Innovative Projects Realized

Development of a Quantum Software Engineering Bootcamp

The Quantum Algorithms Institute (QAI) brings together industry, academia, and the government of British Columbia and launches the first Quantum Software engineering bootcamp. The goal is strengthening and growing the quantum computing ecosystem in British Columbia and Canada.
A major pain point for quantum computing startups is to onboard new researchers and train them on software engineering best practices in order to deliver enterprise class software. On the other hand, lack of short certificate in applied quantum software engineering is making difficult for students in the field or career switchers to build certified quantum related projects portfolios.
This bootcamp will allow these startups to offload their R&D teams and focus on scalable enterprise class software deliveries and will give advantage to students part of the local BC ecosystem when competing for quantum computing related jobs.

The potential of frass as a microbiome-based soil amendment

Industrial-scale production of protein from black soldier fly larvae (BSFL) has emerged as an efficient and sustainable alternative to wild harvesting or large animal farming. The principal waste product, called frass, has plant growth-enhancing properties exceeding those explained from primary nutrient (e.g., N,P,K) profiles, but how does frass confer these growth enhancing properties upon plants? We hypothesize that viable bacteria persist in the dried granular product and that some of these viable bacteria can colonize soils and, in turn the roots of crop plants where they can form beneficial symbiotic associations. In Part 1 of this research, we propose to use using DNA sequencing techniques to test the effect of larval diet on the microbial community in the frass. In Part 2 we will experimentally test whether we can introduce bacteria into the larval feedstock to further improve frass’ plant growth enhancing properties.

Analyse de la performance sportive à l’aide de l’intelligence artificielle

Les différentes données liés à la performance sportive représente un enjeu important dans le monde du sport. Sports Ai se spécialise dans l’analyse de la performance des équipes sportive en temps réel. La solution proposée est composée de plusieurs algorithmes de vision par ordinateur (intelligence artificielle), de serveurs pour le traitement de données ainsi que d’une application client.

Ce projet aura donc pour objectif d’élaborer un processus de prise de données/statistiques en temps réel sur des compétitions sportives (soccer) en plus d’étudier différents processus de traitement de données et de visualisations de données afin de faciliter l’étude de l’analyse de la performance sportive en temps réel. Le défi principal de l’analyse de la performance sportive en temps réel réside dans la capacité à offrir des données importantes pour notre clientèle et qu’elle puissent être en mesure de saisir leurs essences rapidement. Ayant donc designer une application qui répond à ce défi en collaboration avec l’école nationale de design depuis les derniers mois, les stagiaires auront la tâche de suivre ce plan et d’offrir a notre clientèle une experience utilisateur innovante.

Information Management Project

The project involves the development of a human resources information management system using Microsoft’s Office 365 including the Power Platform, Share Point and Teams. This will replace an aging information management system and will benefit the partner non-profit organization greatly by modernizing technology systems, improving work processes, and creating efficiencies HR information management.

PaceZero Summer Analysts

Sustainable Finance, Net-Zero, Transitionary Finance

Design and Demonstration of deformable micro-mirrors – illumiSonics

We will develop adaptive mirrors that will allow Photo-Acoustic Remote Sensing (PARS®) microscopes to autofocus, thereby allowing them to obtain three-dimensional, depth-resolved, images.

Système qui permet le contrôle actif la vitesse d’un véhicule dans des zones de construction

La problématique que le projet veut résoudre est la réduction de la vitesse sur le réseau routier et la sécurité de tous les usagers. 30% des accidents fatals sont dû à la vitesse et 82% arrivent sur les routes secondaires. Un véhicule équipé d’un limiteur de vitesse actif à la capacité de réduire significativement les accidents liés à la vitesse (https://etsc.eu/briefing-intelligent-speed-assistance-isa/ ).

L’objectif du projet est de pouvoir commercialiser une nouvelle génération du produit ESMART qui pourrait déterminer la vitesse maximale sécuritaire d’un véhicule en utilisant des réseaux de neurones profonds et des données de cartographie pour reconnaître l’environnement d’un véhicule. Nous visons dans une première phase à détecter des scénarios tels que des zones de construction. Les limites de vitesse des zones de construction ne sont typiquement pas disponibles dans une cartographie.

L’approche d’ESMART est différente du véhicule autonome puisqu’à court terme, nous ne cherchons pas à remplacer le chauffeur mais lui fournir une approche sécuritaire pour contrôler la vitesse de son véhicule.

Realistic image compositions with deep learning priors

Image composition is an operation that incorporates an object extracted from a source image to incorporate it inside a target image. This simple operation has a lot of practical applications in the advertisement, entertainment,
and movie industries. However, it is tedious for an artist to achieve realistic image compositions. Indeed, it requires different lighting, shadows, or objects present in the target image will change the target object’s appearance. One
of these appearance changes is new contact shadows between the target object and the target environment. This project aims to automate their computation using recent machine learning techniques and achieve a new level of
realism in image composition

GRID (Geo-Registry Integrated Datachain)

The objectives for the project are to formalize the value proposition and business case for a blockchain enabled platform for real-estate information and transactions called GRID (Geo-Registry Integrated Data chain) (a patent-pending technology platform which was developed by Arrowhead Development Company Ltd.). This research aims to: i) investigate aspects related to GRID’s potential financial model and various revenue streams; ii) explore GRID’s network fit within the broader property ecosystem and the emerging Canadian blockchain ecosystem, and iii) quantify the inherent industry risk and estimate the value of mitigating this risk through GRID. The results from this research would be used by Arrowhead to develop a business plan that could be executed.

Aging In Place Medical Device Certification 1 of 2

Home Health Systems has built a sensor and software for Aging In Place monitoring for senior citizens. It supports outpatient care where costs are reduced by 10X and patients get treated at home where they prefer to be. This project will complete the next steps required for Health Canada certification as a class 3 medical device.

Deep Learning Methods for Design of RNA-targeting Small-Molecule Antibiotics

Growing antibiotic resistance is a major threat to global public health. One way to combat this problem is through the development of antibiotics that act against bacteria in new ways. Historically, antibiotics work by interacting with proteins in the bacteria, which are needed for the cells to reproduce. However, recently, it has been shown that molecules that interact with RNA rather than proteins can also inhibit bacterial growth and reproduction. We will use cutting-edge machine learning techniques to improve the capacity of industrial software to identify molecules that are good potential antibiotics that might act through interacting with RNA. The impact will be the identification of new antibiotics and new antibiotic design rules to help ameliorate antibiotic resistance in bacteria.

Estimation of State of Charge in Lithium-Ion and Solid-State Batteries Using Machine Learning Models

Innovation in electric mobility and energy storage from renewable energy resources are two key drivers in the fast growth of a battery industry that is striving to enhance performance of battery systems with great urgency. HQ Center of Excellence is actively working on the development of an advanced battery management system (BMS) and intelligence platform. Machine learning helps extract value from existing data to accelerate the optimization in the design of more effective BMSs. The primary objective is to build BMS technologies that improve the life and performance of lithium-ion and solid-state batteries in power electric vehicles and energy storage systems. Implementations of the BMS asks for the integration of both software and hardware, which includes battery state-of-charge (SOC) estimation, state-of-health (SOH) estimation, fault detection, control and monitoring tasks. This project will help Hydro-Québec to assess methods for predicting electric vehicle battery states. The project develops a data-driven machine learning model offering the most accurate predictions for SOC and SOH. It provides a case study for machine learning techniques accurately predicting the health and life of a battery.