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+<h2>Description</h2>
+<p>
+  The LTPDA method <a href="matlab:doc('ao/cohere')">ao/cohere</a> estimates the  cross-coherence of time-series
+  signals, included in the input <tt>ao</tt>s following the Welch's averaged, modified periodogram method <a href="#references">[1]</a>.
+  Data are windowed prior to the estimation of the spectra, by multiplying
+  it with a <a href="specwin.html">spectral window object</a>, and can be detrended by a polinomial of time in order to reduce the impact
+  of the border discontinuities. The window length is adjustable to shorter lenghts to reduce the spectral
+  density uncertainties, and the percentage of subsequent window overlap can be adjusted as well.
+  <br>
+  <br>
+  <h2>Syntax</h2>
+</p>
+<div class="fragment"><pre>
+    <br>    b = cohere(a1,a2,pl)
+  </pre>
+</div>
+<p>
+  <tt>a1</tt> and <tt>a2</tt> are the 2 <tt>ao</tt>s containing the input time series to be evaluated, <tt>b</tt> is the output object and
+  <tt>pl</tt> is an optional parameters list.
+  <h2>Parameters</h2>
+The parameter list <tt>pl</tt> includes the following parameters:</p>
+<ul>
+  <li> <tt>'Nfft'</tt> - number of samples in each fft [default: length of input data]
+    Notice: analyzing a single segment produces as a result an object full of 1!
+    A string value containing the variable 'fs' can
+  also be used, e.g., plist('Nfft', '2*fs') </li>
+ <li> <tt>'Win'</tt> - the window to be applied to the data to remove the 
+    discontinuities at edges of segments. [default: taken from user prefs].<br>
+    The window is described by a string with its name and, only in the case of Kaiser window,
+  the additional parameter <tt>'psll'</tt>. <br>For instance: plist('Win', 'Kaiser', 'psll', 200). </li>
+  <li> <tt>'Olap'</tt> - segment percent overlap [default: -1, (taken from window function)] </li>
+  <li> <tt>'Order'</tt> - order of segment detrending <ul>
+      <li>      -1 - no detrending  </li>
+      <li>       0 - subtract mean [default] </li>
+      <li>       1 - subtract linear fit </li>
+  <li>       N - subtract fit of polynomial, order N  </li> </ul> </li>
+  <li><tt>'Navs'</tt>  - number of averages. If set, and if Nfft was set to 0 or -1, the number of points for each window will be calculated to match the request. [default: -1, not set] </li> 
+ <li><tt>'Times'</tt>  - interval of time to evaluate the calculation on. If empty [default], it will take the whole section.</li>
+<li><tt>'Type'</tt>  - type of scaling of the coherence function. Choose between:</li>
+<ul>
+   <li> <tt>'C'</tt> - Complex Coherence Sxy / sqrt(Sxx * Syy) [default]</li>
+      <li> <tt>'MS'</tt> - Magnitude-Squared Coherence (abs(Sxy))^2 / (Sxx * Syy) </li>
+  </ul>
+</ul>
+The length of the window is set by the value of the parameter <tt>'Nfft'</tt>, so that the window
+is actually rebuilt using only the key features of the window, i.e. the name and, for Kaiser windows, the PSLL.
+</p>
+
+<p>As an alternative to setting the number of points <tt>'Nfft'</tt> in each window, it's possible to ask for a given number of coherence estimates by setting the  <tt>'Navs'</tt> parameter, and the algorithm takes care of calculating the correct window length, according to the amount of overlap between subsequent segments.</p>
+<p>
+  <table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1">
+    <tr width="90%">
+      <td>
+        If the user doesn't specify the value of a given parameter, the default value is used.
+      </td>
+    </tr>
+  </table>
+</p>
+
+<p>
+  The function makes cross-coherence estimates between the 2 input <tt>ao</tt>s.
+If passing two identical objects or linearly combined signals, the output will be 1 at all frequencies. The same will happen if analyzing only a single window.</p>
+<h2>Algorithm</h2>
+<p>
+  The algorithm is based in standard MATLAB's tools, as the ones used by <a href="matlab:doc('pwelch')">pwelch</a>. The standard deviation of the mean is computed as <a href="#references">[2]</a>
+  <div align="center">
+  <img src="images/cohere_sigma1.png" >
+</div>
+where
+ <div align="center">
+  <img src="images/tfe_sigma2.png" >
+</div>
+is the coherence function.
+<p>
+  <h2>Example</h2>
+</p>
+<p>
+  Evaluation of the cross-coherence of two time-series represented by: a low frequency sinewave signal superimposed to
+  white noise and a linear drift, and a low frequency sinewave signal at the same frequency, phase shifted and with different
+  amplitude, superimposed to white noise.
+</p>
+<div class="fragment"><pre>
+    <br>    <span class="comment">% parameters</span>
+    nsecs = 5000;
+    fs = 10;
+    nfft = 1000;
+    
+    <span class="comment">% build first signal components</span>
+    x1 = ao(plist(<span class="string">'waveform'</span>,<span class="string">'sine wave'</span>,<span class="string">'f'</span>,0.1,<span class="string">'A'</span>,1,<span class="string">'nsecs'</span>,nsecs,<span class="string">'fs'</span>,fs,<span class="string">'yunits'</span>,<span class="string">'m'</span>)) 
+    x2 = ao(plist(<span class="string">'waveform'</span>,<span class="string">'noise'</span>,<span class="string">'type'</span>,<span class="string">'normal'</span>,<span class="string">'nsecs'</span>,nsecs,<span class="string">'fs'</span>,fs,<span class="string">'yunits'</span>,<span class="string">'m'</span>)) 
+    x3 = ao(plist(<span class="string">'tsfcn'</span>, <span class="string">'t'</span>,<span class="string">'nsecs'</span>,nsecs,<span class="string">'fs'</span>,fs,<span class="string">'yunits'</span>,<span class="string">'m'</span>));
+    
+    <span class="comment">% add components</span>
+    x = x1 + x2 + x3;
+    
+    <span class="comment">% build second signal components</span>
+    y1 = ao(plist(<span class="string">'waveform'</span>,<span class="string">'sine wave'</span>,<span class="string">'f'</span>,0.1,<span class="string">'A'</span>,2,<span class="string">'nsecs'</span>,nsecs,<span class="string">'fs'</span>,fs,<span class="string">'phi'</span>,90));
+    y2 =  4*ao(plist(<span class="string">'waveform'</span>,<span class="string">'noise'</span>,<span class="string">'type'</span>,<span class="string">'normal'</span>,<span class="string">'nsecs'</span>,nsecs,<span class="string">'fs'</span>,fs));
+    
+    <span class="comment">% add components and set units</span>
+    y = y1 + y2;
+    y.setYunits(<span class="string">'V'</span>);
+
+    <span class="comment">% compute coherence</span>
+    pl = plist(<span class="string">'win'</span>,<span class="string">'BH92'</span>,<span class="string">'nfft'</span>,nfft, <span class="string">'order'</span>,1);
+    Cxy  = cohere(x,y,pl);
+    
+    <span class="comment">%plot</span>
+    iplot(Cxy);
+  </pre>
+</div>
+<br>
+  
+<img src="images/cohere_1.png" border="3">
+
+<h2><a name="references">References</a></h2>
+<br>
+<ol>
+  <li> P.D. Welch, The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short,
+Modified Periodograms, <i>IEEE Trans. on Audio and Electroacoustics</i>, Vol. 15, No. 2 (1967), pp. 70 - 73.</a></li>
+ <li> G.C. Carter, C.H. Knapp, A.H. Nuttall, Estimation of the Magnitude-Squared Coherence Function Via Overlapped Fast Fourier Transform Processing
+  , <i>IEEE Trans. on Audio and Electroacoustics</i>, Vol. 21, No. 4 (1973), pp. 337 - 344.</a></li>
+</ol>