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Home/News

Category: News

How to make Rounded Edges of rectangle corners with polyshape function ?
Matlab News

How to make Rounded Edges of rectangle corners with polyshape function ?

PuTI / 2025-06-28

hello, everybody
I would like to make the rounded rectangle and I would like them to translated and rotated.
First I tried with rectangle function using ‘Curvature’ of 1. then I can make the rounded rectangle.
However, it is not possible for them to translated and rotated.
Therefore, I made the rectangle with polyshape function. and it is okay for them to translated and rotated.
However, i do not know how I can make them to have rounded edges.
Could you please helpe me how to make them to rounded edges with polyshape function?
clear; close all; clc;
figure(1); hold on; axis equal
% Before translating and rotating
rectangle(‘Position’,[-14.1276, 226.1976, 6.5929, 9.4184],’FaceColor’,’r’,’Curvature’,1)

% After translating and rotating
% translate and rotate polygon
x = [-14.1276; -14.1276+6.5929; -14.1276+6.5929; -14.1276];
y = [226.1976; 226.1976; 226.1976+9.4184; 226.1976+9.4184];
p = [x,y];
pgon = polyshape(p);
pgon = translate(pgon,50,-75);
pgon = rotate(pgon,18,[57, 349]);
plot(pgon,’FaceColor’,’red’,’FaceAlpha’,1)
% object by rectangle function can not translated and rotated.hello, everybody
I would like to make the rounded rectangle and I would like them to translated and rotated.
First I tried with rectangle function using ‘Curvature’ of 1. then I can make the rounded rectangle.
However, it is not possible for them to translated and rotated.
Therefore, I made the rectangle with polyshape function. and it is okay for them to translated and rotated.
However, i do not know how I can make them to have rounded edges.
Could you please helpe me how to make them to rounded edges with polyshape function?
clear; close all; clc;
figure(1); hold on; axis equal
% Before translating and rotating
rectangle(‘Position’,[-14.1276, 226.1976, 6.5929, 9.4184],’FaceColor’,’r’,’Curvature’,1)

% After translating and rotating
% translate and rotate polygon
x = [-14.1276; -14.1276+6.5929; -14.1276+6.5929; -14.1276];
y = [226.1976; 226.1976; 226.1976+9.4184; 226.1976+9.4184];
p = [x,y];
pgon = polyshape(p);
pgon = translate(pgon,50,-75);
pgon = rotate(pgon,18,[57, 349]);
plot(pgon,’FaceColor’,’red’,’FaceAlpha’,1)
% object by rectangle function can not translated and rotated. hello, everybody
I would like to make the rounded rectangle and I would like them to translated and rotated.
First I tried with rectangle function using ‘Curvature’ of 1. then I can make the rounded rectangle.
However, it is not possible for them to translated and rotated.
Therefore, I made the rectangle with polyshape function. and it is okay for them to translated and rotated.
However, i do not know how I can make them to have rounded edges.
Could you please helpe me how to make them to rounded edges with polyshape function?
clear; close all; clc;
figure(1); hold on; axis equal
% Before translating and rotating
rectangle(‘Position’,[-14.1276, 226.1976, 6.5929, 9.4184],’FaceColor’,’r’,’Curvature’,1)

% After translating and rotating
% translate and rotate polygon
x = [-14.1276; -14.1276+6.5929; -14.1276+6.5929; -14.1276];
y = [226.1976; 226.1976; 226.1976+9.4184; 226.1976+9.4184];
p = [x,y];
pgon = polyshape(p);
pgon = translate(pgon,50,-75);
pgon = rotate(pgon,18,[57, 349]);
plot(pgon,’FaceColor’,’red’,’FaceAlpha’,1)
% object by rectangle function can not translated and rotated. curvature MATLAB Answers — New Questions

​

Please help me to run this code
Matlab News

Please help me to run this code

PuTI / 2025-06-28

function sol= proj
clc;clf;clear;
%Relation of base fluid
rhof=997.1*10^-3;kf=0.613*10^5;cpf=4179*10^4;muf=10^-3*10;
alfaf=kf/(rhof*cpf);
bef=21*10^-5;
ky=muf/rhof;
disp(‘ky’);disp((muf/rhof));
%sigf=0.05*10^-8;
%Ag
ph1=0.01;
rho1=10500*10^-3;
cp1=235*10^4;
k1=429*10^5;be1=21*10^-5;
%sig1=0.74*10^-2;

%copper
ph2=0.01;
rho2=8933*10^-3;
cp2=385*10^4;
k2=400*10^5;
%sig2=5.96*10^-1;
be2=1.67*10^-5;
%Alumina
ph3=0.01;
rho3=3970*10^-3;
cp3=765*10^4;
k3=40*10^5;
be3=0.85*10^-5;

%sig3=3.5*10^-1;
%Relation of ternary hyprid
kn=kf*((k3+2*kf-2*ph3*(kf-k3))/(k3+2*kf+ph3*(kf-k3)));
kh=kn*((k2+2*kn-2*ph2*(kn-k2))/(k2+2*kn+ph2*(kn-k2)));
kt=kh*((k1+2*kh-2*ph1*(kh-k1))/(k1+2*kh+ph1*(kh-k1)));
mut= muf/((1-ph1)^2.5*(1-ph2)^2.5*(1-ph3)^2.5);
rhot=(1-ph1)*((1-ph2)*((1-ph3)+ph3*(rho3/rhof))+ph2*(rho2/rhof))+ph1*(rho1/rhof);
%vt=rhot*cpt
vt =(1-ph1)*((1-ph2)*((1-ph3)+ph3*((rho3*cp3)/(rhof*cpf)))+ph2*((rho2*cp2)/(rhof*cpf)))+ph1*((rho1*cp1)/(rhof*cpf));
%disp(‘vt’);disp(vt);
%vb=rho*betb
vb =(1-ph1)*((1-ph2)*((1-ph3)+ph3*((rho3*be3)/(rhof*bef)))+ph2*((rho2*be2)/(rhof*bef)))+ph1*((rho1*be1)/(rhof*bef));
%disp(‘vb’);disp(vb);disp(ky);
myLegend1 = {};myLegend2 = {};
%for i =1:numel(rr)
rr = [0 1 2];
numGr = numel(rr);
m = linspace(0,1);
a=-0.001;b=0.0001;p=-0.15/((1-0.01)*(mut/muf)*(rhof/rhot));
Ec=0.5;
gamma=pi/4;
prf=6.9;Rd=0.5;
Tw=273+50;Ti=273+27;deltaT=Tw-Ti;
disp(‘coe’);disp((mut/muf)*(rhof/rhot));
Lf=rhof*kf;
y0 = [1,0,1,0,0,1,0,1];options =bvpset(‘stats’,’on’,’RelTol’,1e-5);
%solinit = bvpinit(m,y0);
% sol= bvp4c(@projfun,@projbc,solinit,options);
Z = zeros(numGr, length(m));
for i = 1:numGr
Gr= rr(i);
solinit = bvpinit(m, y0);
sol = bvp4c(@projfun, @projbc, solinit, options);
Z(i, 🙂 = deval(sol,m,1); % Store the z-axis data
end
[X, Y] = meshgrid(m, rr);
figure;
surf(X, Y, Z);
xlabel(‘x’);
ylabel(‘Prf’);
zlabel(‘Solution y(6,:)’);
title(‘Surface Plot of Solution’);
grid on;

function dy= projfun(~,y)
dy= zeros(8,1);
% alignComments
E = y(1);
dE = y(2);
F = y(3);
dF= y(4);
w = y(5);
dw=y(6);
t = y(7);
dt = y(8);
dy(1) = dE;
dy(2) = (((rhot/mut)*(a*(muf/rhof)^0.5*(E*F+E^2)+a*(muf/rhof)*w*dE-(mut/muf)*(rhof/rhot)*p*(1-0.01)*E+Gr*a*(muf/rhof)*sin(gamma)*(vb/(rhof*bef))*t)));
dy(3) = dF;
dy(4) = (((rhot/mut)*(b*(muf/rhof)^0.5*(F^2+F*E)+(muf/rhof)*b^0.5*a^(1.5)*dF)));
dy(5) =-(a*F+b*E);
dy(6) = (((rhot/mut)*((muf/rhof)^0.5*w*dw+Gr*b*(muf/rhof)*cos(gamma)*(vb/(rhof*bef))*t)));
dy(7) = dt;
dy(8)=prf*(1/(kt/kf))*(1/(1+((prf*Rd)/((kt/kf)))))*((vt/(rhof*cpf))*(muf/rhof)^0.5*w*dt-(mut/muf)*Ec*1*dw^2) ;

end
end

function res= projbc(ya,yb)

res= [ya(1)+1;
ya(3)-1;
ya(5);
ya(6);
ya(7)-1-(1/0.9)*ya(8);
yb(1)-0.01;
yb(3);
yb(7);
% yb(7);
];
endfunction sol= proj
clc;clf;clear;
%Relation of base fluid
rhof=997.1*10^-3;kf=0.613*10^5;cpf=4179*10^4;muf=10^-3*10;
alfaf=kf/(rhof*cpf);
bef=21*10^-5;
ky=muf/rhof;
disp(‘ky’);disp((muf/rhof));
%sigf=0.05*10^-8;
%Ag
ph1=0.01;
rho1=10500*10^-3;
cp1=235*10^4;
k1=429*10^5;be1=21*10^-5;
%sig1=0.74*10^-2;

%copper
ph2=0.01;
rho2=8933*10^-3;
cp2=385*10^4;
k2=400*10^5;
%sig2=5.96*10^-1;
be2=1.67*10^-5;
%Alumina
ph3=0.01;
rho3=3970*10^-3;
cp3=765*10^4;
k3=40*10^5;
be3=0.85*10^-5;

%sig3=3.5*10^-1;
%Relation of ternary hyprid
kn=kf*((k3+2*kf-2*ph3*(kf-k3))/(k3+2*kf+ph3*(kf-k3)));
kh=kn*((k2+2*kn-2*ph2*(kn-k2))/(k2+2*kn+ph2*(kn-k2)));
kt=kh*((k1+2*kh-2*ph1*(kh-k1))/(k1+2*kh+ph1*(kh-k1)));
mut= muf/((1-ph1)^2.5*(1-ph2)^2.5*(1-ph3)^2.5);
rhot=(1-ph1)*((1-ph2)*((1-ph3)+ph3*(rho3/rhof))+ph2*(rho2/rhof))+ph1*(rho1/rhof);
%vt=rhot*cpt
vt =(1-ph1)*((1-ph2)*((1-ph3)+ph3*((rho3*cp3)/(rhof*cpf)))+ph2*((rho2*cp2)/(rhof*cpf)))+ph1*((rho1*cp1)/(rhof*cpf));
%disp(‘vt’);disp(vt);
%vb=rho*betb
vb =(1-ph1)*((1-ph2)*((1-ph3)+ph3*((rho3*be3)/(rhof*bef)))+ph2*((rho2*be2)/(rhof*bef)))+ph1*((rho1*be1)/(rhof*bef));
%disp(‘vb’);disp(vb);disp(ky);
myLegend1 = {};myLegend2 = {};
%for i =1:numel(rr)
rr = [0 1 2];
numGr = numel(rr);
m = linspace(0,1);
a=-0.001;b=0.0001;p=-0.15/((1-0.01)*(mut/muf)*(rhof/rhot));
Ec=0.5;
gamma=pi/4;
prf=6.9;Rd=0.5;
Tw=273+50;Ti=273+27;deltaT=Tw-Ti;
disp(‘coe’);disp((mut/muf)*(rhof/rhot));
Lf=rhof*kf;
y0 = [1,0,1,0,0,1,0,1];options =bvpset(‘stats’,’on’,’RelTol’,1e-5);
%solinit = bvpinit(m,y0);
% sol= bvp4c(@projfun,@projbc,solinit,options);
Z = zeros(numGr, length(m));
for i = 1:numGr
Gr= rr(i);
solinit = bvpinit(m, y0);
sol = bvp4c(@projfun, @projbc, solinit, options);
Z(i, 🙂 = deval(sol,m,1); % Store the z-axis data
end
[X, Y] = meshgrid(m, rr);
figure;
surf(X, Y, Z);
xlabel(‘x’);
ylabel(‘Prf’);
zlabel(‘Solution y(6,:)’);
title(‘Surface Plot of Solution’);
grid on;

function dy= projfun(~,y)
dy= zeros(8,1);
% alignComments
E = y(1);
dE = y(2);
F = y(3);
dF= y(4);
w = y(5);
dw=y(6);
t = y(7);
dt = y(8);
dy(1) = dE;
dy(2) = (((rhot/mut)*(a*(muf/rhof)^0.5*(E*F+E^2)+a*(muf/rhof)*w*dE-(mut/muf)*(rhof/rhot)*p*(1-0.01)*E+Gr*a*(muf/rhof)*sin(gamma)*(vb/(rhof*bef))*t)));
dy(3) = dF;
dy(4) = (((rhot/mut)*(b*(muf/rhof)^0.5*(F^2+F*E)+(muf/rhof)*b^0.5*a^(1.5)*dF)));
dy(5) =-(a*F+b*E);
dy(6) = (((rhot/mut)*((muf/rhof)^0.5*w*dw+Gr*b*(muf/rhof)*cos(gamma)*(vb/(rhof*bef))*t)));
dy(7) = dt;
dy(8)=prf*(1/(kt/kf))*(1/(1+((prf*Rd)/((kt/kf)))))*((vt/(rhof*cpf))*(muf/rhof)^0.5*w*dt-(mut/muf)*Ec*1*dw^2) ;

end
end

function res= projbc(ya,yb)

res= [ya(1)+1;
ya(3)-1;
ya(5);
ya(6);
ya(7)-1-(1/0.9)*ya(8);
yb(1)-0.01;
yb(3);
yb(7);
% yb(7);
];
end function sol= proj
clc;clf;clear;
%Relation of base fluid
rhof=997.1*10^-3;kf=0.613*10^5;cpf=4179*10^4;muf=10^-3*10;
alfaf=kf/(rhof*cpf);
bef=21*10^-5;
ky=muf/rhof;
disp(‘ky’);disp((muf/rhof));
%sigf=0.05*10^-8;
%Ag
ph1=0.01;
rho1=10500*10^-3;
cp1=235*10^4;
k1=429*10^5;be1=21*10^-5;
%sig1=0.74*10^-2;

%copper
ph2=0.01;
rho2=8933*10^-3;
cp2=385*10^4;
k2=400*10^5;
%sig2=5.96*10^-1;
be2=1.67*10^-5;
%Alumina
ph3=0.01;
rho3=3970*10^-3;
cp3=765*10^4;
k3=40*10^5;
be3=0.85*10^-5;

%sig3=3.5*10^-1;
%Relation of ternary hyprid
kn=kf*((k3+2*kf-2*ph3*(kf-k3))/(k3+2*kf+ph3*(kf-k3)));
kh=kn*((k2+2*kn-2*ph2*(kn-k2))/(k2+2*kn+ph2*(kn-k2)));
kt=kh*((k1+2*kh-2*ph1*(kh-k1))/(k1+2*kh+ph1*(kh-k1)));
mut= muf/((1-ph1)^2.5*(1-ph2)^2.5*(1-ph3)^2.5);
rhot=(1-ph1)*((1-ph2)*((1-ph3)+ph3*(rho3/rhof))+ph2*(rho2/rhof))+ph1*(rho1/rhof);
%vt=rhot*cpt
vt =(1-ph1)*((1-ph2)*((1-ph3)+ph3*((rho3*cp3)/(rhof*cpf)))+ph2*((rho2*cp2)/(rhof*cpf)))+ph1*((rho1*cp1)/(rhof*cpf));
%disp(‘vt’);disp(vt);
%vb=rho*betb
vb =(1-ph1)*((1-ph2)*((1-ph3)+ph3*((rho3*be3)/(rhof*bef)))+ph2*((rho2*be2)/(rhof*bef)))+ph1*((rho1*be1)/(rhof*bef));
%disp(‘vb’);disp(vb);disp(ky);
myLegend1 = {};myLegend2 = {};
%for i =1:numel(rr)
rr = [0 1 2];
numGr = numel(rr);
m = linspace(0,1);
a=-0.001;b=0.0001;p=-0.15/((1-0.01)*(mut/muf)*(rhof/rhot));
Ec=0.5;
gamma=pi/4;
prf=6.9;Rd=0.5;
Tw=273+50;Ti=273+27;deltaT=Tw-Ti;
disp(‘coe’);disp((mut/muf)*(rhof/rhot));
Lf=rhof*kf;
y0 = [1,0,1,0,0,1,0,1];options =bvpset(‘stats’,’on’,’RelTol’,1e-5);
%solinit = bvpinit(m,y0);
% sol= bvp4c(@projfun,@projbc,solinit,options);
Z = zeros(numGr, length(m));
for i = 1:numGr
Gr= rr(i);
solinit = bvpinit(m, y0);
sol = bvp4c(@projfun, @projbc, solinit, options);
Z(i, 🙂 = deval(sol,m,1); % Store the z-axis data
end
[X, Y] = meshgrid(m, rr);
figure;
surf(X, Y, Z);
xlabel(‘x’);
ylabel(‘Prf’);
zlabel(‘Solution y(6,:)’);
title(‘Surface Plot of Solution’);
grid on;

function dy= projfun(~,y)
dy= zeros(8,1);
% alignComments
E = y(1);
dE = y(2);
F = y(3);
dF= y(4);
w = y(5);
dw=y(6);
t = y(7);
dt = y(8);
dy(1) = dE;
dy(2) = (((rhot/mut)*(a*(muf/rhof)^0.5*(E*F+E^2)+a*(muf/rhof)*w*dE-(mut/muf)*(rhof/rhot)*p*(1-0.01)*E+Gr*a*(muf/rhof)*sin(gamma)*(vb/(rhof*bef))*t)));
dy(3) = dF;
dy(4) = (((rhot/mut)*(b*(muf/rhof)^0.5*(F^2+F*E)+(muf/rhof)*b^0.5*a^(1.5)*dF)));
dy(5) =-(a*F+b*E);
dy(6) = (((rhot/mut)*((muf/rhof)^0.5*w*dw+Gr*b*(muf/rhof)*cos(gamma)*(vb/(rhof*bef))*t)));
dy(7) = dt;
dy(8)=prf*(1/(kt/kf))*(1/(1+((prf*Rd)/((kt/kf)))))*((vt/(rhof*cpf))*(muf/rhof)^0.5*w*dt-(mut/muf)*Ec*1*dw^2) ;

end
end

function res= projbc(ya,yb)

res= [ya(1)+1;
ya(3)-1;
ya(5);
ya(6);
ya(7)-1-(1/0.9)*ya(8);
yb(1)-0.01;
yb(3);
yb(7);
% yb(7);
];
end bvp4c, ordinary differential equation MATLAB Answers — New Questions

​

WANT A HELP IN DTM MATLAP CODE
Matlab News

WANT A HELP IN DTM MATLAP CODE

PuTI / 2025-06-28

THIS IS A FILE DESCRIBE THE EQUATION AND THE , the Lagrangian -derived equations of motionTHIS IS A FILE DESCRIBE THE EQUATION AND THE , the Lagrangian -derived equations of motion THIS IS A FILE DESCRIBE THE EQUATION AND THE , the Lagrangian -derived equations of motion matlab, mathematics, code MATLAB Answers — New Questions

​

Question about convert PPO agent to ONNX
Matlab News

Question about convert PPO agent to ONNX

PuTI / 2025-06-27

Greetings,
I encountered the error shown in figure when I tried to execute the ONNX on ubuntu at the first time.
Then I found it was the problem caused by rlContinuousGaussianActor, so I removed the mp_out and sdev layers and it worked!
But I don’t understand why it worked due to as my knowledge after I removed those layers it will mess up the PPO structure.
So what should I do? Keep working without mp_out and sdev layers or trying to fix them?
however, I have no idea about how to fix them, so I am confused.
Thank your time for watching or answering me.
Best wish,
HUANGreetings,
I encountered the error shown in figure when I tried to execute the ONNX on ubuntu at the first time.
Then I found it was the problem caused by rlContinuousGaussianActor, so I removed the mp_out and sdev layers and it worked!
But I don’t understand why it worked due to as my knowledge after I removed those layers it will mess up the PPO structure.
So what should I do? Keep working without mp_out and sdev layers or trying to fix them?
however, I have no idea about how to fix them, so I am confused.
Thank your time for watching or answering me.
Best wish,
HUAN Greetings,
I encountered the error shown in figure when I tried to execute the ONNX on ubuntu at the first time.
Then I found it was the problem caused by rlContinuousGaussianActor, so I removed the mp_out and sdev layers and it worked!
But I don’t understand why it worked due to as my knowledge after I removed those layers it will mess up the PPO structure.
So what should I do? Keep working without mp_out and sdev layers or trying to fix them?
however, I have no idea about how to fix them, so I am confused.
Thank your time for watching or answering me.
Best wish,
HUAN ppo, onnx, rlcontinuousgaussianactor MATLAB Answers — New Questions

​

Error using integral2Calc>integral2t/tensor (line 231) Input function must return ‘double’ or ‘single’ values. Found ‘sym’.
Matlab News

Error using integral2Calc>integral2t/tensor (line 231) Input function must return ‘double’ or ‘single’ values. Found ‘sym’.

PuTI / 2025-06-27

I’d like to know where the mistake in this code, and the solution

h = 0.00172; rho = 1620;
L = 100*h; c=L*L/4;
M=10; N=10;
syms xi eta
shape_w=sym(zeros(M,N)); x_m=sym(zeros(M,1)); y_n=sym(zeros(N,1));

for m=1:M
for n=1:N
x_m(m)=(xi^m)*(1+xi);
y_n(n)=(eta^n)*(1-eta)*(1+eta)^2;
shape_w(m,n)=x_m(m)*y_n(n);
end
end
fun=(shape_w*shape_w’);
M_w =c*(rho*h)*integral2(@(xi,eta)fun,-1,1,-1,1);I’d like to know where the mistake in this code, and the solution

h = 0.00172; rho = 1620;
L = 100*h; c=L*L/4;
M=10; N=10;
syms xi eta
shape_w=sym(zeros(M,N)); x_m=sym(zeros(M,1)); y_n=sym(zeros(N,1));

for m=1:M
for n=1:N
x_m(m)=(xi^m)*(1+xi);
y_n(n)=(eta^n)*(1-eta)*(1+eta)^2;
shape_w(m,n)=x_m(m)*y_n(n);
end
end
fun=(shape_w*shape_w’);
M_w =c*(rho*h)*integral2(@(xi,eta)fun,-1,1,-1,1); I’d like to know where the mistake in this code, and the solution

h = 0.00172; rho = 1620;
L = 100*h; c=L*L/4;
M=10; N=10;
syms xi eta
shape_w=sym(zeros(M,N)); x_m=sym(zeros(M,1)); y_n=sym(zeros(N,1));

for m=1:M
for n=1:N
x_m(m)=(xi^m)*(1+xi);
y_n(n)=(eta^n)*(1-eta)*(1+eta)^2;
shape_w(m,n)=x_m(m)*y_n(n);
end
end
fun=(shape_w*shape_w’);
M_w =c*(rho*h)*integral2(@(xi,eta)fun,-1,1,-1,1); how i can fix error using integral2calc integral2t/tensor (line 231)? MATLAB Answers — New Questions

​

What are the data confidentiality settings for MATLAB Copilot?
Matlab News

What are the data confidentiality settings for MATLAB Copilot?

PuTI / 2025-06-27

As some of the code being developed in MATLAB could be confidential, I am wondering if there are some Terms and Conditions or configuration parameters that must be set so that the MATLAB Copilot does not:
Get trained from the code
Uploads this protected information online
Expose to MathWorks or third parties the protected content
Could you explain how does it work regarding data confidentiality and how to set it up to prevent sharing or training from this confidential information?As some of the code being developed in MATLAB could be confidential, I am wondering if there are some Terms and Conditions or configuration parameters that must be set so that the MATLAB Copilot does not:
Get trained from the code
Uploads this protected information online
Expose to MathWorks or third parties the protected content
Could you explain how does it work regarding data confidentiality and how to set it up to prevent sharing or training from this confidential information? As some of the code being developed in MATLAB could be confidential, I am wondering if there are some Terms and Conditions or configuration parameters that must be set so that the MATLAB Copilot does not:
Get trained from the code
Uploads this protected information online
Expose to MathWorks or third parties the protected content
Could you explain how does it work regarding data confidentiality and how to set it up to prevent sharing or training from this confidential information? matlab, copilot, matlab-copilot MATLAB Answers — New Questions

​

Undefined function handle error when loading a neural network that contain a function layer
Matlab News

Undefined function handle error when loading a neural network that contain a function layer

PuTI / 2025-06-27

Hi everyone,
I created a neural network that uses a functionLayer that I defined by :
functionLayer(@Feature_wise_LM,Name="FiLM_64",Formattable=1,NumInputs=2,NumOutputs=1)
However, when I save the trained neural network with :
save("./trained_networks/FiLM", "trained_network");
and reload it in the same script with :
trained_network = load("./trained_networks/FiLM.mat");
trained_network = trained_network.trained_network;
I get the error :
Warning: While loading an object of class ‘dlnetwork’:
Error using nnet.internal.cnn.layer.GraphExecutor/propagate (line 354)
Execution failed during layer(s) ‘FiLM_64’.

Error in deep.internal.network.ExecutableNetwork/configureForInputsAndForwardOnLayer (line 347)
propagate(this, fcn, Xs, outputLayerIdx, outputLayerPortIdx);

Error in deep.internal.network.EditableNetwork/convertToDlnetwork (line 101)
[executableNetwork, layerOutputSizes] = configureForInputsAndForwardOnLayer(…

Error in dlnetwork.loadobj (line 741)
net = convertToDlnetwork(privateNet, exampleInputs, initializeNetworkWeights);

Caused by:
Undefined function handle.

Error in nnet.cnn.layer.FunctionLayer/predict (line 61)
[varargout{1:layer.NumOutputs}] = layer.PredictFcn(varargin{:});

I tried to save the function in a dedicated file "Feature_wise_LM.m" , but it didn’t workHi everyone,
I created a neural network that uses a functionLayer that I defined by :
functionLayer(@Feature_wise_LM,Name="FiLM_64",Formattable=1,NumInputs=2,NumOutputs=1)
However, when I save the trained neural network with :
save("./trained_networks/FiLM", "trained_network");
and reload it in the same script with :
trained_network = load("./trained_networks/FiLM.mat");
trained_network = trained_network.trained_network;
I get the error :
Warning: While loading an object of class ‘dlnetwork’:
Error using nnet.internal.cnn.layer.GraphExecutor/propagate (line 354)
Execution failed during layer(s) ‘FiLM_64’.

Error in deep.internal.network.ExecutableNetwork/configureForInputsAndForwardOnLayer (line 347)
propagate(this, fcn, Xs, outputLayerIdx, outputLayerPortIdx);

Error in deep.internal.network.EditableNetwork/convertToDlnetwork (line 101)
[executableNetwork, layerOutputSizes] = configureForInputsAndForwardOnLayer(…

Error in dlnetwork.loadobj (line 741)
net = convertToDlnetwork(privateNet, exampleInputs, initializeNetworkWeights);

Caused by:
Undefined function handle.

Error in nnet.cnn.layer.FunctionLayer/predict (line 61)
[varargout{1:layer.NumOutputs}] = layer.PredictFcn(varargin{:});

I tried to save the function in a dedicated file "Feature_wise_LM.m" , but it didn’t work Hi everyone,
I created a neural network that uses a functionLayer that I defined by :
functionLayer(@Feature_wise_LM,Name="FiLM_64",Formattable=1,NumInputs=2,NumOutputs=1)
However, when I save the trained neural network with :
save("./trained_networks/FiLM", "trained_network");
and reload it in the same script with :
trained_network = load("./trained_networks/FiLM.mat");
trained_network = trained_network.trained_network;
I get the error :
Warning: While loading an object of class ‘dlnetwork’:
Error using nnet.internal.cnn.layer.GraphExecutor/propagate (line 354)
Execution failed during layer(s) ‘FiLM_64’.

Error in deep.internal.network.ExecutableNetwork/configureForInputsAndForwardOnLayer (line 347)
propagate(this, fcn, Xs, outputLayerIdx, outputLayerPortIdx);

Error in deep.internal.network.EditableNetwork/convertToDlnetwork (line 101)
[executableNetwork, layerOutputSizes] = configureForInputsAndForwardOnLayer(…

Error in dlnetwork.loadobj (line 741)
net = convertToDlnetwork(privateNet, exampleInputs, initializeNetworkWeights);

Caused by:
Undefined function handle.

Error in nnet.cnn.layer.FunctionLayer/predict (line 61)
[varargout{1:layer.NumOutputs}] = layer.PredictFcn(varargin{:});

I tried to save the function in a dedicated file "Feature_wise_LM.m" , but it didn’t work deep learning, neural networks, function, matlab function MATLAB Answers — New Questions

​

How to process in real-time data from a SoC Device using SoC Blockset?
Matlab News

How to process in real-time data from a SoC Device using SoC Blockset?

PuTI / 2025-06-27

Dear all,

I am using SoC Blockset for a simple receiver design on AMD Zynq Ultrascale+ ZCU111 evaluation board.
Now, when my design is ready, built and deployed, I have an external mode model open and wondering how I can send the data from the ZCU111 board processor to the host computer and operate it in real-time.

I have already tried using Rate Transition block, UDP write block and IO data sink block in the external mode model to save the data to the host, what I received were the files, photos of which are attached
These are binary files, content of which can be loaded to workspace using fopen() and fread() functions. This is working, but would be better if I could receive the data and work with it on the development PC in real-time (e.g. using a different model or some script). Is there a way? Am I using the correct logic for the external mode model (to send the data to the development pc using UDP)?

Thank you!Dear all,

I am using SoC Blockset for a simple receiver design on AMD Zynq Ultrascale+ ZCU111 evaluation board.
Now, when my design is ready, built and deployed, I have an external mode model open and wondering how I can send the data from the ZCU111 board processor to the host computer and operate it in real-time.

I have already tried using Rate Transition block, UDP write block and IO data sink block in the external mode model to save the data to the host, what I received were the files, photos of which are attached
These are binary files, content of which can be loaded to workspace using fopen() and fread() functions. This is working, but would be better if I could receive the data and work with it on the development PC in real-time (e.g. using a different model or some script). Is there a way? Am I using the correct logic for the external mode model (to send the data to the development pc using UDP)?

Thank you! Dear all,

I am using SoC Blockset for a simple receiver design on AMD Zynq Ultrascale+ ZCU111 evaluation board.
Now, when my design is ready, built and deployed, I have an external mode model open and wondering how I can send the data from the ZCU111 board processor to the host computer and operate it in real-time.

I have already tried using Rate Transition block, UDP write block and IO data sink block in the external mode model to save the data to the host, what I received were the files, photos of which are attached
These are binary files, content of which can be loaded to workspace using fopen() and fread() functions. This is working, but would be better if I could receive the data and work with it on the development PC in real-time (e.g. using a different model or some script). Is there a way? Am I using the correct logic for the external mode model (to send the data to the development pc using UDP)?

Thank you! udp, soc blockset, fpga, zcu111, external mode, .csv MATLAB Answers — New Questions

​

Copilot Agent Governance Product Launched by ISV
News

Copilot Agent Governance Product Launched by ISV

Tony Redmond / 2025-06-27

Microsoft Leaves Gaps in Technologies for ISVs to Fill – Like Agent Governance

Every time Microsoft makes a big move, ISVs seek to take advantage with a new product. It’s the way of the work. Microsoft creates technology and ISVs fill the holes left in that technology. In some respects, the cloud is a difficult place for ISVs. There’s less to tweak than in an on-premises environment and although the Graph APIs have extended their coverage to more areas of Microsoft 365 over the last few years, significant gaps still exist for major workloads like Exchange Online and SharePoint Online.

But a new technology creates a new opportunity because everything starts from scratch. Microsoft’s big move into artificial intelligence with Copilot hasn’t created too many opportunities because Copilot depends on a massive infrastructure operated by Microsoft that’s inaccessible except through applications like BizChat. Agents are different. They’re objects that need to be managed. They consume resources that need to be paid for. They represent potential security and compliance problems that require mitigation. In short, agents represent a chance for ISVs to build products to solve customer problems as Microsoft heads full tilt to its agentic future.

Building an Infrastructure for Agent Governance

To be fair to Microsoft, they’ve started to build an infrastructure for agent management. Apart from a whitepaper about managing and governning agents, the first concrete sign is the introduction of agent objects in Entra ID. Microsoft is thinking about how agents can work together, and how that communication can be controlled and monitored. That’s all great stuff and it will deliver benefits in the future, but the immediate risk is the fear that agents might run amok inside Microsoft 365 tenants.

Microsoft reports that there are 56 million monthly active users of Power Platform, or 13% of the 430 million paid Microsoft 365 seats. That’s a lot of citizen developers who could create agents using tools like Copilot Studio. Unless tenant administrators disable ad-hoc email subscriptions for the tenant, developers could be building agents without anyone’s knowledge.

Don’t get me wrong. I see great advantages in agent technology and have even built agents myself, notably a very useful agent to interact with the Office 365 for IT Pros eBook. One thing that we’ve learned over the last 30 years is that when users are allowed to create, they will. And they’ll create objects without thought, and those objects will need to be cleaned up eventually, or, as Microsoft discovered, the mass of SharePoint Online sites created for Teams became a real problem for Microsoft 365 Copilot deployments. Incorporating solid management and governance from the start is of great benefit for new technologies.

Rencore Steps Up with Copilot Agent Governance

All of which brings me to Rencore’s announcement of two new modules for their governance product to deal with Copilot and agent governance and Power Platform governance (Figure 1). Matthias Einig, Rencore’s CEO, has been forceful about the need to take control of these areas and it’s good to see that he’s investing in product development to help Microsoft 365 tenants take control before agents get any chance to become a problem.

Rencore Agent Governance (source: Rencore).
Figure 1: Rencore Agent Governance (source: Rencore)

I have not used the Rencore product and do not endorse it. I just think that it’s great to see an ISV move into this area with purpose and intent. It seems like Rencore aims to address some major pain points, like shadow IT, the cost of running Copilot agents, over-sharing, and “agent sprawl.” All good stuff.

I’m sure other ISVs will enter this space (and there might be some active in the area already that I don’t know of). This will be an interesting area to track as ISVs seek new ways to mitigate the potential risks posed by agents.

No Time to Relax

Product from one ISV does not mean that we can all relax and conclude that agent management is done. It’s not. The continuing huge investment by Microsoft in this space means that agent capabilities will improve and grow over time. Each improvement and new feature has the potential to affect governance and compliance strategies. Don’t let your guard down and make sure that your tenant has agents under control. And keep them that way.


Support the work of the Office 365 for IT Pros team by subscribing to the Office 365 for IT Pros eBook. Your support pays for the time we need to track, analyze, and document the changing world of Microsoft 365 and Office 365.

 

How to simulate a rotor inter-turn fault in synchronous generator,using matlab programme.
Matlab News

How to simulate a rotor inter-turn fault in synchronous generator,using matlab programme.

PuTI / 2025-06-26

I need programme format for simulating rotor inter-turn fault in synchronous generator.I need programme format for simulating rotor inter-turn fault in synchronous generator. I need programme format for simulating rotor inter-turn fault in synchronous generator. rotor inter-turn fault simulation. MATLAB Answers — New Questions

​

Plot browser in R2025a?
Matlab News

Plot browser in R2025a?

PuTI / 2025-06-26

in R2025a I can not find anymore the "plot browser" menu. I used a lot the hide/show options for lines.
Has this function disappeared?
Property Inspector is not really helpfull if many lines are present in the plot.in R2025a I can not find anymore the "plot browser" menu. I used a lot the hide/show options for lines.
Has this function disappeared?
Property Inspector is not really helpfull if many lines are present in the plot. in R2025a I can not find anymore the "plot browser" menu. I used a lot the hide/show options for lines.
Has this function disappeared?
Property Inspector is not really helpfull if many lines are present in the plot. plotedit MATLAB Answers — New Questions

​

Accessing simParameters.Carrier inside MATLAB function block in Simulink
Matlab News

Accessing simParameters.Carrier inside MATLAB function block in Simulink

PuTI / 2025-06-26

I am trying to implement PDSCH transceiver in simulink using simulink blocks and matlab functions.
I have already implemented this in Matlab code now want to implement in simulink, so now while doing this i am facing some problems using simParameters.Carrier in simulink matlab fucntion.
I have used variable carrier = simParameters.Carrier in matlab code. Now i want to access this as when i want to do OFDM Modulation i am using matlab function and while in this function i am writing
function txWaveform = OFDMModulator(pdschGrid, carrier)
txWaveform = nrOFDMModulate(carrier, pdschGrid);
end
But now i dont know how to access this carrier (in which complete configuration is present) in Matlab function.
Any suggestions on how I can get the data stored in this struct variable accessible inside the Simulink MATLAB function block? Thank you.I am trying to implement PDSCH transceiver in simulink using simulink blocks and matlab functions.
I have already implemented this in Matlab code now want to implement in simulink, so now while doing this i am facing some problems using simParameters.Carrier in simulink matlab fucntion.
I have used variable carrier = simParameters.Carrier in matlab code. Now i want to access this as when i want to do OFDM Modulation i am using matlab function and while in this function i am writing
function txWaveform = OFDMModulator(pdschGrid, carrier)
txWaveform = nrOFDMModulate(carrier, pdschGrid);
end
But now i dont know how to access this carrier (in which complete configuration is present) in Matlab function.
Any suggestions on how I can get the data stored in this struct variable accessible inside the Simulink MATLAB function block? Thank you. I am trying to implement PDSCH transceiver in simulink using simulink blocks and matlab functions.
I have already implemented this in Matlab code now want to implement in simulink, so now while doing this i am facing some problems using simParameters.Carrier in simulink matlab fucntion.
I have used variable carrier = simParameters.Carrier in matlab code. Now i want to access this as when i want to do OFDM Modulation i am using matlab function and while in this function i am writing
function txWaveform = OFDMModulator(pdschGrid, carrier)
txWaveform = nrOFDMModulate(carrier, pdschGrid);
end
But now i dont know how to access this carrier (in which complete configuration is present) in Matlab function.
Any suggestions on how I can get the data stored in this struct variable accessible inside the Simulink MATLAB function block? Thank you. #ofdm, #simulink, #modelworkspace, #workspace, #ofdmmodulation, data import MATLAB Answers — New Questions

​

install_unix_legacy not found during installation on MacOS
Matlab News

install_unix_legacy not found during installation on MacOS

PuTI / 2025-06-26

I’m trying to package Matlab R2025A MacOS intel processor version for silent install, I ran the downloader and got the file from download without installing option, however when I ran the install script within the downloaded file I got the following error

/bin/maca64/install_unix_legacy: cannot execute: No such file or directory

When I checked the downloaded files, that install_unix_legacy is indeed missing, is there anything special process to install this version?I’m trying to package Matlab R2025A MacOS intel processor version for silent install, I ran the downloader and got the file from download without installing option, however when I ran the install script within the downloaded file I got the following error

/bin/maca64/install_unix_legacy: cannot execute: No such file or directory

When I checked the downloaded files, that install_unix_legacy is indeed missing, is there anything special process to install this version? I’m trying to package Matlab R2025A MacOS intel processor version for silent install, I ran the downloader and got the file from download without installing option, however when I ran the install script within the downloaded file I got the following error

/bin/maca64/install_unix_legacy: cannot execute: No such file or directory

When I checked the downloaded files, that install_unix_legacy is indeed missing, is there anything special process to install this version? installation, mac, matlab MATLAB Answers — New Questions

​

Why do my Simulink PV module simulation characteristics appear this way?
Matlab News

Why do my Simulink PV module simulation characteristics appear this way?

PuTI / 2025-06-26

I have tried to simulate a PV module with Simulink for a change in radiation and I am unable to get the characteristics.The P-V and I-V characteristics I am expecting are <http://ecee.colorado.edu/~ecen2060/materials/simulink/PV/PV_module_model.pdf here> . I used the repeating sequence for my voltage source and the repeating sequence stair for my insolation. I get incomplete characteristics such as the figures below.

<</matlabcentral/answers/uploaded_files/192/P-V%20and%20I-V%20characteristicspage-0.jpg>>

_ . What could be the problem? I have changed the time values and output values for the repeating sequence source and the simulation time but without success. Anyone with an explanation?I have tried to simulate a PV module with Simulink for a change in radiation and I am unable to get the characteristics.The P-V and I-V characteristics I am expecting are <http://ecee.colorado.edu/~ecen2060/materials/simulink/PV/PV_module_model.pdf here> . I used the repeating sequence for my voltage source and the repeating sequence stair for my insolation. I get incomplete characteristics such as the figures below.

<</matlabcentral/answers/uploaded_files/192/P-V%20and%20I-V%20characteristicspage-0.jpg>>

_ . What could be the problem? I have changed the time values and output values for the repeating sequence source and the simulation time but without success. Anyone with an explanation? I have tried to simulate a PV module with Simulink for a change in radiation and I am unable to get the characteristics.The P-V and I-V characteristics I am expecting are <http://ecee.colorado.edu/~ecen2060/materials/simulink/PV/PV_module_model.pdf here> . I used the repeating sequence for my voltage source and the repeating sequence stair for my insolation. I get incomplete characteristics such as the figures below.

<</matlabcentral/answers/uploaded_files/192/P-V%20and%20I-V%20characteristicspage-0.jpg>>

_ . What could be the problem? I have changed the time values and output values for the repeating sequence source and the simulation time but without success. Anyone with an explanation? pv module, simulink, simulation. MATLAB Answers — New Questions

​

looking for strictly recurrent and fast moving median implementation
Matlab News

looking for strictly recurrent and fast moving median implementation

PuTI / 2025-06-26

I am looking for any suitable trick how to effectively compute moving window (length w) median. I know of course the movmedian function, but I need strictly recurrent native MATLAB function working sample by sample.
My naive solution, which is equivalent to the
output_median = movmedian(input_x,[w-1,0])
is as follows:
rng(‘default’)
% number of samples
N = 25;
% moving median windows length
w = 5;
% init history buffer and median
x_hist = rand;
med_new = x_hist;
% init input x vector
input_x = zeros(1,N);
input_x(1) = x_hist;
% init output median vector of length N
output_median = zeros(1,N);
output_median(1) = med_new;
for i = 2:N
x_new = rand;
[med_new,x_hist] = moving_median(x_hist,x_new,w);
input_x(i) = x_new;
output_median(i) = med_new;
end
where function moving_median is here:
function [med_new,x_hist] = moving_median(x_hist,x_new,w)
% Old length of history
wo = length(x_hist);
% Update history
x_hist = [x_hist(max(1,wo-w+2):wo),x_new]; % Grow history until size w, then append new x and remove oldest x
med_new = median(x_hist);
end
Any idea how to make this algorithm more effective (faster) a still strictly recurrent?

Target use case should works with window length:
w ~ 1e3 – 1e4 (!!!)

Additional notes:
fast moving median computing is always based on advanced data structures use like Heap or Queues, etc.
some sort-structure information could be stored in these structures and used at the next sample step to significant speed-up median computing
similar approach is used in movmedian function, but this function is not directly applicable on running-data streamI am looking for any suitable trick how to effectively compute moving window (length w) median. I know of course the movmedian function, but I need strictly recurrent native MATLAB function working sample by sample.
My naive solution, which is equivalent to the
output_median = movmedian(input_x,[w-1,0])
is as follows:
rng(‘default’)
% number of samples
N = 25;
% moving median windows length
w = 5;
% init history buffer and median
x_hist = rand;
med_new = x_hist;
% init input x vector
input_x = zeros(1,N);
input_x(1) = x_hist;
% init output median vector of length N
output_median = zeros(1,N);
output_median(1) = med_new;
for i = 2:N
x_new = rand;
[med_new,x_hist] = moving_median(x_hist,x_new,w);
input_x(i) = x_new;
output_median(i) = med_new;
end
where function moving_median is here:
function [med_new,x_hist] = moving_median(x_hist,x_new,w)
% Old length of history
wo = length(x_hist);
% Update history
x_hist = [x_hist(max(1,wo-w+2):wo),x_new]; % Grow history until size w, then append new x and remove oldest x
med_new = median(x_hist);
end
Any idea how to make this algorithm more effective (faster) a still strictly recurrent?

Target use case should works with window length:
w ~ 1e3 – 1e4 (!!!)

Additional notes:
fast moving median computing is always based on advanced data structures use like Heap or Queues, etc.
some sort-structure information could be stored in these structures and used at the next sample step to significant speed-up median computing
similar approach is used in movmedian function, but this function is not directly applicable on running-data stream I am looking for any suitable trick how to effectively compute moving window (length w) median. I know of course the movmedian function, but I need strictly recurrent native MATLAB function working sample by sample.
My naive solution, which is equivalent to the
output_median = movmedian(input_x,[w-1,0])
is as follows:
rng(‘default’)
% number of samples
N = 25;
% moving median windows length
w = 5;
% init history buffer and median
x_hist = rand;
med_new = x_hist;
% init input x vector
input_x = zeros(1,N);
input_x(1) = x_hist;
% init output median vector of length N
output_median = zeros(1,N);
output_median(1) = med_new;
for i = 2:N
x_new = rand;
[med_new,x_hist] = moving_median(x_hist,x_new,w);
input_x(i) = x_new;
output_median(i) = med_new;
end
where function moving_median is here:
function [med_new,x_hist] = moving_median(x_hist,x_new,w)
% Old length of history
wo = length(x_hist);
% Update history
x_hist = [x_hist(max(1,wo-w+2):wo),x_new]; % Grow history until size w, then append new x and remove oldest x
med_new = median(x_hist);
end
Any idea how to make this algorithm more effective (faster) a still strictly recurrent?

Target use case should works with window length:
w ~ 1e3 – 1e4 (!!!)

Additional notes:
fast moving median computing is always based on advanced data structures use like Heap or Queues, etc.
some sort-structure information could be stored in these structures and used at the next sample step to significant speed-up median computing
similar approach is used in movmedian function, but this function is not directly applicable on running-data stream moving, median MATLAB Answers — New Questions

​

Get the Microsoft Family Safety app – Microsoft Support
Microsoft News

Get the Microsoft Family Safety app – Microsoft Support

PuTI / 2025-06-26

Post Content

​ 

Solusi Inklusif Berbasis AI dari Mahasiswa UI Menangkan Hackathon AI for Accessibility 2025 Microsoft
Microsoft

Solusi Inklusif Berbasis AI dari Mahasiswa UI Menangkan Hackathon AI for Accessibility 2025 Microsoft

hilfan / 2025-06-26

Tim “The Leporidaes” bersama perwakilan dari Universitas Indonesia, Microsoft dan Suarise.

Microsoft dan Universitas Indonesia (UI) baru saja menyelesaikan rangkaian kompetisi Hackathon AI for Accessibility (AI4A) 2025, sebuah ajang tahunan yang mengajak para inovator muda di Asia Tenggara menciptakan solusi berbasis kecerdasan buatan (AI) dari Microsoft guna memecahkan tantangan dunia nyata yang dihadapi penyandang disabilitas – mulai dari kehidupan sehari-hari, pendidikan, komunikasi, hingga ketenagakerjaan. Memasuki tahun keenam, kali ini Microsoft menggandeng Fakultas Teknik Universitas Indonesia sebagai mitra penyelenggara kompetisi tersebut. Setelah melalui proses penjurian yang ketat, dari 46 tim yang ikut berpartisipasi, terpilihlah 10 tim yang lolos ke grand final.

Selanjutnya, dewan juri yang terdiri dari Rahma Utami, S.Ds., M.A. (Accessibility Director, Suarise), F. Astha Ekadiyanto (Dosen Departemen Teknik Komputer dan Teknik Listrik, Fakultas Teknik UI), serta Edhot Purwoko, S.T., M.T.I. (Senior Technology Specialist, Microsoft) menetapkan tim “The Leporidaes” sebagai pemenang utama dan berhak memperoleh berbagai dukungan eksklusif, mulai dari pelatihan intensif bersama pakar Microsoft, langganan LinkedIn Premium, akses Azure for Students, hingga pendampingan lanjutan untuk mengembangkan solusi mereka di Microsoft Azure.

Tim “The Leporidaes” yang terdiri dari mahasiswa Fakultas Teknik dan Fakultas Ilmu Komputer UI, berhasil terpilih sebagai pemenang utama tahun ini berkat solusi mereka yang diberi nama NeuroBuddy. Mereka membuat sebuah alat deteksi dini neurodivergensi dalam bentuk permainan anak-anak berbasis AI yang menampilkan maskot kelinci untuk mengajak anak berinteraksi. Nantinya, interaksi anak selama bermain akan dievaluasi untuk mendeteksi secara dini potensi disleksia, ASD, atau ADHD, sehingga dapat mendorong inklusi dan menjembatani kesenjangan antara teknologi, disabilitas, dan stigma. Dalam operasinya, Neurobuddy mengintegrasikan beragam layanan dari Azure Cognitive Service.

AI for Accessibility Hackathon Winner 2025 - Neurobuddy

Tampilan solusi dari NeuroBuddy

Microsoft percaya bahwa aksesibilitas adalah kunci untuk mewujudkan misinya: memberdayakan setiap individu dan organisasi di dunia untuk mencapai lebih. Adapun program ini menjadi bagian dari komitmen global perusahaan senilai US$ 25 juta, dan melalui kolaborasi bersama komunitas disabilitas, akademisi, dan developer, Microsoft berupaya memperluas manfaat AI untuk mendukung kehidupan sehari-hari, komunikasi, pendidikan, dan dunia kerja yang lebih inklusif.

“Banyak inovasi teknologi, termasuk AI, berawal dari upaya menjawab tantangan aksesibilitas, seperti fitur closed captions misalnya yang kini digunakan secara luas. Inilah bukti bahwa inovasi yang lahir dari kepedulian terhadap aksesibilitas pada akhirnya membawa manfaat luas bagi semua – karena setiap individu itu unik, teknologi pun harus mampu beradaptasi secara inklusif untuk memenuhi beragam kebutuhan tersebut. Hackathon ini menjadi ruang untuk mewujudkan misi itu, dengan dukungan layanan Microsoft yang berkomitmen pada inklusivitas,” ujar Dharma Simorangkir, Presiden Direktur Microsoft Indonesia

Semangat ini sejalan dengan komitmen Universitas Indonesia (UI) untuk membangun lingkungan pendidikan yang inklusif. UI secara konsisten menghadirkan berbagai inisiatif, mulai dari pendirian Unit Layanan Mahasiswa Disabilitas di sejumlah fakultas—seperti Fakultas Kesehatan Masyarakat dan Fakultas Psikologi—hingga penyediaan layanan pendampingan belajar dan proses seleksi masuk yang inklusif.

“Universitas Indonesia memiliki banyak inovator muda berbakat yang siap menciptakan solusi teknologi demi mendukung inklusivitas. Kami meyakini bahwa inovasi-inovasi yang lahir dari ajang seperti Hackathon AI for Accessibility bersama Microsoft dapat menjadi pemicu perubahan menuju dunia yang lebih ramah dan setara bagi semua kalangan, termasuk penyandang disabilitas. Terima kasih kepada seluruh peserta yang telah mencurahkan energi, waktu, dan gagasan untuk menjawab tantangan nyata di masyarakat,” kata Prof. Kemas Ridwan Kurniawan, S.T., M.Sc., Ph.D., Dekan Fakultas Teknik Universitas Indonesia.

Selain Tim “The Leporidaes”  yang keluar sebagai pemenang utama, sejumlah ide lainnya juga mendapatkan penghargaan. Misalnya, ide dari Tim “UINNOVATORS” dengan solusi bernama Pintaru didapuk sebagai juara kedua. Terinspirasi dari fakta bahwa satu dari lima pelajar di dunia memiliki disleksia, mereka merancang buku digital adaptif yang dapat menyesuaikan ukuran huruf, spasi, dan elemen visual lainnya sesuai kebutuhan pengguna. Solusi yang mereka bawakan didukung oleh Azure OpenAI, Azure Search, dan Azure Speech untuk menciptakan pengalaman belajar yang lebih inklusif.

###

 

Membangun Ekosistem Digital Indonesia yang Siap di Era AI
Microsoft

Membangun Ekosistem Digital Indonesia yang Siap di Era AI

hilfan / 2025-06-26

Read in English here

Transformasi digital di Indonesia kini memasuki babak baru dengan semakin masifnya adopsi kecerdasan buatan (AI). Untuk dapat mengikuti kemajuan ini, kesiapan infrastruktur dan pengembangan talenta harus berjalan beriringan agar ekosistem digital dapat tumbuh secara berkelanjutan. Dalam sesi wawancara langsung bersama CNBC Indonesia melalui program Tech A Look CNBC Indonesia TV, Dharma Simorangkir, Presiden Director Microsoft Indonesia, berbagi pandangan seputar peran Microsoft sebagai mitra jangka panjang yang mendukung transformasi digital secara inklusif, berkelanjutan, dan bertanggung jawab.

Infrastruktur Tangguh untuk Mendukung Ekosistem Digital

Pada April 2025, Microsoft resmi meluncurkan cloud region Indonesia Central sebagai bagian dari investasi sebesar USD 1,7 miliar — investasi terbesar kami selama 30 tahun berkiprah di Indonesia untuk mendukung inovasi dan #BerdayakanIndonesia.

Terintegrasi dengan lebih dari 70 Azure regions dan 300+ datacenter global, Indonesia Central menawarkan infrastruktur cloud terpercaya dengan konektivitas rendah latensi, keamanan data lokal, serta skalabilitas yang mendukung ambisi AI Indonesia.

Infrastruktur ini memungkinkan organisasi di Indonesia menjalankan layanan AI dan cloud secara real-time—baik untuk kebutuhan domestik, maupun untuk membangun solusi dari Indonesia ke panggung global.

Generasi Pembelajar dan Inovator di Era AI

Sejalan dengan komitmen Microsoft untuk #BerdayakanIndonesia, Indonesia membutuhkan talenta yang mampu memanfaatkan teknologi secara inklusif dan bertanggung jawab. Melalui program elevAIte Indonesia bersama Komdigi, kami menargetkan pelatihan bagi 1 juta peserta mulai dari sektor publik, pendidikan, UMKM, hingga komunitas di wilayah 3T.

Berkat inisiatif ini, lahir kisah-kisah inspiratif dari pemanfaatan AI, misalnya, dalam mitigasi bencana di Wonogiri, hingga pertanian yang tahan iklim. Kisah ini membuktikan bahwa kolaborasi antara teknologi AI dan kemampuan manusia mampu menciptakan solusi untuk menghadapi tantangan di masa depan.

Indonesia juga saat ini memiliki lebih dari 3,1 juta developer aktif di GitHub, menjadikannya sebagai komunitas developer terbesar ketiga di Asia Pasifik, yang mencerminkan semangat eksplorasi, kolaborasi, dan keberanian untuk tidak hanya menggunakan teknologi, tapi juga menciptakannya.

Mendorong Adopsi AI yang Bertanggung Jawab

Di tengah percepatan digitalisasi, keamanan siber tidak bisa dianggap sebagai fitur tambahan. Microsoft menerapkan prinsip privacy and security by design di seluruh layanan cloud dan AI.

Setiap hari, Microsoft menganalisis lebih dari 78 triliun sinyal keamanan, didukung oleh 34,000+ engineer keamanan dan inisiatif global seperti Secure Future Initiative (SFI).

Microsoft secara aktif berbagi praktik terbaik melalui publikasi seperti Cyber Signals dan Digital Defense Report, dan berkolaborasi dengan pemerintah Indonesia untuk berbagi praktik terbaik terkait regulasi data dan AI seperti  tercermin dalam Microsoft Responsible AI Standard yang mendorong organisasi dapat menerapkan prinsip dan pengembangan AI yang bertanggung jawab secara luas.

Kekuatan Kolaborasi di Era AI

Untuk membangun ekosistem digital yang inklusif dan berkelanjutan, Microsoft percaya bahwa pendekatan pentahelix – melibatkan pemerintah, industri, akademisi, komunitas, dan media, merupakan kunci.

“Dalam dua tahun terakhir, kami telah melakukan upskilling dan reskilling di bidang digital, keamanan siber, hingga AI kepada lebih dari 700.000 orang di Indonesia melalui program elevAIte. Tentu, upaya ini tidak bisa kami jalankan sendiri—dukungan dari Komdigi, lembaga, dan berbagai komunitas menjadi kunci keberhasilannya. Kini, dengan hadirnya layanan AI dan kebutuhan data residency di dalam negeri, seluruh pelaku usaha dan organisasi dapat #InnovAIteinIndonesia.”

Saksikan rekaman wawancara lengkap saya bersama CNBC Indonesia dalam program Tech a Look di sini:

YouTube Video

Saya mengucapkan terima kasih kepada tim redaksi CNBC Indonesia atas kesempatannya untuk berbagi pandangan mengenai masa depan ekosistem digital Indonesia. Semoga wawancara ini dapat menjadi bagian dari percakapan yang lebih luas tentang bagaimana teknologi, jika diadopsi secara inklusif dan bertanggung jawab, dapat memberikan dampak positif bagi masyarakat luas.

###

 

Building Indonesia’s Digital Ecosystem Ready for the AI Era
Microsoft

Building Indonesia’s Digital Ecosystem Ready for the AI Era

hilfan / 2025-06-26

Read in Bahasa Indonesia here.

Indonesia’s digital transformation has entered a new chapter, marked by the rapid and widespread adoption of artificial intelligence (AI). To keep pace with this momentum, infrastructure readiness and talent development must go hand in hand—ensuring that the country’s digital ecosystem can grow inclusively and sustainably.

In an interview on CNBC Indonesia TV’s Tech A Look program, Dharma Simorangkir, President Director of Microsoft Indonesia, shared insights on Microsoft’s role as a long-term partner supporting an inclusive, sustainable, and responsible digital transformation.

Resilient Infrastructure to Support the Digital Ecosystem

In April 2025, Microsoft officially launched the Indonesia Central cloud region as part of a USD 1.7 billion investment—the largest investment we have made during our 30 years of operation in Indonesia to support innovation and #BerdayakanIndonesia.

Integrated with over 70 Azure regions and more than 300 datacenters worldwide, Indonesia Central delivers trusted cloud infrastructure with low-latency connectivity, local data security, and scalability that underpins Indonesia’s AI ambitions.

This infrastructure enables organizations in Indonesia to run AI and cloud services in real-time—not only to serve domestic needs, but to build solutions that scale globally.

A New Generation of Learners and Innovators

Aligned with Microsoft’s commitment to #BerdayakanIndonesia, Indonesia needs talent capable of leveraging technology inclusively and responsibly. Through the elevAIte Indonesia program in collaboration with Komdigi, we aim to train 1 million participants across the public sector, education, MSMEs, and communities in underdeveloped regions (3T areas).

This initiative has already surfaced inspiring stories of AI in action — from disaster mitigation in Wonogiri to climate-resilient agriculture. These stories demonstrate how AI, when paired with human ingenuity, can help solve real-world challenges.

Indonesia is also home to 3.1 million active developers on GitHub, making it the third-largest developer community in Asia Pacific, reflecting a spirit of exploration, collaboration, and the courage to not only use technology but also create it.

Advancing Responsible AI Adoption

In an era of accelerating digitalization, cybersecurity cannot be an afterthought. Microsoft embeds privacy and security by design across all our cloud and AI services.

Each day, Microsoft analyzes more than 78 trillion security signals, powered by 34,000+ security engineers and global initiatives such as the Secure Future Initiative (SFI).

We actively share best practices through reports like Cyber Signals and the Digital Defense Report and collaborate with the Indonesian government to share best practices on data and AI regulation, as reflected in the Microsoft Responsible AI Standard, encouraging organizations to broadly adopt responsible AI principles and development.

The Power of Collaboration in the AI Era

We believe building a resilient digital ecosystem requires a pentahelix approach – bringing together government, industry, academia, communities, and media, we can create an inclusive and robust digital ecosystem.

“In the past two years, we have upskilled and reskilled more than 700,000 people across digital skills, cybersecurity, and AI in Indonesia through the elevAIte program. Of course, this effort cannot be done alone—we rely on the support of Komdigi, institutions, and various communities. Now, with the availability of AI services and the need for data residency domestically, all businesses and organizations have the opportunity to #InnovAIteinIndonesia.”

Watch the full interview with CNBC Indonesia on Tech A Look here:

YouTube Video

We thank CNBC Indonesia’s editorial team for the opportunity to share our vision for Indonesia’s digital future. We hope this conversation inspires broader dialogue on how inclusive and responsible technology adoption can positively impact society as a whole.

###

 

Token Protection Extends to Microsoft Graph PowerShell SDK Sessions
News

Token Protection Extends to Microsoft Graph PowerShell SDK Sessions

Tony Redmond / 2025-06-26

Token Protection, PRTs, Device Binding, and Session Keys

Last year, I discussed how to use a conditional access policy to apply a new session control called token protection. The idea is to protect against token theft by requiring connections to have a token (the Primary Refresh Token, or PRT) that has a “cryptographically secure tie” with the device that the connection originates from. The PRT is “bound” to a device key that’s securely stored in the device’s Trusted Platform Module (TPM). PRTs are supported on Windows 10 or later devices.

The PRT is an “opaque blob” that’s specific to a user account and device. The Entra ID authentication service issues a PRT following a successful connection by a user when the device is registered, joined, or hybrid joined. Entra ID also issues a session key, an encrypted symmetric key to serve as proof of possession when a PRT attempts to obtain tokens for applications. If an attacker attempts to hijack a connection with an access token they’ve stolen, they’ll fail because they don’t have access to the device key.

Why Does This Matter?

As noted in my article last year, it’s possible to create a conditional access policy with a session control requiring token protection. In other words, when a connection attempts to satisfy the conditions of the policy, it must be able to prove that its PRT is bound to the device where the connection originates and the user making the request. This process is managed by a component called Web Account Manager (WAM).

But conditional access policies can only work if everything involved in the connection understand what’s going on. At the time I wrote the last article, limited support existed for token protection. The reason for this article is that interactive Microsoft Graph PowerShell SDK sessions now support token protection (see details about support for token protection by other applications here). This opens the possibility of extending additional protection for administrators and developers who might work on sensitive data through the Graph SDK.

The reason why you might want to do this is revealed in a recent Entra ID change that shows the resources a user can access when they satisfy a conditional access policy to connect. In this case, the connection is to an interactive Graph PowerShell SDK session, and the resources available in that session depends on the delegated permissions held by the Microsoft Graph Command Line Tools service principal. The set of permissions tends to swell over time as administrators grant consent to permissions needed to work with different cmdlets, but as Figure 1 shows, a Graph PowerShell SDK session can have access to many different resources.

Conditional access policy signin reveals the Resources accessible to the Microsoft Graph PowerShell SDK.
Figure 1: Resources accessible to the Microsoft Graph PowerShell SDK

Enabling Token Protection for Graph Interactive Sessions

Normally, interactive Graph PowerShell SDK sessions don’t use WAM. To enable WAM for Graph sessions, run the Set-MgGraphOption cmdlet before running Connect-MgGraph. As the documentation says, the cmdlet sets global configuration options, so the configuration setting stays in force for all Microsoft Graph interactive sessions on the workstation until it is reversed.

Set-MgGraphOption –EnableLoginByWAM $true
Connect-MgGraph

If the device isn’t registered or joined, the conditional access policy condition for token protection isn’t satisfied and the sign-in attempt is rejected with a 530084 error code. The cause is obvious if you examine the policy details captured in the sign-in event (Figure 2).

The token protection session  control for a conditional access policy rejects a connection attempt.
Figure 2: The token protection session control rejects a connection attempt

WAM doesn’t affect app-only authentication for the Graph SDK, including Azure Automation runbooks that use modules and cmdlets from the Graph PowerShell SDK.

Token Protection and Elevated PowerShell Sessions

The Web Account Manager option doesn’t work in elevated PowerShell sessions (run as administrator). Attempts to connect fail with the error “InteractiveBrowserCredential authentication failed: User canceled authentication.”

The solution is two-fold. First, revert to normal authentication on the workstation by running the Set-MgGraphOption cmdlet to set EnableLoginByWAM to $false. If you don’t, authentication fails because a protected token isn’t available (Figure 3). The second step is to remove users who need to run Graph cmdlets in elevated PowerShell sessions from the scope of the conditional access policy. This avoids the user running into problems on other workstations.

Failure to connect because a conditional access policy condition requires a protected token that isn’t available.
Figure 3: Failure to connect because a conditional access policy condition requires a protected token that isn’t available

Token Protection and Microsoft Graph PowerShell SDK Versions

The WAM option also doesn’t work with the latest versions of the Microsoft Graph PowerShell SDK. This is likely due to Microsoft’s decision to remove support for .NET6 from V2.25 on. In V2.28 of the SDK, the error when running Connect-MgGraph is:

InteractiveBrowserCredential authentication failed: Could not load type 'Microsoft.Identity.Client.AuthScheme.TokenType' from assembly 'Microsoft.Identity.Client, Version=4.67.2.0, Culture=neutral, PublicKeyToken=0a613f4dd989e8ae'.

While Microsoft gets their act together and decides how to fix the issue, the only option is to remain using V2.25. PCs that have upgraded to the current V2.28 release must downgrade to V2.25.

Token Protection is Just Another Tool

Token protection is not for everyone. Its linkup with conditional access policies is another tool for administrators to consider when figuring out how to secure their tenant. My recommendation is that you test the feature and make a measured decision whether it has any value for your organization. Remember that this is an evolving space and other applications are likely to support token protection over time. Maybe one of those applications will be exactly the one you want to secure.


Support the work of the Office 365 for IT Pros team by subscribing to the Office 365 for IT Pros eBook. Your support pays for the time we need to track, analyze, and document the changing world of Microsoft 365 and Office 365.

 

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