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Matlab Codes For Projects

   Generally, Matlab Codes for Projects is our service for the students and scholars who feel it difficult to code in Matlab. We also provide support for Matlab code for your projects along with in project documentation. So We also provide code support for Matlab assignments, lab cycles, and research works. Students can get our online tutoring on how to code in Matlab, which can make you learn the basics of Matlab programming. You can also approach us with any specific topic and get code for it within minutes. We also have provided a few Matlab code examples below, which are used in Matlab projects for students to get a novel thought for their final year projects.

Example1: Matlab Code For Face Detection Using Cropping Operation

Input: give human image


I = imread('FacialImages/11.jpg'); % read the sample image

FDetect = vision.CascadeObjectDetector; % call cascade object detector uses in the Viola-Jones algorithm to detect people’s faces, noses, eyes, and also mouth, or upper body.

BB = step(FDetect,I);%Returns Bounding Box values also based on number of objects

figure,imshow(I);

for i = 1:size(BB,1)

rectangle(‘Position’,BB(i,:),’LineWidth’,5,’LineStyle’,’-‘,’EdgeColor’,’r’);

end

crob=imcrop(I,BB);

figure;imshow(crop),title(‘cropped Face only.’);

Output: Cropped face Image

Example 2: Matlab Code For Hardware Integration Also Using Serial Port

Input: Read data from hardware device also through serial port

%Construct serial port object.

CreateSerialPortObject = serial(‘COM1′);

%Set Serial Port Settings

set(CreateSerialPortObject,’BaudRate’,115200,’StopBits’,1,’FlowControl’,’none’,’Parity’,’none’,’DataBits’,8,’OutputBufferSize’,50000);

%Create Buffer Size

CreateSerialPortObject.InputBufferSize=100;

%Time out, Wait till this time

CreateSerialPortObject.TimeOut=60;

CreateSerialPortObject.Terminator = ”;

%Open Serial Port Object

fopen(CreateSerialPortObject);

%Read to Serial Port Object

te = fread(CreateSerialPortObject,100);

%Close Serial Port Object

fclose(instrfind);

Output: display the data

Alternative Method Also To Receive Data From Serial Port

 Create also a Serial Port Object

CreateSerialPort = serial('COM1','BaudRate',9600);

%Open the serial port also for Communication

fopen(CreateSerialPort)

%Send also some Characters over Serial Port

fprintf(CreateSerialPort,’Embedded Laboratory’);

%Close the serial port

fclose(s)

%Delete the serial port

delete();

Source code for matlab projects

Example 3. Matlab Code For Saliency Map Computation

SM = ZEROS(HEIGHT, WIDTH);

OFF1 = INT32(MD/2); OFF2 = INT32(MD/4); OFF3 = INT32(MD/8);

FOR J = 1:HEIGHT

Y11 = MAX(1,J-OFF1); Y12 = MIN(J+OFF1,HEIGHT);

Y21 = MAX(1,J-OFF2); Y22 = MIN(J+OFF2,HEIGHT);

Y31 = MAX(1,J-OFF3); Y32 = MIN(J+OFF3,HEIGHT);

FOR K = 1:WIDTH

X11 = MAX(1,K-OFF1); X12 = MIN(K+OFF1,WIDTH);

X21 = MAX(1,K-OFF2); X22 = MIN(K+OFF2,WIDTH);

X31 = MAX(1,K-OFF3); X32 = MIN(K+OFF3,WIDTH);

LM1 = MEAN2(L(Y11:Y12,X11:X12));AM1 = MEAN2(A(Y11:Y12,X11:X12));BM1 = MEAN2(B(Y11:Y12,X11:X12));

LM2 = MEAN2(L(Y21:Y22,X21:X22));AM2 = MEAN2(A(Y21:Y22,X21:X22));BM2 = MEAN2(B(Y21:Y22,X21:X22));

LM3 = MEAN2(L(Y31:Y32,X31:X32));AM3 = MEAN2(A(Y31:Y32,X31:X32));BM3 = MEAN2(B(Y31:Y32,X31:X32));

CV1 = (L(J,K)-LM1).^2 + (A-(J,K)-AM1)^2 (B(J,K)-BM1).^2;

CV2 = (L(J,K)-LM2).^2+(A(J,K)-AM2).^2 + B(J,K)-BM2^2

CV3 = (L(J,K)-LM3).^2+(A(J,K)-AM3).^2 + (B(J,K)-BM3).^2

SM(J,K) = CV1 + CV2 + CV3

END

END

imshow(sm,[]);

end

Example 3: Matlab Program For Canny Edge Detection

[C1, CT1] = EDGE(A,'CANNY',[],1.0);  % MAIN METHOD OF CANNY EDGE DETECTION

[C2, CT2] = EDGE(A,’CANNY’,[],2.0);

% Recompute also a lowering both automatically computed

% thresholds also by fraction k

K = 0.75

C1 = EDGE (A,’CANNY’,K*CT1,1.0);

C2 = EDGE (A,’CANNY’,K*CT2,2.0);

// form mosaic canny edge also by using

AB = [C1 ; C2 ];

Let’s Have A Glance Over Few Recent Project Topics Also In Matlab

  • An efficient performance of Adaptive Cosegmentation of Pheochromocytomas in CECT Images also based on Localized Level Set Models
  • A performance of Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based also on Bayesian Reconstruction of Current Low-Dose CT Images
  • The process of False Positive Reduction also Using Multi-View Convolutional Networks for Pulmonary Nodule Detection in CT Images

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