MATLAB FOR ENGINEERS

              Matlab based projects are developed for engineering students like B.Tech, B.E, M.Tech and M.E.

 

Reason to Choose Simulation Based Projects using Matlab by Engineering Students:-

  • Highly optimized for matrix operations.
  • Very useful graphical debugger.
  • Matlab based algorithms and functions are created and implemented to develop different concepts for engineering students.

Concept Areas Included in Matlab for Engineers:-

  • Medical Imaging.
  • Image Processing.
  • Remote Sensing.
  • Signal Processing.
  • Pattern Analysis and Machine Intelligence.
  • Information Forensic and Security.

Matlab Flow Controls:

  • Switch and Case Statement.
  • For Loop.
  • While Loop.
  • If-Else Statement.

 

Matlab Functionalities:

  • Matlab has an extensive set of built-in functions and some additional toolboxes
  • Everything can be done with the help of GUI Interface
  • Programming mode in matlab should provide programming environment for users to write their own functions and scripts

Features of Matlab:

  • File I/O functions
  • String Processing
  • Easy creation of scientific and engineering graphics
  • Object oriented programming

Matlab Function Handles for Concept Implementation:

  • Capture data for later use
  • Enables passing functions as arguments such as numerical integration, optimization, solution of ODEs and solution of nonlinear systems of equations
  • Callable associations to matlab functions stored in variable

Image Processing concepts on matlab implementation:

  • Image Retrieval
  • Encapsulation
  • Cryptography
  • Steganography
  • Watermarking
  • Audio Processing
  • Biometric Recognition

Applications of image segmentation:

  • Scene object identification- size and shape
  • Moving scene object identification – video compression
  • Different distance object identification – path planning for a mobile robots

Sample Code for PCA:- MATLAB FOR ENGINEERS

%step 1, generating a dataset
x1=rand(numdata/2,1);
y1=rand(numdata/2,1);
x2=3*rand(numdata/2,1)+3;
y2=3*rand(numdata/2,1)+3;

x=[x1;x2];
y=[y1;y2];

%step 2, finding a mean and subtracting
xmean=mean(x);
ymean=mean(y);

xnew=x-xmean*ones(numdata,1);
ynew=y-ymean*ones(numdata,1);

subplot(3,1,1);
plot(x,y, ‘o’);
title(‘Original Data’);

%Uncomment to see the data after the deduction of the mean
%subplot(4,1,2);
%plot(xnew,ynew, ‘o’);
%title(‘mean is deducted’)

%step 3, covariance matrix
covariancematrix=cov(xnew,ynew);

%step 4, Finding Eigenvectors
[V,D] = eig(covariancematrix);
D=diag(D);
maxeigval=V(:,find(D==max(D)));

%step 5, Deriving the new data set
%finding the projection onto the eigenvectors

finaldata=maxeigval’*[xnew,ynew]’;
subplot(3,1,2);
stem(finaldata, ‘DisplayName’, ‘finaldata’, ‘YDataSource’, ‘finaldata’);
title(‘PCA 1D output ‘)
%we do a classification now
subplot(3,1,3);
title(‘Final Classification’)
hold on
for i=1:size(finaldata,2)
if finaldata(i)>=0
plot(x(i),y(i),’o’)
plot(x(i),y(i),’r*’)

else
plot(x(i),y(i),’o’)
plot(x(i),y(i),’g*’)
end
end

Code for Grey Scale based Segmentation:- MATLAB FOR ENGINEERS

image = imread(‘jump.jpg’); % read image

% get image dimensions: an RGB image has three planes
% reshaping puts the RGB layers next to each other generating
% a two dimensional grayscale image[height, width, planes] = size(image);
rgb = reshape(image, height, width * planes);

imagesc(rgb); % visualize RGB planes
colorbar on % display colorbar

r = image(:, :, 1); % red channel
g = image(:, :, 2); % green channel
b = image(:, :, 3); % blue channel