Nt = 5;
Nr = 5;
gammaBar = -10:2:30;
SNR = 10 .^(gammaBar/10);
H = sqrt(1/2) * (randn+1j*randn);
for idx = 1:1:length(SNR)
c(idx) = log2(det((eye(Nr))+((SNR(idx)/Nt)*abs(H)*(abs(H’)))));
end
plot(gammaBar,c)
xlabel(‘SNR in dB’);
ylabel(‘Ergodic Channel Capacity’);
Plot this frequency vs Channel CapacityNt = 5;
Nr = 5;
gammaBar = -10:2:30;
SNR = 10 .^(gammaBar/10);
H = sqrt(1/2) * (randn+1j*randn);
for idx = 1:1:length(SNR)
c(idx) = log2(det((eye(Nr))+((SNR(idx)/Nt)*abs(H)*(abs(H’)))));
end
plot(gammaBar,c)
xlabel(‘SNR in dB’);
ylabel(‘Ergodic Channel Capacity’);
Plot this frequency vs Channel Capacity Nt = 5;
Nr = 5;
gammaBar = -10:2:30;
SNR = 10 .^(gammaBar/10);
H = sqrt(1/2) * (randn+1j*randn);
for idx = 1:1:length(SNR)
c(idx) = log2(det((eye(Nr))+((SNR(idx)/Nt)*abs(H)*(abs(H’)))));
end
plot(gammaBar,c)
xlabel(‘SNR in dB’);
ylabel(‘Ergodic Channel Capacity’);
Plot this frequency vs Channel Capacity ergodic channel capacity, mimo antenna MATLAB Answers — New Questions

​

I tried connecting to Chat GPT with the following web instructions and get the error as reflected …
prompt = ‘What is the capital of France?’;
api_key = ‘sk-4Y8TmelxvsdfghfghhdT3BlbkFJepdojXzket1MmQpA9cov’;
url = ‘https://api.openai.com/v1/engines/davinci/completions’;
options = weboptions(‘KeyName’,’Authorization’,’KeyValue’,[‘Bearer ‘ api_key],’MediaType’,’application/json’);
data = webwrite(url,’prompt’,prompt,’max_tokens’,2048,’model’,’text-davinci-003′,’stop’,”,options);
answer = loadjson(data);
answer = answer.choices{1}.text;
disp(answer)
Does anyone know how to connect MATLAB to Chat GPT to send prompts and retrieve back the responses. Also how to send data over to fine tune the model and then to further prompt that model?
It would be good for Mathworks to double quick release a toolbox on FileExchange for this as Python already has an OpenAI library. Seems like Mathworks is always one step behind Python these days.
ThxI tried connecting to Chat GPT with the following web instructions and get the error as reflected …
prompt = ‘What is the capital of France?’;
api_key = ‘sk-4Y8TmelxvsdfghfghhdT3BlbkFJepdojXzket1MmQpA9cov’;
url = ‘https://api.openai.com/v1/engines/davinci/completions’;
options = weboptions(‘KeyName’,’Authorization’,’KeyValue’,[‘Bearer ‘ api_key],’MediaType’,’application/json’);
data = webwrite(url,’prompt’,prompt,’max_tokens’,2048,’model’,’text-davinci-003′,’stop’,”,options);
answer = loadjson(data);
answer = answer.choices{1}.text;
disp(answer)
Does anyone know how to connect MATLAB to Chat GPT to send prompts and retrieve back the responses. Also how to send data over to fine tune the model and then to further prompt that model?
It would be good for Mathworks to double quick release a toolbox on FileExchange for this as Python already has an OpenAI library. Seems like Mathworks is always one step behind Python these days.
Thx I tried connecting to Chat GPT with the following web instructions and get the error as reflected …
prompt = ‘What is the capital of France?’;
api_key = ‘sk-4Y8TmelxvsdfghfghhdT3BlbkFJepdojXzket1MmQpA9cov’;
url = ‘https://api.openai.com/v1/engines/davinci/completions’;
options = weboptions(‘KeyName’,’Authorization’,’KeyValue’,[‘Bearer ‘ api_key],’MediaType’,’application/json’);
data = webwrite(url,’prompt’,prompt,’max_tokens’,2048,’model’,’text-davinci-003′,’stop’,”,options);
answer = loadjson(data);
answer = answer.choices{1}.text;
disp(answer)
Does anyone know how to connect MATLAB to Chat GPT to send prompts and retrieve back the responses. Also how to send data over to fine tune the model and then to further prompt that model?
It would be good for Mathworks to double quick release a toolbox on FileExchange for this as Python already has an OpenAI library. Seems like Mathworks is always one step behind Python these days.
Thx prompts, fine tune, chatgpt MATLAB Answers — New Questions

​

%I can’t make BER=0, I think, something is wrong with demodulation, in the demodulated sequence there are only numbers 5, 7, 13, 15. Now BER is equal to about half of all bit values, so what can I do to resolve this and make BER=0?

clc
close all
clear all
M = 16;
L=10000;
T=10;
W=43; %SNR
bits1 = randi([0 M-1], 1, L);
bits2 = randi([0 3], 1, L/T);
mod1 = qammod(bits1, M,’UnitAveragePower’, true);
% scatterplot(mod1)
mod2 = qammod(bits2, 4, ‘UnitAveragePower’,true);
% scatterplot(mod2)
for i = 1:length(bits2)
sym(i,:)=[mod1((i-1)*T+1:i*T) mod2(i)];
end
f=0;
for k=1:L/T
for j=1:T+1
f=f+1;
symbols(f)=sym(k,j);
end
end
% scatterplot(symbols)
% symbols = awgn(symbols,W,"measured");
pnoise = comm.PhaseNoise(‘Level’,[-70 -104 -110],’FrequencyOffset’,[1e4 1e5 2e5], ‘SampleRate’, 28e6);
symbols2 = pnoise([zeros(1e5,1);symbols’]);
% symbols2 = pnoise(symbols);
fc = 1e6; % Carrier frequency in Hz
fs = 28e6; % Sample rate in Hz.
phNzLevel = [-70 -104 -110]; % in dBc/Hz
phNzFreqOff = [1e4 1e5 2e5]; % in Hz
Nspf = 6e6; % Number of Samples per frame
freqSpan = 400e3; % in Hz, for spectrum computation
sinewave = dsp.SineWave( …
Amplitude=1, …
Frequency=fc, …
SampleRate=fs, …
SamplesPerFrame=Nspf, …
ComplexOutput=true);
pnoise = comm.PhaseNoise( …
Level=phNzLevel, …
FrequencyOffset=phNzFreqOff, …
SampleRate=fs);
sascopeRBW100 = spectrumAnalyzer( …
SampleRate=fs, …
Method="welch", …
FrequencySpan="Span and center frequency", …
CenterFrequency=fc, …
Span=freqSpan, …
RBWSource="Property", …
RBW=100, …
SpectrumType="Power density", …
SpectralAverages=10, …
SpectrumUnits="dBW", …
YLimits=[-150 10], …
Title="Resolution Bandwidth 100 Hz", …
ChannelNames={‘signal’,’signal with phase noise’}, …
Position=[79 147 605 374]);
x = sinewave();
y = pnoise(x);
sascopeRBW100(x,y)
symbols2 = symbols2(1e5+1:end);
prim(1,:)=symbols2(1:T);
for i = 1:length(bits2)-1
prim(i+1,:)=symbols2(i*T+i+1:i*T+i+T);
end
for b = 1:length(mod2)
qam4(b)=symbols2((T+1)*b);
end
h=0;
for u=1:L/T
for l=1:T
h=h+1;
priem(h)=prim(u,l);
end
end
for g = 1:L/T
phase_error(g) = angle(qam4(g) / mod2(g));
compensated4(g) = qam4(g) .* exp(-1i * phase_error(g));
end
for v = 1:L/T
phase_errorM((v-1)*T+1:v*T)=phase_error(v);
end
for f = 1:L
compensatedM(f) = priem(f) .* exp(-1i*phase_errorM(f));
end
demod=qamdemod(compensatedM, M, ‘bin’,’OutputType’,’bit’);
% scatterplot(qam4)
% scatterplot(symbols2)
% scatterplot(compensatedM)
[number,ratio]=biterr(bits1,demod);
evm = lteEVM(demod,bits1);
figure(‘Position’,[200 200 1080 540])
subplot(1,2,1)
scatter(real(symbols2),imag(symbols2),300,".")
subplot(1,2,2)
scatter(real(compensatedM),imag(compensatedM),300,".")%I can’t make BER=0, I think, something is wrong with demodulation, in the demodulated sequence there are only numbers 5, 7, 13, 15. Now BER is equal to about half of all bit values, so what can I do to resolve this and make BER=0?

clc
close all
clear all
M = 16;
L=10000;
T=10;
W=43; %SNR
bits1 = randi([0 M-1], 1, L);
bits2 = randi([0 3], 1, L/T);
mod1 = qammod(bits1, M,’UnitAveragePower’, true);
% scatterplot(mod1)
mod2 = qammod(bits2, 4, ‘UnitAveragePower’,true);
% scatterplot(mod2)
for i = 1:length(bits2)
sym(i,:)=[mod1((i-1)*T+1:i*T) mod2(i)];
end
f=0;
for k=1:L/T
for j=1:T+1
f=f+1;
symbols(f)=sym(k,j);
end
end
% scatterplot(symbols)
% symbols = awgn(symbols,W,"measured");
pnoise = comm.PhaseNoise(‘Level’,[-70 -104 -110],’FrequencyOffset’,[1e4 1e5 2e5], ‘SampleRate’, 28e6);
symbols2 = pnoise([zeros(1e5,1);symbols’]);
% symbols2 = pnoise(symbols);
fc = 1e6; % Carrier frequency in Hz
fs = 28e6; % Sample rate in Hz.
phNzLevel = [-70 -104 -110]; % in dBc/Hz
phNzFreqOff = [1e4 1e5 2e5]; % in Hz
Nspf = 6e6; % Number of Samples per frame
freqSpan = 400e3; % in Hz, for spectrum computation
sinewave = dsp.SineWave( …
Amplitude=1, …
Frequency=fc, …
SampleRate=fs, …
SamplesPerFrame=Nspf, …
ComplexOutput=true);
pnoise = comm.PhaseNoise( …
Level=phNzLevel, …
FrequencyOffset=phNzFreqOff, …
SampleRate=fs);
sascopeRBW100 = spectrumAnalyzer( …
SampleRate=fs, …
Method="welch", …
FrequencySpan="Span and center frequency", …
CenterFrequency=fc, …
Span=freqSpan, …
RBWSource="Property", …
RBW=100, …
SpectrumType="Power density", …
SpectralAverages=10, …
SpectrumUnits="dBW", …
YLimits=[-150 10], …
Title="Resolution Bandwidth 100 Hz", …
ChannelNames={‘signal’,’signal with phase noise’}, …
Position=[79 147 605 374]);
x = sinewave();
y = pnoise(x);
sascopeRBW100(x,y)
symbols2 = symbols2(1e5+1:end);
prim(1,:)=symbols2(1:T);
for i = 1:length(bits2)-1
prim(i+1,:)=symbols2(i*T+i+1:i*T+i+T);
end
for b = 1:length(mod2)
qam4(b)=symbols2((T+1)*b);
end
h=0;
for u=1:L/T
for l=1:T
h=h+1;
priem(h)=prim(u,l);
end
end
for g = 1:L/T
phase_error(g) = angle(qam4(g) / mod2(g));
compensated4(g) = qam4(g) .* exp(-1i * phase_error(g));
end
for v = 1:L/T
phase_errorM((v-1)*T+1:v*T)=phase_error(v);
end
for f = 1:L
compensatedM(f) = priem(f) .* exp(-1i*phase_errorM(f));
end
demod=qamdemod(compensatedM, M, ‘bin’,’OutputType’,’bit’);
% scatterplot(qam4)
% scatterplot(symbols2)
% scatterplot(compensatedM)
[number,ratio]=biterr(bits1,demod);
evm = lteEVM(demod,bits1);
figure(‘Position’,[200 200 1080 540])
subplot(1,2,1)
scatter(real(symbols2),imag(symbols2),300,".")
subplot(1,2,2)
scatter(real(compensatedM),imag(compensatedM),300,".") %I can’t make BER=0, I think, something is wrong with demodulation, in the demodulated sequence there are only numbers 5, 7, 13, 15. Now BER is equal to about half of all bit values, so what can I do to resolve this and make BER=0?

clc
close all
clear all
M = 16;
L=10000;
T=10;
W=43; %SNR
bits1 = randi([0 M-1], 1, L);
bits2 = randi([0 3], 1, L/T);
mod1 = qammod(bits1, M,’UnitAveragePower’, true);
% scatterplot(mod1)
mod2 = qammod(bits2, 4, ‘UnitAveragePower’,true);
% scatterplot(mod2)
for i = 1:length(bits2)
sym(i,:)=[mod1((i-1)*T+1:i*T) mod2(i)];
end
f=0;
for k=1:L/T
for j=1:T+1
f=f+1;
symbols(f)=sym(k,j);
end
end
% scatterplot(symbols)
% symbols = awgn(symbols,W,"measured");
pnoise = comm.PhaseNoise(‘Level’,[-70 -104 -110],’FrequencyOffset’,[1e4 1e5 2e5], ‘SampleRate’, 28e6);
symbols2 = pnoise([zeros(1e5,1);symbols’]);
% symbols2 = pnoise(symbols);
fc = 1e6; % Carrier frequency in Hz
fs = 28e6; % Sample rate in Hz.
phNzLevel = [-70 -104 -110]; % in dBc/Hz
phNzFreqOff = [1e4 1e5 2e5]; % in Hz
Nspf = 6e6; % Number of Samples per frame
freqSpan = 400e3; % in Hz, for spectrum computation
sinewave = dsp.SineWave( …
Amplitude=1, …
Frequency=fc, …
SampleRate=fs, …
SamplesPerFrame=Nspf, …
ComplexOutput=true);
pnoise = comm.PhaseNoise( …
Level=phNzLevel, …
FrequencyOffset=phNzFreqOff, …
SampleRate=fs);
sascopeRBW100 = spectrumAnalyzer( …
SampleRate=fs, …
Method="welch", …
FrequencySpan="Span and center frequency", …
CenterFrequency=fc, …
Span=freqSpan, …
RBWSource="Property", …
RBW=100, …
SpectrumType="Power density", …
SpectralAverages=10, …
SpectrumUnits="dBW", …
YLimits=[-150 10], …
Title="Resolution Bandwidth 100 Hz", …
ChannelNames={‘signal’,’signal with phase noise’}, …
Position=[79 147 605 374]);
x = sinewave();
y = pnoise(x);
sascopeRBW100(x,y)
symbols2 = symbols2(1e5+1:end);
prim(1,:)=symbols2(1:T);
for i = 1:length(bits2)-1
prim(i+1,:)=symbols2(i*T+i+1:i*T+i+T);
end
for b = 1:length(mod2)
qam4(b)=symbols2((T+1)*b);
end
h=0;
for u=1:L/T
for l=1:T
h=h+1;
priem(h)=prim(u,l);
end
end
for g = 1:L/T
phase_error(g) = angle(qam4(g) / mod2(g));
compensated4(g) = qam4(g) .* exp(-1i * phase_error(g));
end
for v = 1:L/T
phase_errorM((v-1)*T+1:v*T)=phase_error(v);
end
for f = 1:L
compensatedM(f) = priem(f) .* exp(-1i*phase_errorM(f));
end
demod=qamdemod(compensatedM, M, ‘bin’,’OutputType’,’bit’);
% scatterplot(qam4)
% scatterplot(symbols2)
% scatterplot(compensatedM)
[number,ratio]=biterr(bits1,demod);
evm = lteEVM(demod,bits1);
figure(‘Position’,[200 200 1080 540])
subplot(1,2,1)
scatter(real(symbols2),imag(symbols2),300,".")
subplot(1,2,2)
scatter(real(compensatedM),imag(compensatedM),300,".") demodulation, qam-16, ber MATLAB Answers — New Questions

​

Hi, I have a function to be fitted to some experimental data, but this function has multiple fitting parameters (6 parameters). I know that one of the ways to find these fitting parameters is to pass the function, the inital guess to the parameters and the lower and upper bounds into the lsqcurvefit function.
I have already created the function, but it seems like the software doesn’t know that the p(1),p(2), p(3)… are parameters. Sorry if the function is very long.
What I wanted was p to be a vector with 6 elements p(1) p(2) p(3) p(4) p(5) p(6) but it seems like the software cant differentiate betwen p and p(1) p(2) p(3) p(4) p(5) p(6). When I try running the code, the error message I got was "Not enough input arguments"
Thank you for helping
function F = EM_SS(p, E_p)
F = p(1)*(2*pi*sqrt(p(4))/E_p)*(1/p(6))*(int(sech(((E_p – E)./p(6)))*(1 + 10*p(5)*(E – p(3)) + 126*p(5)^2*(E – p(3))^2)/(1 – exp(-2*pi*sqrt(p(4)/(E – p(3))))), E, p(3), Inf, ‘ArrayValued’, 1)) + p(2)*(2*pi*p(4)^3/2)*1/p(6)*((1/1^3)*sech((E_p – p(3) + p(4)/1^2)./p(6)) + (1/2^3)*sech((E_p – p(3) + p(4)/2^2)./p(6)) + (1/3^3)*sech((E_p – p(3) + p(4)/3^2)./p(6)) + (1/4^3)*sech((E_p – p(3) + p(4)/4^2)./p(6)) + (1/5^3)*sech((E_p – p(3) + p(4)/5^2)./p(6)) + (1/6^3)*sech((E_p – p(3) + p(4)/6^2)./p(6)) + (1/7^3)*sech((E_p – p(3) + p(4)/7^2)./p(6)));

endHi, I have a function to be fitted to some experimental data, but this function has multiple fitting parameters (6 parameters). I know that one of the ways to find these fitting parameters is to pass the function, the inital guess to the parameters and the lower and upper bounds into the lsqcurvefit function.
I have already created the function, but it seems like the software doesn’t know that the p(1),p(2), p(3)… are parameters. Sorry if the function is very long.
What I wanted was p to be a vector with 6 elements p(1) p(2) p(3) p(4) p(5) p(6) but it seems like the software cant differentiate betwen p and p(1) p(2) p(3) p(4) p(5) p(6). When I try running the code, the error message I got was "Not enough input arguments"
Thank you for helping
function F = EM_SS(p, E_p)
F = p(1)*(2*pi*sqrt(p(4))/E_p)*(1/p(6))*(int(sech(((E_p – E)./p(6)))*(1 + 10*p(5)*(E – p(3)) + 126*p(5)^2*(E – p(3))^2)/(1 – exp(-2*pi*sqrt(p(4)/(E – p(3))))), E, p(3), Inf, ‘ArrayValued’, 1)) + p(2)*(2*pi*p(4)^3/2)*1/p(6)*((1/1^3)*sech((E_p – p(3) + p(4)/1^2)./p(6)) + (1/2^3)*sech((E_p – p(3) + p(4)/2^2)./p(6)) + (1/3^3)*sech((E_p – p(3) + p(4)/3^2)./p(6)) + (1/4^3)*sech((E_p – p(3) + p(4)/4^2)./p(6)) + (1/5^3)*sech((E_p – p(3) + p(4)/5^2)./p(6)) + (1/6^3)*sech((E_p – p(3) + p(4)/6^2)./p(6)) + (1/7^3)*sech((E_p – p(3) + p(4)/7^2)./p(6)));

end Hi, I have a function to be fitted to some experimental data, but this function has multiple fitting parameters (6 parameters). I know that one of the ways to find these fitting parameters is to pass the function, the inital guess to the parameters and the lower and upper bounds into the lsqcurvefit function.
I have already created the function, but it seems like the software doesn’t know that the p(1),p(2), p(3)… are parameters. Sorry if the function is very long.
What I wanted was p to be a vector with 6 elements p(1) p(2) p(3) p(4) p(5) p(6) but it seems like the software cant differentiate betwen p and p(1) p(2) p(3) p(4) p(5) p(6). When I try running the code, the error message I got was "Not enough input arguments"
Thank you for helping
function F = EM_SS(p, E_p)
F = p(1)*(2*pi*sqrt(p(4))/E_p)*(1/p(6))*(int(sech(((E_p – E)./p(6)))*(1 + 10*p(5)*(E – p(3)) + 126*p(5)^2*(E – p(3))^2)/(1 – exp(-2*pi*sqrt(p(4)/(E – p(3))))), E, p(3), Inf, ‘ArrayValued’, 1)) + p(2)*(2*pi*p(4)^3/2)*1/p(6)*((1/1^3)*sech((E_p – p(3) + p(4)/1^2)./p(6)) + (1/2^3)*sech((E_p – p(3) + p(4)/2^2)./p(6)) + (1/3^3)*sech((E_p – p(3) + p(4)/3^2)./p(6)) + (1/4^3)*sech((E_p – p(3) + p(4)/4^2)./p(6)) + (1/5^3)*sech((E_p – p(3) + p(4)/5^2)./p(6)) + (1/6^3)*sech((E_p – p(3) + p(4)/6^2)./p(6)) + (1/7^3)*sech((E_p – p(3) + p(4)/7^2)./p(6)));

end curve fitting, lsqcurvefit MATLAB Answers — New Questions

​

Hello!
I have 5 matrices: M1 = randi(100,10) , M2, M3 etc.
I want to be able to make new matrices with all of the same pixels in each matrix (so N1 = M1(1), M2(1) etc. until I reach N100 = M1(100), M2(100), etc.). I can do this manually, but the size of each matrix(10×10) would make this a very long process. I have been trying to mess around with for loops but cannot seem to figure out how to properly use them to create all 100 1×5 matrices. Are there any tips or ways to make this process happen? I tried using cell arrays as well in a for loop and yet I still am unable to do so.Hello!
I have 5 matrices: M1 = randi(100,10) , M2, M3 etc.
I want to be able to make new matrices with all of the same pixels in each matrix (so N1 = M1(1), M2(1) etc. until I reach N100 = M1(100), M2(100), etc.). I can do this manually, but the size of each matrix(10×10) would make this a very long process. I have been trying to mess around with for loops but cannot seem to figure out how to properly use them to create all 100 1×5 matrices. Are there any tips or ways to make this process happen? I tried using cell arrays as well in a for loop and yet I still am unable to do so. Hello!
I have 5 matrices: M1 = randi(100,10) , M2, M3 etc.
I want to be able to make new matrices with all of the same pixels in each matrix (so N1 = M1(1), M2(1) etc. until I reach N100 = M1(100), M2(100), etc.). I can do this manually, but the size of each matrix(10×10) would make this a very long process. I have been trying to mess around with for loops but cannot seem to figure out how to properly use them to create all 100 1×5 matrices. Are there any tips or ways to make this process happen? I tried using cell arrays as well in a for loop and yet I still am unable to do so. matrices, image segmentation, for loop, help MATLAB Answers — New Questions

​

Hello there I am tying to build a model to test what a 1D convolution layer will do to my data. I have 3 input accelerometer mesurements in the x,y,z directions and am trying to predict a continuous output. The Xtrain is a 3×500 double with the 3 input accelerometer mesurements over 500 time steps and the Ytrain is a 1×500 double of the target output variable. The code I have developed is here:
numFilters = 64;
filterSize = 5;
droupoutFactor = 0.005;
numBlocks = 4;
dilationFactor = 1;

net = dlnetwork;

layers = sequenceInputLayer(NumFeatures,Normalization="rescale-symmetric",Name="inputaccelerometers")
convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal",Name="conv1_")

net = addLayers(net,layers)

options = trainingOptions("adam", …
MaxEpochs=60, …
miniBatchSize=1, …
InputDataFormats="CTB", …
Plots="training-progress", …
Metrics="rmse", …
Verbose=0);

net = trainnet(Xtrain’,Ytrain’,net,"mse",options)
But when I try and train the model I get and error that says: Number of channels in predictions (3) must match the number of channels in the targets (1). Do I need to add another layer to convert the inputs into one predicted output and which layer should I use to do this? Any suggestions on how to remove this error and get the network to run accurately? Thanks so much!Hello there I am tying to build a model to test what a 1D convolution layer will do to my data. I have 3 input accelerometer mesurements in the x,y,z directions and am trying to predict a continuous output. The Xtrain is a 3×500 double with the 3 input accelerometer mesurements over 500 time steps and the Ytrain is a 1×500 double of the target output variable. The code I have developed is here:
numFilters = 64;
filterSize = 5;
droupoutFactor = 0.005;
numBlocks = 4;
dilationFactor = 1;

net = dlnetwork;

layers = sequenceInputLayer(NumFeatures,Normalization="rescale-symmetric",Name="inputaccelerometers")
convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal",Name="conv1_")

net = addLayers(net,layers)

options = trainingOptions("adam", …
MaxEpochs=60, …
miniBatchSize=1, …
InputDataFormats="CTB", …
Plots="training-progress", …
Metrics="rmse", …
Verbose=0);

net = trainnet(Xtrain’,Ytrain’,net,"mse",options)
But when I try and train the model I get and error that says: Number of channels in predictions (3) must match the number of channels in the targets (1). Do I need to add another layer to convert the inputs into one predicted output and which layer should I use to do this? Any suggestions on how to remove this error and get the network to run accurately? Thanks so much! Hello there I am tying to build a model to test what a 1D convolution layer will do to my data. I have 3 input accelerometer mesurements in the x,y,z directions and am trying to predict a continuous output. The Xtrain is a 3×500 double with the 3 input accelerometer mesurements over 500 time steps and the Ytrain is a 1×500 double of the target output variable. The code I have developed is here:
numFilters = 64;
filterSize = 5;
droupoutFactor = 0.005;
numBlocks = 4;
dilationFactor = 1;

net = dlnetwork;

layers = sequenceInputLayer(NumFeatures,Normalization="rescale-symmetric",Name="inputaccelerometers")
convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal",Name="conv1_")

net = addLayers(net,layers)

options = trainingOptions("adam", …
MaxEpochs=60, …
miniBatchSize=1, …
InputDataFormats="CTB", …
Plots="training-progress", …
Metrics="rmse", …
Verbose=0);

net = trainnet(Xtrain’,Ytrain’,net,"mse",options)
But when I try and train the model I get and error that says: Number of channels in predictions (3) must match the number of channels in the targets (1). Do I need to add another layer to convert the inputs into one predicted output and which layer should I use to do this? Any suggestions on how to remove this error and get the network to run accurately? Thanks so much! neural network, convolution layer, input MATLAB Answers — New Questions

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the files attached contains the code that i used , and when i upload, it shows the results in the screenshots below,
Task : sending dht11 data without Wi Fi to thingspeakthe files attached contains the code that i used , and when i upload, it shows the results in the screenshots below,
Task : sending dht11 data without Wi Fi to thingspeak the files attached contains the code that i used , and when i upload, it shows the results in the screenshots below,
Task : sending dht11 data without Wi Fi to thingspeak sim800l, ttgo t-call, data, dht11, arduino ide, tcp/ip MATLAB Answers — New Questions

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I am working on integrating a MEX function into a Simulink model to protect the source code of an algorithm from direct visibility. To achieve this, I converted the original MATLAB function into a MEX function, which I then call within a MATLAB Function block using the coder.extrinsic command.
During my testing, I’ve observed that the MEX function executes at each simulation step and is significantly slower compared to the original MATLAB function, resulting in a much higher overall simulation time.
I have several questions and would appreciate insights from the community:
Why might the MEX function be performing slower than the MATLAB function within the Simulink environment?
How does Simulink handle MEX functions during simulation and compilation?
What are the best practices or correct methods for integrating a MEX function into a Simulink model to ensure optimal performance?
Any detailed explanations or guidance on troubleshooting and improving the performance of MEX functions in Simulink would be highly appreciated. Thank you!I am working on integrating a MEX function into a Simulink model to protect the source code of an algorithm from direct visibility. To achieve this, I converted the original MATLAB function into a MEX function, which I then call within a MATLAB Function block using the coder.extrinsic command.
During my testing, I’ve observed that the MEX function executes at each simulation step and is significantly slower compared to the original MATLAB function, resulting in a much higher overall simulation time.
I have several questions and would appreciate insights from the community:
Why might the MEX function be performing slower than the MATLAB function within the Simulink environment?
How does Simulink handle MEX functions during simulation and compilation?
What are the best practices or correct methods for integrating a MEX function into a Simulink model to ensure optimal performance?
Any detailed explanations or guidance on troubleshooting and improving the performance of MEX functions in Simulink would be highly appreciated. Thank you! I am working on integrating a MEX function into a Simulink model to protect the source code of an algorithm from direct visibility. To achieve this, I converted the original MATLAB function into a MEX function, which I then call within a MATLAB Function block using the coder.extrinsic command.
During my testing, I’ve observed that the MEX function executes at each simulation step and is significantly slower compared to the original MATLAB function, resulting in a much higher overall simulation time.
I have several questions and would appreciate insights from the community:
Why might the MEX function be performing slower than the MATLAB function within the Simulink environment?
How does Simulink handle MEX functions during simulation and compilation?
What are the best practices or correct methods for integrating a MEX function into a Simulink model to ensure optimal performance?
Any detailed explanations or guidance on troubleshooting and improving the performance of MEX functions in Simulink would be highly appreciated. Thank you! simulink, mex function, coder.extrinsic, simulation speed, mex compiler, execution time, matlab compiler MATLAB Answers — New Questions

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Hi, there is a BLDC Motor Control Simulink model in Matlab and it works perfect. It uses buck converter to regulate the dc voltage. I created a boost converter model to increase my 24V battery instead of constant dc voltage. It works perfect separate from BLDC model but when I add this model to BLDC control model, the duty cycle that is coming out of PID block is always 0 and the desired speed input doesnt change anything. This is BLDC Motor Control Simulink model with Buck converter:

And this is my boost converter and complete model:

And these is duty cycle:

Anyone who can help solving this problem? Thank you!Hi, there is a BLDC Motor Control Simulink model in Matlab and it works perfect. It uses buck converter to regulate the dc voltage. I created a boost converter model to increase my 24V battery instead of constant dc voltage. It works perfect separate from BLDC model but when I add this model to BLDC control model, the duty cycle that is coming out of PID block is always 0 and the desired speed input doesnt change anything. This is BLDC Motor Control Simulink model with Buck converter:

And this is my boost converter and complete model:

And these is duty cycle:

Anyone who can help solving this problem? Thank you! Hi, there is a BLDC Motor Control Simulink model in Matlab and it works perfect. It uses buck converter to regulate the dc voltage. I created a boost converter model to increase my 24V battery instead of constant dc voltage. It works perfect separate from BLDC model but when I add this model to BLDC control model, the duty cycle that is coming out of PID block is always 0 and the desired speed input doesnt change anything. This is BLDC Motor Control Simulink model with Buck converter:

And this is my boost converter and complete model:

And these is duty cycle:

Anyone who can help solving this problem? Thank you! bldc, electric_motor_control, boost converter MATLAB Answers — New Questions

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how to compute ERGODIC CHANNEL CAPACITY for 8 element mimo antenna .also plot frequency vs channel capacityhow to compute ERGODIC CHANNEL CAPACITY for 8 element mimo antenna .also plot frequency vs channel capacity how to compute ERGODIC CHANNEL CAPACITY for 8 element mimo antenna .also plot frequency vs channel capacity antenna, mimo MATLAB Answers — New Questions

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