[Priberam ML Seminars] Priberam Machine Learning Lunch Seminars (T11) - 7 - "Can 5G and Machine Learning Replace the GPS?", João Gante (INESC-ID/IST)

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Rúben Cardoso

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Jun 2, 2020, 8:02:46 AM6/2/20
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Hello all,

Hope you are all safe and healthy, the 
Priberam Machine Learning Seminars will continue to take place remotely via zoom on Tuesdays at 1 p.m.

Next Tuesday, June 9th, João Gante, an INESC-ID / IST Ph.D student will present his work "Can 5G and Machine Learning Replace the GPS?at 13:00h (zoom link: https://zoom.us/j/84517076196 )

You can register for this event and keep watch on future seminars below:
Food will not be provided but feel free to eat at the same time :) Please note that the seminar is limited to 100 people and this will work on a 1st come 1st served basis. So please try to be on time if you wish to attend.

Best regards,
Rúben Cardoso

Priberam Labs
http://labs.priberam.com/

Priberam is hiring!
If you are interested in working with us please consult the available positions at priberam.com/careers. 

Image result for priberam logoPRIBERAM SEMINARS   --  Zoom 84517076196
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Priberam Machine Learning Lunch Seminar
Speaker:  João Gante (INESC-ID / IST)
Venue: https://zoom.us/j/84517076196
Date: Tuesday, June 9th, 2020
Time: 13:00 
Title:
Can 5G and Machine Learning Replace the GPS?

Abstract:
Whereas physical obstacles were mostly associated with signal attenuation in telecommunications, their presence in 5G's millimeter wave systems adds complex, non-linear phenomena, including reflections and scattering. The result is a multi-path propagation environment, shaped by the obstacles encountered during transmission, indicating a strong and highly non-linear relationship between a device's received radiation and its position. In this presentation, new ways to shape these signals will be discussed so as to estimate a mobile device’s position, including the physical intuition behind those accuracy-enhancing manipulations. To untangle the information hidden in the received signal into a mobile device position, different neural networks architectures can be employed, enabling a low-power single anchor positioning system. This positioning system can be further enhanced so as to track users, using short-term historical data and sequence learning approaches. The discussed system sets a new state-of-the-art for non-line-of-sight millimeter wave outdoor positioning accuracy, while having a much higher energy efficiency when compared to low-power GPS implementations, and thus answering the question in the title: yes, they can!
Short Bio:
João Gante is a PhD Candidate at IST, researching Machine Learning-based algorithms for positioning and tracking with 5G, supervised by Professor Leonel Sousa. João is currently an ML Engineer at nPlan, London, where Machine Learning is the key to predict the outcome of large construction projects.

Eventbrite:
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