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

Intelligent Tracking Infrastructures Using Wireless Networks with Vision-Audio Monitoring Capabilities

Wireless technologies offer new opportunities in the field of telecommunications and computer networks. Wireless sensor networks are a new technology that has emerged after the great technological progress in the development of smart sensors and powerful processors. The city of Trois-Rivieres is currently in the heart of a project to develop a smart public lighting system subject to motion detection. The system will be equipped with vision-audio capabilities for public safety. The main objectives of this research project is to improve the massive data analyzing efficiency and to enhance the information security through optimized design for the wireless multimedia sensor network. The development and proof of concept of such system will allow the partner organization to have a technology watch on security and monitoring using mesh technology and to develop new mobile products. The latest will extend its market to other regions and eventually at the international level.

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Faculty Supervisor:

Adel Omar Dahmane

Student:

Ghassene Gadhoum & Ahmed Refaey Hussein

Partner:

Centre collégial de transfert de technologie en télécommunications

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Université du Québec à Trois-Rivières

Program:

Accelerate

Fiber-optic based point of care genetic testing device

Point-of-Care DNA tests have grown in importance throughout recent years. Incorporation of devices in patient beside treatment requires fast turn-around times for results as well as economic feasibility. Spartan Bioscience, a local biotechnology company, has developed a technology that can detect abnormalities in a person’s DNA. In an effort to develop next generation technology, Spartan Bioscience has collaborated with several faculties at Carleton to develop a platform that will have improved performance. The device is making use of Spartan’s core technology and optical fiber techniques pioneered at Carleton University. The joint teams will be making a proof-of-concept device that will hopefully, lead to a full-on product development.

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Faculty Supervisor:

Drs. Jacques Albert & William Willmore

Student:

Hubert Jean-Ruel & Jason Koppert

Partner:

Spartan Bioscience

Discipline:

Engineering - biomedical

Sector:

Life sciences

University:

Carleton University

Program:

Accelerate

Mechanical Design and Power Drive Improvements for Moovee’s One-seater Prototype

An emerging concept in urban transportation systems is utilization of small electric vehicles that meet the demands for enclosed personal mobility. These types of vehicles are generally small and lightweight but require much less space than more conventional vehicles such as the Smart Car. Furthermore, the vehicle is all battery electric. Recent developments have utilized innovative in-wheel electric motors mounted on carbon fiber platforms. In such systems, each wheel unit contains a drive motor enabled with regenerative braking, steering, and suspension, all digitally controlled by a computer. The in-wheel motor concept enables maneuvers such as spinning on the wheel's own axis, moving sideways into parallel parking spaces, and lane changes while facing straight ahead. Furthermore, the vehicle is foldable, resulting in smaller space requirements when parked. The folding mechanism also allows for safety in crash scenarios. The above features make the vehicle ideal for crowded cities with limited spaces. The proposed activity involves improvements in power drive and mechanism design for the Moovee's Insectra vehicle and their proof-of-concept demonstration.

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Faculty Supervisor:

Drs. Mehrdad Moallem & Farid Golnaraghi

Student:

Chen-yu Hsieh & Amir Maravandi

Partner:

Moovee Innovation Inc.

Discipline:

Engineering

Sector:

Automotive and transportation

University:

Simon Fraser University

Program:

Accelerate

Real-time 3D object detection and pose estimation from multiple cameras

The project aims to build a 3D object detection system by using a number of images from multiple cameras. The system will train on instances of objects to detect other instances of the same object. This means that if, for example, we want to detect a sphere, we will make the system learn how a sphere looks like by giving some example images. Now when the system encounters a new object which looks similar to the example images, the system will detect the new object to be a sphere. Similarly the system will train for detection of more complex objects. Another component of the system is pose detection which will detect the orientation of the object defected. This means if the object detected is a cylinder, the system will detect whether the cylinder is standing upright, lying horizontally or placed in some other orientation.

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Faculty Supervisor:

Dr. Sven Dickinson

Student:

Amanjot Kaur

Partner:

Epson Canada Ltd

Discipline:

Computer science

Sector:

Consumer goods

University:

University of Toronto

Program:

Accelerate

Research on 3D software user interface design

How to define user requirements accurately and design user interface appropriately have been a very active research area concerned with human-computer interaction and graphic design. Because of the scale and the complexity of three-dimensional (3D) animation software, making correct design decisions becomes tougher. Houdini is a 3D animation software as well as the main product of Side Effects. Using 3D animation software is always very challenge for users. It requires longer learning curves as well as interdisciplinary knowledge. With the increase of usage time, users become familiar with the functions and layout of Houdini and they turns out to be expert users. On the other hand, the apprentice users may carry experience from other 3D animation software or have no experience with any similar software. The conflicts between expert users and apprentice users emerge from the different requirements and expectation of Houdini. This research project will help Houdini to setup a standard design process including collecting data, extracting user requirements and making design decisions.

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Faculty Supervisor:

Dr. Eugene Fiume

Student:

Shuyuan Ma

Partner:

Side Effects Software

Discipline:

Computer science

Sector:

Information and communications technologies

University:

University of Toronto

Program:

Accelerate

Beyond the Book

The ultimate aim of this project is to design and develop methods and tools for classifying attributes of books such as genre, style, tone, and likelihood of being popular. Towards this end we will make use of various information types available on books and users of the Kobo catalog, including the text, meta-data associated with the text, and user features associated with readers of the text. This is a large undertaking. As a first step, the intern and research team will tackle the problem of genre classification, while keeping in mind that the task is one among a collection of desired automatic tagging tools for books. Through the use of NLP and machine learning techniques, books can be categorized and referred to readers via interests that they have expressed. This project is likely to provide valuable insight into both books and readers, allowing Kobo to provide users with higher quality recommendations, interesting reading lists, and deeper understanding of both books and users.

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Faculty Supervisor:

Dr. Brendan Frey

Student:

Sagun Bajracharya

Partner:

Kobo Inc.

Discipline:

Engineering - computer / electrical

Sector:

Digital media

University:

University of Toronto

Program:

Accelerate

Big Data Processing and Analysis

Addictive Mobility is a leading online advertising company in Canada. The success of ad campaigns drives the majority of the company revenue. Exploring advanced machine-learning techniques to efficiently control an ad's performance is crucial to the company strategy. The objective of the proposed project is to optimize the real-time bidding system in the sense that delivery has been carried out in real-time and within a time-interval of 100 ms. As we mentioned, this problem is highly complex and we can break it into several subproblems each of which can be a major area in machine learning.

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Faculty Supervisor:

Dr. Anthony Bonner

Student:

Guilherme Trein

Partner:

Addictive Mobility

Discipline:

Computer science

Sector:

Digital media

University:

University of Toronto

Program:

Accelerate

User Profile generation for Mobile Ad Targeting

Personalized ad targeting is one of the most important features to ensure a successful advertising campaign—e.g., F-150 ford pickup trucks are best shown to construction workers than teenage girls. Another important aspect of ad personalization is frequency capping the ads. For example, showing the same ad 100 times to 1 user will result in not having any budget left over for others, and this can make or break an advertising campaign. To generalize the targeting strategy across users that have not been observed in the past, either due to lack of data or due to new users, we aim to develop clustering methods based on similarity measure that receives user features as input and then assign the new user to a group of users.

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Faculty Supervisor:

Dr. Anthony Bonner

Student:

Megha Lakshmi Narayanan

Partner:

Addictive Mobility

Discipline:

Computer science

Sector:

Digital media

University:

University of Toronto

Program:

Accelerate

Green Roofs as a Stormwater Management Tool

The intern will undertake a project which analyses several drainage structures of an experimental green roof while developing a numerical model to mimic the experimentally obtained results. In addition, the intern will also be involved in working on an Integrated Stormwater Management Plan, and low impact development methods. The partner organization will benefit by having a more effective green roof drainage design developed as well as a numerical model created to simulate the design changes.

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Faculty Supervisor:

Dr. Loretta Li

Student:

Calvin Kemm

Partner:

Kerr Wood Leidal Associates Ltd.

Discipline:

Engineering - civil

Sector:

Construction and infrastructure

University:

University of British Columbia

Program:

Accelerate

Data Visualization Incorporating Social Collaboration for Asset Management

Through this research project the intern will investigate the potential of data visualization incorporating a social collaboration element tied to typical data types in asset management. The partner organization Riva Modeling Systems will benefit from the proposed research by providing its clients with an enhanced asset management tool that allows various groups or individuals within the client organization to collaborate in various data views and to see their interaction records on the central dashboard with visualized data. The impact of this enhancement would include that it would set apart the company from its competitors with this novel collaboration functionality and that it would enable client organizations to engage in more effective collaboration and to achieve more optimal results in asset management.

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Faculty Supervisor:

Dr. Eugene Fiume

Student:

Qi Wang

Partner:

Riva Modeling Systems

Discipline:

Computer science

Sector:

Information and communications technologies

University:

University of Toronto

Program:

Accelerate

Asset Management using machine learning techniques

Perform an exploratory research to investigate the potential to extract knowledge by aggregating historical data across the company's clients and applying machine learning techniques on them. The goal is to device prediction models that can forecast the condition of certain events(such as life of pipelines) using statistical analysis. Riva Modeling( partner organization) will benefit from the results of the research by being able to draw more meaningful inferences from the large amount of historical data available from the clients. The greatest benefit of the research goes to the clients of Riva, which includes more than 15 Canadian municipalities and additional cities and utilities in the USA, Australia and New Zealand. The research would lead to a large savings in the expenditure towards the maintenance of their assets.

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Faculty Supervisor:

Dr. Eugene Fiume

Student:

Sreekumar Rajan

Partner:

Riva Modeling Systems

Discipline:

Computer science

Sector:

Information and communications technologies

University:

University of Toronto

Program:

Accelerate

Big Data Analytics for GPS Fleet Management

The project will develop a data mining strategy on how to efficiently use Geotab’s data from multiple sources to isolate network connectivity logs in Geotab’s data, interpolate the GPS coordinates associated with network logs, acquire fuel consumption rates and related opportunities. It will involve a literature survey of the existing algorithms to gain valuable insights. It will also include an important research component to analyze, understand, and develop trends using the available data to compare and corelate the results. The interns shall work toward enriching the data files as required, compare the performance of big data source providers, upload the data to the big data source and use appropriate data visualization tools for the fleet productivity and safety.

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Faculty Supervisor:

Dr. Mariano Consens

Student:

Vandana Saini & Fiona (Yi) Zhao

Partner:

Geotab

Discipline:

Computer science

Sector:

Information and communications technologies

University:

University of Toronto

Program:

Accelerate

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