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Urban planners, architects and filmmakers could all benefit from new technology developed by a research team led by À¶Ý®ÊÓƵ Engineering.

The automated system can generate 3D computer models of buildings or entire cities using only 2D aerial photographs, a much cheaper, faster way than relying on specially trained 3D artists and computer graphics programs.

Artificial intelligence (AI) technology developed at À¶Ý®ÊÓƵ Engineering gives baseball scouts a powerful new tool to accurately analyze pitcher performance and biomechanics using low-resolution video.

The system, known as PitcherNet, is the product of a three-year partnership between researchers at the Vision and Image Processing (VIP) Lab and the Baltimore Orioles of Major League Baseball (MLB).

Two professors at À¶Ý®ÊÓƵ Engineering were awarded funding today to advance quantum communications, sensing and detection.

Dr. Eihab Abdel-Rahman, from systems design engineering, and Dr. Mustafa Yavuz, from mechanical and mechatronics engineering, were among three projects campus-wide to receive more than $1.3 million from a collaboration with the Natural Sciences and Engineering Research Council of Canada (NSERC) and the United Kingdom Research and Innovation (UKRI) programs.

A À¶Ý®ÊÓƵ Engineering team from the Vision and Image Processing (VIP) Lab is working with the Inuit-driven Arctic Eider Society (AES) to use deep learning to detect hazardous ice areas.  

Led by Neil Brubacher (BASc ‘21 and MASc ‘24, systems design engineering), the team partnered with AES to add data about ice conditions to an app used by locals in Nunavut.