<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Calibration on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/calibration/</link><description>Recent content in Calibration on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sun, 12 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/calibration/index.xml" rel="self" type="application/rss+xml"/><item><title>Data Handling, Storage, and Calibration Strategies</title><link>https://ai-blog.noorshomelab.dev/quadrf-phased-array-sdr-2026-07/data-handling-storage-calibration/</link><pubDate>Sun, 12 Jul 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/quadrf-phased-array-sdr-2026-07/data-handling-storage-calibration/</guid><description>&lt;p&gt;Managing the deluge of raw radio frequency (RF) data generated by a multi-element phased array, like our hypothetical QuadRF system, is a significant engineering challenge. Without robust data handling, efficient storage, and rigorous calibration, even the most sophisticated hardware will fail to deliver accurate beamforming or precise direction finding.&lt;/p&gt;
&lt;p&gt;This chapter delves into the internal strategies a high-performance SDR system employs to acquire, process, store, and, critically, calibrate its RF data streams. We&amp;rsquo;ll explore the roles of the FPGA and Raspberry Pi 5 in this pipeline, the implications of high data rates, and the non-negotiable importance of maintaining phase and amplitude coherence across all array elements. A solid grasp of these principles is essential for anyone looking to design, operate, or troubleshoot advanced SDR platforms.&lt;/p&gt;</description></item></channel></rss>