I’m trying to do neural network with 2 hidden layers and one neuron in the output layer without any toolboxes and just with matrix and vectors multiplications. To do this, I created fictional simple data as below to help me in this task:
%Data
x = 1:1000;
y1 = sind(x);
y2 = sind(x+30);
y3 = cosd(x);
y4 = cosd(x+30);
y5 = cosd(x+45);
% y6 will be the desired output data taht I would like my neural network
% try to predict
y6 = (y1 + y2 + y3 + y4 + y5);
Then, I coded as I thought to be be the right way, but my neural network can´t reach a good result, as below:

My doubt is if the result isn´t good because my implementation isn´t right or because I need to add more mechanisms im my neural network (like momentum, regularization and etc.) ?
I will post my code below sorry about the naem of some variables, but originally I wrote this code in portuguese. I will comment the code to help undestand it
%Nueral network achictecture
n_h1 = 10;
n_h2 = 11;
n_out = 1;

%Adjustable parameters
w1 = rand(5,n_h1);
b1 = ones(1,n_h1)*rand(1,1);
w2 = rand(n_h1,n_h2);
b2 = ones(1,n_h2)*rand(1,1);
w_out = rand(n_h2,n_out);
b_out = ones(1,n_out)*rand(1,1);
sig_a = 1;
learning_rate = 0.001;
limiar = 0.002;

%Helpful variables
max_epocas = 1000;
conj_entrada = [y1;y2;y3;y4;y5];
erros_epoca = [];

%Backpropagation

for epoch = 1:max_epocas

for i = 1:size(conj_entrada,2)
if i ==1
soma = 0;

else

end
enter = conj_entrada(:,i);

h1_in = [w1;b1]’*[enter;1];
h1_out = sig(h1_in,sig_a,’False’);

h2_in = [w2;b2]’*[h1_out;1];
h2_out = sig(h2_in,sig_a,’False’);

saida_in = [w_out;b_out]’*[h2_out;1];
saida_out = saida_in;

erro = y6(i) – saida_out;
soma = soma + (erro^2);

%Here starts the part of the code where the gradients are being
%calculated. Note that, here, I tried to folllow the chain rule.

%let me try to help in the understanding. Saida in portuguese is
%like output in english so when you read ,for example,
%d_erro_d_saida_out you need to know that this is the derivative of
%the error in relation with the output of the output layer. In the
%same way, entrada means input and pesos means weights

%output layer
%chain rule

d_erro_d_saida_out = -1*erro;
d_saida_d_entrada_out = 1; %linear
grad_saida = erro*d_saida_d_entrada_out;
d_entrada_d_pesos_out = h2_out;

d_erro_d_pesos_out = d_erro_d_saida_out*d_saida_d_entrada_out*d_entrada_d_pesos_out;

% Update the wights and bias
w_out = w_out -learning_rate*d_erro_d_pesos_out;
b_out = b_out -learning_rate*d_erro_d_saida_out*d_saida_d_entrada_out;

%Second hidden layer (The neighbor layer of the output layer)
%chain rule

d_erro_d_saida_h2 = -1*w_out*grad_saida;
d_saida_d_entrada_h2 = sig(h2_in,sig_a,’True’);
grad_h2 = sum(grad_saida)*d_saida_d_entrada_h2;
d_entrada_d_pesos_h2 = h1_out;

d_erro_d_pesos_h2 = d_entrada_d_pesos_h2*grad_h2′;

% Update the wights and bias
w2 = w2 -1*learning_rate*d_erro_d_pesos_h2;
b2 = b2 -1*learning_rate*sum(d_erro_d_saida_h2.*d_saida_d_entrada_h2,1);

%First hidden layer (The neighbor layer of the seccond hidden layer)
%chain rule

d_erro_d_saida_h1 = -1*w2*grad_h2;
d_saida_d_entrada_h1 = sig(h1_in,sig_a,’True’);
grad_h1 = sum(grad_h2)*d_saida_d_entrada_h1; %então daqui, tem que sair um 3×1
d_entrada_d_pesos_h1 = enter;

d_erro_d_pesos_h1 = d_entrada_d_pesos_h1*grad_h1′; %a segunda variável tem que resultar em um 1×3

% Update the wights and bias
w1 = w1 -1*learning_rate*d_erro_d_pesos_h1;
b1 = b1 -1*learning_rate*sum(d_erro_d_saida_h1.*d_saida_d_entrada_h1,1);
end
erro_atual = (soma/(2*size(x,2)));
erros_epoca = [erros_epoca;erro_atual];
if erros_epoca(epoch) <limiar
break
else
end
end

%testing the output of neural network
vetor_teste = 1:1000;
resposta_teste = zeros(1,size(vetor_teste,2));
for i = 1:size(vetor_teste,2)
enter_teste = conj_entrada(:,i);

h1_in_teste = [w1;b1]’*[enter_teste;1];
h1_out_teste = sig(h1_in_teste,sig_a,’False’);

h2_in_teste = [w2;b2]’*[h1_out_teste;1];
h2_out_teste = sig(h2_in_teste,sig_a,’False’);

saida_in_teste = [w_out;b_out]’*[h2_out_teste;1];
saida_out_teste = saida_in_teste; % a função de saída é linear;

resposta_teste(i) = saida_out_teste;
end
plot(1:size(erros_epoca,1),erros_epoca);
% plot(x,y3,’b’,vetor_teste,resposta_teste,’r’);
The code of my sigmoid activation function is below:
function [vetor_saida] = sig(vetor_entrada, const1, derivative)
if strcmp(derivative, ‘False’) == 1
vetor_saida = 1 ./ (1 + exp(-const1 * vetor_entrada));
else
sig_value = sig(vetor_entrada, const1, ‘False’);
vetor_saida = const1 * sig_value .* (1 – sig_value);
end
endI’m trying to do neural network with 2 hidden layers and one neuron in the output layer without any toolboxes and just with matrix and vectors multiplications. To do this, I created fictional simple data as below to help me in this task:
%Data
x = 1:1000;
y1 = sind(x);
y2 = sind(x+30);
y3 = cosd(x);
y4 = cosd(x+30);
y5 = cosd(x+45);
% y6 will be the desired output data taht I would like my neural network
% try to predict
y6 = (y1 + y2 + y3 + y4 + y5);
Then, I coded as I thought to be be the right way, but my neural network can´t reach a good result, as below:

My doubt is if the result isn´t good because my implementation isn´t right or because I need to add more mechanisms im my neural network (like momentum, regularization and etc.) ?
I will post my code below sorry about the naem of some variables, but originally I wrote this code in portuguese. I will comment the code to help undestand it
%Nueral network achictecture
n_h1 = 10;
n_h2 = 11;
n_out = 1;

%Adjustable parameters
w1 = rand(5,n_h1);
b1 = ones(1,n_h1)*rand(1,1);
w2 = rand(n_h1,n_h2);
b2 = ones(1,n_h2)*rand(1,1);
w_out = rand(n_h2,n_out);
b_out = ones(1,n_out)*rand(1,1);
sig_a = 1;
learning_rate = 0.001;
limiar = 0.002;

%Helpful variables
max_epocas = 1000;
conj_entrada = [y1;y2;y3;y4;y5];
erros_epoca = [];

%Backpropagation

for epoch = 1:max_epocas

for i = 1:size(conj_entrada,2)
if i ==1
soma = 0;

else

end
enter = conj_entrada(:,i);

h1_in = [w1;b1]’*[enter;1];
h1_out = sig(h1_in,sig_a,’False’);

h2_in = [w2;b2]’*[h1_out;1];
h2_out = sig(h2_in,sig_a,’False’);

saida_in = [w_out;b_out]’*[h2_out;1];
saida_out = saida_in;

erro = y6(i) – saida_out;
soma = soma + (erro^2);

%Here starts the part of the code where the gradients are being
%calculated. Note that, here, I tried to folllow the chain rule.

%let me try to help in the understanding. Saida in portuguese is
%like output in english so when you read ,for example,
%d_erro_d_saida_out you need to know that this is the derivative of
%the error in relation with the output of the output layer. In the
%same way, entrada means input and pesos means weights

%output layer
%chain rule

d_erro_d_saida_out = -1*erro;
d_saida_d_entrada_out = 1; %linear
grad_saida = erro*d_saida_d_entrada_out;
d_entrada_d_pesos_out = h2_out;

d_erro_d_pesos_out = d_erro_d_saida_out*d_saida_d_entrada_out*d_entrada_d_pesos_out;

% Update the wights and bias
w_out = w_out -learning_rate*d_erro_d_pesos_out;
b_out = b_out -learning_rate*d_erro_d_saida_out*d_saida_d_entrada_out;

%Second hidden layer (The neighbor layer of the output layer)
%chain rule

d_erro_d_saida_h2 = -1*w_out*grad_saida;
d_saida_d_entrada_h2 = sig(h2_in,sig_a,’True’);
grad_h2 = sum(grad_saida)*d_saida_d_entrada_h2;
d_entrada_d_pesos_h2 = h1_out;

d_erro_d_pesos_h2 = d_entrada_d_pesos_h2*grad_h2′;

% Update the wights and bias
w2 = w2 -1*learning_rate*d_erro_d_pesos_h2;
b2 = b2 -1*learning_rate*sum(d_erro_d_saida_h2.*d_saida_d_entrada_h2,1);

%First hidden layer (The neighbor layer of the seccond hidden layer)
%chain rule

d_erro_d_saida_h1 = -1*w2*grad_h2;
d_saida_d_entrada_h1 = sig(h1_in,sig_a,’True’);
grad_h1 = sum(grad_h2)*d_saida_d_entrada_h1; %então daqui, tem que sair um 3×1
d_entrada_d_pesos_h1 = enter;

d_erro_d_pesos_h1 = d_entrada_d_pesos_h1*grad_h1′; %a segunda variável tem que resultar em um 1×3

% Update the wights and bias
w1 = w1 -1*learning_rate*d_erro_d_pesos_h1;
b1 = b1 -1*learning_rate*sum(d_erro_d_saida_h1.*d_saida_d_entrada_h1,1);
end
erro_atual = (soma/(2*size(x,2)));
erros_epoca = [erros_epoca;erro_atual];
if erros_epoca(epoch) <limiar
break
else
end
end

%testing the output of neural network
vetor_teste = 1:1000;
resposta_teste = zeros(1,size(vetor_teste,2));
for i = 1:size(vetor_teste,2)
enter_teste = conj_entrada(:,i);

h1_in_teste = [w1;b1]’*[enter_teste;1];
h1_out_teste = sig(h1_in_teste,sig_a,’False’);

h2_in_teste = [w2;b2]’*[h1_out_teste;1];
h2_out_teste = sig(h2_in_teste,sig_a,’False’);

saida_in_teste = [w_out;b_out]’*[h2_out_teste;1];
saida_out_teste = saida_in_teste; % a função de saída é linear;

resposta_teste(i) = saida_out_teste;
end
plot(1:size(erros_epoca,1),erros_epoca);
% plot(x,y3,’b’,vetor_teste,resposta_teste,’r’);
The code of my sigmoid activation function is below:
function [vetor_saida] = sig(vetor_entrada, const1, derivative)
if strcmp(derivative, ‘False’) == 1
vetor_saida = 1 ./ (1 + exp(-const1 * vetor_entrada));
else
sig_value = sig(vetor_entrada, const1, ‘False’);
vetor_saida = const1 * sig_value .* (1 – sig_value);
end
end I’m trying to do neural network with 2 hidden layers and one neuron in the output layer without any toolboxes and just with matrix and vectors multiplications. To do this, I created fictional simple data as below to help me in this task:
%Data
x = 1:1000;
y1 = sind(x);
y2 = sind(x+30);
y3 = cosd(x);
y4 = cosd(x+30);
y5 = cosd(x+45);
% y6 will be the desired output data taht I would like my neural network
% try to predict
y6 = (y1 + y2 + y3 + y4 + y5);
Then, I coded as I thought to be be the right way, but my neural network can´t reach a good result, as below:

My doubt is if the result isn´t good because my implementation isn´t right or because I need to add more mechanisms im my neural network (like momentum, regularization and etc.) ?
I will post my code below sorry about the naem of some variables, but originally I wrote this code in portuguese. I will comment the code to help undestand it
%Nueral network achictecture
n_h1 = 10;
n_h2 = 11;
n_out = 1;

%Adjustable parameters
w1 = rand(5,n_h1);
b1 = ones(1,n_h1)*rand(1,1);
w2 = rand(n_h1,n_h2);
b2 = ones(1,n_h2)*rand(1,1);
w_out = rand(n_h2,n_out);
b_out = ones(1,n_out)*rand(1,1);
sig_a = 1;
learning_rate = 0.001;
limiar = 0.002;

%Helpful variables
max_epocas = 1000;
conj_entrada = [y1;y2;y3;y4;y5];
erros_epoca = [];

%Backpropagation

for epoch = 1:max_epocas

for i = 1:size(conj_entrada,2)
if i ==1
soma = 0;

else

end
enter = conj_entrada(:,i);

h1_in = [w1;b1]’*[enter;1];
h1_out = sig(h1_in,sig_a,’False’);

h2_in = [w2;b2]’*[h1_out;1];
h2_out = sig(h2_in,sig_a,’False’);

saida_in = [w_out;b_out]’*[h2_out;1];
saida_out = saida_in;

erro = y6(i) – saida_out;
soma = soma + (erro^2);

%Here starts the part of the code where the gradients are being
%calculated. Note that, here, I tried to folllow the chain rule.

%let me try to help in the understanding. Saida in portuguese is
%like output in english so when you read ,for example,
%d_erro_d_saida_out you need to know that this is the derivative of
%the error in relation with the output of the output layer. In the
%same way, entrada means input and pesos means weights

%output layer
%chain rule

d_erro_d_saida_out = -1*erro;
d_saida_d_entrada_out = 1; %linear
grad_saida = erro*d_saida_d_entrada_out;
d_entrada_d_pesos_out = h2_out;

d_erro_d_pesos_out = d_erro_d_saida_out*d_saida_d_entrada_out*d_entrada_d_pesos_out;

% Update the wights and bias
w_out = w_out -learning_rate*d_erro_d_pesos_out;
b_out = b_out -learning_rate*d_erro_d_saida_out*d_saida_d_entrada_out;

%Second hidden layer (The neighbor layer of the output layer)
%chain rule

d_erro_d_saida_h2 = -1*w_out*grad_saida;
d_saida_d_entrada_h2 = sig(h2_in,sig_a,’True’);
grad_h2 = sum(grad_saida)*d_saida_d_entrada_h2;
d_entrada_d_pesos_h2 = h1_out;

d_erro_d_pesos_h2 = d_entrada_d_pesos_h2*grad_h2′;

% Update the wights and bias
w2 = w2 -1*learning_rate*d_erro_d_pesos_h2;
b2 = b2 -1*learning_rate*sum(d_erro_d_saida_h2.*d_saida_d_entrada_h2,1);

%First hidden layer (The neighbor layer of the seccond hidden layer)
%chain rule

d_erro_d_saida_h1 = -1*w2*grad_h2;
d_saida_d_entrada_h1 = sig(h1_in,sig_a,’True’);
grad_h1 = sum(grad_h2)*d_saida_d_entrada_h1; %então daqui, tem que sair um 3×1
d_entrada_d_pesos_h1 = enter;

d_erro_d_pesos_h1 = d_entrada_d_pesos_h1*grad_h1′; %a segunda variável tem que resultar em um 1×3

% Update the wights and bias
w1 = w1 -1*learning_rate*d_erro_d_pesos_h1;
b1 = b1 -1*learning_rate*sum(d_erro_d_saida_h1.*d_saida_d_entrada_h1,1);
end
erro_atual = (soma/(2*size(x,2)));
erros_epoca = [erros_epoca;erro_atual];
if erros_epoca(epoch) <limiar
break
else
end
end

%testing the output of neural network
vetor_teste = 1:1000;
resposta_teste = zeros(1,size(vetor_teste,2));
for i = 1:size(vetor_teste,2)
enter_teste = conj_entrada(:,i);

h1_in_teste = [w1;b1]’*[enter_teste;1];
h1_out_teste = sig(h1_in_teste,sig_a,’False’);

h2_in_teste = [w2;b2]’*[h1_out_teste;1];
h2_out_teste = sig(h2_in_teste,sig_a,’False’);

saida_in_teste = [w_out;b_out]’*[h2_out_teste;1];
saida_out_teste = saida_in_teste; % a função de saída é linear;

resposta_teste(i) = saida_out_teste;
end
plot(1:size(erros_epoca,1),erros_epoca);
% plot(x,y3,’b’,vetor_teste,resposta_teste,’r’);
The code of my sigmoid activation function is below:
function [vetor_saida] = sig(vetor_entrada, const1, derivative)
if strcmp(derivative, ‘False’) == 1
vetor_saida = 1 ./ (1 + exp(-const1 * vetor_entrada));
else
sig_value = sig(vetor_entrada, const1, ‘False’);
vetor_saida = const1 * sig_value .* (1 – sig_value);
end
end neural network, backpropagation MATLAB Answers — New Questions

​

As I asked, I want to design a fractional pid controller for a model. Unfortunately, the results I got with step in matlab are quite different from those in simulink
.
Here the orange line is the output and the blue line is the offset,Now let me show you the situation of matlab.The specific parameters of the controller are consistent with the code below
cs=fopid(9.503084707494814e+03,1.889631254823942e+02,2.304958553299564e+02,0.058440555124293,1.558103446462550);%This is parameter of controller
gs=tf(118700,[1 1085 1224000]);%This is the transfer function of the controlled object
step(feedback(cs*gs,1))%Step Response of Closed-loop Transfer Function

This is the general situation. I feel confused now.As I asked, I want to design a fractional pid controller for a model. Unfortunately, the results I got with step in matlab are quite different from those in simulink
.
Here the orange line is the output and the blue line is the offset,Now let me show you the situation of matlab.The specific parameters of the controller are consistent with the code below
cs=fopid(9.503084707494814e+03,1.889631254823942e+02,2.304958553299564e+02,0.058440555124293,1.558103446462550);%This is parameter of controller
gs=tf(118700,[1 1085 1224000]);%This is the transfer function of the controlled object
step(feedback(cs*gs,1))%Step Response of Closed-loop Transfer Function

This is the general situation. I feel confused now. As I asked, I want to design a fractional pid controller for a model. Unfortunately, the results I got with step in matlab are quite different from those in simulink
.
Here the orange line is the output and the blue line is the offset,Now let me show you the situation of matlab.The specific parameters of the controller are consistent with the code below
cs=fopid(9.503084707494814e+03,1.889631254823942e+02,2.304958553299564e+02,0.058440555124293,1.558103446462550);%This is parameter of controller
gs=tf(118700,[1 1085 1224000]);%This is the transfer function of the controlled object
step(feedback(cs*gs,1))%Step Response of Closed-loop Transfer Function

This is the general situation. I feel confused now. matlab, simulink MATLAB Answers — New Questions

​

area = roc(fileName,exp)
%
% fileName: The name of the output file. It is an image
% exp: a structure representing an experiment with three fields
% – exp.name: a string specifying the experiment name
% – exp.positive_n: the number of positive samples in the experiment
% – exp.likelihoods: likelihoods of each sample where the first
% exp.positive_n number of likelihoods belongs to the positive
% samples while others belong to the negative samples.
% area: a 1×1-dimensional cell array of the area under the roc curve.

true positive,false positive values of ground truth,edge detected image was calculated…then how apply these values to roc program…??????area = roc(fileName,exp)
%
% fileName: The name of the output file. It is an image
% exp: a structure representing an experiment with three fields
% – exp.name: a string specifying the experiment name
% – exp.positive_n: the number of positive samples in the experiment
% – exp.likelihoods: likelihoods of each sample where the first
% exp.positive_n number of likelihoods belongs to the positive
% samples while others belong to the negative samples.
% area: a 1×1-dimensional cell array of the area under the roc curve.

true positive,false positive values of ground truth,edge detected image was calculated…then how apply these values to roc program…?????? area = roc(fileName,exp)
%
% fileName: The name of the output file. It is an image
% exp: a structure representing an experiment with three fields
% – exp.name: a string specifying the experiment name
% – exp.positive_n: the number of positive samples in the experiment
% – exp.likelihoods: likelihoods of each sample where the first
% exp.positive_n number of likelihoods belongs to the positive
% samples while others belong to the negative samples.
% area: a 1×1-dimensional cell array of the area under the roc curve.

true positive,false positive values of ground truth,edge detected image was calculated…then how apply these values to roc program…?????? roc MATLAB Answers — New Questions

​

I have a problem installing apps, the command windows gives me this:

com.mathworks.jmi.MatlabException: The specified path is invalid.

at com.mathworks.jmi.NativeMatlab.SendMatlabMessage(Native Method)
at com.mathworks.jmi.NativeMatlab.sendMatlabMessage(NativeMatlab.java:265)
at com.mathworks.jmi.MatlabLooper.sendMatlabMessage(MatlabLooper.java:120)
at com.mathworks.jmi.Matlab.mtFeval(Matlab.java:1541)
at com.mathworks.jmi.MatlabWorker.feval(MatlabWorker.java:197)
at com.mathworks.appmanagement.InstallAppMatlabWorker.doOnMatlabThread(InstallAppMatlabWorker.java:20)
at com.mathworks.appmanagement.InstallAppMatlabWorker.doOnMatlabThread(InstallAppMatlabWorker.java:7)
at com.mathworks.appmanagement.AbstractAppManagementMatlabWorker.runOnMatlabThread(AbstractAppManagementMatlabWorker.java:21)
at com.mathworks.jmi.MatlabWorker$2.run(MatlabWorker.java:79)
at com.mathworks.jmi.NativeMatlab.dispatchMTRequests(NativeMatlab.java:440)

What should I do?I have a problem installing apps, the command windows gives me this:

com.mathworks.jmi.MatlabException: The specified path is invalid.

at com.mathworks.jmi.NativeMatlab.SendMatlabMessage(Native Method)
at com.mathworks.jmi.NativeMatlab.sendMatlabMessage(NativeMatlab.java:265)
at com.mathworks.jmi.MatlabLooper.sendMatlabMessage(MatlabLooper.java:120)
at com.mathworks.jmi.Matlab.mtFeval(Matlab.java:1541)
at com.mathworks.jmi.MatlabWorker.feval(MatlabWorker.java:197)
at com.mathworks.appmanagement.InstallAppMatlabWorker.doOnMatlabThread(InstallAppMatlabWorker.java:20)
at com.mathworks.appmanagement.InstallAppMatlabWorker.doOnMatlabThread(InstallAppMatlabWorker.java:7)
at com.mathworks.appmanagement.AbstractAppManagementMatlabWorker.runOnMatlabThread(AbstractAppManagementMatlabWorker.java:21)
at com.mathworks.jmi.MatlabWorker$2.run(MatlabWorker.java:79)
at com.mathworks.jmi.NativeMatlab.dispatchMTRequests(NativeMatlab.java:440)

What should I do? I have a problem installing apps, the command windows gives me this:

com.mathworks.jmi.MatlabException: The specified path is invalid.

at com.mathworks.jmi.NativeMatlab.SendMatlabMessage(Native Method)
at com.mathworks.jmi.NativeMatlab.sendMatlabMessage(NativeMatlab.java:265)
at com.mathworks.jmi.MatlabLooper.sendMatlabMessage(MatlabLooper.java:120)
at com.mathworks.jmi.Matlab.mtFeval(Matlab.java:1541)
at com.mathworks.jmi.MatlabWorker.feval(MatlabWorker.java:197)
at com.mathworks.appmanagement.InstallAppMatlabWorker.doOnMatlabThread(InstallAppMatlabWorker.java:20)
at com.mathworks.appmanagement.InstallAppMatlabWorker.doOnMatlabThread(InstallAppMatlabWorker.java:7)
at com.mathworks.appmanagement.AbstractAppManagementMatlabWorker.runOnMatlabThread(AbstractAppManagementMatlabWorker.java:21)
at com.mathworks.jmi.MatlabWorker$2.run(MatlabWorker.java:79)
at com.mathworks.jmi.NativeMatlab.dispatchMTRequests(NativeMatlab.java:440)

What should I do? install, apps MATLAB Answers — New Questions

​

I want to add a new property to a pointcloud, which is not in the standard properties of pointcloud.

I’ve got a pointcloud with one color image, from where I extract the Color inforamtion, and one NIR image, from where I get a greyscale value for each point, and I want to have every point to have an additional Property called ‘NIR’ with this value.

I tried :

pointcloud=pointCloud(posxyz,’Intensity’,intensityvalue,’NIR’,nirvalue)

Matlab gives me the following error:

Error using pointCloud>validateAndParseInputs (line 818)
‘NIR’ is not a recognized parameter. For a list of valid name-value pair arguments, see the
documentation for this function.

Error in pointCloud (line 142)
[xyzPoints, C, nv, I] = validateAndParseInputs(varargin{:});I want to add a new property to a pointcloud, which is not in the standard properties of pointcloud.

I’ve got a pointcloud with one color image, from where I extract the Color inforamtion, and one NIR image, from where I get a greyscale value for each point, and I want to have every point to have an additional Property called ‘NIR’ with this value.

I tried :

pointcloud=pointCloud(posxyz,’Intensity’,intensityvalue,’NIR’,nirvalue)

Matlab gives me the following error:

Error using pointCloud>validateAndParseInputs (line 818)
‘NIR’ is not a recognized parameter. For a list of valid name-value pair arguments, see the
documentation for this function.

Error in pointCloud (line 142)
[xyzPoints, C, nv, I] = validateAndParseInputs(varargin{:}); I want to add a new property to a pointcloud, which is not in the standard properties of pointcloud.

I’ve got a pointcloud with one color image, from where I extract the Color inforamtion, and one NIR image, from where I get a greyscale value for each point, and I want to have every point to have an additional Property called ‘NIR’ with this value.

I tried :

pointcloud=pointCloud(posxyz,’Intensity’,intensityvalue,’NIR’,nirvalue)

Matlab gives me the following error:

Error using pointCloud>validateAndParseInputs (line 818)
‘NIR’ is not a recognized parameter. For a list of valid name-value pair arguments, see the
documentation for this function.

Error in pointCloud (line 142)
[xyzPoints, C, nv, I] = validateAndParseInputs(varargin{:}); pointcloud, porperty MATLAB Answers — New Questions

​

Dose any one knows to generate CDDF polots for OFDM …plese help..strugling with itDose any one knows to generate CDDF polots for OFDM …plese help..strugling with it Dose any one knows to generate CDDF polots for OFDM …plese help..strugling with it ccdf plot, ofdm MATLAB Answers — New Questions

​

when I compiled my mlapp with simulink model,it has warning me PSB option menu block block (mask) does not have a parameter named ‘MechanicalLoad’。when I compiled my mlapp with simulink model,it has warning me PSB option menu block block (mask) does not have a parameter named ‘MechanicalLoad’。 when I compiled my mlapp with simulink model,it has warning me PSB option menu block block (mask) does not have a parameter named ‘MechanicalLoad’。 simulink app designer MATLAB Answers — New Questions

​

Hi everyone!
I want to plot a function like that:

<http://i44.tinypic.com/hu3v2q.jpg>

the plot is in fig. 138 (c) and its expression in Ms.

What’s the code?

because, I think, there is a scale factor, I use this code:

ts=linspace(0,2*pi,500);
Ms = 1-ts.*sin(ts)-0.5*cos(ts);
polar(ts,Ms)

but this generates a wrong plot.
How can I do this?

Thanks a lot for your answer!
PincoHi everyone!
I want to plot a function like that:

<http://i44.tinypic.com/hu3v2q.jpg>

the plot is in fig. 138 (c) and its expression in Ms.

What’s the code?

because, I think, there is a scale factor, I use this code:

ts=linspace(0,2*pi,500);
Ms = 1-ts.*sin(ts)-0.5*cos(ts);
polar(ts,Ms)

but this generates a wrong plot.
How can I do this?

Thanks a lot for your answer!
Pinco Hi everyone!
I want to plot a function like that:

<http://i44.tinypic.com/hu3v2q.jpg>

the plot is in fig. 138 (c) and its expression in Ms.

What’s the code?

because, I think, there is a scale factor, I use this code:

ts=linspace(0,2*pi,500);
Ms = 1-ts.*sin(ts)-0.5*cos(ts);
polar(ts,Ms)

but this generates a wrong plot.
How can I do this?

Thanks a lot for your answer!
Pinco polar, plot MATLAB Answers — New Questions

​

I want to maximize rate using fmincon function. the problem is I have an error as "Arrays have incompatible sizes for this operation." and "Caused by: Failure in initial objective function evaluation. FMINCON cannot continue.". I provide a part of my code and the objective function here. I guess the problem is in the reshape part and it might be wrong. I would really appreciate it if someone could help me to write the objective function correctly.

when . I should mention that is a vector and is a matrix for .

here is my code.
%initial guess for P2 and W2
P2_0 = ones(Ns,1) * (tilde_P2 / Ns) / 2; % initial guess for P2
W2_0 = ones(Ms,Ns); %initial guess for W2

%combine initial guess in a single vector
x0 = [P2_0 ; W2_0(:)];

% Define the linear constraint (C2)
A = zeros(Np, Ns + Ms * Ns);
for l = 1:Np
A(l, 1:Ns) = xi’; % Place xi’ in the first Ns columns of each row
end

% Define the bounds(C1)
lb = [zeros(Ns, 1); -Inf(Ms * Ns, 1)]; % Lower bound (P2 >= 0, no lower bound for W2)
ub = [repmat(tilde_P2 / Ns, Ns, 1); Inf(Ms * Ns, 1)]; % Upper bound (P2 <= tilde_P2_max / Ns, no upper bound for W2)
%%%objective function
obj_fun = @(x) -((Nup – Nt) / Nup * sum(log(1 + x(1:Ns) ./ (sigma2 * reshape(x(Ns+1:end), Ms, Ns).^2))));

% Set options for fmincon
options = optimoptions(‘fmincon’, ‘Display’, ‘iter’, ‘Algorithm’, ‘interior-point’);

% Run the optimization
[x_opt, fval] = fmincon(obj_fun, x0, A, hat_zeta1, [], [], lb, ub, [], options);

% Extract optimized values for P2 and W2
P2_opt = x_opt(1:Ns);
W2_opt = reshape(x_opt(Ns+1:end), Ms, Ns);I want to maximize rate using fmincon function. the problem is I have an error as "Arrays have incompatible sizes for this operation." and "Caused by: Failure in initial objective function evaluation. FMINCON cannot continue.". I provide a part of my code and the objective function here. I guess the problem is in the reshape part and it might be wrong. I would really appreciate it if someone could help me to write the objective function correctly.

when . I should mention that is a vector and is a matrix for .

here is my code.
%initial guess for P2 and W2
P2_0 = ones(Ns,1) * (tilde_P2 / Ns) / 2; % initial guess for P2
W2_0 = ones(Ms,Ns); %initial guess for W2

%combine initial guess in a single vector
x0 = [P2_0 ; W2_0(:)];

% Define the linear constraint (C2)
A = zeros(Np, Ns + Ms * Ns);
for l = 1:Np
A(l, 1:Ns) = xi’; % Place xi’ in the first Ns columns of each row
end

% Define the bounds(C1)
lb = [zeros(Ns, 1); -Inf(Ms * Ns, 1)]; % Lower bound (P2 >= 0, no lower bound for W2)
ub = [repmat(tilde_P2 / Ns, Ns, 1); Inf(Ms * Ns, 1)]; % Upper bound (P2 <= tilde_P2_max / Ns, no upper bound for W2)
%%%objective function
obj_fun = @(x) -((Nup – Nt) / Nup * sum(log(1 + x(1:Ns) ./ (sigma2 * reshape(x(Ns+1:end), Ms, Ns).^2))));

% Set options for fmincon
options = optimoptions(‘fmincon’, ‘Display’, ‘iter’, ‘Algorithm’, ‘interior-point’);

% Run the optimization
[x_opt, fval] = fmincon(obj_fun, x0, A, hat_zeta1, [], [], lb, ub, [], options);

% Extract optimized values for P2 and W2
P2_opt = x_opt(1:Ns);
W2_opt = reshape(x_opt(Ns+1:end), Ms, Ns); I want to maximize rate using fmincon function. the problem is I have an error as "Arrays have incompatible sizes for this operation." and "Caused by: Failure in initial objective function evaluation. FMINCON cannot continue.". I provide a part of my code and the objective function here. I guess the problem is in the reshape part and it might be wrong. I would really appreciate it if someone could help me to write the objective function correctly.

when . I should mention that is a vector and is a matrix for .

here is my code.
%initial guess for P2 and W2
P2_0 = ones(Ns,1) * (tilde_P2 / Ns) / 2; % initial guess for P2
W2_0 = ones(Ms,Ns); %initial guess for W2

%combine initial guess in a single vector
x0 = [P2_0 ; W2_0(:)];

% Define the linear constraint (C2)
A = zeros(Np, Ns + Ms * Ns);
for l = 1:Np
A(l, 1:Ns) = xi’; % Place xi’ in the first Ns columns of each row
end

% Define the bounds(C1)
lb = [zeros(Ns, 1); -Inf(Ms * Ns, 1)]; % Lower bound (P2 >= 0, no lower bound for W2)
ub = [repmat(tilde_P2 / Ns, Ns, 1); Inf(Ms * Ns, 1)]; % Upper bound (P2 <= tilde_P2_max / Ns, no upper bound for W2)
%%%objective function
obj_fun = @(x) -((Nup – Nt) / Nup * sum(log(1 + x(1:Ns) ./ (sigma2 * reshape(x(Ns+1:end), Ms, Ns).^2))));

% Set options for fmincon
options = optimoptions(‘fmincon’, ‘Display’, ‘iter’, ‘Algorithm’, ‘interior-point’);

% Run the optimization
[x_opt, fval] = fmincon(obj_fun, x0, A, hat_zeta1, [], [], lb, ub, [], options);

% Extract optimized values for P2 and W2
P2_opt = x_opt(1:Ns);
W2_opt = reshape(x_opt(Ns+1:end), Ms, Ns); fmincon, error MATLAB Answers — New Questions

​

I am trying to use the GPU coder to send converted code to my Jetson Orin Nano. However, when I use the coder.checkGpuInstall command, it says that it can’t find nvcc.
I followed the setup instructions with the variables here:
Documentation
and referenced the following questions before posting this one:
https://www.mathworks.com/matlabcentral/answers/506483-ncc-problem-in-jetson-nano
https://www.mathworks.com/matlabcentral/answers/2068206-checking-for-cuda-availability-on-the-target-checking-for-nvcc-in-the-target-system-path-war

This is a cropped version of my script:
if (boardName == "jetson")
if isempty(deviceAddress)
hwobj = jetson();
else
hwobj = jetson(deviceAddress,userName,password);
end
else
if isempty(deviceAddress)
hwobj = drive();
else
hwobj = drive(deviceAddress,userName,password);
end
end

if (boardName == "jetson")
envCfg = coder.gpuEnvConfig(‘jetson’);
else
envCfg = coder.gpuEnvConfig(‘drive’);
end
envCfg.BasicCodegen = 1;
envCfg.HardwareObject = hwobj;
coder.checkGpuInstall(envCfg);
and the output:
Checking for CUDA availability on the Target…
Checking for ‘nvcc’ in the target system path…
Checking for cuDNN library availability on the Target…
Checking for TensorRT library availability on the Target…
Checking for prerequisite libraries is complete.
Gathering hardware details…
Checking for third-party library availability on the Target…
Warning: Unable to find the SDL 1.2 library on the target.
> In nvidiaio.internal.checkForSdlLibs (line 14)
In nvidiaboard/checkAndGetHardwareConfig
In jetson
In gpu_jetson_setup (line 10)
Warning: Unable to find one of the packages "sox", "libsox-fmt-all" or "libsox-dev". Make sure to have installed these
packages on the target hardware using "apt-get". These are required for successful deployment of Audio File Read block
in Simulink.
> In nvidiaio.internal.checkSoXVersion (line 17)
In nvidiaboard/checkAndGetHardwareConfig
In jetson
In gpu_jetson_setup (line 10)
Warning: Unable to fetch information about GPU devices.
> In nvidiaio.internal.getGpuInfo (line 77)
In nvidiaboard/getGpuInfo
In nvidiaboard/checkAndGetHardwareConfig
In jetson
In gpu_jetson_setup (line 10)
Gathering hardware details is complete.
Board name : NVIDIA Jetson Orin Nano Developer Kit
CUDA Version : 12.2
cuDNN Version : 8.9
TensorRT Version : 8.6
GStreamer Version : 1.20.3
V4L2 Version : 1.22.1-2build1
SDL Version :
OpenCV Version : 4.8.0
Available Webcams :
Available GPUs :
Available Digital Pins : 7 11 12 13 15 16 18 19 21 22 23 24 26 29 31 32 33 35 36 37 38 40
Compatible GPU : FAILED (Unable to find GPU information. This is due to the missing of ‘nvcc’ on the system path. Update the ‘.bashrc’ script on the target to set up the required environment variables.)
CUDA Environment : PASSED
Runtime : PASSED
cuFFT : PASSED
cuSOLVER : PASSED
cuBLAS : PASSED
Basic Code Generation : PASSED

In accordance with the setup guide, I updated both the .bashrc file (and /etc/environment file for good measure) on the jetson, I have pasted them below:
.bashrc excerpt:
# ~/.bashrc: executed by bash(1) for non-login shells.
# see /usr/share/doc/bash/examples/startup-files (in the package bash-doc)
# for examples

# If not running interactively, don’t do anything
case $- in
*i*) ;;
*)
export PATH=$PATH:/usr/local/cuda/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
export ARM_COMPUTELIB=$ARM_COMPUTELIB:/usr/local/arm_compute
return;;
esac

# don’t put duplicate lines or lines starting with space in the history.
# See bash(1) for more options
HISTCONTROL=ignoreboth
/etc/environment:
PATH="/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin"
LANG="en_US.UTF-8"
LD_LIBRARY_PATH="/usr/local/cuda/lib64"
And to confirm, the jetson does show nvcc is installed correctly:
$ nvcc –version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Tue_Aug_15_22:08:11_PDT_2023
Cuda compilation tools, release 12.2, V12.2.140
Build cuda_12.2.r12.2/compiler.33191640_0
Matlab version info:
MATLAB Version: 24.1.0.2628055 (R2024a) Update 4
Operating System: Linux 5.15.0-116-generic #126~20.04.1-Ubuntu SMP Mon Jul 1 15:40:07 UTC 2024 x86_64
Java Version: Java 1.8.0_202-b08 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode
Any help on this topic would be much appreciated. Thank you!I am trying to use the GPU coder to send converted code to my Jetson Orin Nano. However, when I use the coder.checkGpuInstall command, it says that it can’t find nvcc.
I followed the setup instructions with the variables here:
Documentation
and referenced the following questions before posting this one:
https://www.mathworks.com/matlabcentral/answers/506483-ncc-problem-in-jetson-nano
https://www.mathworks.com/matlabcentral/answers/2068206-checking-for-cuda-availability-on-the-target-checking-for-nvcc-in-the-target-system-path-war

This is a cropped version of my script:
if (boardName == "jetson")
if isempty(deviceAddress)
hwobj = jetson();
else
hwobj = jetson(deviceAddress,userName,password);
end
else
if isempty(deviceAddress)
hwobj = drive();
else
hwobj = drive(deviceAddress,userName,password);
end
end

if (boardName == "jetson")
envCfg = coder.gpuEnvConfig(‘jetson’);
else
envCfg = coder.gpuEnvConfig(‘drive’);
end
envCfg.BasicCodegen = 1;
envCfg.HardwareObject = hwobj;
coder.checkGpuInstall(envCfg);
and the output:
Checking for CUDA availability on the Target…
Checking for ‘nvcc’ in the target system path…
Checking for cuDNN library availability on the Target…
Checking for TensorRT library availability on the Target…
Checking for prerequisite libraries is complete.
Gathering hardware details…
Checking for third-party library availability on the Target…
Warning: Unable to find the SDL 1.2 library on the target.
> In nvidiaio.internal.checkForSdlLibs (line 14)
In nvidiaboard/checkAndGetHardwareConfig
In jetson
In gpu_jetson_setup (line 10)
Warning: Unable to find one of the packages "sox", "libsox-fmt-all" or "libsox-dev". Make sure to have installed these
packages on the target hardware using "apt-get". These are required for successful deployment of Audio File Read block
in Simulink.
> In nvidiaio.internal.checkSoXVersion (line 17)
In nvidiaboard/checkAndGetHardwareConfig
In jetson
In gpu_jetson_setup (line 10)
Warning: Unable to fetch information about GPU devices.
> In nvidiaio.internal.getGpuInfo (line 77)
In nvidiaboard/getGpuInfo
In nvidiaboard/checkAndGetHardwareConfig
In jetson
In gpu_jetson_setup (line 10)
Gathering hardware details is complete.
Board name : NVIDIA Jetson Orin Nano Developer Kit
CUDA Version : 12.2
cuDNN Version : 8.9
TensorRT Version : 8.6
GStreamer Version : 1.20.3
V4L2 Version : 1.22.1-2build1
SDL Version :
OpenCV Version : 4.8.0
Available Webcams :
Available GPUs :
Available Digital Pins : 7 11 12 13 15 16 18 19 21 22 23 24 26 29 31 32 33 35 36 37 38 40
Compatible GPU : FAILED (Unable to find GPU information. This is due to the missing of ‘nvcc’ on the system path. Update the ‘.bashrc’ script on the target to set up the required environment variables.)
CUDA Environment : PASSED
Runtime : PASSED
cuFFT : PASSED
cuSOLVER : PASSED
cuBLAS : PASSED
Basic Code Generation : PASSED

In accordance with the setup guide, I updated both the .bashrc file (and /etc/environment file for good measure) on the jetson, I have pasted them below:
.bashrc excerpt:
# ~/.bashrc: executed by bash(1) for non-login shells.
# see /usr/share/doc/bash/examples/startup-files (in the package bash-doc)
# for examples

# If not running interactively, don’t do anything
case $- in
*i*) ;;
*)
export PATH=$PATH:/usr/local/cuda/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
export ARM_COMPUTELIB=$ARM_COMPUTELIB:/usr/local/arm_compute
return;;
esac

# don’t put duplicate lines or lines starting with space in the history.
# See bash(1) for more options
HISTCONTROL=ignoreboth
/etc/environment:
PATH="/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin"
LANG="en_US.UTF-8"
LD_LIBRARY_PATH="/usr/local/cuda/lib64"
And to confirm, the jetson does show nvcc is installed correctly:
$ nvcc –version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Tue_Aug_15_22:08:11_PDT_2023
Cuda compilation tools, release 12.2, V12.2.140
Build cuda_12.2.r12.2/compiler.33191640_0
Matlab version info:
MATLAB Version: 24.1.0.2628055 (R2024a) Update 4
Operating System: Linux 5.15.0-116-generic #126~20.04.1-Ubuntu SMP Mon Jul 1 15:40:07 UTC 2024 x86_64
Java Version: Java 1.8.0_202-b08 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode
Any help on this topic would be much appreciated. Thank you! I am trying to use the GPU coder to send converted code to my Jetson Orin Nano. However, when I use the coder.checkGpuInstall command, it says that it can’t find nvcc.
I followed the setup instructions with the variables here:
Documentation
and referenced the following questions before posting this one:
https://www.mathworks.com/matlabcentral/answers/506483-ncc-problem-in-jetson-nano
https://www.mathworks.com/matlabcentral/answers/2068206-checking-for-cuda-availability-on-the-target-checking-for-nvcc-in-the-target-system-path-war

This is a cropped version of my script:
if (boardName == "jetson")
if isempty(deviceAddress)
hwobj = jetson();
else
hwobj = jetson(deviceAddress,userName,password);
end
else
if isempty(deviceAddress)
hwobj = drive();
else
hwobj = drive(deviceAddress,userName,password);
end
end

if (boardName == "jetson")
envCfg = coder.gpuEnvConfig(‘jetson’);
else
envCfg = coder.gpuEnvConfig(‘drive’);
end
envCfg.BasicCodegen = 1;
envCfg.HardwareObject = hwobj;
coder.checkGpuInstall(envCfg);
and the output:
Checking for CUDA availability on the Target…
Checking for ‘nvcc’ in the target system path…
Checking for cuDNN library availability on the Target…
Checking for TensorRT library availability on the Target…
Checking for prerequisite libraries is complete.
Gathering hardware details…
Checking for third-party library availability on the Target…
Warning: Unable to find the SDL 1.2 library on the target.
> In nvidiaio.internal.checkForSdlLibs (line 14)
In nvidiaboard/checkAndGetHardwareConfig
In jetson
In gpu_jetson_setup (line 10)
Warning: Unable to find one of the packages "sox", "libsox-fmt-all" or "libsox-dev". Make sure to have installed these
packages on the target hardware using "apt-get". These are required for successful deployment of Audio File Read block
in Simulink.
> In nvidiaio.internal.checkSoXVersion (line 17)
In nvidiaboard/checkAndGetHardwareConfig
In jetson
In gpu_jetson_setup (line 10)
Warning: Unable to fetch information about GPU devices.
> In nvidiaio.internal.getGpuInfo (line 77)
In nvidiaboard/getGpuInfo
In nvidiaboard/checkAndGetHardwareConfig
In jetson
In gpu_jetson_setup (line 10)
Gathering hardware details is complete.
Board name : NVIDIA Jetson Orin Nano Developer Kit
CUDA Version : 12.2
cuDNN Version : 8.9
TensorRT Version : 8.6
GStreamer Version : 1.20.3
V4L2 Version : 1.22.1-2build1
SDL Version :
OpenCV Version : 4.8.0
Available Webcams :
Available GPUs :
Available Digital Pins : 7 11 12 13 15 16 18 19 21 22 23 24 26 29 31 32 33 35 36 37 38 40
Compatible GPU : FAILED (Unable to find GPU information. This is due to the missing of ‘nvcc’ on the system path. Update the ‘.bashrc’ script on the target to set up the required environment variables.)
CUDA Environment : PASSED
Runtime : PASSED
cuFFT : PASSED
cuSOLVER : PASSED
cuBLAS : PASSED
Basic Code Generation : PASSED

In accordance with the setup guide, I updated both the .bashrc file (and /etc/environment file for good measure) on the jetson, I have pasted them below:
.bashrc excerpt:
# ~/.bashrc: executed by bash(1) for non-login shells.
# see /usr/share/doc/bash/examples/startup-files (in the package bash-doc)
# for examples

# If not running interactively, don’t do anything
case $- in
*i*) ;;
*)
export PATH=$PATH:/usr/local/cuda/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
export ARM_COMPUTELIB=$ARM_COMPUTELIB:/usr/local/arm_compute
return;;
esac

# don’t put duplicate lines or lines starting with space in the history.
# See bash(1) for more options
HISTCONTROL=ignoreboth
/etc/environment:
PATH="/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin"
LANG="en_US.UTF-8"
LD_LIBRARY_PATH="/usr/local/cuda/lib64"
And to confirm, the jetson does show nvcc is installed correctly:
$ nvcc –version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Tue_Aug_15_22:08:11_PDT_2023
Cuda compilation tools, release 12.2, V12.2.140
Build cuda_12.2.r12.2/compiler.33191640_0
Matlab version info:
MATLAB Version: 24.1.0.2628055 (R2024a) Update 4
Operating System: Linux 5.15.0-116-generic #126~20.04.1-Ubuntu SMP Mon Jul 1 15:40:07 UTC 2024 x86_64
Java Version: Java 1.8.0_202-b08 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode
Any help on this topic would be much appreciated. Thank you! gpu, nvidia, jetson, nvcc, cuda, error, env, matlab coder, embedded coder, hardware, gpu coder MATLAB Answers — New Questions

​