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		<title>Home on Brandon Castellano</title>
		<link>https://www.bcastell.com/</link>
		<description>Recent content in Home on Brandon Castellano</description>
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				<title>Triclysm</title>
				<link>https://www.bcastell.com/projects/triclysm/</link>
				<pubDate>Thu, 28 Sep 2017 12:57:00 -0500</pubDate>
				<guid>https://www.bcastell.com/projects/triclysm/</guid>
				<description>&lt;h2 id=&#34;triclysm&#34;&gt;Triclysm&lt;/h2&gt;&#xA;&lt;hr&gt;&#xA;&lt;h3 id=&#34;project-overview&#34;&gt;Project Overview&lt;/h3&gt;&#xA;&lt;p&gt;See the Triclysm project on Github at the following URL: &lt;a href=&#34;https://github.com/Triclysm&#34;&gt;https://github.com/Triclysm&lt;/a&gt;&lt;/p&gt;&#xA;&lt;h3 id=&#34;triclysm-previewer&#34;&gt;Triclysm Previewer&lt;/h3&gt;&#xA;&lt;p&gt;&lt;a href=&#34;https://github.com/Triclysm/Previewer-Legacy&#34;&gt;Triclysm Previewer (link)&lt;/a&gt; is a cross-platform application for developing, previewing, and streaming (via Ethernet or Wi-Fi) to a physical LED cube/voxel-based display.  Cross-platform, written in C++, using SDL/OpenGL as a back-end, with Lua as the primary scripting language for animations.&lt;/p&gt;&#xA;&lt;p&gt;In the future, a rewrite is planned, utilizing Python for a majority of the code, and allowing it to be used for animation development as well.  However, the existing code-base has been successfully tested on an 8x8x8 monochrome LED cube (giving 1024 total levels of brightness at the maximum refresh rate), as well as a 4x4x4 RGB LED cube, with a total of 4096 individual colour combinations.&lt;/p&gt;</description>
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				<title>Scene Detection with Python and OpenCV, Part 2</title>
				<link>https://www.bcastell.com/posts/scene-detection-tutorial-part-2/</link>
				<pubDate>Wed, 06 Sep 2017 01:14:08 +0200</pubDate>
				<guid>https://www.bcastell.com/posts/scene-detection-tutorial-part-2/</guid>
				<description>&lt;h1 id=&#34;part-2-adaptive-threshold-detection&#34;&gt;Part 2: Adaptive Threshold Detection&lt;/h1&gt;&#xA;&lt;p&gt;This post is currently being migrated from the old location, &lt;a href=&#34;https://web.archive.org/web/20160316124732/http://www.bcastell.com/tech-articles/pyscenedetect-tutorial-part-2/&#34;&gt;click here to view the old one on The Wayback Machine&lt;/a&gt;.&lt;/p&gt;</description>
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				<title>Scene Detection with Python and OpenCV, Part 1</title>
				<link>https://www.bcastell.com/posts/scene-detection-tutorial-part-1/</link>
				<pubDate>Sat, 19 Oct 2013 00:00:00 +0000</pubDate>
				<guid>https://www.bcastell.com/posts/scene-detection-tutorial-part-1/</guid>
				<description>&lt;h2 id=&#34;part-1-thresholdfade-to-black-detection&#34;&gt;Part 1: Threshold/Fade-to-Black Detection&lt;/h2&gt;&#xA;&lt;p&gt;This tutorial is currently being migrated from the old location.  In the meantime, you can view the cached version of the previous, complete version on The Wayback Machine &lt;a href=&#34;https://web.archive.org/web/20160316225649/http://www.bcastell.com/tech-articles/pyscenedetect-tutorial-part-1/&#34;&gt;by clicking here&lt;/a&gt;.  Thank you for your patience during this time.&lt;/p&gt;&#xA;&lt;hr&gt;&#xA;&lt;p&gt;In the first part of this three-part tutorial, we will write a Python program, using the OpenCV library, to perform threshold-based scene detection, to determine the exact frames where scene transitions (fade ins/outs to/from black in this case) occur. In the following tutorials, we will optimize our scene detection algorithm, and use the output to create a scene list with proper timecodes (&lt;a href=&#34;https://www.bcastell.com/posts/scene-detection-tutorial-part-2/&#34;&gt;Part 2&lt;/a&gt;) so a video can be split automatically into scenes. Lastly, we will cover how to detect content-based scene changes (in a future Part 3, coming soon), and combine this with the concepts from the previous tutorials to create a robust scene detection program.&lt;/p&gt;</description>
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				<title>How does word length affect the performance and operation of a CPU?</title>
				<link>https://www.bcastell.com/posts/word-length-vs-performance/</link>
				<pubDate>Wed, 17 Oct 2012 00:00:00 +0000</pubDate>
				<guid>https://www.bcastell.com/posts/word-length-vs-performance/</guid>
				<description>&lt;p&gt;About a year ago, I came across a question on Super User titled &amp;ldquo;How much faster is a 64-bit CPU than a 32-bit CPU?&amp;rdquo;, which was promptly closed and deleted since it&amp;rsquo;s a very open ended question. However, the author (a software developer) referred to benchmarks regarding system performance in 32-bit versus 64-bit. The purpose of this blog post is to investigate how the performance of a computer is affected, as a function of the word length.&lt;/p&gt;</description>
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				<title>Fixing the XAnalogTV Screensaver</title>
				<link>https://www.bcastell.com/posts/fixing-xanalogtv/</link>
				<pubDate>Mon, 10 Sep 2012 00:00:00 +0000</pubDate>
				<guid>https://www.bcastell.com/posts/fixing-xanalogtv/</guid>
				<description>&lt;p&gt;This post is currently being migrated from the old location, &lt;a href=&#34;https://web.archive.org/web/20160307233103/http://www.bcastell.com/tech-articles/fixing-the-xanalogtv-screensaver/&#34;&gt;click here to view the old one on The Wayback Machine&lt;/a&gt;.&lt;/p&gt;</description>
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				<title>Biopsy Bot</title>
				<link>https://www.bcastell.com/projects/biopsybot/</link>
				<pubDate>Mon, 05 Dec 2011 00:00:00 +0000</pubDate>
				<guid>https://www.bcastell.com/projects/biopsybot/</guid>
				<description>&lt;img src=&#34;https://www.bcastell.com/img/projects/biopsybot/header.jpg&#34; alt=&#34;Biopsy Bot in Action&#34; width=&#34;100%&#34; /&gt;&#xA;&lt;h2 id=&#34;general-overview&#34;&gt;General Overview&lt;/h2&gt;&#xA;&lt;p&gt;A remote-control robot capable of gathering a biological sample in unknown areas, Biopsy Bot was developed as a final project for the 4th year Mechatronic System Design course offered at UWO. The purpose of the project was to develop a robot capable of remotely navigating (with the aid of an on-board wireless camera) an area of unknown, rocky terrain, in order to gather and return a biological sample from a mysterious blob (made of Jello). Furthermore, the robot is required to overcome several obstacles, while having a limited size.&lt;/p&gt;</description>
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			<item>
				<title>About</title>
				<link>https://www.bcastell.com/about/</link>
				<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
				<guid>https://www.bcastell.com/about/</guid>
				<description>&lt;div class=&#34;card&#34;&gt;&#xA;&lt;p&gt;Hi there!  This is the personal website of Brandon Castellano (sometimes known as Breakthrough).  Here, you can find my blog, technical articles, and various projects (software &amp;amp; hardware) I&amp;rsquo;m working on.&lt;/p&gt;&#xA;&lt;p&gt;I&amp;rsquo;m a software engineer with experience developing for real-time and embedded systems.  The languages I have the most experience with are C/C++, Rust, and Python, but I&amp;rsquo;m also proficient in many others. This includes Go, Dart, and C#, and I&amp;rsquo;m always interested in learning new languages (Zig). I&amp;rsquo;m comfortable with Windows/Linux/OSX, as well as developing on specialized hardware and new platforms.&lt;/p&gt;</description>
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				<title>Contact</title>
				<link>https://www.bcastell.com/contact/</link>
				<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
				<guid>https://www.bcastell.com/contact/</guid>
				<description>&lt;h2 id=&#34;social&#34;&gt;Social&lt;/h2&gt;&#xA;&lt;ul&gt;&#xA;&lt;li&gt;&lt;a href=&#34;https://github.com/Breakthrough&#34;&gt;Github: Breakthrough&lt;/a&gt; &lt;img alt=&#34;GitHub followers&#34; src=&#34;https://img.shields.io/github/followers/Breakthrough?style=flat&#34;&gt;&lt;/li&gt;&#xA;&lt;li&gt;&lt;a href=&#34;https://superuser.com/users/6887/breakthrough&#34;&gt;Super User: Breakthrough&lt;/a&gt; &lt;img alt=&#34;Stack Exchange reputation&#34; src=&#34;https://img.shields.io/stackexchange/superuser/r/6887?style=flat&#34;&gt;&lt;/li&gt;&#xA;&lt;li&gt;&lt;a href=&#34;https://stackoverflow.com/users/159447/breakthrough&#34;&gt;Stack Overflow: Breakthrough&lt;/a&gt; &lt;img alt=&#34;Stack Exchange reputation&#34; src=&#34;https://img.shields.io/stackexchange/stackoverflow/r/159447?style=flat&#34;&gt;&lt;/li&gt;&#xA;&lt;li&gt;&lt;a href=&#34;https://www.youtube.com/@breakthrough248&#34;&gt;Youtube: breakthrough248&lt;/a&gt;&lt;/li&gt;&#xA;&lt;li&gt;&lt;a href=&#34;https://en.wikipedia.org/wiki/User:Bcastell&#34;&gt;Wikipedia: bcastell&lt;/a&gt;&lt;/li&gt;&#xA;&lt;/ul&gt;&#xA;&lt;h2 id=&#34;direct&#34;&gt;Direct&lt;/h2&gt;&#xA;&lt;p&gt;For professional inquiries, use the contact form below. You can also contact me through any of my other official accounts/services listed on &lt;a href=&#34;https://www.bcastell.com/about/#across-the-web-social&#34;&gt;the About page&lt;/a&gt;.&lt;/p&gt;&#xA;&lt;p&gt;This is especially preferred in regards to any bug reports or feature requests, which should be submitted as an issue on the relevant project&amp;rsquo;s Github Issues page.&lt;/p&gt;</description>
			</item>
			<item>
				<title>Projects</title>
				<link>https://www.bcastell.com/projects/</link>
				<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
				<guid>https://www.bcastell.com/projects/</guid>
				<description>&lt;h2 id=&#34;active&#34;&gt;Active&lt;/h2&gt;&#xA;&lt;div class=&#34;card&#34;&gt;&#xA;&lt;h3&gt;&lt;a href=&#34;https://www.scenedetect.com&#34;&gt;&lt;picture&gt;&lt;source media=&#34;(prefers-color-scheme: dark)&#34; srcset=&#34;https://www.bcastell.com/img/projects/pyscenedetect-dark.png&#34;&gt;&lt;img src=&#34;https://www.bcastell.com/img/projects/pyscenedetect.png&#34; alt=&#34;PySceneDetect&#34; width=&#34;240&#34;&gt;&lt;/picture&gt;&lt;/a&gt;&lt;/h3&gt;&#xA;&lt;p&gt;&lt;a href=&#34;https://www.scenedetect.com&#34;&gt;PySceneDetect (www.scenedetect.com)&lt;/a&gt; is a scene/shot detection tool and Python library. It is free and open source software, used in both industry and academia. Example applications include preprocessing video data for training AI models, developing plugins for non-linear video editors, and research analysis of film. PySceneDetect was built to be extensible, and easily integrates into most workflows.&lt;/p&gt;&#xA;&lt;p&gt;&lt;a href=&#34;https://github.com/Breakthrough/PySceneDetect&#34;&gt;&lt;img alt=&#34;GitHub Repo stars&#34; src=&#34;https://img.shields.io/github/stars/Breakthrough/PySceneDetect?style=flat&#34;&gt;&lt;/a&gt;&#xA;&lt;a href=&#34;https://pypi.org/project/scenedetect/&#34;&gt;&lt;img alt=&#34;PyPI - Downloads&#34; src=&#34;https://img.shields.io/pepy/dt/scenedetect?style=flat&amp;label=downloads&#34;&gt;&lt;/a&gt;&#xA;&lt;a href=&#34;https://discord.gg/H83HbJngk7&#34;&gt;&lt;img alt=&#34;Discord&#34; src=&#34;https://img.shields.io/discord/800190909605871626?style=flat&#34;&gt;&lt;/a&gt;&lt;/p&gt;&#xA;&lt;/div&gt;&#xA;&lt;div class=&#34;card&#34;&gt;&#xA;&lt;h3&gt;&lt;a href=&#34;https://www.dvr-scan.com&#34;&gt;&lt;picture&gt;&lt;source media=&#34;(prefers-color-scheme: dark)&#34; srcset=&#34;https://www.bcastell.com/img/projects/dvr-scan-dark.png&#34;&gt;&lt;img src=&#34;https://www.bcastell.com/img/projects/dvr-scan.png&#34; alt=&#34;DVR-Scan&#34; width=&#34;240&#34;&gt;&lt;/picture&gt;&lt;/a&gt;&lt;/h3&gt;&#xA;&lt;p&gt;&lt;a href=&#34;https://www.dvr-scan.com&#34;&gt;DVR-Scan (www.dvr-scan.com)&lt;/a&gt; is a tool that automatically detects motion events in video files (e.g. security camera footage). It extracts each event into a separate video clip for further analysis. DVR-Scan is free and open-source software written in Python, and was built to be extendable and hackable.&lt;/p&gt;</description>
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