Projets novateurs réalisés

Performance of Fiber-Reinforced Lightweight Self-Consolidating Concrete Columns Reinforced with Glass Fiber-Reinforced Polymer Bars and Spirals under Axial and Eccentric Loads

One of the main interests of the construction industry is the use of innovative materials to facilitate construction, extend service life and minimize maintenance and rehabilitation costs. Lightweight aggregate self-consolidating concrete (LWSCC) can be of great interest for reducing dead loads, section dimensions and project costs, especially for precast elements. Integrating GFRP reinforcement into LWSCC would effectively contribute to producing lighter and more durable concrete members for precast applications. Lightweight concrete is more brittle than normal-weight concrete (NWC). Furthermore, the brittleness of concrete may affect not only the failure mode but also the axial capacity of concrete columns. Adding fiber into LWSCC is an effective way to solve the brittleness of concrete and improve the tensile strength and crack resistance of concrete. This research project aims to develop fiber-reinforced lightweight aggregate self-consolidating concrete (FR-LWSCC) mixes for precast applications
and to provide an experimental work as well as extensive theoretical analysis and design recommendations of RC columns reinforced with FRP bars. The experimental results will be discussed in terms of moment–deflection behavior, flexural capacity, mode of failure, crack patterns, and crack widths.

Cloud platform of machine learning

Surgical Safety Technologies Inc. is expanding upon its existing OR Black Box® platform, which will allow users of the platform to build a personalized, user-created library of surgical videos in the cloud. There are many people and groups around the world who will use this video library to make sure that performance evaluations are fair and can be done quickly and easily. Among the methodologies used in this project are research on human-computer interaction, video relevance ranking, and business intelligence based on meta-data that comes from the cloud platform. The main goal of the project is to work with existing teams at SST to make prototypes and products that can be used on the cloud, and to make sure that the cloud solution can be used more widely.

Neural Networks for Observable Market Data Validation

Observable Market Data is critical for effective valuation of trades for risk management purposes within the investment bank. The valuation process requires the existence of good quality data day by day, and dating back into the mid-2000’s. Not all assets have highly liquid data available either historically or at present, and there is significant interest within the industry in building models to both predict missing data and qualify available data. Historically this process has been highly manual, and due to the volume of data statistical methods are used to identify potentially suspect data. The use of these simplistic methods to gate incoming data results in known blind spots and false positives. This project seeks to use deep neural networks to tackle these two tasks : 1) flagging suspect data efficiently, 2) generating quality data that can be used to improve modeling where real data is unavailable.

Assessment and modelling of salmon streams in western Newfoundland

The approach to assessing Atlantic salmon in Newfoundland is based on patterns of salmon production and habitat that were established for populations in rivers much further south. The physical and biological characteristics of watersheds and rivers in Newfoundland are different from the southern rivers and the bases for conservation decisions are likely inappropriate. Establishing relationships between stream physical and biological characteristics that are unique to Newfoundland is prudent for better management of Newfoundland salmon populations.

Advancing Gender Equality for Women in Venture Capital – Part 2

Women are vastly underrepresented in venture capital (VC) firms. There is also a lack of women entering the field of venture capital due to systemic barriers and a lack of momentum to shift existing barriers for women to enter the venture capital world. This research aims to understand the current status of women in VC firms, identify structural barriers facing them, and promote gender parity in VC firms and the investing community. The methods include a literature review, in-depth interviews with VC capitalists or senior leaders from VC firms, as well as the creation of an action plan for the partner organizations to drive changes in the investing community.

Cross-Modal Recipe Retrieval

The goal of cross-modal recipe retrieval is to design systems that are able to find a digital recipe, given the user’s image of the food, or find its image, given its ingredients or cooking instructions. For such a cross- modal retrieval task, a common image-text representation space is needed to embed the semantic information of each modality along with the cross-modal mutual information. With the advent of large- scale datasets, such as Recipe1M, the scalability-accuracy tradeoff of the cross-modal embedding methods has increasingly gained more attention in the last few years. The main goal of this project is to use (and improve) the SOTA cross-modal embedding methods to efficiently retrieve a recipe in a large dataset of recipes with low latency and computational demand, and recommend similar recipes based on the queried recipe.

Lifelong learning for service robots

Traditionally, software for home products could not be changed once they were shipped. Furthermore, they could not run complex machine learning (ML) models because their computing and storage capacities are limited due to budget constraints. Recent advancements of cloud infrastructure, however, may allow such products to collect a large amount of data and continuously update the software system throughout their lifetime. Lifelong learning can potentially improve the user experience by making the software system more intelligent and adaptive to specific user preferences. On the other hand, we need to solve problems such as incremental learning, data representation, and catastrophic forgetting. There may also be security and privacy issues associated with processing user data on the cloud.

Multimodal Procedure Understanding

Procedural content (text and video) is abundant on the internet, and is regularly used in our daily lives, e.g., when we follow a cooking recipe to make a dish, or watch an instructional furniture assembly video. Automatically understanding such content allows for the development of various types of AI assistants, including those that can provide answers to our questions (e.g., asking a cooking assistant how much milk we needed in the recipe), and those that can guide users follow through a procedure (e.g., if the user forgets an important step). This project focuses on various aspects of automatically understanding procedural content.

Rapid photocatalytic determination of soil organic carbon content: development and validation of protocols

Global warming is a well-known global phenomenon that has been in the headlines for the past few decades. Greenhouse gases (Carbon Dioxide, Methane, Carbon Monoxide, etc.) are the major contributors to global warming and several governments have been working towards limiting such emissions by introducing emission guidelines. Carbon Dioxide is the gas of interest in this research project as recent studies show that Canadian agricultural soils could remove 11.9 million tonnes of CO2 from the atmosphere annually. The intern will be conducting research to investigate analysis techniques for organic carbon in soil through optimizing previous common analysis methods and under multiple real-life conditions to ensure the reliability of the project’s outcome that would enable the readers to explore a new approach for the analysis of organic carbon in the soil, carbon credits, and providing a green sustainable future for the coming generations.

University of Guelph and IndigenousTech.ai Mentorship Program for Indigenous Youth

IndigenousTech.ai Corporation (IndigenousTech.ai) is is one of the corporations inside of the Forrest Green Group of Companies. All Forrest Green corporations perform web development, solution development, project management and professional services in the technology industry. IndigenousTech.ai has become certified in Ontario and to our knowledge is Canada’s first Indigenous-owned consumer credit reporting agency. IndigenousTech.ai’s mission is to train and hire Indigenous youth on-reserve to increase self-sufficiency, own-source revenue and economic development. To this end, IndigenousTech.ai conducts summer mentorship programs for Indigenous youth focused on financial, digital and accounting literacy. This summer mentorship program is designed to achieve IndigenousTech’s mission described above. Success of the mentorship program requires the contribution from knowledgeable personnel in the fields of technology, business, finance and accounting. This collaboration with the University of Guelph will enable IndigenousTech.ai to conduct a successful Indigenous mentorship program and grow further.

Seleste Western

Seleste is developing an app to integrate with our smart glasses hardware. For this project the intern will help us develop this app which includes two major components. The focus will mainly be the React Native app where the intern will add features to our app and setup our in-house volunteer network for users to call and adding edge ML to our app. Our interface is going to be unique from most since our users are visually impaired and it is incredibly important to create an interface that is simple, accessible and easy-to-use with a screen reader. The second part of the app is adding the ability for guides to remotely take a photo from the glasses. This involves computer networking and sending data from the glasses to the guide using both Wi-Fi and LTE. Both parts of this project will be a core part of Seleste’s IP and value proposition. Successful completion of both parts of this project will allow us to start shipping out our initial pre-order of glasses and run a successful pilot.

Development and testing of a business development strategy for the world’s first and only ubiquitous edge voice AI platform

Picovoice is the first and only ubiquitous edge voice AI platform. The world’s first production-grade local Speech-to-Text technology has been recently added to its portfolio. Currently, there is no direct competition on the market, but alternatives. The intern will be responsible for developing and testing business development and go-to-market strategy with vertical-specific value propositions and communications.
Voice AI is a big market mainly dominated by big players such as Amazon, Google, Microsoft and IBM. Those offerings are all cloud-based. Picovoice differentiates itself with its easy-to-train developer console and its superior speech recognition technology that achieves higher accuracy than the cloud-based alternatives with the benefits of edge computing: up to 100x cost-effectiveness, privacy, and zero-latency.

Finding the niche for on-device, private and high volume use cases and the correct positioning of Picovoice technology within this crowded market will contribute to the success of Picovoice. The project will include crafting the strategy and also execution to test whether the strategy works and iterate accordingly when needed.