The development of autonomous unmanned aerial vehicles (UAVs) is a growing area of interest. An important step in the creation of a fully autonomous flying vehicle is the ability to precisely and smoothly land on a target. The goal of this research is to develop a UAV landing system that is able to track and land on a moving platform. The project will involve developing a guidance and control system that can plan a descent trajectory and track it down to the platform. The proposed system will be robust to strong wind gusts and still provide a smooth touchdown to avoid damage.

Addem Labs is developing an office-friendly machine to manufacture single- and double- layer printed circuit boards (PCBs) on a user’s desktop. A core part of the machine is a miniature tool-changer mechanism that can pick up and use the tools required for various mechanical operations - the intern will be designing and developing this automated tool changer mechanism. The intern’s work will radically accelerate Addem Labs’ product development efforts and enable the company to bring its product to market months earlier than expected.

Concrete elements are extensively used in urban structures including high-rises, bridges, dams and tunnels, and testing their quality and monitoring their health is of extremely high importance in the industry.
In this project, use of piezoelectric transducers along with wireless communication and memory elements is suggested to overcome the limitations with the current methods. Piezoelectric elements are embedded in a concrete element at the time of construction. They will later generate alternating forces in the concrete and sense the reaction of the elements.

SYNEX Medical, a Toronto based biotechnology startup has developed a non-invasive biomarker technology that comes in the form of a wearable ring enabling users to track concentration of different biomarkers in their blood. The device employs Nuclear Magnetic Resonance (NMR) technology to determine the concentration of different biomarkers. One fundamental instrumentation for this NMR based wearable device is a permanent magnet.

This project aims at studying the powder characteristics in the laser powder-bed fusion of Ti-6Al-4V. As an additive manufacturing technique, the laser powder-bed fusion process produces metal objects layer-by-layer using a laser source. Ti-6Al-4V is being used in the aerospace industry because it offers high strength-to-weight ratio and outstanding corrosion resistance.

Additive Manufacturing (AM), also known as 3D printing, uses computer-aided design to build objects layer by layer. AM enables complex designs with reduced component lead time, cost, material waste, energy usage and carbon footprint. Large scale robotic AM (> 1 m cubed) using wire arc welding is an emerging field offering scalability of AM to heavy industry. A particularly limiting factor for large-scale AM, however, is the need for support structures for overhanging features with current AM process planning and tool path planning methods.

In this research, occupancy monitoring data, temperature and humidity, and water quality parameters data collected through image processing and Internet of Things (IoT) sensors from multiple swimming pools are going to be processed and analyzed to identify the meaningful relations between these parameters and freshwater usage. The aim is to identify correlations between parameters and formulate the addition of freshwater as a function of number of swimmers, time spent, and activities.

Companies rely on financial reports which are generated through various transactions such as sales and expenses to understand the discrepancies between actual performance and financial forecast. Accordingly, generating commentaries on financial data might be considered as a routine operation for many companies. The previous studies indicate that machine learning algorithms can be used to automate the process of commentary generation. Specifically, such approaches use product forecasts and actuals in addition to inventory and point-of-sales data for the underlying prediction task.

Since the COVID-19 outbreak, social distancing became the most effective approach to guarantee safety. In manufacturing operations this has been achieved through a work from home policy for non-essential personnel and staggered shift operations for essential personnel. However, it is near impossible to track essential personnel compliance to social distancing as well as optimize production operations manually due to the COVID-19 safety restrictions. Therefore, automated computer-vision-based alternatives have attracted interest for such applications.

Electric buses have achieved first demonstration deployments in Canada. As bus fleet operators ramp up their implementation over the next 5-10 years, with many planning to fully convert their fleets to electric, charging capacity will become a challenge. This project will build up tools to examine the efficacy of energy storage located on the power grid side of the bus chargers to alleviate the high power requirements of the bus charges. This will help to mitigate technical challenges as well as costs associated with the charging the fleets.