<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Shayan Gerami |</title><link>https://omerkurkutlu.github.io/authors/shayan-gerami/</link><atom:link href="https://omerkurkutlu.github.io/authors/shayan-gerami/index.xml" rel="self" type="application/rss+xml"/><description>Shayan Gerami</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 01 Sep 2025 00:00:00 +0000</lastBuildDate><item><title>SunSift: Solar-Powered Intelligent Sensing through Informative Sample Selection</title><link>https://omerkurkutlu.github.io/publications/sunsift-acsos-2025/</link><pubDate>Mon, 01 Sep 2025 00:00:00 +0000</pubDate><guid>https://omerkurkutlu.github.io/publications/sunsift-acsos-2025/</guid><description>&lt;h2 id="abstract"&gt;Abstract&lt;/h2&gt;
&lt;p&gt;SunSift is a lightweight framework for batteryless CNN inference on energy-harvesting IoT devices. It uses intelligent voltage-based state transitions and selective MRAM checkpointing to support intermittent execution under frequent power failures. The system is implemented using Arduino Nano 33 BLE boards and TensorFlow Lite for Microcontrollers, achieving &lt;strong&gt;71.51% accuracy&lt;/strong&gt; in identifying informative samples from CIFAR-10 for distributed federated learning. This work demonstrates a practical path toward sustainable, batteryless edge intelligence.&lt;/p&gt;</description></item></channel></rss>