Category: Matlab
Category Archives: Matlab
Question regarding Oracle Java Vulnerability
Subject: Regarding Oracle Java Vulnerabilities for QC and LRE Servers
Hi Team,
I am reaching out to inquire about the Oracle Java vulnerabilities present in server of MATLAB. I would like to confirm if JRE version 8.0.411 or higher will be suitable for 2023a version of the application.
The paths where vulnerabilities have been identified are-
MATLAB (GD)
vpwv0140 :
Plugin Output:
Path : C:Program FilesMATLABR2023asysjavajrewin64
Installed version : 1.8.0.202.8 / build 8.0.202
Fixed version : Upgrade to version 8.0.411 or greater
Path : C:Program FilesMATLABR2018bsysjavajrewin64
Installed version : 1.8.0.152.16 / build 8.0.152
Fixed version : Upgrade to version 8.0.411 or greater
-Thanks
Rithika Chugh
Consultant
GDS—
email address : rithika.chugh@enbridge.comSubject: Regarding Oracle Java Vulnerabilities for QC and LRE Servers
Hi Team,
I am reaching out to inquire about the Oracle Java vulnerabilities present in server of MATLAB. I would like to confirm if JRE version 8.0.411 or higher will be suitable for 2023a version of the application.
The paths where vulnerabilities have been identified are-
MATLAB (GD)
vpwv0140 :
Plugin Output:
Path : C:Program FilesMATLABR2023asysjavajrewin64
Installed version : 1.8.0.202.8 / build 8.0.202
Fixed version : Upgrade to version 8.0.411 or greater
Path : C:Program FilesMATLABR2018bsysjavajrewin64
Installed version : 1.8.0.152.16 / build 8.0.152
Fixed version : Upgrade to version 8.0.411 or greater
-Thanks
Rithika Chugh
Consultant
GDS—
email address : rithika.chugh@enbridge.com Subject: Regarding Oracle Java Vulnerabilities for QC and LRE Servers
Hi Team,
I am reaching out to inquire about the Oracle Java vulnerabilities present in server of MATLAB. I would like to confirm if JRE version 8.0.411 or higher will be suitable for 2023a version of the application.
The paths where vulnerabilities have been identified are-
MATLAB (GD)
vpwv0140 :
Plugin Output:
Path : C:Program FilesMATLABR2023asysjavajrewin64
Installed version : 1.8.0.202.8 / build 8.0.202
Fixed version : Upgrade to version 8.0.411 or greater
Path : C:Program FilesMATLABR2018bsysjavajrewin64
Installed version : 1.8.0.152.16 / build 8.0.152
Fixed version : Upgrade to version 8.0.411 or greater
-Thanks
Rithika Chugh
Consultant
GDS—
email address : rithika.chugh@enbridge.com jre, java MATLAB Answers — New Questions
Error:External Mode Open Protocol Connect command failed Caused by: Could not connect to target application: XCP internal error: timeout expired, in response to XCP CONNEC
External Mode Open Protocol Connect command failed
Caused by:
Could not connect to target application: XCP internal error: timeout expired, in response to XCP CONNECT command
How To fix this problem?External Mode Open Protocol Connect command failed
Caused by:
Could not connect to target application: XCP internal error: timeout expired, in response to XCP CONNECT command
How To fix this problem? External Mode Open Protocol Connect command failed
Caused by:
Could not connect to target application: XCP internal error: timeout expired, in response to XCP CONNECT command
How To fix this problem? xcp MATLAB Answers — New Questions
detect the blue color
Below is the code which i used there is no error in the code but not detecting the blue color
% Capture image from Kinect sensor
v = videoinput("kinect", 1, "RGB_640x480");
v.ReturnedColorspace = "rgb";
src = getselectedsource(v);
src.CameraElevationAngle = 15;
snapshot4 = getsnapshot(v);
delete(v);
clear src v;
% Convert RGB image to LAB color space
labImage = rgb2lab(snapshot4);
% Extract the L-channel (luminance)
LChannel = labImage(:,:,1);
% Define a threshold for blue color in the L-channel
blueThresholdLow = 0.85; % Adjust as needed
blueThresholdHigh = 0.10; % Adjust as needed
% Create binary mask for blue regions
blueMask = (LChannel >= blueThresholdLow) & (LChannel <= blueThresholdHigh);
% Apply the blue mask to the original RGB image to get the refined blue objects
refinedBlueObjects = bsxfun(@times, snapshot4, cast(blueMask, ‘like’, snapshot4));
% Display the original image and the refined blue objects
figure;
subplot(1, 2, 1), imshow(snapshot4), title(‘Original Image’);
subplot(1, 2, 2), imshow(refinedBlueObjects), title(‘Refined Blue Objects’);Below is the code which i used there is no error in the code but not detecting the blue color
% Capture image from Kinect sensor
v = videoinput("kinect", 1, "RGB_640x480");
v.ReturnedColorspace = "rgb";
src = getselectedsource(v);
src.CameraElevationAngle = 15;
snapshot4 = getsnapshot(v);
delete(v);
clear src v;
% Convert RGB image to LAB color space
labImage = rgb2lab(snapshot4);
% Extract the L-channel (luminance)
LChannel = labImage(:,:,1);
% Define a threshold for blue color in the L-channel
blueThresholdLow = 0.85; % Adjust as needed
blueThresholdHigh = 0.10; % Adjust as needed
% Create binary mask for blue regions
blueMask = (LChannel >= blueThresholdLow) & (LChannel <= blueThresholdHigh);
% Apply the blue mask to the original RGB image to get the refined blue objects
refinedBlueObjects = bsxfun(@times, snapshot4, cast(blueMask, ‘like’, snapshot4));
% Display the original image and the refined blue objects
figure;
subplot(1, 2, 1), imshow(snapshot4), title(‘Original Image’);
subplot(1, 2, 2), imshow(refinedBlueObjects), title(‘Refined Blue Objects’); Below is the code which i used there is no error in the code but not detecting the blue color
% Capture image from Kinect sensor
v = videoinput("kinect", 1, "RGB_640x480");
v.ReturnedColorspace = "rgb";
src = getselectedsource(v);
src.CameraElevationAngle = 15;
snapshot4 = getsnapshot(v);
delete(v);
clear src v;
% Convert RGB image to LAB color space
labImage = rgb2lab(snapshot4);
% Extract the L-channel (luminance)
LChannel = labImage(:,:,1);
% Define a threshold for blue color in the L-channel
blueThresholdLow = 0.85; % Adjust as needed
blueThresholdHigh = 0.10; % Adjust as needed
% Create binary mask for blue regions
blueMask = (LChannel >= blueThresholdLow) & (LChannel <= blueThresholdHigh);
% Apply the blue mask to the original RGB image to get the refined blue objects
refinedBlueObjects = bsxfun(@times, snapshot4, cast(blueMask, ‘like’, snapshot4));
% Display the original image and the refined blue objects
figure;
subplot(1, 2, 1), imshow(snapshot4), title(‘Original Image’);
subplot(1, 2, 2), imshow(refinedBlueObjects), title(‘Refined Blue Objects’); detect color, kinect MATLAB Answers — New Questions
How do I access elevation data capabilities for TI MM Wave Radar sensors (AWR1843 Boost).
The TIAWR1843Boost MM Wave Radar sensor has elevation sensing capabilities. During seup, when I create the .cfg file through the mmWave demo tool provided by Texas Instruments i can plot this data. The .cfg file stipulates that the sensor is configured for Azimuth 15 + Elevation. When i run the file though, the radar object has the elevation resolution as read only and set as NaN. It is only configurable through the .cfg file. I have verified that I am not recording any Z-axis values (only 0).
Has anybody found a work around for this?
RADAR OBJECT PROPERTIES:
BoardName: "TI AWR1843BOOST"
ConfigPort: "COM6"
DataPort: "COM5"
ConfigFile: "C:ProgramDataMATLABSupportPackagesR2023btoolboxtargetsupportpackagestimmwaveradarconfigfilesxwr18xx_BestRange_UpdateRate_1.cfg"
MMWave SDK Version: "03.06.00.00"
SensorIndex: 1
MountingLocation: [0, 0, 0] (m)
MountingAngles: [0, 0, 0] (degrees)
UpdateRate: 1 (samples/s)
RangeResolution: 2.441211e-01 (m)
RangeRateResolution: 1.242369e-01 (m/s)
AzimuthResolution: 1.447751e+01 (degrees)
ElevationResolution: NaN (degrees)
MaximumRange: 6.249500e+01 (m)
MaximumRangeRate: 9.938949e-01 (m/s)
BaudRate: 921600 (bits/s)
ReadMode: "latest"
StartTime: "25-Dec-2023 07:03:52.375"
CenterFrequency: 7.751722e+10 (Hz)
Bandwidth: 6.144492e+08 (Hz)
EnableRangeGroups: 1
EnableDopplerGroups: 1
RemoveStaticClutter: 1
RangeCFAR: 15 (dB)
DopplerCFAR: 15 (dB)
RangeLimits: [0, 4.999000e+01] (m)
RangeRateLimits: [-1, 1] (m/s)
AzimuthLimits: [-90, 90] (degrees)
ElevationLimits: [-90, 90] (degrees)
DetectionCoordinates: "Sensor rectangular"
This is the .cfg file i am using:
% ***************************************************************
% Created for SDK ver:03.06
% Created using Visualizer ver:3.6.0.0
% Frequency:77
% Platform:xWR18xx
% Scene Classifier:best_range
% Azimuth Resolution(deg):15 + Elevation
% Range Resolution(m):0.244
% Maximum unambiguous Range(m):50
% Maximum Radial Velocity(m/s):1
% Radial velocity resolution(m/s):0.13
% Frame Duration(msec):1000
% RF calibration data:None
% Range Detection Threshold (dB):15
% Doppler Detection Threshold (dB):15
% Range Peak Grouping:enabled
% Doppler Peak Grouping:enabled
% Static clutter removal:disabled
% Angle of Arrival FoV: Full FoV
% Range FoV: Full FoV
% Doppler FoV: Full FoV
% ***************************************************************
sensorStop
flushCfg
dfeDataOutputMode 1
channelCfg 15 7 0
adcCfg 2 1
adcbufCfg -1 0 1 1 1
profileCfg 0 77 296 7 28.49 0 0 30 1 256 12499 0 0 30
chirpCfg 0 0 0 0 0 0 0 1
chirpCfg 1 1 0 0 0 0 0 4
chirpCfg 2 2 0 0 0 0 0 2
frameCfg 0 2 16 0 1000 1 0
lowPower 0 0
guiMonitor -1 1 0 0 1 1 0
cfarCfg -1 0 2 8 4 3 0 15 1
cfarCfg -1 1 0 4 2 3 1 15 1
multiObjBeamForming -1 1 0.5
clutterRemoval -1 0
calibDcRangeSig -1 0 -5 8 256
extendedMaxVelocity -1 0
lvdsStreamCfg -1 0 0 0
compRangeBiasAndRxChanPhase 0.0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0
measureRangeBiasAndRxChanPhase 0 1.5 0.2
CQRxSatMonitor 0 3 4 63 0
CQSigImgMonitor 0 127 4
analogMonitor 0 0
aoaFovCfg -1 -90 90 -90 90
cfarFovCfg -1 0 0 49.99
cfarFovCfg -1 1 -1 1.00
calibData 0 0 0
sensorStartThe TIAWR1843Boost MM Wave Radar sensor has elevation sensing capabilities. During seup, when I create the .cfg file through the mmWave demo tool provided by Texas Instruments i can plot this data. The .cfg file stipulates that the sensor is configured for Azimuth 15 + Elevation. When i run the file though, the radar object has the elevation resolution as read only and set as NaN. It is only configurable through the .cfg file. I have verified that I am not recording any Z-axis values (only 0).
Has anybody found a work around for this?
RADAR OBJECT PROPERTIES:
BoardName: "TI AWR1843BOOST"
ConfigPort: "COM6"
DataPort: "COM5"
ConfigFile: "C:ProgramDataMATLABSupportPackagesR2023btoolboxtargetsupportpackagestimmwaveradarconfigfilesxwr18xx_BestRange_UpdateRate_1.cfg"
MMWave SDK Version: "03.06.00.00"
SensorIndex: 1
MountingLocation: [0, 0, 0] (m)
MountingAngles: [0, 0, 0] (degrees)
UpdateRate: 1 (samples/s)
RangeResolution: 2.441211e-01 (m)
RangeRateResolution: 1.242369e-01 (m/s)
AzimuthResolution: 1.447751e+01 (degrees)
ElevationResolution: NaN (degrees)
MaximumRange: 6.249500e+01 (m)
MaximumRangeRate: 9.938949e-01 (m/s)
BaudRate: 921600 (bits/s)
ReadMode: "latest"
StartTime: "25-Dec-2023 07:03:52.375"
CenterFrequency: 7.751722e+10 (Hz)
Bandwidth: 6.144492e+08 (Hz)
EnableRangeGroups: 1
EnableDopplerGroups: 1
RemoveStaticClutter: 1
RangeCFAR: 15 (dB)
DopplerCFAR: 15 (dB)
RangeLimits: [0, 4.999000e+01] (m)
RangeRateLimits: [-1, 1] (m/s)
AzimuthLimits: [-90, 90] (degrees)
ElevationLimits: [-90, 90] (degrees)
DetectionCoordinates: "Sensor rectangular"
This is the .cfg file i am using:
% ***************************************************************
% Created for SDK ver:03.06
% Created using Visualizer ver:3.6.0.0
% Frequency:77
% Platform:xWR18xx
% Scene Classifier:best_range
% Azimuth Resolution(deg):15 + Elevation
% Range Resolution(m):0.244
% Maximum unambiguous Range(m):50
% Maximum Radial Velocity(m/s):1
% Radial velocity resolution(m/s):0.13
% Frame Duration(msec):1000
% RF calibration data:None
% Range Detection Threshold (dB):15
% Doppler Detection Threshold (dB):15
% Range Peak Grouping:enabled
% Doppler Peak Grouping:enabled
% Static clutter removal:disabled
% Angle of Arrival FoV: Full FoV
% Range FoV: Full FoV
% Doppler FoV: Full FoV
% ***************************************************************
sensorStop
flushCfg
dfeDataOutputMode 1
channelCfg 15 7 0
adcCfg 2 1
adcbufCfg -1 0 1 1 1
profileCfg 0 77 296 7 28.49 0 0 30 1 256 12499 0 0 30
chirpCfg 0 0 0 0 0 0 0 1
chirpCfg 1 1 0 0 0 0 0 4
chirpCfg 2 2 0 0 0 0 0 2
frameCfg 0 2 16 0 1000 1 0
lowPower 0 0
guiMonitor -1 1 0 0 1 1 0
cfarCfg -1 0 2 8 4 3 0 15 1
cfarCfg -1 1 0 4 2 3 1 15 1
multiObjBeamForming -1 1 0.5
clutterRemoval -1 0
calibDcRangeSig -1 0 -5 8 256
extendedMaxVelocity -1 0
lvdsStreamCfg -1 0 0 0
compRangeBiasAndRxChanPhase 0.0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0
measureRangeBiasAndRxChanPhase 0 1.5 0.2
CQRxSatMonitor 0 3 4 63 0
CQSigImgMonitor 0 127 4
analogMonitor 0 0
aoaFovCfg -1 -90 90 -90 90
cfarFovCfg -1 0 0 49.99
cfarFovCfg -1 1 -1 1.00
calibData 0 0 0
sensorStart The TIAWR1843Boost MM Wave Radar sensor has elevation sensing capabilities. During seup, when I create the .cfg file through the mmWave demo tool provided by Texas Instruments i can plot this data. The .cfg file stipulates that the sensor is configured for Azimuth 15 + Elevation. When i run the file though, the radar object has the elevation resolution as read only and set as NaN. It is only configurable through the .cfg file. I have verified that I am not recording any Z-axis values (only 0).
Has anybody found a work around for this?
RADAR OBJECT PROPERTIES:
BoardName: "TI AWR1843BOOST"
ConfigPort: "COM6"
DataPort: "COM5"
ConfigFile: "C:ProgramDataMATLABSupportPackagesR2023btoolboxtargetsupportpackagestimmwaveradarconfigfilesxwr18xx_BestRange_UpdateRate_1.cfg"
MMWave SDK Version: "03.06.00.00"
SensorIndex: 1
MountingLocation: [0, 0, 0] (m)
MountingAngles: [0, 0, 0] (degrees)
UpdateRate: 1 (samples/s)
RangeResolution: 2.441211e-01 (m)
RangeRateResolution: 1.242369e-01 (m/s)
AzimuthResolution: 1.447751e+01 (degrees)
ElevationResolution: NaN (degrees)
MaximumRange: 6.249500e+01 (m)
MaximumRangeRate: 9.938949e-01 (m/s)
BaudRate: 921600 (bits/s)
ReadMode: "latest"
StartTime: "25-Dec-2023 07:03:52.375"
CenterFrequency: 7.751722e+10 (Hz)
Bandwidth: 6.144492e+08 (Hz)
EnableRangeGroups: 1
EnableDopplerGroups: 1
RemoveStaticClutter: 1
RangeCFAR: 15 (dB)
DopplerCFAR: 15 (dB)
RangeLimits: [0, 4.999000e+01] (m)
RangeRateLimits: [-1, 1] (m/s)
AzimuthLimits: [-90, 90] (degrees)
ElevationLimits: [-90, 90] (degrees)
DetectionCoordinates: "Sensor rectangular"
This is the .cfg file i am using:
% ***************************************************************
% Created for SDK ver:03.06
% Created using Visualizer ver:3.6.0.0
% Frequency:77
% Platform:xWR18xx
% Scene Classifier:best_range
% Azimuth Resolution(deg):15 + Elevation
% Range Resolution(m):0.244
% Maximum unambiguous Range(m):50
% Maximum Radial Velocity(m/s):1
% Radial velocity resolution(m/s):0.13
% Frame Duration(msec):1000
% RF calibration data:None
% Range Detection Threshold (dB):15
% Doppler Detection Threshold (dB):15
% Range Peak Grouping:enabled
% Doppler Peak Grouping:enabled
% Static clutter removal:disabled
% Angle of Arrival FoV: Full FoV
% Range FoV: Full FoV
% Doppler FoV: Full FoV
% ***************************************************************
sensorStop
flushCfg
dfeDataOutputMode 1
channelCfg 15 7 0
adcCfg 2 1
adcbufCfg -1 0 1 1 1
profileCfg 0 77 296 7 28.49 0 0 30 1 256 12499 0 0 30
chirpCfg 0 0 0 0 0 0 0 1
chirpCfg 1 1 0 0 0 0 0 4
chirpCfg 2 2 0 0 0 0 0 2
frameCfg 0 2 16 0 1000 1 0
lowPower 0 0
guiMonitor -1 1 0 0 1 1 0
cfarCfg -1 0 2 8 4 3 0 15 1
cfarCfg -1 1 0 4 2 3 1 15 1
multiObjBeamForming -1 1 0.5
clutterRemoval -1 0
calibDcRangeSig -1 0 -5 8 256
extendedMaxVelocity -1 0
lvdsStreamCfg -1 0 0 0
compRangeBiasAndRxChanPhase 0.0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0
measureRangeBiasAndRxChanPhase 0 1.5 0.2
CQRxSatMonitor 0 3 4 63 0
CQSigImgMonitor 0 127 4
analogMonitor 0 0
aoaFovCfg -1 -90 90 -90 90
cfarFovCfg -1 0 0 49.99
cfarFovCfg -1 1 -1 1.00
calibData 0 0 0
sensorStart ti mmwave radar, radar, elevation data, radar toolbox MATLAB Answers — New Questions
IWR1843BOOST Parameter: “ElevationResolution value: NaN”, which causes the z axis missing in 3D point cloud data.
Device: TI IWR1843BOOST
SDK version: 3.06
Test procedures: 1. Use hardware setup to flash the bin and load the cfg file (generated from mmWave demo visualizer 3.6.0).
2. Run mmWaveRadar() to read the properties on the device.
The value of the mmWaveRadar() output:
val =
BoardName: "TI IWR1843BOOST"
ConfigPort: "COM5"
DataPort: "COM6"
ConfigFile: "C:ProgramDataMATLABSupportPackagesR2023btoolboxtargetsupportpackagestimmwaveradarconfigfilesxwr18xx_BestRangeRateResolution_UpdateRate_10.cfg"
MMWave SDK Version: "03.06.00.00"
SensorIndex: 1
MountingLocation: [0, 0, 0] (m)
MountingAngles: [0, 0, 0] (degrees)
UpdateRate: 10 (samples/s)
RangeResolution: 2.142656e-01 (m)
RangeRateResolution: 3.849848e-02 (m/s)
AzimuthResolution: 1.447751e+01 (degrees)
ElevationResolution: NaN (degrees)
MaximumRange: 1.371300e+01 (m)
MaximumRangeRate: 2.463903e+00 (m/s)
The ElevationResolution is NaN.
However, the 3D point cloud data can be correctly read from Demo visualizer but fails to be read in the MATLAB. Is there any differences from the cfg extract procedure in MATLAB?Device: TI IWR1843BOOST
SDK version: 3.06
Test procedures: 1. Use hardware setup to flash the bin and load the cfg file (generated from mmWave demo visualizer 3.6.0).
2. Run mmWaveRadar() to read the properties on the device.
The value of the mmWaveRadar() output:
val =
BoardName: "TI IWR1843BOOST"
ConfigPort: "COM5"
DataPort: "COM6"
ConfigFile: "C:ProgramDataMATLABSupportPackagesR2023btoolboxtargetsupportpackagestimmwaveradarconfigfilesxwr18xx_BestRangeRateResolution_UpdateRate_10.cfg"
MMWave SDK Version: "03.06.00.00"
SensorIndex: 1
MountingLocation: [0, 0, 0] (m)
MountingAngles: [0, 0, 0] (degrees)
UpdateRate: 10 (samples/s)
RangeResolution: 2.142656e-01 (m)
RangeRateResolution: 3.849848e-02 (m/s)
AzimuthResolution: 1.447751e+01 (degrees)
ElevationResolution: NaN (degrees)
MaximumRange: 1.371300e+01 (m)
MaximumRangeRate: 2.463903e+00 (m/s)
The ElevationResolution is NaN.
However, the 3D point cloud data can be correctly read from Demo visualizer but fails to be read in the MATLAB. Is there any differences from the cfg extract procedure in MATLAB? Device: TI IWR1843BOOST
SDK version: 3.06
Test procedures: 1. Use hardware setup to flash the bin and load the cfg file (generated from mmWave demo visualizer 3.6.0).
2. Run mmWaveRadar() to read the properties on the device.
The value of the mmWaveRadar() output:
val =
BoardName: "TI IWR1843BOOST"
ConfigPort: "COM5"
DataPort: "COM6"
ConfigFile: "C:ProgramDataMATLABSupportPackagesR2023btoolboxtargetsupportpackagestimmwaveradarconfigfilesxwr18xx_BestRangeRateResolution_UpdateRate_10.cfg"
MMWave SDK Version: "03.06.00.00"
SensorIndex: 1
MountingLocation: [0, 0, 0] (m)
MountingAngles: [0, 0, 0] (degrees)
UpdateRate: 10 (samples/s)
RangeResolution: 2.142656e-01 (m)
RangeRateResolution: 3.849848e-02 (m/s)
AzimuthResolution: 1.447751e+01 (degrees)
ElevationResolution: NaN (degrees)
MaximumRange: 1.371300e+01 (m)
MaximumRangeRate: 2.463903e+00 (m/s)
The ElevationResolution is NaN.
However, the 3D point cloud data can be correctly read from Demo visualizer but fails to be read in the MATLAB. Is there any differences from the cfg extract procedure in MATLAB? mmwave radar, iwr1843boost, point cloud MATLAB Answers — New Questions
IMCLIPBOARD doesn’t recognise function – import failed.
Hello, I got a problem with the IMCLIPBOARD-Add-On on Mac. When I try to import an Image (png) from clipboard it crashes when getData is called. My ‘investigations’ led to the result that the imBuffer is now receiving an object of the class "MultiResolutionCachedImage" instead of a proper ImagerBuffer-datatype. Does anybody know how to solve this problem? Thanks.Hello, I got a problem with the IMCLIPBOARD-Add-On on Mac. When I try to import an Image (png) from clipboard it crashes when getData is called. My ‘investigations’ led to the result that the imBuffer is now receiving an object of the class "MultiResolutionCachedImage" instead of a proper ImagerBuffer-datatype. Does anybody know how to solve this problem? Thanks. Hello, I got a problem with the IMCLIPBOARD-Add-On on Mac. When I try to import an Image (png) from clipboard it crashes when getData is called. My ‘investigations’ led to the result that the imBuffer is now receiving an object of the class "MultiResolutionCachedImage" instead of a proper ImagerBuffer-datatype. Does anybody know how to solve this problem? Thanks. imclipboard, java, function, error, clipboard, image MATLAB Answers — New Questions
parfor works but parcluster fails using Cellpose in linux
Hello all,
I am trying to run parallel jobs in Matlab in linux system. I used the Matlab Add-on Cellpose, which needs Matlab versoion later than 2023b.
parfor i=1:4
img = imread(‘calibrated-P01.001.tif’);
cp=cellpose;
segmentCells2D(cp,img,ImageCellDiameter=110);
end
The parfor using Cellpose works.
But when I try to use parcluster/createJob/createTask, it falls. Here is the code
clustLocal = parcluster(‘Processes’)
j = createJob(clustLocal,’AutoAddClientPath’,true)
createTask(j,@TestCellpose,0,{},’CaptureDiary’,true)
submit(j)
function TestCellpose
img = imread(‘calibrated-P01.001.tif’);
cp=cellpose;
segmentCells2D(cp,img,ImageCellDiameter=110);
end
The error message
Error: Unable to resolve the name ‘py.MWCellposeWrapper.setModelsFolderPath’
I prefer using parcluster because it can run the code in backgound.
Does anyone know how to solve the problem?
Thanks a lot!Hello all,
I am trying to run parallel jobs in Matlab in linux system. I used the Matlab Add-on Cellpose, which needs Matlab versoion later than 2023b.
parfor i=1:4
img = imread(‘calibrated-P01.001.tif’);
cp=cellpose;
segmentCells2D(cp,img,ImageCellDiameter=110);
end
The parfor using Cellpose works.
But when I try to use parcluster/createJob/createTask, it falls. Here is the code
clustLocal = parcluster(‘Processes’)
j = createJob(clustLocal,’AutoAddClientPath’,true)
createTask(j,@TestCellpose,0,{},’CaptureDiary’,true)
submit(j)
function TestCellpose
img = imread(‘calibrated-P01.001.tif’);
cp=cellpose;
segmentCells2D(cp,img,ImageCellDiameter=110);
end
The error message
Error: Unable to resolve the name ‘py.MWCellposeWrapper.setModelsFolderPath’
I prefer using parcluster because it can run the code in backgound.
Does anyone know how to solve the problem?
Thanks a lot! Hello all,
I am trying to run parallel jobs in Matlab in linux system. I used the Matlab Add-on Cellpose, which needs Matlab versoion later than 2023b.
parfor i=1:4
img = imread(‘calibrated-P01.001.tif’);
cp=cellpose;
segmentCells2D(cp,img,ImageCellDiameter=110);
end
The parfor using Cellpose works.
But when I try to use parcluster/createJob/createTask, it falls. Here is the code
clustLocal = parcluster(‘Processes’)
j = createJob(clustLocal,’AutoAddClientPath’,true)
createTask(j,@TestCellpose,0,{},’CaptureDiary’,true)
submit(j)
function TestCellpose
img = imread(‘calibrated-P01.001.tif’);
cp=cellpose;
segmentCells2D(cp,img,ImageCellDiameter=110);
end
The error message
Error: Unable to resolve the name ‘py.MWCellposeWrapper.setModelsFolderPath’
I prefer using parcluster because it can run the code in backgound.
Does anyone know how to solve the problem?
Thanks a lot! parfor, parcluster, linux, cellpose MATLAB Answers — New Questions
Unreal engine crashes when I run the simulation with a car mesh exported from blender
When I run the simulation with a mesh created by me in blender and exported to unreal, the 3D simulator crashes before starting displaying the following message:When I run the simulation with a mesh created by me in blender and exported to unreal, the 3D simulator crashes before starting displaying the following message: When I run the simulation with a mesh created by me in blender and exported to unreal, the 3D simulator crashes before starting displaying the following message: unreal engine simulation MATLAB Answers — New Questions
How to read VideoDevice into 1D array?
We have a custom frame grabber that is recognized as a video device by
v=imaq.VideoDevice("winvideo",DeviceID)
The ReturnedDataType defaults to ‘single’ but can be set to ‘uint16’. The frame grabber outputs 16 bits per image sensor pixel
The ReturnedColorSpace does not show ‘Bayer’ as an option, only ‘rgb’ ‘grayscale’ and ‘YCbCr’. But the frame grabber outputs the 2D image sensor pixels in row-major order (ie row1, then row2, etc) which can be transposed and then demosaiced using a ‘gbrg’ BayerSensorAlignment.
The ROI defaults to the [1, 1, Height, Width] of the sensor
It seems that step(v) reshapes the data in column-major order.
Since the ReturnedColorSpace does not offer ‘Bayer’ as an option, and seems to default to colum-major reshaping of the output of step(v), is there a way to execute step(v) such that the output is a 1D vector with length=Height*Width? This would allow the image data to be reshaped in row-major order into a 2D ‘Bayer’ image, transposed, and then demosaiced.
For reference, a video capture object can be generated in python
v=cv2.VideoCapture(DeviceID)
and the reshaping of the v.capture() output can be halted using
v.set(cv2.CAP_PROP_CONVERT_RGB, 0)
v.capture() results in a 1D vector (although the length is then 2*Height*Width of ‘uint8’ values that can be typecast to uint16)We have a custom frame grabber that is recognized as a video device by
v=imaq.VideoDevice("winvideo",DeviceID)
The ReturnedDataType defaults to ‘single’ but can be set to ‘uint16’. The frame grabber outputs 16 bits per image sensor pixel
The ReturnedColorSpace does not show ‘Bayer’ as an option, only ‘rgb’ ‘grayscale’ and ‘YCbCr’. But the frame grabber outputs the 2D image sensor pixels in row-major order (ie row1, then row2, etc) which can be transposed and then demosaiced using a ‘gbrg’ BayerSensorAlignment.
The ROI defaults to the [1, 1, Height, Width] of the sensor
It seems that step(v) reshapes the data in column-major order.
Since the ReturnedColorSpace does not offer ‘Bayer’ as an option, and seems to default to colum-major reshaping of the output of step(v), is there a way to execute step(v) such that the output is a 1D vector with length=Height*Width? This would allow the image data to be reshaped in row-major order into a 2D ‘Bayer’ image, transposed, and then demosaiced.
For reference, a video capture object can be generated in python
v=cv2.VideoCapture(DeviceID)
and the reshaping of the v.capture() output can be halted using
v.set(cv2.CAP_PROP_CONVERT_RGB, 0)
v.capture() results in a 1D vector (although the length is then 2*Height*Width of ‘uint8’ values that can be typecast to uint16) We have a custom frame grabber that is recognized as a video device by
v=imaq.VideoDevice("winvideo",DeviceID)
The ReturnedDataType defaults to ‘single’ but can be set to ‘uint16’. The frame grabber outputs 16 bits per image sensor pixel
The ReturnedColorSpace does not show ‘Bayer’ as an option, only ‘rgb’ ‘grayscale’ and ‘YCbCr’. But the frame grabber outputs the 2D image sensor pixels in row-major order (ie row1, then row2, etc) which can be transposed and then demosaiced using a ‘gbrg’ BayerSensorAlignment.
The ROI defaults to the [1, 1, Height, Width] of the sensor
It seems that step(v) reshapes the data in column-major order.
Since the ReturnedColorSpace does not offer ‘Bayer’ as an option, and seems to default to colum-major reshaping of the output of step(v), is there a way to execute step(v) such that the output is a 1D vector with length=Height*Width? This would allow the image data to be reshaped in row-major order into a 2D ‘Bayer’ image, transposed, and then demosaiced.
For reference, a video capture object can be generated in python
v=cv2.VideoCapture(DeviceID)
and the reshaping of the v.capture() output can be halted using
v.set(cv2.CAP_PROP_CONVERT_RGB, 0)
v.capture() results in a 1D vector (although the length is then 2*Height*Width of ‘uint8’ values that can be typecast to uint16) imaq, roi MATLAB Answers — New Questions
How to Define C Caller Function Argument as Input
Hello,
I’ve been having a problem with the C-Caller block in that it always thinks my function argument is an output. The function accepts a pointer to a structure and outputs a calculated result. Here’s the function declaration and the definition of the input structure that it uses:
typedef struct
{
unsigned char data[10];
unsigned short numbytes;
} CRC_In;
unsigned short Calc_CRC(CRC_In* input);
The "C-Caller" block accepts the function ok, but insists on defining CRC_In* as an Output in the ‘Port Specification’. Although this can be manually changed to ‘Input’ from the drop-down list, clicking on ‘Apply’ sets it back to ‘Output’. Please could anyone suggest how to tell Simulink that the function argument is an input and not an output?
On a probably related note, when I try to compile the model, I get the error message ‘Need to specify the exact size of the output argument ‘in0′ for the C Caller block’. I would have expected Simulink to be able to work out the structure size from its typedef declaration. Why is this not so?
Thanks,
John.Hello,
I’ve been having a problem with the C-Caller block in that it always thinks my function argument is an output. The function accepts a pointer to a structure and outputs a calculated result. Here’s the function declaration and the definition of the input structure that it uses:
typedef struct
{
unsigned char data[10];
unsigned short numbytes;
} CRC_In;
unsigned short Calc_CRC(CRC_In* input);
The "C-Caller" block accepts the function ok, but insists on defining CRC_In* as an Output in the ‘Port Specification’. Although this can be manually changed to ‘Input’ from the drop-down list, clicking on ‘Apply’ sets it back to ‘Output’. Please could anyone suggest how to tell Simulink that the function argument is an input and not an output?
On a probably related note, when I try to compile the model, I get the error message ‘Need to specify the exact size of the output argument ‘in0′ for the C Caller block’. I would have expected Simulink to be able to work out the structure size from its typedef declaration. Why is this not so?
Thanks,
John. Hello,
I’ve been having a problem with the C-Caller block in that it always thinks my function argument is an output. The function accepts a pointer to a structure and outputs a calculated result. Here’s the function declaration and the definition of the input structure that it uses:
typedef struct
{
unsigned char data[10];
unsigned short numbytes;
} CRC_In;
unsigned short Calc_CRC(CRC_In* input);
The "C-Caller" block accepts the function ok, but insists on defining CRC_In* as an Output in the ‘Port Specification’. Although this can be manually changed to ‘Input’ from the drop-down list, clicking on ‘Apply’ sets it back to ‘Output’. Please could anyone suggest how to tell Simulink that the function argument is an input and not an output?
On a probably related note, when I try to compile the model, I get the error message ‘Need to specify the exact size of the output argument ‘in0′ for the C Caller block’. I would have expected Simulink to be able to work out the structure size from its typedef declaration. Why is this not so?
Thanks,
John. c caller MATLAB Answers — New Questions
Impulse Response Measurer App – Audio Device List refresh doesn’t work.
When connecting a full duplex audio device (e.g. via USB) after starting Matlab & IR-Meas App, the refresh button doesn’t. Thus, it’s necessary to close/reopen the App. Most often, it’s even necessary to re-start Matlab itself, since by checking available audio devices by cmd audiodevinfo later connected devices won’t be found. Is there any way for initializing them without re-starting Matlab?
Cheers, CorneliaWhen connecting a full duplex audio device (e.g. via USB) after starting Matlab & IR-Meas App, the refresh button doesn’t. Thus, it’s necessary to close/reopen the App. Most often, it’s even necessary to re-start Matlab itself, since by checking available audio devices by cmd audiodevinfo later connected devices won’t be found. Is there any way for initializing them without re-starting Matlab?
Cheers, Cornelia When connecting a full duplex audio device (e.g. via USB) after starting Matlab & IR-Meas App, the refresh button doesn’t. Thus, it’s necessary to close/reopen the App. Most often, it’s even necessary to re-start Matlab itself, since by checking available audio devices by cmd audiodevinfo later connected devices won’t be found. Is there any way for initializing them without re-starting Matlab?
Cheers, Cornelia audiodevice list MATLAB Answers — New Questions
Tuned PID motor controller leads to a slow response
I am modelling an Electronic Throttle with the aim of choosing the right sized motor. I have tried to implement a PID control to my motor controller but it is leading to a slower response than expected. When I remove the PID controller and run the motor simply the response it much faster. I have used the tune function on matlab but it is still not as fast as it should be. I have attached the simulink model and some images describing my issue.
My model including the PID control, leads to the follwing response.
Ideally the settling time should be <0.25s for this kind of input
Now the same mechanical model with a simple step input is much much faster in responding
Its response
Zoomed in
You can see the response to 50deg in this case is ~0.1s.
Is there anything I can do? on the PID tuner workspace I get the settling time should be 0.011s. Maybe I should control the brake port on the H bridge controller with a second derative?
Thanks for your helpI am modelling an Electronic Throttle with the aim of choosing the right sized motor. I have tried to implement a PID control to my motor controller but it is leading to a slower response than expected. When I remove the PID controller and run the motor simply the response it much faster. I have used the tune function on matlab but it is still not as fast as it should be. I have attached the simulink model and some images describing my issue.
My model including the PID control, leads to the follwing response.
Ideally the settling time should be <0.25s for this kind of input
Now the same mechanical model with a simple step input is much much faster in responding
Its response
Zoomed in
You can see the response to 50deg in this case is ~0.1s.
Is there anything I can do? on the PID tuner workspace I get the settling time should be 0.011s. Maybe I should control the brake port on the H bridge controller with a second derative?
Thanks for your help I am modelling an Electronic Throttle with the aim of choosing the right sized motor. I have tried to implement a PID control to my motor controller but it is leading to a slower response than expected. When I remove the PID controller and run the motor simply the response it much faster. I have used the tune function on matlab but it is still not as fast as it should be. I have attached the simulink model and some images describing my issue.
My model including the PID control, leads to the follwing response.
Ideally the settling time should be <0.25s for this kind of input
Now the same mechanical model with a simple step input is much much faster in responding
Its response
Zoomed in
You can see the response to 50deg in this case is ~0.1s.
Is there anything I can do? on the PID tuner workspace I get the settling time should be 0.011s. Maybe I should control the brake port on the H bridge controller with a second derative?
Thanks for your help simulink, electric_motor_control, simscape, control MATLAB Answers — New Questions
Simulink course not properly
Hi, I am trying to complete the Simulink Onramp course on my MacBook Pro. I am using the latest version of MATLAB R2022a.
However, the Simulink environment is extremely slow and buggy (although MATLAB works absolutely fine). I identified 3 problems:
When I load the Simulink environment, only parts of it load. Sometimes the "Training-Tasks" bit on the left won’t show up, sometimes the top part with the "simulation", "debug", etc. buttons won’t even show up.
Another problem I am facing is that some buttons don’t work most of the times. I try clicking the submit button, but all it does it turn white. The "Training-Assessment" bit on the right shows that the signal was correct, but I can’t move to the next assignment as if Simulink just crashed. I have attached a picture that shows this situation. In addition, sometimes clicking on the "debug", "modeling", etc. buttons on the top won’t do anything.
Another problem I am facing is that it takes Simulink about 5 seconds every time to load search results from the quick insert. Why does the quick insert take so much time to load?
I would like some advice on how to fix this issue. The problems 1-3 happen on the Onramp course, and problem 3 happens even in regular Simulink.
Thank you in advance.Hi, I am trying to complete the Simulink Onramp course on my MacBook Pro. I am using the latest version of MATLAB R2022a.
However, the Simulink environment is extremely slow and buggy (although MATLAB works absolutely fine). I identified 3 problems:
When I load the Simulink environment, only parts of it load. Sometimes the "Training-Tasks" bit on the left won’t show up, sometimes the top part with the "simulation", "debug", etc. buttons won’t even show up.
Another problem I am facing is that some buttons don’t work most of the times. I try clicking the submit button, but all it does it turn white. The "Training-Assessment" bit on the right shows that the signal was correct, but I can’t move to the next assignment as if Simulink just crashed. I have attached a picture that shows this situation. In addition, sometimes clicking on the "debug", "modeling", etc. buttons on the top won’t do anything.
Another problem I am facing is that it takes Simulink about 5 seconds every time to load search results from the quick insert. Why does the quick insert take so much time to load?
I would like some advice on how to fix this issue. The problems 1-3 happen on the Onramp course, and problem 3 happens even in regular Simulink.
Thank you in advance. Hi, I am trying to complete the Simulink Onramp course on my MacBook Pro. I am using the latest version of MATLAB R2022a.
However, the Simulink environment is extremely slow and buggy (although MATLAB works absolutely fine). I identified 3 problems:
When I load the Simulink environment, only parts of it load. Sometimes the "Training-Tasks" bit on the left won’t show up, sometimes the top part with the "simulation", "debug", etc. buttons won’t even show up.
Another problem I am facing is that some buttons don’t work most of the times. I try clicking the submit button, but all it does it turn white. The "Training-Assessment" bit on the right shows that the signal was correct, but I can’t move to the next assignment as if Simulink just crashed. I have attached a picture that shows this situation. In addition, sometimes clicking on the "debug", "modeling", etc. buttons on the top won’t do anything.
Another problem I am facing is that it takes Simulink about 5 seconds every time to load search results from the quick insert. Why does the quick insert take so much time to load?
I would like some advice on how to fix this issue. The problems 1-3 happen on the Onramp course, and problem 3 happens even in regular Simulink.
Thank you in advance. simulink MATLAB Answers — New Questions
how to implement pso algoritm
i used regression linear and perdiect my parameter which i need … i want to use pso algoritm in my code to optimize my code…i saw some video on youtube but all of them focus on something like it f(x)=sum xi^2 but i have dataset that i got them from experimental test and used it in svr model ..i can’t understand use database in pso code …
give me help to solve my problem
thanks in advancei used regression linear and perdiect my parameter which i need … i want to use pso algoritm in my code to optimize my code…i saw some video on youtube but all of them focus on something like it f(x)=sum xi^2 but i have dataset that i got them from experimental test and used it in svr model ..i can’t understand use database in pso code …
give me help to solve my problem
thanks in advance i used regression linear and perdiect my parameter which i need … i want to use pso algoritm in my code to optimize my code…i saw some video on youtube but all of them focus on something like it f(x)=sum xi^2 but i have dataset that i got them from experimental test and used it in svr model ..i can’t understand use database in pso code …
give me help to solve my problem
thanks in advance pso algoritm MATLAB Answers — New Questions
Model Advisor – Is is possible to obtain the number of individual violations?
Would like to obtain, as a metric on the project I am working on, the number of individual violations when running a Model Advisor configuration.
For example, in the case below I would like to obtain the value 5 (ideally per MA Check), as there are 5 individual violations (the blue highlights) preventing the check to be a PASS.Would like to obtain, as a metric on the project I am working on, the number of individual violations when running a Model Advisor configuration.
For example, in the case below I would like to obtain the value 5 (ideally per MA Check), as there are 5 individual violations (the blue highlights) preventing the check to be a PASS. Would like to obtain, as a metric on the project I am working on, the number of individual violations when running a Model Advisor configuration.
For example, in the case below I would like to obtain the value 5 (ideally per MA Check), as there are 5 individual violations (the blue highlights) preventing the check to be a PASS. simulink, modeladvisor MATLAB Answers — New Questions
finding the mean/average of the smallest values in a row
hi ,
i have a row and i want to find the average of the smallest values and the position of this minimum.hi ,
i have a row and i want to find the average of the smallest values and the position of this minimum. hi ,
i have a row and i want to find the average of the smallest values and the position of this minimum. minimum MATLAB Answers — New Questions
Reduce noise and highlight peaks in DWS data
Introduction
Hi my name’s Mark and I’m an Italian CS undegraduate working on a internship regarding data analysis about Diffusing Wave Spectroscopy data coming from the ISS, from the FSL Soft Matter Dynamics facility. I decided to ask the help of you most experts in the field as I’ve never worked with MATLAB this extensively in all my career and I struggle to come up with simple solutions without implementing them myself (e.g. creating a function when it exists already). I do have the professors to ask help to if things get grim.
The topic
The raw data that we start with is composed of multiple grayscale colored .tif images. A single .tif is a single measurement. Please see the [myTif.png] attachment. After reading the image with tiffreadVolume and after some local normalization and Gaussian filtering, I am left with a graph. See [norm_of_changes.png] attachment. This graph has lots of noise. I suppose the noise is testament of the method used to get the graph in the first place (computing the norm of changes between blocks).
The following code is the one used to compute the data to be plotted.
% blocks = 1;
% [s, ~] = size(V_norm_smooth);
% V_norm_smooth 10000×200 tif after imgaussfilt
for i = 1 : s – blocks
change = V_norm_smooth(i + blocks, 🙂 – V_norm_smooth(i, :);
avg_change(i) = norm(change);
end
window_size = 21;
med_res = medfilt2(avg_change, [1, window_size]);
The objective
My goal is to highlight peaks present in the final plot.
Peaks are not always guaranteed to be as evident as they appear in the attached example. There might be only one peak, two, three or more. Several parameters already help with the reduction of "false positives", as we’ve experimented with them a lot and figured that an universal solution does not exist.
I’m quite of the idea there is no simple answer to the "problem". What I’d like is some pointers and directions on what a good approach would be for our case. The actual objective would be to higlight that region in [myTif.png] that looks like a barrier of unordered pixels with lots of different intensities. We are of the idea that this method might not be the best one, but as a research topic, it is interesting and worthwhile.Introduction
Hi my name’s Mark and I’m an Italian CS undegraduate working on a internship regarding data analysis about Diffusing Wave Spectroscopy data coming from the ISS, from the FSL Soft Matter Dynamics facility. I decided to ask the help of you most experts in the field as I’ve never worked with MATLAB this extensively in all my career and I struggle to come up with simple solutions without implementing them myself (e.g. creating a function when it exists already). I do have the professors to ask help to if things get grim.
The topic
The raw data that we start with is composed of multiple grayscale colored .tif images. A single .tif is a single measurement. Please see the [myTif.png] attachment. After reading the image with tiffreadVolume and after some local normalization and Gaussian filtering, I am left with a graph. See [norm_of_changes.png] attachment. This graph has lots of noise. I suppose the noise is testament of the method used to get the graph in the first place (computing the norm of changes between blocks).
The following code is the one used to compute the data to be plotted.
% blocks = 1;
% [s, ~] = size(V_norm_smooth);
% V_norm_smooth 10000×200 tif after imgaussfilt
for i = 1 : s – blocks
change = V_norm_smooth(i + blocks, 🙂 – V_norm_smooth(i, :);
avg_change(i) = norm(change);
end
window_size = 21;
med_res = medfilt2(avg_change, [1, window_size]);
The objective
My goal is to highlight peaks present in the final plot.
Peaks are not always guaranteed to be as evident as they appear in the attached example. There might be only one peak, two, three or more. Several parameters already help with the reduction of "false positives", as we’ve experimented with them a lot and figured that an universal solution does not exist.
I’m quite of the idea there is no simple answer to the "problem". What I’d like is some pointers and directions on what a good approach would be for our case. The actual objective would be to higlight that region in [myTif.png] that looks like a barrier of unordered pixels with lots of different intensities. We are of the idea that this method might not be the best one, but as a research topic, it is interesting and worthwhile. Introduction
Hi my name’s Mark and I’m an Italian CS undegraduate working on a internship regarding data analysis about Diffusing Wave Spectroscopy data coming from the ISS, from the FSL Soft Matter Dynamics facility. I decided to ask the help of you most experts in the field as I’ve never worked with MATLAB this extensively in all my career and I struggle to come up with simple solutions without implementing them myself (e.g. creating a function when it exists already). I do have the professors to ask help to if things get grim.
The topic
The raw data that we start with is composed of multiple grayscale colored .tif images. A single .tif is a single measurement. Please see the [myTif.png] attachment. After reading the image with tiffreadVolume and after some local normalization and Gaussian filtering, I am left with a graph. See [norm_of_changes.png] attachment. This graph has lots of noise. I suppose the noise is testament of the method used to get the graph in the first place (computing the norm of changes between blocks).
The following code is the one used to compute the data to be plotted.
% blocks = 1;
% [s, ~] = size(V_norm_smooth);
% V_norm_smooth 10000×200 tif after imgaussfilt
for i = 1 : s – blocks
change = V_norm_smooth(i + blocks, 🙂 – V_norm_smooth(i, :);
avg_change(i) = norm(change);
end
window_size = 21;
med_res = medfilt2(avg_change, [1, window_size]);
The objective
My goal is to highlight peaks present in the final plot.
Peaks are not always guaranteed to be as evident as they appear in the attached example. There might be only one peak, two, three or more. Several parameters already help with the reduction of "false positives", as we’ve experimented with them a lot and figured that an universal solution does not exist.
I’m quite of the idea there is no simple answer to the "problem". What I’d like is some pointers and directions on what a good approach would be for our case. The actual objective would be to higlight that region in [myTif.png] that looks like a barrier of unordered pixels with lots of different intensities. We are of the idea that this method might not be the best one, but as a research topic, it is interesting and worthwhile. noise reduction, image processing, plot, matlab function MATLAB Answers — New Questions
App Designer Crashes on start up
Hello, I am using matlab 2017b in Win10. Whenever I run "appdesigner" in the command line, I see a window pop up and close immediately. I also cannot open any .mlapp file. Also, there are not any warnings or errors popping up. I am very confused. Any ideas?Hello, I am using matlab 2017b in Win10. Whenever I run "appdesigner" in the command line, I see a window pop up and close immediately. I also cannot open any .mlapp file. Also, there are not any warnings or errors popping up. I am very confused. Any ideas? Hello, I am using matlab 2017b in Win10. Whenever I run "appdesigner" in the command line, I see a window pop up and close immediately. I also cannot open any .mlapp file. Also, there are not any warnings or errors popping up. I am very confused. Any ideas? app designer MATLAB Answers — New Questions
Time- series inputs for ODE Function
Dear all,
I hope you are all doing well.
I am trying to introduce the external inputs (forces) data which is time series. I have refered to a number of Matlab discussions and used the interp1 method to calculate in the ode function.
However, I found the results of the interplotion are different with the original data which I used. Furthermore, I have no idea about how to include the external inputs in ode. It does not accept the time-series data when I use it directly. I have attached the file and my code is following:
In the script, I just try to find the problem so, only 1 input (force) which is F_wave is enabled.
Thank you for your kind help.
Best wishes,
Yu
clear; clc;
global all_F
global ex_F
all_F = [];
ex_F = [];
syms z_T
filename = ‘Inputs_Force.xlsx’;
data = readtable(filename);
use_data=table2cell(data(:,1:6));
use_data=cell2mat(use_data);
Waveforce = use_data(:,2);
Wavemoment =use_data(:,3);
Windforce = use_data(:,5);
Windmoment = use_data(:,6);
tspan = 0:0.025:200;
figure,
subplot(3,1,2), plot(tspan, Windforce), xlabel(‘time/s’),ylabel(‘WindForce/N’);
subplot(3,1,1), plot(tspan, Waveforce), xlabel(‘time/s’),ylabel(‘WaveForce/N’);
subplot(3,1,3), plot(tspan, Wavemoment), xlabel(‘time/s’),ylabel(‘WaveMoment/N.m’)
h_R = 144.582; % m the height from the MSL to tower top;
H_T = 129.582; % m tower height from the tower bottom;
h_T = 15; % m height from tower base to tower bottom;
h = 29.94;
h_1 = 164.94;
z = 14.94; % m the distance from rotational centre to mooring line
h_t = 92.61; % m the distance from the centre of gravity of tower to rotational centre
h_p = 14.94; % m the distance from the centre of gravity of platform to rotational centre
g = 9.81;
m = 20093000; % kg /total mass
m_T = 1.263e6; % 8.6e5; % kg / tower
m_N = 1.017e6; % kg / nacelle
m_p = 1.7838e7; % kg /platform mass
xi_TA = 0.01;
I_p = 1.2507*10^10;% kg m2 /mass moment of inertia of platform
m_as = 9.4*10^6; % kg / Added mass for platform surge
I_a = 1.13*10^10; % kg m2 / Added mass for platform pitch
m_asp = -1.01*10^8; % kg m / Coupling effects of added mass bewteen surge and pitch
c_s = 1e5; % N s2/m2 / damping in surge motion (x-axis translation)
c_sp = -2e5; % coupled damping value between surge and platform pitch
c_p = 6e8; % viscous damping in pitch motionS
k_p = 2.190e9; % rotational stiffness of platform
k_mooringS = 7.965e4;
k_mooringSP = 1.162e6;
k_mooringP = 2.65e8;
% definition of TMD parameters
m_TMD = 1.2e5; % kg
k_TMD = 6.064e3; % kg/m
c_TMD = 1.2678e4; % kg/(m s)
X0 = [0 0 0 0 0 0]; % initial pitch motion
% ==================== definition of the tower properties===============
mu = 0.0084*z_T^3-1.077*z_T^2-171.5*z_T+2.96e04; % mass per length
EI = 1.905e06*z_T^3-2.47e08*z_T^2-5.208e10*z_T+6.851e12; % tower bending stiffness
Phi_TA = 0.9414*(z_T/H_T)^2+0.3468*(z_T/H_T)^3-1.073*(z_T/H_T)^4+1.3139*(z_T/H_T)^5-0.5289*(z_T/H_T)^6; % tower fore-aft first mode shape
% mass component
fun1 = mu*Phi_TA^2; m_TA = double(int(fun1,z_T,0,H_T));
fun2 = mu*Phi_TA; m_1 = double(int(fun2,z_T,0,H_T));
fun3 = mu*(z_T)*Phi_TA; m_2 = double(int(fun3,z_T,0,H_T));
fun4 = mu*(z_T); m_3 = double(int(fun4,z_T,0,H_T));
fun5 = mu*(z_T)^2; m_4 = double(int(fun5,z_T,0,H_T));
% fun6 = mu; m_T = double(int(fun6,z_T,0,H_T));
% Stiffness of the tower
D2y = diff(Phi_TA,z_T,2); Dy = diff(Phi_TA,z_T,1);
fun6 = EI*D2y^2; f1 = int(fun6,0,H_T);
fun7 = mu; f2 = int(fun7,z_T,H_T);
fun8 = g*(m_N+f2)*Dy^2; f3 = int(fun8,0,H_T);
k_TA = double(f1-f3);
% ================= definition of matrices ======================
M = [m_N+m_TA m_N+m_1 m_N*h_R+m_2;
m_N+m_1 m_N+m_p+m_T (m_N*h_R-m_p*h_p+m_3+m_T*h_T);
m_N*h_R+m_2 (m_N*h_R-m_p*h_p+m_3) m_N*h_R^2+I_p+m_4];
C = [2*xi_TA*sqrt(m_TA*k_TA) 0 0;
0 0 0;
0 0 0];
K = [k_TA 0 -(m_N+m_T)*g;
0 0 0;
-(m_N+m_T)*g 0 -(m_N*h_R+m_3-m_p*h_p)*g];
M_add = [0 0 0;
0 m_as m_asp;
0 m_asp I_a];
C_add = [0 0 0; % problem_
0 c_s c_sp;
0 c_sp c_p];
K_add = [0 0 0;
0 0 0;
0 0 k_p];
K_mooring = [0 0 0;
0 k_mooringS k_mooringSP;
0 k_mooringSP k_mooringP];
M_1 = M+M_add;
K_1 = K+K_mooring+K_add;
% the forces and moments are extracted from Orcaflex
F_wave = Waveforce;
M_wave = Wavemoment;
F_aero = Windforce;
M_aero = Windmoment;
% [V,D,W] = eig(K_1,M_1);
%
% w = diag(D).^0.5;
%
% T = (2*pi./w);
options = odeset(‘RelTol’,1e-10,’AbsTol’,1e-10);
[t,X] = ode45(@(t,X) reducedmodel(z,h_R,tspan,t,X,M,M_add,C,C_add,K,K_add,K_mooring,F_wave,M_wave,F_aero,M_aero),tspan,X0,options);
PtfmPitch_deg = X(:,3)*180/pi;
figure,
subplot(3,1,1), plot(t,X(:,1)),grid, xlabel(‘time/ s’), ylabel(‘TTDspFA/ m’)
subplot(3,1,2), plot(t,X(:,2)),grid, xlabel(‘time/ s’), ylabel(‘surge/ m’)
subplot(3,1,3), plot(t,PtfmPitch_deg),grid, xlabel(‘time/ s’), ylabel(‘platform pitch/ deg’)
function dXdt = reducedmodel(z,h_R,tspan,t,X,M,M_add,C,C_add,K,K_add,K_mooring,F_wave,M_wave,F_aero,M_aero)
global all_F
global ex_F
x = X(1:3);
xdot = X(4:6);
coe = 9.225e5;
coe1 = 1.16e10;
F1 = -coe*xdot(2)*abs(xdot(2));
F2 = -coe1*xdot(3)*abs(xdot(3));
F3 = interp1(tspan, F_wave, t,’spline’);
F4 = interp1(tspan, M_wave, ‘spline’);
F5 = interp1(tspan, F_aero, ‘spline’);
F6 = interp1(tspan, F_aero*h_R, ‘spline’);
F_external = [0;F3;0];
F=[0;F1;F2];
all_F = [all_F,F]; % drag force
ex_F = [ex_F, F_external]; % wave and wind forces
xddot = (M+M_add)(F+F_external-(K+K_add+K_mooring)*x-(C+C_add)*xdot);
dXdt = [xdot; xddot];
end
% function [u,wn]=eigsort(K_1,M_1)
% Omega=sqrt(eig(K_1,M_1));
% [vtem,~]=eig(K_1,M_1);
% [wn,isort]=sort(Omega);
% il=length(wn);
% for i=1:il
% v(:,i)=vtem(:,isort(i));
% end
% disp("The natural frequencies are (rad/sec)")
% wn
% endDear all,
I hope you are all doing well.
I am trying to introduce the external inputs (forces) data which is time series. I have refered to a number of Matlab discussions and used the interp1 method to calculate in the ode function.
However, I found the results of the interplotion are different with the original data which I used. Furthermore, I have no idea about how to include the external inputs in ode. It does not accept the time-series data when I use it directly. I have attached the file and my code is following:
In the script, I just try to find the problem so, only 1 input (force) which is F_wave is enabled.
Thank you for your kind help.
Best wishes,
Yu
clear; clc;
global all_F
global ex_F
all_F = [];
ex_F = [];
syms z_T
filename = ‘Inputs_Force.xlsx’;
data = readtable(filename);
use_data=table2cell(data(:,1:6));
use_data=cell2mat(use_data);
Waveforce = use_data(:,2);
Wavemoment =use_data(:,3);
Windforce = use_data(:,5);
Windmoment = use_data(:,6);
tspan = 0:0.025:200;
figure,
subplot(3,1,2), plot(tspan, Windforce), xlabel(‘time/s’),ylabel(‘WindForce/N’);
subplot(3,1,1), plot(tspan, Waveforce), xlabel(‘time/s’),ylabel(‘WaveForce/N’);
subplot(3,1,3), plot(tspan, Wavemoment), xlabel(‘time/s’),ylabel(‘WaveMoment/N.m’)
h_R = 144.582; % m the height from the MSL to tower top;
H_T = 129.582; % m tower height from the tower bottom;
h_T = 15; % m height from tower base to tower bottom;
h = 29.94;
h_1 = 164.94;
z = 14.94; % m the distance from rotational centre to mooring line
h_t = 92.61; % m the distance from the centre of gravity of tower to rotational centre
h_p = 14.94; % m the distance from the centre of gravity of platform to rotational centre
g = 9.81;
m = 20093000; % kg /total mass
m_T = 1.263e6; % 8.6e5; % kg / tower
m_N = 1.017e6; % kg / nacelle
m_p = 1.7838e7; % kg /platform mass
xi_TA = 0.01;
I_p = 1.2507*10^10;% kg m2 /mass moment of inertia of platform
m_as = 9.4*10^6; % kg / Added mass for platform surge
I_a = 1.13*10^10; % kg m2 / Added mass for platform pitch
m_asp = -1.01*10^8; % kg m / Coupling effects of added mass bewteen surge and pitch
c_s = 1e5; % N s2/m2 / damping in surge motion (x-axis translation)
c_sp = -2e5; % coupled damping value between surge and platform pitch
c_p = 6e8; % viscous damping in pitch motionS
k_p = 2.190e9; % rotational stiffness of platform
k_mooringS = 7.965e4;
k_mooringSP = 1.162e6;
k_mooringP = 2.65e8;
% definition of TMD parameters
m_TMD = 1.2e5; % kg
k_TMD = 6.064e3; % kg/m
c_TMD = 1.2678e4; % kg/(m s)
X0 = [0 0 0 0 0 0]; % initial pitch motion
% ==================== definition of the tower properties===============
mu = 0.0084*z_T^3-1.077*z_T^2-171.5*z_T+2.96e04; % mass per length
EI = 1.905e06*z_T^3-2.47e08*z_T^2-5.208e10*z_T+6.851e12; % tower bending stiffness
Phi_TA = 0.9414*(z_T/H_T)^2+0.3468*(z_T/H_T)^3-1.073*(z_T/H_T)^4+1.3139*(z_T/H_T)^5-0.5289*(z_T/H_T)^6; % tower fore-aft first mode shape
% mass component
fun1 = mu*Phi_TA^2; m_TA = double(int(fun1,z_T,0,H_T));
fun2 = mu*Phi_TA; m_1 = double(int(fun2,z_T,0,H_T));
fun3 = mu*(z_T)*Phi_TA; m_2 = double(int(fun3,z_T,0,H_T));
fun4 = mu*(z_T); m_3 = double(int(fun4,z_T,0,H_T));
fun5 = mu*(z_T)^2; m_4 = double(int(fun5,z_T,0,H_T));
% fun6 = mu; m_T = double(int(fun6,z_T,0,H_T));
% Stiffness of the tower
D2y = diff(Phi_TA,z_T,2); Dy = diff(Phi_TA,z_T,1);
fun6 = EI*D2y^2; f1 = int(fun6,0,H_T);
fun7 = mu; f2 = int(fun7,z_T,H_T);
fun8 = g*(m_N+f2)*Dy^2; f3 = int(fun8,0,H_T);
k_TA = double(f1-f3);
% ================= definition of matrices ======================
M = [m_N+m_TA m_N+m_1 m_N*h_R+m_2;
m_N+m_1 m_N+m_p+m_T (m_N*h_R-m_p*h_p+m_3+m_T*h_T);
m_N*h_R+m_2 (m_N*h_R-m_p*h_p+m_3) m_N*h_R^2+I_p+m_4];
C = [2*xi_TA*sqrt(m_TA*k_TA) 0 0;
0 0 0;
0 0 0];
K = [k_TA 0 -(m_N+m_T)*g;
0 0 0;
-(m_N+m_T)*g 0 -(m_N*h_R+m_3-m_p*h_p)*g];
M_add = [0 0 0;
0 m_as m_asp;
0 m_asp I_a];
C_add = [0 0 0; % problem_
0 c_s c_sp;
0 c_sp c_p];
K_add = [0 0 0;
0 0 0;
0 0 k_p];
K_mooring = [0 0 0;
0 k_mooringS k_mooringSP;
0 k_mooringSP k_mooringP];
M_1 = M+M_add;
K_1 = K+K_mooring+K_add;
% the forces and moments are extracted from Orcaflex
F_wave = Waveforce;
M_wave = Wavemoment;
F_aero = Windforce;
M_aero = Windmoment;
% [V,D,W] = eig(K_1,M_1);
%
% w = diag(D).^0.5;
%
% T = (2*pi./w);
options = odeset(‘RelTol’,1e-10,’AbsTol’,1e-10);
[t,X] = ode45(@(t,X) reducedmodel(z,h_R,tspan,t,X,M,M_add,C,C_add,K,K_add,K_mooring,F_wave,M_wave,F_aero,M_aero),tspan,X0,options);
PtfmPitch_deg = X(:,3)*180/pi;
figure,
subplot(3,1,1), plot(t,X(:,1)),grid, xlabel(‘time/ s’), ylabel(‘TTDspFA/ m’)
subplot(3,1,2), plot(t,X(:,2)),grid, xlabel(‘time/ s’), ylabel(‘surge/ m’)
subplot(3,1,3), plot(t,PtfmPitch_deg),grid, xlabel(‘time/ s’), ylabel(‘platform pitch/ deg’)
function dXdt = reducedmodel(z,h_R,tspan,t,X,M,M_add,C,C_add,K,K_add,K_mooring,F_wave,M_wave,F_aero,M_aero)
global all_F
global ex_F
x = X(1:3);
xdot = X(4:6);
coe = 9.225e5;
coe1 = 1.16e10;
F1 = -coe*xdot(2)*abs(xdot(2));
F2 = -coe1*xdot(3)*abs(xdot(3));
F3 = interp1(tspan, F_wave, t,’spline’);
F4 = interp1(tspan, M_wave, ‘spline’);
F5 = interp1(tspan, F_aero, ‘spline’);
F6 = interp1(tspan, F_aero*h_R, ‘spline’);
F_external = [0;F3;0];
F=[0;F1;F2];
all_F = [all_F,F]; % drag force
ex_F = [ex_F, F_external]; % wave and wind forces
xddot = (M+M_add)(F+F_external-(K+K_add+K_mooring)*x-(C+C_add)*xdot);
dXdt = [xdot; xddot];
end
% function [u,wn]=eigsort(K_1,M_1)
% Omega=sqrt(eig(K_1,M_1));
% [vtem,~]=eig(K_1,M_1);
% [wn,isort]=sort(Omega);
% il=length(wn);
% for i=1:il
% v(:,i)=vtem(:,isort(i));
% end
% disp("The natural frequencies are (rad/sec)")
% wn
% end Dear all,
I hope you are all doing well.
I am trying to introduce the external inputs (forces) data which is time series. I have refered to a number of Matlab discussions and used the interp1 method to calculate in the ode function.
However, I found the results of the interplotion are different with the original data which I used. Furthermore, I have no idea about how to include the external inputs in ode. It does not accept the time-series data when I use it directly. I have attached the file and my code is following:
In the script, I just try to find the problem so, only 1 input (force) which is F_wave is enabled.
Thank you for your kind help.
Best wishes,
Yu
clear; clc;
global all_F
global ex_F
all_F = [];
ex_F = [];
syms z_T
filename = ‘Inputs_Force.xlsx’;
data = readtable(filename);
use_data=table2cell(data(:,1:6));
use_data=cell2mat(use_data);
Waveforce = use_data(:,2);
Wavemoment =use_data(:,3);
Windforce = use_data(:,5);
Windmoment = use_data(:,6);
tspan = 0:0.025:200;
figure,
subplot(3,1,2), plot(tspan, Windforce), xlabel(‘time/s’),ylabel(‘WindForce/N’);
subplot(3,1,1), plot(tspan, Waveforce), xlabel(‘time/s’),ylabel(‘WaveForce/N’);
subplot(3,1,3), plot(tspan, Wavemoment), xlabel(‘time/s’),ylabel(‘WaveMoment/N.m’)
h_R = 144.582; % m the height from the MSL to tower top;
H_T = 129.582; % m tower height from the tower bottom;
h_T = 15; % m height from tower base to tower bottom;
h = 29.94;
h_1 = 164.94;
z = 14.94; % m the distance from rotational centre to mooring line
h_t = 92.61; % m the distance from the centre of gravity of tower to rotational centre
h_p = 14.94; % m the distance from the centre of gravity of platform to rotational centre
g = 9.81;
m = 20093000; % kg /total mass
m_T = 1.263e6; % 8.6e5; % kg / tower
m_N = 1.017e6; % kg / nacelle
m_p = 1.7838e7; % kg /platform mass
xi_TA = 0.01;
I_p = 1.2507*10^10;% kg m2 /mass moment of inertia of platform
m_as = 9.4*10^6; % kg / Added mass for platform surge
I_a = 1.13*10^10; % kg m2 / Added mass for platform pitch
m_asp = -1.01*10^8; % kg m / Coupling effects of added mass bewteen surge and pitch
c_s = 1e5; % N s2/m2 / damping in surge motion (x-axis translation)
c_sp = -2e5; % coupled damping value between surge and platform pitch
c_p = 6e8; % viscous damping in pitch motionS
k_p = 2.190e9; % rotational stiffness of platform
k_mooringS = 7.965e4;
k_mooringSP = 1.162e6;
k_mooringP = 2.65e8;
% definition of TMD parameters
m_TMD = 1.2e5; % kg
k_TMD = 6.064e3; % kg/m
c_TMD = 1.2678e4; % kg/(m s)
X0 = [0 0 0 0 0 0]; % initial pitch motion
% ==================== definition of the tower properties===============
mu = 0.0084*z_T^3-1.077*z_T^2-171.5*z_T+2.96e04; % mass per length
EI = 1.905e06*z_T^3-2.47e08*z_T^2-5.208e10*z_T+6.851e12; % tower bending stiffness
Phi_TA = 0.9414*(z_T/H_T)^2+0.3468*(z_T/H_T)^3-1.073*(z_T/H_T)^4+1.3139*(z_T/H_T)^5-0.5289*(z_T/H_T)^6; % tower fore-aft first mode shape
% mass component
fun1 = mu*Phi_TA^2; m_TA = double(int(fun1,z_T,0,H_T));
fun2 = mu*Phi_TA; m_1 = double(int(fun2,z_T,0,H_T));
fun3 = mu*(z_T)*Phi_TA; m_2 = double(int(fun3,z_T,0,H_T));
fun4 = mu*(z_T); m_3 = double(int(fun4,z_T,0,H_T));
fun5 = mu*(z_T)^2; m_4 = double(int(fun5,z_T,0,H_T));
% fun6 = mu; m_T = double(int(fun6,z_T,0,H_T));
% Stiffness of the tower
D2y = diff(Phi_TA,z_T,2); Dy = diff(Phi_TA,z_T,1);
fun6 = EI*D2y^2; f1 = int(fun6,0,H_T);
fun7 = mu; f2 = int(fun7,z_T,H_T);
fun8 = g*(m_N+f2)*Dy^2; f3 = int(fun8,0,H_T);
k_TA = double(f1-f3);
% ================= definition of matrices ======================
M = [m_N+m_TA m_N+m_1 m_N*h_R+m_2;
m_N+m_1 m_N+m_p+m_T (m_N*h_R-m_p*h_p+m_3+m_T*h_T);
m_N*h_R+m_2 (m_N*h_R-m_p*h_p+m_3) m_N*h_R^2+I_p+m_4];
C = [2*xi_TA*sqrt(m_TA*k_TA) 0 0;
0 0 0;
0 0 0];
K = [k_TA 0 -(m_N+m_T)*g;
0 0 0;
-(m_N+m_T)*g 0 -(m_N*h_R+m_3-m_p*h_p)*g];
M_add = [0 0 0;
0 m_as m_asp;
0 m_asp I_a];
C_add = [0 0 0; % problem_
0 c_s c_sp;
0 c_sp c_p];
K_add = [0 0 0;
0 0 0;
0 0 k_p];
K_mooring = [0 0 0;
0 k_mooringS k_mooringSP;
0 k_mooringSP k_mooringP];
M_1 = M+M_add;
K_1 = K+K_mooring+K_add;
% the forces and moments are extracted from Orcaflex
F_wave = Waveforce;
M_wave = Wavemoment;
F_aero = Windforce;
M_aero = Windmoment;
% [V,D,W] = eig(K_1,M_1);
%
% w = diag(D).^0.5;
%
% T = (2*pi./w);
options = odeset(‘RelTol’,1e-10,’AbsTol’,1e-10);
[t,X] = ode45(@(t,X) reducedmodel(z,h_R,tspan,t,X,M,M_add,C,C_add,K,K_add,K_mooring,F_wave,M_wave,F_aero,M_aero),tspan,X0,options);
PtfmPitch_deg = X(:,3)*180/pi;
figure,
subplot(3,1,1), plot(t,X(:,1)),grid, xlabel(‘time/ s’), ylabel(‘TTDspFA/ m’)
subplot(3,1,2), plot(t,X(:,2)),grid, xlabel(‘time/ s’), ylabel(‘surge/ m’)
subplot(3,1,3), plot(t,PtfmPitch_deg),grid, xlabel(‘time/ s’), ylabel(‘platform pitch/ deg’)
function dXdt = reducedmodel(z,h_R,tspan,t,X,M,M_add,C,C_add,K,K_add,K_mooring,F_wave,M_wave,F_aero,M_aero)
global all_F
global ex_F
x = X(1:3);
xdot = X(4:6);
coe = 9.225e5;
coe1 = 1.16e10;
F1 = -coe*xdot(2)*abs(xdot(2));
F2 = -coe1*xdot(3)*abs(xdot(3));
F3 = interp1(tspan, F_wave, t,’spline’);
F4 = interp1(tspan, M_wave, ‘spline’);
F5 = interp1(tspan, F_aero, ‘spline’);
F6 = interp1(tspan, F_aero*h_R, ‘spline’);
F_external = [0;F3;0];
F=[0;F1;F2];
all_F = [all_F,F]; % drag force
ex_F = [ex_F, F_external]; % wave and wind forces
xddot = (M+M_add)(F+F_external-(K+K_add+K_mooring)*x-(C+C_add)*xdot);
dXdt = [xdot; xddot];
end
% function [u,wn]=eigsort(K_1,M_1)
% Omega=sqrt(eig(K_1,M_1));
% [vtem,~]=eig(K_1,M_1);
% [wn,isort]=sort(Omega);
% il=length(wn);
% for i=1:il
% v(:,i)=vtem(:,isort(i));
% end
% disp("The natural frequencies are (rad/sec)")
% wn
% end ode45, differential equations, time series MATLAB Answers — New Questions
Index exceeds the number of array elements. Index must not exceed 4.
Index exceeds the number of array elements. Index must not exceed 4. I get it in Simulink. I’m getting this error and I couldn’t find a solution. Can you help me.Index exceeds the number of array elements. Index must not exceed 4. I get it in Simulink. I’m getting this error and I couldn’t find a solution. Can you help me. Index exceeds the number of array elements. Index must not exceed 4. I get it in Simulink. I’m getting this error and I couldn’t find a solution. Can you help me. simulink, matlab MATLAB Answers — New Questions