tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File ExchangeUser-contributed code library2014-12-19T09:52:41-05:00226151100tag:www.mathworks.com,2005:FileInfo/488062014-12-19T14:27:08Z2014-12-19T14:27:08ZA study on the biodynamic models of seated human (6-DOF Model by Muksian and Nash, 1974)body vibrations<p>The lumped-parameter models from literature have been analyzed and validated.</p>Olexander Zhytenkohttp://www.mathworks.com/matlabcentral/fileexchange/authors/216465MATLAB 7.14 (R2012a)falsetag:www.mathworks.com,2005:FileInfo/488052014-12-19T14:11:07Z2014-12-19T14:11:07ZA study on the biodynamic models of seated human (4-DOF Model by Boileau and Rakheja, 1998)Body vibrations<p>The lumped-parameter models from literature have been analyzed and validated.</p>Olexander Zhytenkohttp://www.mathworks.com/matlabcentral/fileexchange/authors/216465MATLAB 7.14 (R2012a)falsetag:www.mathworks.com,2005:FileInfo/488042014-12-19T14:04:38Z2014-12-19T14:04:38ZMean acquisition time in a multi-dwell hybrid or parallel acquisition structure in GNSSMean acquisition time calculation for the acquisition of GNSS signals (GPS, Galileo, Glonass, ...)<p>%The main function is Fct_MeanTacq_hybr_and_parr, which computes the mean acquisition
<br />%time in a multi-dwell hybrid or parallel structure in GNSS.
<br />%It is based on the formulas derived and presented in:
<br />%%Lohan, E.-S. , Lakhzouri, A. , Renfors, M, "Selection of the multiple-dwell hybrid-search strategy for the acqusition
<br />%of Galileo signals in fading channels",
<br />%IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
<br />%Volume 4, 2004, Pages 2352-2356
<br />%The function is called via:
<br />%Tacq=Fct_MeanTacq_hybr_and_parr(Pd, Pfa_H0, Pfa_H1, tau_d, Kpenalty, Nr_states);
<br />%where we have the following input parameters
<br />%
<br />%INPUTS
<br />%Pd = vector of detection probabilities for each dwell =[Pd1,
<br />% Pd2,..PdK]; there are K dwells. For example, for a 2 dwell
<br />% structure with 95% acquisition probability in the first dwell
<br />% and 94% acquisition probability in the second dwell, Pd=[0.95
<br />% 0.94];
<br />% Pfa_H0 = vector of false alarm probabilities under the hypothesis
<br />% that we are in an incorrect window; For example, for a 2 dwell
<br />% structure with 1% false alarm probability in the first dwell
<br />% and 0.5% acquisition probability in the second dwell in the incorrect windows, Pfa_H0=[0.01
<br />% 0.005];
<br />% Pfa_H1 = vector of false alarm probabilities under the hypothesis
<br />% that we are in a correct window (typically, at high
<br />% CNRs, this is zero, i.e: Pfa_H1=[0 0] for a 2-dwell acquisition)
<br />%tau_d = vector of dwell times [in seconds], i.e. the time to take a decision,
<br />% usually equal to the integration times in each dwell. For
<br />% example, for a 2-dwell approach, if we use 2 ms coherent
<br />% integration time and 1 block non-coherent integration in the first dwell and a 4ms coherent
<br />% integration time and 2 blocks non-coherent integration in the
<br />% second dwell , tau_d=[2 8]*1e-3 [seconds];
<br />%Kpenalty = penatlty factor for the last dwell, associated with a
<br />% false alarm [in seconds]. This is a scalar value, some example is
<br />% Kpenalty=1e6;
<br />%Nr_states= the number of windows(states) to cover the whole
<br />% time-frequency uncertainty; if Nr_states==1 => fully parallel;
<br />% otherwise (Nr_states >1), we have hybrid acquisition. For
<br />% example, if we use a window size of 2046 half-chips and 1 frequency bin and we want to cover a
<br />% full uncertainty space of 9 frequency bins and 4098 chips code
<br />% length, Nr_states=9*4=36.
<br />%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
<br />%OUTPUT
<br />%Tacq = mean acquisition time in seconds</p>Elena L.http://www.mathworks.com/matlabcentral/fileexchange/authors/537148MATLAB 8.4 (R2014b)MATLABfalsetag:www.mathworks.com,2005:FileInfo/488032014-12-19T13:59:36Z2014-12-19T13:59:36ZA study on the biodynamic models of seated human (4-DOF Model by Wan and Schimmels, 1995)body vibration<p>The lumped-parameter models from literature have been analyzed and validated.</p>Olexander Zhytenkohttp://www.mathworks.com/matlabcentral/fileexchange/authors/216465MATLAB 7.14 (R2012a)falsetag:www.mathworks.com,2005:FileInfo/488022014-12-19T13:47:48Z2014-12-19T13:47:48ZA study on the biodynamic models of seated human (2-DOF Models by Allen, 1978)body vibration under low-frequency excitation in seated posture<p>The lumped-parameter models from literature have been analyzed and validated.</p>Olexander Zhytenkohttp://www.mathworks.com/matlabcentral/fileexchange/authors/216465MATLAB 7.14 (R2012a)falsetag:www.mathworks.com,2005:FileInfo/488012014-12-19T13:37:49Z2014-12-19T13:37:49ZBiodynamics model of seated human (2-DOF Models by Muksian and Nash, 1976)A study on the biodynamic models of seated human.<p>A complete study on lumped-parameter models for seated human subjects without backrest support under vertical vibration excitation has been carried out.</p>Olexander Zhytenkohttp://www.mathworks.com/matlabcentral/fileexchange/authors/216465MATLAB 7.14 (R2012a)falsetag:www.mathworks.com,2005:FileInfo/488002014-12-19T13:22:24Z2014-12-19T13:22:24ZModel of seated human (1-DOF Models Coermann, 1962)The simulations of the lumped-parameter model in this study for seated human.<p>Biodynamics of seated human subjects has been a topic of interest over the years, and a number of mathematical models have been established.
<br /> While much research has been performed on building up speciļ¬c biodynamic models based on certain experimental data under prescribed testing conditions, a thorough investigation of mathematical human models in seated posture has not yet received the same level of attention.</p>Olexander Zhytenkohttp://www.mathworks.com/matlabcentral/fileexchange/authors/216465MATLAB 7.14 (R2012a)falsetag:www.mathworks.com,2005:FileInfo/487992014-12-19T10:56:52Z2014-12-19T11:00:03ZpowersmoothSmooth noisy time-series faithfully, without distorting lower-order time-derivatives<p>Smoothing noisy time-series using ordinary "smooth.m" may cause artifacts, especially if one wants to estimate time-derivatives of the underlying noisy-free dynamics. The function "powersmooth.m" solves this problem, providing a smoothed time-series with faithful estimates of the first n time-derivatives of the noise-free dynamics. The function uses quadratic programming to simultaneously minimize (i) the residuals between the original, noisy time-series and the smoothed curve, and (ii) the (n+1)-th time-derivative of the smoothed curve. The user has to specify the noisy time-series (vec), the desired order n (order), and a regularization weight (weight).</p>Benjamin Friedrichhttp://www.mathworks.com/matlabcentral/fileexchange/authors/334378MATLAB 7.11 (R2010b)falsetag:www.mathworks.com,2005:FileInfo/487982014-12-19T10:50:36Z2014-12-19T10:50:36ZCSV reader supporting stringsThis function provides you with the data contained in a CSV file, column names and a term dictionary<p>This function reads the data contain in a .csv file and transform it for its use. The function returns the next values:
<br /> data: contains the readed data. The columns that contains nominal values are transformed into numerical ones.
<br /> column_names: if the file contains a header, this variable saves the name of each column(*).
<br /> string_conversions: this variable contains the nominal values of the columns that have been transformed. If the column has numerical value, it contains {}. Otherwise, the nominal values are saved in column position.
<br /> (*)NOTE: In case of a entire nominal dataset with no header, the first example can be confused with header.</p>
<p> Example: the file contains the following data:
<br /> A,B,C,D
<br /> 1,2,Sun,YES
<br /> 3,1,Rain,YES
<br /> 3,5,Sun,NO
<br />
<br /> Then, the function returns:
<br /> - data = [ 1 2 2 2
<br /> 3 1 1 2
<br /> 3 5 2 1]
<br /> - column_names = {'A','B','C', 'D'}
<br /> - string_conversions = {{}
<br /> {}
<br /> {'Rain' 'Sun'}
<br /> {'NO' 'YES'}}</p>Pedrohttp://www.mathworks.com/matlabcentral/fileexchange/authors/537088MATLAB 8.4 (R2014b)falsetag:www.mathworks.com,2005:FileInfo/487972014-12-19T08:47:33Z2014-12-19T08:47:33ZGuide To ObjectTool for converting GUIDE UIs to Object Oriented Matlab<p>Convert your GUIDE GUIs to Matlab objects.
<br />Usage Instructions
<br />==================</p>
<p>1. Export the GUIDE UI to a Matlab file
<br />2. Run GuideToObject('guideFile_export.m', 'output.Class', debugFlag)
<br /> Parameters:
<br /> guideFile - name of the exported UI file
<br /> outputClass - name (including namespace) of class to create
<br /> debugFlag - true to print callback names in base class
<br />3. GuideToObject creates two files on the first run, a base class
<br /> (output.ClassBase) and a template implementation class
<br /> (output.Class). Subsequent runs only update the base class, leaving
<br /> any existing implementation untouched.
<br />4. Implement required functionality in the implementation class,
<br /> callbacks are raised in response to user actions in the UI
<br />5. Any updates to the GUIDE file can be incorporated by running
<br /> outputClass.Recreate()</p>Neil Hopcrofthttp://www.mathworks.com/matlabcentral/fileexchange/authors/535728MATLAB 8.0 (R2012b)false