Projets novateurs réalisés

An ensemble machine learning framework for streamflow data reconstruction

This project will develop a new framework to reconstruct missing streamflow data using advanced machine learning techniques. Reliable streamflow records are essential for flood forecasting, drought monitoring, and water resource planning, but many stations have missing or incomplete data. The proposed approach will combine traditional statistical methods with modern single-learner and ensemble machine learning models to estimate missing values more accurately across North American river basins. The collaboration between the University of Saskatchewan and UNAM will strengthen expertise in statistical hydrology and deliver practical tools that contribute to efforts toward improving water management and water security for both countries.

Understanding the effects of Early-Life Adversity on Microglia in the Developing Brain

Early-life adversity (ELA) is one of the most consistent and robust predictors of poor mental health outcomes across the lifespan. These are developmental periods characterized by high neural plasticity, in which the brain is shaped by experience. Thus, ELA can provoke detrimental, long-lasting changes in the brain and it is critical to understand the mechanisms of how ELA changes the developing brain that remain unclear. Thus, we propose to use the well-established Limited Bedding and Nesting (LBN) model, to perform bulk-RNA seq in the hypothalamus; a stress-sensitive brain region. Previous literature showed that ELA-caused developmental changes in the brain are mediated by microglia, the immune cells of the brain. Therefore, we will isolate microglia from the paraventricular nucleus of the hypothalamus of P8 mice from ELA or control conditions, to look for changes in gene expression that could provide potential therapeutic targets. This collaboration will capitalize in Dr. Ciernia’s expertise in RNA-seq and bioinformatics, as well as Dr. Bolton’s expertise in ELA and brain development to produce data that will be used for future grant writing to bring funding for future research.

System-of-Systems Solution with Quantum Simulation for Monitoring, Reporting, Validation, and Verification of GHGs/Carbon Cycle in Biodiversity Ecosystems for SEPLS to Strengthen Sustainable Frameworks Development

Title :System-of-Systems Solution with Quantum Simulation for Monitoring, Reporting, Validation, and Verification of GHGs/Carbon Cycle in Biodiversity Ecosystems for SEPLS to Strengthen Sustainable

Overview:
This project aims to develop a System-of-Systems solution using quantum simulation to monitor, report, validate, and verify greenhouse gases (CO2, CH4, N2O) and the carbon cycle in biodiversity ecosystems, particularly in SEPLS (Socio-Ecological Production Landscapes and Seascapes). By integrating satellite data, field sampling, AI, and quantum-based spectral analysis, the project will create accurate regional carbon-sequestration models, maps of soil and water carbon, and identify carbon-sink hotspots. Participating institutions will benefit by gaining access to cutting-edge tools and data for ecosystem carbon monitoring, enhancing research capacity, and strengthening collaborations for sustainable ecosystem management and climate action.

Academic supervisor: Cynthia Goh and Yen-Hsun Su
Intern:Ke-Hsin Chen

Mitigation des impacts opérationnels des erreurs de contextualisation dans une scène routière

E-Smart commercialise une solution innovante qui offre aux gestionnaires un moyen automatisé d’assurer la conformité aux limites de vitesse, sans qu’ils aient à surveiller les camionneurs. Une interface placée à la commande d’accélération contrôle activement la vitesse du camion en temps réel, en se basant sur la cartographie des limites de vitesse. Toutefois, lorsque la localisation GPS est imprécise ou que les vitesses sont absentes de la carte ou erronées, le système produit des erreurs. Ceci entraine un manque de confiance envers l’outil. En effet, les systèmes de vision artificielle basés sur la détection d’objets, la classification et la segmentation sémantique monoculaires, ne permettent pas de comprendre ce qui se passe dans une scène, comme les positions relatives d’un objet par rapport à l’autre ou leur orientation respective [1] [2]. Ces limitations découlent des défis inhérents à l’estimation de la profondeur et à la compréhension de la scène avec des images monoculaires [3]. Ces systèmes sont encore moins capables de comprendre la sémantique (signification des relations entre le sujet et les objets) d’une scène. Donc pour améliorer le produit et offrir du coup l’ensemble des fonctionnalités de vidéo-télématique classique aux gestionnaires de flotte, E-Smart souhaite développer une nouvelle génération de produit avec une caméra et des réseaux de neurones profonds. Ce système devra faire la détection d’objets et de conditions sur le réseau routier (ex. piétons et cyclistes, neige), afin de bonifier les informations disponibles, en plus d’accroître la précision globale de la solution.

Agrilo and Agrilo-VR: Field Validation and Real-World Deployment of AI-Driven Soil and Nutrient Sensing Systems in South Africa

Healthy soil is crucial for global food security, yet farmers face declining fertility, nutrient depletion, and rising fertilizer costs. These challenges are particularly pressing for smallholder farmers in South Africa, where timely soil information is limited. This project connects researchers from Canada and South Africa to advance Agrilo, a real-time soil nutrient sensing system, and Agrilo-VR, an immersive virtual reality platform for environmental education.
Agrilo’s portable, low-cost sensors measure essential soil indicators—including nitrate, phosphate, potassium, pH, boron, sulfur, CEC, NOM, magnesium, manganese, iron, and calcium—with near-laboratory accuracy. These data help farmers optimize fertilizer application, reduce nutrient runoff, and improve crop performance. Field validation will take place across South Africa’s diverse agricultural landscapes, allowing for assessment under real farming conditions.
A student intern will travel to South Africa to support field validation, collect soil datasets, and assist with the deployment and testing of Agrilo-VR in schools, training centres, and conservation programs. Agrilo-VR will transform real soil and ecological data into interactive learning environments that make environmental science accessible and engaging.
By integrating sensing technology, AI-driven analytics, and VR-based education, this project promotes climate-smart agriculture, strengthens international collaboration, and expands access to sustainable soil management tools across both partner regions.

Research and Development to Enhance Data Collection Functionality, Usability, and Outreach For An Educational Survey Platform

Xello is a leading college and career readiness platform designed to engage K-12 students in career exploration, academic planning, and skills development. It provides educators with data-driven insights to support student success, helping school districts make informed decisions.
Xello is looking to enhance its survey platform to improve their means of data collection, enhance the way the data can be visualized and interpreted by the educator, as well as supporting high concurrency access and the exporting of large datasets in a fast and reliable manner without performance issues. Xello believes that their data collection framework can be expanded as well as the efficiency and seamlessness of their exporting tool to further improve their educational platform. This is the case as efficient data collection is an important tool for helping educators better tailor their current and future curriculum to their students which would lead to an optimal experience for both the student and educator. Xello aims to collaborate with the WIMTACH team to develop these expanded survey tools such as student activity and data integration, survey reporting upgrades, and alumni surveys.
All in all, the development of the improved survey tools/data collection framework would benefit both the students and educators of their platform by allowing the educators to visualize and seamlessly export important insights provided by the students to better improve their curriculum, and it would allow the students to receive more optimal curriculum that is tailored to them based on the insights provided.

Quantum network sensor for speed tracking

The advance of sensors can have important impacts to our lives, ranging from developing safer self-driving cars to more precise medical imaging. Quantum properties like entanglement can dramatically improve the sensitivity of sensors. This project will study a new type of quantum sensing that aims to track speed. A new design will be considered that involves a spatially distributed quantum sensor network to detect the signal generated by a traveling object. The efficacy of this unexplored scheme will be verified through obtaining the optimal quantum control and understanding the role of entanglement. This Globalink internship will be a pilot project of this direction of research. A PhD student from Simon Fraser University will conduct 3-month internship at Chuo University to model the sensing scenario, and analyze the performance of many-body quantum sensors. The intern will learn analytical and numerical techniques that are valuable assets for quantum industries in Canada. The results will also lay the foundation of quantum speed sensing, facilitate collaboration and joint grant application between Profs. Lau and Matsuzaki.

G-BSIEM-PQC: Hybrid Consensus Architecture for Quantum-Resistant Security Information and Event Management (SIEM)

The G-BSIEM-PQC project is a pioneering cybersecurity initiative designed to fortify enterprise Security Information and Event Management (SIEM) systems against the existential threats posed by the post-quantum era. As quantum computing capabilities advance, the traditional cryptographic algorithms currently protecting critical infrastructure are becoming vulnerable to “harvest now, decrypt later” attacks. This research addresses this urgent vulnerability by developing a hybrid, quantum-resistant SIEM architecture that integrates the newly standardized NIST Post-Quantum Cryptography (PQC) algorithms – specifically ML-KEM-512 for secure key encapsulation and ML-DSA-44 for tamper-proof digital signatures. These advanced cryptographic primitives are synthesized with robust distributed consensus protocols, utilizing Practical Byzantine Fault Tolerance (PBFT) to ensure the integrity of critical event logs and RAFT for the high-availability replication of media streams. Developed using the memory-safe Rust programming language to minimize software vulnerabilities, the G-BSIEM-PQC system provides a scalable, standards-compliant blueprint for securing Canadian enterprise networks and data sovereignty in a quantum-enabled future.

Enhancing Sawmill Residue Utilization Through Mobile Biochar Production

This project will demonstrate on-site biochar production from sawmill residues at Patterson Sawmill (Hay River, NWT) using Saskatchewan Polytechnic’s mobile kiln. The mobile system avoids high transport and capital costs associated with stationary facilities and can process diverse residue types directly on-site, offering a practical solution for sustainable management of waste biomass at northern sawmills.
Patterson Sawmill in Hay River, Northwest Territories, produces substantial volumes of sawmill residues that have limited local disposal or value-added options. Transporting these materials is costly, and open-burning remains a common practice, resulting in lost revenue potential and avoidable emissions. Reclaimit Ltd., a northern reclamation and resource-management consulting firm, is working with the mill to identify practical ways to convert these residues into higher-value products while improving environmental outcomes.
This project will demonstrate that mobile biochar kilns are a cost-effective, flexible solution for increasing the sustainability of remote sawmill operations and diversifying their income. Unlike stationary biochar systems – which require high capital investment and consistent feedstock supply – mobile kilns can be deployed directly at the location of the material and can process diverse residue types. However, to support adoption of the technology, operational data, performance benchmarks, and hands-on experience are needed to ensure effective and efficient implementation for best results.

Agrilo and Agrilo-VR: Field Validation and Real-World Deployment of AI-Driven Soil and Nutrient Sensing Systems in Kenya

Healthy soil is crucial for global food security, yet farmers face declining fertility, nutrient depletion, and rising fertilizer costs. These challenges are particularly pressing for smallholder farmers in Kenya, where timely soil information is limited. This project connects researchers from Canada and Kenya to advance Agrilo, a real-time soil nutrient sensing system, and Agrilo-VR, an immersive virtual reality platform for environmental education.

Agrilo’s portable, low-cost sensors measure essential soil indicators—including nitrate, phosphate, potassium, pH, boron, sulfur, CEC, NOM, magnesium, manganese, iron, and calcium—with near-laboratory accuracy. These data help farmers optimize fertilizer application, reduce nutrient runoff, and improve crop performance. Field validation will take place across Kenya’s diverse agricultural landscapes, allowing for assessment under real farming conditions.
A student intern will travel to Kenya to support field validation, collect soil datasets, and assist with the deployment and testing of Agrilo-VR in schools, training centres, and conservation programs. Agrilo-VR will transform real soil and ecological data into interactive learning environments that make environmental science accessible and engaging.

By integrating sensing technology, AI-driven analytics, and VR-based education, this project promotes climate-smart agriculture, strengthens international collaboration, and expands access to sustainable soil management tools across both partner regions.

L2M – Expert Validation of IPPM Now and Assembly of a Smart Trap Prototype

This project will help Insect Track Solutions improve and validate IPPM Now, a digital tool designed to make insect monitoring easier and more accurate for farmers, agronomists, and researchers. Today, insect data is collected in many different ways, making it hard to compare results or make fast decisions. The project will work with expert users to test the app, improve its accuracy, and ensure the information it collects is useful for real field conditions. At the same time, the team will assemble the first smart trap prototype that automatically detects insects and sends data to the platform. By the end of the project, Insect Track Solutions will have a validated app, a functional smart-trap prototype, and a basic data flow between the device and the platform, important steps that bring the company much closer to commercial readiness and support better integrated pest management across the Prairies.

Development of a Novel Dicalcium Phosphate Cement-Based Construct [Brushite-Shell/Monetite-Core] for Controlled Degradation and Drug Release in Craniofacial Defect Repair Surgery

Craniofacial and maxillofacial reconstruction is often limited by insufficient autologous bone and the shortcomings of current graft alternatives, which can degrade unevenly, bond poorly, or fail to support vascular growth. These issues are especially critical in cases of trauma, congenital defects, or cancer-related bone loss, where reliable, bioresorbable grafts are essential. This project evaluates and adapts Brushite-Shell/Monetite-Core calcium phosphate scaffolds for craniofacial, maxillofacial, and reconstructive surgery. Brushite (CaHPO4·2H2O) transforms into a more stable phase, slowing resorption, while monetite (CaHPO4) degrades faster, supporting early bone regeneration. Combining them enables controlled resorption and improved integration, and the monetite core can also be modified to deliver natural growth factors that promote bone and vessel formation. The aim of this project is to determine where this hybrid material provides the greatest clinical benefit and what design adaptations are needed for surgical use. The collaboration between Dalhousie University and the University of Illinois Health connects biomaterial innovation with clinical translation; Dalhousie focusing on material design, fabrication, and testing, and Illinois advancing understanding of clinical needs and real-world applications. This partnership will strengthen research collaboration, promote knowledge exchange, and support the development of safer, more effective bone grafts to improve outcomes in craniofacial and maxillofacial reconstruction.