Congratulations 2018 Imagine Cup Championship Teams!

Published Jan 08 2019 07:03 PM 12K Views
First published on MSDN on Jul 24, 2018
Microsoft’s Imagine Cup empowers student developers to bring their original technology solutions to the global stage for the potential to change the world. Utilizing the forefront of technological resources, such as Artificial Intelligence, Mixed Reality, and Big Data, this year’s finalist teams have brought their very best to the World Finals. We are blown away by their outstanding projects, passion for innovation, and coding for good

Out of 40,000 students registered, 49 finalist teams were selected to advance to the World Finals. After two days of competition, the judges have narrowed down the top projects to compete in the final round. These three teams will present their projects in front of a trio of Championship judges in the hopes of winning the 2018 Imagine Cup.

We are thrilled to announce the three top teams who will be moving forwards the 2018 World Championships tomorrow! Which team will win it all and take home the Imagine Cup trophy? Tune in to the Championships live July 25 th at 9:00 PT to find out!
iCry2Talk , Greece, Aristotle University of Thessaloniki
iCry2Talk proposes a low-cost and non-invasive intelligent interface between the infant and the parent that translates in real time the baby’s cry and associates it with a specific physiological and psychological state, depicting the result in a text, image and voice message.
Mediated Ear , Japan, University of Tokyo
Mediated Ear is software for hearing-impaired individuals to focus on a specific speaker among a multitude of conversations. Mediated Ear can relay specific sounds in audio waveforms through deep learning.
smartARM , Canada, University of Ontario Institute of Technology
smartARM is a robotic hand prosthetic, created using Microsoft Azure Computer Vision, Machine Learning and Cloud Storage. The robotic hand uses a camera embedded in its palm to recognize objects and calculate the most appropriate grip for an object. Using Machine Learning, the more the model is used the more accurate it becomes.

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