<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>IoT |</title><link>https://www.adityapaliwal.net/tags/iot/</link><atom:link href="https://www.adityapaliwal.net/tags/iot/index.xml" rel="self" type="application/rss+xml"/><description>IoT</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 15 Feb 2021 00:00:00 +0000</lastBuildDate><image><url>https://www.adityapaliwal.net/media/icon_hu_982c5d63a71b2961.png</url><title>IoT</title><link>https://www.adityapaliwal.net/tags/iot/</link></image><item><title>BLE-based Indoor Positioning System</title><link>https://www.adityapaliwal.net/projects/master-thesis/</link><pubDate>Mon, 15 Feb 2021 00:00:00 +0000</pubDate><guid>https://www.adityapaliwal.net/projects/master-thesis/</guid><description>&lt;p>Master thesis project at KU Leuven in collaboration with NXP Semiconductors.&lt;/p>
&lt;p>Compared different Machine Learning algorithms for efficient Bluetooth Low Energy (BLE) based indoor positioning. The system uses Raspberry Pi as signal scanners and NXH3670 for signal advertising.&lt;/p>
&lt;p>Key achievements:&lt;/p>
&lt;ul>
&lt;li>Implemented both supervised and semi-supervised regression approaches&lt;/li>
&lt;li>Achieved accurate location prediction using RSSI values&lt;/li>
&lt;li>Semi-supervised approach showed lower RMSE&lt;/li>
&lt;/ul></description></item></channel></rss>