<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bay-Fi |</title><link>https://cnardin.github.io/tags/bay-fi/</link><atom:link href="https://cnardin.github.io/tags/bay-fi/index.xml" rel="self" type="application/rss+xml"/><description>Bay-Fi</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 27 Oct 2025 00:00:00 +0000</lastBuildDate><image><url>https://cnardin.github.io/media/icon_hu_2d2b1e39e19355d7.png</url><title>Bay-Fi</title><link>https://cnardin.github.io/tags/bay-fi/</link></image><item><title>Bay-Fi</title><link>https://cnardin.github.io/projects/bayfi/</link><pubDate>Mon, 27 Oct 2025 00:00:00 +0000</pubDate><guid>https://cnardin.github.io/projects/bayfi/</guid><description>&lt;p&gt;This study introduces a novel general method, named Bayesian Frequency Identification (&lt;strong&gt;Bay-Fi&lt;/strong&gt;), which enables direct identification of the main modal frequency without requiring signal reconstruction and any prior tuning. The proposed approach employs a Bayesian framework to optimize a curve-fitting algorithm applied to highly under-sampled and randomly acquired vibration signals.&lt;/p&gt;</description></item></channel></rss>