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- title: "On-Device IoT-Based Predictive Maintenance Analytics Model: Comparing TinyLSTM and TinyModel from Edge Impulse"
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authors: "Irene Niyonambaza Mihigo, Marco Zennaro, Alfred Uwitonze, James Rwigema, Marcelo Rovai"
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pub: "Sensors"
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doi: "10.3390/s22145174"
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url: "https://www.mdpi.com/1424-8220/22/14/5174"
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date: "June 2022"
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thumbnails:
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- image: "/assets/images/thumbnails/UoRwanda.png"
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alt: "University of Rwanda"
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url: "https://ur.ac.rw/"
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- image: "/assets/images/thumbnails/ictp.svg"
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alt: "The Abdus Salam International Centre for Theoretical Physics (ICTP)"
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url: "https://www.ictp.it/"
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- image: "/assets/images/thumbnails/unifei.png"
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alt: "Universidade Federal de Itajubá (UNIFEI)"
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url: "https://unifei.edu.br/"
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- title: "Widening Access to Applied Machine Learning with TinyML"
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authors: "Vijay Janapa Reddi, Brian Plancher, Susan Kennedy, Laurence Moroney, Pete Warden, Anant Agarwal, Colby Banbury, Massimo Banzi, Matthew Bennett, Benjamin Brown, Sharad Chitlangia, Radhika Ghosal, Sarah Grafman, Rupert Jaeger, Srivatsan Krishnan, Maximilian Lam, Daniel Leiker, Cara Mann, Mark Mazumder, Dominic Pajak, Dhilan Ramaprasad, J. Evan Smith, Matthew Stewart, Dustin Tingley"
@@ -35,6 +74,51 @@ journal:
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url: "https://www.edx.org/"
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conference:
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- title: "How TinyML Can be Leveraged to Solve Environmental Problems: A Survey"
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authors: "Hatim Bamoumen, Anas Temouden, Nabil Benamar, Yousra Chtouki"
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pub: "IEEE International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies"
- title: "Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots"
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authors: "Sabrina M. Neuman, Brian Plancher, Bardienus Duisterhof, Srivatsan Krishnan, Colby Banbury, Mark Mazumder, Shvetank Prakash, Jason Jabbour, Aleksandra Faust, Guido C.H.E. de Croon, Vijay Janapa Reddi"
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pub: "IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)"
Copy file name to clipboardExpand all lines: _pages/workingGroups/4D/academicNetwork.md
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excerpt: "The TinyML4D working group is building a network of academic institutions, based in Developing Countries, interested in expanding access to Applied Machine Learning by establishing best practices in education. We aim to ultimately develop a community of researchers and practitioners focused on both improving access to TinyML education and enabling innovative solutions for the unique challenges faced by Developing Countries."
excerpt: "With support from the [**International Centre for Theoretical Physics (ICTP)**](https://www.ictp.it/) a first group of 20 Universities joined the network in Summer 2021 and another 20 in Summer 2022."
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excerpt2: "We have also partnered with the [Edge Impulse Academic Network](https://www.edgeimpulse.com/university) to further expand our reach by co-promoting events, workshops, seminars, and activities!"
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