The industry partner for this NSERC ARD Grant is MHIRJ Aviation Group (https://mhirj.com, Mitsubishi Heavy Industries - Regional Jet, formerly Bombardier Inc. Regional Jet). MHIRJ provides comprehensive critical operational, engineering and customer support solutions for the global regional aircraft industry. MHIRJ needs applied research assistance from Humber College to address two complementary maintenance-related needs.

The first need is for Automated Maintenance Data Processing system that will work with maintenance data submitted - in multiple formats and over varying intervals - by aircraft operators (airline companies). This data will be ingested, processed, analyzed, and outputted as databases for further analytics of the individual performance of aircraft in the individual fleets; as well as how the overall performance of individual fleets compare to an aggregated and anonymized view of the fleets of competing operators. The proposed solution can reduce significantly the time and effort required by MHIRJ personnel to administer and maintain the process. The automated data processing is not time critical as long as the output is available within 24 hours of data submission.

The second need is for an Integrated Analytical Tool to assist ground maintenance crews in their critical and time-sensitive 'dispatchability' decision: 'go,' 'go with limitation,' and 'no go,' in a very narrow window of time between flights, based on real-time operational data from each flight. The design of the Integrated Analytic Tool will be based upon the same data processing and analytics algorithms developed for Automated Maintenance Data Processing. This will result in more consistent data interpretation, improved efficiency by aircraft maintenance crews, faster turn-around times of aircraft with improved safety.

Humber College's applied research team will provide relevant expertise in textual data mining, language processing, machine learning, data analytics, and database design, to assist in developing software algorithms and tools necessary for the intended solutions. The project will improve the competitiveness for MIHRJ and establish a stronger collaboration between MHIRJ and Humber College.

As part of our effort to develop a technological platform, using IoT technology to control HVAC systems for energy consumption optimization, we want to data mine the vast amount of data our different systems logged to derive intelligence leading to automated decision making and execution.

Torstar’s flagship news product, The Toronto Star Newspapers Ltd (TSNL), has become increasingly interested in covering cybersecurity issues (data breaches, ransomware, etc.) but lacks the expertise and technical framework for delving deeper into these issues. Specifically, they lack the expertise to safely extract, analyze, and share data retrieved from the un-indexed “Dark Web”, where primary documents related to data breaches and ransomware attacks are regularly shared publicly. This would be accomplished by working with the Faculty of Social & Community Services’ PSI Program to develop a technical and logistical framework (a data server solution as well as reporter training in cybersecurity fundamentals) to bolster the Star’s reporting in this sector. Potential outcomes include the establishment of a new cybersecurity beat, or even a separate subscription publication to create additional revenue streams for the company.

​The objective is to develop a next-generation ultra-long-range wireless backhaul solution for operators of point-to-point communication systems Using the physical layer of the Advanced Television Systems Committee 3.0 broadcast standard (ATSC 3.0), and available sub-1GHz frequencies, the ULRWB project aim is to increase the achievable distance of backhaul hops for high throughput and low-latency broadband access. The solution would be future-ready, facilitating integration with Internet Service Providers (ISP) with current and emerging fixed and cellular technologies, including 5G infrastructure.

The industrial partner Mechatronic Diagnostics LTD is a for-profit automotive diagnostic and repair centre and is challenged in finding expert painters, as well as reducing the health hazards for its existing painters. This grant will develop an automated painting robotic system that is installable in the painting booth. The proposed solution will allow the industrial partner to perform the operation of vehicle painting in their repair shop booth in an automatic manner, which will optimize the workforce and increase the safety of staff. Humber students involved in the research project will participate in the design of the robotic placement in the booth, development of the algorithms required for automation of the process and help build and test the device prototype.

This project will work with the industry partner, InCube, and a Honey Producer in the Niagara Region of  Ontario on the Traceability of Honey. Using GS1 and EPCIS 1.2 and 2.0 standard technology, every step of the production, collection, pasteurization, packaging and distribution of honey will be collected, and traceable via an individualized serial number on each container of honey for retail sale.  Purchasers of the container will be able, via a QR code scanned by a smartphone, to see every step in the value chain in the production of the honey they have purchased.

This project will be a study pilot study tracing honey using the global GS1 and EPCIS 1.2 and 2.0 Traceability Standards via InCube’s global IT platform and utilizing a study of the feasibility of moving the Blockchain’s immutable ledger data in further phases of the project, to allow all of the participants of the value-chain in the production of honey to ensure that the single-use serial number will ensure that there is no doubt about the authenticity of the product and its origins.