<?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/it/tags/bay-fi/</link><atom:link href="https://cnardin.github.io/it/tags/bay-fi/index.xml" rel="self" type="application/rss+xml"/><description>Bay-Fi</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>it-it</language><lastBuildDate>Thu, 01 Jan 2026 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/it/tags/bay-fi/</link></image><item><title>Bay-Fi</title><link>https://cnardin.github.io/it/projects/bayfi/</link><pubDate>Mon, 27 Oct 2025 00:00:00 +0000</pubDate><guid>https://cnardin.github.io/it/projects/bayfi/</guid><description>&lt;p&gt;Questa è la pagina del progetto &lt;strong&gt;Bay-Fi&lt;/strong&gt;, acronimo di &lt;strong&gt;Bayesian Frequency Identification&lt;/strong&gt;. Lo studio introduce un nuovo metodo generale che permette di identificare direttamente la frequenza modale principale senza ricostruire il segnale e senza alcuna taratura preliminare. L&amp;rsquo;approccio usa un framework bayesiano per ottimizzare un algoritmo di curve fitting applicato a segnali di vibrazione fortemente sottocampionati e acquisiti in modo casuale.&lt;/p&gt;
&lt;p&gt;Il metodo è particolarmente adatto agli scenari di &lt;strong&gt;campionamento Sub-Nyquist&lt;/strong&gt;, in cui le tecniche convenzionali di elaborazione del segnale falliscono a causa dell&amp;rsquo;aliasing e di frequenze di campionamento insufficienti. Grazie all&amp;rsquo;inferenza bayesiana, Bay-Fi fornisce stime di frequenza robuste e accurate anche da misure fortemente compresse.&lt;/p&gt;
&lt;p&gt;Qui trovi maggiori informazioni sul progetto e sulle attività di ricerca in corso:&lt;/p&gt;
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&lt;/ul&gt;</description></item><item><title>Bay-Fi: Approximate Bayesian main Frequency Identification for extremely undersampled signals</title><link>https://cnardin.github.io/it/publication/articles/2026-mssp-bayfi/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://cnardin.github.io/it/publication/articles/2026-mssp-bayfi/</guid><description>
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&lt;/div&gt;</description></item><item><title>Bayesian Approach for Main Frequency Identification on Extremely Under-Sampled Signals</title><link>https://cnardin.github.io/it/publication/conferences/2025-evaces-bayfi/</link><pubDate>Wed, 02 Jul 2025 00:00:00 +0000</pubDate><guid>https://cnardin.github.io/it/publication/conferences/2025-evaces-bayfi/</guid><description>
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