Which parts of this code is correct? I’m getting confused on which sections are relevant for my question above?
N = length(sig1);
xdft1 = fft(sig1);
xdft1 = xdft1(1:N/2+1);
psdx1 = (1/(Fs*N)) * abs(xdft1).^2;
psdx1(2:end-1) = 2*psdx1(2:end-1);
freq1 = 0:Fs/length(sig1):Fs/2;
P1_band = psdx1(freq1 >= 20 & freq1 <= 100);
f1_band = freq1(freq1 >= 20 & freq1 <= 100);
power_sig1 = trapz(f1_band, P1_band);

noise = x_new – x_notch;
SNR_dB = snr(x_notch, noise);
P_signal = mean(signalSeg.^2, ‘omitnan’);
P_noise = mean(noiseSeg.^2, ‘omitnan’);
SNR_dB = 10*log10(P_signal / P_noise);

f_notch = 60;
BW_notch = 2;
wo = f_notch/(Fs/2);
Q = f_notch/BW_notch;
bw = wo/Q;
[b_n,a_n] = iirnotch(wo,bw);
x_notch = filtfilt(b_n,a_n,x_bp);

x = x(:);
t = (0:length(x)-1)’/Fs;
% Frequency analysis
x_dm = x – mean(x);
N = length(x_dm);
X = fft(x_dm);
K = floor(N/2);
X = X(1:K+1);
PSD = (1/(Fs*N)) * abs(X).^2;
if K > 1
PSD(2:end-1) = 2*PSD(2:end-1);
end
f = (0:K)’ * (Fs/N);
% Find dominant frequency
[~, idxPeak] = max(PSD(2:end)); % ignore 0 Hz
idxPeak = idxPeak + 1;
domFreq = f(idxPeak);
fprintf(‘Dominant frequency = %.2f Hzn’, domFreq);Which parts of this code is correct? I’m getting confused on which sections are relevant for my question above?
N = length(sig1);
xdft1 = fft(sig1);
xdft1 = xdft1(1:N/2+1);
psdx1 = (1/(Fs*N)) * abs(xdft1).^2;
psdx1(2:end-1) = 2*psdx1(2:end-1);
freq1 = 0:Fs/length(sig1):Fs/2;
P1_band = psdx1(freq1 >= 20 & freq1 <= 100);
f1_band = freq1(freq1 >= 20 & freq1 <= 100);
power_sig1 = trapz(f1_band, P1_band);

noise = x_new – x_notch;
SNR_dB = snr(x_notch, noise);
P_signal = mean(signalSeg.^2, ‘omitnan’);
P_noise = mean(noiseSeg.^2, ‘omitnan’);
SNR_dB = 10*log10(P_signal / P_noise);

f_notch = 60;
BW_notch = 2;
wo = f_notch/(Fs/2);
Q = f_notch/BW_notch;
bw = wo/Q;
[b_n,a_n] = iirnotch(wo,bw);
x_notch = filtfilt(b_n,a_n,x_bp);

x = x(:);
t = (0:length(x)-1)’/Fs;
% Frequency analysis
x_dm = x – mean(x);
N = length(x_dm);
X = fft(x_dm);
K = floor(N/2);
X = X(1:K+1);
PSD = (1/(Fs*N)) * abs(X).^2;
if K > 1
PSD(2:end-1) = 2*PSD(2:end-1);
end
f = (0:K)’ * (Fs/N);
% Find dominant frequency
[~, idxPeak] = max(PSD(2:end)); % ignore 0 Hz
idxPeak = idxPeak + 1;
domFreq = f(idxPeak);
fprintf(‘Dominant frequency = %.2f Hzn’, domFreq); Which parts of this code is correct? I’m getting confused on which sections are relevant for my question above?
N = length(sig1);
xdft1 = fft(sig1);
xdft1 = xdft1(1:N/2+1);
psdx1 = (1/(Fs*N)) * abs(xdft1).^2;
psdx1(2:end-1) = 2*psdx1(2:end-1);
freq1 = 0:Fs/length(sig1):Fs/2;
P1_band = psdx1(freq1 >= 20 & freq1 <= 100);
f1_band = freq1(freq1 >= 20 & freq1 <= 100);
power_sig1 = trapz(f1_band, P1_band);

noise = x_new – x_notch;
SNR_dB = snr(x_notch, noise);
P_signal = mean(signalSeg.^2, ‘omitnan’);
P_noise = mean(noiseSeg.^2, ‘omitnan’);
SNR_dB = 10*log10(P_signal / P_noise);

f_notch = 60;
BW_notch = 2;
wo = f_notch/(Fs/2);
Q = f_notch/BW_notch;
bw = wo/Q;
[b_n,a_n] = iirnotch(wo,bw);
x_notch = filtfilt(b_n,a_n,x_bp);

x = x(:);
t = (0:length(x)-1)’/Fs;
% Frequency analysis
x_dm = x – mean(x);
N = length(x_dm);
X = fft(x_dm);
K = floor(N/2);
X = X(1:K+1);
PSD = (1/(Fs*N)) * abs(X).^2;
if K > 1
PSD(2:end-1) = 2*PSD(2:end-1);
end
f = (0:K)’ * (Fs/N);
% Find dominant frequency
[~, idxPeak] = max(PSD(2:end)); % ignore 0 Hz
idxPeak = idxPeak + 1;
domFreq = f(idxPeak);
fprintf(‘Dominant frequency = %.2f Hzn’, domFreq); bandpower MATLAB Answers — New Questions

​

I have a dataset Y representing the real part Y(:,1) and the imaginary part (Y:,2) of a function against the frequency fq. I want to find the coefficient of the transfer function to have the a good fit with the dataset. The transfer function is given assumed to have a fifth order, (so there are totally 10 unknowns) T(p,q,s)=p(5)*s^4+p(4)*s^3+p(3)*s^2+p(2)*s+p(1))/(s^5+q(5)*s^4+q(4)*s^3+q(3)*s^2+q(2)*s+q(1));
The code is given in the attached, I split the objective function as the imaginary and real part respectively, the objective function is (real(T)-Y(:,1))^2+(imag(T)-Y(:,2)) over all the selected points in the Y-fq curve.
The blue circle is the fitted result from the NLSQ, but they are slightly away from the original data. My question is how to improve the code to improve the fitting result.
In the attached files,"nonlsqfitting.mat" is the Y and fq data; NLSFmat is the runing code; OBJrealimag.m is the objective function, where Yout(:,1) is the real part of T and Yout(:,2) is the imaginary part of T.I have a dataset Y representing the real part Y(:,1) and the imaginary part (Y:,2) of a function against the frequency fq. I want to find the coefficient of the transfer function to have the a good fit with the dataset. The transfer function is given assumed to have a fifth order, (so there are totally 10 unknowns) T(p,q,s)=p(5)*s^4+p(4)*s^3+p(3)*s^2+p(2)*s+p(1))/(s^5+q(5)*s^4+q(4)*s^3+q(3)*s^2+q(2)*s+q(1));
The code is given in the attached, I split the objective function as the imaginary and real part respectively, the objective function is (real(T)-Y(:,1))^2+(imag(T)-Y(:,2)) over all the selected points in the Y-fq curve.
The blue circle is the fitted result from the NLSQ, but they are slightly away from the original data. My question is how to improve the code to improve the fitting result.
In the attached files,"nonlsqfitting.mat" is the Y and fq data; NLSFmat is the runing code; OBJrealimag.m is the objective function, where Yout(:,1) is the real part of T and Yout(:,2) is the imaginary part of T. I have a dataset Y representing the real part Y(:,1) and the imaginary part (Y:,2) of a function against the frequency fq. I want to find the coefficient of the transfer function to have the a good fit with the dataset. The transfer function is given assumed to have a fifth order, (so there are totally 10 unknowns) T(p,q,s)=p(5)*s^4+p(4)*s^3+p(3)*s^2+p(2)*s+p(1))/(s^5+q(5)*s^4+q(4)*s^3+q(3)*s^2+q(2)*s+q(1));
The code is given in the attached, I split the objective function as the imaginary and real part respectively, the objective function is (real(T)-Y(:,1))^2+(imag(T)-Y(:,2)) over all the selected points in the Y-fq curve.
The blue circle is the fitted result from the NLSQ, but they are slightly away from the original data. My question is how to improve the code to improve the fitting result.
In the attached files,"nonlsqfitting.mat" is the Y and fq data; NLSFmat is the runing code; OBJrealimag.m is the objective function, where Yout(:,1) is the real part of T and Yout(:,2) is the imaginary part of T. nonlinear least square fitting transfer function MATLAB Answers — New Questions

​

Basic discontinuety was simulated 0Hz to 40Ghz 40000 samples as presented in the photos . S-parameters were extracted as S2P file(attached in the link). My goal is to see the discontinuety using time domain reflectometry. what is the proper way to do IFFT in this case? What windows should I multiply with? Thanks.Basic discontinuety was simulated 0Hz to 40Ghz 40000 samples as presented in the photos . S-parameters were extracted as S2P file(attached in the link). My goal is to see the discontinuety using time domain reflectometry. what is the proper way to do IFFT in this case? What windows should I multiply with? Thanks. Basic discontinuety was simulated 0Hz to 40Ghz 40000 samples as presented in the photos . S-parameters were extracted as S2P file(attached in the link). My goal is to see the discontinuety using time domain reflectometry. what is the proper way to do IFFT in this case? What windows should I multiply with? Thanks. ifft, tdr, s-parameters MATLAB Answers — New Questions

​

Elapsed time is 8.597842 seconds.

clear, clc
tic
load(‘Erros_a.mat’); load(‘Sp1_b.mat’);
Relatorio = zeros(5000,4,’uint32′);
Relatorio_final = zeros(10000,4,’uint32′);
a = 10;
for i1 = 1:1
% BLOCO 1 — usa cache se existir, senão gera e salva
cache_file = sprintf(‘Erros_b_cache_%d.mat’, i1);
if exist(cache_file, ‘file’)
load(cache_file);
else
linha_a = Erros_a(a,:);
vals = linha_a(linha_a > 0);
vals = vals(1:end-2);
nRef = numel(vals);
cols_b1 = 2*(double(vals)-1) + 1;
cols_b2 = 2*(double(vals)-1) + 2;
passa = (Sp1_b(:, cols_b1) == 0) & (Sp1_b(:, cols_b2) == 0);
col_dest_matrix = uint32(cumsum(passa, 2) – passa + 1);
Erros_b = zeros(3268760, 37, ‘uint32’);
for d = 1:nRef
linhas_ok = find(passa(:, d));
if ~isempty(linhas_ok)
idx = sub2ind([3268760, 37], linhas_ok, double(col_dest_matrix(linhas_ok, d)));
Erros_b(idx) = vals(d);
end
end
save(cache_file, ‘Erros_b’, ‘-v7.3’);
end
% BLOCO 2 — preparação
load(‘Sp1_c.mat’);
Erros_b(:,38) = (1:size(Erros_b,1))’;
mascara = Erros_b(:, 13) > 50;
Erros_reduzido = Erros_b(mascara, :);
[lin_u, col_u] = find(Sp1_c == 1);
uns_nas_colunas = accumarray(col_u, lin_u, [size(Sp1_c,2) 1], @(x){sort(x)}, {[]});
clear Erros_b Sp1_c lin_u col_u mascara;
[~, ordem] = sortrows(Erros_reduzido(:, 1:38));
Erros_reduzido_ordem = Erros_reduzido(ordem, :);
[~, ordem] = sortrows(Erros_reduzido_ordem(:, 1:38));
Erros_reduzido_ordem = Erros_reduzido_ordem(ordem, :);
clear Erros_reduzido ordem;
ERO = double(Erros_reduzido_ordem);
clear Erros_reduzido_ordem;
nCols_c = numel(uns_nas_colunas);
uns_sets = cell(nCols_c, 1);
for cc = 1:nCols_c
s = uns_nas_colunas{cc};
if ~isempty(s)
m = false(max(s), 1);
m(s) = true;
uns_sets{cc} = m;
else
uns_sets{cc} = false(0,1);
end
end
% BLOCO 3 — while principal
num_linhas = size(ERO, 1);
y = 1;
offset = 0;
while (offset + 1) <= num_linhas
i = offset + 1;
linha_at = ERO(i, 1:38);
a_valid = linha_at(linha_at > 0 & linha_at <= 100);
% k=2: dois ponteiros
A = uns_nas_colunas{a_valid(1)};
B = uns_nas_colunas{a_valid(2)};
buf = zeros(min(numel(A), numel(B)), 1);
ia = 1; ib = 1; ic = 0;
na = numel(A); nb = numel(B);
while ia <= na && ib <= nb
if A(ia) == B(ib)
ic = ic + 1; buf(ic) = A(ia);
ia = ia + 1; ib = ib + 1;
elseif A(ia) < B(ib); ia = ia + 1;
else; ib = ib + 1;
end
end
candidatas = buf(1:ic);
w=1; z=1;
n(w,1) = a_valid(w,1);
achou = false;
if isempty(candidatas)
melhor_erro = a_valid(2) – 1;
melhor_linha = linha_at(38);
n(w,2) = a_valid(w,2);
nP = length(n);
lo = offset + 1; hi = num_linhas; ultlin_abs = offset;
if all(ERO(lo, 1:nP) == n)
while lo <= hi
mid = floor((lo + hi) / 2);
if all(ERO(mid, 1:nP) == n)
ultlin_abs = mid; lo = mid + 1;
else; hi = mid – 1; end
end
end
if ultlin_abs > offset; offset = ultlin_abs;
else; offset = offset + 1; end
n = [];
Relatorio(y,1) = Erros_a(i1,40);
Relatorio(y,2) = A(1);
Relatorio(y,3) = melhor_linha;
Relatorio(y,4) = melhor_erro;
y = y + 1;
achou = true;
end
% k>=3: lookup lógico
for k = 3:length(a_valid)
if achou; break; end
n(w,k-1) = a_valid(w,k-1);
col_atual = a_valid(k);
s2 = uns_sets{col_atual};
if isempty(candidatas) || isempty(s2)
proximas = [];
else
mask = candidatas <= numel(s2);
cand_validas = candidatas(mask);
if isempty(cand_validas); proximas = [];
else; proximas = cand_validas(s2(cand_validas)); end
end
if isempty(proximas)
melhor_erro = col_atual – 1;
melhor_linha = linha_at(38);
n(w,k) = a_valid(w,k);
nP = length(n);
lo = offset + 1; hi = num_linhas; ultlin_abs = offset;
if all(ERO(lo, 1:nP) == n)
while lo <= hi
mid = floor((lo + hi) / 2);
if all(ERO(mid, 1:nP) == n)
ultlin_abs = mid; lo = mid + 1;
else; hi = mid – 1; end
end
end
if ultlin_abs > offset; offset = ultlin_abs;
else; offset = offset + 1; end
n = [];
Relatorio(y,1) = Erros_a(i1,40);
Relatorio(y,2) = candidatas(1);
Relatorio(y,3) = melhor_linha;
Relatorio(y,4) = melhor_erro;
y = y + 1;
achou = true;
break;
end
candidatas = proximas;
melhor_linha = candidatas(1);
end % for k

if ~achou; offset = offset + 1; end
end % while
clear ERO uns_sets;
Relatorio = sortrows(Relatorio, 4, ‘descend’);
Relatorio_final(i1,:) = Relatorio(1,:);
Relatorio(:,:) = 0;
a = a + 1;
end % i1
tocElapsed time is 8.597842 seconds.

clear, clc
tic
load(‘Erros_a.mat’); load(‘Sp1_b.mat’);
Relatorio = zeros(5000,4,’uint32′);
Relatorio_final = zeros(10000,4,’uint32′);
a = 10;
for i1 = 1:1
% BLOCO 1 — usa cache se existir, senão gera e salva
cache_file = sprintf(‘Erros_b_cache_%d.mat’, i1);
if exist(cache_file, ‘file’)
load(cache_file);
else
linha_a = Erros_a(a,:);
vals = linha_a(linha_a > 0);
vals = vals(1:end-2);
nRef = numel(vals);
cols_b1 = 2*(double(vals)-1) + 1;
cols_b2 = 2*(double(vals)-1) + 2;
passa = (Sp1_b(:, cols_b1) == 0) & (Sp1_b(:, cols_b2) == 0);
col_dest_matrix = uint32(cumsum(passa, 2) – passa + 1);
Erros_b = zeros(3268760, 37, ‘uint32’);
for d = 1:nRef
linhas_ok = find(passa(:, d));
if ~isempty(linhas_ok)
idx = sub2ind([3268760, 37], linhas_ok, double(col_dest_matrix(linhas_ok, d)));
Erros_b(idx) = vals(d);
end
end
save(cache_file, ‘Erros_b’, ‘-v7.3’);
end
% BLOCO 2 — preparação
load(‘Sp1_c.mat’);
Erros_b(:,38) = (1:size(Erros_b,1))’;
mascara = Erros_b(:, 13) > 50;
Erros_reduzido = Erros_b(mascara, :);
[lin_u, col_u] = find(Sp1_c == 1);
uns_nas_colunas = accumarray(col_u, lin_u, [size(Sp1_c,2) 1], @(x){sort(x)}, {[]});
clear Erros_b Sp1_c lin_u col_u mascara;
[~, ordem] = sortrows(Erros_reduzido(:, 1:38));
Erros_reduzido_ordem = Erros_reduzido(ordem, :);
[~, ordem] = sortrows(Erros_reduzido_ordem(:, 1:38));
Erros_reduzido_ordem = Erros_reduzido_ordem(ordem, :);
clear Erros_reduzido ordem;
ERO = double(Erros_reduzido_ordem);
clear Erros_reduzido_ordem;
nCols_c = numel(uns_nas_colunas);
uns_sets = cell(nCols_c, 1);
for cc = 1:nCols_c
s = uns_nas_colunas{cc};
if ~isempty(s)
m = false(max(s), 1);
m(s) = true;
uns_sets{cc} = m;
else
uns_sets{cc} = false(0,1);
end
end
% BLOCO 3 — while principal
num_linhas = size(ERO, 1);
y = 1;
offset = 0;
while (offset + 1) <= num_linhas
i = offset + 1;
linha_at = ERO(i, 1:38);
a_valid = linha_at(linha_at > 0 & linha_at <= 100);
% k=2: dois ponteiros
A = uns_nas_colunas{a_valid(1)};
B = uns_nas_colunas{a_valid(2)};
buf = zeros(min(numel(A), numel(B)), 1);
ia = 1; ib = 1; ic = 0;
na = numel(A); nb = numel(B);
while ia <= na && ib <= nb
if A(ia) == B(ib)
ic = ic + 1; buf(ic) = A(ia);
ia = ia + 1; ib = ib + 1;
elseif A(ia) < B(ib); ia = ia + 1;
else; ib = ib + 1;
end
end
candidatas = buf(1:ic);
w=1; z=1;
n(w,1) = a_valid(w,1);
achou = false;
if isempty(candidatas)
melhor_erro = a_valid(2) – 1;
melhor_linha = linha_at(38);
n(w,2) = a_valid(w,2);
nP = length(n);
lo = offset + 1; hi = num_linhas; ultlin_abs = offset;
if all(ERO(lo, 1:nP) == n)
while lo <= hi
mid = floor((lo + hi) / 2);
if all(ERO(mid, 1:nP) == n)
ultlin_abs = mid; lo = mid + 1;
else; hi = mid – 1; end
end
end
if ultlin_abs > offset; offset = ultlin_abs;
else; offset = offset + 1; end
n = [];
Relatorio(y,1) = Erros_a(i1,40);
Relatorio(y,2) = A(1);
Relatorio(y,3) = melhor_linha;
Relatorio(y,4) = melhor_erro;
y = y + 1;
achou = true;
end
% k>=3: lookup lógico
for k = 3:length(a_valid)
if achou; break; end
n(w,k-1) = a_valid(w,k-1);
col_atual = a_valid(k);
s2 = uns_sets{col_atual};
if isempty(candidatas) || isempty(s2)
proximas = [];
else
mask = candidatas <= numel(s2);
cand_validas = candidatas(mask);
if isempty(cand_validas); proximas = [];
else; proximas = cand_validas(s2(cand_validas)); end
end
if isempty(proximas)
melhor_erro = col_atual – 1;
melhor_linha = linha_at(38);
n(w,k) = a_valid(w,k);
nP = length(n);
lo = offset + 1; hi = num_linhas; ultlin_abs = offset;
if all(ERO(lo, 1:nP) == n)
while lo <= hi
mid = floor((lo + hi) / 2);
if all(ERO(mid, 1:nP) == n)
ultlin_abs = mid; lo = mid + 1;
else; hi = mid – 1; end
end
end
if ultlin_abs > offset; offset = ultlin_abs;
else; offset = offset + 1; end
n = [];
Relatorio(y,1) = Erros_a(i1,40);
Relatorio(y,2) = candidatas(1);
Relatorio(y,3) = melhor_linha;
Relatorio(y,4) = melhor_erro;
y = y + 1;
achou = true;
break;
end
candidatas = proximas;
melhor_linha = candidatas(1);
end % for k

if ~achou; offset = offset + 1; end
end % while
clear ERO uns_sets;
Relatorio = sortrows(Relatorio, 4, ‘descend’);
Relatorio_final(i1,:) = Relatorio(1,:);
Relatorio(:,:) = 0;
a = a + 1;
end % i1
toc Elapsed time is 8.597842 seconds.

clear, clc
tic
load(‘Erros_a.mat’); load(‘Sp1_b.mat’);
Relatorio = zeros(5000,4,’uint32′);
Relatorio_final = zeros(10000,4,’uint32′);
a = 10;
for i1 = 1:1
% BLOCO 1 — usa cache se existir, senão gera e salva
cache_file = sprintf(‘Erros_b_cache_%d.mat’, i1);
if exist(cache_file, ‘file’)
load(cache_file);
else
linha_a = Erros_a(a,:);
vals = linha_a(linha_a > 0);
vals = vals(1:end-2);
nRef = numel(vals);
cols_b1 = 2*(double(vals)-1) + 1;
cols_b2 = 2*(double(vals)-1) + 2;
passa = (Sp1_b(:, cols_b1) == 0) & (Sp1_b(:, cols_b2) == 0);
col_dest_matrix = uint32(cumsum(passa, 2) – passa + 1);
Erros_b = zeros(3268760, 37, ‘uint32’);
for d = 1:nRef
linhas_ok = find(passa(:, d));
if ~isempty(linhas_ok)
idx = sub2ind([3268760, 37], linhas_ok, double(col_dest_matrix(linhas_ok, d)));
Erros_b(idx) = vals(d);
end
end
save(cache_file, ‘Erros_b’, ‘-v7.3’);
end
% BLOCO 2 — preparação
load(‘Sp1_c.mat’);
Erros_b(:,38) = (1:size(Erros_b,1))’;
mascara = Erros_b(:, 13) > 50;
Erros_reduzido = Erros_b(mascara, :);
[lin_u, col_u] = find(Sp1_c == 1);
uns_nas_colunas = accumarray(col_u, lin_u, [size(Sp1_c,2) 1], @(x){sort(x)}, {[]});
clear Erros_b Sp1_c lin_u col_u mascara;
[~, ordem] = sortrows(Erros_reduzido(:, 1:38));
Erros_reduzido_ordem = Erros_reduzido(ordem, :);
[~, ordem] = sortrows(Erros_reduzido_ordem(:, 1:38));
Erros_reduzido_ordem = Erros_reduzido_ordem(ordem, :);
clear Erros_reduzido ordem;
ERO = double(Erros_reduzido_ordem);
clear Erros_reduzido_ordem;
nCols_c = numel(uns_nas_colunas);
uns_sets = cell(nCols_c, 1);
for cc = 1:nCols_c
s = uns_nas_colunas{cc};
if ~isempty(s)
m = false(max(s), 1);
m(s) = true;
uns_sets{cc} = m;
else
uns_sets{cc} = false(0,1);
end
end
% BLOCO 3 — while principal
num_linhas = size(ERO, 1);
y = 1;
offset = 0;
while (offset + 1) <= num_linhas
i = offset + 1;
linha_at = ERO(i, 1:38);
a_valid = linha_at(linha_at > 0 & linha_at <= 100);
% k=2: dois ponteiros
A = uns_nas_colunas{a_valid(1)};
B = uns_nas_colunas{a_valid(2)};
buf = zeros(min(numel(A), numel(B)), 1);
ia = 1; ib = 1; ic = 0;
na = numel(A); nb = numel(B);
while ia <= na && ib <= nb
if A(ia) == B(ib)
ic = ic + 1; buf(ic) = A(ia);
ia = ia + 1; ib = ib + 1;
elseif A(ia) < B(ib); ia = ia + 1;
else; ib = ib + 1;
end
end
candidatas = buf(1:ic);
w=1; z=1;
n(w,1) = a_valid(w,1);
achou = false;
if isempty(candidatas)
melhor_erro = a_valid(2) – 1;
melhor_linha = linha_at(38);
n(w,2) = a_valid(w,2);
nP = length(n);
lo = offset + 1; hi = num_linhas; ultlin_abs = offset;
if all(ERO(lo, 1:nP) == n)
while lo <= hi
mid = floor((lo + hi) / 2);
if all(ERO(mid, 1:nP) == n)
ultlin_abs = mid; lo = mid + 1;
else; hi = mid – 1; end
end
end
if ultlin_abs > offset; offset = ultlin_abs;
else; offset = offset + 1; end
n = [];
Relatorio(y,1) = Erros_a(i1,40);
Relatorio(y,2) = A(1);
Relatorio(y,3) = melhor_linha;
Relatorio(y,4) = melhor_erro;
y = y + 1;
achou = true;
end
% k>=3: lookup lógico
for k = 3:length(a_valid)
if achou; break; end
n(w,k-1) = a_valid(w,k-1);
col_atual = a_valid(k);
s2 = uns_sets{col_atual};
if isempty(candidatas) || isempty(s2)
proximas = [];
else
mask = candidatas <= numel(s2);
cand_validas = candidatas(mask);
if isempty(cand_validas); proximas = [];
else; proximas = cand_validas(s2(cand_validas)); end
end
if isempty(proximas)
melhor_erro = col_atual – 1;
melhor_linha = linha_at(38);
n(w,k) = a_valid(w,k);
nP = length(n);
lo = offset + 1; hi = num_linhas; ultlin_abs = offset;
if all(ERO(lo, 1:nP) == n)
while lo <= hi
mid = floor((lo + hi) / 2);
if all(ERO(mid, 1:nP) == n)
ultlin_abs = mid; lo = mid + 1;
else; hi = mid – 1; end
end
end
if ultlin_abs > offset; offset = ultlin_abs;
else; offset = offset + 1; end
n = [];
Relatorio(y,1) = Erros_a(i1,40);
Relatorio(y,2) = candidatas(1);
Relatorio(y,3) = melhor_linha;
Relatorio(y,4) = melhor_erro;
y = y + 1;
achou = true;
break;
end
candidatas = proximas;
melhor_linha = candidatas(1);
end % for k

if ~achou; offset = offset + 1; end
end % while
clear ERO uns_sets;
Relatorio = sortrows(Relatorio, 4, ‘descend’);
Relatorio_final(i1,:) = Relatorio(1,:);
Relatorio(:,:) = 0;
a = a + 1;
end % i1
toc script slow MATLAB Answers — New Questions

​

Using Share to "Standalone Desktop App" in MATLAB App Designer, Is there a way to sign the installer .exe file that is generated, and also sign the application .exe file that is generated after the installation file is run? Currently, when using App Designer to package the app, it creates a build of the app and then packages it into an installation .exe file in the same workflow.

The issue I’m forseeing is that if I sign the installation .exe file, the application .exe file within will still be un-signed when it is generated after the installer is run. Am I right in thinking this? If so, is there a method of ensuring the application .exe file within is signed?Using Share to "Standalone Desktop App" in MATLAB App Designer, Is there a way to sign the installer .exe file that is generated, and also sign the application .exe file that is generated after the installation file is run? Currently, when using App Designer to package the app, it creates a build of the app and then packages it into an installation .exe file in the same workflow.

The issue I’m forseeing is that if I sign the installation .exe file, the application .exe file within will still be un-signed when it is generated after the installer is run. Am I right in thinking this? If so, is there a method of ensuring the application .exe file within is signed? Using Share to "Standalone Desktop App" in MATLAB App Designer, Is there a way to sign the installer .exe file that is generated, and also sign the application .exe file that is generated after the installation file is run? Currently, when using App Designer to package the app, it creates a build of the app and then packages it into an installation .exe file in the same workflow.

The issue I’m forseeing is that if I sign the installation .exe file, the application .exe file within will still be un-signed when it is generated after the installer is run. Am I right in thinking this? If so, is there a method of ensuring the application .exe file within is signed? app designer, compiler, certificate, signing MATLAB Answers — New Questions

​

I have a force-displacement graph (the attached file) and I want to find out exactly at what point the linear portion of the graph ends. Please see the picture for more clarification.

I’ve already smoothed out the curve, so it is noiseless.
I did a quick search but couldn’t find anything useful.
Any help would be highly appreciated.I have a force-displacement graph (the attached file) and I want to find out exactly at what point the linear portion of the graph ends. Please see the picture for more clarification.

I’ve already smoothed out the curve, so it is noiseless.
I did a quick search but couldn’t find anything useful.
Any help would be highly appreciated. I have a force-displacement graph (the attached file) and I want to find out exactly at what point the linear portion of the graph ends. Please see the picture for more clarification.

I’ve already smoothed out the curve, so it is noiseless.
I did a quick search but couldn’t find anything useful.
Any help would be highly appreciated. linear part, nonlinear part MATLAB Answers — New Questions

​

Bonjour la communauté,
enfaite présentatement je travail sur un programme qui permets de recueillir les paramétre du modèle de Cole_cole à partir des mesures que j’ai eu. Donc l’idéé c’est de faire un programme qui va tracer une courbe qui se superpose à la mesure et me donner les paramétre du modèle correspondant. là j’utilise le lsqcurvefit mais le programme me retourne les valeurs d’initialisation et je sais pas ca peut être dû quoi ?
Merci d’avance de votre réponseBonjour la communauté,
enfaite présentatement je travail sur un programme qui permets de recueillir les paramétre du modèle de Cole_cole à partir des mesures que j’ai eu. Donc l’idéé c’est de faire un programme qui va tracer une courbe qui se superpose à la mesure et me donner les paramétre du modèle correspondant. là j’utilise le lsqcurvefit mais le programme me retourne les valeurs d’initialisation et je sais pas ca peut être dû quoi ?
Merci d’avance de votre réponse Bonjour la communauté,
enfaite présentatement je travail sur un programme qui permets de recueillir les paramétre du modèle de Cole_cole à partir des mesures que j’ai eu. Donc l’idéé c’est de faire un programme qui va tracer une courbe qui se superpose à la mesure et me donner les paramétre du modèle correspondant. là j’utilise le lsqcurvefit mais le programme me retourne les valeurs d’initialisation et je sais pas ca peut être dû quoi ?
Merci d’avance de votre réponse mbengue MATLAB Answers — New Questions

​

We are building an app with AppDesigner (24b currently). To get help designing we are using AI features. The suggestions seem good, and I want to adapt my AppDesigner Code. However, the code of the UI is greyed out. Secondly, unzipping the .mlapp and editing the matlab/document.xml does not work, as zipping again creates an not running (and smaller in size) .mlapp file. Is there a way to get access to the UI Code and to edit it effectively? Thank you.We are building an app with AppDesigner (24b currently). To get help designing we are using AI features. The suggestions seem good, and I want to adapt my AppDesigner Code. However, the code of the UI is greyed out. Secondly, unzipping the .mlapp and editing the matlab/document.xml does not work, as zipping again creates an not running (and smaller in size) .mlapp file. Is there a way to get access to the UI Code and to edit it effectively? Thank you. We are building an app with AppDesigner (24b currently). To get help designing we are using AI features. The suggestions seem good, and I want to adapt my AppDesigner Code. However, the code of the UI is greyed out. Secondly, unzipping the .mlapp and editing the matlab/document.xml does not work, as zipping again creates an not running (and smaller in size) .mlapp file. Is there a way to get access to the UI Code and to edit it effectively? Thank you. appdesigner, ai MATLAB Answers — New Questions

​

New toolboxes have just been added to my license. How do I install additional toolboxes into an existing installation of MATLAB?New toolboxes have just been added to my license. How do I install additional toolboxes into an existing installation of MATLAB? New toolboxes have just been added to my license. How do I install additional toolboxes into an existing installation of MATLAB?  MATLAB Answers — New Questions

​

Hello everyone,
I’ve been working with Matlab pdepe to solve numerically a set of coupled partial differential equations. I’m fairly new and I’ve encountered a problem with the numerical solution that I get from my code and can’t seem to get rid of it. The equations model the diffusion and decay of two species in a material over 100s of ns. The problem I’m getting is that when I look at the solution in a log time scale, several kinks appear. Here are different things I’ve tried and haven’t helped:
Make tspan finer. (30-1000 points)
Make xmesh finer. (50-1000 points)
Increase or decrease relative tolerance (1E0 to 1E-6).
Make tspan time separation linear after t = 0.
Make tspan time separation increase logarithmically after t = 0.1 s
Any insights into how I could solve the problem would be useful.
% Physical constants
%params = [DR TR TT A B];
DR_guess = 1E6; % in nm^2 /ns
DT_guess = 1E1; % in nm^2 /ns
TR_guess = 14; % in ns
TT_guess = 130; % in ns
T0 = 295; % in K
R0 = 0.016;% in e/nm^3
A = 1;
B = 200;
Y = 0.3056;
CR = -80000;
CT = 220;

params = [DR_guess DT_guess TR_guess TT_guess T0 R0 A B Y CR CT];

% Setting up the mesh for space and time
xmesh = linspace(0,2000,55); %in nm
tspan = [-5, -2, -1, -0.5, 0, logspace(-1, 3, 80)];% in ns

% Solving the differential equation
options = odeset(‘RelTol’,1e-0,’OutputFcn’,@myOut,’Stats’,’on’);
pde = @(x, t, u, dudx) AgSepde(x, t, u, dudx, params);
soln = pdepe(1, pde, @AgSeic, @AgSebc, xmesh, tspan, options);

% Partial differential equation function
function [c,f,s] = AgSepde(x,t,u,dudx,params)
% Parameters for PDEs
DR = params(1);
DT = params(2);
TR = params(3);
TT = params(4);
T0 = params(5);
R0 = params(6);
A = params(7);
B = params(8);
Y = params(9);

% Generation function
sigmar = 180; % in nm
sigmat = 0.2; %in ns
t0 = 0.00; % in ns
G = (1 / (sigmar * sqrt(2*pi))) * exp(-x.^2 / (2 * sigmar^2)) …
* (1 / (sigmat * sqrt(2*pi))) * exp(-(t – t0).^2 / (2 * sigmat^2));

c = [1; 1];
f = [DR*dudx(1); DT*dudx(2)];
s = [-(u(1) – R0)/TR + A*G; Y*(u(1)- R0) – (u(2) – T0)/TT + B*G];
end

% Intial conditions for the PDEs
function u0 = AgSeic(x)
T0 = 295;
R0 = 0.016;
u0 = [R0; T0];
end

function [pl, ql, pr, qr] = AgSebc(xl, ul, xr, ur, t)
% Left boundary: u(0,t) = 1
pl = [0; 0];
ql = [1; 1];

% Right boundary: u(x’,t) = 0
pr = [0; 0];
qr = [1; 1];
end

function status = myOut(t,y,flag)
persistent last
if strcmp(flag,’init’), last = tic; end
if isempty(flag) && toc(last) > 1
fprintf(‘t = %.3g nsn’, t(end));
last = tic;
end
status = 0;
end

%% Figures
tiledlayout(2,1)
nexttile(1,[1,1])
hold on;
plot(tspan,soln(:,1,1),’Marker’,’o’);
set(gca,’XScale’,’log’);
xlabel(‘Time / ns’);
ylabel(‘Species 1’);
hold off;

nexttile(2,[1,1])
hold on;
plot(tspan,soln(:,1,2),’Marker’,’square’)
set(gca,’XScale’,’log’);
xlabel(‘Time / ns’);
ylabel(‘Species 2’);
hold off;Hello everyone,
I’ve been working with Matlab pdepe to solve numerically a set of coupled partial differential equations. I’m fairly new and I’ve encountered a problem with the numerical solution that I get from my code and can’t seem to get rid of it. The equations model the diffusion and decay of two species in a material over 100s of ns. The problem I’m getting is that when I look at the solution in a log time scale, several kinks appear. Here are different things I’ve tried and haven’t helped:
Make tspan finer. (30-1000 points)
Make xmesh finer. (50-1000 points)
Increase or decrease relative tolerance (1E0 to 1E-6).
Make tspan time separation linear after t = 0.
Make tspan time separation increase logarithmically after t = 0.1 s
Any insights into how I could solve the problem would be useful.
% Physical constants
%params = [DR TR TT A B];
DR_guess = 1E6; % in nm^2 /ns
DT_guess = 1E1; % in nm^2 /ns
TR_guess = 14; % in ns
TT_guess = 130; % in ns
T0 = 295; % in K
R0 = 0.016;% in e/nm^3
A = 1;
B = 200;
Y = 0.3056;
CR = -80000;
CT = 220;

params = [DR_guess DT_guess TR_guess TT_guess T0 R0 A B Y CR CT];

% Setting up the mesh for space and time
xmesh = linspace(0,2000,55); %in nm
tspan = [-5, -2, -1, -0.5, 0, logspace(-1, 3, 80)];% in ns

% Solving the differential equation
options = odeset(‘RelTol’,1e-0,’OutputFcn’,@myOut,’Stats’,’on’);
pde = @(x, t, u, dudx) AgSepde(x, t, u, dudx, params);
soln = pdepe(1, pde, @AgSeic, @AgSebc, xmesh, tspan, options);

% Partial differential equation function
function [c,f,s] = AgSepde(x,t,u,dudx,params)
% Parameters for PDEs
DR = params(1);
DT = params(2);
TR = params(3);
TT = params(4);
T0 = params(5);
R0 = params(6);
A = params(7);
B = params(8);
Y = params(9);

% Generation function
sigmar = 180; % in nm
sigmat = 0.2; %in ns
t0 = 0.00; % in ns
G = (1 / (sigmar * sqrt(2*pi))) * exp(-x.^2 / (2 * sigmar^2)) …
* (1 / (sigmat * sqrt(2*pi))) * exp(-(t – t0).^2 / (2 * sigmat^2));

c = [1; 1];
f = [DR*dudx(1); DT*dudx(2)];
s = [-(u(1) – R0)/TR + A*G; Y*(u(1)- R0) – (u(2) – T0)/TT + B*G];
end

% Intial conditions for the PDEs
function u0 = AgSeic(x)
T0 = 295;
R0 = 0.016;
u0 = [R0; T0];
end

function [pl, ql, pr, qr] = AgSebc(xl, ul, xr, ur, t)
% Left boundary: u(0,t) = 1
pl = [0; 0];
ql = [1; 1];

% Right boundary: u(x’,t) = 0
pr = [0; 0];
qr = [1; 1];
end

function status = myOut(t,y,flag)
persistent last
if strcmp(flag,’init’), last = tic; end
if isempty(flag) && toc(last) > 1
fprintf(‘t = %.3g nsn’, t(end));
last = tic;
end
status = 0;
end

%% Figures
tiledlayout(2,1)
nexttile(1,[1,1])
hold on;
plot(tspan,soln(:,1,1),’Marker’,’o’);
set(gca,’XScale’,’log’);
xlabel(‘Time / ns’);
ylabel(‘Species 1’);
hold off;

nexttile(2,[1,1])
hold on;
plot(tspan,soln(:,1,2),’Marker’,’square’)
set(gca,’XScale’,’log’);
xlabel(‘Time / ns’);
ylabel(‘Species 2’);
hold off; Hello everyone,
I’ve been working with Matlab pdepe to solve numerically a set of coupled partial differential equations. I’m fairly new and I’ve encountered a problem with the numerical solution that I get from my code and can’t seem to get rid of it. The equations model the diffusion and decay of two species in a material over 100s of ns. The problem I’m getting is that when I look at the solution in a log time scale, several kinks appear. Here are different things I’ve tried and haven’t helped:
Make tspan finer. (30-1000 points)
Make xmesh finer. (50-1000 points)
Increase or decrease relative tolerance (1E0 to 1E-6).
Make tspan time separation linear after t = 0.
Make tspan time separation increase logarithmically after t = 0.1 s
Any insights into how I could solve the problem would be useful.
% Physical constants
%params = [DR TR TT A B];
DR_guess = 1E6; % in nm^2 /ns
DT_guess = 1E1; % in nm^2 /ns
TR_guess = 14; % in ns
TT_guess = 130; % in ns
T0 = 295; % in K
R0 = 0.016;% in e/nm^3
A = 1;
B = 200;
Y = 0.3056;
CR = -80000;
CT = 220;

params = [DR_guess DT_guess TR_guess TT_guess T0 R0 A B Y CR CT];

% Setting up the mesh for space and time
xmesh = linspace(0,2000,55); %in nm
tspan = [-5, -2, -1, -0.5, 0, logspace(-1, 3, 80)];% in ns

% Solving the differential equation
options = odeset(‘RelTol’,1e-0,’OutputFcn’,@myOut,’Stats’,’on’);
pde = @(x, t, u, dudx) AgSepde(x, t, u, dudx, params);
soln = pdepe(1, pde, @AgSeic, @AgSebc, xmesh, tspan, options);

% Partial differential equation function
function [c,f,s] = AgSepde(x,t,u,dudx,params)
% Parameters for PDEs
DR = params(1);
DT = params(2);
TR = params(3);
TT = params(4);
T0 = params(5);
R0 = params(6);
A = params(7);
B = params(8);
Y = params(9);

% Generation function
sigmar = 180; % in nm
sigmat = 0.2; %in ns
t0 = 0.00; % in ns
G = (1 / (sigmar * sqrt(2*pi))) * exp(-x.^2 / (2 * sigmar^2)) …
* (1 / (sigmat * sqrt(2*pi))) * exp(-(t – t0).^2 / (2 * sigmat^2));

c = [1; 1];
f = [DR*dudx(1); DT*dudx(2)];
s = [-(u(1) – R0)/TR + A*G; Y*(u(1)- R0) – (u(2) – T0)/TT + B*G];
end

% Intial conditions for the PDEs
function u0 = AgSeic(x)
T0 = 295;
R0 = 0.016;
u0 = [R0; T0];
end

function [pl, ql, pr, qr] = AgSebc(xl, ul, xr, ur, t)
% Left boundary: u(0,t) = 1
pl = [0; 0];
ql = [1; 1];

% Right boundary: u(x’,t) = 0
pr = [0; 0];
qr = [1; 1];
end

function status = myOut(t,y,flag)
persistent last
if strcmp(flag,’init’), last = tic; end
if isempty(flag) && toc(last) > 1
fprintf(‘t = %.3g nsn’, t(end));
last = tic;
end
status = 0;
end

%% Figures
tiledlayout(2,1)
nexttile(1,[1,1])
hold on;
plot(tspan,soln(:,1,1),’Marker’,’o’);
set(gca,’XScale’,’log’);
xlabel(‘Time / ns’);
ylabel(‘Species 1’);
hold off;

nexttile(2,[1,1])
hold on;
plot(tspan,soln(:,1,2),’Marker’,’square’)
set(gca,’XScale’,’log’);
xlabel(‘Time / ns’);
ylabel(‘Species 2’);
hold off; pde, pdepe MATLAB Answers — New Questions

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