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  <refnamediv>
    <refname>enbw</refname>
    <refpurpose>Estimate the Equivalent Noise Bandwidth (ENBW) of a window.</refpurpose>
  </refnamediv>


<refsynopsisdiv>
   <title>Syntax</title>
   <synopsis>
   bw = enbw(window)
   bw = enbw(window, fs)
   
   </synopsis>
</refsynopsisdiv>

<refsection>
   <title>Parameters</title>
   <variablelist>
        <varlistentry>
            <term>window:</term>
            <listitem><para> Vector. Specifies the sample window.</para></listitem>
        </varlistentry>
        <varlistentry>
            <term>fs:</term>
            <listitem><para> Positive scalar (optional). Specifies the sampling rate of the window. Default is 1.</para></listitem>
        </varlistentry>
        <varlistentry>
            <term>bw:</term>
            <listitem><para> Scalar. Returns the two-sided equivalent noise bandwidth for a uniformly sampled window.</para></listitem>
        </varlistentry>
   </variablelist>
</refsection>

<refsection>
   <title>Description</title>
   <para>
This function estimates the Equivalent Noise Bandwidth (ENBW) of a given window. The ENBW is calculated as the ratio of the root mean square (RMS) value of the window to its mean, squared. If the sampling rate `fs` is provided, the bandwidth is scaled accordingly.
   </para>
   <para>
</para>
</refsection>

<refsection>
   <title>Examples</title>
   <programlisting role="example"><![CDATA[
// Without sampling rate:
window = 1:10
bw = enbw(window)
// With sampling rate:
window = 1:10
fs = 2.5
bw = enbw(window, fs)


   ]]></programlisting>
</refsection>

<refsection>
   <title>Authors</title>
   <simplelist type="vert">
   <member>Jitendra Singh</member>
   </simplelist>
</refsection>
</refentry>
