Tag Archives: matlab
Hi, is there a matlab function to read .grib2 files (these are files similar to netcdf but Matlab nclibraries cant seem to be able to read them) thank you
Hi,
I need to read .grib2 files fast.
I m currently using a java toolbox (njvarget) to read them but it takes a lot of time as the files are many.
Is there a way to read them faster (changing function or reformatting them?)? thank youHi,
I need to read .grib2 files fast.
I m currently using a java toolbox (njvarget) to read them but it takes a lot of time as the files are many.
Is there a way to read them faster (changing function or reformatting them?)? thank you Hi,
I need to read .grib2 files fast.
I m currently using a java toolbox (njvarget) to read them but it takes a lot of time as the files are many.
Is there a way to read them faster (changing function or reformatting them?)? thank you .grib, grib2 MATLAB Answers — New Questions
Kramers Kronig versus Hilbert
Dear Matlab community,
A quick question about Kramers-Kronig.
I am aware that KK is used extensively in optics, but here the application is slightly different. I measure a complex impedance over a frequency range. Now I’d like to run the real part as well as the imaginary part of that impedance through KK and compare the calculate the error (residuals).
This is done to see if the measurement was in equilibrium and the values measured are sensible. There are some parts of code out there that I’ve tried but they all seem to fail at some point. I found that there is the built-in function ‘hilbert’ that might just do the trick with some adjustment, but it is exactly these adjustments, that I do not know.
As the original data I have an array with col1, col2, col3 being frequency, real part of the impedance, and imaginary part of the impedance, respectively.
Here is the KK result as plotted from a commercially available software …
When running my code I get:
blue: KK
red: original data
I tend to believe that the commercial SW is working properly 🙂
If that is true, then the overall shape of my results looks alright, but there seems to be a significant shift in the x-coordinate… (to lower values)
The two pieces of code are shown below.
Input values for the calculating the imKK value are:
the real part of the impedance, re
the angular frequency, omega (which is: freq. * 2 * pi())
Input values for the calculating the reKK value are:
the NEGATIVE imaginary part of the impedance, im (as over most of the frequency range ImZ<0)
the angular frequency, omega (which is: freq. * 2 * pi())
The imKK calc is done like this:
++++++++++++++++++++++++++++
function imkk = kkre2im(re,omega)
% Perform Kramers-Kronig transform of real data re and
% angular frequencies omega to get imaginary parts. Handle
% division by zero by letting integrand be the mean
% of the adjoining values.
imkk = zeros(size(re)); % set up vector for reconstructed imag parts
N = length(re); % number of data points
warning off Matlab:divideByZero % avoid warning, since division
% for each data point, do the transform
for k = 1:N;
rek = re(k);
omegak = omega(k);
% compute the integrand
inte = (re – rek)./(omega.^2 – omegak.^2);
% handle division by zero by taking mean and end-points
% by setting equal to adjoining point
switch k
case 1
inte(k) = inte(2);
case N
inte(k) = inte(N-1);
otherwise
inte(k) = ( inte(k-1) + inte(k+1) )/2;
end
% integrate
%intecur = integrate(omega,inte);
intecur = integrate(log10(omega),inte.*omega*log(10));
% scale by constant to get final imaginary parts
imkk(k) = 2*omegak/pi*intecur;
end
warning on Matlab:divideByZero
++++++++++++++++++++++++++++
++++++++++++++++++++++++++++
The reKK calc is done like this:
++++++++++++++++++++++++++++
function rekk = kkim2re(im,omega)
% Perform Kramers-Kronig transform of imaginary data im and
% angular frequencies omega to get real parts. Handle
% division by zero by letting integrand be the mean
% of the adjoining values.
rekk = zeros(size(im)); % set up vector for reconstructed real parts
N = length(im); % number of data points
warning off Matlab:divideByZero % avoid warning, since division
% for each data point, do the transform
for k = 1:N;
imk = im(k);
omegak = omega(k);
% compute the integrand
inte = (omega.*im – omegak*imk)./(omega.^2 – omegak.^2);
% handle division by zero by taking mean and end-points
% by setting equal to adjoining point
switch k
case 1
inte(k) = inte(2);
case N
inte(k) = inte(N-1);
otherwise
inte(k) = ( inte(k-1) + inte(k+1) )/2;
end
% integrate
intecur = integrate(log10(omega),inte.*omega*log(10));
% scale by constant to get final real parts
rekk(k) = -2/pi*intecur;
end
warning on Matlab:divideByZero
++++++++++++++++++++++++++++
++++++++++++++++++++++++++++
Any suggestions as to why the results are different and (in case you also believe the results shown by the commercial software) what am I doing wrong?
The original data is like this (Again, with col1, col2, col3 being frequency, real part of the impedance, and imaginary part of the impedance, respectively):
10078.1250000000 0.00130600000000000 0.00293600000000000
8296.87500000000 0.00128000000000000 0.00241600000000000
6755.51464800000 0.00125700000000000 0.00196100000000000
5671.87500000000 0.00124500000000000 0.00163200000000000
4641.54394500000 0.00123400000000000 0.00131700000000000
3814.33813500000 0.00121800000000000 0.00106100000000000
3170.95581100000 0.00120900000000000 0.000850000000000000
2619.48535200000 0.00120900000000000 0.000663000000000000
2159.92651400000 0.00118400000000000 0.000476000000000000
1792.27941900000 0.00123500000000000 0.000333000000000000
1459.31604000000 0.00125700000000000 0.000168000000000000
1216.94714400000 0.00128500000000000 9.50000000000000e-05
998.263855000000 0.00132900000000000 -1.70000000000000e-05
824.652771000000 0.00133600000000000 -9.50000000000000e-05
676.081726000000 0.00138800000000000 -0.000189000000000000
564.236084000000 0.00142800000000000 -0.000267000000000000
460.379456000000 0.00147600000000000 -0.000349000000000000
383.522705000000 0.00152400000000000 -0.000422000000000000
315.504822000000 0.00158000000000000 -0.000500000000000000
260.416656000000 0.00164300000000000 -0.000581000000000000
217.013885000000 0.00171000000000000 -0.000662000000000000
177.556824000000 0.00179700000000000 -0.000755000000000000
146.484375000000 0.00190100000000000 -0.000845000000000000
121.228455000000 0.00201500000000000 -0.000938000000000000
100.446426000000 0.00214900000000000 -0.00102400000000000
82.7205890000000 0.00230000000000000 -0.00110900000000000
68.2645650000000 0.00247300000000000 -0.00116800000000000
56.2500000000000 0.00265000000000000 -0.00122600000000000
46.8750000000000 0.00282800000000000 -0.00126100000000000
38.4221310000000 0.00301600000000000 -0.00128000000000000
31.6722980000000 0.00320600000000000 -0.00130800000000000
26.0416680000000 0.00339900000000000 -0.00132300000000000
21.7013890000000 0.00359200000000000 -0.00131800000000000
17.7556820000000 0.00376700000000000 -0.00129100000000000
14.7405660000000 0.00393000000000000 -0.00127200000000000
12.2070310000000 0.00409400000000000 -0.00122200000000000
9.93114500000000 0.00426600000000000 -0.00116900000000000
8.22368400000000 0.00440700000000000 -0.00110700000000000
6.85307000000000 0.00446700000000000 -0.00105100000000000
5.63401500000000 0.00462400000000000 -0.00100000000000000
4.68750000000000 0.00464700000000000 -0.000957000000000000
3.82965700000000 0.00471500000000000 -0.00101000000000000
3.15869300000000 0.00470100000000000 -0.00113200000000000
2.60127600000000 0.00484600000000000 -0.00126300000000000
2.14629100000000 0.00507800000000000 -0.00141500000000000
1.76753400000000 0.00525200000000000 -0.00152900000000000
1.45393900000000 0.00559300000000000 -0.00157000000000000
1.20936500000000 0.00589200000000000 -0.00159500000000000
0.999041000000000 0.00619300000000000 -0.00156500000000000
0.820641000000000 0.00652000000000000 -0.00129400000000000
0.675822000000000 0.00654400000000000 -0.00134600000000000
0.564759000000000 0.00680100000000000 -0.00107200000000000
0.468750000000000 0.00689900000000000 -0.000870000000000000
0.384221000000000 0.00717100000000000 -0.000851000000000000
0.316723000000000 0.00713500000000000 -0.000612000000000000
0.261872000000000 0.00715500000000000 -0.000508000000000000
0.216014000000000 0.00713200000000000 -0.000361000000000000
0.178232000000000 0.00721300000000000 -0.000297000000000000
0.146944000000000 0.00732800000000000 -0.000258000000000000
0.121438000000000 0.00730000000000000 -0.000120000000000000
0.100160000000000 0.00726600000000000 -7.70000000000000e-05Dear Matlab community,
A quick question about Kramers-Kronig.
I am aware that KK is used extensively in optics, but here the application is slightly different. I measure a complex impedance over a frequency range. Now I’d like to run the real part as well as the imaginary part of that impedance through KK and compare the calculate the error (residuals).
This is done to see if the measurement was in equilibrium and the values measured are sensible. There are some parts of code out there that I’ve tried but they all seem to fail at some point. I found that there is the built-in function ‘hilbert’ that might just do the trick with some adjustment, but it is exactly these adjustments, that I do not know.
As the original data I have an array with col1, col2, col3 being frequency, real part of the impedance, and imaginary part of the impedance, respectively.
Here is the KK result as plotted from a commercially available software …
When running my code I get:
blue: KK
red: original data
I tend to believe that the commercial SW is working properly 🙂
If that is true, then the overall shape of my results looks alright, but there seems to be a significant shift in the x-coordinate… (to lower values)
The two pieces of code are shown below.
Input values for the calculating the imKK value are:
the real part of the impedance, re
the angular frequency, omega (which is: freq. * 2 * pi())
Input values for the calculating the reKK value are:
the NEGATIVE imaginary part of the impedance, im (as over most of the frequency range ImZ<0)
the angular frequency, omega (which is: freq. * 2 * pi())
The imKK calc is done like this:
++++++++++++++++++++++++++++
function imkk = kkre2im(re,omega)
% Perform Kramers-Kronig transform of real data re and
% angular frequencies omega to get imaginary parts. Handle
% division by zero by letting integrand be the mean
% of the adjoining values.
imkk = zeros(size(re)); % set up vector for reconstructed imag parts
N = length(re); % number of data points
warning off Matlab:divideByZero % avoid warning, since division
% for each data point, do the transform
for k = 1:N;
rek = re(k);
omegak = omega(k);
% compute the integrand
inte = (re – rek)./(omega.^2 – omegak.^2);
% handle division by zero by taking mean and end-points
% by setting equal to adjoining point
switch k
case 1
inte(k) = inte(2);
case N
inte(k) = inte(N-1);
otherwise
inte(k) = ( inte(k-1) + inte(k+1) )/2;
end
% integrate
%intecur = integrate(omega,inte);
intecur = integrate(log10(omega),inte.*omega*log(10));
% scale by constant to get final imaginary parts
imkk(k) = 2*omegak/pi*intecur;
end
warning on Matlab:divideByZero
++++++++++++++++++++++++++++
++++++++++++++++++++++++++++
The reKK calc is done like this:
++++++++++++++++++++++++++++
function rekk = kkim2re(im,omega)
% Perform Kramers-Kronig transform of imaginary data im and
% angular frequencies omega to get real parts. Handle
% division by zero by letting integrand be the mean
% of the adjoining values.
rekk = zeros(size(im)); % set up vector for reconstructed real parts
N = length(im); % number of data points
warning off Matlab:divideByZero % avoid warning, since division
% for each data point, do the transform
for k = 1:N;
imk = im(k);
omegak = omega(k);
% compute the integrand
inte = (omega.*im – omegak*imk)./(omega.^2 – omegak.^2);
% handle division by zero by taking mean and end-points
% by setting equal to adjoining point
switch k
case 1
inte(k) = inte(2);
case N
inte(k) = inte(N-1);
otherwise
inte(k) = ( inte(k-1) + inte(k+1) )/2;
end
% integrate
intecur = integrate(log10(omega),inte.*omega*log(10));
% scale by constant to get final real parts
rekk(k) = -2/pi*intecur;
end
warning on Matlab:divideByZero
++++++++++++++++++++++++++++
++++++++++++++++++++++++++++
Any suggestions as to why the results are different and (in case you also believe the results shown by the commercial software) what am I doing wrong?
The original data is like this (Again, with col1, col2, col3 being frequency, real part of the impedance, and imaginary part of the impedance, respectively):
10078.1250000000 0.00130600000000000 0.00293600000000000
8296.87500000000 0.00128000000000000 0.00241600000000000
6755.51464800000 0.00125700000000000 0.00196100000000000
5671.87500000000 0.00124500000000000 0.00163200000000000
4641.54394500000 0.00123400000000000 0.00131700000000000
3814.33813500000 0.00121800000000000 0.00106100000000000
3170.95581100000 0.00120900000000000 0.000850000000000000
2619.48535200000 0.00120900000000000 0.000663000000000000
2159.92651400000 0.00118400000000000 0.000476000000000000
1792.27941900000 0.00123500000000000 0.000333000000000000
1459.31604000000 0.00125700000000000 0.000168000000000000
1216.94714400000 0.00128500000000000 9.50000000000000e-05
998.263855000000 0.00132900000000000 -1.70000000000000e-05
824.652771000000 0.00133600000000000 -9.50000000000000e-05
676.081726000000 0.00138800000000000 -0.000189000000000000
564.236084000000 0.00142800000000000 -0.000267000000000000
460.379456000000 0.00147600000000000 -0.000349000000000000
383.522705000000 0.00152400000000000 -0.000422000000000000
315.504822000000 0.00158000000000000 -0.000500000000000000
260.416656000000 0.00164300000000000 -0.000581000000000000
217.013885000000 0.00171000000000000 -0.000662000000000000
177.556824000000 0.00179700000000000 -0.000755000000000000
146.484375000000 0.00190100000000000 -0.000845000000000000
121.228455000000 0.00201500000000000 -0.000938000000000000
100.446426000000 0.00214900000000000 -0.00102400000000000
82.7205890000000 0.00230000000000000 -0.00110900000000000
68.2645650000000 0.00247300000000000 -0.00116800000000000
56.2500000000000 0.00265000000000000 -0.00122600000000000
46.8750000000000 0.00282800000000000 -0.00126100000000000
38.4221310000000 0.00301600000000000 -0.00128000000000000
31.6722980000000 0.00320600000000000 -0.00130800000000000
26.0416680000000 0.00339900000000000 -0.00132300000000000
21.7013890000000 0.00359200000000000 -0.00131800000000000
17.7556820000000 0.00376700000000000 -0.00129100000000000
14.7405660000000 0.00393000000000000 -0.00127200000000000
12.2070310000000 0.00409400000000000 -0.00122200000000000
9.93114500000000 0.00426600000000000 -0.00116900000000000
8.22368400000000 0.00440700000000000 -0.00110700000000000
6.85307000000000 0.00446700000000000 -0.00105100000000000
5.63401500000000 0.00462400000000000 -0.00100000000000000
4.68750000000000 0.00464700000000000 -0.000957000000000000
3.82965700000000 0.00471500000000000 -0.00101000000000000
3.15869300000000 0.00470100000000000 -0.00113200000000000
2.60127600000000 0.00484600000000000 -0.00126300000000000
2.14629100000000 0.00507800000000000 -0.00141500000000000
1.76753400000000 0.00525200000000000 -0.00152900000000000
1.45393900000000 0.00559300000000000 -0.00157000000000000
1.20936500000000 0.00589200000000000 -0.00159500000000000
0.999041000000000 0.00619300000000000 -0.00156500000000000
0.820641000000000 0.00652000000000000 -0.00129400000000000
0.675822000000000 0.00654400000000000 -0.00134600000000000
0.564759000000000 0.00680100000000000 -0.00107200000000000
0.468750000000000 0.00689900000000000 -0.000870000000000000
0.384221000000000 0.00717100000000000 -0.000851000000000000
0.316723000000000 0.00713500000000000 -0.000612000000000000
0.261872000000000 0.00715500000000000 -0.000508000000000000
0.216014000000000 0.00713200000000000 -0.000361000000000000
0.178232000000000 0.00721300000000000 -0.000297000000000000
0.146944000000000 0.00732800000000000 -0.000258000000000000
0.121438000000000 0.00730000000000000 -0.000120000000000000
0.100160000000000 0.00726600000000000 -7.70000000000000e-05 Dear Matlab community,
A quick question about Kramers-Kronig.
I am aware that KK is used extensively in optics, but here the application is slightly different. I measure a complex impedance over a frequency range. Now I’d like to run the real part as well as the imaginary part of that impedance through KK and compare the calculate the error (residuals).
This is done to see if the measurement was in equilibrium and the values measured are sensible. There are some parts of code out there that I’ve tried but they all seem to fail at some point. I found that there is the built-in function ‘hilbert’ that might just do the trick with some adjustment, but it is exactly these adjustments, that I do not know.
As the original data I have an array with col1, col2, col3 being frequency, real part of the impedance, and imaginary part of the impedance, respectively.
Here is the KK result as plotted from a commercially available software …
When running my code I get:
blue: KK
red: original data
I tend to believe that the commercial SW is working properly 🙂
If that is true, then the overall shape of my results looks alright, but there seems to be a significant shift in the x-coordinate… (to lower values)
The two pieces of code are shown below.
Input values for the calculating the imKK value are:
the real part of the impedance, re
the angular frequency, omega (which is: freq. * 2 * pi())
Input values for the calculating the reKK value are:
the NEGATIVE imaginary part of the impedance, im (as over most of the frequency range ImZ<0)
the angular frequency, omega (which is: freq. * 2 * pi())
The imKK calc is done like this:
++++++++++++++++++++++++++++
function imkk = kkre2im(re,omega)
% Perform Kramers-Kronig transform of real data re and
% angular frequencies omega to get imaginary parts. Handle
% division by zero by letting integrand be the mean
% of the adjoining values.
imkk = zeros(size(re)); % set up vector for reconstructed imag parts
N = length(re); % number of data points
warning off Matlab:divideByZero % avoid warning, since division
% for each data point, do the transform
for k = 1:N;
rek = re(k);
omegak = omega(k);
% compute the integrand
inte = (re – rek)./(omega.^2 – omegak.^2);
% handle division by zero by taking mean and end-points
% by setting equal to adjoining point
switch k
case 1
inte(k) = inte(2);
case N
inte(k) = inte(N-1);
otherwise
inte(k) = ( inte(k-1) + inte(k+1) )/2;
end
% integrate
%intecur = integrate(omega,inte);
intecur = integrate(log10(omega),inte.*omega*log(10));
% scale by constant to get final imaginary parts
imkk(k) = 2*omegak/pi*intecur;
end
warning on Matlab:divideByZero
++++++++++++++++++++++++++++
++++++++++++++++++++++++++++
The reKK calc is done like this:
++++++++++++++++++++++++++++
function rekk = kkim2re(im,omega)
% Perform Kramers-Kronig transform of imaginary data im and
% angular frequencies omega to get real parts. Handle
% division by zero by letting integrand be the mean
% of the adjoining values.
rekk = zeros(size(im)); % set up vector for reconstructed real parts
N = length(im); % number of data points
warning off Matlab:divideByZero % avoid warning, since division
% for each data point, do the transform
for k = 1:N;
imk = im(k);
omegak = omega(k);
% compute the integrand
inte = (omega.*im – omegak*imk)./(omega.^2 – omegak.^2);
% handle division by zero by taking mean and end-points
% by setting equal to adjoining point
switch k
case 1
inte(k) = inte(2);
case N
inte(k) = inte(N-1);
otherwise
inte(k) = ( inte(k-1) + inte(k+1) )/2;
end
% integrate
intecur = integrate(log10(omega),inte.*omega*log(10));
% scale by constant to get final real parts
rekk(k) = -2/pi*intecur;
end
warning on Matlab:divideByZero
++++++++++++++++++++++++++++
++++++++++++++++++++++++++++
Any suggestions as to why the results are different and (in case you also believe the results shown by the commercial software) what am I doing wrong?
The original data is like this (Again, with col1, col2, col3 being frequency, real part of the impedance, and imaginary part of the impedance, respectively):
10078.1250000000 0.00130600000000000 0.00293600000000000
8296.87500000000 0.00128000000000000 0.00241600000000000
6755.51464800000 0.00125700000000000 0.00196100000000000
5671.87500000000 0.00124500000000000 0.00163200000000000
4641.54394500000 0.00123400000000000 0.00131700000000000
3814.33813500000 0.00121800000000000 0.00106100000000000
3170.95581100000 0.00120900000000000 0.000850000000000000
2619.48535200000 0.00120900000000000 0.000663000000000000
2159.92651400000 0.00118400000000000 0.000476000000000000
1792.27941900000 0.00123500000000000 0.000333000000000000
1459.31604000000 0.00125700000000000 0.000168000000000000
1216.94714400000 0.00128500000000000 9.50000000000000e-05
998.263855000000 0.00132900000000000 -1.70000000000000e-05
824.652771000000 0.00133600000000000 -9.50000000000000e-05
676.081726000000 0.00138800000000000 -0.000189000000000000
564.236084000000 0.00142800000000000 -0.000267000000000000
460.379456000000 0.00147600000000000 -0.000349000000000000
383.522705000000 0.00152400000000000 -0.000422000000000000
315.504822000000 0.00158000000000000 -0.000500000000000000
260.416656000000 0.00164300000000000 -0.000581000000000000
217.013885000000 0.00171000000000000 -0.000662000000000000
177.556824000000 0.00179700000000000 -0.000755000000000000
146.484375000000 0.00190100000000000 -0.000845000000000000
121.228455000000 0.00201500000000000 -0.000938000000000000
100.446426000000 0.00214900000000000 -0.00102400000000000
82.7205890000000 0.00230000000000000 -0.00110900000000000
68.2645650000000 0.00247300000000000 -0.00116800000000000
56.2500000000000 0.00265000000000000 -0.00122600000000000
46.8750000000000 0.00282800000000000 -0.00126100000000000
38.4221310000000 0.00301600000000000 -0.00128000000000000
31.6722980000000 0.00320600000000000 -0.00130800000000000
26.0416680000000 0.00339900000000000 -0.00132300000000000
21.7013890000000 0.00359200000000000 -0.00131800000000000
17.7556820000000 0.00376700000000000 -0.00129100000000000
14.7405660000000 0.00393000000000000 -0.00127200000000000
12.2070310000000 0.00409400000000000 -0.00122200000000000
9.93114500000000 0.00426600000000000 -0.00116900000000000
8.22368400000000 0.00440700000000000 -0.00110700000000000
6.85307000000000 0.00446700000000000 -0.00105100000000000
5.63401500000000 0.00462400000000000 -0.00100000000000000
4.68750000000000 0.00464700000000000 -0.000957000000000000
3.82965700000000 0.00471500000000000 -0.00101000000000000
3.15869300000000 0.00470100000000000 -0.00113200000000000
2.60127600000000 0.00484600000000000 -0.00126300000000000
2.14629100000000 0.00507800000000000 -0.00141500000000000
1.76753400000000 0.00525200000000000 -0.00152900000000000
1.45393900000000 0.00559300000000000 -0.00157000000000000
1.20936500000000 0.00589200000000000 -0.00159500000000000
0.999041000000000 0.00619300000000000 -0.00156500000000000
0.820641000000000 0.00652000000000000 -0.00129400000000000
0.675822000000000 0.00654400000000000 -0.00134600000000000
0.564759000000000 0.00680100000000000 -0.00107200000000000
0.468750000000000 0.00689900000000000 -0.000870000000000000
0.384221000000000 0.00717100000000000 -0.000851000000000000
0.316723000000000 0.00713500000000000 -0.000612000000000000
0.261872000000000 0.00715500000000000 -0.000508000000000000
0.216014000000000 0.00713200000000000 -0.000361000000000000
0.178232000000000 0.00721300000000000 -0.000297000000000000
0.146944000000000 0.00732800000000000 -0.000258000000000000
0.121438000000000 0.00730000000000000 -0.000120000000000000
0.100160000000000 0.00726600000000000 -7.70000000000000e-05 hilbert, kramers-kronig, kk, impedance spectroscopy MATLAB Answers — New Questions
if i change convolution encoder to 3/4 or 7/8 code rate can u tell BER shd improve wid comp to1/2 ?
i have implemented MODEM which has Convolution encoder. when i change 1/2 to 3/4 or 7/8 . My BER for 3/4 or 7/8 comes out to be zero …..with AWGN as 1 db for Eb/No .is it right ?plese give ur suggestioni have implemented MODEM which has Convolution encoder. when i change 1/2 to 3/4 or 7/8 . My BER for 3/4 or 7/8 comes out to be zero …..with AWGN as 1 db for Eb/No .is it right ?plese give ur suggestion i have implemented MODEM which has Convolution encoder. when i change 1/2 to 3/4 or 7/8 . My BER for 3/4 or 7/8 comes out to be zero …..with AWGN as 1 db for Eb/No .is it right ?plese give ur suggestion communication, modem, convolution encoder MATLAB Answers — New Questions
sqlread specific columns/variables from a MySQL table
I want to import to Matlab one or a couple specific columns from a table that is stored in MySQL database. sqlread(conn, tablename) would import the whole table. I tried playing around with opts. it didn’t work. Is there any way to make this work? Or should I try using sql query script? Thanks!
opts = databaseImportOptions(conn, tablename);
opts = setoptions(opts, ‘SelectedVaraibleNames’, {‘wanted_column’}); % error messageI want to import to Matlab one or a couple specific columns from a table that is stored in MySQL database. sqlread(conn, tablename) would import the whole table. I tried playing around with opts. it didn’t work. Is there any way to make this work? Or should I try using sql query script? Thanks!
opts = databaseImportOptions(conn, tablename);
opts = setoptions(opts, ‘SelectedVaraibleNames’, {‘wanted_column’}); % error message I want to import to Matlab one or a couple specific columns from a table that is stored in MySQL database. sqlread(conn, tablename) would import the whole table. I tried playing around with opts. it didn’t work. Is there any way to make this work? Or should I try using sql query script? Thanks!
opts = databaseImportOptions(conn, tablename);
opts = setoptions(opts, ‘SelectedVaraibleNames’, {‘wanted_column’}); % error message sqlread MATLAB Answers — New Questions
draw more date in the axis
I would like to report more dates: for example every month or every week
I use plot to draw it..(
plot(Ax_Sys,XDates(locs(:,i)),sig,’DisplayName’,STR_name{i},’Color’,SumCTR_color(i,:),’LineWidth’,1);)I would like to report more dates: for example every month or every week
I use plot to draw it..(
plot(Ax_Sys,XDates(locs(:,i)),sig,’DisplayName’,STR_name{i},’Color’,SumCTR_color(i,:),’LineWidth’,1);) I would like to report more dates: for example every month or every week
I use plot to draw it..(
plot(Ax_Sys,XDates(locs(:,i)),sig,’DisplayName’,STR_name{i},’Color’,SumCTR_color(i,:),’LineWidth’,1);) draw more date in the axis MATLAB Answers — New Questions
Does matlab supports imgcodes of openCV
Iam trying to use cv2.imwrite in jpeg format for which imagecodes of opencv is required. Ultimately want to use the C++ code using openCV for convertion to matlab readable format using mexOpenCV function.
I have installed "computer vision toolbox interface for OpenCV in Matlab". But the conversion throws error because when i check the precences of imgcodes.hpp inside opencv2 folder its not present.
The automatically generated file opencv_modules.hpp also does not list down imgcodecsIam trying to use cv2.imwrite in jpeg format for which imagecodes of opencv is required. Ultimately want to use the C++ code using openCV for convertion to matlab readable format using mexOpenCV function.
I have installed "computer vision toolbox interface for OpenCV in Matlab". But the conversion throws error because when i check the precences of imgcodes.hpp inside opencv2 folder its not present.
The automatically generated file opencv_modules.hpp also does not list down imgcodecs Iam trying to use cv2.imwrite in jpeg format for which imagecodes of opencv is required. Ultimately want to use the C++ code using openCV for convertion to matlab readable format using mexOpenCV function.
I have installed "computer vision toolbox interface for OpenCV in Matlab". But the conversion throws error because when i check the precences of imgcodes.hpp inside opencv2 folder its not present.
The automatically generated file opencv_modules.hpp also does not list down imgcodecs openccv MATLAB Answers — New Questions
Open end winding permanent magnet synchronous motor Simulink data or library?
Hello
I would like to make open end winding PMSM model with dual inverter.
But, open end winding PMSM doesn’t provide basic library in Matlab 2020b.
Is there someone who can share library or simulink data for me?
It would be really appreciated if you can give me your idea.
ThanksHello
I would like to make open end winding PMSM model with dual inverter.
But, open end winding PMSM doesn’t provide basic library in Matlab 2020b.
Is there someone who can share library or simulink data for me?
It would be really appreciated if you can give me your idea.
Thanks Hello
I would like to make open end winding PMSM model with dual inverter.
But, open end winding PMSM doesn’t provide basic library in Matlab 2020b.
Is there someone who can share library or simulink data for me?
It would be really appreciated if you can give me your idea.
Thanks oew pmsm MATLAB Answers — New Questions
Calculation with three dimensional matrices
I want to calculate a vector , with each element defined as follows
, where is the set of dimensional vector, and is also the set of dimensional vector, is a constant. The superscript is denoted as the transpose operation.
In my matlab code, is stored as a three dimensional matrix , its dimension is (where ).
is also stored as a three dimensional matrix , its dimension is (where ). is a scalar.
I know i can use multiple for loop to calculate this vector , but it is too inefficient. Is there any some fast way to calculate it, maybe use three-dimensional matrix calculation method?I want to calculate a vector , with each element defined as follows
, where is the set of dimensional vector, and is also the set of dimensional vector, is a constant. The superscript is denoted as the transpose operation.
In my matlab code, is stored as a three dimensional matrix , its dimension is (where ).
is also stored as a three dimensional matrix , its dimension is (where ). is a scalar.
I know i can use multiple for loop to calculate this vector , but it is too inefficient. Is there any some fast way to calculate it, maybe use three-dimensional matrix calculation method? I want to calculate a vector , with each element defined as follows
, where is the set of dimensional vector, and is also the set of dimensional vector, is a constant. The superscript is denoted as the transpose operation.
In my matlab code, is stored as a three dimensional matrix , its dimension is (where ).
is also stored as a three dimensional matrix , its dimension is (where ). is a scalar.
I know i can use multiple for loop to calculate this vector , but it is too inefficient. Is there any some fast way to calculate it, maybe use three-dimensional matrix calculation method? matrix manipulation MATLAB Answers — New Questions
machine learning and artificial intelligence
hello every one
i need help with my artificial intelligence Project .
i am a Power-Electronics student and i have a project in which i have to use Artificial intelligence But i have no idea about this issue. i want to learn about
artificial Inteligence and machine learning and also how to implement it in Matlab .
could you Please introduce me some sources Or tutorial which is usefull for me ??hello every one
i need help with my artificial intelligence Project .
i am a Power-Electronics student and i have a project in which i have to use Artificial intelligence But i have no idea about this issue. i want to learn about
artificial Inteligence and machine learning and also how to implement it in Matlab .
could you Please introduce me some sources Or tutorial which is usefull for me ?? hello every one
i need help with my artificial intelligence Project .
i am a Power-Electronics student and i have a project in which i have to use Artificial intelligence But i have no idea about this issue. i want to learn about
artificial Inteligence and machine learning and also how to implement it in Matlab .
could you Please introduce me some sources Or tutorial which is usefull for me ?? artificial intelligence-machine learning-neural network-matlab projects MATLAB Answers — New Questions
Does gather() clear memory
I am running in to memory limits on my GPUs. I know I can reset(gpuDevice) to clear all memory on the device, however, I would like to move arrays one at a time from GPU memory to memory and then clear the orginal GPU version. Does gather() also clear the GPU memory after copying/moving to memory?
If not, what would be the best way to achieve this?I am running in to memory limits on my GPUs. I know I can reset(gpuDevice) to clear all memory on the device, however, I would like to move arrays one at a time from GPU memory to memory and then clear the orginal GPU version. Does gather() also clear the GPU memory after copying/moving to memory?
If not, what would be the best way to achieve this? I am running in to memory limits on my GPUs. I know I can reset(gpuDevice) to clear all memory on the device, however, I would like to move arrays one at a time from GPU memory to memory and then clear the orginal GPU version. Does gather() also clear the GPU memory after copying/moving to memory?
If not, what would be the best way to achieve this? gather, gpu, memory MATLAB Answers — New Questions
regarding fake currency detection matlab code…it is showing “real” even if i upload fake image,please rectify my code
% Step 1: Load the Image
[FILENAME, PATHNAME] = uigetfile(‘*.jpg’, ‘Select an Image’);
if isequal(FILENAME, 0)
disp(‘User canceled the image selection.’);
return;
end
FilePath = strcat(PATHNAME, FILENAME);
disp(‘Selected Image Location:’);
disp(FilePath);
% Step 2: Read and Resize the Image
DataArray = imread(FilePath); % Read the image
DataArray = imresize(DataArray, [300, 650]); % Resize image to a standard size
figure, imshow(DataArray); % Display image
title(‘Original Image’);
% Step 3: Convert to Grayscale
Igray = rgb2gray(DataArray); % Convert to grayscale
figure, imshow(Igray); % Display grayscale image
title(‘Grayscale Image’);
% Step 4: Noise Removal using Median Filter
Igray = medfilt2(Igray); % Apply median filter to remove noise
figure, imshow(Igray);
title(‘Denoised Grayscale Image’);
% Step 5: Edge Detection (using Sobel operator)
edges = edge(Igray, ‘Sobel’); % Detect edges using Sobel method
figure, imshow(edges);
title(‘Edge Detection’);
% Step 6: Feature Extraction – Texture Features using GLCM
% Calculate the Gray Level Co-occurrence Matrix (GLCM)
glcm = graycomatrix(Igray, ‘Offset’, [0 1; -1 1; -1 0; -1 -1]);
stats = graycoprops(glcm, {‘Contrast’, ‘Correlation’, ‘Energy’, ‘Homogeneity’});
% Extract features from GLCM
Contrast = stats.Contrast;
Correlation = stats.Correlation;
Energy = stats.Energy;
Homogeneity = stats.Homogeneity;
% Step 7: Feature Extraction – Shape Features using Regionprops
props = regionprops(edges, ‘Area’, ‘Eccentricity’, ‘Solidity’);
Area = [props.Area]; % Extract Area
Eccentricity = [props.Eccentricity]; % Extract Eccentricity
Solidity = [props.Solidity]; % Extract Solidity
% Step 8: Create Feature Vector
Features = [mean(Contrast), mean(Correlation), mean(Energy), mean(Homogeneity), mean(Area), mean(Eccentricity), mean(Solidity)];
disp(‘Extracted Features:’);
disp(Features);
% Step 9: Prepare Training Data (for SVM)
% Example of real and fake currency training data (manually labeled)
% In real projects, you’d use a dataset with images of real and fake currency
Real_Data = [
% Example feature values for real currency
0.1, 0.9, 0.8, 0.7, 2500, 0.6, 0.95; % Example feature values for real currency
% Add more real currency data points
];
Fake_Data = [
% Example feature values for fake currency
0.5, 0.4, 0.2, 0.3, 2000, 0.8, 0.3; % Example feature values for fake currency
% Add more fake currency data points
];
% Step 10: Labels for Training Data
% 1 for real currency, 0 for fake currency
Real_Labels = ones(size(Real_Data, 1), 1);
Fake_Labels = zeros(size(Fake_Data, 1), 1);
% Combine real and fake data
Train_Data = [Real_Data; Fake_Data];
Train_Labels = [Real_Labels; Fake_Labels];
% Step 11: Train an SVM Classifier
svmModel = fitcsvm(Train_Data, Train_Labels, ‘KernelFunction’, ‘rbf’, ‘Standardize’, true);
% Step 12: Test the classifier with the current image features
[Label, Score] = predict(svmModel, Features); % Classify using the SVM model
% Step 13: Display the result
if Label == 1
msgbox(‘Currency Type: Real’);
else
msgbox(‘Currency Type: Fake’);
end
% Step 14: Display Final Image with Result
figure, imshow(DataArray);
if Label == 1
title(‘Detected: Real Currency’);
else
title(‘Detected: Fake Currency’);
end% Step 1: Load the Image
[FILENAME, PATHNAME] = uigetfile(‘*.jpg’, ‘Select an Image’);
if isequal(FILENAME, 0)
disp(‘User canceled the image selection.’);
return;
end
FilePath = strcat(PATHNAME, FILENAME);
disp(‘Selected Image Location:’);
disp(FilePath);
% Step 2: Read and Resize the Image
DataArray = imread(FilePath); % Read the image
DataArray = imresize(DataArray, [300, 650]); % Resize image to a standard size
figure, imshow(DataArray); % Display image
title(‘Original Image’);
% Step 3: Convert to Grayscale
Igray = rgb2gray(DataArray); % Convert to grayscale
figure, imshow(Igray); % Display grayscale image
title(‘Grayscale Image’);
% Step 4: Noise Removal using Median Filter
Igray = medfilt2(Igray); % Apply median filter to remove noise
figure, imshow(Igray);
title(‘Denoised Grayscale Image’);
% Step 5: Edge Detection (using Sobel operator)
edges = edge(Igray, ‘Sobel’); % Detect edges using Sobel method
figure, imshow(edges);
title(‘Edge Detection’);
% Step 6: Feature Extraction – Texture Features using GLCM
% Calculate the Gray Level Co-occurrence Matrix (GLCM)
glcm = graycomatrix(Igray, ‘Offset’, [0 1; -1 1; -1 0; -1 -1]);
stats = graycoprops(glcm, {‘Contrast’, ‘Correlation’, ‘Energy’, ‘Homogeneity’});
% Extract features from GLCM
Contrast = stats.Contrast;
Correlation = stats.Correlation;
Energy = stats.Energy;
Homogeneity = stats.Homogeneity;
% Step 7: Feature Extraction – Shape Features using Regionprops
props = regionprops(edges, ‘Area’, ‘Eccentricity’, ‘Solidity’);
Area = [props.Area]; % Extract Area
Eccentricity = [props.Eccentricity]; % Extract Eccentricity
Solidity = [props.Solidity]; % Extract Solidity
% Step 8: Create Feature Vector
Features = [mean(Contrast), mean(Correlation), mean(Energy), mean(Homogeneity), mean(Area), mean(Eccentricity), mean(Solidity)];
disp(‘Extracted Features:’);
disp(Features);
% Step 9: Prepare Training Data (for SVM)
% Example of real and fake currency training data (manually labeled)
% In real projects, you’d use a dataset with images of real and fake currency
Real_Data = [
% Example feature values for real currency
0.1, 0.9, 0.8, 0.7, 2500, 0.6, 0.95; % Example feature values for real currency
% Add more real currency data points
];
Fake_Data = [
% Example feature values for fake currency
0.5, 0.4, 0.2, 0.3, 2000, 0.8, 0.3; % Example feature values for fake currency
% Add more fake currency data points
];
% Step 10: Labels for Training Data
% 1 for real currency, 0 for fake currency
Real_Labels = ones(size(Real_Data, 1), 1);
Fake_Labels = zeros(size(Fake_Data, 1), 1);
% Combine real and fake data
Train_Data = [Real_Data; Fake_Data];
Train_Labels = [Real_Labels; Fake_Labels];
% Step 11: Train an SVM Classifier
svmModel = fitcsvm(Train_Data, Train_Labels, ‘KernelFunction’, ‘rbf’, ‘Standardize’, true);
% Step 12: Test the classifier with the current image features
[Label, Score] = predict(svmModel, Features); % Classify using the SVM model
% Step 13: Display the result
if Label == 1
msgbox(‘Currency Type: Real’);
else
msgbox(‘Currency Type: Fake’);
end
% Step 14: Display Final Image with Result
figure, imshow(DataArray);
if Label == 1
title(‘Detected: Real Currency’);
else
title(‘Detected: Fake Currency’);
end % Step 1: Load the Image
[FILENAME, PATHNAME] = uigetfile(‘*.jpg’, ‘Select an Image’);
if isequal(FILENAME, 0)
disp(‘User canceled the image selection.’);
return;
end
FilePath = strcat(PATHNAME, FILENAME);
disp(‘Selected Image Location:’);
disp(FilePath);
% Step 2: Read and Resize the Image
DataArray = imread(FilePath); % Read the image
DataArray = imresize(DataArray, [300, 650]); % Resize image to a standard size
figure, imshow(DataArray); % Display image
title(‘Original Image’);
% Step 3: Convert to Grayscale
Igray = rgb2gray(DataArray); % Convert to grayscale
figure, imshow(Igray); % Display grayscale image
title(‘Grayscale Image’);
% Step 4: Noise Removal using Median Filter
Igray = medfilt2(Igray); % Apply median filter to remove noise
figure, imshow(Igray);
title(‘Denoised Grayscale Image’);
% Step 5: Edge Detection (using Sobel operator)
edges = edge(Igray, ‘Sobel’); % Detect edges using Sobel method
figure, imshow(edges);
title(‘Edge Detection’);
% Step 6: Feature Extraction – Texture Features using GLCM
% Calculate the Gray Level Co-occurrence Matrix (GLCM)
glcm = graycomatrix(Igray, ‘Offset’, [0 1; -1 1; -1 0; -1 -1]);
stats = graycoprops(glcm, {‘Contrast’, ‘Correlation’, ‘Energy’, ‘Homogeneity’});
% Extract features from GLCM
Contrast = stats.Contrast;
Correlation = stats.Correlation;
Energy = stats.Energy;
Homogeneity = stats.Homogeneity;
% Step 7: Feature Extraction – Shape Features using Regionprops
props = regionprops(edges, ‘Area’, ‘Eccentricity’, ‘Solidity’);
Area = [props.Area]; % Extract Area
Eccentricity = [props.Eccentricity]; % Extract Eccentricity
Solidity = [props.Solidity]; % Extract Solidity
% Step 8: Create Feature Vector
Features = [mean(Contrast), mean(Correlation), mean(Energy), mean(Homogeneity), mean(Area), mean(Eccentricity), mean(Solidity)];
disp(‘Extracted Features:’);
disp(Features);
% Step 9: Prepare Training Data (for SVM)
% Example of real and fake currency training data (manually labeled)
% In real projects, you’d use a dataset with images of real and fake currency
Real_Data = [
% Example feature values for real currency
0.1, 0.9, 0.8, 0.7, 2500, 0.6, 0.95; % Example feature values for real currency
% Add more real currency data points
];
Fake_Data = [
% Example feature values for fake currency
0.5, 0.4, 0.2, 0.3, 2000, 0.8, 0.3; % Example feature values for fake currency
% Add more fake currency data points
];
% Step 10: Labels for Training Data
% 1 for real currency, 0 for fake currency
Real_Labels = ones(size(Real_Data, 1), 1);
Fake_Labels = zeros(size(Fake_Data, 1), 1);
% Combine real and fake data
Train_Data = [Real_Data; Fake_Data];
Train_Labels = [Real_Labels; Fake_Labels];
% Step 11: Train an SVM Classifier
svmModel = fitcsvm(Train_Data, Train_Labels, ‘KernelFunction’, ‘rbf’, ‘Standardize’, true);
% Step 12: Test the classifier with the current image features
[Label, Score] = predict(svmModel, Features); % Classify using the SVM model
% Step 13: Display the result
if Label == 1
msgbox(‘Currency Type: Real’);
else
msgbox(‘Currency Type: Fake’);
end
% Step 14: Display Final Image with Result
figure, imshow(DataArray);
if Label == 1
title(‘Detected: Real Currency’);
else
title(‘Detected: Fake Currency’);
end image processing MATLAB Answers — New Questions
Issue with parameter estimation in Battery(Table-based) block
Hi,
I am in between estimating the parameter of 1RC battery model(using battery(table based) block). I have set initial parameters for V0,R0,R1 and Tau1. Since i have experimental pulse discharge data values of 6190 seconds, my paramters for estimations are also column vector of size 6190X1. i am using parameter estimation toolbox for optimization and least square algorithm. in new experiment tab, i have set pulse discharge data values as "Output" variable. in parameter, i set V0,R0,R1 and Tau1 with initials guess of all paramters. when running the estimation, the app shows its estimating (at bottom), and the model is running the iterations. but at the end, the the estimation output is "Initial point is local minimum" and iteration=0 and F-Count=513. also the estimated parameters plot is empty. I am unable to guess what is happening?? please help. ThanksHi,
I am in between estimating the parameter of 1RC battery model(using battery(table based) block). I have set initial parameters for V0,R0,R1 and Tau1. Since i have experimental pulse discharge data values of 6190 seconds, my paramters for estimations are also column vector of size 6190X1. i am using parameter estimation toolbox for optimization and least square algorithm. in new experiment tab, i have set pulse discharge data values as "Output" variable. in parameter, i set V0,R0,R1 and Tau1 with initials guess of all paramters. when running the estimation, the app shows its estimating (at bottom), and the model is running the iterations. but at the end, the the estimation output is "Initial point is local minimum" and iteration=0 and F-Count=513. also the estimated parameters plot is empty. I am unable to guess what is happening?? please help. Thanks Hi,
I am in between estimating the parameter of 1RC battery model(using battery(table based) block). I have set initial parameters for V0,R0,R1 and Tau1. Since i have experimental pulse discharge data values of 6190 seconds, my paramters for estimations are also column vector of size 6190X1. i am using parameter estimation toolbox for optimization and least square algorithm. in new experiment tab, i have set pulse discharge data values as "Output" variable. in parameter, i set V0,R0,R1 and Tau1 with initials guess of all paramters. when running the estimation, the app shows its estimating (at bottom), and the model is running the iterations. but at the end, the the estimation output is "Initial point is local minimum" and iteration=0 and F-Count=513. also the estimated parameters plot is empty. I am unable to guess what is happening?? please help. Thanks parameter estimation, simulink, optimization MATLAB Answers — New Questions
How to clear serial port buffers from matlab to arduino?
Hi there, I tried to use ‘flush’ to clear the serial buffer sent from MATLAB to Arduino, but I can’t make it. Here is a simple code in MATLAB:
device = serialport("/dev/cu.usbmodem2101",9600);
write(device, 1:5, "uint8");
The device.NumBytesAvailable = 0, and device.NumBytesWritten = 5, and after I run this:
flush(device);
the device.NumBytesWritten is still 5.
I’ve tried different MATLAB versions (2022b,and 2019b, and tried to clear this in arduino, but neither works.
Please help me. I don’t know what’s going wrong and I’ve been stuck in this for several days. Thanks.Hi there, I tried to use ‘flush’ to clear the serial buffer sent from MATLAB to Arduino, but I can’t make it. Here is a simple code in MATLAB:
device = serialport("/dev/cu.usbmodem2101",9600);
write(device, 1:5, "uint8");
The device.NumBytesAvailable = 0, and device.NumBytesWritten = 5, and after I run this:
flush(device);
the device.NumBytesWritten is still 5.
I’ve tried different MATLAB versions (2022b,and 2019b, and tried to clear this in arduino, but neither works.
Please help me. I don’t know what’s going wrong and I’ve been stuck in this for several days. Thanks. Hi there, I tried to use ‘flush’ to clear the serial buffer sent from MATLAB to Arduino, but I can’t make it. Here is a simple code in MATLAB:
device = serialport("/dev/cu.usbmodem2101",9600);
write(device, 1:5, "uint8");
The device.NumBytesAvailable = 0, and device.NumBytesWritten = 5, and after I run this:
flush(device);
the device.NumBytesWritten is still 5.
I’ve tried different MATLAB versions (2022b,and 2019b, and tried to clear this in arduino, but neither works.
Please help me. I don’t know what’s going wrong and I’ve been stuck in this for several days. Thanks. arduino, matlab, buffer, clear, flush MATLAB Answers — New Questions
I want to replace the hyphens with en dashes in a plot axes.
Dear all
I need help with replacing the hyphens in the y-axis with en dashes in a plot. In other words, I want to increase the length of the negative sing in the plot axis.
Thanks
AzizDear all
I need help with replacing the hyphens in the y-axis with en dashes in a plot. In other words, I want to increase the length of the negative sing in the plot axis.
Thanks
Aziz Dear all
I need help with replacing the hyphens in the y-axis with en dashes in a plot. In other words, I want to increase the length of the negative sing in the plot axis.
Thanks
Aziz replace the hyphens with en dashes MATLAB Answers — New Questions
First order partial differential equations system – Numerical solution
Hello everyone!
I would like to solve a first order partial differential equations (2 coupled equations) system numerically. I just want to make sure that my thoughts are correct.
So firstly, I will start by doing a discretization to each of the two equations and then I will use ode15s to solve the ordinary differential equations that I got from the first step. Am I right? Which method of discretization do you recommend me to use?
Note: My equations are similar to the linear advection equation but with a source term.
Thank you very much.
MalakHello everyone!
I would like to solve a first order partial differential equations (2 coupled equations) system numerically. I just want to make sure that my thoughts are correct.
So firstly, I will start by doing a discretization to each of the two equations and then I will use ode15s to solve the ordinary differential equations that I got from the first step. Am I right? Which method of discretization do you recommend me to use?
Note: My equations are similar to the linear advection equation but with a source term.
Thank you very much.
Malak Hello everyone!
I would like to solve a first order partial differential equations (2 coupled equations) system numerically. I just want to make sure that my thoughts are correct.
So firstly, I will start by doing a discretization to each of the two equations and then I will use ode15s to solve the ordinary differential equations that I got from the first step. Am I right? Which method of discretization do you recommend me to use?
Note: My equations are similar to the linear advection equation but with a source term.
Thank you very much.
Malak partial differential equations, first order, hyperbolic, discretization, pdepe MATLAB Answers — New Questions
I want to measure the line voltages in a three phase system, can I connect the voltage measurement between the two phases? Does it mean short circuiting the two phases?
I want to know whether voltage measurement short circuits or not when connected between two phases to measure line voltage.I want to know whether voltage measurement short circuits or not when connected between two phases to measure line voltage. I want to know whether voltage measurement short circuits or not when connected between two phases to measure line voltage. voltage measurement MATLAB Answers — New Questions
about ‘matlab function’ using refprom to get properties of methane
Hi there,
I want to link REFPROP with Simulink for methane. The pressure and temperature of the working fluid change. I want to import the fluid properties to Simulink, considering the changing pressure and temperature. Is it possible, and how can I do that?Hi there,
I want to link REFPROP with Simulink for methane. The pressure and temperature of the working fluid change. I want to import the fluid properties to Simulink, considering the changing pressure and temperature. Is it possible, and how can I do that? Hi there,
I want to link REFPROP with Simulink for methane. The pressure and temperature of the working fluid change. I want to import the fluid properties to Simulink, considering the changing pressure and temperature. Is it possible, and how can I do that? simulink, simulink refpropm MATLAB Answers — New Questions
My previous question was closed by John D’Errico – fair enough – but I am not a student doing homework but 69 years old – and in the past you helped me to solve 7 polynomial
s*x+i = (x^2*v^2/w^2+v^2)^(1/2) wanted that solved for xs*x+i = (x^2*v^2/w^2+v^2)^(1/2) wanted that solved for x s*x+i = (x^2*v^2/w^2+v^2)^(1/2) wanted that solved for x qualified questions MATLAB Answers — New Questions
Which matlab will i be able to use in my pc?
the specs of my pc are windows 7 32 bit operating system
processor : pentium(R) dual core cpu E5700@ 3.00Ghzthe specs of my pc are windows 7 32 bit operating system
processor : pentium(R) dual core cpu E5700@ 3.00Ghz the specs of my pc are windows 7 32 bit operating system
processor : pentium(R) dual core cpu E5700@ 3.00Ghz release MATLAB Answers — New Questions
Axis ticks and colorbar labels in bold (heatmap)?
I created a heatmap and I’ve got my axis labels in bold for better visibility. I had to use this ‘bf thing to be able to do that, because, apparently, the normal way of:
h = heatmap(X, Y, C, ‘FontName’, ‘Times New Roman’, ‘FontSize’, 13, ‘FontWeight’, ‘bold’);
doesn’t work with heatmaps.
Now, I’d like to have the axis ticks and the colorbar labels in bold as well. Is that a way to do so in matlab? I couldn’t find a solution for that by myself.I created a heatmap and I’ve got my axis labels in bold for better visibility. I had to use this ‘bf thing to be able to do that, because, apparently, the normal way of:
h = heatmap(X, Y, C, ‘FontName’, ‘Times New Roman’, ‘FontSize’, 13, ‘FontWeight’, ‘bold’);
doesn’t work with heatmaps.
Now, I’d like to have the axis ticks and the colorbar labels in bold as well. Is that a way to do so in matlab? I couldn’t find a solution for that by myself. I created a heatmap and I’ve got my axis labels in bold for better visibility. I had to use this ‘bf thing to be able to do that, because, apparently, the normal way of:
h = heatmap(X, Y, C, ‘FontName’, ‘Times New Roman’, ‘FontSize’, 13, ‘FontWeight’, ‘bold’);
doesn’t work with heatmaps.
Now, I’d like to have the axis ticks and the colorbar labels in bold as well. Is that a way to do so in matlab? I couldn’t find a solution for that by myself. heatmap, axis ticks, colorbar, bold MATLAB Answers — New Questions
