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

Heavy Rare Earth Elements: New Insight into Mineralogical Parameters That Impact Mine Processing

Current Heavy Rare Earth Element (HREE) processing techniques are expensive, environmentally-challenging, and slow. Kinetic models predict that the rate of acid permeation of a mineral is the rate controlling step. Therefore, permeation rate controls the acid quantity and residency time of the mineral in the acid bath; impacting costs. Kinetic models are based on structural assumptions including the uniform distribution of elements within a mineral. This study proposes to employ atom probe tomography on the HREE-mineral gadolinite: an ore mineral in the resource at Strange Lake, Quebec. TO BE CONT’D

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Faculty Supervisor:

Desmond Moser

Student:

Natalie Pietrzak-Renaud

Partner:

Juniper Associates Ltd

Discipline:

Geography / Geology / Earth science

Sector:

Information and communications technologies

University:

Program:

Elevate

Analysis of techno-economic-environmental feasibility of zero emission buses on public transit routes in Canadian context

Substitution of existing diesel buses by zero-emission propulsion technologies (electric batteries and hydrogen fuel cell) in vehicles – specifically public transit fleets – can play an instrumental role in realizing Canada’s obligation towards green house gas emission reduction. It is imperative to enable transit agencies to assess the capabilities of existing technology variants in meeting the demands of existing operations to achieve successful, long-term integration while maintaining commercially viability. Most of Canada’s transit agencies today lack a comprehensive understanding of the impact of these buses, in terms of their operational performance, cost, degree of interoperability, infrastructure requirements and environmental impacts. In the proposed project, the post-doctoral fellow (PDF) will develop a modeling-based predictive and comparative analysis tool. TO BE CONT’D

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Faculty Supervisor:

Heather MacLean

Student:

Abhishek Raj

Partner:

Canadian Urban Transit Research and Innovation Consortium

Discipline:

Engineering - civil

Sector:

Alternative energy

University:

Program:

Elevate

Better predictions of employee events

Machine learning can be used to predict employee events around retention, promotion or movement. This project explores how to generate better predictions by exploring correlations and exploiting them through features that increase predictive strength. Furthermore, the project explores how to reliably fine-tune the predictive model to a particular data set in the presence of interdependence of data points. The results will enable improved Machine learning predictions related to employee events.

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Faculty Supervisor:

Leonid Chindelevitch

Student:

Nafiseh Sedaghat

Partner:

Visier Solutions Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

High Performance of Sulfide-based Electrolytes in All Solid-State Batteries for Safe Applications of Electric Vehicles

Lithium-ion batteries (LIBs) have become a key player in the growing need for electric vehicles (EVs). State-of-the-art LIBs, using liquid electrolytes, still have significant challenges in their safety, lifespan, and energy density. Accordingly, solid-state lithium-ion batteries (SSLBs) have recently been attracting increasing research and industrial attention due to their ability to overcome intrinsic disadvantages of flammable liquid electrolytes used in current LIBs. The objective of this proposed research is to develop safe and high-performance SSLBs with sulfide-based electrolytes. The project includes two main directions: (1) synthesis of highly conductive and stable sulfide-based electrolytes; and (2) design of high-performance SSLBs with a stable interface between sulfide electrolytes and Li-ion cathode materials. GLABAT SOLID-STATE BATTERY INC. as an industrial partner will support and be involved in this project. The innovative research will help both GLABAT and Canada increase their global competitiveness and create new economic ventures.

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Faculty Supervisor:

Xueliang Sun

Student:

Xia Li

Partner:

Glabat Solid-state Battery Inc

Discipline:

Engineering - mechanical

Sector:

Alternative energy

University:

Program:

Elevate

A melanoma diagnosis and prognosis framework with human readable explanation

Melanoma is the most lethal skin cancer, accounting for 2% of all skin cancer types, yet approximately 75% of skin cancer deaths. It often evolves from clear skin or existing moles, making it difficult to diagnose at early stage. Besides, the treatment of melanoma is a complex decision making process, which is affected by a large number of internal and external factors, e.g. disease location, staging, etc. Our objective is to utilize the medical data collected by CMRN to design an electronic tool to save valuable time of clinicians in routine pathology assessment and ultimately assist evidence based decision making. The system will help to identify high risk pathological observations. It combines state-of-the-art pattern recognition algorithms with natural language processing (NLP), which generates human readable explanation. Moreover, our system can generate easy-understandable analysis results to patients, which enables patients to better understand their own conditions. TO BE CONT’D

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Faculty Supervisor:

Scott Ernst

Student:

Xue Teng

Partner:

Pulse InfoFrame Inc.

Discipline:

Dentistry

Sector:

Medical devices

University:

Program:

Elevate

Machine Learning Based Encrypted Traffic Analysis

Accurate network traffic identification would assist network operations and management teams effectively on many different network tasks such as managing bandwidth and ensuring security. The demand for network management methods that optimize network performance and provide quality of service guarantees has increased substantially in recent years. As new social networking and voice over internet protocol (VoIP) applications such as Facebook and WeChat have dramatically grown in popularity over the past several years, they now constitute a significant share of the total traffic on the Internet. Therefore, identification of such network traffic plays an important role in many areas such as network management, traffic shaping, cyber security and so on. In this research, we aim to investigate how encrypted social media and VoIP traffic could be accurately and robustly identified using a machine learning based approach in network traffic data.

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Faculty Supervisor:

Nur Zincir-Heywood

Student:

Ali Safari Khatouni

Partner:

Solana Networks

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Real-Time Operating System for Safety Critical Systems

Mannarino Systems & Software Inc. is currently developing M-RTOS, which is a Real-Time Operating System (RTOS), designed specifically to meet the requirements of the aeronautical industry’s ARINC 653 Avionics Application Software Standard Interface specification, and supporting a wide range of avionic systems. Throughout this phase of the project, the Concordia partner will provide a synthesis document covering: (i) The existing standards and guidance currently governing the aerospace industry, (ii) An evaluation of future threads and trends regarding cyber security in the aerospace industry, and (iii) A catalog of the different threats scenario and mitigation measures commonly identified and discussed in the aerospace industry. TO BE CONT’D

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Faculty Supervisor:

Amr Youssef

Student:

Mohamed Tolba

Partner:

Mannarino Systems & Software Inc

Discipline:

Computer science

Sector:

Aerospace and defense

University:

Program:

Accelerate

Evaluating Natural Channel Design Performance in Southern Ontario

Natural channel design practices are continually evolving, but monitoring the performance and success of these urban river engineering projects is often limited to sparse point measurements of streamflow, stream morphology, and species inventories during the 2 – 5 years following construction. The result is relatively few data on the overall performance of natural channel design projects, both in terms of the original project goals and geomorphic function (no net erosion and deposition). Advances in survey technologies (e.g., Mobile Laser Scanning) allow high-resolution topographic data to be collected quickly in the field, which has the potential to improve both the quality of data collected as well as our overall understanding of how natural channel designs function geomorphically post-construction. TO BE CONT’D

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Faculty Supervisor:

Peter Ashmore

Student:

Sarah Peirce

Partner:

Matrix Solutions Inc.

Discipline:

Geography / Geology / Earth science

Sector:

Natural resources

University:

Program:

Elevate

Radio Frequency Identification (RFID) Based Multi Agent System in Banking Environment

The wide adoption and development of wireless sensing technologies for the monitoring and autonomous identification of financial activities have affected financial institutions in the past decade. However, wider utilization of RFID technologies in the banking sector has introduced challenges regarding the security and privacy of sensitive financial data. The proposed innovations and technological developments will revolutionize the banking sector by increasing efficiency, decreasing cost and provide secure and privacy sensitive financial transactions. In this work, we will deliberately build up a RFID based comprehensive framework and its application to expertly and automatically matching profile of customer and banker according to a number of selected weighted attributes. We will develop a RFID framework which collects, communicates and manages the financial data and customer’s account details securely. TO BE CONT’D

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Faculty Supervisor:

Dimitrios Hatzinakso

Student:

Sonam Kaul

Partner:

RBC Financial Group

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program:

Elevate

Advanced Hybrid Solid-State Lithium(-ion) Batteries for Electric Vehicle Applications

As the dominating power supplies for current electric vehicles (EVs), the state-of-the-art LIBs are yet sulfuring from severe challenges in terms of safety, lifespan, and energy density due to the adoption of liquid electrolytes (LEs). Accordingly, developing next generation solid-state lithium(-ion) batteries (SSLBs) is considered to be a feasible approach to achieve safe and high energy density power supplies for future EVs with long driving distance and short charging time. This project will aim at developing innovative hybrid solid-state Li(-ion)batteries (HSSLBs) based on hybrid/composite solid-state electrolytes (SSEs). To realize high performance HSSLBs, many scientific and technological interface challenges are urgently required to be addressed. TO BE CONT’D

View Full Project Description
Faculty Supervisor:

Xueliang Sun

Student:

Qian Sun

Partner:

Glabat Solid-state Battery Inc

Discipline:

Engineering - mechanical

Sector:

Alternative energy

University:

Program:

Elevate

Potential roads for recovery of Atlantic salmon in Nova Scotian rivers

Atlantic salmon and its associated fisheries have a rich and complicated history in Nova Scotia. Commercial and recreational overfishing, as well as habitat damage and environmental pollution have all contributed to the species’ decline. For a century, work on rebuilding populations has met with varied successes. This history, and relative successes of different measures, will be reviewed and synthesized in this project, with a specific focus on potential recovery options for the Margaree in Cape Breton. Primary and secondary literature, combined with field work and key informant interviews will be used to make management recommendations for salmon recovery. Wild Salmon Unlimited, the partner in this proposal, will use the recommendations to chart a path forward for Atlantic salmon enhancement and recovery in the short and long term.

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Faculty Supervisor:

Megan Bailey

Student:

Seth Jenks

Partner:

Wild Salmon Unlimited

Discipline:

Sector:

Environmental industry

University:

Program:

Accelerate

Open-domain Contextual Conversation Generation

The objective of the project is to design a system that is able to generate context-wise reasonable and meaningful responses to open-domain conversation queries. In open-domain conversation generation, the retrieval-based methods and neural network generative models are two main approaches; there are also some recent research about improving the context consistency of conversation generation. In this project, we try to use context resolution model to complete the queries to include more information from context, and use ranking models to rank the candidates from the combination of retrieval-based generation and generative model, based on their relevance to both queries and context. We will also try to use generative adversarial network and reinforcement learning in generative models to make responses with higher qualities.

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Faculty Supervisor:

Ali Ghodsi

Student:

Wei Yang

Partner:

RSVP Technologies Inc

Discipline:

Computer science

Sector:

Digital media

University:

Program:

Accelerate

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