I have this single-channel ECG signal with motion artifacts (I believe from breathing).
I’ve tried several filters, but none have given me a good output.
Not being an expert, I followed the instructions from Kher, 2019 with this code, but a compatibility issue (since I am using version 2018b) prevents me from obtaining any results; by changing some commands the result is unsuitable:
y1 = load(‘EKG.mat’);
y2= (y1 (:,1)); % ECG signal data
a1= (y1 (:,1)); % accelerometer x-axis data
a2= (y1 (:,1)); % accelerometer y-axis data
a3= (y1 (:,1)); % accelerometer z-axis data
y2 = y2/max(y2);
Subplot (3, 1, 1), plot (y2), title (‘ECG Signal with motion artifacts’), grid on
a = a1+a2+a3;
a = a/max(a);
mu= 0.0008;

%Hd = adaptfilt.lms(32, mu); %original command
Hd = dsp.LMSFilter(‘Length’, 32, ‘StepSize’, mu);

% [s2, e] = filter(Hd, a, y2); % original command, don’t work in 2018b version

[s2, e] = Hd(a, y2); % command adapted

fig = figure
subplot (3, 1, 2)
plot (s2)
title (‘Noise (motion artifact) estimate’)
grid on
subplot (3, 1, 3)
plot (e)
title (‘Adaptively filtered/ Noise free ECG signal’)
grid on

I also tried filtering in this other way, but the result is very poor.
ecg_signal = load(‘EKG.mat’);
Fs = 256;
t = (0:length(ecg_signal)-1) / Fs;

fc = 45; % cut frequency
[b, a] = butter(4, fc / (Fs / 2), ‘low’);

% filter
ecg_filtered = filtfilt(b, a, ecg_signal);

With simple low-pass or high-pass filters I wasn’t able to obtain at least acceptable results.
If anyone can help me?
thank you in advanceI have this single-channel ECG signal with motion artifacts (I believe from breathing).
I’ve tried several filters, but none have given me a good output.
Not being an expert, I followed the instructions from Kher, 2019 with this code, but a compatibility issue (since I am using version 2018b) prevents me from obtaining any results; by changing some commands the result is unsuitable:
y1 = load(‘EKG.mat’);
y2= (y1 (:,1)); % ECG signal data
a1= (y1 (:,1)); % accelerometer x-axis data
a2= (y1 (:,1)); % accelerometer y-axis data
a3= (y1 (:,1)); % accelerometer z-axis data
y2 = y2/max(y2);
Subplot (3, 1, 1), plot (y2), title (‘ECG Signal with motion artifacts’), grid on
a = a1+a2+a3;
a = a/max(a);
mu= 0.0008;

%Hd = adaptfilt.lms(32, mu); %original command
Hd = dsp.LMSFilter(‘Length’, 32, ‘StepSize’, mu);

% [s2, e] = filter(Hd, a, y2); % original command, don’t work in 2018b version

[s2, e] = Hd(a, y2); % command adapted

fig = figure
subplot (3, 1, 2)
plot (s2)
title (‘Noise (motion artifact) estimate’)
grid on
subplot (3, 1, 3)
plot (e)
title (‘Adaptively filtered/ Noise free ECG signal’)
grid on

I also tried filtering in this other way, but the result is very poor.
ecg_signal = load(‘EKG.mat’);
Fs = 256;
t = (0:length(ecg_signal)-1) / Fs;

fc = 45; % cut frequency
[b, a] = butter(4, fc / (Fs / 2), ‘low’);

% filter
ecg_filtered = filtfilt(b, a, ecg_signal);

With simple low-pass or high-pass filters I wasn’t able to obtain at least acceptable results.
If anyone can help me?
thank you in advance I have this single-channel ECG signal with motion artifacts (I believe from breathing).
I’ve tried several filters, but none have given me a good output.
Not being an expert, I followed the instructions from Kher, 2019 with this code, but a compatibility issue (since I am using version 2018b) prevents me from obtaining any results; by changing some commands the result is unsuitable:
y1 = load(‘EKG.mat’);
y2= (y1 (:,1)); % ECG signal data
a1= (y1 (:,1)); % accelerometer x-axis data
a2= (y1 (:,1)); % accelerometer y-axis data
a3= (y1 (:,1)); % accelerometer z-axis data
y2 = y2/max(y2);
Subplot (3, 1, 1), plot (y2), title (‘ECG Signal with motion artifacts’), grid on
a = a1+a2+a3;
a = a/max(a);
mu= 0.0008;

%Hd = adaptfilt.lms(32, mu); %original command
Hd = dsp.LMSFilter(‘Length’, 32, ‘StepSize’, mu);

% [s2, e] = filter(Hd, a, y2); % original command, don’t work in 2018b version

[s2, e] = Hd(a, y2); % command adapted

fig = figure
subplot (3, 1, 2)
plot (s2)
title (‘Noise (motion artifact) estimate’)
grid on
subplot (3, 1, 3)
plot (e)
title (‘Adaptively filtered/ Noise free ECG signal’)
grid on

I also tried filtering in this other way, but the result is very poor.
ecg_signal = load(‘EKG.mat’);
Fs = 256;
t = (0:length(ecg_signal)-1) / Fs;

fc = 45; % cut frequency
[b, a] = butter(4, fc / (Fs / 2), ‘low’);

% filter
ecg_filtered = filtfilt(b, a, ecg_signal);

With simple low-pass or high-pass filters I wasn’t able to obtain at least acceptable results.
If anyone can help me?
thank you in advance filter, artifact MATLAB Answers — New Questions

​

Hi,
I am trying to plot a lunar terrain from a *.stl file. You can see the real image and plot in photos. MATLAB is plotting the deepest place as dark blue, the highest as yellow, but the thing is MATLAB’s dark blue is not actually the deepest place, it is just shadow, likewise MATLAB’s yellow actually isn’t the highest place, it just takes the most light so its brighter. This is my code snippet to plot it. My real aim is to create a simulation, therefore I need the surface model.
[dem, ~] = readgeoraster(‘moon1m-a.tif’);

gridSize = 3900;
start = 1;

[X, Y] = meshgrid(1:gridSize, 1:gridSize);
Z = dem(start:start+gridSize-1, start+gridSize-1:-1:start);

figure
mesh(X, Y, Z);

Images are aligned, you can see the shadow places at the bottom right.
I can’t put the file into the attachments, it is too big even after compressing. I can put the *.tif into a 3D drawing tool, this is the output.

Any help would be appreciated.
Thanks in advance.Hi,
I am trying to plot a lunar terrain from a *.stl file. You can see the real image and plot in photos. MATLAB is plotting the deepest place as dark blue, the highest as yellow, but the thing is MATLAB’s dark blue is not actually the deepest place, it is just shadow, likewise MATLAB’s yellow actually isn’t the highest place, it just takes the most light so its brighter. This is my code snippet to plot it. My real aim is to create a simulation, therefore I need the surface model.
[dem, ~] = readgeoraster(‘moon1m-a.tif’);

gridSize = 3900;
start = 1;

[X, Y] = meshgrid(1:gridSize, 1:gridSize);
Z = dem(start:start+gridSize-1, start+gridSize-1:-1:start);

figure
mesh(X, Y, Z);

Images are aligned, you can see the shadow places at the bottom right.
I can’t put the file into the attachments, it is too big even after compressing. I can put the *.tif into a 3D drawing tool, this is the output.

Any help would be appreciated.
Thanks in advance. Hi,
I am trying to plot a lunar terrain from a *.stl file. You can see the real image and plot in photos. MATLAB is plotting the deepest place as dark blue, the highest as yellow, but the thing is MATLAB’s dark blue is not actually the deepest place, it is just shadow, likewise MATLAB’s yellow actually isn’t the highest place, it just takes the most light so its brighter. This is my code snippet to plot it. My real aim is to create a simulation, therefore I need the surface model.
[dem, ~] = readgeoraster(‘moon1m-a.tif’);

gridSize = 3900;
start = 1;

[X, Y] = meshgrid(1:gridSize, 1:gridSize);
Z = dem(start:start+gridSize-1, start+gridSize-1:-1:start);

figure
mesh(X, Y, Z);

Images are aligned, you can see the shadow places at the bottom right.
I can’t put the file into the attachments, it is too big even after compressing. I can put the *.tif into a 3D drawing tool, this is the output.

Any help would be appreciated.
Thanks in advance. 3d plots, plot, mesh, figure MATLAB Answers — New Questions

​

Hi,

Which function should i use if I want to test whether two samples with different sizes have different means? I see many ttest functions in matlabHi,

Which function should i use if I want to test whether two samples with different sizes have different means? I see many ttest functions in matlab Hi,

Which function should i use if I want to test whether two samples with different sizes have different means? I see many ttest functions in matlab t test MATLAB Answers — New Questions

​

I get this error OPTIMOPTIONS does not support code generation for function ‘ga’ when I try to run a matlab function in simulink using genetic algorithm optimization methode;
options = optimoptions(‘ga’, ‘Display’, ‘iter’, …
‘PopulationSize’, 50, … % You can adjust this
‘MaxGenerations’, 100, … % You can adjust this
‘UseParallel’, true); % Use parallel computing if available
could someone help me please ?I get this error OPTIMOPTIONS does not support code generation for function ‘ga’ when I try to run a matlab function in simulink using genetic algorithm optimization methode;
options = optimoptions(‘ga’, ‘Display’, ‘iter’, …
‘PopulationSize’, 50, … % You can adjust this
‘MaxGenerations’, 100, … % You can adjust this
‘UseParallel’, true); % Use parallel computing if available
could someone help me please ? I get this error OPTIMOPTIONS does not support code generation for function ‘ga’ when I try to run a matlab function in simulink using genetic algorithm optimization methode;
options = optimoptions(‘ga’, ‘Display’, ‘iter’, …
‘PopulationSize’, 50, … % You can adjust this
‘MaxGenerations’, 100, … % You can adjust this
‘UseParallel’, true); % Use parallel computing if available
could someone help me please ? genetic algorithm, simulink, optimization MATLAB Answers — New Questions

​

I have been following and modifying the example for LIDAR Complex-YOLOv4 Object Detection, and I am running into an issue when creating training data using the createTrainingData.m file. The Standard pcRange block [xMin = -25.0 xMax = 25.0 yMin = 0.0 yMax = 50.0 zMin = -7.0 and zMax = 15.0] is too small for my data set, and the origin position cuts off most of the point cloud regions I want to capture (see image ROI_within_PointCloud). I would like to adjust the pcRange to allow for an ROI as seen in the attached image DesiredROI_2D_PointCloud.
When I drop the xMin and/or yMin values below the standard, i.e. xMin < -25 and/or yMin < 0, to capture the desired region I receive the following error:
———————————————————————————————————–
Error using sub2ind (line 71)
Out of range subscript.

Error in helper.preprocess (line 53)
mapIndices = sub2ind([bevHeight,bevWidth],locMod(:,1),locMod(:,2));

Error in createTrainingData (line 42)
[processedData,~] = helper.preprocess(ptCloud,gridParams);
———————————————————————————————————-
As far as I can tell after diving into the sub2ind function, this error is occuring due to v, the newly sorted array of points, failing this portion of the if statement: min(v,[],’all’) < 1. I can adjust xMax and yMax to be larger in the positive direction and have no issues.
How do I get past this issue and move/modify the region of interest within the point cloud scene? Can I adjust the origin location of the point cloud some how? Or can I remove the restriction on expanding the ROI into the negative quadrants?I have been following and modifying the example for LIDAR Complex-YOLOv4 Object Detection, and I am running into an issue when creating training data using the createTrainingData.m file. The Standard pcRange block [xMin = -25.0 xMax = 25.0 yMin = 0.0 yMax = 50.0 zMin = -7.0 and zMax = 15.0] is too small for my data set, and the origin position cuts off most of the point cloud regions I want to capture (see image ROI_within_PointCloud). I would like to adjust the pcRange to allow for an ROI as seen in the attached image DesiredROI_2D_PointCloud.
When I drop the xMin and/or yMin values below the standard, i.e. xMin < -25 and/or yMin < 0, to capture the desired region I receive the following error:
———————————————————————————————————–
Error using sub2ind (line 71)
Out of range subscript.

Error in helper.preprocess (line 53)
mapIndices = sub2ind([bevHeight,bevWidth],locMod(:,1),locMod(:,2));

Error in createTrainingData (line 42)
[processedData,~] = helper.preprocess(ptCloud,gridParams);
———————————————————————————————————-
As far as I can tell after diving into the sub2ind function, this error is occuring due to v, the newly sorted array of points, failing this portion of the if statement: min(v,[],’all’) < 1. I can adjust xMax and yMax to be larger in the positive direction and have no issues.
How do I get past this issue and move/modify the region of interest within the point cloud scene? Can I adjust the origin location of the point cloud some how? Or can I remove the restriction on expanding the ROI into the negative quadrants? I have been following and modifying the example for LIDAR Complex-YOLOv4 Object Detection, and I am running into an issue when creating training data using the createTrainingData.m file. The Standard pcRange block [xMin = -25.0 xMax = 25.0 yMin = 0.0 yMax = 50.0 zMin = -7.0 and zMax = 15.0] is too small for my data set, and the origin position cuts off most of the point cloud regions I want to capture (see image ROI_within_PointCloud). I would like to adjust the pcRange to allow for an ROI as seen in the attached image DesiredROI_2D_PointCloud.
When I drop the xMin and/or yMin values below the standard, i.e. xMin < -25 and/or yMin < 0, to capture the desired region I receive the following error:
———————————————————————————————————–
Error using sub2ind (line 71)
Out of range subscript.

Error in helper.preprocess (line 53)
mapIndices = sub2ind([bevHeight,bevWidth],locMod(:,1),locMod(:,2));

Error in createTrainingData (line 42)
[processedData,~] = helper.preprocess(ptCloud,gridParams);
———————————————————————————————————-
As far as I can tell after diving into the sub2ind function, this error is occuring due to v, the newly sorted array of points, failing this portion of the if statement: min(v,[],’all’) < 1. I can adjust xMax and yMax to be larger in the positive direction and have no issues.
How do I get past this issue and move/modify the region of interest within the point cloud scene? Can I adjust the origin location of the point cloud some how? Or can I remove the restriction on expanding the ROI into the negative quadrants? yolov4, matlab, object detection, grid, parameters, lidar, roi MATLAB Answers — New Questions

​

I want to model PID-controlled butterfly throttling valve to control the flow. the butterfly valve put before the well turbine to avoid the stalling ,this behavior which demonstrates when the flow coefficient surpasses a critical value
and how can i draw Airflow control scheme strategy for OWC plant equipped with Wells turbine like this in matlabI want to model PID-controlled butterfly throttling valve to control the flow. the butterfly valve put before the well turbine to avoid the stalling ,this behavior which demonstrates when the flow coefficient surpasses a critical value
and how can i draw Airflow control scheme strategy for OWC plant equipped with Wells turbine like this in matlab I want to model PID-controlled butterfly throttling valve to control the flow. the butterfly valve put before the well turbine to avoid the stalling ,this behavior which demonstrates when the flow coefficient surpasses a critical value
and how can i draw Airflow control scheme strategy for OWC plant equipped with Wells turbine like this in matlab owc, simulink, butterfly valve, pid controller MATLAB Answers — New Questions

​

Hello,

I attempted to upload an MLTBX file, but I received the following error message: "This submission seems to have been previously rejected or deleted. Please contact the File Exchange admin if you wish to upload it again."

Could you please advise on how to resolve this issue or provide the contact information for the File Exchange admin?

Thank you for your assistance.

Best regards,
DanielHello,

I attempted to upload an MLTBX file, but I received the following error message: "This submission seems to have been previously rejected or deleted. Please contact the File Exchange admin if you wish to upload it again."

Could you please advise on how to resolve this issue or provide the contact information for the File Exchange admin?

Thank you for your assistance.

Best regards,
Daniel Hello,

I attempted to upload an MLTBX file, but I received the following error message: "This submission seems to have been previously rejected or deleted. Please contact the File Exchange admin if you wish to upload it again."

Could you please advise on how to resolve this issue or provide the contact information for the File Exchange admin?

Thank you for your assistance.

Best regards,
Daniel mltbx, file-exchange MATLAB Answers — New Questions

​

Hello everyone,
I am trying to plot only the diffusion and FDEP diffusion using the formulas below. However, the FDEP diffusion plot is incorrect. I am also attaching the reference graph that it should match.
formulas:

reference graph:

my graph:

% Define parameters
epsilon0 = 8.85e-12; % F/m (Permittivity of free space)
epsilon_m = 79; % F/m (Relative permittivity of the medium)
CM = 0.5; % Clausius-Mossotti factor
k_B = 1.38e-23; % J/K (Boltzmann constant)
R = 1e-6; % m (Particle radius)
gamma = 1.88e-8; % kg/s (Friction coefficient)
q = 1e-14; % C (Charge of the particle)
dt = 1e-3; % s (Time step)
T = 300; % K (Room temperature)
x0 = 10e-6; % Initial position (slightly adjusted from zero)
N = 100000; % Number of simulations
num_steps = 1000; % Number of steps (simulation for 1 second)
epsilon = 1e-9; % Small offset to avoid division by zero
k = 1 / (4 * pi * epsilon_m); % Constant for force coefficient

% Generate random numbers
rng(0); % Reset random number generator
W = randn(num_steps, N); % Random numbers from standard normal distribution

% Define position vectors (with and without DEP force)
x_dep = zeros(num_steps, N);
x_diff = zeros(num_steps, N);
x_dep(1, 🙂 = x0;
x_diff(1, 🙂 = x0;

% Perform iterations using the Euler-Maruyama method
for i = 1:num_steps-1
% With DEP force (FDEP is present)
FDEP = 4 * pi * R^3 * epsilon0 * epsilon_m * CM * (-2 * k^2 * q^2) ./ ((abs(x_dep(i, 🙂 – x0) + epsilon).^5);
x_dep(i+1, 🙂 = x_dep(i, 🙂 + (FDEP / gamma) * dt + sqrt(2 * k_B * T / gamma) * sqrt(dt) * W(i, :); % sqrt(dt) added here

% Only diffusion (FDEP is absent)
x_diff(i+1, 🙂 = x_diff(i, 🙂 + sqrt(2 * k_B * T / gamma) * sqrt(dt) * W(i, :); % sqrt(dt) added here
end

% Calculate means
x_mean_dep = mean(x_dep, 2);
x_mean_diff = mean(x_diff, 2);

% Plot results (y-axis scaled by 10^-6)
figure;
plot((0:num_steps-1) * dt, x_mean_dep * 1e6, ‘b’, ‘LineWidth’, 1.5); % y-axis scaled by 10^-6
hold on;
plot((0:num_steps-1) * dt, x_mean_diff * 1e6, ‘r–‘, ‘LineWidth’, 1.5); % y-axis scaled by 10^-6
xlabel(‘Time (s)’);
ylabel(‘Particle Position (μm)’); % Units updated to micrometers
title(‘Particle Positions with and without DEP Force’);
legend(‘DEP Force Present’, ‘Only Diffusion’);
grid on;Hello everyone,
I am trying to plot only the diffusion and FDEP diffusion using the formulas below. However, the FDEP diffusion plot is incorrect. I am also attaching the reference graph that it should match.
formulas:

reference graph:

my graph:

% Define parameters
epsilon0 = 8.85e-12; % F/m (Permittivity of free space)
epsilon_m = 79; % F/m (Relative permittivity of the medium)
CM = 0.5; % Clausius-Mossotti factor
k_B = 1.38e-23; % J/K (Boltzmann constant)
R = 1e-6; % m (Particle radius)
gamma = 1.88e-8; % kg/s (Friction coefficient)
q = 1e-14; % C (Charge of the particle)
dt = 1e-3; % s (Time step)
T = 300; % K (Room temperature)
x0 = 10e-6; % Initial position (slightly adjusted from zero)
N = 100000; % Number of simulations
num_steps = 1000; % Number of steps (simulation for 1 second)
epsilon = 1e-9; % Small offset to avoid division by zero
k = 1 / (4 * pi * epsilon_m); % Constant for force coefficient

% Generate random numbers
rng(0); % Reset random number generator
W = randn(num_steps, N); % Random numbers from standard normal distribution

% Define position vectors (with and without DEP force)
x_dep = zeros(num_steps, N);
x_diff = zeros(num_steps, N);
x_dep(1, 🙂 = x0;
x_diff(1, 🙂 = x0;

% Perform iterations using the Euler-Maruyama method
for i = 1:num_steps-1
% With DEP force (FDEP is present)
FDEP = 4 * pi * R^3 * epsilon0 * epsilon_m * CM * (-2 * k^2 * q^2) ./ ((abs(x_dep(i, 🙂 – x0) + epsilon).^5);
x_dep(i+1, 🙂 = x_dep(i, 🙂 + (FDEP / gamma) * dt + sqrt(2 * k_B * T / gamma) * sqrt(dt) * W(i, :); % sqrt(dt) added here

% Only diffusion (FDEP is absent)
x_diff(i+1, 🙂 = x_diff(i, 🙂 + sqrt(2 * k_B * T / gamma) * sqrt(dt) * W(i, :); % sqrt(dt) added here
end

% Calculate means
x_mean_dep = mean(x_dep, 2);
x_mean_diff = mean(x_diff, 2);

% Plot results (y-axis scaled by 10^-6)
figure;
plot((0:num_steps-1) * dt, x_mean_dep * 1e6, ‘b’, ‘LineWidth’, 1.5); % y-axis scaled by 10^-6
hold on;
plot((0:num_steps-1) * dt, x_mean_diff * 1e6, ‘r–‘, ‘LineWidth’, 1.5); % y-axis scaled by 10^-6
xlabel(‘Time (s)’);
ylabel(‘Particle Position (μm)’); % Units updated to micrometers
title(‘Particle Positions with and without DEP Force’);
legend(‘DEP Force Present’, ‘Only Diffusion’);
grid on; Hello everyone,
I am trying to plot only the diffusion and FDEP diffusion using the formulas below. However, the FDEP diffusion plot is incorrect. I am also attaching the reference graph that it should match.
formulas:

reference graph:

my graph:

% Define parameters
epsilon0 = 8.85e-12; % F/m (Permittivity of free space)
epsilon_m = 79; % F/m (Relative permittivity of the medium)
CM = 0.5; % Clausius-Mossotti factor
k_B = 1.38e-23; % J/K (Boltzmann constant)
R = 1e-6; % m (Particle radius)
gamma = 1.88e-8; % kg/s (Friction coefficient)
q = 1e-14; % C (Charge of the particle)
dt = 1e-3; % s (Time step)
T = 300; % K (Room temperature)
x0 = 10e-6; % Initial position (slightly adjusted from zero)
N = 100000; % Number of simulations
num_steps = 1000; % Number of steps (simulation for 1 second)
epsilon = 1e-9; % Small offset to avoid division by zero
k = 1 / (4 * pi * epsilon_m); % Constant for force coefficient

% Generate random numbers
rng(0); % Reset random number generator
W = randn(num_steps, N); % Random numbers from standard normal distribution

% Define position vectors (with and without DEP force)
x_dep = zeros(num_steps, N);
x_diff = zeros(num_steps, N);
x_dep(1, 🙂 = x0;
x_diff(1, 🙂 = x0;

% Perform iterations using the Euler-Maruyama method
for i = 1:num_steps-1
% With DEP force (FDEP is present)
FDEP = 4 * pi * R^3 * epsilon0 * epsilon_m * CM * (-2 * k^2 * q^2) ./ ((abs(x_dep(i, 🙂 – x0) + epsilon).^5);
x_dep(i+1, 🙂 = x_dep(i, 🙂 + (FDEP / gamma) * dt + sqrt(2 * k_B * T / gamma) * sqrt(dt) * W(i, :); % sqrt(dt) added here

% Only diffusion (FDEP is absent)
x_diff(i+1, 🙂 = x_diff(i, 🙂 + sqrt(2 * k_B * T / gamma) * sqrt(dt) * W(i, :); % sqrt(dt) added here
end

% Calculate means
x_mean_dep = mean(x_dep, 2);
x_mean_diff = mean(x_diff, 2);

% Plot results (y-axis scaled by 10^-6)
figure;
plot((0:num_steps-1) * dt, x_mean_dep * 1e6, ‘b’, ‘LineWidth’, 1.5); % y-axis scaled by 10^-6
hold on;
plot((0:num_steps-1) * dt, x_mean_diff * 1e6, ‘r–‘, ‘LineWidth’, 1.5); % y-axis scaled by 10^-6
xlabel(‘Time (s)’);
ylabel(‘Particle Position (μm)’); % Units updated to micrometers
title(‘Particle Positions with and without DEP Force’);
legend(‘DEP Force Present’, ‘Only Diffusion’);
grid on; graph, graphics, mathematics, matlab, loop MATLAB Answers — New Questions

​

I have data of a heated plate (2m x 2m = L x L) defined by a grid (x- and y-coordinates from 0 to 2 in same or different refinements) and a corresponding matrix containing the temperatures of the centres of the plates fields (the field dimensions are defined by the x- and y-grid).
The imagesc() plot function from matlab creates good results on equidistant grids since every field has the same length and width. However working with an non-equidistant grid requires to adjust all fields dimensions according to x-y-grid.
Is it possible to do this within the imagesc() function?

Code:
imagesc(x,y, sol)
set(gca,’YDir’,’normal’)
colormap(jet(2000)); colorbar;
xlabel(‘x[m]’)
ylabel(‘y[m]’)
x and y are vectors containing the coordinates of the grid, sol is the matrix containing the field centres temperatures.

Plot:
[Heated Plate with equidistant grid]

[Heated Plate with non-equidistant grid]

The non-equidistant grid performs a refinement on the right side of the plate since numerical error is biggest there.
The refinement is defined by

– same discretization for 0 <= x <= L/2 (same for y)
– double fine discretization for L/2 <= x <= L*3/4 (same for y)
– four times discretization for L*3/4 <= x <= L (same for y)

As you can see, the refinement’s new field dimensions are not considered which ends up ruining the visulization.
I’ve already tried different plot methods like surf or pcolor. Both are not well suited for a field-centre-oriented solution.
Thank’s for any advice.I have data of a heated plate (2m x 2m = L x L) defined by a grid (x- and y-coordinates from 0 to 2 in same or different refinements) and a corresponding matrix containing the temperatures of the centres of the plates fields (the field dimensions are defined by the x- and y-grid).
The imagesc() plot function from matlab creates good results on equidistant grids since every field has the same length and width. However working with an non-equidistant grid requires to adjust all fields dimensions according to x-y-grid.
Is it possible to do this within the imagesc() function?

Code:
imagesc(x,y, sol)
set(gca,’YDir’,’normal’)
colormap(jet(2000)); colorbar;
xlabel(‘x[m]’)
ylabel(‘y[m]’)
x and y are vectors containing the coordinates of the grid, sol is the matrix containing the field centres temperatures.

Plot:
[Heated Plate with equidistant grid]

[Heated Plate with non-equidistant grid]

The non-equidistant grid performs a refinement on the right side of the plate since numerical error is biggest there.
The refinement is defined by

– same discretization for 0 <= x <= L/2 (same for y)
– double fine discretization for L/2 <= x <= L*3/4 (same for y)
– four times discretization for L*3/4 <= x <= L (same for y)

As you can see, the refinement’s new field dimensions are not considered which ends up ruining the visulization.
I’ve already tried different plot methods like surf or pcolor. Both are not well suited for a field-centre-oriented solution.
Thank’s for any advice. I have data of a heated plate (2m x 2m = L x L) defined by a grid (x- and y-coordinates from 0 to 2 in same or different refinements) and a corresponding matrix containing the temperatures of the centres of the plates fields (the field dimensions are defined by the x- and y-grid).
The imagesc() plot function from matlab creates good results on equidistant grids since every field has the same length and width. However working with an non-equidistant grid requires to adjust all fields dimensions according to x-y-grid.
Is it possible to do this within the imagesc() function?

Code:
imagesc(x,y, sol)
set(gca,’YDir’,’normal’)
colormap(jet(2000)); colorbar;
xlabel(‘x[m]’)
ylabel(‘y[m]’)
x and y are vectors containing the coordinates of the grid, sol is the matrix containing the field centres temperatures.

Plot:
[Heated Plate with equidistant grid]

[Heated Plate with non-equidistant grid]

The non-equidistant grid performs a refinement on the right side of the plate since numerical error is biggest there.
The refinement is defined by

– same discretization for 0 <= x <= L/2 (same for y)
– double fine discretization for L/2 <= x <= L*3/4 (same for y)
– four times discretization for L*3/4 <= x <= L (same for y)

As you can see, the refinement’s new field dimensions are not considered which ends up ruining the visulization.
I’ve already tried different plot methods like surf or pcolor. Both are not well suited for a field-centre-oriented solution.
Thank’s for any advice. imagesc, plot, grid, refinement MATLAB Answers — New Questions

​

Hi there,
In my project, I want to control two motors, one is the load motor and the other is MUT. I used two F28379D launchpads with two three-phase inverter. The first motor connected to the first inverter that controlled by first launchpad, and the other motor connected in the same way. The DC link, and axuiliuary supply are shared between them. The ground is connected between all of them. However, when I control the first motor (load), the ADCs for the other launchpad get lots of noises. I am not sure, whether, all these two launchpad should be supplied individualy or they have to communicate with each other. Could you please help in this matter.Hi there,
In my project, I want to control two motors, one is the load motor and the other is MUT. I used two F28379D launchpads with two three-phase inverter. The first motor connected to the first inverter that controlled by first launchpad, and the other motor connected in the same way. The DC link, and axuiliuary supply are shared between them. The ground is connected between all of them. However, when I control the first motor (load), the ADCs for the other launchpad get lots of noises. I am not sure, whether, all these two launchpad should be supplied individualy or they have to communicate with each other. Could you please help in this matter. Hi there,
In my project, I want to control two motors, one is the load motor and the other is MUT. I used two F28379D launchpads with two three-phase inverter. The first motor connected to the first inverter that controlled by first launchpad, and the other motor connected in the same way. The DC link, and axuiliuary supply are shared between them. The ground is connected between all of them. However, when I control the first motor (load), the ADCs for the other launchpad get lots of noises. I am not sure, whether, all these two launchpad should be supplied individualy or they have to communicate with each other. Could you please help in this matter. launchpad f28379d MATLAB Answers — New Questions

​