function [outputPicture] = smoothMe (inputPicture)
G=fspecial('gaussian', [10 10],3); %Create Gaussian mask
outputPicture = conv2(inputPicture,G,'same'); %Convolve the grayscale image with Gaussian filters
end
Ex1 :

function [H_outputPicture, V_outputPicture] = sobelOperators(inputPicture)
%Convolve the grayscale image with horizontal Sobel filters
H_outputPicture = conv2(inputPicture, [1 2 1; 0 0 0; -1 -2 -1])
%Convolve the grayscale image with vertical Sobel filters
V_outputPicture = conv2(inputPicture, [1 0 -1; 2 0 -2; 1 0 -1])
end
Ex2 :

function[ match ] = recogniseFace( Unknown_face, Train_face )
%Training
[U,S,D]=svds(Train_Face, 30) %First 30 Eigen vectors
1TFaces= U'*Train_Face %low dimension Training Face with 20 dimension
%Prediction
1UnknownFace = U'*Unknown_Face
EuclideanDist=sqrt(sum((1TFaces-repmat(1UnknownFace,[1 34])).^2))
[match_score, match]=min(EuclideanDist);
end
Ex3 :

function [ ranks, frequency ] = querystring( query, Tf, uniqueCell )
queryCell = strsplit(query)';
number_of_documents=size(Tf,2);
for i = 1 : size(queryCell,1)
 frequency(i,1:number_of_documents)= Tf(find(strcmp(uniqueCell,queryCell(i))),:);
end
frequency=sum(frequency);
[frequency, ranks]=sort(frequency,'descend');
end
Ex4

function [tfidfM] = tfidf(Tf)
%Input Tf is the term frequency matrix (importance of a document to a term)
%N is the total number of documents in the corpus
N = size(Tf, 2); %number of columns
n = sum (Tf > 0, 2); %n(t) is the number of the documents in the corpus that contains term t
idf = log(N./(1+n)); %Inverse Document Frequency
%Term frequency inverse document frequency Matrix
tfidfM = Tf.*repmat(idf,[1 size(Tf, 2)]);
end
Ex5
function [ s ] = kalmanfilter( s )
%Kalman filter store state in variable s
%s is a structure with field
%s.x = state variables
%s.F = transformation matrix
%s.P = covariance of variables (how to initialize? with R)
%s.R = covariance of sensor (Radar) / sampling error
%s.z = new observation
%preduction
s.x = s.F * s.x
s.P = s.F * s.P * s.F';
%update
K = s.P * inv(s.P + s.R)
s.x = s.x + K *(s.z - s.x);
s.P = s.P - K * s.P;
end