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+<h2>Description</h2>
+<p>
+  The LTPDA method <a href="matlab:doc('ao/tfe')">ao/tfe</a> estimates the transfer function 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 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 = tfe(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.
+</p>
+<h2>Parameters</h2>
+The parameter list <tt>pl</tt> includes the following parameters:
+<ul>
+  <li> <tt>'Nfft'</tt> - number of samples in each fft [default: length of input data]
+    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>
+</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 TFE 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 transfer functions estimates between the 2 input <tt>ao</tt>s, and the output will contain the transfer function estimate from the first <tt>ao</tt> to the second.</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
+  <div align="center">
+  <img src="images/tfe_sigma1.png" >
+</div>
+where
+ <div align="center">
+  <img src="images/tfe_sigma2.png" >
+</div>
+is the coherence function.
+</p>
+<h2>Example</h2>
+<p>
+  Evaluation of the transfer function between two time-series represented by:
+  a low frequency sinewave signal superimposed to
+  white noise, 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 = 1000;
+    fs = 10;
+    
+    <span class="comment">% create first signal AO</span>
+    x = 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)) + ...
+    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));
+    x.setYunits(<span class="string">'m'</span>);
+    
+    <span class="comment">% create second signal AO</span>
+    y = 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)) + ...
+    0.1*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));
+    y.setYunits(<span class="string">'rad'</span>);
+    
+    <span class="comment">% compute transfer function</span>
+    nfft = 1000;
+    psll = 200;
+    Txy = tfe(x,y,plist(<span class="string">'win'</span>,<span class="string">'Kaiser'</span>,<span class="string">'psll'</span>,psll,<span class="string">'nfft'</span>,nfft));
+    
+    <span class="comment">% plot</span>
+    iplot(Txy)   
+  </pre>
+</div>
+<br>
+<img src="images/transfer_1.png" alt="" border="3">
+
+<h2><a name="references">References</a></h2>
+
+<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>
+</ol>
+
+