ECE Capstone Faculty Award Recipients

UBC ECE’s 2020 spring cohort’s perseverance and persistence has paid off, and they’ve seen their Capstone projects to completion. Each team overcame many challenges to provide timely deliverables spanning a wide array of ECE-related subjects. Over 230 students formed 48 teams that leveraged four years worth of learning in order to design solutions to challenges proposed by industry and community partners.

ECE is proud to present this year’s Capstone Faculty Award Winners:

  • Propbot (Divya Budihal, Jack Guo, Nancy Hong, Zhaosheng Li, Hannah Sawiuk)
  • Project Skynet (Peter Deutsch, Arthur Hsueh, Ardell Wilson, Muchen He, Meng Wang)
  • Procedural Generation Tool (Ian McCall, Matthew Berends, Mitch Duffield, Mathew MacDougall, Simong Song)
  • Digital Health & Wellness: Video Fall Detection using Deep Learning (Mohamed Hamdan, Alessandro Narciso, Abdul Moiz, Winnie Gong, Kirsten Kwan)

Congratulations to all Capstone students on your hard work over this past year.


Propbot, sponsored by the UBC Radio Science Lab

Divya Budihal, Jack Guo, Nancy Hong, Zhaosheng Li, Hannah Sawiuk

Propbot aims to provide researchers with key information to design robust and transformative communication systems—and it does so completely autonomously. This team of fourth-year students created a robot architecture capable of traversing the UBC campus and completing large-scale data collection. The restrictions of COVID-19 posed a serious obstacle to the Propbot developers; to continue testing, they constructed a completely virtual environment to simulate Propbot and ensure its functionality. The team’s development of Propbot is only the first phase in a multi-year project that will shape the future of communication systems at UBC.


Project Skynet, sponsored by the UBC ECE System-on-Chip (SoC) Lab

Peter Deutsch, Arthur Hsueh, Ardell Wilson, Muchen He, Meng Wang

Generic, off-the-shelf component architectures available in CPUs and GPUs are not optimized for the ever-expanding field of machine learning. This team focused on showcasing the promise of tailor-made hardware being developed by Dr. Mieszko Li and team at the UBC SoC Lab. One common method of achieving this custom performance is through the use of field-programmable gate arrays (FPGAs), a kind of reconfigurable digital circuit, allowing for rapid iteration of hardware designs.

The team mounted an FPGA and the necessary circuitry on a drone equipped with a camera and CPU. The final project is capable of performing machine learning—quite literally—on the fly. The drone performs object-detection using YOLOv2, a popular open-source machine learning algorithm. The team faced countless obstacles due to the COVID crisis, such as trouble ordering parts or having to build each subcomponent separately in respect of social distancing regulations. However, they persevered and ended up with not only a fantastic final product, but also a feeling of preparedness for the unforeseen challenges in a career of engineering.


Procedural Generation Tool, sponsored by Blackbird Interactive

Ian McCall, Matthew Berends, Mitch Duffield, Mathew MacDougall, Simong Song

While the current pandemic may have forced UBC students and staff indoors, these students were already developing new ways of creating procedurally-generated virtual environments for us to explore from home. “Procedural generation” refers to the use of algorithms to create entire landscapes using various noise patterns and simulation. This team created a tool which eases the process of designing and fine-tuning those very algorithms, allowing their client, Blackbird Interactive, to more rapidly construct the worlds they envision. This, in turn, will reduce costs and provide an effectively-infinite number of high-quality environments for the studio’s fans to explore.

The software-based solution was developed in C++ and comes equipped with an editor to allow developers to preview their results. The final result can be exported and works with a variety of engines. It even ships with 33 types of terrain generation functions, right out of the box.


Digital Health & Wellness: Video Fall Detection using Deep Learning, sponsored by TELUS

Mohamed Hamdan, Alessandro Narciso, Abdul Moiz, Winnie Gong, Kirsten Kwan

Remote health monitoring is of tremendous value to the field of healthcare, improving quality-of-life for patients and greatly reducing costs in the healthcare industry. This team looked to tackle fall detection, which is of great value for the elderly and people with disabilities. Additionally, fall detection can be a life-saving signal in the event of a heart attack or stroke. Leveraging the latest advancements in machine learning technology, the team created their own labelled data set and trained a deep learning based neural network. The final model detects falls with over 90% accuracy from the video feed.

Though COVID-19 restrictions prevented in-person meetings, the team was able to do a live-demo at their last in-person meeting, as they had completed their project ahead of schedule. Their impressive accomplishment will surely support the healthcare system in preventing lives from being lost.


All projects completed in this year’s Capstone course were exemplary and showcased the grit and character of UBC engineers, while also giving back to UBC’s local community and industry. Congratulations to all who participated, and a special congratulations to this year’s award winners.

Though COVID-19 restrictions prevented in-person meetings, the team was able to do a live-demo at their last in-person meeting, as they had completed their project ahead of schedule. Their impressive accomplishment will surely support the healthcare system in preventing lives from being lost.