The quality of the air we breathe is vital to our health, the environment and overall quality of life. Airborne particulate matter poses significant health risks when concentrations exceed safe levels. Our device enhances the method for aerosol particulate monitoring, by utilizing a focused laser beam with the principles of photoionization and absorption spectroscopy to identify and quantify foreign substances in the air.

The project aims to develop a reliable pressure sensor system using Velostat and a customized pressure sensor design. It initially explored both electrical and optical approaches but settled on an electrical system due to feasibility and familiarity. The mission involves overcoming challenges related to wire management, sensor measurement, and noise while ensuring accurate pressure readings. The goal is to create a robust sensor system capable of providing precise measurements for various applications.

EasyEntry is built to enhance accessibility in everydayenvironments by addressing the issue of non-functionaland inadequate handicap door systems. EasyEntry offers users a universal solution to deploying accessibility features in buildings, such as automated doors, ramps, and more. Using a convenient handheld device, users can have seamless access to the places they need to get.

This project capitalizes on 3D LIDAR technology for a cutting-edge Human Activity Recognition (HAR) system, aimed at transforming physical fitness through precise exercise pose detection. By integrating LIDAR, our initiative seeks to refine motion analysis, offering real-time, precise feedback on exercise form. This technology not only elevates workout efficiency and safety, but also brings advantages of high accuracy in diverse lighting conditions and ensures user privacy. Our goal is to harness LIDAR's unmatched spatial accuracy to revolutionize health and wellness, optimizing exercise routines for the active lifestyles of today.

⁤The TESERACT, a 3U Astrophotonic CubeSat Prototype marks the beginning of a project to send the first astrophotonic chip-based instrument into space. It is designed to detect gas(s) using transit spectroscopy via the photonic chip, alongside the Raspberry Pi serving as the onboard computer, and the Cube-Orange as the fight-controller. Additionally, various instrumentations are in place to monitor the system's environment and health. This project marks a significant first step in exoplanetary exploration join us as we embark on this groundbreaking journey.

The purpose of this project is to investigate and determine the efficiency of image classification deep learning models for both thermal and dash cameras. To maintain consistency between the two models, each trained model uses near identical scenes. This consistency makes comparative results more effective. This project will create a unique dataset for varying weather conditions of pedestrians and cyclists in Ottawa. This dataset can be used by other projects, once published on a site such as Roboflow. By merging the training from both the thermal and pedestrian models, we will have many edge cases for general pedestrian and cyclist detection. The Indvidual weight modifications of the training parameters will optimize the final detection model for the purpose of a more uniform system.