We are creating a powerful tool for improvements in surgeries of the brain and spine. A new magnetic resonance imaging (MRI) machine will be used inside operating rooms so patients undergoing surgery can be imaged in the middle of the surgery for quality control. For instance, surgeons can obtain an MRI to ensure the entire tumor was removed before ending the surgery.
In order to collect MR images, special electronics are needed to create and receive signals from the patient. These electronics are called radio-frequency (RF) coils.
Rangelands store carbon, regulate water, and conserve biodiversity. This research will study the effect of cattle grazing on wet meadow rangelands and their soils. Specifically, this project will measure the effect of changes in plants on soil nematodes. Soil nematodes are important in the cycling of nutrients and carbon and help sustain soil health. We will use surveys of soil nematodes to measure the changing function of soil food webs following grazing.
We explore the creation of accurate representations of social issues in factual and fictional media; and the relative effectiveness of different media representation modes and types, to support and improve Partner Eagle Vision’s effectiveness as a media producer. Our objective in collaborating is to link the researcher and intern’s expertise in qualitative research with Eagle Vision’s media production knowledge and experience to inform our shared concerns for different forms of narratives in culture and their relation to the justice.
Funded through a series of NSERC partnership grants, Cubresa Inc. have been working with the applicants to develop technologies for pre-clinical imaging for positron emission tomography (PET) imaging and PET friendly radiofrequency (RF) coils for magnetic resonance imaging (MRI) of small animals.
In order to better understand the role of Canada’s vast forested area in our country’s carbon budget, further work is needed to monitor ‘hot spots’ of carbon activity – the boundaries between land and lake, and how these landscape positions will react to a changing climate. Further, less is known about the timing around the activation of these hot spots outside of regularly spaced traditional monitoring practices.
Graphics Processing Units (GPUs) are usually employed to quickly render images on everyday computer screens, and do so quickly and efficiently for relatively little cost compared to the use of Central Processing Units (CPUs) that make up the “brain” of the computer. Modern GPUs are able to do hundreds or thousands of simultaneous calculations; rewriting conventional computer problems in the language of GPUs offers the po-tential to dramatically decrease the computation time for complex problems such as Electromagnetic Trans-mission (EMT) simulations.
The Canadian Arctic is warming at an alarming rate. The coastal community of Pangnirtung, Nunavut has long witnessed and experienced the reality of climate change. Country food is the main resource for Panniqtuumiut all year round and practices related to hunting and fishing are key to family and community well-being. Local organizations and community members contribute to numerous academic studies and endeavours devoted to climate change.
The problem considered in this work is how to produce highly accurate and consistent land-use/land-cover (LULC) maps significantly faster than current semi?automated methods for use by Manitoba Hydro. The goal is to improve the ability to produce maps quickly and efficiently as priority needs arise. This project will use an approach for automated LULC mapping from satellite images using deep learning methods pioneered by the applicants. By classifying each pixel in a satellite image into LULC categories using neural networks, rapid and accurate LULC maps can be successfully produced.
We envision a future where it will be possible to lavish the same attention on individual plants in a large prairie crop farm as one might on those in a backyard garden. As camera sensors shrink in size, and self-driving vehicles continue to improve, such an idea is no longer the realm of science fiction. The remaining piece of the puzzle, however, is the need for a very large number of pre-identified images of crop plants and weeds with which to train a computer to recognize one from the other.