Innovative Projects Realized

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

31133 Completed Projects

2940
AB
5159
BC
837
MB
685
NL
882
SK
9292
ON
9695
QC
97
PE
601
NB
1161
NS

Projects by Category

Smart Battery Research

The proposed project seeks to develop a Machine Learning-based software solution that accurately measures the capacity, State of Health (SoH), State of Charge (SoC), and cycle count of non-smart batteries utilized in mobile fleets. The project’s primary objective is to bridge the gap between smart and non-smart batteries by monitoring non-smart battery capacity and other pertinent parameters. It includes conducting experiments on Lithium-Ion batteries to obtain valuable data and parameters, which will be utilized to develop mathematical models and Machine Learning algorithms for predicting those parameters for non-smart batteries. The aim is to integrate non-smart batteries into SOTI’s XSight dashboard, providing customers with precise information for a broader range of battery models. This project is expected to benefit SOTI and its customers significantly, while also contributing to sustainable technology development.

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Faculty Supervisor:

Arvind Gupta;Huaxiong Huang

Student:

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Simulation of Remote Control on a Mobile Device

Mobile devices have become a crucial tool for businesses, and SOTI MobiControl is a leading mobile device management solution that provides remote control capabilities. However, to ensure proper product functionality and scalability of SOTI MobiControl, the company is looking to research the simulation of remote controlling a mobile device for automation testing. By testing the remote-control feature under various scenarios and conditions, SOTI can identify and address any issues that may arise, resulting in a better-performing product and improved user experience. This research will enable SOTI to maintain worker productivity by ensuring that the remote-control functionality is optimized and scalable. The simulation of remote controlling a mobile device for automation testing is a critical aspect of product development for SOTI MobiControl, contributing to the productivity of workers who rely on these devices to perform their job.

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Faculty Supervisor:

Eyal de Lara

Student:

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Mining Event Tracing for Windows (ETW)

As cyber adversaries are becoming more creative, analysts are required to figure out more innovative ways to detect them to be able to respond before it’s too late. To detect any underlying threat inside a system, data logs are collected showing events and activities occurring inside the system. Adversaries nowadays are capable of evading detection and doing activities that do not always get recorded. Event Tracing for Windows (ETW) offers new data sources to collect logs from that can be of great benefit in detecting adversaries and their movement inside computer systems. ETW is quite flexible and spans many different log providers that can cover a huge deal of logs. This project will work on mining data obtained from ETW logs to create a tool that detects malicious patterns that indicate that a system is compromised or if it’s under attack.

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Faculty Supervisor:

Charlie Obimbo

Student:

Partner:

eSentire

Discipline:

Computer science

Sector:

Cyber Security; Information and Communications Technology; Technology

University:

University of Guelph

Program:

Accelerate

Cloud Hosting Cost Optimization

The proposed research project will focus on analyzing and optimizing the cloud infrastructure used by SOTI to manage mobile devices globally. The intern will analyze the current cloud architecture and hosting costs, identify areas for improvement, and propose and implement optimizations to reduce system requirements and minimize costs. The expected benefit to SOTI is a more cost-effective and efficient cloud infrastructure that maintains the performance and quality of their technology solutions. This project will also benefit the Canadian community by promoting cost-effective and sustainable technology solutions for mobile device management.

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Faculty Supervisor:

Baochun Li

Student:

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Application of Machine Learning and Data Science for classification of BDD (Behavior Driven Development) Test Development and Execution

Continuous integration (CI) and continuous delivery (CD) are practices that help software development teams deliver code changes more often and with fewer issues. To ensure that code changes are working as they should, developers use Behavior Driven Development (BDD) tests. But running all these tests against every code change can be time-consuming and costly. This project aims at classifying and categorizing the BDD tests into smaller categories and creating a recommendation system that assigns the right tests to each code change. Instead of running all the tests, the proposed solution would recommend running only the tests that are relevant to the specific code change, resulting in faster and more efficient software development for the partner organization.

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Faculty Supervisor:

Shurui Zhou

Student:

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Command and Control Automation and Reporting

A red team is a group of cybersecurity experts who are tasked with simulating real-world attacks on an organization’s systems and networks. They do this by using a variety of tools and techniques to identify vulnerabilities and weaknesses in an organization’s defenses. This project implements command-and-control infrastructure, which is critical for the red team or simulated attackers to remotely control systems compromised by them and receive stolen data from the compromised systems.

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Faculty Supervisor:

Xiaodong Lin

Student:

Partner:

Lares LLC

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Guelph

Program:

Accelerate

Automating Insider Threat monitoring and detection

Insider threat involves individuals who have access to company resources and causes harm to the institution. These insiders can be employees, consultants, contractors, and third-party companies. Different types of insiders include people who intentionally harm the company, those to masquerade as a trusted entity, and those who unintentionally cause harm. Insider threats can lead to the disclosure, alteration, or destruction of sensitive information. To defend against such threats, companies need a system to detect and reduce insider threat risk. This project focuses on developing a system that automates the identification of high-risk groups for insider threats and generating remediation strategies for each risk a group may pose. The project will benefit EQ Bank’s Insider risk team in categorizing and identifying key hallmarks of insiders. This will provide more context to the team in making strategies to mitigate the risk emerging from potential insiders.

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Faculty Supervisor:

Ali Dehghantanha

Student:

Partner:

Equitable Bank

Discipline:

Computer science

Sector:

Finance and Insurance

University:

University of Guelph

Program:

Accelerate

Optimizing Security Orchestration, Automation, and Response for Incident response

This research project aims to develop cost-effective solutions to aid organizations in defending against cyber-attacks. With limited resources, security operations centers are struggling to defend against the vast volume of cyber-attacks. The project proposes reducing the work effort and amount of labor needed to perform tasks such as manual inspection and incident responses. By enhancing the functionality of current plugins and developing new ones for the Security Orchestration, Automation, and Response tool, cybersecurity experts will spend less time monitoring and more time implementing better security metrics, resulting in a more secure system and organization. The project’s expected benefit to partner organizations is a reduction in the cost of services and improved cybersecurity measures, resulting in fewer cyber-attacks and better protection for both the organization and its clients.

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Faculty Supervisor:

Rozita Dara

Student:

Partner:

ISA Cybersecurity

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Guelph

Program:

Accelerate

Monitoring and optimizing environmental conditions for improved CEA productivity

Consistent plant production is critical for plant product quality and marketability. Plant quality (leafy greens, cannabis or other plants) is influenced by the plant species/cultivar and environmental conditions (temperature, relative humidity, light level, light quality, CO2 levels, water quality and quantity, nutrient levels and air movement). Understanding existing variability of the environmental conditions is critical for any plant production operation with the knowledge quantified in one location transferrable to other locations. The objective of this study is to evaluate and monitor the environmental conditions in a CEA operation and modify existing sensor systems to understand temporal and spatial variability.

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Faculty Supervisor:

Mark Lefsrud

Student:

Partner:

Fermes Urbaines Ôplant

Discipline:

Engineering

Sector:

Agriculture

University:

McGill University

Program:

Accelerate

Adversarial Threats on a Penetration Testing Solution

Malicious adversaries are increasingly aiming to bypass security controls. There is a race to “owning” vulnerable machines and it is advantageous to malicious adversaries if the existing vulnerabilities are not patched. The research will be performed on a vulnerability assessment and management platform, specifically designed to assist organizations in identifying and mitigating cyber risks. It is unclear how effective the solution is against malicious insiders. For instance, in an enterprise environment, a malicious insider may circumvent the alerts that the platform may generate if it has taken over that machine. The research will focus on testing exploit techniques that can allow a threat actor to bypass the platform’s detection mechanisms and establish malware and persistence mechanisms within the target. By taking this approach, the project aims to identify the susceptibility of the platform to insider attacks and develop strategies to address them before malicious actors can exploit them. Proactively identifying and mitigating potential cyber threats is critical in today’s digital landscape, and this research can help organizations better understand and address their vulnerabilities.

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Faculty Supervisor:

Hassan Khan

Student:

Partner:

Fiera Capital

Discipline:

Computer science

Sector:

Finance and Insurance

University:

University of Guelph

Program:

Accelerate

Threat actor group profiling

Understanding the current panorama of threat actor groups worldwide is critical to building efficient cybersecurity programs. Information about threat actor groups’ motivations, tools, tactics and techniques they use to attack, and the type of targets they have in their sights provide valuable information to cybersecurity teams. To achieve this goal is essential to generate intelligence processing such information. Unfortunately, humans cannot process the amount of data generated daily, so implementing machine learning models is required for data processing and categorizing threat actor groups. With the proper profiling of threat actor groups, cybersecurity teams will strengthen their policies, security controls and processes, efficiently targeting their resources to threats that could impact them instead of wasting resources on pointless activities.

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Faculty Supervisor:

Charlie Obimbo

Student:

Partner:

eSentire

Discipline:

Computer science

Sector:

Cyber Security; Information and Communications Technology; Technology

University:

University of Guelph

Program:

Accelerate

Towards Fully Automated Tumor and Organ-at-Risk Detection and Segmentation from PSMA PET and SPECT Scans of Prostate Cancer Patients

Prostate cancer is the third deadliest cancer in men and early detection is crucial. PSMA is a protein that is highly present in prostate cells, making it a promising target for imaging and treatment. Total metabolic tumor volume (TMTV) is a measure of tumors’ characteristics, but it is currently not measured in clinical settings due to the labor-intensive and time-consuming process of manually delineating the borders of all tumors in PET images. AI can automate this process, but it struggles with low-quality images and small tumors. Our proposal is to develop AI-based object detection methods to locate lesions before segmenting them to improve accuracy. PSMA can also be used for personalized radioligand therapy, where radioactive drugs attached to PSMA molecules are injected into the patient to kill cancer cells. AI can aid in automating the process of manually delineating the borders of tumors and organs at risk in PET images, simplifying existing protocols, and predicting patient response and outcome. Personalized radioligand therapy could maximize cancer irradiation while minimizing toxicity to healthy organs, leading to better efficacy.

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Faculty Supervisor:

Arman Rahmim

Student:

Partner:

Microsoft Canada Development Centre

Discipline:

Life Sciences

Sector:

Technology; Health and Related Sciences & Technology; Artificial Intelligence

University:

The University of British Columbia

Program:

Elevate

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Mitacs — Return on Innovation

Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.