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Publications.html

@@ -170,6 +170,46 @@ <h2>List of research articles citing EntropyHub<a class="headerlink" href="#list
 <h3>2023<a class="headerlink" href="#id1" title="Permalink to this heading"></a></h3>
 <ul>
 <li><dl>
+<dt><a class="reference external" href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4643418">Radiological Complexity of Nuclear Facilities: An Information Complexity Approach to Workplace Monitoring</a></dt><dd><div class="line-block">
+<div class="line">Abinash Chakraborty, Neeraj Parashar, DK Pandey, Pankaj Kumar, UV Deokar, JPN Pandey</div>
+<div class="line"><em>SSRN - Social Science Research Network</em></div>
+</div>
+</dd>
+</dl>
+</li>
+<li><dl>
+<dt><a class="reference external" href="https://mhnursing.or.kr/index.php/JKPMHN/article/view/153">Nonlinear Analysis of the Dynamical Characteristics of EEG Signals in Epileptic Brains</a></dt><dd><div class="line-block">
+<div class="line">Shervin Skaria, Sreelatha KS</div>
+<div class="line"><em>Journal of the Korean Academy of Pyschiatric and Mental Health Nursing</em></div>
+</div>
+</dd>
+</dl>
+</li>
+<li><dl>
+<dt><a class="reference external" href="https://www.doi.org/10.1016/j.cmpb.2023.107941">NLDyn - An open source MATLAB toolbox for the univariate and multivariate nonlinear dynamical analysis of physiological data</a></dt><dd><div class="line-block">
+<div class="line">Mostafa Rostaghi, Sadegh Rostaghi, Anne Humeau-Heurtier, Tarek K. Rajji, Hamed Azami</div>
+<div class="line"><em>Computer Methods and Programs in Biomedicine</em></div>
+</div>
+</dd>
+</dl>
+</li>
+<li><dl>
+<dt><a class="reference external" href="https://doi.org/10.22541/au.170004063.32719671/v1">Functional near-infrared spectroscopy for human brain age group classification by machine learning</a></dt><dd><div class="line-block">
+<div class="line">Martti Ilvesmäki, Hany Ferdinando, Kai Noponen, Tapio Seppänen, Vesa Korhonen, Vesa Kiviniemi, Teemu Myllylä</div>
+<div class="line"><em>Authorea</em></div>
+</div>
+</dd>
+</dl>
+</li>
+<li><dl>
+<dt><a class="reference external" href="https://trace.tennessee.edu/utk_graddiss/8578/">Acceleration profiles of adolescent soccer players across a season</a></dt><dd><div class="line-block">
+<div class="line">Jake Melaro</div>
+<div class="line"><em>University of Tennessee (Knoxville) Repository</em></div>
+</div>
+</dd>
+</dl>
+</li>
+<li><dl>
 <dt><a class="reference external" href="https://www.mdpi.com/1424-8220/23/20/8609">Entropy-Based Machine Learning Model for Fast Diagnosis and Monitoring of Parkinson’s Disease</a></dt><dd><div class="line-block">
 <div class="line">Maksim Belyaev, Murugappan Murugappan, Andrei Velichko, Dmitry Korzun</div>
 <div class="line"><em>Sensors</em></div>

_sources/Publications.rst.txt

@@ -28,6 +28,27 @@ List of research articles citing EntropyHub
 2023
 ----
 
+
+* `Radiological Complexity of Nuclear Facilities: An Information Complexity Approach to Workplace Monitoring <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4643418>`_
+    | Abinash Chakraborty, Neeraj Parashar, DK Pandey, Pankaj Kumar, UV Deokar, JPN Pandey
+    | *SSRN - Social Science Research Network*
+
+* `Nonlinear Analysis of the Dynamical Characteristics of EEG Signals in Epileptic Brains <https://mhnursing.or.kr/index.php/JKPMHN/article/view/153>`_
+    | Shervin Skaria, Sreelatha KS
+    | *Journal of the Korean Academy of Pyschiatric and Mental Health Nursing*
+
+* `NLDyn - An open source MATLAB toolbox for the univariate and multivariate nonlinear dynamical analysis of physiological data <https://www.doi.org/10.1016/j.cmpb.2023.107941>`_
+    | Mostafa Rostaghi, Sadegh Rostaghi, Anne Humeau-Heurtier, Tarek K. Rajji, Hamed Azami
+    | *Computer Methods and Programs in Biomedicine*
+
+* `Functional near-infrared spectroscopy for human brain age group classification by machine learning <https://doi.org/10.22541/au.170004063.32719671/v1>`_
+    | Martti Ilvesmäki, Hany Ferdinando, Kai Noponen, Tapio Seppänen, Vesa Korhonen, Vesa Kiviniemi, Teemu Myllylä
+    | *Authorea*
+
+* `Acceleration profiles of adolescent soccer players across a season <https://trace.tennessee.edu/utk_graddiss/8578/>`_
+    | Jake Melaro
+    | *University of Tennessee (Knoxville) Repository*
+
 * `Entropy-Based Machine Learning Model for Fast Diagnosis and Monitoring of Parkinson’s Disease <https://www.mdpi.com/1424-8220/23/20/8609>`_
     | Maksim Belyaev, Murugappan Murugappan, Andrei Velichko, Dmitry Korzun
     | *Sensors*

python/Functions/Base.html

@@ -692,7 +692,7 @@ <h2>Functions for estimating the entropy of a single univariate time series.<a c
 
 <dl class="py function">
 <dt class="sig sig-object py" id="EntropyHub.FuzzEn">
-<span class="sig-name descname"><span class="pre">FuzzEn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Sig</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">r</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.2,</span> <span class="pre">2)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Fx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Logx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">numpy.exp</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#EntropyHub.FuzzEn" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">FuzzEn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Sig</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">r</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.2,</span> <span class="pre">2.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Fx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Logx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">numpy.exp</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#EntropyHub.FuzzEn" title="Permalink to this definition"></a></dt>
 <dd><p>FuzzEn  estimates the fuzzy entropy of a univariate data sequence.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">Fuzz</span><span class="p">,</span> <span class="n">Ps1</span><span class="p">,</span> <span class="n">Ps2</span> <span class="o">=</span> <span class="n">FuzzEn</span><span class="p">(</span><span class="n">Sig</span><span class="p">)</span> 
 </pre></div>
@@ -1120,7 +1120,7 @@ <h2>Functions for estimating the entropy of a single univariate time series.<a c
 <li><p>Permutation entropy variation, one of the following:</p></li>
 </ul>
 <dl class="simple">
-<dt>{<code class="docutils literal notranslate"><span class="pre">'uniquant'</span></code>, <code class="docutils literal notranslate"><span class="pre">'finegrain'</span></code>, <code class="docutils literal notranslate"><span class="pre">'modified'</span></code>, <code class="docutils literal notranslate"><span class="pre">'ampaware'</span></code>, <code class="docutils literal notranslate"><span class="pre">'weighted'</span></code>, <code class="docutils literal notranslate"><span class="pre">'edge'</span></code>}</dt><dd><p>See the <a class="reference external" href="https://github.com/MattWillFlood/EntropyHub/blob/main/EntropyHub%20Guide.pdf">EntropyHub guide</a> for more info on PermEn variants.</p>
+<dt>{<code class="docutils literal notranslate"><span class="pre">'uniquant'</span></code>, <code class="docutils literal notranslate"><span class="pre">'finegrain'</span></code>, <code class="docutils literal notranslate"><span class="pre">'modified'</span></code>, <code class="docutils literal notranslate"><span class="pre">'ampaware'</span></code>, <code class="docutils literal notranslate"><span class="pre">'weighted'</span></code>, <code class="docutils literal notranslate"><span class="pre">'edge'</span></code>, <code class="docutils literal notranslate"><span class="pre">'phase'</span></code>}</dt><dd><p>See the <a class="reference external" href="https://github.com/MattWillFlood/EntropyHub/blob/main/EntropyHub%20Guide.pdf">EntropyHub guide</a> for more info on PermEn variants.</p>
 </dd>
 </dl>
 </dd>
@@ -1184,6 +1184,11 @@ <h2>Functions for estimating the entropy of a single univariate time series.<a c
 The European Physical Journal Special Topics 
 222.2 (2013): 249-262.</p>
 </dd>
+<dt>[9] Kang Huan, Xiaofeng Zhang, and Guangbin Zhang,</dt><dd><p>“Phase permutation entropy: A complexity measure for nonlinear time 
+series incorporating phase information.” 
+Physica A: Statistical Mechanics and its Applications 
+568 (2021): 125686.</p>
+</dd>
 </dl>
 </dd>
 </dl>