or the trend is going down, it is a lay on side. If there are
 continuously same poses, and different from the last turning over, it will be count as
 another turning over.
 
-\begin{figure}[ht]
+\begin{figure}[tbp]
   \centering
   \minipage{0.3\columnwidth}
     \includegraphics[width=\linewidth]{figures/Lepton_residual_heat.png}

trunk/RTCSA_SS/04Evaluation.tex

@@ -8,7 +8,7 @@ For training SRCNN model, we let a person lay on bed and randomly change pose
 or move his arms and legs. Collect the about 600 images from Grideye sensors and
 Lepton 3, and align them at the same timestamps. Figure~\ref{fig:resolution_compare} shows the result of SRCNN model.
 
-\begin{figure}[ht]
+\begin{figure}[tbp]
 	\centering
 	\subfloat[Grideye Image]{
 		\includegraphics[width=0.32\columnwidth]{figures/LR.png}