Researching strategic and business opportunities: using AI to foster animal (pet) health

Empawerpet (empawerpet.com) is pioneering the field of canine mental health diagnostics through their veterinary clinic-based biomarker testing platform. While they have established a foundation in biochemical analysis, they recognize the need to expand their diagnostic capabilities through a new AI non-invasive, accessible methods. The company is therefore interested in expanding their current clinic-based biomarker testing […]

Read More
PRISM Development/Enhancements

PRISM is a proprietary LAMP stack-based platform developed by Levis Tech, a software development consultancy headquartered in Saskatoon. Serving as the backbone for approximately 90% of the company’s projects, PRISM represents over 15 years of accumulated intellectual property and technological advancements. This project focuses on enhancing key components of PRISM to ensure continued innovation and […]

Read More
Assessment of surgical tool inpainting techniques in epilepsy neurosurgery

Inpainting refers to the process of removing a foreground object from an image and seamlessly replacing it with consistent and contextually relevant background pixels. In the context of epilepsy neurosurgery, training videos are recorded using a microscope-mounted camera to document surgical procedures. However, surgical instruments frequently obstruct the camera’s line of sight, partially occluding brain […]

Read More
Estimation de la pose de la caméra physique en production virtuelle

Cette recherche explore l’intégration des espaces physique et virtuel en production cinématographique. L’espace physique regroupe acteurs, décors, éclairages et caméras, tandis que l’espace virtuel crée des environnements numériques immersifs. Pour un rendu visuel réaliste, les mouvements de la caméra physique doivent être précisément suivis et reproduits dans l’espace virtuel. Actuellement, ce suivi repose sur des […]

Read More
artolog – Prototype

Le portail d’archives collectives artolog.ca a comme objectif de permettre aux artistes d’obtenir une visibilité équitable. Son mandat est de réduire la perte du patrimoine culturel Canadien. L’objectif est de finaliser le prototype afin que le portail soit sécurisé, accessible, rapide, facile à utiliser et compatible avec d’autres plateformes. S’y faisant, nous pourrons promouvoir le […]

Read More
Metaverse plugin

Development of a system (template, plugin, API) to integrate cloud based services such as GIS, IoT and interactive gaming streams into a browser based user interface.

Read More
Monitoring Housing Diversity Project – Sustainability Scholar

The Edmonton City Plan was approved by Council in 2020 and sets ambitious diversified housing and land use targets to avoid premature fragmentation of agricultural lands while accommodating another one million residents. The Monitoring & Analysis Team, within the Urban Growth Unit lead the annual Growth Monitoring Program, the Geodemographic Projection Program, and Urban Growth […]

Read More
Enhancing Digital Product Strategy and Workflow Automation for Scalable Innovation

This project aims to enhance SolvedAF’s digital product strategy by developing a workflow automation system that streamlines client onboarding, project tracking, and internal collaboration. By integrating automation tools and optimizing business process workflows, SolvedAF will improve efficiency, reduce manual bottlenecks, and enhance the user experience for both internal teams and clients. The project will contribute […]

Read More
Data collection and cross-domain representation models for trajectory analysis

Due to the vast amount of available tracking sensors in recent years, high-frequency and high-volume streams of data are generated every day. Such tracking sensors include but are not limited to vessel, airplane or vehicle tracking data, drones, smartwatches and smart bands as well as cameras and earth observation sensors. Despite the overabundance of data […]

Read More
Improving Differentially Private Deep Learning Models

Machine Learning (ML) models are known to leak information about the data they were trained on, enabling membership and reconstruction attacks [7]. Such privacy risks damage trust in ML, and hinder the broad adoption of model co-training, through Federated Learning or cloud-based co-training. This is especially true in sensitive domains that could significantly benefit from […]

Read More