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Import.
| author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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| date | Wed, 23 Nov 2011 19:22:13 +0100 |
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| 1 <h2>Description</h2> | |
| 2 <p> | |
| 3 Spikes in data due to different nature can be removed, if desired, from the original data. LTPDA <a href="matlab:doc('ao/spikecleaning')"><tt>spikecleaning</tt></a> detects and replaces | |
| 4 spikes of the input AOs. A spike in data is defined as a single sample exceeding a certain value (usually, the | |
| 5 floor noise of the data) defined by the user: | |
| 6 </p> | |
| 7 <br> | |
| 8 <p> | |
| 9 <div align="center"> | |
| 10 <IMG src="images/spike10x.png" width="147" height="15" border="0" /> | |
| 11 </div> | |
| 12 </p> | |
| 13 <br> | |
| 14 <p> | |
| 15 where <IMG src="images/spike41x.png" width="50" height="14" border="0" /> is the input data high-pass filtered, <IMG src="images/spike42x.png" width="34" height="13" border="0"/> is a value defined by the user (by default is 3.3) and <IMG src="images/spike43x.png" width="35" height="8" border="0"/> is the standard deviation of <IMG src="images/spike41x.png" width="50" height="14" border="0" /> . In consequence, a spike is defined as the value that exceeds the floor noise of the data by a factor <IMG src="images/spike42x.png" width="34" height="13" border="0"/>, the higher of this parameter the more difficult to "detect" a spike. | |
| 16 <br> | |
| 17 <br> | |
| 18 <h2>Syntax</h2> | |
| 19 </p> | |
| 20 <div class="fragment"><pre> | |
| 21 <br> | |
| 22 b = spikecleaning(a, pl) | |
| 23 </pre> | |
| 24 </div> | |
| 25 <h2>Parameters</h2> | |
| 26 <p> | |
| 27 The following parameters can be set in this method: | |
| 28 <ul> | |
| 29 <li> <tt>'kspike'</tt> - set the <IMG src="images/spike42x.png" width="34" height="13" border="0"/> value (default is 3.3) </li> | |
| 30 <li> <tt>'method'</tt> - method used to replace the "spiky" sample. Three methods are available ---see below for details---:</li> | |
| 31 <ul> | |
| 32 <li> <tt>'random'</tt> </li> | |
| 33 <li> <tt>'mean'</tt> </li> | |
| 34 <li> <tt>'previous'</tt> </li> | |
| 35 </ul> | |
| 36 <li> <tt>'fc'</tt> - frequency cut-off of the high-pass IIR filter (default is 0.025) </li> | |
| 37 <li> <tt>'order'</tt> - specifies the order of the IIR filter (default is 2) </li> | |
| 38 <li> <tt>'ripple'</tt> - specifies pass/stop-band ripple for bandpass and bandreject filters (default is 0.5) </li> | |
| 39 </ul> | |
| 40 </p> | |
| 41 <p> | |
| 42 <h2>Algorithm</h2> | |
| 43 </p> | |
| 44 <p> | |
| 45 <b>Random: </b> this method substitutes the spiky sample by: | |
| 46 </p> | |
| 47 <p> | |
| 48 <br> | |
| 49 <div align="center"> | |
| 50 <IMG src="images/spike20x.png" width="197" height="15" align="center" border="0"/> | |
| 51 </div> | |
| 52 <br> | |
| 53 </p> | |
| 54 <p> | |
| 55 where <IMG src="images/spike44x.png" width="42" height="14" border="0"/> is a random number of mean zero and standard deviation 1. | |
| 56 </p> | |
| 57 <p> | |
| 58 <b>Mean: </b>this method uses the following equation to replace the spike detected in data. | |
| 59 </p> | |
| 60 <br> | |
| 61 <p> | |
| 62 <div align="center"> | |
| 63 <IMG src="images/spike30x.png" width="158" height="29" align="center" border="0"/> | |
| 64 </div> | |
| 65 </p> | |
| 66 <br> | |
| 67 <p> | |
| 68 <b>Previous: </b>the spike is substitued by the previous sample, i.e.: | |
| 69 </p> | |
| 70 <br> | |
| 71 <p> | |
| 72 <div align="center"> | |
| 73 <IMG src="images/spike40x.png" width="90" height="14" align="center" border="0"/> | |
| 74 </div> | |
| 75 </p> | |
| 76 <br> | |
| 77 <p> | |
| 78 <h2>Examples</h2> | |
| 79 </p> | |
| 80 <p> | |
| 81 1. Spike cleaning of a sequence of random data with <tt>kspike = 2</tt>. | |
| 82 </p> | |
| 83 <p> | |
| 84 <div class="fragment"><pre> | |
| 85 <br> | |
| 86 x = ao(plist( <span class="string">'waveform'</span>, <span class="string">'noise'</span>, <span class="string">'nsecs'</span>,1e4, <span class="string">'fs'</span>,10)); <span class="comment">% create an AO of random data sampled at 1 Hz.</span> | |
| 87 pl = plist( <span class="string">'kspike'</span>, 2); <span class="comment">% kspike = 2</span> | |
| 88 y = spikecleaning(x, pl); <span class="comment">% spike cleaning function applied to the input AO, x</span> | |
| 89 iplot(x, y) <span class="comment">% plot original and "cleaned" data</span> | |
| 90 </pre> | |
| 91 </div> | |
| 92 </p> | |
| 93 <p> | |
| 94 <div align="center"> | |
| 95 <img src="images/spike1.png" border="1" > | |
| 96 </div> | |
| 97 </p> | |
| 98 <!-- <p> | |
| 99 2. Example of real data: the first image shows data from the real world prior to | |
| 100 the application of the spike cleaning tool. It is clear that some spikes are | |
| 101 present in data and might be convenient to remove them. The second image shows | |
| 102 the same data after the spike samples supression. | |
| 103 </p> | |
| 104 <p> | |
| 105 <img src="images/stat1x.png" border="1" width="400px"> | |
| 106 </p> | |
| 107 <p> | |
| 108 <img src="images/stat2x.png" border="1" width="400px"> | |
| 109 </p> | |
| 110 --> | |
| 111 | |
| 112 |