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| <!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport content="width=device-width,initial-scale=1,viewport-fit=cover"><link rel=stylesheet href="/style.min.6aded9aabec22e872a6f2b12c8ad60b2a6c803fd8401499d4c134899fade4af6.css" integrity="sha256-at7Zqr7CLocqbysSyK1gsqbIA/2EAUmdTBNImfreSvY=" crossorigin=anonymous><script defer type=text/javascript src=https://sensorlab.github.io/scripts/app.min.93058f41bd1dbb13445944582228d7c47003915c378d0ffa6daef4f14b5e04d7.js integrity="sha256-kwWPQb0duxNEWURYIijXxHADkVw3jQ/6ba708UteBNc="></script><meta name=generator content="Hugo 0.117.0"><meta name=author content="SensorLab"><meta name=keywords content="bilateral,graph neural networks,LiDAR,reinforcement learning,Japan,Slovenia,Shinkuma,sensor fusion"><meta name=description content="The MISA (Multi-lidar Intelligence for Smart City Applications) project aims to strengthen cooperation between the research group at Jozef Stefan Institute (JSI) in Slovenia and Shinkuma Lab at Shibaura Institute of Technology (SIT) in Tokyo, Japan. The scientific cooperation will focus on optimizing LiDAR (Light Detection and Ranging) sensors using machine learning, with JSI contributing expertise in time series analysis and SIT leveraging its experience with smart city applications. The collaboration will address two key challenges: enhancing LiDAR sensor fusion accuracy through deep reinforcement learning and mitigating sparsity in LiDAR data using Graph Neural Networks."><meta name=robots content="noindex,nofollow"><link rel=canonical href=https://sensorlab.github.io/projects/misa/><link rel=alternate hreflang=en href=https://sensorlab.github.io/projects/misa/><link rel=icon type=image/png href=https://sensorlab.github.io/images/favicon.png><meta property="og:title" content="MISA: Multi-lidar Intelligence for Smart City Applications"><meta property="og:description" content="The MISA (Multi-lidar Intelligence for Smart City Applications) project aims to strengthen cooperation between the research group at Jozef Stefan Institute (JSI) in Slovenia and Shinkuma Lab at Shibaura Institute of Technology (SIT) in Tokyo, Japan. The scientific cooperation will focus on optimizing LiDAR (Light Detection and Ranging) sensors using machine learning, with JSI contributing expertise in time series analysis and SIT leveraging its experience with smart city applications. The collaboration will address two key challenges: enhancing LiDAR sensor fusion accuracy through deep reinforcement learning and mitigating sparsity in LiDAR data using Graph Neural Networks."><meta property="og:type" content="article"><meta property="og:url" content="https://sensorlab.github.io/projects/misa/"><meta property="article:section" content="projects"><meta name=twitter:card content="summary"><meta name=twitter:title content="MISA: Multi-lidar Intelligence for Smart City Applications"><meta name=twitter:description content="The MISA (Multi-lidar Intelligence for Smart City Applications) project aims to strengthen cooperation between the research group at Jozef Stefan Institute (JSI) in Slovenia and Shinkuma Lab at Shibaura Institute of Technology (SIT) in Tokyo, Japan. The scientific cooperation will focus on optimizing LiDAR (Light Detection and Ranging) sensors using machine learning, with JSI contributing expertise in time series analysis and SIT leveraging its experience with smart city applications. The collaboration will address two key challenges: enhancing LiDAR sensor fusion accuracy through deep reinforcement learning and mitigating sparsity in LiDAR data using Graph Neural Networks."><title>MISA: Multi-lidar Intelligence for Smart City Applications — SensorLab — Jozef Stefan Institute</title></head><body><header class="navbar navbar-expand-md"><div class=container><a class=navbar-brand href=https://sensorlab.github.io/><img src=https://sensorlab.github.io/images/sensorlab-white.min.svg alt="SensorLab logo" class="d-inline-block align-top me-2" height=42></a> | ||
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| <span class=navbar-toggler-icon></span></button><nav class="collapse navbar-collapse" id=navbarToggler><ul class="navbar-nav ms-0 ms-md-auto ps-4"><li class=nav-item><a class="nav-link active" href=https://sensorlab.github.io/projects><span>Projects</span></a></li><li class=nav-item><a class=nav-link href=https://sensorlab.github.io/results><span>Results</span></a></li><li class=nav-item><a class=nav-link href=https://sensorlab.github.io/publications><span>Publications</span></a></li><li class=nav-item><a class=nav-link href=https://sensorlab.github.io/people><span>People</span></a></li><li class=nav-item><a class=nav-link href=https://sensorlab.github.io/opportunities><span>Join Us</span></a></li><li class=nav-item><a class=nav-link href=https://sensorlab.github.io/about><span>About</span></a></li></ul></nav></div></header><main class="flex-fill container post my-4" aria-role=main><aside class=my-4></aside><article class=mt-4><header class=mb-4><div><img src=https://sensorlab.github.io/projects/misa/aris_logo_hub08622e5c39b8cb3af485e5245357ede_10856_200x0_resize_q75_box.jpg alt="MISA: Multi-lidar Intelligence for Smart City Applications logo" class="me-3 mb-2" height=200 width=200 style=max-width:min(200px,100vw)></div><div><h1>MISA: Multi-lidar Intelligence for Smart City Applications</h1><p><span>Duration: Apr 2024 — Apr 2026</span></p></div></header><section class=my-4><p>The MISA (Multi-lidar Intelligence for Smart City Applications) project aims to strengthen cooperation between the research group at Jozef Stefan Institute (JSI) in Slovenia and Shinkuma Lab at Shibaura Institute of Technology (SIT) in Tokyo, Japan. The scientific cooperation will focus on optimizing LiDAR (Light Detection and Ranging) sensors using machine learning, with JSI contributing expertise in time series analysis and SIT leveraging its experience with smart city applications. The collaboration will address two key challenges: enhancing LiDAR sensor fusion accuracy through deep reinforcement learning and mitigating sparsity in LiDAR data using Graph Neural Networks.</p><h2 id=funding>Funding</h2><p>Τhe MISA bilateral project receives funding from the Slovenian Research and Inovation Agency (ARIS) under Grant Agreement No. BI-JP/24-26-001.</p></section></article></main><footer class="container d-flex flex-wrap justify-content-between align-items-center py-3 my-4 border-top"><div class="col-md-6 d-flex align-items-center"><a href=https://sensorlab.github.io/ class="mb-3 me-2 mb-md-0 text-body-secondary text-decoration-none lh-1"><img src=https://sensorlab.github.io/images/sensorlab-color.min.svg style=height:2.5rem></a> | ||
| <span class=text-body-secondary>© 2014 ‐ 2024 SensorLab, Jozef Stefan Institute</span></div><ul class="nav col-md-6 justify-content-center justify-content-xs-right list-unstyled d-flex flex-wrap"><li class=ms-3><a class=text-body-secondary target=_blank href=https://github.com/sensorlab>GitHub</a></li><li class=ms-3><a class=text-body-secondary target=_blank href=https://twitter.com/CommSysJSI>Twitter</a></li><li class=ms-3><a class=text-body-secondary target=_blank href=https://www.researchgate.net/institution/Joef_Stefan_Institute/department/Komunikacijski_sistemi>ResearchGate</a></li><li class=ms-3><a class=text-body-secondary target=_blank href=https://e6.ijs.si/>Department's site</a></li></ul></footer><script async src="https://www.googletagmanager.com/gtag/js?id=G-KQGSFFY1XV"></script> | ||
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| <!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport content="width=device-width,initial-scale=1,viewport-fit=cover"><link rel=stylesheet href="/style.min.6aded9aabec22e872a6f2b12c8ad60b2a6c803fd8401499d4c134899fade4af6.css" integrity="sha256-at7Zqr7CLocqbysSyK1gsqbIA/2EAUmdTBNImfreSvY=" crossorigin=anonymous><script defer type=text/javascript src=https://sensorlab.github.io/scripts/app.min.93058f41bd1dbb13445944582228d7c47003915c378d0ffa6daef4f14b5e04d7.js integrity="sha256-kwWPQb0duxNEWURYIijXxHADkVw3jQ/6ba708UteBNc="></script><meta name=generator content="Hugo 0.117.0"><meta name=author content="SensorLab"><meta name=robots content="noindex,nofollow"><link rel=canonical href=https://sensorlab.github.io/tags/bilateral/><link rel=alternate hreflang=en href=https://sensorlab.github.io/tags/bilateral/><link rel=alternate type=application/rss+xml href=https://sensorlab.github.io/tags/bilateral/index.xml title="SensorLab — Jozef Stefan Institute"><link rel=icon type=image/png href=https://sensorlab.github.io/images/favicon.png><meta property="og:title" content="bilateral"><meta property="og:description" content><meta property="og:type" content="website"><meta property="og:url" content="https://sensorlab.github.io/tags/bilateral/"><meta name=twitter:card content="summary"><meta name=twitter:title content="bilateral"><meta name=twitter:description content><title>bilateral — SensorLab — Jozef Stefan Institute</title></head><body><header class="navbar navbar-expand-md"><div class=container><a class=navbar-brand href=https://sensorlab.github.io/><img src=https://sensorlab.github.io/images/sensorlab-white.min.svg alt="SensorLab logo" class="d-inline-block align-top me-2" height=42></a> | ||
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| <span class=navbar-toggler-icon></span></button><nav class="collapse navbar-collapse" id=navbarToggler><ul class="navbar-nav ms-0 ms-md-auto ps-4"><li class=nav-item><a class=nav-link href=https://sensorlab.github.io/projects><span>Projects</span></a></li><li class=nav-item><a class=nav-link href=https://sensorlab.github.io/results><span>Results</span></a></li><li class=nav-item><a class=nav-link href=https://sensorlab.github.io/publications><span>Publications</span></a></li><li class=nav-item><a class=nav-link href=https://sensorlab.github.io/people><span>People</span></a></li><li class=nav-item><a class=nav-link href=https://sensorlab.github.io/opportunities><span>Join Us</span></a></li><li class=nav-item><a class=nav-link href=https://sensorlab.github.io/about><span>About</span></a></li></ul></nav></div></header><main class="flex-fill container" aria-role=main><aside class=mt-4></aside><article><header><h1>bilateral</h1></header><section></section><section class="list-group my-5"><a href=https://sensorlab.github.io/projects/misa/ class="list-group-item list-group-item-action flex-column align-items-start"><header class="d-flex w-100 justify-content-between"><h5 class=mb-1>MISA: Multi-lidar Intelligence for Smart City Applications</h5><small><time datetime=0001-01-01T00:00:00Z>Monday, January 1, 1</time></small></header><p class=mb-1>The MISA (Multi-lidar Intelligence for Smart City Applications) project aims to strengthen cooperation between the research group at Jozef Stefan Institute (JSI) in Slovenia and Shinkuma Lab at Shibaura Institute of Technology (SIT) in Tokyo, Japan. The scientific cooperation will focus on optimizing LiDAR (Light Detection and Ranging) sensors using machine learning, with JSI contributing expertise in time series analysis and SIT leveraging its experience with smart city applications. The collaboration will address two key challenges: enhancing LiDAR sensor fusion accuracy through deep reinforcement learning and mitigating sparsity in LiDAR data using Graph Neural Networks.</p><small><time datetime=0001-01-01T00:00:00Z>Monday, January 1, 1</time></small></a></section></article><nav class="d-flex d-content justify-content-center"></nav></main><footer class="container d-flex flex-wrap justify-content-between align-items-center py-3 my-4 border-top"><div class="col-md-6 d-flex align-items-center"><a href=https://sensorlab.github.io/ class="mb-3 me-2 mb-md-0 text-body-secondary text-decoration-none lh-1"><img src=https://sensorlab.github.io/images/sensorlab-color.min.svg style=height:2.5rem></a> | ||
| <span class=text-body-secondary>© 2014 ‐ 2024 SensorLab, Jozef Stefan Institute</span></div><ul class="nav col-md-6 justify-content-center justify-content-xs-right list-unstyled d-flex flex-wrap"><li class=ms-3><a class=text-body-secondary target=_blank href=https://github.com/sensorlab>GitHub</a></li><li class=ms-3><a class=text-body-secondary target=_blank href=https://twitter.com/CommSysJSI>Twitter</a></li><li class=ms-3><a class=text-body-secondary target=_blank href=https://www.researchgate.net/institution/Joef_Stefan_Institute/department/Komunikacijski_sistemi>ResearchGate</a></li><li class=ms-3><a class=text-body-secondary target=_blank href=https://e6.ijs.si/>Department's site</a></li></ul></footer><script async src="https://www.googletagmanager.com/gtag/js?id=G-KQGSFFY1XV"></script> | ||
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| <?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>bilateral on SensorLab — Jozef Stefan Institute</title><link>https://sensorlab.github.io/tags/bilateral/</link><description>Recent content in bilateral on SensorLab — Jozef Stefan Institute</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>SensorLab</copyright><atom:link href="https://sensorlab.github.io/tags/bilateral/index.xml" rel="self" type="application/rss+xml"/><item><title>MISA: Multi-lidar Intelligence for Smart City Applications</title><link>https://sensorlab.github.io/projects/misa/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://sensorlab.github.io/projects/misa/</guid><description>The MISA (Multi-lidar Intelligence for Smart City Applications) project aims to strengthen cooperation between the research group at Jozef Stefan Institute (JSI) in Slovenia and Shinkuma Lab at Shibaura Institute of Technology (SIT) in Tokyo, Japan. The scientific cooperation will focus on optimizing LiDAR (Light Detection and Ranging) sensors using machine learning, with JSI contributing expertise in time series analysis and SIT leveraging its experience with smart city applications. The collaboration will address two key challenges: enhancing LiDAR sensor fusion accuracy through deep reinforcement learning and mitigating sparsity in LiDAR data using Graph Neural Networks.</description></item></channel></rss> |
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