Matlab Task on sphereVol

Matlab Task on sphereVol

function A09Prob1_sphereVol_kang401(radius)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% ENGR 132
% Program Description
% The code computes volume of a sphere as a function of height of fluid
% The input to function call is radius
% The function does not return any variable Continue reading

Matlab Task on Computer Programming & Numerical Methods

Matlab Task on Computer Programming & Numerical Methods

A=imread(‘one.png’)
signImage=rgb2gray(A);
figure(1)
imshow(signImage)
%Detect features of first image that is read above%
signPoints=detectSURFFeatures(signImage)
figure(3)
imshow(signImage)
title(‘100 strongest features from sign image’)
hold on
%Top 100 strongest features
plot(selectStrongest(signPoints,100))

[signFeatures2, signPoints2] = extractFeatures(signImage, signPoints)

B=imread(‘two.png’)
signImage2=rgb2gray(B);
figure(4)
imshow(signImage2)
%Detect features of first image that is read above%
signPoints2=detectSURFFeatures(signImage2)
figure(5)
imshow(signImage2)
title(‘100 strongest features from sign image’)
hold on
%Top 100 strongest features
plot(selectStrongest(signPoints2,300))
[signFeatures2, signPoints2] = extractFeatures(signImage2, signPoints2)

%Punitive point matches in both the images%

picPairs=matchFeatures(signFeatures, signFeatures2);
matchedSignPoints = signPoints(picPairs(:,1),:);
matchedFindPoints = signPoints2(picPairs(:,2),:);
figure(6)
showMatchedFeatures(signImage,signImage2,matchedSignPoints, matchedFindPoints, ‘montage’)
title(‘Matched points both images’)

%Locate objects using Punitive matches%
%[tform, inlierBoxPoints, inlierScenePoints] =estimateGeometricTransform(matchedSignPoints, matchedFindPoints);
%figure;
%showMatchedFeatures(signImage, signImage2, inlierBoxPoints,inlierScenePoints, ‘montage’);
%title(‘Matched Points (Inliers Only)’);

R Programming Assignment Help

R Task on ANOVA Model

rm(list = ls())
options(warn = -1)
library(readxl)

## Reading the data from excel
Project_2_Data <- read_excel(“Stat 481 Project 2 Data.xls”)
str(Project_2_Data)

## Cleaning and attributing the dtaa
Project_2_Data$courses = as.factor(Project_2_Data$courses)
Project_2_Data$gender = as.factor(Project_2_Data$gender)
levels(Project_2_Data$gender) <- c(“Female”, “Male”)
levels(Project_2_Data$courses) <- c(“Algebra”, “Algebra&Geometry”, “Calculus”)

attach(Project_2_Data)

## Descriptives
library(ggplot2)
library(hrbrthemes)
library(dplyr)
library(tidyr)
library(viridis)
temp = aggregate(score~courses+gender, Project_2_Data, FUN = mean)

qqnorm(score)
ggplot(Project_2_Data, aes(x = score)) + geom_histogram()

summary(Project_2_Data)
p1 <- ggplot(data=Project_2_Data, aes(x=score, fill=courses)) + geom_density(adjust=1.5, alpha=.4) + theme_ipsum()

p2 <- ggplot(data=Project_2_Data, aes(x=score, fill=gender)) + geom_density(adjust=1.5, alpha=.4) + theme_ipsum()
## Model
## Test of normality and other assumptions
ks.test(score, pnorm, mean = mean(score), sd= sd(score))
bartlett.test(score~courses, data = Project_2_Data)
bartlett.test(score~gender, data = Project_2_Data)

## Linear model
model1 = anova(score ~ courses + gender, data = Project_2_Data)
model1
summary(model1)
## Post Hoc
library(DescTools)
PostHocTest(model1, method = “bonferroni”)
PostHocTest(model1, method = “hsd”)