<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>C on Everardo Shain</title><link>https://Everardo-shain.github.io/tags/c/</link><description>Recent content in C on Everardo Shain</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Tue, 03 Dec 2024 08:06:25 +0600</lastBuildDate><atom:link href="https://Everardo-shain.github.io/tags/c/index.xml" rel="self" type="application/rss+xml"/><item><title>Next Best View | Master's Thesis</title><link>https://Everardo-shain.github.io/posts/projects/nbv/</link><pubDate>Tue, 03 Dec 2024 08:06:25 +0600</pubDate><guid>https://Everardo-shain.github.io/posts/projects/nbv/</guid><description>&lt;h2 id="overview"&gt;Overview&lt;/h2&gt;
&lt;p&gt;This study was made with the purpose of understanding the impact of the objective function and optimization methods on the Next Best View problem, which consists in finding the next position that the sensor or camera needs to take to scan an object or scenery in its totality.&lt;/p&gt;
&lt;p&gt;A simulated 5-Degree-of-Freedom mobile robot with a mounted simulated range sensor was used on a Virtual Reality Modeling Language environment, and the space discretization was made using a voxel map. For the objective function, two main factors were included: an area factor to make sure that the image taken by the sensor provides the best possible information, and a motion factor made up of distance and energy sub-factors to reduce the resources used by the robot, making multiple experiments on a laboratory scene to determine their best arrangement on the final objective function. Global optimization tasks such as a backstepping technique to escape local minima and a dynamic change in the objective function were implemented. The retrievement of the scene was made on an iterative process, with each iteration needing an optimization process for which three different methods were tested: Nelder-Mead, an Evolution Strategy, and Simulated Annealing.&lt;/p&gt;</description></item><item><title>Poker Simulation</title><link>https://Everardo-shain.github.io/posts/projects/poker-simulation/</link><pubDate>Tue, 13 Aug 2024 08:06:25 +0600</pubDate><guid>https://Everardo-shain.github.io/posts/projects/poker-simulation/</guid><description>&lt;h2 id="overview"&gt;Overview&lt;/h2&gt;
&lt;p&gt;This project was part of the Introductory C Programming specialization from Coursera, and it was developed through the entire duration of the different courses divided into smaller sections. All the code was written in C language, with the use of advanced concepts such as arrays, pointers, debugging and memory allocation.&lt;/p&gt;
&lt;p&gt;The main task was to develop a functional poker simulator, starting with a correct card representation, building decks, implementing an extensive hand evaluation function to end with a Monte Carlo simulation system with 10000 trials for evaluating hand probabilities.&lt;/p&gt;</description></item></channel></rss>