A Scalable Solution for Sensing and Sorting Orein the Mineral Mining Process

This project intends to research the building of scalable, low-cost and robust alternatives andimprovements to existing systems for mineral sensing and sorting in order to achieve greaterproductivity and efficiency by means of improving speed and accuracy in the process of miningminerals from low grade rocks. The project will combine developments in embedded and streamingsystems, parallel computing and machine-learning to create a more specialized system for thisdomain. The project will be based on an existing platform of MineSense, a leading company in thisarea. This research will provide MineSense with insights into alternative hardware and softwaresystems, as well as useful research data with regards to trade-offs in light of scalability, latency andaccuracy, which should prove beneficial for design and planning for their current and future miningsystems.

Faculty Supervisor:

Alan Wagner

Student:

Partner:

MineSense Technologies Inc.

Discipline:

Computer science

Sector:

Information and Communications Technology

University:

The University of British Columbia

Program: