Machine Learning algorithms proved to be very helpful in medical science in the presence of enormous amounts of data. Particularly, it analyzes medical data, such as images, genetic information, and patient records, aiding healthcare professionals in faster and more accurate diagnoses. Deep learning has gained significance in automated report generation, expediting the diagnostic process and […]

Autonomous vehicles (AVs) promise to enhance safety, reduce emissions, and improve transportation system efficiency and reliability. The growing demand for AVs is shaping the future of the automotive industry by transforming the in-vehicle experience and paving the way for large-scale implementation of autonomous driving. AV technology requires onboard intelligence relying on sensors and systems such […]

Anessa is developing software that assesses the feasibility of an anaerobic digestion facility at the earliest stages when trying to determine if a facility should be constructed. The challenge is that little information is fully known and there are a lot of factors that can affect the outcome to be positive or negative in the […]

The demand for higher bandwidth and ultra-lower latency has increased due to the rise of wireless devices. To meet these challenges, fifth generation (5G) new radio (NR) offers enhanced bandwidths with higher data rates and ultra-lower latency for real-time applications. The ultra-dense networks in 5G NR can improve the coverage. However, it can cause problem […]

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Skin diseases, like psoriasis, are a growing global health concern, especially in North America. Understanding the factors that increase the risk of these diseases are important for prevention and treatment. The introduction of targeted and biologic agents has significantly transformed the treatment of prevalent skin disease like psoriasis in recent years. However, due to their […]

In recent advancements, researchers are leveraging deep learning and machine learning for improving medical care, particularly within the veterinary field. The development of smart, wearable biosensing devices, equipped with non-invasive sensors and integrated with machine learning algorithms, facilitates real-time health monitoring. Continuous monitoring of health data through these devices offers valuable insights into adverse health […]

In this study, we are exploring how Artificial Neural Networks compare to the brain on the task of face recognition. We are using a FaceNet model trained using different loss functions and comparing them to how the brain works. We are curious to see how well these instructions make the computer’s recognition similar to how […]

Our project seeks funding through the MITACS Globalink internship to introduce a revolutionary approach to pilot training by integrating virtual flight simulation and biometric machine learning. Dr. Michael Barnett-Cowan leads this cross-disciplinary initiative, utilizing his expertise in flight simulation and motion sickness to develop a sustainable, inclusive, and cost-effective flight training system. At the core […]

La planification des horaires des médecins joue un rôle central pour assurer le bon fonctionnement des services de santé, notamment aux urgences. Une planification efficace des horaires des médecins est cruciale pour maintenir des soins optimaux aux patients, une utilisation des ressources et une efficacité organisationnelle globale. Cependant, l’une des questions les moins explorées dans […]

This project in collaboration with Oxford University focuses on improving how robots work together through coarse Collaborative Simultaneous Localization and Mapping (SLAM). SLAM technology is crucial for autonomous navigation, allowing robots to map and understand their surroundings while providing real-time localization estimates. The coarse approach we aim to develop would simplify how robots map their […]

The proposed research aims to apply the capabilities of machine learning to study molecular models of cell membranes. Cell membranes are composed of fluid lipid bilayers with a highly dynamic nature. By developing neural network models for the simulation of lipid bilayers, we seek to overcome the shortcomings of current computational methods, providing superior accuracy […]