Skip to main content

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

How can I identify COM port devices on Windows

 I am working on several projects that involve using Arduinos and other serial connected devices with a MATLAB GUI. I have a solution for identifying the devices that opens all the COM ports one at a time and queries them. By checking the responses I can identify which devices are connected to which COM ports. This only works if the devices have unique responses to the query sent. I am currently using *IDN? as my query because it seems to be fairly standard. Of course, to interact with the Arduinos, I have to make sure they respond to this query in a predictable way.

 
 
My problem is that this process is fairly slow and a little error prone. In Windows Device Manager, each Port is identified by name and COM number. Is there any way to get these names inside of MATLAB? I want to be able to ID the ports without having to open connections to them all.



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.

It will only work on Windows (7 for sure but maybe not others). I use DOS commands to query the registry in two places to identify which COM ports are connected and then check the USB section of the CurrentContrlSet to match up friendly names.
 
My code my not be completely optimized but it runs in about .1 seconds so it suits my purposes.
 
The result is a cell array with friendly names and COM number pairs for each connected USB-serial device that has a friendly name.
 
Code posted below:
 
Skey = 'HKEY_LOCAL_MACHINE\HARDWARE\DEVICEMAP\SERIALCOMM';
% Find connected serial devices and clean up the output
[~, list] = dos(['REG QUERY ' Skey]);
list = strread(list,'%s','delimiter',' ');
coms = 0;
for i = 1:numel(list)
  if strcmp(list{i}(1:3),'COM')
      if ~iscell(coms)
          coms = list(i);
      else
          coms{end+1} = list{i};
      end
  end
end



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