MATLAB is a numerical computing environment and proprietary fourth-generation programming language. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Although MATLAB is intended primarily for numerical computing, an optional toolbox uses the MuPAD symbolic engine, allowing access to symbolic computing capabilities. An additional package, Simulink, adds graphical multi-domain simulation and model-based design for dynamic and embedded systems.

In MATLAB, I have used importdata to read a mixed type .csv in, creating a 1x1 structure with two fields (data (96x2) and textdata (97x7)). data should go in the last two columns of textdata under the header row. I eventually want to create a cell array (num2cell) of the concatenated matrix of data/textdata so I can index specific values and run descriptive statistics on them but vertcat/ horzcat aren't working because the field dims are different.

I've tried textscan also, but it creates a 1x4 cell array with each cell being 96x1 (such that I can't see any of the data).

Here is my code:

% This code runs descriptive statistics and creates graphical
% represntations of m-sit data to see how participants responded to negative/
% neutral pictures that were presented after fMRI scan session

% Bring folder to current directory
cd 'C:\Users\krublab\Desktop\Raisa\MSIT_post_rate_raisa\msitPostRate_data'
dirpath = 'C:\Users\krublab\Desktop\Raisa\MSIT_post_rate_raisa\msitPostRate_data';
% Add folder to search path
addpath(dirpath)

% Create flexible file names structure to read from
filePattern = fullfile(dirpath, '*-post.csv');
fileNames = dir(filePattern);

% Pad for loop to begin iterating at 01 instead of 1
pad = sprintf('%02d', 1);
for pad = 01:length(fileNames)
…

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The problem is that I need to run the code in a Matlab version which doesn't have the Signal Processing Toolbox. Is there any solution to write a similar function without the need of the SP Toolbox?

%Define the Fuzzy Inferences System
edgeFIS = newfis('edgeDetection');

%Specify the image gradients, Ix and Iy, as the inputs of edgeFIS
edgeFIS = addvar(edgeFIS,'input','Ix',[-1 1]);
edgeFIS = addvar(edgeFIS,'input','Iy',[-1 1]);

I need to wrap a Fortran program to run from Matlab. Has anyone done this before? I did not find much information in my search. Thank you for any help you can give.

I have an array (raw_acceleration) of 1600 elements acquired every 10 seconds at 160Hz from a sensor.
i use this array to calculate the fft and the Power Spectral Density and it works very well:

I obtain this plot:
As you can see, I obtain four peaks centered around four different frequencies.

I would like to have a Matlab code which is able to automatically find these four maximum peaks and their frequency.
At the moment, I always need to plot the graph and manually detect the maximum peak and its frequency.

Is there any way to do this by code?
I hope you an help me.

I have a set of seven parameters which are numerical values (i.e. current readings, pitch, roll and yaw angles, acceleration, etc..) and I use them to classify a vehicle behavior.
For example, if:

What is the best way to implement this kind of classification in Matlab?
Should I use a simple neural network? Or a classification method like SVM?
Is there any tutorial or example?

I have a set of data where each set of parameters is related to a specific behavior, so I can use this to train my model.

I have a vector in Matlab which contains samples acquired at 10Hz from an accelerometer mounted on a frame's vehicle.
This vector contains the acceleration values along the Z-axis.
The acquisition time is 103 seconds.

I would like to plot the frequency values in Hz since I'm trying to study the terrain frequency response.

I tried to use the fft function available in Matlab, but I do not know how to proceed.
Can you help me, please?

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I have a vector " imu" with a size of 11497 elements. These elements are samples captured at a very high rate (the total time is 71 seconds so I think the frequency is about 160 Hz). Since I acquired all the other values at 10Hz, the size of all my other vectors is 719.

I would like to remove the oversampled elements from the vector " imu" in order to have only 10 samples per second.

Is there any way to do this? Can you help me, please?

i'm having a problem with one of my old matlab scripts.
This script worked very well on a previous version of Matlab, but now I'm running it on a different machine and on a different Matlab version (R2017b) and it gives me this error:

Index exceeds matrix dimensions.Error in cusum (line 36)y = x1(:,c_y) - mean(x1(:,c_y));Error in original (line 269) [ckc,slc]=cusum(Ic(:,2)); %% PROBLEMA

Please, can you try to help me?
I attached to this post all the matlab files (i renamed them with *.txt extension) and also the txt file to use to populate the vectors.

I am trying to use Matlab 2016 as a neural network. I have large amounts of comma-delimited ASCII data that I would like to analyze. Each row of data contains about 1500 elements of alphanumerics. There would be about 100 records/rows of 1500 elements each. The first task I need to do is determine the correlation, if any, between the various elements. I think that would be a clustering issue. Is it practical, or advised, to try to run the entire block of data at one time, or to pick out what I think are the relevant factors and just analyze those for correlations? I have reasonably powerful computing equipment and I don't know if time factors would make looking at the entire data set prohibitive. Once I determined which way to do that, and have figured out some of the correlations, I would then have to set up an actual neural network to try to get some insight into larger blocks of that same type of data.

Any help will be appreciated and points awarded. Thanks.

(i) Develop an algorithm to determine whether a graph represented by a node-node incidence matrix is connected or not. Provide a clear algorithm statement.
(ii) Code your algorithm in any programming language you prefer, run your code on the node-node adjacency matrices. The matrix is 300x300. Also, I need to report my results along with the computation time in seconds

When I compile my mex with the "-g" argument, a PDB file is created which allows me to Attach to the Matlab Process.

This works OK.

However, I'm finding that if I change any part of the source code, then recompile the mex and Attach to Matlab again, my breakpoints are not ever hit again.

The solution is to exit Matlab, restart Matlab, rebuild the mex and re-attach. Then, magically, it works again.

But it's a royal pain to have to continue exiting Matlab every time I make a small code change.

Hi,
I am trying to check integration using two different methods, first by using [function (int)] and second by using summation. I am integration from high to low values (0.1 to 0). I got same result. However, from function I got positive value (0.005) and from summation, negative value (-0.005). I am not sure if that because I am integrating from high to low values so I need to use (dr) in negative value when finding the integration using summation.

I used below code

clear all; clc; syms x y y = -x; z=int(y,x); v=[0:0.1/19:0.1]; dr=0.1/20; for i=1:20 x=v(i); kk(i)=eval(y)*(dr); end R1=sum(kk(1,:)); x=0.1; z1=eval(z); x=0; z2=eval(z); R2=z2-z1;

I am very new to image processing and Matlab. I am working with RGB image and used SLIC algorithm to generate superpixel for an image. By using regionprops to calculate the superpixel properties.

[Rcounts binlocations]=imhist(ImageRED);
[Gcounts binlocations]=imhist(ImageGREEN);
[Bcounts binlocations]=imhist(ImageBLUE);
Q1: I have 48 superpixels with Counts(R,G,B channels) it is giving me 256*1 double(most of them are zero). Can anybody explain how to refine them to get each superpixel histogram?

Q2: How can I compare and calculate the distance between two specific superpixel histograms?

Any help is greatly appreciated. Thanks in advance

I have a covariance matrix and I am using Matlab's chol function for cholesky decomposition. However, since my matrix is not a positive definite matrix. I am getting an error. How can I figure out which correlations/covariances in the matrix are leading to the problem.

how i can apply fuzzy logic for this problem? Or is there any other technique i can use for my problem?

Input : parameter values Output : (1) OLTP , (2) DSS , (3) % of OLTP and % of DSS

I have to classify between 3 classes as OLTP, DSS and mix type. if my input matches with OLTP or DSS type it return crisp class. if it matches with mix type it returns the percentage of OLTP and DSS

How can i apply fuzzy logic for this problem? Or is there any other technique i can use for my problem?

Input : parameter values Output : (1) OLTP , (2) DSS , (3) % of OLTP and % of DSS

I have to classify between 3 classes as OLTP, DSS and mix type. if my input matches with OLTP or DSS type it return crisp class. if it matches with mix type it returns the percentage of OLTP and DSS.

MATLAB is a numerical computing environment and proprietary fourth-generation programming language. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Although MATLAB is intended primarily for numerical computing, an optional toolbox uses the MuPAD symbolic engine, allowing access to symbolic computing capabilities. An additional package, Simulink, adds graphical multi-domain simulation and model-based design for dynamic and embedded systems.