Innovative Projects Realized

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

13270 Completed Projects

1072
AB
2795
BC
430
MB
106
NF
348
SK
4184
ON
2671
QC
43
PE
209
NB
474
NS

Projects by Category

10%
Computer science
9%
Engineering
1%
Engineering - biomedical
4%
Engineering - chemical / biological

Numerical modeling and evaluation of mixing behavior and grinding efficiency in FLSmidth VXP vertical stirred mill

Stirred mill grinding is a complex and energy-intensive process that involves multiple phases. Multi-scale phenomena that drive hydrodynamic and particle breakage processes in the stirred mill are still not fully understood, due to a number of operating and design parameters that affect mill performance, including disk and barrel geometry, agitation rate, grinding media size and fill volume, and slurry properties, which are further limiting our abilities to monitor, model, and predict this complex operation. To this end, the proposed study will focus on the development and validation of high-performance (GPU- and MIC-accelerated) multiscale numerical models that will allow evaluation of mill design and process state parameters for a range of FLSmidth VXPmills (with different geometries) running over different operating conditions. The primary goal of this research is to develop a better understanding of the effects of operating and design parameters on grinding efficiency, specific power consumption, and level of mixing (residence time) in existing and new VXPmills. Obtained results will help development of new scale-up rules and power models, which will capture parametric dependences between operating and design variables and help optimize VXPmill grinding performance and energy consumption

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Faculty Supervisor:

Sanja Miskovic

Student:

Chaitrali Ghodke

Partner:

Discipline:

Engineering

Sector:

University:

Program:

Accelerate

Factors Related to Braille Acquisition Among Adult and Senior Learners: Establishing Evidence-Based Practice

The ability to read carries important implications for overall self-esteem and independence, as reading is necessary to perform many daily tasks (making grocery lists, reading prescriptions, following recipes). Rehabilitation professionals provide training and support to adults and seniors who are born with visual impairments or who acquire it later in life due to age-related conditions. Within this context, reading related difficulties are among the most common reasons for referral to vision rehabilitation services. Braille, a tactile system of reading for the blind, is one option for individuals who are unable to read print; however, very little is known about the impact of aging on braille learning and usage. The goal of this research is to explore the impact of age-related declines on the ability to read braille, and to investigate the influence of emerging technologies on the training outcomes of older adults who learn braille. TO BE CONT’D

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Faculty Supervisor:

Walter Wittich

Student:

Natalina Martiniello

Partner:

Canadian National Institute for the Blind

Discipline:

Medicine

Sector:

Medical devices

University:

Program:

Accelerate

Detection of Fights in Crowd Video

Detection of fights and anomalous behavior of individuals in a crowd is a common problem in computer vision. Some tools that currently exist rely on optical flow of tracked features is a sequence of video frames. These motion algorithms are sensitive to independently moving objects in the frame. What constitutes an “anomaly” is context (eg. location) specific, thereby adding to the complexity. We aim to expand on existing work to create a baseline algorithm which can be used in a general use case, while in parallel investigate the state of the art; and if time permits, tune the algorithm to a specific use case

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Faculty Supervisor:

Viqar Husain

Student:

Suprit Singh

Partner:

EhEye

Discipline:

Mathematics

Sector:

Information and communications technologies

University:

Program:

Accelerate

Medium and Macro Scale Editing for the Synthesis of Facial Meshes for Video Game Applications

Ubisoft has an extensive database of 3D scanned heads. It would be convenient to use it to mix-and-match parts of characters to create new human-like character heads. Let’s say we want to adjust medium-scale features of the face, such as replacing the nose of one character with another nose. We will design an editing workflow allowing the artist to create a new nose from mixtures of noses found in the database. Also, we want to derive a workflow to edit the macro features of the face, for example to change a head from “oblong” to “square” or to adjust large regions such as the cheeks and forehead. This macro feature editing should not interfere with the medium scale features: changing the chin and jaw should preserve the shape of the mouth. We will derive approaches to ease both types of editing.

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Faculty Supervisor:

Eric Paquette

Student:

Donya Ghafourzadeh

Partner:

Ubisoft

Discipline:

Visual arts

Sector:

Information and communications technologies

University:

Program:

Accelerate

Stateful Intrusion Detection using Algebraic State-Transition Diagrams

Increasingly, cyber threats evolve targeting companies, industries and governments. As defense systems are strengthening, threat actors developed new tactics, strategies and techniques to break down security perimeters. Generally, the security of the perimeters are enforced by multiples intrusion prevention and detection tools responsible to provide proactive insights, real-time insights and operational insights for the detection, prevention and mitigation of eventual threatening activities on the monitored system. The performance of such tools depends of the different criteria including detection technique, state awareness, usage frequency and structure. Tools like Snort offer a real-time detection based on rules (or signatures) to detect threatening behaviours from its knowledge base. Snort signatures are expressed in a low-level language that limits the expression of more complex attacks such as advanced persistent threats, distributed and multi-step attacks. They offer basic options for dynamic or stateful analysis, which is necessary to detect aforementioned attacks. TO BE CONt’D

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Faculty Supervisor:

Marc Frappier

Student:

Lionel Tidjon

Partner:

Nokia Canada Inc.

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Development of signal processing techniques for animal movement data

In the past decade, the development of sophisticated sensors attached to animals (tags) have researchers to infer of horizontal and vertical movement of marine animals across time and space. The amount of data collected from these tags along with the analytical challenges surrounding the extraction of behavioural patterns has presented a significant barrier for researchers to adopt this technology. This project aims to address these challenges in two ways: First, to develop an analytical framework to extract relevant information from such tags and second, to develop an analytical approach that integrates different data streams from lower resolution sensors (e.g. depth, location) and higher resolution sensors (acceleration) to facilitate the extraction of broad scale patterns in animal behavior and movement in relation to environmental variables. TO BE CONT’D

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Faculty Supervisor:

Michael Dowd

Student:

Franziska Broell

Partner:

Maritime BioLoggers

Discipline:

Mathematics

Sector:

Information and communications technologies

University:

Program:

Measurement-Based Geospatial Data Management

This project will develop and test a geospatial data management system for land surveyors. The system will of a mobile client for data collection and a web service that integrates and stores the data long term. Data processing will use the most accurate geodetic techniques to ensure data quality and optimal data integration strategies will be determined in the course of the project. The Intern will have the opportunity to develop project management and software development skills through hands on experience and will reinforce his knowledge of geodesy and geomatics. The partner organization expects that the software developed by this project will have commercial value as a business-to-business software and intends to develop a line of software products based the results.

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Faculty Supervisor:

Marcelo Santos

Student:

Mike Bremner

Partner:

Dim Ideas Un Ltd

Discipline:

Engineering

Sector:

Information and communications technologies

University:

Program:

Accelerate

Pavement Distress Detection Using Conventional Unmanned Autonomous Vehicle LiDAR

In Montreal, pavement distresses are causing serious problem to the road network with more than half of the road considered in a bad and a very bad shape. Many pavement inspection methods are developed in order to inspect, detect, locate, and classify pavement distresses; however, these methods are not efficient in term of time, cost, and accuracy. In our project, we aim to develop a new approach in detecting, classifying, and locating pavement distresses using conventional unmanned autonomous vehicle LiDAR. This approach will create a new platform involving large number of vehicles equipped with LiDAR in detecting pavement distresses with no extra cost, less time, and more detection accuracy than the traditional methods.

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Faculty Supervisor:

Maarouf Saad

Student:

Nizar Tarabay

Partner:

WSP

Discipline:

Engineering - computer / electrical

Sector:

Automotive and transportation

University:

Program:

Accelerate

Learning PDF Document Structures using Recursive Neural Networks

Portable Document Format or PDF is the de facto standard for presenting textual-visual content. In this project, we aim to develop a machine learning framework for PDF document understanding. Despite the recent proliferation of deep learning-based methods for the analysis and processing of natural images, there have been considerably less efforts on designing similar approaches for highly structured data such as documents. Our project will explore two novel ideas. First, we will develop a structured and organizational representation of PDF documents which is built on labeled content blocks (e.g., heading, figure, list, caption, etc.). Second, we will investigate how recursive neural networks (RvNN), one type of deep neural networks that have been utilized to language parsing, can be adopted and formulated for learning PDF document structures.

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Faculty Supervisor:

Richard Zhang

Student:

Chenyang Zhu

Partner:

PDFTron Systems

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Impact of Post-Quantum Cryptography on PKI, Common Libraries, Protocols and Crypto Agility Requirements

Advances in quantum computing have Entrust Datacard and their customers concerned about whether the industry is ready to move to post quantum cryptographic algorithms, particularly for PKI use cases. Entrust Datacard and University of Ottawa will test the quantum-readiness of commercially-available PKI. The end goal is to provide guidance to the community about the impact of particular PQ algorithms on common infrastructure, provide examples of safe migration paths where they exist, and recommend changes or mitigations based on discovered issues. This research will help establish Entrust Datacard’s leadership moving towards post-quantum cryptography and prepare the market to meet the challenges of a post-quantum world.

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Faculty Supervisor:

Carlisle Adams

Student:

Jinnan Fan

Partner:

Entrust Datacard Limited

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program:

Accelerate

Accelerate Transaction Latency of Pool Mining in Cryptocurrency Networks

In this project, using such mainstream cryptocurrencies as BitCoin and Ethereum as representatives, the intern will analyze the transaction collection strategies of their mining pools, and then collect transactions and the corresponding blocks data to build a large dataset, from which the computing power of different mining pools and their proportions will be analyzed, together with the transaction latencies of pool mining. We will also identify potential enhancement through the analysis and measurement, particularly on energy and delay optimization. Coinchain is a BC-based startup company focusing on advanced cryptocurrency and blockchain technologies, and their application in industrial and commercial scenarios. It delivers global enterprise-level blockchain solutions to leading companies worldwide, and provides one-stop customized services such as product and information platforms, as well as smart contracts and trading platforms. TO BE CONT’D

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Faculty Supervisor:

Jiangchuan Liu

Student:

Lei Zhang

Partner:

Coinchain Capital

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Generative Adversarial Networks for Addressing Data Privacy Issues

It is extremely important to preserve privacy of our citizens. One way to do it is to detect private information in the document and to indicate to owner of the documents that the documents contain privacy information. In order to develop machine learning algorithms to detect privacy data in the documents, the algorithms need to be trained with the existing documents that are annotated to point out private information. Access to those documents for training is limited since in many cases they are private as well. In addition, annotating large number of documents to indicate private information is extremely expensive and long process. Therefore, in this project we propose to develop algorithms for generating large number of documents that contain fictitious private information that will be used for training the algorithms. TO BE CONT’D

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Faculty Supervisor:

Miodrag Bolic

Student:

Rajitha Prabath

Partner:

IMRSV Data Labs

Discipline:

Engineering

Sector:

Information and communications technologies

University:

Program:

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