Matlab play an important role for research scholars to effectively done their PHD thesis work. We Provide Phd Thesis Matlab Code for academic students. Image processing concepts are selected to done PHD thesis and it should be implemented using matlab simulation.

**Matlab Processes:**

- Matlab has several vectorized operations.
- Matlab graphical output is optimized for interaction.
- Matlab allows to perform numerical calculations and visualize results.
- No need for complicated and time consuming programming.
- Matlab allows users to accurately solve problems, produce graphics easily and produce code efficiently.

**Interested Phd Thesis Topics Using Matlab code:**

- Dictionary Learning.
- Sparse and Redundant Representations.
- Nonparametric Approximations.
- Adaptive Transforms.
- Biologically Inspired Models.

**Applications of Thesis Domain (**Image processing**):**

- Denoising and artifacts removal
- Medical Images like CT, MRI and ultrasound
- HD/3D mobile displays
- Web-scale data

** **

**Thesis Topics:**

- Water resource management using artificial neural networking and image processing
- Fabric Wrinkle Measurement based on image processing
- Signal and image processing for biometrics
- Image processing for identifying different stages of diabetic retinopathy
- Integration of 3D Anthropmetry in computer aided design
- Virtual colon dissection for colorectal cancer screening
- Human body modeling

**Matlab Code Optimization:**

- Nonlinear Optimization
- Quadratic Programming
- Mixed-Integer Programming
- Least Squares

**PhD Thesis Matlab Code for Signal generation and visualization:**

set(gcf,’NumberTitle’,’off’);

set(gcf, ‘Name’,’Signal Generation using Signal Processing Toolbox’);

Fs = 2000; % Sampling Frequency: 2000 Hz

t = 0:1/Fs:1; % Time 1-sec duration

f = 50; % 50 Hz signal

% Generate Known Signals

% Square Wave

sqWave = square(2*pi*f*t);

plotSig(1,sqWave,’50 Hz Square Wave’);

% Sawtooth wave

sawtoothWave = sawtooth(2*pi*f*t);

plotSig(2,sawtoothWave,’50 Hz Sawtooth Wave’);

% Sine waveform

sineWave = sin(2*pi*f*t);

plotSig(3,sineWave,’50 Hz Sine Wave’);

%Chirp Signal

chirpSig = chirp(t, 0,0.5,150);

plotSig(4,chirpSig,’Linear Chirp Signal’);

x = quadprog(H,f)

x = quadprog(H,f,A,b)

x = quadprog(H,f,A,b,Aeq,beq)

x = quadprog(H,f,A,b,Aeq,beq,lb,ub)

x = quadprog(H,f,A,b,Aeq,beq,lb,ub,x0)

x = quadprog(H,f,A,b,Aeq,beq,lb,ub,x0,options)

x = quadprog(problem)[x,fval] = quadprog(H,f,…)[x,fval,exitflag] = quadprog(H,f,…)[x,fval,exitflag,output] = quadprog(H,f,…)[x,fval,exitflag,output,lambda] = quadprog(H,f,…)

**PhD Thesis Matlab Code for Background Removal:**

input_image_path = ‘image1.jpg’;

im = double(imread(input_image_path));

[masked_image] = separate(im,1);imshow(uint8(masked_image.*im))

**PhD Thesis Matlab Code for skin color detection:**

I=imread(‘filename’);

I=double(I);[hue,s,v]=rgb2hsv(I);

cb = 0.148* I(:,:,1) – 0.291* I(:,:,2) + 0.439 * I(:,:,3) + 128;

cr = 0.439 * I(:,:,1) – 0.368 * I(:,:,2) -0.071 * I(:,:,3) + 128;[w h]=size(I(:,:,1));

for i=1:w

for j=1:h

if 140<=cr(i,j) && cr(i,j)<=165 && 140<=cb(i,j) && cb(i,j)<=195 && 0.01<=hue(i,j) && hue(i,j)<=0.1

segment(i,j)=1;

else

segment(i,j)=0;

end

end

end

% imshow(segment);

im(:,:,1)=I(:,:,1).*segment;