海角社区

SeeVita: AI-Powered Contactless Realtime Vital Sign Estimation

Thi Kieu Khanh Ho, and Professor Narges Armanfard Electrical and Computer Engineering, for "SeeVita"

Executive summary:

Hypertension and hypotension are leading contributors to cardiovascular disease worldwide, yet current blood pressure (BP) monitoring methods remain uncomfortable and unsuitable for continuous use. This project develops SeeVita, an AI-driven, video-based BP monitoring system that enables real-time, contact-free, and continuous measurement. By extracting physiological signals from standard cameras, SeeVita offers a scalable, low-cost solution for individuals, healthcare providers, and wellness organizations. The project focuses on enhancing algorithm robustness, validating performance across diverse populations, and preparing the system for real-world deployment鈥攎aking BP monitoring more accessible, convenient, and effective for proactive cardiovascular care.

AquaHQL: A Smart AI-Holographic Platform for Portable, Real-Time Water Quality Monitoring

Devendra Pal, and Professor Parisa Ariya both from Department of Chemistry, Atmospheric and Oceanic Sciences, for "AquaHQL"

Executive summary:

Under new Canadian Environmental Protection Act (CEPA) 2023 registry and EU Drinking Water directives, utilities and industries must demonstrate control of these emerging contaminants, yet no field-deployable solution can identify them in real time. AquaHQL鈩�, based on 海角社区鈥檚 patented Nano-Digital In-Line Holographic Microscopy (Nano-DIHM) and DaPi鈩� AI platform, fills this gap. It is the first portable, reagent-free holographic system that classifies and quantifies individual particles in flow using 3D morphology and refractive-phase reconstruction. The device measures plastics, oils, microbial content, and metallic colloids within seconds, streaming data to a cloud dashboard that integrates seamlessly with existing SCADA and water-quality systems. This provides a new layer of 鈥減article intelligence鈥� that complements, rather than replaces, existing chemical sensors, offering faster and reagent-free analysis at field settings.

Through this fellowship, we will advance AquaHQL from TRL5 to TRL7 by miniaturizing and ruggedizing the optical module for remote, continuous operation, expanding AI classification (DaPi v2.0) for mixed-contaminant datasets, and validating the system with municipal wastewater partners and natural-water research observatories. This dual validation bridges operational (wastewater) and lab research (environmental) markets, establishing both technical and market readiness. The resulting prototype will deliver the first real-time, particle-resolved datasets linking wastewater discharge to downstream surface-water quality, creating actionable insight for utilities, regulators, and environmental researchers. The fellowship will catalyze AquaHQL鈥檚 transition from a validated laboratory innovation to a commercial spin-off platform offering hardware and cloud-based analytics subscriptions, positioning 海角社区 at the forefront of AI-enabled, sustainable water-technology solutions.

Co-Planning Assistant for Navigation of Harvesting Machines, Kuiper AutonomI

Ehsan Yousefi, and Professor Inna Sharf both Mechanical Engineering

Executive summary:

We propose to bring the AI-based co-planning assistant for navigation of timber-harvesting machines to a build鈥搈easure鈥� learn loop in the field. This co-planner falls under the shared autonomy framework for field machines---the umbrella core technology developed over the past six years. Canadian timber-harvesting industry is in dire need of innovation, and our SA technology can provide the next productivity boost to ensure its global competitiveness. Our solution co-plans the navigation tasks of the operator, and progressively co-learns to offer optimal, operator-friendly assistance. Navigation tasks and decision-making required of harvester operators are challenging, and our solution is uniquely positioned to offer productivity and safety benefits through a human-in-the-loop design. This project builds on achievements under previous Engine grants and our expanded ties with key industry partners: Groupe Remabec, Domtar, and Chantiers Chibougamau. We will start with field-data preprocessing, progressively train and lab-validate models on real-time machine data, and culminate with field trials. Our solution is game changing and we are not aware of competing solutions that provide operator-assistive technology for harvesting machines navigation capable of online adaptation to operator decisions.

Scaling-up of Manufacturable All-Solid-State Lithium Metal Battery (ASSLB) Technology

Fan Jason Yu, and Professor George Demopoulos both Mining and Materials Engineering

Executive summary:

Li-ion batteries (LIBs) are the transformative technology that has enabled the tremendous proliferation of mobile electronics, the electrification of transportation, and increasingly the stationary storage of electricity generated from renewable sources. These batteries however suffer from an inherent safety vulnerability due to the use of flammable organic solvent electrolytes. At the same time the energy density of current state LIBs is rather limited to meet the demand for more packaged power. To address these needs, the industry is working towards commercialization of solid-state lithium metal batteries (SSLB) with the advantage of Li metal anode鈥檚 high energy capacity and the safety of replacing the flammable electrolyte with ceramic and/or polymer solid analogues. The challenge lies however in the processability and interfacial resistance of ceramic electrolytes or the lack of mechanical strength of the polymeric electrolytes. The HydroMET lab has addressed these challenges by developing (patents pending) a hybrid solid-state electrolyte (HSE) which features a porous ceramic (garnet oxide) membrane infiltrated with a small amount (~5%) of conductive polymer. Our innovative technology has the potential for low cost production of size tunable garnet ceramic powders which subsequently are fabricated via standard slurry deposition and sintering processing into porous membranes promising very competitive manufacturing. The new HSE membrane has been successfully integrated with commercial LiFePO4 and high-voltage NMC811 cathodes and tested in coin cell assemblies with Li metal for over 500 cycles. With the support of the Chwang Seto Innovation Fellowship, we aim to work towards scale up of the new technology with the objective of developing and testing of a small prototype of >400 Wh/kg all-solid-state lithium metal battery (ASSLB) in a pouch cell format. Undertaking this next critical step, it will accelerate commercialization by promoting Technology Readiness from TRL4 to TRL6 paving the way for licensing opportunities and strategic partnerships in the US$500B battery market.

This program was made possible thanks to the Chwang Seto family, in honour of the late Ronald Chwang (B.Eng.'72, D. Sc.'12), a pioneering entrepreneur and venture capitalist who served on the Faculty of Engineering Advancement Board.

Development of an Automated In-Field Nutrient Ion Sampling System

Minh Tran and Professor Thomas Szkopek, both Electrical and Computer Engineering, for 鈥淚kei Systems鈥�

Executive summary:

Nutrient imbalance is a significant source of inefficiency in hydroponics and indoor farming. The common approach听to address this issue involves flushing and replacing the water growth medium, which leads to substantial waste of听both water and nutrients. Current nutrient-ion analysis techniques, such as atomic absorption spectroscopy and听conventional liquid-filled ion-selective electrodes, are costly to acquire and maintain, making automated operation听impractical. Our patented ion-selective field-effect transistor and patent-pending solid reservoir reference听electrode provide a cost-effective solution for nutrient-ion monitoring, enabling the development of an automated听nutrient balancing system. This grant will support the creation of an intervention-free sampling system for our听sensors, allowing us to conduct pilot projects with indoor and hydroponic farms across Canada. In turn, this will听elevate the technology readiness level of our sensors and bring us closer to realizing a fully automated nutrient听balancing system.

Biosensor for Detecting Specific Bacteria in Food Safety Applications

Reza Abbasi and Professor Sebastian Wachsmann-Hogiu, both Bioengineering, for 鈥淧hoela Health鈥�

Executive summary:

Ensuring food safety is a critical challenge for the food storage industry, where bacterial contamination can lead to听significant health risks, costly recalls, and reputational damage. Current solutions, such as ATP-based听bioluminescence testing, are costly and limited to detecting the presence of bacteria without identifying specific strains.
Our invention introduces a CMOS-based biosensor that not only detects bacterial contamination but also specifies听bacterial strains, addressing an unmet need in on-site food safety diagnostics. Leveraging a proprietary bioluminescent听immunoassay, our biosensor offers comparable performance to traditional luminometers at approximately 1% of the听cost. This device is portable, cost-effective, and user-friendly, providing essential strain specific information that听enables food safety professionals to make informed, targeted decisions to mitigate contamination. Initial results,听published in a high-impact journal, and recent feedback from industry experts highlight the market demand and听potential impact of our technology in revolutionizing food safety practices. Support from the Chwang Seto Innovation听Fellowship will allow us to advance our prototype to a market-ready solution, validating its performance in real-world听environments with target users in food storage and processing facilities.

This program was made possible thanks to the Chwang Seto family, in honour of the late Ronald Chwang (B.Eng.'72, D. Sc.'12), a pioneering entrepreneur and venture capitalist who served on the Faculty of Engineering Advancement Board.

Dr. Dusan Gostimirovic听and Professor Odile Liboiron-Ladouceur, both Electrical and Computer Engineering, for 鈥淧reFab AI Photonics鈥�

Executive summary:

The semiconductor industry can now integrate light on a chip, leading to higher data capacity in communications and many emerging applicationssuch as sensors, optical quantum computing, and optical neuromorphic computing. Light, however, is more susceptible to fabrication process deviations than its electronic counterpart. Our invention uses machine learning (ML) to predict and correct deviations in the design of photonic (optical) integrated circuits prior to nanofabrication, saving on cost, time, and energy. Since the publication of our paper and recent discussions with potential customers at an international conference earlier in November, it is evident that our solution addresses an invaluable need for better design tools that enable the next generation of photonics. Indeed, our technology is the first ML-based solution to correct design prior to fabrication, which will have considerable impact in the industry.听

This program was made possible thanks to the Chwang-Seto families, in honour of the late Ronald Chwang (B.Eng.'72, D. Sc.'12), a pioneering entrepreneur and venture capitalist who served on the Faculty of Engineering Advancement Board.

Dr. Marc-Antoine Campeau and Professor Corinne Hoesli, both Chemical Engineering, for 鈥淭owards the commercialization of a pro-healing bifunctional surface modification to improve endothelialization of prosthetic vascular grafts鈥�

Executive summary:

Prosthetic vascular graft of small diameter remains a challenging type of implants to use due to the high risk of thrombosis and the rapid loss of patency. Coatings have been developed and commercialized to limit these risks but they fail to fully address the current limitation of hemocompatibility, resulting in a lack of proper alternatives to autologous vein graft for bypass surgery. The proposed application aims to translate our patented coating technology to polytetrafluoroethylene, an inherently inert material extensively used in the manufacture of blood-contacting implants. Our coating consists of antibodies and biomimetic peptides which respectively enable the capture and firm adhesion of endothelial progenitor cells promoting the in situ endothelialization of the implant surface. In contrast to current solutions, this approach allows for the rebuilding of the artery lining, the endothelium, which has innate anti-thrombotic properties. Our coating technology has the potential to have broad implications for the manufacture of blood-contacting medical implants where enhanced regeneration and integration into human tissues is critical to avoid long-term complications.

This program was made possible thanks to the generosity of 海角社区 alum Pasquale Di Pierro.

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Dr. Hamed Rafezi (post-doc) and Professor Ferri Hassani for 鈥淒rill bit condition monitoring system for mining applications鈥�

Executive summary:

The proposed application aims to further develop our patent-pending approach (PCT/CA2018/051236) for tricone drill Bit Condition Monitoring System (BCMS) in surface mining. The mining industry is moving toward automation and autonomous machinery for increasing the efficiency, precision and safety in production. A successful automated blasthole drilling condition monitoring and control system is a vital step forward. Drilling and blasting are two preliminary tasks in large surface mining operations and constitute more than 15% of the total costs. Tricone bits are preferred in most rotary drilling applications for blasthole drilling in a surface mining operation. Bit wear and subsequent failure of drill in the hole create major delays in removing the detached cone(s) from the hole to avoid damage to the rock crusher equipment. Fully autonomous drilling would not be achievable without a machine-sensing system for recognizing when the drill bit is worn and requires replacing.

This program was made possible thanks to the generosity of 海角社区 alum Pasquale Di Pierro.

Winners of the 2017 Engine Innovation Fellowship Awards Competition
Presented by Professor Benoit Boulet, Director of the 海角社区 Engine

Dr. William Lepry and Professor Showan Nazhat, both Materials Engineering for their project on "Treating Sensitive Teeth and Beyond: A Multifunctional Bioactive Borate Glass" and Dr. Sajad Arabnejad, along with Professor Damiano Pasini and Dr. Michael Tanzer for their project on "The Engineering and Manufacturing of High Strength Fully Porous Biomaterials and Implants for Orthopaedic Applications".

This program is made possible thanks to the generosity of 海角社区 alumni Pasquale Di Pierro and听Cesar Cesaratto.