# 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