tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File ExchangeUser-contributed code library2015-04-19T15:04:53-04:00236731100tag:www.mathworks.com,2005:FileInfo/505622015-04-19T18:11:32Z2015-04-19T18:11:32ZConditional entropy of ordinal patterns in sliding windowsCondEn.m computes efficiently empirical conditional entropy of ordinal patterns in sliding windows.<p>CondEn.m computes efficiently empirical conditional entropy of ordinal patterns in sliding windows [1]. The method is based on precomputing values of successive ordinal patterns of order d, using the fact that they are "overlapped" in d points [2].
<br />1 Unakafov A.M., Keller, K. (2014) Conditional entropy of ordinal patterns. Physica D, 269, 94–102.
<br />2 Unakafova, V. A., Keller, K. (2013). Efficiently Measuring Complexity on the Basis of Real-World Data. Entropy, 15(10), 4392-4415.</p>Valentina Unakafovahttp://www.mathworks.com/matlabcentral/profile/authors/4762096-valentina-unakafovaMATLAB 8.2 (R2013b)falsetag:www.mathworks.com,2005:FileInfo/505612015-04-19T18:10:00Z2015-04-19T18:10:00Zfoto week2a<p>a</p>xabin olaskoagahttp://www.mathworks.com/matlabcentral/profile/authors/6431632-xabin-olaskoagaMATLAB 8.5 (R2015a)falsetag:www.mathworks.com,2005:FileInfo/505602015-04-19T18:09:11Z2015-04-19T18:09:11ZRegression reportregression<p>regression</p>Xiao Lihttp://www.mathworks.com/matlabcentral/profile/authors/6431511-xiao-liMATLAB 8.5 (R2015a)falsetag:www.mathworks.com,2005:FileInfo/505592015-04-19T17:54:32Z2015-04-19T17:54:32ZAmplitude Ratio and Phase Difference Measurement with Matlab ImplementationAmplitude ratio and phase difference measurement between two signals via DFT.<p>The present code is a Matlab function that provides a measurement of the amplitude ratio and phase difference between two signals. The measurement is based on Discrete Fourier Transform (DFT) and Maximum Likelihood (ML) estimation of the signals properties.
<br />An example is given in order to clarify the usage of the function. The input and output arguments are given in the beginning of the code.</p>
<p>The code is based on the theory described in:</p>
<p>[1] D. Rife, R. Boorstyn. Single-tone parameter estimation from discrete-time observations. IEEE Trans. Inform. Theory, Vol. IT–20, pp. 591-598, Sept. 1974.</p>
<p>[2] T. Wescott. Applied control theory for embedded systems. Oxford, Elsevier, 2006.</p>Hristo Zhivomirovhttp://www.mathworks.com/matlabcentral/profile/authors/3571337-hristo-zhivomirovMATLAB 7.11 (R2010b)Signal Processing ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/480252014-10-05T15:16:01Z2015-04-19T17:47:30ZPhase Difference Measurement with Matlab ImplementationPhase difference measurement between two signals via DFT.<p>The present code is a Matlab function that provides a measurement of the phase difference between two signals. The measurement is based on Discrete Fourier Transform (DFT) and Maximum Likelihood (ML) estimation of the signals properties.
<br />An example is given in order to clarify the usage of the function. The input and output arguments are given in the beginning of the code.</p>
<p>The code is based on the theory described in:</p>
<p>[1] M. Sedlacek, M. Krumpholc. “Digital measurement of phase difference – a comparative study of DSP algorithms”. Metrology and Measurement Systems, Vol. XII, No. 4, pp. 427-448, 2005.</p>
<p>[2] M. Sedlacek. “Digital Measurement of Phase Difference of LF Signals A Comparison of DSP Algorithms”. Proceedings of XVII IMEKO World Congress, pp. 639-644, 2003.</p>Hristo Zhivomirovhttp://www.mathworks.com/matlabcentral/profile/authors/3571337-hristo-zhivomirovMATLAB 7.11 (R2010b)Signal Processing ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/505582015-04-19T17:38:26Z2015-04-19T17:38:26ZDecade (Log Scale) Vector Generation with Matlab ImplementationDecade (log scale) vector generation of the type [1 2 3 4 5 ... 10 20 30 40 50 ... ]<p>The present code is a Matlab function that provides a generation of decade (log-space) vector. The vector start value is of type (10^m), the end value – (10^n), and the step size in every one decade is for example 0.1 (i.e. step = 0.1*first term of every decade).
<br />Example decade vectors:
<br />[1 2 3 4 5 6 7 8 9 10 20 30 40 50 60 70 80 90 100 200 …]
<br />[100 110 120 130 140 … 1000 1100 1200 1300 1400 …]</p>
<p>Two examples are given in order to clarify the usage of the function. The input and output arguments are given in the beginning of the code.</p>Hristo Zhivomirovhttp://www.mathworks.com/matlabcentral/profile/authors/3571337-hristo-zhivomirovMATLAB 7.11 (R2010b)MATLABfalsetag:www.mathworks.com,2005:FileInfo/441612013-11-01T20:48:24Z2015-04-19T16:57:25ZFast permutation entropyEfficient computing of empirical permutation entropy<p>PE.m computes efficiently empirical permutation entropy. PEeq.m computes efficiently empirical permutation entropy for the case of modified ordinal patterns. The methods are based on precomputing values of successive ordinal patterns of order d, using the fact that they are "overlapped" in d points, and on precomputing successive values of the permutation entropy related to "overlapping" successive time-windows [1].
<br />1 Unakafova, V. A., & Keller, K. (2013). Efficiently Measuring Complexity on the Basis of Real-World Data. Entropy, 15(10), 4392-4415.</p>Valentina Unakafovahttp://www.mathworks.com/matlabcentral/profile/authors/4762096-valentina-unakafovaMATLAB 8.0 (R2012b)MATLABfalsetag:www.mathworks.com,2005:FileInfo/505572015-04-19T16:03:19Z2015-04-19T16:03:19ZFast robust empirical permutation entropyFast calculation of a robust empirical permutation entropy<p>Robust empirical permutation entropy (rePE) is introduced in [1,2] on the basis of empirical permutation entropy (ePE), rePE uses also a metric information from a time series in comparison with ePE. rePE has shown better robustness than ePE with respect to noise for simulated data [1,2] and better results than ePE for epileptic seizures detection [1,2]. The choice of the lower (thr1) and upper (thr2) thresholds is ambiguous, we recommend to play with these parameters, see [1,2] for more details.
<br />1 Keller, K., Unakafov A.M., and Unakafova V.A. "Ordinal Patterns, Entropy, and EEG." Entropy 16.12 (2014): 6212-6239.
<br />2 Unakafova, V.A. Investigating measures of complexity for dynamical systems and for time series.
<br />Ph.D. Thesis, draft version, University of Lubeck, Lubeck, Germany, 2015.</p>Valentina Unakafovahttp://www.mathworks.com/matlabcentral/profile/authors/4762096-valentina-unakafovaMATLAB 8.2 (R2013b)falsetag:www.mathworks.com,2005:FileInfo/408542013-03-18T20:32:41Z2015-04-19T15:11:17ZBradley local image thresholdingA fast an well performing local image thresholding method.<p>The brief idea of the algorithm is that every image's pixel is set to black if its brightness is T percent lower than the average brightness of the surrounding pixels in the window of the specified size, otherwise it is set to white. The detail description of the algorithm is available at:
<br />Derek Bradley, Gerhard Roth (2005). Adaptive Thresholding Using the Integral Image. Retrieved from <a href="http://www.scs.carleton.ca/~roth/iit-publications-iti/docs/gerh-50002.pdf">http://www.scs.carleton.ca/~roth/iit-publications-iti/docs/gerh-50002.pdf</a>
<br />The advantage of this method is that the binarized images are subjectively almost as good as from Sauvola's method but the calculation is two times faster than Sauvola's method. Sauvola’s method calculates local mean and local variance, while Bradley’s method calculates just local mean. And because the variance can be calculated using following formula for variance:</p>
<p> Var(X) = E(X^2) – [E(X)]^2,</p>
<p>the calculation of variance reuses the result from the calculation of the local mean (E(X))^2 and just calculates E(X^2). And that takes the same amount of time as calculation of the local mean. Since calculation of local mean and variance is the most time consuming operation performed by these two methods, Bradley’s method is effectively two times faster than Sauvola’s method. A brief comparison of Bradley’s and Sauvola’s method is available at a blog:</p>
<p>Altun Nazmi (2010). Adaptive (Local) Thresholding For AForge.NET. Retrieved from <a href="http://nazmialtun.blogspot.com/2010/10/adaptive-local-thresholding-for.html">http://nazmialtun.blogspot.com/2010/10/adaptive-local-thresholding-for.html</a></p>
<p>The calculation of the local mean is performed with integral image method in constant time regardless of the kernel size. </p>
<p>Examples of real word applications are: bar-code scanners, license plate registration.</p>Jan Motlhttp://www.mathworks.com/matlabcentral/profile/authors/3909588-jan-motlMATLAB 6.5 (R13)falsetag:www.mathworks.com,2005:FileInfo/390762012-11-16T15:32:36Z2015-04-19T12:51:07ZTransfunction2plot_01App for Control Calculator for Beginners<p>This GUI provides results from a unit-step input, impulse input and diagrams of Root Locus, Bode, and Nyquist. It is very simple to use; just fill out transfer function and gain after selecting radio buttone of zero & pole and Coefficient, then click 'calculate' button. You can see multi-results of the transfer function you inserted. In addition, This code shows a math equation of the transfer function on the figure. Furthermore, you can see zoom in/out on the figure by using 'max' button.
<br />When I was preparing a qualifying exam, I wanted to easily/quickly see and check the results from Root Locus, Bode, and Nyquist based on the transfer function. it was very useful to understand about them. Please enjoy :)</p>Jong Kimhttp://www.mathworks.com/matlabcentral/profile/authors/3056886-jong-kimMATLAB 8.4 (R2014b)Control System ToolboxMATLABfalse