Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

29670 Completed Projects

2811
AB
4990
BC
801
MB
663
NL
825
SK
8841
ON
9197
QC
95
PE
568
NB
1088
NS

Projects by Category

Development and commercialization of bio-sensor using cholesteric structure of magnetoplasmonic nanoparticle chain

Recently, bio-sensing R&D using plasmonic has been actively carried out in Korea and overseas. The global bio-sensing market is projected to grow rapidly by an average of 11.6% per year, with a high potential for future development and increased demand in the field. Helical nanostructure using magneto-plasmonic nanoparticles exhibit signals in visible light regions such as DNA and RNA, which are very easy to apply to biomedical fields. Signals represented by the structure of self-assembled helical nano chains are influenced by electron cloud signals of organic matter, resulting in wavelength shifts and these databases can be an indicator of the presence and qualitative determination of organic chiral moieties handedness. Therefore, micro-and miniaturization development, which implements the chiral three-dimensional structure of magneto-plasmonic nanoparticles on the substrate, is very necessary. The development of new micro-size bio-sensing devices provides a breakthrough technology that enables the early diagnosis of many intractable diseases.

View Full Project Description
Faculty Supervisor:

Simon Park

Student:

Partner:

Chungnam National University

Discipline:

Physics

Sector:

Nanotechnology; Biotechnology; Health and Related Sciences & Technology

University:

University of Calgary

Program:

Globalink Research Award

Artificial Intelligence to Support Autonomous Seabed Mapping Operations

Presently the Ocean Mapping Community is spending a lot of time manually cleaning the raw sonar survey files. If this cleaning process is done by an Artificial Intelligence system as clean as a human would do, this could be transforming the entire Ocean Mapping community. Therefore, we propose a novel solution of using an Artificial Intelligence Algorithm – Reinforcement Learning to clean the sonar survey files. The proposed algorithm was used by Google’s DeepMind to beat GO Grandmasters. We believe that by tapping this algorithm we would be able to clean the raw sonar survey files as efficient as the manual cleaning of the raw sonar survey files.

View Full Project Description
Faculty Supervisor:

Ian Church

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Engineering

Sector:

Ocean Tech; Artificial Intelligence; Environmental Science and Technology

University:

University of New Brunswick

Program:

Accelerate

UofT-JMIR Paper-Peer Reviewer Recommender System

Rapid and open dissemination of research is critical for the advancement of science. Preprint servers (such as MedRxiv, BioRxiv, PsyRxiv, and aRxiv) are becoming increasingly popular in health and medicine to share early research results, particularly in the context of the current COVID-19 pandemic. Given the need for rapid peer review, we need to innovate in rapid prioritization, classification, assignment, and evaluation of research papers (with a focus on medicine). A proposed AI-based method to match STM papers/submissions with suitable reviewers will significantly speed up the review process and improve the quality of feedback provided to the researchers. The proposed approach will also help in identifying the reviewers with similar expertise and interests to build peer-reviewer communities.

View Full Project Description
Faculty Supervisor:

Eldan Cohen;Michael Guerzhoy

Student:

Partner:

JMIR Publications Inc

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Toronto

Program:

Accelerate

An AI Approach to VLT Games Modeling

Video Lottery Terminals (VLT) are common gaming machines for money usually found in various venues. These machines are anonymous and stateless and do not record any information about player’s identity. The data obtained from these machines is very complex and hard to analyse. The gaming industry is keen to understand how a game or a possible change to a game will be perceived in different environments by different types of customers. This understanding will provide insights into both gaming performance and responsible gaming related behaviours. The most common current approach is to release a game, monitor the data and the analyze the results along with player interviews. This is expensive and inefficient, and many players choose to abstain from participating in these experiments. One goal in this project is to identify the most relevant VLT data features to predict positive user experiences and train models that imitate the behaviour of players to get a good estimation of a machine learning model as a reliable substitute for real world players. TO BE CONT’.

View Full Project Description
Faculty Supervisor:

Vlado Keselj;Colin Conrad

Student:

Partner:

IGT

Discipline:

Computer science

Sector:

Arts, entertainment and recreation; Information and cultural industries

University:

Dalhousie University

Program:

Accelerate

Gait and Balance Parameters Inferred with the PROTXX Head Accelerometer (Phase 2)

The vestibular organs detect head movement and are involved in the coordination of standing balance. With balance problems being a common and expensive healthcare cost internationally, there is a growing need for new diagnostic and therapeutic medical devices that target vestibular balance function. In particular, a wearable device that could be used outside the clinic could provide a convenient, low-cost alternative. Here we explore the feasibility of integrating Electrical Vestibular Stimulation (EVS) – a relatively new technique for probing vestibular- specific balance function – with the commercially available head motion sensor from PROTXX. The wearable technology we are co-developing will enable frequent, accurate, and mobile assessments of vestibular function, as well as provide a novel therapeutic approach for enhancing balance control in patients at risk of falling (analogous to a vestibular prosthetic).

View Full Project Description
Faculty Supervisor:

Ryan Peters

Student:

Partner:

PROTXX

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

University of Calgary

Program:

Accelerate

The economics of Mi’kmaq communal commercial fisheries in Nova Scotia

The proposed internship project seeks to answer two questions for KMK Mi’kmaq Rights Initiative and Gardner Pinfold. First, what are the operational costs and fishery revenues of two communal lobster fisheries within Nova Scotia? Second, what are the possible avenues for internalizing fishery benefits locally in each of these fishing communities and how might they be applied to future moderate livelihood fishing communities? A financial analysis of two communal commercial lobster fisheries will be done and information will be gathered
from each community via the KMK Mi’kmaq Rights Initiative. The intern will then analyze the collected data with the guidance of her supervisor and will produce a report to present to the KMK Mi’kmaq Rights Initiative. The second question will be answered through a partial economic impact assessment of the two lobster fisheries, looking specifically at how the addition of or absence of value chain activities may impact the local economy.

View Full Project Description
Faculty Supervisor:

Peter Tyedmers

Student:

Partner:

Gardner Pinfold Consultants Inc

Discipline:

Sociology

Sector:

Professional, scientific and technical services

University:

Dalhousie University

Program:

Accelerate

Développement d’algorithmes et de méthodes d’interprétation automatique de données structurées et non structurées dans le cadre d’un outil d’AI d’aide à la décision pédagogique au Québec

L’objectif général de ce projet de recherche est d’étudier des approches statistiques et computationnelles pour permettre l’analyse automatique des données structurées et non-structurées dans le contexte de décision péda-gogique et dans le cadre d’un outil tel que Reussito. Afin d’étudier la pertinence de ces approches, le projet se concentrera sur le développement des algorithmes d’analyse du langage naturel à partir des enregistrements vocaux en considérant le contexte québécois, de l’enseignement et de données bruitées. Le projet consiste en l’étude des méthodes d’analyse du langage naturel, pour la détection de mots-clés, l’analyse de sentiment ainsi que des méthodes pour l’interprétation automatique afin de soulever des enjeux permettant une prise de décision de la part du corps enseignant.

View Full Project Description
Faculty Supervisor:

Jean-François Plante

Student:

Partner:

Videns Analytics

Discipline:

Sociology

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

HEC Montréal

Program:

Accelerate

Spatial and temporal settlement patterns of the American oyster (Crassostrea virginica) along the North Shore of Nova Scotia (Canada)

An oyster seed source is essential for regular operations in the oyster aquaculture industry. Currently, oyster seed production in Nova Scotia is minimal. Instead, seed is collected in New Brunswick and Prince Edward Island and then sold to farmers in Nova Scotia. The threat of invasive species and disease exposes a serious vulnerability of Nova Scotia’s seed source security. To address this issue, wild spat collection must be explored in Nova Scotia’s coastal waters. By exploring several locations with potential for seed collection; the magnitude, timing, and predictability of wild seed production will be investigated to develop a predictive model for optimizing wild spat collection in a sustainable manner.

View Full Project Description
Faculty Supervisor:

Ramon Filgueira

Student:

Partner:

Perennia Food and Agriculture Inc

Discipline:

Life Sciences

Sector:

Agriculture; Information and cultural industries

University:

Dalhousie University

Program:

Accelerate

Neural implicit functions for multi-image low-level computer vision on smartphones

Modern smartphone cameras commonly employ multi-image (or burst photography) for tasks related to image super-resolution and high-dynamic-range imaging. This project is focused on developing novel multi-image techniques that leverage the power of recently proposed neural implicit functions. Neural implicit functions (NIFs) are a new way to represent images not as a 2D grid of pixels values but instead as functions. This functional representation offers many benefits, including a continuous representation, effective interpolation, and compact representation. We are interested in using NIFs for burst photography. The goal is to fuse the multiple images into a new image with enhanced properties, such as improved image detail (super-resolution) or higher dynamic range (HDR). This project will help Samsung Electronics Canada develop improved camera performance for its smartphone devices.

View Full Project Description
Faculty Supervisor:

Michael S. Brown

Student:

Partner:

Samsung Electronics Canada

Discipline:

Computer science

Sector:

Manufacturing

University:

York University

Program:

Accelerate

Sensor development for in-situ microplastics monitoring in water bodies

Plastics are all around us, and unfortunately, they either do not go through decomposition or take decades to decompose. Consequently, tiny plastic particles, called microplastics, are found in oceans, rivers, and even in drinking water. Living species in the oceans consume them, and these microplastics enter to our food chain and pose significant health concerns as they can be toxic to humans and animals. Currently, the detection of microplastics takes too much time and effort, and it requires laboratory equipment. In this project, we propose developing a sensor to detect microplastics in water bodies in real-time. The sensor will attach to autonomous robots to identify their size and concentration in oceans. Using GPS, we are going to map their locations. Developing and using the proposed sensor is the first step understanding their concentration and toxicity levels in oceans. 7.3. Participant

View Full Project Description
Faculty Supervisor:

Cagri Ayranci

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Engineering

Sector:

Sustainability & the Environment; Ocean Tech; Clean Technology

University:

University of Alberta

Program:

Accelerate

Synthesis of Pheromone Analopgs for the Control of Parasitic Insect Infestation

SemiosBIO is an entrepreneurial business trying to find a chemical basis for fighting bed bug infestation. Bed bugs (Cimex lectularius) are parasitic insects feeding exclusively on warm-blooded animals including humans. Their preferred habitat includes beds and other areas used by humans to sleep. An infestation with bed bugs can provoke a number of health problems such as skin rashes, allergies or psychological problems. This project deals with the chemical preparation of insect pheromone analogs which will be tested for their potential as bed bug attractants. Should they prove efficient, they will be used as a means to control parasitic infestations by luring the insects away from the infested areas. SemiosBIO Technology perceives a strong market for efficient and cost-effective tools to detect and monitor bed bug infestations and the academic group at UBC is supporting their business goal by providing expertise in the field of chemical synthesis.

View Full Project Description
Faculty Supervisor:

Gregory Dake

Student:

Partner:

SemiosBio Technologies Inc

Discipline:

Physics

Sector:

Biotechnology

University:

The University of British Columbia

Program:

Accelerate

Delineation and phenology of the invasive box tree moth (BTM) in Toronto

The box tree moth (BTM) (Cydalima perspectalis) is an invasive insect pest from Asia that was confirmed to be present in Etobicoke, Ontario by the CFIA in November, 2018. This is the first known introduction of BTM to North America. BTM attacks boxwood (Buxus spp.), a popular broadleaf plant used in residential and commercial gardens, hedges and topiary in Canada. For the nursery sector, boxwood represents a very high value, slow-growing crop in Ontario, Quebec and BC. BTM is a distinct threat to this industry causing severe defoliation that quickly leads to death of the plant in both young and mature specimens. This proposed project will help inform current IPM programs for BTM in Ontario by (1) surveying and monitoring to define the spread of BTM from confirmed locations in the Greater Toronto Area and (2) investigating the ecology, behaviour, and phenology of BTM populations in Ontario. This research is part of the ongoing research which is the first to focus on management of box tree moth in Canada, and more specifically in Ontario.

View Full Project Description
Faculty Supervisor:

Sandy Smith

Student:

Partner:

Landscape Ontario

Discipline:

Life Sciences

Sector:

Agriculture

University:

University of Toronto

Program:

Accelerate

Previous
1…1,5101,5111,5121,5131,5141,5151,516…2,473
Next

Join a thriving innovation ecosystem.

Subscribe to our newsletter now!

Subscribe