1 files changed, 68 insertions, 29 deletions
diff --git a/help/en_US/medfilt1.xml b/help/en_US/medfilt1.xml
index 2766e88..2760fe9 100644
--- a/help/en_US/medfilt1.xml
+++ b/help/en_US/medfilt1.xml
@@ -18,68 +18,107 @@
   <refnamediv>
     <refname>medfilt1</refname>
     <refpurpose>1D median filtering</refpurpose>
+    <para> </para>
   </refnamediv>
 
 
 <refsynopsisdiv>
    <title>Calling Sequence</title>
    <synopsis>
+ y = medfilt1(x)
+ y = medfilt1(x, n)
+ y = medfilt1(x, n, dim)
+ y = medfitl1(__, nanflag, padding)
    </synopsis>
+   <para> </para>
 </refsynopsisdiv>
 
 <refsection>
    <title>Description</title>
    <para>
 y = medfilt1(x)
-Applies a 3rd order 1-dimensional median filter to input x along the
-first non-zero dimension. The function appropriately pads the signal
+   </para>
+<para>Applies a 3rd order 1-dimensional median filter to input x along the
+first non-zero dimension.</para>
+ <para>The function appropriately pads the signal
 with zeros at the endings. For a segment, a median is calculated as
 the middle value (average of two middle values) for odd number
-number (even number) of data points.
-y = medfilt1(x,n)
-Applies a nth order 1-dimensional median filter.
-y = medfilt1(x,n,dim)
-Applies the median filter along the n-th dimension
-y = medfilt1(__, nanflag, padding)
-nanflag specifies how NaN values are treated. padding specifies the
-type of filtering to be performed at the signal edges.
-   </para>
-   <para>
-</para>
+number (even number) of data points.</para>
+<para> </para>
+<para>y = medfilt1(x,n)</para>
+<para>Applies a nth order 1-dimensional median filter.</para>
+<para> </para>
+<para>y = medfilt1(x,n,dim)</para>
+<para>Applies the median filter along the n-th dimension</para>
+<para> </para>
+<para>y = medfilt1(__, nanflag, padding)</para>
+<para>nanflag specifies how NaN values are treated. padding specifies the
+type of filtering to be performed at the signal edges.</para>
+
+   <para> </para>
 </refsection>
 
 <refsection>
    <title>Parameters</title>
    <variablelist>
    <varlistentry><term>x:</term>
-      <listitem><para> int | double</para></listitem></varlistentry>
+      <listitem><para> int | double</para> <para>Input signal.</para></listitem></varlistentry>
    <varlistentry><term>n:</term>
-      <listitem><para> positive integer scalar</para></listitem></varlistentry>
+      <listitem><para> positive integer, scalar</para><para>
+      Filter order. </para>
+     <para> Defaults to 3.The order of the median filter. Must be less than
+      (length of the signal) where signals are 1D vectors along the
+      dimension of x to be filtered </para></listitem></varlistentry>
    <varlistentry><term>dim:</term>
-      <listitem><para> positive integer scalar</para></listitem></varlistentry>
+      <listitem><para> positive integer scalar</para><para>
+      Dimension to filter along. </para>
+     <para> Defaults to first non-singleton dimension of x</para></listitem></varlistentry>
    <varlistentry><term>nanflag:</term>
-      <listitem><para> 'includenan' (default) | 'omitnan'</para></listitem></varlistentry>
-   <varlistentry><term>* includenan:</term>
-      <listitem><para> Filtering such that the median of any segment</para></listitem></varlistentry>
-   <varlistentry><term>* omitnan:</term>
-      <listitem><para> Filtering with NaNs omitted in each segment. If a segment</para></listitem></varlistentry>
+      <listitem><para> 'includenan' (default) | 'omitnan'</para> <para>
+      NaN condition.</para>
+     <para> * includenan: Filtering such that the median of any segment containing a NaN is also a NaN. </para>
+     <para>* omitnan: Filtering with NaNs omitted in each segment. If a segment contains all NaNs, the result is NaN</para>
+</listitem></varlistentry>
+
    <varlistentry><term>y:</term>
-      <listitem><para> int | double</para></listitem></varlistentry>
+      <listitem><para> int | double</para><para>
+      The filtered signal.</para>
+      <para>y has the same size as x</para></listitem></varlistentry>
    </variablelist>
+   <para> </para>
 </refsection>
 
 <refsection>
    <title>Examples</title>
    <programlisting role="example"><![CDATA[
-1) Noise supression using median filtering
-fs = 1e3;
-t =  1:1/fs:1;
-s = sin(2*%pi*2*t)+ cos(2*%pi*5*t);
-// Adding noise
-x = s + 0.1*randn(size(s));
-y = medfilt1(x);
+//Generate a sinusoidal signal sampled for 1 second at 100 Hz. Add a higher-frequency sinusoid to simulate noise.
+fs = 100;
+t = 0:1/fs:1;
+x = sin(2*%pi*t*3)+0.25*sin(2*%pi*t*40);
 
+//Use a 10th-order median filter to smooth the signal. Plot the result.
+y = medfilt1(x,10);
+plot(t,x,t,y)
+legend('Original','Filtered');
+y = round(y*10000)/10000;
+y = y'
    ]]></programlisting>
+
+<scilab:image>
+//Generate a sinusoidal signal sampled for 1 second at 100 Hz. Add a higher-frequency sinusoid to simulate noise.
+fs = 100;
+t = 0:1/fs:1;
+x = sin(2*%pi*t*3)+0.25*sin(2*%pi*t*40);
+
+//Use a 10th-order median filter to smooth the signal. Plot the result.
+y = medfilt1(x,10);
+plot(t,x,t,y)
+legend('Original','Filtered');
+y = round(y*10000)/10000;
+y = y'
+</scilab:image>
+
+
 </refsection>
 
 <refsection>