Skip to main content

Stretch the dynamic range of the given 8-bit grayscale image using MATL...

Why do my UTM WGS84 calculations not match what MATLAB has calculated?

 I have longitude and latitude data and I have converted them to UTM using WGS 84 standards. When I compare these to the UTM values generated from my lat and long they do not match. Default values for my conversion are from

 

 <http://www.cellspark.com/UTM.html>

For example:

test=defaultm('utm');

utmzone=35;

origin=(utmzone-31).*6+3;

test.origin=[0 origin 0];

test=defaultm(test);

[eastutm2,northutm2]=mfwdtran(test,44.3536,28.4981);

%default values taken from manual lat -> UTM conversion

diffnorth2=4912239-northutm2

diffeast2=619393.56-eastutm2

 


NOTE:-

Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.

This difference occurs because MATLAB’s default reference ellipsoid is "International", not "WGS 84"
Although people often use UTM with WGS 84, UTM was originally specified to work the various ellipsoids in different parts of the world, and the toolbox honors this specification.
 
The following code is accurate to within a few centimeters.
 
mUtm=defaultm('utm');

mUtm.zone = '35T';

mUtm.geoid = almanac('earth','wgs84','meters');

mUtm=defaultm(mUtm);

[eastutm2,northutm2]=mfwdtran(mUtm,44.3536,28.4981);

%default values taken from manual lat -> UTM conversion

diffnorth2=4912239-northutm2

diffeast2=619393.56-eastutm2

% Perform inverse mapping to verify lat and lon

[lat, lon] = minvtran(mUtm, eastutm2,northutm2)

Note that starting in MATLAB 7.14 (R2012a), it is recommended to use the wgs84Ellipsoid function instead of almanac . To do so, replace the line containing the almanac function with the following:


SEE COMPLETE ANSWER CLICK THE LINK

Comments

Popular posts from this blog

https://journals.worldnomads.com/scholarships/story/70330/Worldwide/Dat-shares-his-photos-from-Bhutan https://www.blogger.com/comment.g?blogID=441349916452722960&postID=9118208214656837886&page=2&token=1554200958385 https://todaysinspiration.blogspot.com/2016/08/lp-have-look-at-this-this-is-from.html?showComment=1554201056566#c578424769512920148 https://behaviorpsych.blogspot.com/p/goal-bank.html?showComment=1554201200695 https://billlumaye.blogspot.com/2012/10/tagg-romney-drops-by-bill-show.html?showComment=1550657710334#c7928008051819098612 http://blog.phdays.com/2014/07/review-of-waf-bypass-tasks.html?showComment=1554201301305#c6351671948289526101 http://www.readyshelby.org/blog/gifts-of-preparedness/#comment_form http://www.hanabilkova.svet-stranek.cz/nakup/ http://www.23hq.com/shailendrasingh/photo/21681053 http://blogs.stlawu.edu/jbpcultureandmedia/2013/11/18/blog-entry-10-guns-as-free-speech/comment-page-1443/#comment-198345 https://journals.worldnomads.com

USING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR DETECTING SPAM AND NON-SPAM EMAILS

    ABSTRACT We know the increasing volume of unwanted volume of emails as spam. As per statistical analysis 40% of all messages are spam which about 15.4 billion email for every day and that cost web clients about $355 million every year. Spammers to use a few dubious techniques to defeat the filtering strategies like utilizing irregular sender addresses or potentially add irregular characters to the start or the finish of the message subject line. A particular calculation is at that point used to take in the order rules from these email messages. Machine learning has been contemplated and there are loads of calculations can be used in email filtering. To classify these mails as spam and non-spam mails implementation of machine learning algorithm  such as KNN, SVM, Bayesian classification  and ANN  to develop better filtering tool.   Contents ABSTRACT 2 1. INTRODUCTION 4 1.1 Objective : 5 2. Literature Review 5 2.1. Existing Machine learning technique. 6 2.2 Existing

Why are Fourier series important? Are there any real life applications of Fourier series?

A  Fourier series  is a way of representing a periodic function as a (possibly infinite) sum of sine and cosine functions. It is analogous to a Taylor series, which represents functions as possibly infinite sums of monomial terms. A sawtooth wave represented by a successively larger sum of trigonometric terms. For functions that are not periodic, the Fourier series is replaced by the Fourier transform. For functions of two variables that are periodic in both variables, the trigonometric basis in the Fourier series is replaced by the spherical harmonics. The Fourier series, as well as its generalizations, are essential throughout the physical sciences since the trigonometric functions are eigenfunctions of the Laplacian, which appears in many physical equations. Real-life applications: Signal Processing . It may be the best application of Fourier analysis. Approximation Theory . We use Fourier series to write a function as a trigonometric polynomial. Control Theory . The F