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

Investigation into the Radiation Damage Effect on Failure Mechanism of CANDU Spacer Material X-750 Ni-based Su

This study focus on the understanding of radiation-induced embrittlement in CANDU reactor spacer material, Inconel X-750. The helium pre-implantation following by proton irradiation will be employed as a surrogate for neutron irradiation to simulate the radiation damage on the microstructure of Inconel X-750. Micro-tensile test on irradiation X-750 material will be carried out to evaluate the mechanical properties and furthermore explore the failure or fracture mechanism. The step by step monitoring the deformation behavior of irradiated X-750 alloy during in-situ SEM straining test will be employed to understand the failure mechanism of CANDU spacer after irradiation. Also, post-deformation TEM observation on irradiated Inconel X-750 will help to investigate the effect of radiation-induced defects on fracture/failure property of the spacer material.

View Full Project Description
Faculty Supervisor:

Zhongwen Yao

Student:

Pooyan Changizian

Partner:

Kinectrics Inc

Discipline:

Engineering - mechanical

Sector:

Energy

University:

Program:

Accelerate

Blockchain Enabled Land Registry: Towards Improving Transparency, Accountability & Compliance

The current land registry system in Ontario lacks efficiency and transparency; and is susceptible to information quality problems due to lack of a uniform and integrated system to record and share real-time data about land property transactions across stakeholder organizations. To overcome such issues, many countries are turning to blockchain technology to enable land registration transactions. An end-to-end implementation of a blockchain enabled land registry platform has the potential to create a decentralized, transparent, and trustworthy system capable of tracking all events of significance related to a specific property. However, no such initiatives exist in Canada that explore the opportunities afforded by blockchain technology for land registry transactions. Our research aims to address this gap. The objective of this research is to utilize a proof-of-concept approach and develop a blockchain platform prototype that meets specific use-case requirements related to property events and transactions among business entities. TO BE CONT’D

View Full Project Description
Faculty Supervisor:

Umar Ruhi

Student:

Karim Sultan

Partner:

Arrowhead Development

Discipline:

Business

Sector:

Information and communications technologies

University:

Program:

Accelerate

Development of Dielectric Coating for the Next Generation Wearable Heart Monitoring System

Electrocardiography or ECG is a technology that can be used to monitor the electrical activity of the heart over a long period of time. Such signals can be utilized for interpreting both the structure and function of the heart. Traditional ECG designs consist of several electrodes that need to be placed directly in contact with a patient’s skin. These metallic sensory electrodes are usually made into disc-like configuration and one of the major drawbacks is the inconsistency in signal recording due to poor skin contact from the non-flexible nature of the electrode design. Thus, HelpWear Inc., is intended to manufacture next generation ECG sensors by utilization of flexible polymeric based material to replace the traditional bulky ECG design and integrate novel materials for wearable healthcare applications.

View Full Project Description
Faculty Supervisor:

Hani Naguib

Student:

Yu-Chen Sun

Partner:

HelpWear

Discipline:

Engineering - mechanical

Sector:

Medical devices

University:

Program:

Accelerate

Hyperstate: Communication between organizations In the Music Business

The current project intends to create a software platform solution that solves the communication between organizations in the Music Industry. Overall, the platform uses third party systems to achieve the requirements of the problem statement. Moreover, the platform is built over abstraction levels to integrate different components. The research of the integration of these parties including blockchain technologies will solve the data transmission and accountability responsible for the payment distribution of value in the specific use case of the Partner in the Music Industry. Specifically, transmit data among several organizations (labels, artists, distributors, etc efficiently) to make payments fast, securely, and reliable so achieving trust between the participants. The chain of the processes that are going to be defined will automate the accountable traditional processes. Each participant will get their payoffs more fairly and faster, improving and innovating the industry at the same time.

View Full Project Description
Faculty Supervisor:

Ralph Deters

Student:

Marco Antonio Maigua Teran

Partner:

Membran Canada

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

3D Perception and Prediction for Autonomous Driving

For an Autonomous Vehicle (AV) to make decisions and drive independently on urban streets, the problem at hand can be broken down into many phases, two of which are perception and prediction. Perception refers to the process of extracting valuable information from the environment using data collected by sensors such as LIDAR and camera. This includes detection of cars, ped estrians, lanes among many objects. Prediction refers to the process of tracking all the known objects and predicting the possible future actions so as to enable the autonomous vehicle to make informed decisions. Traditionally these tasks are done sequentially and independently one after another. This makes uncertainty hard to propagate from perception to prediction. The aim of this project is to build a deep learning model that does combined 3D perception and prediction.

View Full Project Description
Faculty Supervisor:

Sanja Fidler

Student:

Satya Krishna Gorti

Partner:

Uber Advanced Technologies Group

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Development of Drug-eluting Bio-absorbable Scaffolds for Body Piercing Applications

Nearly 20% of all piercings lead to local infection, and therefore, it is imperative to develop alternative and commercially-viable methods of piercing aftercare to prevent infection. The general objective of the proposed project is to optimize the design of drug-eluting bio-absorbable scaffolds for human and animal tissue piercing applications with a focus on scaffold degradation and drug release properties. Methods of low temperature fabrication of drug-eluting bio-absorbable scaffolds will be developed and optimized. Moreover, the required scaffold geometry and concentration of drugs to be embedded within the scaffold will be determined based on the drug release profile and degradation rate (in-vitro).

View Full Project Description
Faculty Supervisor:

Ali Ahmadi

Student:

Tartela Alkayyali

Partner:

BioPierce Canada Ltd

Discipline:

Engineering - other

Sector:

Advanced manufacturing

University:

Program:

Accelerate

Anomaly detection and simulation for unlabeled sensor data

The rapid development in the areas of statistics and machine learning demonstrate unprecedented performance in making cognitive business decisions. Quartic.ai aims to use state-of-the-art machine learning technology to help manufacturers assess and maintain the quality of their industrial units, which suffer damage due to continuous usage and normal wear and tear. Such damage needs to be detected early to prevent further losses. The data in this domain are recorded using sensors at various stages in the process flow. Major challenges of analyzing these sensor data are (1) unlabeled data, which may contain very few unobserved anomalies or outliers; (2) the development and evaluation of algorithms that can robustly detect anomalies. Due to the lack of labels, the performance of algorithms can not be directly evaluated. To tackle the problems, we will carefully design simulations by taking into account of various types of outliers and develop novel robust one-class classification algorithms.

View Full Project Description
Faculty Supervisor:

Linglong Kong

Student:

Sile Tao

Partner:

Quartic.ai Canada Inc

Discipline:

Mathematics

Sector:

Information and communications technologies

University:

Program:

Accelerate

Predicting recovery from concussion during military cadet training using multimodal MRI data and machine learning

In the military, concussions are common and many occur while non-deployed, including during cadet training exercises. For the majority of those with concussions, symptoms resolve on their own but for a “miserable minority” symptoms persist beyond the typical 3-month recovery period, impacting quality of life. Most concussion research produces group level inferences which cannot be used to make individual predictions. We propose a supervised machine learning approach to build a model to predict symptom recovery from multiple MRI brain measures. The ability to identify those in the acute phase likely to have poor symptom recovery at 6 months post injury is incredibly useful for clinical decision making, concussion management, optimized treatment and personalized medicine. This project will contribute to bridging the gap between research and clinical use, by adapting and validating machine learning applications in neuroimaging. TO BE CONT’D

View Full Project Description
Faculty Supervisor:

Douglas Cook

Student:

Ashley Ptinis

Partner:

Synaptive Medical Inc.

Discipline:

Medicine

Sector:

Medical devices

University:

Program:

Accelerate

The Genetics of Blood Biomarkers in COPD

COPD is a progressive inflammatory airway disease characterized by persistent and progressive airway inflammation. It is a major cause of global morbidity and mortality and is predicted to become the third leading cause of death by 2020. Biomarkers may be useful for diagnosing disease considering that the usually used lung function measures have poor correlation with both symptoms and other measures of disease progression. However, the relationship between biomarkers and COPD is still elusive. Establishing causality for selected proteins and pathways is a promising step toward their development as both biomarkers and therapeutic targets. Our group has found surfactant protein D is a novel biomarker, plays a causal role in the pathogenesis of COPD and its progression. TO BE CONT’D

View Full Project Description
Faculty Supervisor:

Xuekui Zhang

Student:

Ailan Shi

Partner:

Providence Health Care

Discipline:

Mathematics

Sector:

Medical devices

University:

Program:

Accelerate

Machine learning towards intelligent steel refining processes

In the steelmaking industry, process control models need to be based on a sound physical understanding of the process but should also account for many uncertainties due to the nature and complexity of the environment in which the process is carried out. As a result, it is crucial to extract useful process control information from the raw data stream acquired by the industrial sensors. The proposed project aims at developing advanced algorithms to improve the estimation of key control parameters in the Argon-Oxygen Decarburization (AOD) process, by leveraging on Machine Learning approaches and tools applied to manufacturing data. This research, while being a valuable training for a high-talented student in Canada, will help the partner organization Tenova Goodfellow Inc. in maintaining its leadership in process optimization applied to steel making furnaces.

View Full Project Description
Faculty Supervisor:

Abdallah Shami

Student:

Elena Uchiteleva

Partner:

Tenova Goodfellow Inc

Discipline:

Engineering - computer / electrical

Sector:

Advanced manufacturing

University:

Program:

Accelerate

Lessons Learned and Decision-Making Tool for Engineering Projects

According to the World Petroleum Council (WPC), the average age of employees in Oil and Gas companies is 50 years, and it is estimated that in the next 5 years 40-60% of them will retire. The consequence is an age-related crisis in the sector given that, in many cases, the knowledge accumulated goes with the retiring gray-beards. The objective of this project is to start paving the way for the development of a novel software system that supports timely decision-making tasks based on lessons learned and best practices in the oil and gas industry, addressing a current need of a major company in the Maritime region of reducing the overhead of the complicated process of retrieving all necessary pieces of information to solve a problem.

View Full Project Description
Faculty Supervisor:

Fernando Paulovich

Student:

Mitchell Kane

Partner:

Waterford Energy Services Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Robust WiFi-based Indoor Presence Detection and Localization

In this project, we are interested in device-free methods that passively sense, monitor, and track people’s indoor presence, location, and movement using off-the-shelf Wi-Fi-enabled devices. We use information extracted from the physical layer of wireless links to detect and interpret human presence, location, and physical activities. The current design and implementation of Wi-Fi-based systems exhibit some temporal inconsistencies and limitations due to the complexity of the wireless signal propagation in indoor environment and the challenging nature of human’s behavior itself. This project focus on feature extraction techniques to reduce data inconsistencies and improving the performance of classical machine learning algorithms and deep learning models, for building robust smart-home applications such as presence detection and indoor localization.

View Full Project Description
Faculty Supervisor:

Xue (Steve) Liu

Student:

Qianyu Liu

Partner:

Aerial Technologies Inc.

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Previous
1…804805806807808809810…1,106
Next

Join a thriving innovation ecosystem.

Subscribe to our newsletter now!

Subscribe