## Month: June 2024

## setting up infinite sequence and plotting

How do I set up and plot:

Sum (n=0 to infinity) (n^3+1)

Thank youHow do I set up and plot:

Sum (n=0 to infinity) (n^3+1)

Thank you How do I set up and plot:

Sum (n=0 to infinity) (n^3+1)

Thank you infinite sequence, matlab, plotting MATLAB Answers — New Questions

## PDE and indefinite integral defining in Matlab

Hello,

I’m setting up the indefinite integral based on time and heat u

Integral t from 0 to infinity (u) * du/dt

How do I set this up and plot it?

Thank you.Hello,

I’m setting up the indefinite integral based on time and heat u

Integral t from 0 to infinity (u) * du/dt

How do I set this up and plot it?

Thank you. Hello,

I’m setting up the indefinite integral based on time and heat u

Integral t from 0 to infinity (u) * du/dt

How do I set this up and plot it?

Thank you. pde, indefinite integral, plotting MATLAB Answers — New Questions

## what is the error here?

here is the code .

function slipflow

format long g

%Define all parameters

% Boundary layer thickness & stepsize

etaMin = 0;

etaMax1 = 15;

etaMax2 = 15; %15, 10

stepsize1 = etaMax1;

stepsize2 = etaMax2;

% Input for the parameters

A=1; %velocity slip

B=0.2; %thermal slip

beta=0.02; %heat gen/abs

S=2.4; %suction(2.3,2.4,2.5)

Pr=6.2; %prandtl number

lambda=-1; %stretching shrinking

a=0.01; %phil-1st nanoparticle concentration

b=0.01; %(0.01,0.05)phi2-2nd nanoparticle concentration

c=a+b; %phi-hnf concentration of hybrid nanoparticle

%%%%%%%%%%% 1st nanoparticle properties (Al2O3)%%%%%%%%%%%%

C1=765;

P1=3970;

K1=40;

B1=0.85/((10)^5);

s1=35*(10)^6; %MHD

%%%%%%%%%%% 2nd nanoparticle properties (Cu)%%%%%%%%%%%%

C2=385; %specific heat

P2=8933; %density

K2=400; %thermal conductivity

B2=1.67/((10)^5); %thermal expansion

s2=(59.6)*(10)^6; %MHD

%%%%%%%%%%% Base fluid properties %%%%%%%%%%%%

C3=4179; %specific heat

P3=997.1; %density

K3=0.613; %thermal conductivity

B3=21/((10)^5); %thermal expansion

s3=0.05; %MHD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%multiplier%%%%%%%%%%%%%%%%%%%

H1=P1*C1; %pho*cp nanoparticle 1

H2=P2*C2; %pho*cp nanoparticle 2

H3=P3*C3; %pho*cp base fluid

H4=a*H1+b*H2+(1-c)*H3; %pho*cp hybrid nanofluid

H5=a*P1+b*P2+(1-c)*P3; %pho hybrid nanofluid

H6=1/((1-c)^2.5); % mu hybrid nanofluid / mu base fluid

H7=a*(P1*B1)+b*(P2*B2)+(1-c)*(P3*B3); % thermal expansion of hybrid nanofluid

%Kn=K3*(K1+2*K3-2*a*(K3-K1))/(K1+2*K3+a*(K3-K1)); %thermal conductivity of nanofluid

Kh=(((a*K1+b*K2)/c)+2*K3+2*(a*K1+b*K2)-2*c*K3)/(((a*K1+b*K2)/c)+2*K3-(a*K1+b*K2)-2*c*K3); %khnf/kf

H8=(((a*s1+b*s2)/c)+2*s3+2*(a*s1+b*s2)-2*c*s3)/(((a*s1+b*s2)/c)+2*s3-(a*s1+b*s2)-2*c*s3); % sigma hnf/ sigma f

D1=(H5/P3)/H6;

D3=(H7/(P3*B3))/(H5/P3); % multiplier of boundary parameter

D2= Pr*((H4/H3)/Kh);

D4=H8/(H5/P3); %multiplier MHD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% First solution %%%%%%%%%%%%%%%%%%%

options = bvpset(‘stats’,’off’,’RelTol’,1e-10);

solinit = bvpinit (linspace (etaMin, etaMax1, stepsize1),@(x)OdeInit1(x,A,S,lambda));

sol = bvp4c (@(x,y)OdeBVP(x,y,Pr,D1,Kh,H4,H3), @(ya,yb)OdeBC(ya, yb, A, S, lambda), solinit, options);

eta = linspace (etaMin, etaMax1, stepsize1);

y= deval (sol,eta);

figure(1) %velocity profile

plot(sol.x,sol.y(2,:),’-‘)

xlabel(‘eta’)

ylabel(‘f`(eta)’)

hold on

figure(2) %temperature profile

plot(sol.x,sol.y(4,:),’-‘)

xlabel(‘eta’)

ylabel(‘theta(eta)’)

hold on

% saving the out put in text file for first solution

descris =[sol.x; sol.y];

save ‘sliphybrid_upper.txt’ descris -ascii

% Displaying the output for first solution

fprintf(‘n First solution:n’);

fprintf(‘f"(0)=%7.9fn’,y(3)); % reduced skin friction

fprintf(‘-theta(0)=%7.9fn’,-y(5)); %reduced local nusselt number

fprintf(‘Cfx=%7.9fn’,H6*(y(3))); % skin friction

fprintf(‘Nux=%7.9fn’,-Kh*y(5)); % local nusselt number

fprintf(‘n’);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% second solution %%%%%%%%%%%%%%%%%%%

options = bvpset(‘stats’,’off’,’RelTol’,1e-10);

solinit = bvpinit (linspace (etaMin, etaMax2, stepsize2),@(x)OdeInit2(x,A,S,lambda));

sol = bvp4c (@(x,y)OdeBVP(x,y,Pr,D1,Kh,H4,H3), @(ya,yb)OdeBC(ya, yb, A, S, lambda), solinit, options);

eta= linspace (etaMin, etaMax2, stepsize2);

y = deval (sol,eta);

figure(1) %velocity profile

plot(sol.x,sol.y(2,:),’–‘)

xlabel(‘eta’)

ylabel(‘f`(eta)’)

hold on

figure(2) %temperature profile

plot(sol.x,sol.y(4,:),’–‘)

xlabel(‘eta’)

ylabel(‘theta(eta)’)

hold on

% saving the out put in text file for second solution

descris=[sol.x; sol.y];

save ‘sliphybrid_lower.txt’descris -ascii

% Displaying the output for first solution

fprintf(‘nSecond solution:n’);

fprintf(‘f"(0)=%7.9fn’,y(3)); % reduced skin friction

fprintf(‘-theta(0)=%7.9fn’,-y(5)); %reduced local nusselt number

fprintf(‘Cfx=%7.9fn’,H6*(y(3))); % skin friction

fprintf(‘Nux=%7.9fn’,-Kh*y(5)); % local nusselt number

fprintf(‘n’);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

end

% Define the ODE function

function f = OdeBVP(x,y,Pr,D1,Kh,H4,H3)

f =[y(2);y(3);(1/3)*D1*((y(2)^2)-2*(y(1)*y(3))+2*S);y(5);-2/3*(Pr*((H4/H3)/kh)*y(1)*y(5))];

end

% Define the boundary conditions

function res = OdeBC(ya, yb, A, S, lambda)

res= [ya(1)-S;ya(2)-lambda;ya(4)-1;yb(3)-1;yb(4)];

end

% setting the initial guess for first solution

function v = OdeInit1(x,A,S,lambda)

v=[S+0.56;0;0;0;0];

end

% setting the initial guess for second solution

function v1 =OdeInit2(x, A, S,lambda)

v1 = [exp(-x);exp(-x);-exp(-x);-exp(-x);-exp(-x)];

endhere is the code .

function slipflow

format long g

%Define all parameters

% Boundary layer thickness & stepsize

etaMin = 0;

etaMax1 = 15;

etaMax2 = 15; %15, 10

stepsize1 = etaMax1;

stepsize2 = etaMax2;

% Input for the parameters

A=1; %velocity slip

B=0.2; %thermal slip

beta=0.02; %heat gen/abs

S=2.4; %suction(2.3,2.4,2.5)

Pr=6.2; %prandtl number

lambda=-1; %stretching shrinking

a=0.01; %phil-1st nanoparticle concentration

b=0.01; %(0.01,0.05)phi2-2nd nanoparticle concentration

c=a+b; %phi-hnf concentration of hybrid nanoparticle

%%%%%%%%%%% 1st nanoparticle properties (Al2O3)%%%%%%%%%%%%

C1=765;

P1=3970;

K1=40;

B1=0.85/((10)^5);

s1=35*(10)^6; %MHD

%%%%%%%%%%% 2nd nanoparticle properties (Cu)%%%%%%%%%%%%

C2=385; %specific heat

P2=8933; %density

K2=400; %thermal conductivity

B2=1.67/((10)^5); %thermal expansion

s2=(59.6)*(10)^6; %MHD

%%%%%%%%%%% Base fluid properties %%%%%%%%%%%%

C3=4179; %specific heat

P3=997.1; %density

K3=0.613; %thermal conductivity

B3=21/((10)^5); %thermal expansion

s3=0.05; %MHD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%multiplier%%%%%%%%%%%%%%%%%%%

H1=P1*C1; %pho*cp nanoparticle 1

H2=P2*C2; %pho*cp nanoparticle 2

H3=P3*C3; %pho*cp base fluid

H4=a*H1+b*H2+(1-c)*H3; %pho*cp hybrid nanofluid

H5=a*P1+b*P2+(1-c)*P3; %pho hybrid nanofluid

H6=1/((1-c)^2.5); % mu hybrid nanofluid / mu base fluid

H7=a*(P1*B1)+b*(P2*B2)+(1-c)*(P3*B3); % thermal expansion of hybrid nanofluid

%Kn=K3*(K1+2*K3-2*a*(K3-K1))/(K1+2*K3+a*(K3-K1)); %thermal conductivity of nanofluid

Kh=(((a*K1+b*K2)/c)+2*K3+2*(a*K1+b*K2)-2*c*K3)/(((a*K1+b*K2)/c)+2*K3-(a*K1+b*K2)-2*c*K3); %khnf/kf

H8=(((a*s1+b*s2)/c)+2*s3+2*(a*s1+b*s2)-2*c*s3)/(((a*s1+b*s2)/c)+2*s3-(a*s1+b*s2)-2*c*s3); % sigma hnf/ sigma f

D1=(H5/P3)/H6;

D3=(H7/(P3*B3))/(H5/P3); % multiplier of boundary parameter

D2= Pr*((H4/H3)/Kh);

D4=H8/(H5/P3); %multiplier MHD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% First solution %%%%%%%%%%%%%%%%%%%

options = bvpset(‘stats’,’off’,’RelTol’,1e-10);

solinit = bvpinit (linspace (etaMin, etaMax1, stepsize1),@(x)OdeInit1(x,A,S,lambda));

sol = bvp4c (@(x,y)OdeBVP(x,y,Pr,D1,Kh,H4,H3), @(ya,yb)OdeBC(ya, yb, A, S, lambda), solinit, options);

eta = linspace (etaMin, etaMax1, stepsize1);

y= deval (sol,eta);

figure(1) %velocity profile

plot(sol.x,sol.y(2,:),’-‘)

xlabel(‘eta’)

ylabel(‘f`(eta)’)

hold on

figure(2) %temperature profile

plot(sol.x,sol.y(4,:),’-‘)

xlabel(‘eta’)

ylabel(‘theta(eta)’)

hold on

% saving the out put in text file for first solution

descris =[sol.x; sol.y];

save ‘sliphybrid_upper.txt’ descris -ascii

% Displaying the output for first solution

fprintf(‘n First solution:n’);

fprintf(‘f"(0)=%7.9fn’,y(3)); % reduced skin friction

fprintf(‘-theta(0)=%7.9fn’,-y(5)); %reduced local nusselt number

fprintf(‘Cfx=%7.9fn’,H6*(y(3))); % skin friction

fprintf(‘Nux=%7.9fn’,-Kh*y(5)); % local nusselt number

fprintf(‘n’);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% second solution %%%%%%%%%%%%%%%%%%%

options = bvpset(‘stats’,’off’,’RelTol’,1e-10);

solinit = bvpinit (linspace (etaMin, etaMax2, stepsize2),@(x)OdeInit2(x,A,S,lambda));

sol = bvp4c (@(x,y)OdeBVP(x,y,Pr,D1,Kh,H4,H3), @(ya,yb)OdeBC(ya, yb, A, S, lambda), solinit, options);

eta= linspace (etaMin, etaMax2, stepsize2);

y = deval (sol,eta);

figure(1) %velocity profile

plot(sol.x,sol.y(2,:),’–‘)

xlabel(‘eta’)

ylabel(‘f`(eta)’)

hold on

figure(2) %temperature profile

plot(sol.x,sol.y(4,:),’–‘)

xlabel(‘eta’)

ylabel(‘theta(eta)’)

hold on

% saving the out put in text file for second solution

descris=[sol.x; sol.y];

save ‘sliphybrid_lower.txt’descris -ascii

% Displaying the output for first solution

fprintf(‘nSecond solution:n’);

fprintf(‘f"(0)=%7.9fn’,y(3)); % reduced skin friction

fprintf(‘-theta(0)=%7.9fn’,-y(5)); %reduced local nusselt number

fprintf(‘Cfx=%7.9fn’,H6*(y(3))); % skin friction

fprintf(‘Nux=%7.9fn’,-Kh*y(5)); % local nusselt number

fprintf(‘n’);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

end

% Define the ODE function

function f = OdeBVP(x,y,Pr,D1,Kh,H4,H3)

f =[y(2);y(3);(1/3)*D1*((y(2)^2)-2*(y(1)*y(3))+2*S);y(5);-2/3*(Pr*((H4/H3)/kh)*y(1)*y(5))];

end

% Define the boundary conditions

function res = OdeBC(ya, yb, A, S, lambda)

res= [ya(1)-S;ya(2)-lambda;ya(4)-1;yb(3)-1;yb(4)];

end

% setting the initial guess for first solution

function v = OdeInit1(x,A,S,lambda)

v=[S+0.56;0;0;0;0];

end

% setting the initial guess for second solution

function v1 =OdeInit2(x, A, S,lambda)

v1 = [exp(-x);exp(-x);-exp(-x);-exp(-x);-exp(-x)];

end here is the code .

function slipflow

format long g

%Define all parameters

% Boundary layer thickness & stepsize

etaMin = 0;

etaMax1 = 15;

etaMax2 = 15; %15, 10

stepsize1 = etaMax1;

stepsize2 = etaMax2;

% Input for the parameters

A=1; %velocity slip

B=0.2; %thermal slip

beta=0.02; %heat gen/abs

S=2.4; %suction(2.3,2.4,2.5)

Pr=6.2; %prandtl number

lambda=-1; %stretching shrinking

a=0.01; %phil-1st nanoparticle concentration

b=0.01; %(0.01,0.05)phi2-2nd nanoparticle concentration

c=a+b; %phi-hnf concentration of hybrid nanoparticle

%%%%%%%%%%% 1st nanoparticle properties (Al2O3)%%%%%%%%%%%%

C1=765;

P1=3970;

K1=40;

B1=0.85/((10)^5);

s1=35*(10)^6; %MHD

%%%%%%%%%%% 2nd nanoparticle properties (Cu)%%%%%%%%%%%%

C2=385; %specific heat

P2=8933; %density

K2=400; %thermal conductivity

B2=1.67/((10)^5); %thermal expansion

s2=(59.6)*(10)^6; %MHD

%%%%%%%%%%% Base fluid properties %%%%%%%%%%%%

C3=4179; %specific heat

P3=997.1; %density

K3=0.613; %thermal conductivity

B3=21/((10)^5); %thermal expansion

s3=0.05; %MHD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%multiplier%%%%%%%%%%%%%%%%%%%

H1=P1*C1; %pho*cp nanoparticle 1

H2=P2*C2; %pho*cp nanoparticle 2

H3=P3*C3; %pho*cp base fluid

H4=a*H1+b*H2+(1-c)*H3; %pho*cp hybrid nanofluid

H5=a*P1+b*P2+(1-c)*P3; %pho hybrid nanofluid

H6=1/((1-c)^2.5); % mu hybrid nanofluid / mu base fluid

H7=a*(P1*B1)+b*(P2*B2)+(1-c)*(P3*B3); % thermal expansion of hybrid nanofluid

%Kn=K3*(K1+2*K3-2*a*(K3-K1))/(K1+2*K3+a*(K3-K1)); %thermal conductivity of nanofluid

Kh=(((a*K1+b*K2)/c)+2*K3+2*(a*K1+b*K2)-2*c*K3)/(((a*K1+b*K2)/c)+2*K3-(a*K1+b*K2)-2*c*K3); %khnf/kf

H8=(((a*s1+b*s2)/c)+2*s3+2*(a*s1+b*s2)-2*c*s3)/(((a*s1+b*s2)/c)+2*s3-(a*s1+b*s2)-2*c*s3); % sigma hnf/ sigma f

D1=(H5/P3)/H6;

D3=(H7/(P3*B3))/(H5/P3); % multiplier of boundary parameter

D2= Pr*((H4/H3)/Kh);

D4=H8/(H5/P3); %multiplier MHD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% First solution %%%%%%%%%%%%%%%%%%%

options = bvpset(‘stats’,’off’,’RelTol’,1e-10);

solinit = bvpinit (linspace (etaMin, etaMax1, stepsize1),@(x)OdeInit1(x,A,S,lambda));

sol = bvp4c (@(x,y)OdeBVP(x,y,Pr,D1,Kh,H4,H3), @(ya,yb)OdeBC(ya, yb, A, S, lambda), solinit, options);

eta = linspace (etaMin, etaMax1, stepsize1);

y= deval (sol,eta);

figure(1) %velocity profile

plot(sol.x,sol.y(2,:),’-‘)

xlabel(‘eta’)

ylabel(‘f`(eta)’)

hold on

figure(2) %temperature profile

plot(sol.x,sol.y(4,:),’-‘)

xlabel(‘eta’)

ylabel(‘theta(eta)’)

hold on

% saving the out put in text file for first solution

descris =[sol.x; sol.y];

save ‘sliphybrid_upper.txt’ descris -ascii

% Displaying the output for first solution

fprintf(‘n First solution:n’);

fprintf(‘f"(0)=%7.9fn’,y(3)); % reduced skin friction

fprintf(‘-theta(0)=%7.9fn’,-y(5)); %reduced local nusselt number

fprintf(‘Cfx=%7.9fn’,H6*(y(3))); % skin friction

fprintf(‘Nux=%7.9fn’,-Kh*y(5)); % local nusselt number

fprintf(‘n’);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% second solution %%%%%%%%%%%%%%%%%%%

options = bvpset(‘stats’,’off’,’RelTol’,1e-10);

solinit = bvpinit (linspace (etaMin, etaMax2, stepsize2),@(x)OdeInit2(x,A,S,lambda));

sol = bvp4c (@(x,y)OdeBVP(x,y,Pr,D1,Kh,H4,H3), @(ya,yb)OdeBC(ya, yb, A, S, lambda), solinit, options);

eta= linspace (etaMin, etaMax2, stepsize2);

y = deval (sol,eta);

figure(1) %velocity profile

plot(sol.x,sol.y(2,:),’–‘)

xlabel(‘eta’)

ylabel(‘f`(eta)’)

hold on

figure(2) %temperature profile

plot(sol.x,sol.y(4,:),’–‘)

xlabel(‘eta’)

ylabel(‘theta(eta)’)

hold on

% saving the out put in text file for second solution

descris=[sol.x; sol.y];

save ‘sliphybrid_lower.txt’descris -ascii

% Displaying the output for first solution

fprintf(‘nSecond solution:n’);

fprintf(‘f"(0)=%7.9fn’,y(3)); % reduced skin friction

fprintf(‘-theta(0)=%7.9fn’,-y(5)); %reduced local nusselt number

fprintf(‘Cfx=%7.9fn’,H6*(y(3))); % skin friction

fprintf(‘Nux=%7.9fn’,-Kh*y(5)); % local nusselt number

fprintf(‘n’);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

end

% Define the ODE function

function f = OdeBVP(x,y,Pr,D1,Kh,H4,H3)

f =[y(2);y(3);(1/3)*D1*((y(2)^2)-2*(y(1)*y(3))+2*S);y(5);-2/3*(Pr*((H4/H3)/kh)*y(1)*y(5))];

end

% Define the boundary conditions

function res = OdeBC(ya, yb, A, S, lambda)

res= [ya(1)-S;ya(2)-lambda;ya(4)-1;yb(3)-1;yb(4)];

end

% setting the initial guess for first solution

function v = OdeInit1(x,A,S,lambda)

v=[S+0.56;0;0;0;0];

end

% setting the initial guess for second solution

function v1 =OdeInit2(x, A, S,lambda)

v1 = [exp(-x);exp(-x);-exp(-x);-exp(-x);-exp(-x)];

end matlab MATLAB Answers — New Questions

## Sort columns of a table by the value corresponding

Ive attached a sc of my table, although it continues to 86 columns, I would like to sort the values corresponding to each variable in decending order, and have the variable names be sorted along with the value.

The table dimensions is 1×86 as seen in the screenshotIve attached a sc of my table, although it continues to 86 columns, I would like to sort the values corresponding to each variable in decending order, and have the variable names be sorted along with the value.

The table dimensions is 1×86 as seen in the screenshot Ive attached a sc of my table, although it continues to 86 columns, I would like to sort the values corresponding to each variable in decending order, and have the variable names be sorted along with the value.

The table dimensions is 1×86 as seen in the screenshot table, sort MATLAB Answers — New Questions

## FFT multiple input waves and plot on same graph

I am trying to plot the fft of multiple input waves on the same graph. The input epquation is the same but I want varying A values (A1,A2,…An) and varying freq values (freq1,freq2,…,freqn). I want to first plot these equations all on one graph, and plot all of the ffts of these inputs on another plot. This is what I have, I feel that there is a more efficient/better way to do this, as I want to have about 10 inputs. Thank you for your time!

%% Input Signal

fs = 20e3;

numFilts = 32; %

filter_number = numFilts;

freq1 = 1000; % Hz

Nperiods = 15; % we need more than 1 period of signal to reach the steady state output (look a the IR samples)

t = linspace(0,Nperiods/freq1,200*Nperiods); %

A1=1;

input1 = A1*sin(2*pi*freq1*t) + 0*rand(size(t));

A2=3;

freq2=2000;

input2 = A2*sin(2*pi*freq2*t) + 0*rand(size(t));

%FFT Input

FFT_Input1=fft(input1);

plot(t,FFT_Input1)

FFT_Input2=fft(input2);

plot(t,FFT_Input2)I am trying to plot the fft of multiple input waves on the same graph. The input epquation is the same but I want varying A values (A1,A2,…An) and varying freq values (freq1,freq2,…,freqn). I want to first plot these equations all on one graph, and plot all of the ffts of these inputs on another plot. This is what I have, I feel that there is a more efficient/better way to do this, as I want to have about 10 inputs. Thank you for your time!

%% Input Signal

fs = 20e3;

numFilts = 32; %

filter_number = numFilts;

freq1 = 1000; % Hz

Nperiods = 15; % we need more than 1 period of signal to reach the steady state output (look a the IR samples)

t = linspace(0,Nperiods/freq1,200*Nperiods); %

A1=1;

input1 = A1*sin(2*pi*freq1*t) + 0*rand(size(t));

A2=3;

freq2=2000;

input2 = A2*sin(2*pi*freq2*t) + 0*rand(size(t));

%FFT Input

FFT_Input1=fft(input1);

plot(t,FFT_Input1)

FFT_Input2=fft(input2);

plot(t,FFT_Input2) I am trying to plot the fft of multiple input waves on the same graph. The input epquation is the same but I want varying A values (A1,A2,…An) and varying freq values (freq1,freq2,…,freqn). I want to first plot these equations all on one graph, and plot all of the ffts of these inputs on another plot. This is what I have, I feel that there is a more efficient/better way to do this, as I want to have about 10 inputs. Thank you for your time!

%% Input Signal

fs = 20e3;

numFilts = 32; %

filter_number = numFilts;

freq1 = 1000; % Hz

Nperiods = 15; % we need more than 1 period of signal to reach the steady state output (look a the IR samples)

t = linspace(0,Nperiods/freq1,200*Nperiods); %

A1=1;

input1 = A1*sin(2*pi*freq1*t) + 0*rand(size(t));

A2=3;

freq2=2000;

input2 = A2*sin(2*pi*freq2*t) + 0*rand(size(t));

%FFT Input

FFT_Input1=fft(input1);

plot(t,FFT_Input1)

FFT_Input2=fft(input2);

plot(t,FFT_Input2) fft, plot MATLAB Answers — New Questions

## Building a survey GUI that records the answers of different study participants

Hello everyone,

I’m pretty new to MATLAB and was hoping if I could get some advice. I’m trying to build a MATLAB GUI for a study where I want the participants to listen to multiple audio files and give ratings for each (by slider values). I plan on deploying a web app and making it available on the Web Server so that pariticipants (that don’t have MATLAB) can access a link and send in their anonymous responses. I would like these responses to be saved in an excel sheet for all participants for analysis later.

I haven’t reached the deployment stage of this GUI (currently waiting on a license for Web Server access), but I was wondering if anyone had any suggestions when it comes to building a participant survery in MATLAB. Additionally, if anyone has an pariticipant survey scripts that already do that, I would really appreciate it if you could share it with me!Hello everyone,

I’m pretty new to MATLAB and was hoping if I could get some advice. I’m trying to build a MATLAB GUI for a study where I want the participants to listen to multiple audio files and give ratings for each (by slider values). I plan on deploying a web app and making it available on the Web Server so that pariticipants (that don’t have MATLAB) can access a link and send in their anonymous responses. I would like these responses to be saved in an excel sheet for all participants for analysis later.

I haven’t reached the deployment stage of this GUI (currently waiting on a license for Web Server access), but I was wondering if anyone had any suggestions when it comes to building a participant survery in MATLAB. Additionally, if anyone has an pariticipant survey scripts that already do that, I would really appreciate it if you could share it with me! Hello everyone,

I’m pretty new to MATLAB and was hoping if I could get some advice. I’m trying to build a MATLAB GUI for a study where I want the participants to listen to multiple audio files and give ratings for each (by slider values). I plan on deploying a web app and making it available on the Web Server so that pariticipants (that don’t have MATLAB) can access a link and send in their anonymous responses. I would like these responses to be saved in an excel sheet for all participants for analysis later.

I haven’t reached the deployment stage of this GUI (currently waiting on a license for Web Server access), but I was wondering if anyone had any suggestions when it comes to building a participant survery in MATLAB. Additionally, if anyone has an pariticipant survey scripts that already do that, I would really appreciate it if you could share it with me! data acquisition MATLAB Answers — New Questions

## Registrieren im Cloud-Partner-Programm

Hallo,

ich verfüge über ein Azure-Konto und will mich nun für das Cloud-Partner-Programm registrieren.

Es wird aber kein E-Mail-Account akzeptiert. Ich kann mich weder mit dem Azure-Konto anmelden, noch ein neues erstellen. Was mache ich falsch?

Vielen Dank

Klaus Götzer

Hallo, ich verfüge über ein Azure-Konto und will mich nun für das Cloud-Partner-Programm registrieren. Es wird aber kein E-Mail-Account akzeptiert. Ich kann mich weder mit dem Azure-Konto anmelden, noch ein neues erstellen. Was mache ich falsch? Vielen Dank Klaus Götzer Read More

## Stuck on a register loop when logging in to Microsoft Learn

When I log in to Microsoft Learn, I am transferred to a register page:

—

Before registering with this Microsoft Account, please check if you may have previously registered with

a different account.

—

Even though I have a account that before.

I can complete the registration process and view my profile, but when I try to access ms learn content, the registration process restarts.

So I can only read ms learn content as logged out. But then my progress is not saved, and I cannot do Applied Skills or register for a exam, that I had planned to do on Friday next week.

I have restarted my computer, tried incognito mode, other browsers (Edge/Chrome) and also on Android phone (Edge/Chrome), both on Wifi and Cellular, but the same happens there. So the problem should not be on my side.

I have had help from Microsoft Support Ambassador that tried to get past this error, but after a while he/she directed me to this support forum.

Regards,

/Tommy

When I log in to Microsoft Learn, I am transferred to a register page: —Before registering with this Microsoft Account, please check if you may have previously registered witha different account.–Even though I have a account that before.I can complete the registration process and view my profile, but when I try to access ms learn content, the registration process restarts. So I can only read ms learn content as logged out. But then my progress is not saved, and I cannot do Applied Skills or register for a exam, that I had planned to do on Friday next week.I have restarted my computer, tried incognito mode, other browsers (Edge/Chrome) and also on Android phone (Edge/Chrome), both on Wifi and Cellular, but the same happens there. So the problem should not be on my side. I have had help from Microsoft Support Ambassador that tried to get past this error, but after a while he/she directed me to this support forum. Regards, /Tommy Read More

## Error in opening MATLAB device file for LED trigger source.

Hi, I am using the ‘MATLAB support package for Raspberry Pi hardware’ using MATLAB 2023a. I have installed all the necessary files on the Pi. I began following the tutorial here:

Getting Started with MATLAB Support Package for Raspberry Pi Hardware – MATLAB & Simulink Example – MathWorks United Kingdom

I got to the first step, ran the code

rpi = raspi(‘IP Address’,’username’,’password’);

and was met with the following error (Practice_1 is the name of the script):

Error using Practice_1

Cannot open the device file for LED trigger source.

Any help please? I followed the tutorials accurately on the MathWorks websiteHi, I am using the ‘MATLAB support package for Raspberry Pi hardware’ using MATLAB 2023a. I have installed all the necessary files on the Pi. I began following the tutorial here:

Getting Started with MATLAB Support Package for Raspberry Pi Hardware – MATLAB & Simulink Example – MathWorks United Kingdom

I got to the first step, ran the code

rpi = raspi(‘IP Address’,’username’,’password’);

and was met with the following error (Practice_1 is the name of the script):

Error using Practice_1

Cannot open the device file for LED trigger source.

Any help please? I followed the tutorials accurately on the MathWorks website Hi, I am using the ‘MATLAB support package for Raspberry Pi hardware’ using MATLAB 2023a. I have installed all the necessary files on the Pi. I began following the tutorial here:

Getting Started with MATLAB Support Package for Raspberry Pi Hardware – MATLAB & Simulink Example – MathWorks United Kingdom

I got to the first step, ran the code

rpi = raspi(‘IP Address’,’username’,’password’);

and was met with the following error (Practice_1 is the name of the script):

Error using Practice_1

Cannot open the device file for LED trigger source.

Any help please? I followed the tutorials accurately on the MathWorks website raspberry pi, hardware, add-ons, led MATLAB Answers — New Questions

## Implicit plot with multi variable assumptions (symbolic math)

Hello,

I have to plot the following problem in symbolic form:

x*y + constant > 0 if x + y – constant2 > 0

i have used fimplicit and assume for the other condition but two problems arise: assume only seems to read one variable while fimplicit doesn’t seem to work with assumptions.

Can anybody give me any helpful suggestion?

thank youHello,

I have to plot the following problem in symbolic form:

x*y + constant > 0 if x + y – constant2 > 0

i have used fimplicit and assume for the other condition but two problems arise: assume only seems to read one variable while fimplicit doesn’t seem to work with assumptions.

Can anybody give me any helpful suggestion?

thank you Hello,

I have to plot the following problem in symbolic form:

x*y + constant > 0 if x + y – constant2 > 0

i have used fimplicit and assume for the other condition but two problems arise: assume only seems to read one variable while fimplicit doesn’t seem to work with assumptions.

Can anybody give me any helpful suggestion?

thank you symbolic math, fimplicit, assume, plotting MATLAB Answers — New Questions

## Can I adjust the hyperparameters when using the Classification Learner app?

I already read how to adjust hyperparameters from this website: https://www.mathworks.com/help/stats/train-classifier-using-hyperparameter-optimization-in-classification-learner-app.html, however, I seem to be unable to adjust the hyperparameters. Is this due to a difference in MATLAB versions?I already read how to adjust hyperparameters from this website: https://www.mathworks.com/help/stats/train-classifier-using-hyperparameter-optimization-in-classification-learner-app.html, however, I seem to be unable to adjust the hyperparameters. Is this due to a difference in MATLAB versions? I already read how to adjust hyperparameters from this website: https://www.mathworks.com/help/stats/train-classifier-using-hyperparameter-optimization-in-classification-learner-app.html, however, I seem to be unable to adjust the hyperparameters. Is this due to a difference in MATLAB versions? toolbox, classification, hyperparameters MATLAB Answers — New Questions

## Curve fitting with loop

Hi all, I have multiple sets of data to be fitted using a custom fitting function. I would like to do it such that I will only have to provide one initial guess for the first set of data, and after the computer managed to get the solution of the parameters for the first set of data, it will use the solution of the first set of data as the initial guess for the second set of data, so on and forth.

I have attached my data and code for the fitting:

%% Preparation

clear;clc

%data = importdata("Experimental dataTransient AbsorptionFCPIB-293K-2.5mW-400nm-Jan072021 -ibg -bg -chirp.csv"); % insert file path within parenthesis

data = importdata("FCPIB-293K-2.5mW-400nm-Jan072021 -ibg -bg -chirp.csv"); % insert file path within parenthesis

%% Preamble

% Fundamental constants

h = 4.0135667696*10^-15; % units: eV/ Hz

c = 3*10^8; % SI units

kB = 8.617333268*10^-5; % units: eV/ K

% Clean up of data to select range of values

wavelength = data(1:end, 1);

delay_t = data(1, 1:end); % conatains all of the delay times

E = (h*c)./(wavelength*10^-9); % contains all of the probe energies

Range_E = E>=1.5 & E<=2.2;

Range_T = delay_t>=0.5 & delay_t<=1000;

% for one delay time

T = find(Range_T);

T_min = min(T);

T_max = max(T);

t = min(T); % choose an integer b/w T_min and T_max

delaytime = delay_t(1, t);

% Data for fitting

E_p = E(Range_E); % selected probe energies

delta_Abs = -1*data(Range_E,t);

delta_Abs_norm = delta_Abs./max(delta_Abs); % normalised delta_Abs

Range_Efit = E_p>=1.65 & E_p<=max(E_p);

E_fit = E_p(Range_Efit);

delta_Abs_norm_fit = delta_Abs_norm(Range_Efit);

% Fitting function

function F = MB(y, E_fit)

F = y(1).*exp(-(E_fit./(8.617333268*10^-5.*y(2)))) + y(3);

end

%% Curve fitting options

% Initial parameter guess and bounds

lb = [0, 293, -Inf]; ub = [Inf, Inf, Inf];

y0 = [4*10^7, 900, 2];

% lsqcurvefit and choose between different algorithm that lsqcurvefit employs (3C1, comment those lines that are not choosen and uncomment the line that is choosen, if not, matlab will take the last line of "optim_lsq" by default)

% optim_lsq = optimoptions(‘lsqcurvefit’, ‘Algorithm’, ‘levenberg-marquardt’, ‘MaxFunctionEvaluations’,10^5, ‘MaxIterations’, 10^5, ‘FunctionTolerance’,10^-10, ‘StepTolerance’, 10^-10);

optim_lsq = optimoptions(‘lsqcurvefit’, ‘Algorithm’, ‘trust-region-reflective’, ‘MaxFunctionEvaluations’,10^5, ‘MaxIterations’,10^5, ‘FunctionTolerance’,10^-20, ‘StepTolerance’, 10^-20);

% optim_lsq = optimoptions(‘lsqcurvefit’, ‘Algorithm’, ‘interior-point’, ‘MaxFunctionEvaluations’,1000, ‘MaxIterations’, 1000, ‘FunctionTolerance’,10^-20, ‘StepTolerance’, 10^-20);

% Solver for lsqcurvefit

[y, residualnorm, residual, exitflag, output, lambda, jacobian] = lsqcurvefit(@MB, y0, E_fit, delta_Abs_norm_fit, lb, ub);

%% Error bars calculation

ci = nlparci(y, residual, ‘Jacobian’, jacobian);

PCI = table(ci(:,1), y(:), ci(:,2),’VariableNames’,{‘CI 0.025′,’y’,’CI 0.975′});

Parameter_CI = table2array(PCI);

upper_bar = (Parameter_CI(:,3) – Parameter_CI(:,2))./2;

lower_bar = (Parameter_CI(:,2) – Parameter_CI(:,1))./2;

%% Plot command

plot(E_p, delta_Abs_norm,’Black’)

hold on

plot(E_fit, MB(y, E_fit), ‘LineWidth’, 1.0, ‘Color’, ‘red’)

xlabel(‘Probe Photon Energy (eV)’)

ylabel(‘Normalised Delta A (a.u.)’)

legend(‘Experimental Data’, ‘Fitted Curve’)

Some background info regarding the data:

There’s a range of values in the first column and these corresponds to this quantity called wavelength. After I have extracted the wavelength from there, I’ll process it into this quantitiy call E. However, E takes a range of values and I’ll only need the values ranging from E = 1.5 to E = 2.2 (this is the data used for the scatterplot, it is named as E_p in my code)

After the first step is done, I’ll now need to choose the range of E_p (E_p = 1.65 to E_p = 2.2) for the fitting of my data (this is named as E_fit in my code) note: the range of values for fitting range of values for scatter plot

Subsequently, I’ll need to look at the first row of my data and select some of them (they need to be positive). Once I have selected them, I’ll need to select the data in the same column as them and this needs to correspond to both E_p and E_fit.

In conclusion, the data selected in 1. and 2. goes to the horizontal axis and the data selected in 3. goes to the vertical axis.

Thank you.Hi all, I have multiple sets of data to be fitted using a custom fitting function. I would like to do it such that I will only have to provide one initial guess for the first set of data, and after the computer managed to get the solution of the parameters for the first set of data, it will use the solution of the first set of data as the initial guess for the second set of data, so on and forth.

I have attached my data and code for the fitting:

%% Preparation

clear;clc

%data = importdata("Experimental dataTransient AbsorptionFCPIB-293K-2.5mW-400nm-Jan072021 -ibg -bg -chirp.csv"); % insert file path within parenthesis

data = importdata("FCPIB-293K-2.5mW-400nm-Jan072021 -ibg -bg -chirp.csv"); % insert file path within parenthesis

%% Preamble

% Fundamental constants

h = 4.0135667696*10^-15; % units: eV/ Hz

c = 3*10^8; % SI units

kB = 8.617333268*10^-5; % units: eV/ K

% Clean up of data to select range of values

wavelength = data(1:end, 1);

delay_t = data(1, 1:end); % conatains all of the delay times

E = (h*c)./(wavelength*10^-9); % contains all of the probe energies

Range_E = E>=1.5 & E<=2.2;

Range_T = delay_t>=0.5 & delay_t<=1000;

% for one delay time

T = find(Range_T);

T_min = min(T);

T_max = max(T);

t = min(T); % choose an integer b/w T_min and T_max

delaytime = delay_t(1, t);

% Data for fitting

E_p = E(Range_E); % selected probe energies

delta_Abs = -1*data(Range_E,t);

delta_Abs_norm = delta_Abs./max(delta_Abs); % normalised delta_Abs

Range_Efit = E_p>=1.65 & E_p<=max(E_p);

E_fit = E_p(Range_Efit);

delta_Abs_norm_fit = delta_Abs_norm(Range_Efit);

% Fitting function

function F = MB(y, E_fit)

F = y(1).*exp(-(E_fit./(8.617333268*10^-5.*y(2)))) + y(3);

end

%% Curve fitting options

% Initial parameter guess and bounds

lb = [0, 293, -Inf]; ub = [Inf, Inf, Inf];

y0 = [4*10^7, 900, 2];

% lsqcurvefit and choose between different algorithm that lsqcurvefit employs (3C1, comment those lines that are not choosen and uncomment the line that is choosen, if not, matlab will take the last line of "optim_lsq" by default)

% optim_lsq = optimoptions(‘lsqcurvefit’, ‘Algorithm’, ‘levenberg-marquardt’, ‘MaxFunctionEvaluations’,10^5, ‘MaxIterations’, 10^5, ‘FunctionTolerance’,10^-10, ‘StepTolerance’, 10^-10);

optim_lsq = optimoptions(‘lsqcurvefit’, ‘Algorithm’, ‘trust-region-reflective’, ‘MaxFunctionEvaluations’,10^5, ‘MaxIterations’,10^5, ‘FunctionTolerance’,10^-20, ‘StepTolerance’, 10^-20);

% optim_lsq = optimoptions(‘lsqcurvefit’, ‘Algorithm’, ‘interior-point’, ‘MaxFunctionEvaluations’,1000, ‘MaxIterations’, 1000, ‘FunctionTolerance’,10^-20, ‘StepTolerance’, 10^-20);

% Solver for lsqcurvefit

[y, residualnorm, residual, exitflag, output, lambda, jacobian] = lsqcurvefit(@MB, y0, E_fit, delta_Abs_norm_fit, lb, ub);

%% Error bars calculation

ci = nlparci(y, residual, ‘Jacobian’, jacobian);

PCI = table(ci(:,1), y(:), ci(:,2),’VariableNames’,{‘CI 0.025′,’y’,’CI 0.975′});

Parameter_CI = table2array(PCI);

upper_bar = (Parameter_CI(:,3) – Parameter_CI(:,2))./2;

lower_bar = (Parameter_CI(:,2) – Parameter_CI(:,1))./2;

%% Plot command

plot(E_p, delta_Abs_norm,’Black’)

hold on

plot(E_fit, MB(y, E_fit), ‘LineWidth’, 1.0, ‘Color’, ‘red’)

xlabel(‘Probe Photon Energy (eV)’)

ylabel(‘Normalised Delta A (a.u.)’)

legend(‘Experimental Data’, ‘Fitted Curve’)

Some background info regarding the data:

There’s a range of values in the first column and these corresponds to this quantity called wavelength. After I have extracted the wavelength from there, I’ll process it into this quantitiy call E. However, E takes a range of values and I’ll only need the values ranging from E = 1.5 to E = 2.2 (this is the data used for the scatterplot, it is named as E_p in my code)

After the first step is done, I’ll now need to choose the range of E_p (E_p = 1.65 to E_p = 2.2) for the fitting of my data (this is named as E_fit in my code) note: the range of values for fitting range of values for scatter plot

Subsequently, I’ll need to look at the first row of my data and select some of them (they need to be positive). Once I have selected them, I’ll need to select the data in the same column as them and this needs to correspond to both E_p and E_fit.

In conclusion, the data selected in 1. and 2. goes to the horizontal axis and the data selected in 3. goes to the vertical axis.

Thank you. Hi all, I have multiple sets of data to be fitted using a custom fitting function. I would like to do it such that I will only have to provide one initial guess for the first set of data, and after the computer managed to get the solution of the parameters for the first set of data, it will use the solution of the first set of data as the initial guess for the second set of data, so on and forth.

I have attached my data and code for the fitting:

%% Preparation

clear;clc

%data = importdata("Experimental dataTransient AbsorptionFCPIB-293K-2.5mW-400nm-Jan072021 -ibg -bg -chirp.csv"); % insert file path within parenthesis

data = importdata("FCPIB-293K-2.5mW-400nm-Jan072021 -ibg -bg -chirp.csv"); % insert file path within parenthesis

%% Preamble

% Fundamental constants

h = 4.0135667696*10^-15; % units: eV/ Hz

c = 3*10^8; % SI units

kB = 8.617333268*10^-5; % units: eV/ K

% Clean up of data to select range of values

wavelength = data(1:end, 1);

delay_t = data(1, 1:end); % conatains all of the delay times

E = (h*c)./(wavelength*10^-9); % contains all of the probe energies

Range_E = E>=1.5 & E<=2.2;

Range_T = delay_t>=0.5 & delay_t<=1000;

% for one delay time

T = find(Range_T);

T_min = min(T);

T_max = max(T);

t = min(T); % choose an integer b/w T_min and T_max

delaytime = delay_t(1, t);

% Data for fitting

E_p = E(Range_E); % selected probe energies

delta_Abs = -1*data(Range_E,t);

delta_Abs_norm = delta_Abs./max(delta_Abs); % normalised delta_Abs

Range_Efit = E_p>=1.65 & E_p<=max(E_p);

E_fit = E_p(Range_Efit);

delta_Abs_norm_fit = delta_Abs_norm(Range_Efit);

% Fitting function

function F = MB(y, E_fit)

F = y(1).*exp(-(E_fit./(8.617333268*10^-5.*y(2)))) + y(3);

end

%% Curve fitting options

% Initial parameter guess and bounds

lb = [0, 293, -Inf]; ub = [Inf, Inf, Inf];

y0 = [4*10^7, 900, 2];

% lsqcurvefit and choose between different algorithm that lsqcurvefit employs (3C1, comment those lines that are not choosen and uncomment the line that is choosen, if not, matlab will take the last line of "optim_lsq" by default)

% optim_lsq = optimoptions(‘lsqcurvefit’, ‘Algorithm’, ‘levenberg-marquardt’, ‘MaxFunctionEvaluations’,10^5, ‘MaxIterations’, 10^5, ‘FunctionTolerance’,10^-10, ‘StepTolerance’, 10^-10);

optim_lsq = optimoptions(‘lsqcurvefit’, ‘Algorithm’, ‘trust-region-reflective’, ‘MaxFunctionEvaluations’,10^5, ‘MaxIterations’,10^5, ‘FunctionTolerance’,10^-20, ‘StepTolerance’, 10^-20);

% optim_lsq = optimoptions(‘lsqcurvefit’, ‘Algorithm’, ‘interior-point’, ‘MaxFunctionEvaluations’,1000, ‘MaxIterations’, 1000, ‘FunctionTolerance’,10^-20, ‘StepTolerance’, 10^-20);

% Solver for lsqcurvefit

[y, residualnorm, residual, exitflag, output, lambda, jacobian] = lsqcurvefit(@MB, y0, E_fit, delta_Abs_norm_fit, lb, ub);

%% Error bars calculation

ci = nlparci(y, residual, ‘Jacobian’, jacobian);

PCI = table(ci(:,1), y(:), ci(:,2),’VariableNames’,{‘CI 0.025′,’y’,’CI 0.975′});

Parameter_CI = table2array(PCI);

upper_bar = (Parameter_CI(:,3) – Parameter_CI(:,2))./2;

lower_bar = (Parameter_CI(:,2) – Parameter_CI(:,1))./2;

%% Plot command

plot(E_p, delta_Abs_norm,’Black’)

hold on

plot(E_fit, MB(y, E_fit), ‘LineWidth’, 1.0, ‘Color’, ‘red’)

xlabel(‘Probe Photon Energy (eV)’)

ylabel(‘Normalised Delta A (a.u.)’)

legend(‘Experimental Data’, ‘Fitted Curve’)

Some background info regarding the data:

There’s a range of values in the first column and these corresponds to this quantity called wavelength. After I have extracted the wavelength from there, I’ll process it into this quantitiy call E. However, E takes a range of values and I’ll only need the values ranging from E = 1.5 to E = 2.2 (this is the data used for the scatterplot, it is named as E_p in my code)

After the first step is done, I’ll now need to choose the range of E_p (E_p = 1.65 to E_p = 2.2) for the fitting of my data (this is named as E_fit in my code) note: the range of values for fitting range of values for scatter plot

Subsequently, I’ll need to look at the first row of my data and select some of them (they need to be positive). Once I have selected them, I’ll need to select the data in the same column as them and this needs to correspond to both E_p and E_fit.

In conclusion, the data selected in 1. and 2. goes to the horizontal axis and the data selected in 3. goes to the vertical axis.

Thank you. curve fitting MATLAB Answers — New Questions

## “bookee” calendar times, not person’s doing booking

Hi, we are (urgently) trying to create a Booking calendar so people from anywhere in the world can attend a clinic to discuss a specific technical area, with one of our technicians, who are also all over the world.

I cannot find a way to make the calendar reflect that of those who are being booked, but there are plenty of options for allowing the calendar of the person making the booking.

Anyone got any ideas about this?

Thanks in advance.

Hi, we are (urgently) trying to create a Booking calendar so people from anywhere in the world can attend a clinic to discuss a specific technical area, with one of our technicians, who are also all over the world.I cannot find a way to make the calendar reflect that of those who are being booked, but there are plenty of options for allowing the calendar of the person making the booking.Anyone got any ideas about this?Thanks in advance. Read More

## regsvr32.exe command causes dammage to the Windows security app

Hi. I recently noticed that running the regsvr32.exe C:Windowssystem32SecurityHealthProxyStub.dll causes the Windows security app to stop functionning and show only a blank screen. I think this is a severe issue, because it allows attackers to easily disable the system protection for Microsoft Defender users. I also could not find any easy way to revert the issue, only an in-place Windows reinstall or System restore can fix it.

Hi. I recently noticed that running the regsvr32.exe C:Windowssystem32SecurityHealthProxyStub.dll causes the Windows security app to stop functionning and show only a blank screen. I think this is a severe issue, because it allows attackers to easily disable the system protection for Microsoft Defender users. I also could not find any easy way to revert the issue, only an in-place Windows reinstall or System restore can fix it. Read More

## plutosdr get chan error

hello I am tring to use matlab with ad9363+ zynq ,the firmware is plutosdr v0.38 built by myself,the iio version is v0.25

the matlab code is

warning(‘off’,’plutoradio:sysobj:FirmwareIncompatible’);

%%ad9361配置

tx = sdrtx(‘AD936x’,…

‘IPAddress’,’192.168.5.23′,…

‘ChannelMapping’,1)

radioSettings = info(tx)

tx.CenterFrequency = 2.415e9;

tx.BasebandSampleRate = 2e6;

tx.Gain = -5;

%% 信号波形设置

sw = dsp.SineWave;

sw.Amplitude = 0.5;

sw.Frequency = 100e3;

sw.ComplexOutput = true;

sw.SampleRate = tx.BasebandSampleRate;

sw.SamplesPerFrame = 1000;

txWaveform = sw();

%%发送信号

transmitRepeat(tx,txWaveform)

release(tx);

the error is

包含以下字段的 struct:

Status: ‘Unable to setup device’

MsgDetails: ‘Channel: altvoltage4 not found.’

11 tx.CenterFrequency = 2.415e9;

## Establishing connection to hardware. This process can take several seconds.

错误使用 matlabshared.libiio.base/cstatusid

Channel: altvoltage4 not found.

出错 matlabshared.libiio.base/getChan

出错 comm.libiio.AD9361.tx_control/setupDDSChannel

出错 comm.libiio.AD9361.tx_control/SetupDDS

出错 comm.libiio.AD9361.tx_control/sdrDevPreInit

出错 comm.libiio.AD9361.tx/sdrDevPreInit

出错 comm.zynqradioshared.tx/sdrDevPreInit

出错 comm.libiio.AD9361.base_control/configureChanBuffers

出错 comm.libiio.AD9361.base/configureChanBuffers

出错 matlabshared.libiio.base/setupImpl

出错 comm.libiio.AD9361.base_control/setupImpl

出错 comm.libiio.AD9361.base/setupImpl

出错 comm.libiio.AD9361.tx_control/setupImpl

出错 comm.libiio.AD9361.tx/setupImpl

出错 comm.zynqradioshared.tx/setupImpl

出错 comm.libiio.AD9361.tx/transmitRepeat

出错 Untitled (第 28 行)

transmitRepeat(tx,txWaveform)

but I think the firmware is ok because I test it on GNU Radio and ADI IIO Osi they both work.

when I swith the sdrtx(‘AD936x’ to sdrtx(‘pluto’,I get the error

The radio with ID usb:0 is already owned by a block,hello I am tring to use matlab with ad9363+ zynq ,the firmware is plutosdr v0.38 built by myself,the iio version is v0.25

the matlab code is

warning(‘off’,’plutoradio:sysobj:FirmwareIncompatible’);

%%ad9361配置

tx = sdrtx(‘AD936x’,…

‘IPAddress’,’192.168.5.23′,…

‘ChannelMapping’,1)

radioSettings = info(tx)

tx.CenterFrequency = 2.415e9;

tx.BasebandSampleRate = 2e6;

tx.Gain = -5;

%% 信号波形设置

sw = dsp.SineWave;

sw.Amplitude = 0.5;

sw.Frequency = 100e3;

sw.ComplexOutput = true;

sw.SampleRate = tx.BasebandSampleRate;

sw.SamplesPerFrame = 1000;

txWaveform = sw();

%%发送信号

transmitRepeat(tx,txWaveform)

release(tx);

the error is

包含以下字段的 struct:

Status: ‘Unable to setup device’

MsgDetails: ‘Channel: altvoltage4 not found.’

11 tx.CenterFrequency = 2.415e9;

## Establishing connection to hardware. This process can take several seconds.

错误使用 matlabshared.libiio.base/cstatusid

Channel: altvoltage4 not found.

出错 matlabshared.libiio.base/getChan

出错 comm.libiio.AD9361.tx_control/setupDDSChannel

出错 comm.libiio.AD9361.tx_control/SetupDDS

出错 comm.libiio.AD9361.tx_control/sdrDevPreInit

出错 comm.libiio.AD9361.tx/sdrDevPreInit

出错 comm.zynqradioshared.tx/sdrDevPreInit

出错 comm.libiio.AD9361.base_control/configureChanBuffers

出错 comm.libiio.AD9361.base/configureChanBuffers

出错 matlabshared.libiio.base/setupImpl

出错 comm.libiio.AD9361.base_control/setupImpl

出错 comm.libiio.AD9361.base/setupImpl

出错 comm.libiio.AD9361.tx_control/setupImpl

出错 comm.libiio.AD9361.tx/setupImpl

出错 comm.zynqradioshared.tx/setupImpl

出错 comm.libiio.AD9361.tx/transmitRepeat

出错 Untitled (第 28 行)

transmitRepeat(tx,txWaveform)

but I think the firmware is ok because I test it on GNU Radio and ADI IIO Osi they both work.

when I swith the sdrtx(‘AD936x’ to sdrtx(‘pluto’,I get the error

The radio with ID usb:0 is already owned by a block, hello I am tring to use matlab with ad9363+ zynq ,the firmware is plutosdr v0.38 built by myself,the iio version is v0.25

the matlab code is

warning(‘off’,’plutoradio:sysobj:FirmwareIncompatible’);

%%ad9361配置

tx = sdrtx(‘AD936x’,…

‘IPAddress’,’192.168.5.23′,…

‘ChannelMapping’,1)

radioSettings = info(tx)

tx.CenterFrequency = 2.415e9;

tx.BasebandSampleRate = 2e6;

tx.Gain = -5;

%% 信号波形设置

sw = dsp.SineWave;

sw.Amplitude = 0.5;

sw.Frequency = 100e3;

sw.ComplexOutput = true;

sw.SampleRate = tx.BasebandSampleRate;

sw.SamplesPerFrame = 1000;

txWaveform = sw();

%%发送信号

transmitRepeat(tx,txWaveform)

release(tx);

the error is

包含以下字段的 struct:

Status: ‘Unable to setup device’

MsgDetails: ‘Channel: altvoltage4 not found.’

11 tx.CenterFrequency = 2.415e9;

## Establishing connection to hardware. This process can take several seconds.

错误使用 matlabshared.libiio.base/cstatusid

Channel: altvoltage4 not found.

出错 matlabshared.libiio.base/getChan

出错 comm.libiio.AD9361.tx_control/setupDDSChannel

出错 comm.libiio.AD9361.tx_control/SetupDDS

出错 comm.libiio.AD9361.tx_control/sdrDevPreInit

出错 comm.libiio.AD9361.tx/sdrDevPreInit

出错 comm.zynqradioshared.tx/sdrDevPreInit

出错 comm.libiio.AD9361.base_control/configureChanBuffers

出错 comm.libiio.AD9361.base/configureChanBuffers

出错 matlabshared.libiio.base/setupImpl

出错 comm.libiio.AD9361.base_control/setupImpl

出错 comm.libiio.AD9361.base/setupImpl

出错 comm.libiio.AD9361.tx_control/setupImpl

出错 comm.libiio.AD9361.tx/setupImpl

出错 comm.zynqradioshared.tx/setupImpl

出错 comm.libiio.AD9361.tx/transmitRepeat

出错 Untitled (第 28 行)

transmitRepeat(tx,txWaveform)

but I think the firmware is ok because I test it on GNU Radio and ADI IIO Osi they both work.

when I swith the sdrtx(‘AD936x’ to sdrtx(‘pluto’,I get the error

The radio with ID usb:0 is already owned by a block, ad936x MATLAB Answers — New Questions

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